"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "I3KSeq2Zy8tt"
+ },
+ "source": [
+ "**Welcome to Homework 2!** In this homework, you will be using techniques you learned in Working with Data using Pandas. The purpose of this homework is for you to try on your own data manipulation with ```Pandas``` and to explore what you observe. \n",
+ "\n",
+ "Feel free to refer back to the notebook and ask questions on Piazza!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xVLcYzB-y8tt"
+ },
+ "source": [
+ "### Grading"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "j8Q3BiiXy8tu"
+ },
+ "source": [
+ "There are a total of ten questions and each question is worth 1 point.\n",
+ "\n",
+ "In order to work on the Homework sections and submit them for grading, you'll need to run the code block below. It will ask for your student ID number and then create a folder that will house your answers for each question. At the very end of the notebook, there is a code section that will download this folder as a zip file to your computer. This zip file will be your final submission."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1TDpHLqgzCcu"
+ },
+ "source": [
+ "import os\n",
+ "import shutil\n",
+ "\n",
+ "!rm -rf sample_data\n",
+ "\n",
+ "student_id = input('Please Enter your Student ID: ') # Enter Student ID.\n",
+ "\n",
+ "while len(student_id) != 9:\n",
+ " student_id = int('Please Enter your Student ID: ') \n",
+ " \n",
+ "folder_location = f'{student_id}/Week_Four/Homework'\n",
+ "if not os.path.exists(folder_location):\n",
+ " os.makedirs(folder_location)\n",
+ " print('Successfully Created Directory, Lets get started')\n",
+ "else:\n",
+ " print('Directory Already Exists')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2rT0Pt-Ny8tu"
+ },
+ "source": [
+ "### Import Library"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EtymSgmry8tv"
+ },
+ "source": [
+ "import pandas as pd"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qQLwxdZOy8tz"
+ },
+ "source": [
+ "### Load dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uI4M3fVfy8t0",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 626
+ },
+ "outputId": "1b03cd1e-5bc9-46f5-9bba-59c2db933c4d"
+ },
+ "source": [
+ "url = 'https://raw.githubusercontent.com/bitprj/BitUniversity/master/Digital_History/Week3-Introduction-to-Open-Data-Importing-Data-and-Basic-Data-Wrangling/data/acs2017_county_data.csv'\n",
+ "df = pd.read_csv(url)\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ " CountyId State County TotalPop Men Women Hispanic \\\n",
+ "0 1001 Alabama Autauga County 55036 26899 28137 2.7 \n",
+ "1 1003 Alabama Baldwin County 203360 99527 103833 4.4 \n",
+ "2 1005 Alabama Barbour County 26201 13976 12225 4.2 \n",
+ "3 1007 Alabama Bibb County 22580 12251 10329 2.4 \n",
+ "4 1009 Alabama Blount County 57667 28490 29177 9.0 \n",
+ "\n",
+ " White Black Native ... Walk OtherTransp WorkAtHome MeanCommute \\\n",
+ "0 75.4 18.9 0.3 ... 0.6 1.3 2.5 25.8 \n",
+ "1 83.1 9.5 0.8 ... 0.8 1.1 5.6 27.0 \n",
+ "2 45.7 47.8 0.2 ... 2.2 1.7 1.3 23.4 \n",
+ "3 74.6 22.0 0.4 ... 0.3 1.7 1.5 30.0 \n",
+ "4 87.4 1.5 0.3 ... 0.4 0.4 2.1 35.0 \n",
+ "\n",
+ " Employed PrivateWork PublicWork SelfEmployed FamilyWork Unemployment \n",
+ "0 24112 74.1 20.2 5.6 0.1 5.2 \n",
+ "1 89527 80.7 12.9 6.3 0.1 5.5 \n",
+ "2 8878 74.1 19.1 6.5 0.3 12.4 \n",
+ "3 8171 76.0 17.4 6.3 0.3 8.2 \n",
+ "4 21380 83.9 11.9 4.0 0.1 4.9 \n",
+ "\n",
+ "[5 rows x 37 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 3
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TmFuSeZ7y8t6"
+ },
+ "source": [
+ "### Q1. Display the summary statistics of ```df```. (1 pt)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e8QcdyIvy8t6"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0ALiWgu5y8t7"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/1.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fc-tBI3_y8t9"
+ },
+ "source": [
+ "### Q2. Set ```CountyId``` as an index for ```df```. (1 pt)\n",
+ "\n",
+ "Hint: Use ```set_index```."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PxljsQ2yy8t9"
+ },
+ "source": [
+ "#### Answer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-6Ty46ZVy8t-"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/2.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6iGk8ClRy8uA"
+ },
+ "source": [
+ "### Q3 (a) Find the rows where ```Total Pop (Total Population)``` is greater than 50000. (0.5 pt)\n",
+ "\n",
+ "Hint: Use ```df.loc[]```."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fzg10xVgy8uA"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9mBh6gX-y8uB"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/3.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5lDXE_Hby8uD"
+ },
+ "source": [
+ "### Q3 (b) How many counties have ```Total Pop``` > 50000? Type your answer in the Answer section. (0.5 pt)\n",
+ "\n",
+ "Hint: Look at how many rows Q3 (a) displays."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CeVUzurIy8uF"
+ },
+ "source": [
+ "#### Answer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "n-diR-tHy8uG"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/3.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qxu6WpxVy8uK"
+ },
+ "source": [
+ "### Q4. Find the state with lowest unemployment rate. (1 pt)\n",
+ "\n",
+ "Hint: Refer to the 9.0 Now Try This in the notebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kz0kzrnBy8uL"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DiIvYNZpy8uL"
+ },
+ "source": [
+ "# INSERT CODE BELOW\n",
+ "# This is not part of grading.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i2oeBkApy8uN"
+ },
+ "source": [
+ "#### Answer: type your answer in a string form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KHxQm6ezy8uN"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/4.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LVQzoi-Cy8uP"
+ },
+ "source": [
+ "### Q5 (a) Select the state with lowest unemployment rates and save the dataframe as ```df_lowest_unemp```. (0.5 pt)\n",
+ "\n",
+ "Hint: Use ```df.loc[]``` and select the rows where ```State``` is the one you found in Q4."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WdLjdBfqy8uQ"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZUP1OjtYy8uQ"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/5.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PCdhzi4_y8uS"
+ },
+ "source": [
+ "### Q5 (b) Find the top 3 most popular sources of employment in ```df_lowest_unemp```. (0.5 pt)\n",
+ "\n",
+ "Hint: Use the following columns: 'PrivateWork', 'PublicWork', 'SelfEmployed', 'FamilyWork' and an AGGREGATE function."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Pk9_KJiHy8uS"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KcW0POmsy8uS"
+ },
+ "source": [
+ "# INSERT CODE BELOW\n",
+ "# This is not part of grading.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "26qagyQpy8uU"
+ },
+ "source": [
+ "#### Answer: type your answer in a string form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_QXx_IGgy8uU"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/5.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "haUCCnNLy8uX"
+ },
+ "source": [
+ "### Q6. What are the top 3 states with highest rate of people who work at home ( ```WorkAtHome```)? (1 pt)\n",
+ "\n",
+ "Hint: This one is similar to Q4."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nsjshYAHy8uX"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kl3Q6nmNy8uX"
+ },
+ "source": [
+ "# INSERT CODE BELOW\n",
+ "# This is not part of grading.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "W7OtLlWKy8uZ"
+ },
+ "source": [
+ "#### Answer: type your answer in a string form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "34qN6HWpy8uZ"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/6.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "D67NR8bKy8ub"
+ },
+ "source": [
+ "### Q7 (a) Select the subset of data of the top 3 states from Q6 and save it as ```top_three```. (.33 point)\n",
+ "\n",
+ "Hint: Use ```df['State'].isin()```. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jZe1rAdsy8ub"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Gh_lNk7Iy8uc"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/7.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3rTnGpety8ud"
+ },
+ "source": [
+ "### Q7 (b) How many counties are there in the top three states? (.33 pt)\n",
+ "\n",
+ "Hint: Use one of the aggregate functions. (```sum()```, ```mean()```, or ```count()```)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "V4rDqsgjy8ud"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_VziNPHfy8ue"
+ },
+ "source": [
+ "# INSERT CODE BELOW\n",
+ "# This is not part of grading.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "z1sTB-8wPKdC"
+ },
+ "source": [
+ "#### Answer: type your answer in a string form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sxkKIuoKPPe2"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/7.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5SanZc0Ry8uf"
+ },
+ "source": [
+ "### Q7 (c) Find the County with the most ```SelfEmployed``` in ```top_three``` (.34 pt).\n",
+ "\n",
+ "Hint: Use ```sort_values(by= )```."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J_7HAJCly8ug"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "o59reUbAy8ug"
+ },
+ "source": [
+ "# INSERT CODE BELOW\n",
+ "# This is not part of grading.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e0WdoWqqy8ui"
+ },
+ "source": [
+ "#### Answer: type your answer in a string form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AL8otbJfy8uj"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/7.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wgAeWZbSy8ul"
+ },
+ "source": [
+ "### Q8. Find the top 5 states with highest number of employed people and find out their top professional working field: 'Professional', 'Service', 'Office', 'Construction', or 'Production.' \n",
+ "\n",
+ "Hint: This question is a combination of Q6 and Q7. You may add more blocks to write codes and use the same approach as in Q6 and Q7."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wb_makRBy8ul"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DqAEFELhy8um"
+ },
+ "source": [
+ "# INSERT CODE BELOW\n",
+ "# This is not part of grading.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bjXwfuqYy8un"
+ },
+ "source": [
+ "#### What are the top five states with highest number of employed people? Type your answer in a string form. (0.5 pts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SzFntG8Ry8uo"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/8.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PzStbWG3y8up"
+ },
+ "source": [
+ "#### What is the most popular working field in the top five states? Type your answer in a string form. (0.5 pts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iKS-S78xy8up"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/8.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hW96XGAEy8ur"
+ },
+ "source": [
+ "### Q9. Find the state with highest percentage of people who walk to their work. (1 pt)\n",
+ "\n",
+ "Hint: This is similar to Q4."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "udRRvME-y8ur"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3ceJbLRwy8ur"
+ },
+ "source": [
+ "# INSERT CODE BELOW\n",
+ "# This is not part of grading.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "M8BmiT3Ty8uu"
+ },
+ "source": [
+ "#### Answer: type your answer in a string form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QMMpBaUay8uu"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/9.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7pIT8Z4ay8uw"
+ },
+ "source": [
+ "### Q10. Find the states where median income (```income```) is higher than income per capita (```incomepercap```). (1pt)\n",
+ "\n",
+ "Hint: Using ```total_pop.loc[criteria]```, find out the states with a greater median income.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KWqechtpy8uw"
+ },
+ "source": [
+ "#### Code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3jnjij4py8uw"
+ },
+ "source": [
+ "# INSERT CODE BELOW\n",
+ "# This is not part of grading.\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dEGjsuVJy8uy"
+ },
+ "source": [
+ "#### Answer: type your answer in a string form."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6pE9RP-Ty8uy"
+ },
+ "source": [
+ "# Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/10.py\n",
+ "# Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT YOUR ANSWER BELOW\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "O-Fklmq9zSBX"
+ },
+ "source": [
+ "\n",
+ "## Submission\n",
+ "Run this code block to download your answers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XZresUd5zRrY"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "!zip -r \"{student_id}.zip\" \"{student_id}\"\n",
+ "files.download(f\"{student_id}.zip\")"
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Digital_History/Week4-Introduction-to-data-visualization-and-graphs-with-matplotlib/Data_analysis_and_visualizations_using_Matplotlib_and_Pandas.ipynb b/Digital_History/Week4-Introduction-to-data-visualization-and-graphs-with-matplotlib/Data_analysis_and_visualizations_using_Matplotlib_and_Pandas.ipynb
new file mode 100644
index 0000000..fe73b62
--- /dev/null
+++ b/Digital_History/Week4-Introduction-to-data-visualization-and-graphs-with-matplotlib/Data_analysis_and_visualizations_using_Matplotlib_and_Pandas.ipynb
@@ -0,0 +1,2512 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Week3-Data-analysis-and-visualizations-using-Matplotlib-and-Pandas.ipynb",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gUF-FN8uahXq"
+ },
+ "source": [
+ " \r\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "B-bG6hEs0W2A"
+ },
+ "source": [
+ "#
Data analysis and visualizations using Matplotlib and Pandas
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0LZ4eo7EEfJV"
+ },
+ "source": [
+ "For **Table of Contents** click the icon to the left --> "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e6w-ktN0YRrd"
+ },
+ "source": [
+ "## What is Data Visualization? \n",
+ "Data visualization is the use of graphic and visual representations to present a given dataset. It helps us in detecting patterns, trends and correlations that usually go undetected in text-based data. In simple terms, We primarily use data visualization:\n",
+ "- To *explore* data\n",
+ "- To *communicate* data\n",
+ "\n",
+ "## Why do we need data visualization?\n",
+ "As the world becomes more digitally connected due to an increasing number of electronic devices, the volume of data will also continue to grow at an unprecedented rate. Data visualization makes datasets of any size (both big and small) easier for the human brain to understand, as well as easier to detect patterns, trends, and outliers in groups of data.\n",
+ "\n",
+ "Data visualization is truly important for any career; from teachers trying to make sense of student test results to computer scientists trying to develop the next big thing in artificial intelligence, it’s hard to imagine a field where people don’t need to better understand data.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vSstc5lqHPyL"
+ },
+ "source": [
+ "# About the Datasets\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0oBrAh8SmAl9"
+ },
+ "source": [
+ "### Stock Market Index\n",
+ "\n",
+ "\n",
+ "\n",
+ "This is a simple introductory dataset. The dataset contains a shape of 5472 rows and 10 columns, the first row is the header row, therefore the shape of our dataset is (5472,10). The first column is the datetime ranging from 1990 to 2012. The remaining 9 columns are numerical values that indicate the stock prices of companies such as:\n",
+ "- AA \n",
+ "- AAPL \n",
+ "- GE \n",
+ "- IBM \n",
+ "- JNJ \n",
+ "- MSFT \n",
+ "- PEP \n",
+ "- SPX \n",
+ "- XOM\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H0En23wfdMLc"
+ },
+ "source": [
+ "### California Housing Information \n",
+ "\n",
+ "This dataset serves as an excellent introduction to visualizing simple numerical data. The data contains information from the 1990 California census.\n",
+ "\n",
+ "The following is the data methodology described in the paper where this dataset was published.\n",
+ "\n",
+ "**Content**\n",
+ "The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. The columns are as follows, their names are self explanatory:\n",
+ "\n",
+ "- longitude\n",
+ "- latitude\n",
+ "- housing median age\n",
+ "- total_rooms\n",
+ "- total_bedrooms\n",
+ "- population\n",
+ "- households\n",
+ "- median_income\n",
+ "- median house value\n",
+ "- ocean_proximity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gMheundcWewc"
+ },
+ "source": [
+ "##Goals\n",
+ "- Building a plot step by step using matplotlib.\n",
+ "- Loading datasets using pandas and visualizing selected columns.\n",
+ "- Using the stock-market dataset to visualize trends in data from 1990-2016 for 6 listed companies.\n",
+ "- Breaking down the ```matplotlib``` function ```pyplot.plot```\n",
+ "- Introduction to Matplotlib\n",
+ " - Figures and subplots\n",
+ " - Colors, Markers, and Line Styles\n",
+ " - Ticks, Labels and Legends\n",
+ "- Plotting simple relational graphs such as:\n",
+ " - Bar plots\n",
+ " - Histograms\n",
+ " - Line plots\n",
+ " - Scatter plots\n",
+ "- Using our tutorial to map longitude and latitude data.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xlQspYFJxYHj"
+ },
+ "source": [
+ "# Grading\n",
+ "\n",
+ "In order to work on the NTT sections and submit them for grading, you'll need to run the code block below. It will ask for your student ID number and then create a folder that will house your answers for each question. At the very end of the notebook, there is a code section that will download this folder as a zip file to your computer. This zip file will be your final submission."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "glNwYzOyxYHl",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 52
+ },
+ "outputId": "b63d2882-4589-400d-9e47-5fa94fb1dd93"
+ },
+ "source": [
+ "import os\n",
+ "import shutil\n",
+ "\n",
+ "!rm -rf sample_data\n",
+ "\n",
+ "student_id = input('Please Enter your Student ID: ') # Enter Student ID.\n",
+ "\n",
+ "while len(student_id) != 9:\n",
+ " student_id = int('Please Enter your Student ID: ') \n",
+ " \n",
+ "folder_location = f'{student_id}/Week_Three/Now_Try_This'\n",
+ "if not os.path.exists(folder_location):\n",
+ " os.makedirs(folder_location)\n",
+ " print('Successfully Created Directory, Lets get started')\n",
+ "else:\n",
+ " print('Directory Already Exists')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Please Enter your Student ID: 123456789\n",
+ "Successfully Created Directory, Lets get started\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "keXK_L7B7oRI"
+ },
+ "source": [
+ "## Plotting with Pandas and matplotlib.\n",
+ "\n",
+ "By now you have been introduced to what Pandas primarily does, if you want a quick refresher here are some important points:\n",
+ "- A DataFrame is a collection or rows and columns (Just like any single Excel Sheet).\n",
+ "- Using pandas we can modify our DataFrames, this can involve dropping null(empty cells) values, changing column names, modifying column properties or sometimes trivial things as moving columns around.\n",
+ "- We can also load `.csv` files and convert them to DataFrames using `read_csv`.\n",
+ "\n",
+ "By using simple `pandas` operations we can get the most out of our dataset. However, pandas also has another super useful tool that comes with it. This tool allows us to directly plot columns from our data frames and visualize them as forms of graphs. This method is called ```plot``` and we will go though it in more detail throughout this notebook\n",
+ "\n",
+ "### Matplotlib\n",
+ "Although pandas completes most of our visualization needs. There are somecases where we want to be more precise with how we want to visualize our data. This is where `matplotlib` comes in. Infact, most of the plotting **options** that pandas has, come directly from the matplotlib. Although we won't strictly be focusing on matplotlib, we will explain both pandas and matplotlib imeplementations side by side for every **feature** we go through.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T7UcLg97_4PW"
+ },
+ "source": [
+ "For this exercise and tutorial we will be using a publicaly available dataset. This is the stock price for 8 companies and 1 index. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0vqPgVtxAhls"
+ },
+ "source": [
+ "### Importing libraries "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VCR3sTSJAliI"
+ },
+ "source": [
+ "import pandas as pd\r\n",
+ "import matplotlib.pyplot as plt\r\n",
+ "import datetime"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lm1hE6J4AJ8f"
+ },
+ "source": [
+ "### Using Pandas to load `csv` files - \n",
+ "\n",
+ "\n",
+ "In order to get started with the visualizations we first need to load our dataset. As mentioned earlier we can do this simply by using the built in pandas method `read_csv`. A wayr to do this would be to store the data (the URL link) as a variable called `url`, then you would would call pandas' `pd.read_csv()` function to convert the actual data into a pandas dataframe and store that into a variable called `df`, and then you can finally call the `df` variable again in the notebook to see the informtion of the dataset as a Pandas dataframe.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NT8x3dQ1A03C",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 424
+ },
+ "outputId": "437d7ff7-8641-4978-be0b-8b71a67bcfde"
+ },
+ "source": [
+ "url = 'https://raw.githubusercontent.com/bitprj/DigitalHistory/master/Week4-Introduction-to-data-visualization-and-graphs-with-matplotlib/data/stock_px/stock_px.csv'\n",
+ "\n",
+ "df = pd.read_csv(url,index_col = 0,parse_dates = True)\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
AA
\n",
+ "
AAPL
\n",
+ "
GE
\n",
+ "
IBM
\n",
+ "
JNJ
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+ "
MSFT
\n",
+ "
PEP
\n",
+ "
SPX
\n",
+ "
XOM
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+ "
\n",
+ " \n",
+ " \n",
+ "
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+ "
1990-02-01
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+ "
4.98
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+ "
7.86
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+ "
2.87
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+ "
16.79
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4.27
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0.51
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6.04
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+ "
328.79
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+ "
6.12
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+ "
\n",
+ "
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+ "
1990-02-02
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+ "
5.04
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+ "
8.00
\n",
+ "
2.87
\n",
+ "
16.89
\n",
+ "
4.37
\n",
+ "
0.51
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+ "
6.09
\n",
+ "
330.92
\n",
+ "
6.24
\n",
+ "
\n",
+ "
\n",
+ "
1990-02-05
\n",
+ "
5.07
\n",
+ "
8.18
\n",
+ "
2.87
\n",
+ "
17.32
\n",
+ "
4.34
\n",
+ "
0.51
\n",
+ "
6.05
\n",
+ "
331.85
\n",
+ "
6.25
\n",
+ "
\n",
+ "
\n",
+ "
1990-02-06
\n",
+ "
5.01
\n",
+ "
8.12
\n",
+ "
2.88
\n",
+ "
17.56
\n",
+ "
4.32
\n",
+ "
0.51
\n",
+ "
6.15
\n",
+ "
329.66
\n",
+ "
6.23
\n",
+ "
\n",
+ "
\n",
+ "
1990-02-07
\n",
+ "
5.04
\n",
+ "
7.77
\n",
+ "
2.91
\n",
+ "
17.93
\n",
+ "
4.38
\n",
+ "
0.51
\n",
+ "
6.17
\n",
+ "
333.75
\n",
+ "
6.33
\n",
+ "
\n",
+ "
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
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+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
\n",
+ "
\n",
+ "
2011-10-10
\n",
+ "
10.09
\n",
+ "
388.81
\n",
+ "
16.14
\n",
+ "
186.62
\n",
+ "
64.43
\n",
+ "
26.94
\n",
+ "
61.87
\n",
+ "
1194.89
\n",
+ "
76.28
\n",
+ "
\n",
+ "
\n",
+ "
2011-10-11
\n",
+ "
10.30
\n",
+ "
400.29
\n",
+ "
16.14
\n",
+ "
185.00
\n",
+ "
63.96
\n",
+ "
27.00
\n",
+ "
60.95
\n",
+ "
1195.54
\n",
+ "
76.27
\n",
+ "
\n",
+ "
\n",
+ "
2011-10-12
\n",
+ "
10.05
\n",
+ "
402.19
\n",
+ "
16.40
\n",
+ "
186.12
\n",
+ "
64.33
\n",
+ "
26.96
\n",
+ "
62.70
\n",
+ "
1207.25
\n",
+ "
77.16
\n",
+ "
\n",
+ "
\n",
+ "
2011-10-13
\n",
+ "
10.10
\n",
+ "
408.43
\n",
+ "
16.22
\n",
+ "
186.82
\n",
+ "
64.23
\n",
+ "
27.18
\n",
+ "
62.36
\n",
+ "
1203.66
\n",
+ "
76.37
\n",
+ "
\n",
+ "
\n",
+ "
2011-10-14
\n",
+ "
10.26
\n",
+ "
422.00
\n",
+ "
16.60
\n",
+ "
190.53
\n",
+ "
64.72
\n",
+ "
27.27
\n",
+ "
62.24
\n",
+ "
1224.58
\n",
+ "
78.11
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5472 rows × 9 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " AA AAPL GE IBM JNJ MSFT PEP SPX XOM\n",
+ "1990-02-01 4.98 7.86 2.87 16.79 4.27 0.51 6.04 328.79 6.12\n",
+ "1990-02-02 5.04 8.00 2.87 16.89 4.37 0.51 6.09 330.92 6.24\n",
+ "1990-02-05 5.07 8.18 2.87 17.32 4.34 0.51 6.05 331.85 6.25\n",
+ "1990-02-06 5.01 8.12 2.88 17.56 4.32 0.51 6.15 329.66 6.23\n",
+ "1990-02-07 5.04 7.77 2.91 17.93 4.38 0.51 6.17 333.75 6.33\n",
+ "... ... ... ... ... ... ... ... ... ...\n",
+ "2011-10-10 10.09 388.81 16.14 186.62 64.43 26.94 61.87 1194.89 76.28\n",
+ "2011-10-11 10.30 400.29 16.14 185.00 63.96 27.00 60.95 1195.54 76.27\n",
+ "2011-10-12 10.05 402.19 16.40 186.12 64.33 26.96 62.70 1207.25 77.16\n",
+ "2011-10-13 10.10 408.43 16.22 186.82 64.23 27.18 62.36 1203.66 76.37\n",
+ "2011-10-14 10.26 422.00 16.60 190.53 64.72 27.27 62.24 1224.58 78.11\n",
+ "\n",
+ "[5472 rows x 9 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6OQRKdnyCM6S"
+ },
+ "source": [
+ "### What is ```df.plot``` and how do we use it?\n",
+ "\n",
+ "As mentioned earlier our primary method of plotting will be using the ```plot``` method that comes with the pandas library. In order to call this method we simply attach it to our dataframe. For example, in our case the data frame is called ```df``` so we can simply call ```df.plot()``` in order to plot our dataframe. Lets go ahead and do that."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rmtZspMoCnga",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 272
+ },
+ "outputId": "00e89393-0cd1-4bea-d2ce-12a6ecac7d8b"
+ },
+ "source": [
+ "df.plot()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 15
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SJfzZw0uCqfm"
+ },
+ "source": [
+ "As you can see above we have a complete visualization of our dataframe. We can see all the 9 listings and a line chart of the stock market price for each of them (from 1992-2012). \n",
+ "\n",
+ "Although this is a good visualization, it has alot of information in it. When starting with visualization, it might not be the best case to put every thing on one chart. Next we'll look into how to plot columns of a DataFrame one by one, gradually adding more important features to it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7QQpytgrBXXD"
+ },
+ "source": [
+ "### Plotting a Column - [`y`]\n",
+ "\n",
+ "Lets plot a single column and see how it works:\n",
+ "- we add the `.plot()` function to our defined dataframe. Inside the brackets we simply declare the `y` as `'AA'` (which is our column name). Also, the `y` variable indicates that we're ploting specifically for the y-axis of the graph itself. Since we're setting out `y` variable as `AA`, we're looking at the y-axis of the stock market growth for the company `AA`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Xh7u10Q-BamQ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 273
+ },
+ "outputId": "99e3fced-c113-42c6-ff56-48b4d63638e8"
+ },
+ "source": [
+ "df.plot(y = 'AA') "
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 5
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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OCD3rDjE/UbvupX/1oND+McAo4FW7/QWssmqKkjXeX7w5pfObNnB3eevYvAGPTxgY2K/0GdqEeEv48UdG/mfm2rT7gyeT47yiysekb9by4ZItaRnDBUd3CWxv2l3GR0u38utXF7j23bKnjFXb9id0fecLz4Nn9g06NvrwNgldqy4Qlw1cRLwiMh/YCkwDVgK7jDH+97n1QMcI514hIrNFZPa2bdnPp6zkLx8t3ZrS+Y3rR16wPLp7q8B2ZZWJ6AXx5rXDAZizZidTvk1vvo7lW/bF7hTUfy+/fCW9uVmKHW6M/u1I2QFXbk1svBBscunUomHQsaN7tArtXueJS4AbY6qMMQOBTljV5w+L9wbGmKeMMUOMMUOKi4uTHKaixMafvyRZmjeMbLcuKqz+qlT6TMRAEmcE6M0vp1d47j5QEbuTg5Me/Yy3vk1vbhZnlkf/msHmCDnJI73RRMPphRJqElPPk3AS8kIxxuwCpgPHAM1FxO/U2QnQ9GBKVkn1FTtabEj9gmrtvMrni+iNXRiHnTdZonld1FQaW7csj2P7ua8HxErp+6dzBjB+cKegNqeQDinaE2YfD81TUxeJxwulWESa29sNgJOAJViC/Gy72yXAlEwNUlHiIVWTc6hW3atN48C2U7Ou8JlAyH4oboE/6SLa/KYu3Eh5ZVVUIZ+O8PPt+8JzgDeKkDjs0Q+jL7qOP7ITfzp3QFCb04QSWnYN4Os7Tgxst2taFPX6dYF4/tvaA9NFZAEwC5hmjHkbuA24RURWAK2AZzM3TEWJzcINu1M6P1RArnDYcD0eoWTiOA5t24SqKDZwpwbulkclU6zdfoAznviSPne/D8CPH/0srE+qOdLBWkgNjaYMjbb04zeB1Cvw8Nh5A137hOK8lNNs5addsyL+7yd9gPgiQfOdeLxQFhhjBhlj+htj+hpjfmu3rzLGHGWM6WWMOccYU3MZdRTFhf98szasrdvtU+Ou3hJPDnGvR6j0+Vi80f1hkUkN/KxBrn4CAJRXVrFkk5WfxRjD9y6ufamYWbrdPpX7/reYqioTVu4sUv1Qf2HiWXeN5owoY3fifACG+vX7uWxEd1o1qpf1qNTagEZiKnnPBU9/E1e/W19zd4dzUugVKqpMoEJ6KE5f8nTapQ8crOL1eRvo2LwBd9sa6OXHdQ8cdyZ66n7HO67XSHU8z31RQqXPF7a4GCpH124v5ZZX5geKWzSsF386AqcA31UaedG2wCtZj0qtDagAV/KGSItp6cTrkaiautNWns56lYPut0L8N+w6wKXDu/Hpr0dy17g+gYIH/hqZ0Vi4YTdVPsMDb3/Hpt0HkhpHpc/ELON2/MPTeX1utU9Dsgu7PaOYoAo8nrgiQfMdFeBK3lDcuH7S+afHPBZuM3ajwOOJKpidnhLlFYkH3kTCmQdEROjayhJupw20Cljc+7/vYl7jmklzmV2yg2c+X83Pn48/MvPLFdUmqCqfoXtx46DjK7cl7u8dD5Fs62C9CakGrgJcySMqfIaCGNphlc8wfOLHPDMj2ASyNI4q9GC9uh9wCOY/nlPtRdG1VXDgSTYqxkej9GAVpfbYl2zawwtflsR13nWTq+tZzly9IyitK8C8tbvSNkYn0TIjeD0S002xLqACXMlZDlYGV8aprPJR4PFE1cI37jrAhl0HeGDqkpjXdxbZ9eP1CAccaU3PPrJTWJ/ZvxnN5cd1p7zSx1EPfsjO/eGud+kiUSG2r6za/HPPW4vjOseZgbC80sfeskpO6tM2ofsmQ7S8KIVej2vl+rqGCnAlJ/H5DL1/8y6/fbvadFDpMxR4hd+f2S+sv99u7bTflkUwcRze3kp52swlkrDQ6wnSwAEGd2kOQE/btNC6cf1AVOfWveV8uTJzhYfjEd/OeSxYH6wtR/oMYvHz4d1jd6L6s0mGaBp4gVfUCwUV4EqO4l/AevHrNYG2yipDodfDER2ahfU/4p73KauoCjKx3B7B66S5LfDcNPlQDRzg9WuG8/iEgTzmSHjlXMwMdbtLJ8N7xs4Pctbgahe+p2esDjoWS4PfHcETJJq7pNOUFFoCLhGiGcN27q/g4xRz3+QDKsCVnMQvE51aWKXPh9cjeL3uX/2PlmwNyrXhd3ML5dB2TQBo3yzcs6MwxAbu5/SBHWlaVC3wgwR4BhfbYqVo7dO+KbefEjl1USwt1s2HvkfrRmFrDe8urK6A5HQzLEthHSCa98qGXZYXTV0vbqwCXMk5pszf4FrzsqLKWsQsjLCQaTBR8534uXPs4Uy5dnhQKL0fr0t4txvBuVPSJ2RevSq8JFk0Hj1vIPULvBE15lgpb90SfJ0xqGOYK+HVk+YGtp0PhXiCoyIRa0Ea0vvZ5iLRK4wqSi2j5If93PjSfAZ0DretVlb5KPR6IvopGxOfQKlX4HG9PhDx4RDKdsfCXzqq87RvVsTwXq0ZEqHEWyT8ecsfOL2va6CSU9hO/34rzRoUMrhLi0CbW451r0coiPCWs6esgrWOakXJCPD3bzqeBet3xVUAosqYOi3E6vLclRxkv52saeOu8ECUSp+h0CuuSZDAWrAb+cdPwtpnlcRf2cX5cCiOUNQBgutxppqz4/W569m0uyzhdLIAjey8JT/u285VgDs12EvtEmUlE8cF2p53cTUsKvS6Vj869+9fsdWuwON389ufRN3KQ9s1CZixYlHXNXA1oSg5hf8L6zSF/PNza2FuZ+lBDES0gZeGLD76ZbE/h0g8ODVPZyh7KE75lqq3hH+hNlb0ZKN63qAK7yUTxwVMJ24eNQBXT5rj2h6Nod1a0KQo/HozS3ZQst3Svv3T31+evmAmN1SAK0qOYIyhwmVB0O9KuGjDHuat3RUx8X/oa//wXq2B4PqV0bRqCE5x2rJR5L7n2iHukLqQ2Wl7gpTGEIZllb6gCu/xMG/trqhmjqHdWoS19e/UnHbNivjP5Uez6L4fu55X6TP079SM37m4dKaTuh5NryYUJWe44/WFvDRrnesxZ7V25wJiUJ+yYEHlDwRxplkNLWwcitOEcoYdxu7G6QM7cuNL84HUNXC/G+KqH6LXl3Q+KGK57115Qo9AQq6yiqqAqcVJWUUVs0p2BvafvWRIUIbAY3u2jnj935/Vj/OP6hLxeLqoUi+U6IhIZxGZLiLfichiEbnRbr9XRDaIyHz7Z2zmh6vUVYwxQcI71Avl4feWBra9du7uUPaUBduQ/drbc19U+0bHKstW6NDi41lkg9Rt4IM6W1pwvCXjlvx2DJ/f9qOIx3/940MZ7hC+VcYw/ftwn+r7HPlVSiaO48TD2wbeWqLRpKigRoQ3WGazukw8/4GVwC+NMX2AYcC1ItLHPvaoMWag/eOew1JR0kAsReuFryw78TFRCt+GplP1a28/OKrMPHnhkVHvs+dA4oty6dLAY30Gh9hujw3qeSPm0gYY1qMVIxyCuMpnAguYQCBQafLM8Pzqbpwe8iZy2oDIbybp5h+frqyxe9VG4inosMkYM9fe3otVTi2+7OyKkibiFYGR3NvACuRxEmqbbtmoHi1juPwl8sr+xe2jXO+TCO8t2sw7Cy1XvkhBK4/bEaChybQi0axBQVBA00/+/HnQ8Y0hi6UXDouuTT8+YVDQ/prtpRF6pg//A2jltuhmpXwnoUVMEekGDAL8GfKvE5EFIvJPEQlf7bDOuUJEZovI7G3btrl1UZSYRKpBGco5jsVDPzeP7g0E25A7Nm8Qds3HJ8Qu+xXvOACa2hVpUimkcNWL1V4i/7hoiGufYfZbRyyzhT9/SQs7OOfR86xMittDkm2FPigeOCOxhcjOLeN7kKSC/wFU1/OhxC3ARaQx8BpwkzFmD/Ak0BMYCGwC/uR2njHmKWPMEGPMkOLi4jQMWamLRJKbp4a8rruZUK4b1SusrdLnC9OM2zSJXSTXWaggFv7F1PLK5FzpnL7uXVo2pF+n8BwvAG2bFgVs1NG4c+xhfHXHKFrZC5xuBYoBdidhJjrxsDaB7dGHt4nSMz34ozSr6rgbSlwCXEQKsYT3JGPM6wDGmC3GmCpjjA94Gjgqc8NU6jqRNN9tduCIH6cXySFtGtOkqMDVrbDKZ8IEeLTsd8lQ6BU8knxe8GMnfhzY3hoyz2Qo8HqC8rtEGtcf7AVht6LCkXjmkiGBqE83j5Z04y/2UNczysb8pMVKyvsssMQY84ijvb0xxp/B5kxgUWaGqCiRCX2DLiyolsLTbjkh4nk9Wjdm14HkPRj85pFoiAj1C7xJp2x14qzIky7qR8iP8s1qKzK1S8uGdG8duayZExHhlL7teOGrNQFBnkn8z+lYuVzynXgescOBi4BRIS6DfxCRhSKyAPgRcHMmB6rUbSJp4KH22njqL/79wsEUN63Psi37mDI/fpNI0H3j7Fe/MHoJtmwSWjDheoepad2OUpZt2RcWvRqNX485jMmXD6NHcXgSsHTj98ev637gMdUIY8znuKfmVbdBpcaI9D0deWiboGCTeDLYbdlTHsjl4Q+2gej5p/20aFjIztIKhnR1XbMPoyhJDXzb3mA/93ZNY9vnEyX04efUnP05UGYsD08nG4nG9Qs4Jo785OngyuN78s7CzZzQu26vq2kovZITRNLArxnZk9euPjawH60Mlx9j3Curd4ijsnvfjtZC4iXHdovZF5LXwB+cGlykePOe1G3goQwNyWxY3+E77ncFzMSDIx34qya1SLKIdb6gAlzJCSK9KIsIfewvcySbbigej7hWPE9k8S2eBwUkX7tx0cb4E2wly4DOzVlw78mB/T0HKrjlJMvlsmUjSzC+ce2xrudmm4AJpXZap2oMFeBKTmBCvqj/95M+gQhAf36pxgkI4Dij4MO44vgeAPTr6O7SF3YfERZt2MMjH3yf3A0zjLOK0MZdZQzv1SqwDbhmHawN+F+g1AauKDmACdHBLxtRncq1foGX//tJH0YeGtkeWuiVQCZDIf48JqEcd0ixa56VSFT4fKzdUcqfP17BDSceEtd9N+w6wOAuzVmxdV9SY0yUpfeP4e+fruSqE3oG7umvRN8gSkh+NhGxXDTVC0VRcoBY39PLRnQPVIV34+GzBwTtx8o6mC5WOUK9d0YoEBzK8Ikf88rs9QBcfEzXjIzLSVGhl5tG96aosLr02p6yCuoXRK5uVBvweqTOa+AqwJWcINXitacP7BBY8BKRqFXVM8XSzbHt2qFRm/ecegQA/SNEYaYbvxfP7tKKGgnISQWPiGrg2R6AosRDqt9TEeGnR1vabJOighrTwJ1Eqorj5Mp/V+c+adWoHl6P8N5Nx/HiL47O5NAC+P3o95ZXJrSmkA38ZdvqMrX7L6QoNqE28GS4/sRedGjegFP7d+AvH69Iw6gSIx5Z88n31Qnf/EmmDmvXNFNDCsP5ZlLrBbioCaV2/4UUxabSUUotVsrXSNQv8HLB0VbGPmfIfU2RC9qiM5K1cRzpArKJx6MmFDWhKDnB79+1EiyN7deOj6LkOImXbJhQ/Hb8v368nJ8/P8u1z1mDsptq31lxKF6/+myx+0AFCzbszvYwskrtfsQqis3/vt0IwNJNe2mRpAbuJBuLmH4N/I8fLIvcJ8smgUb1qkVCImH02WLe2l3ZHkJWqd2PWEUJIVZh33gJ1cAPbdskLdeNRjwh9enIXJgKnlrsNqiEowJcySmu/VHPtFzHaesd3KU57954XFquG43HPoyseft5f/GWjI9DyR9SqUrfUkSmichy+3d86dkUJQn8SZUuP65HWq5X6DChGGpG89x9oCLIn/39xZtTrlifSR47L3aJOSW7pFKV/nbgI2PMIcBH9r6iZIRT+rWjSVEBzRumbv+G4FJcySSbSobLj+sRlBb3yn/P4R+frXLt+/5Nx/PmtcNrZFyR6NUm83m9ldSIJx/4Jqyalxhj9oqIvyr96cBIu9sLwCfAbRkZpVLnMSa+fN3xsq+suu6j11MzlkSPCD/sC87zHZr32yNwxfE9ObRd5m3ykahX4OFgpS8rC71KYiTkhRJSlb6to6TaZsC1oqqIXAFcAdClS/Sq2YoSCWNMWs0cDR3eFt+uqxlPhltfWxDW1qBedbKokh/24zPZ8ZBxMukXR/PA1CV0qYHq8qkwpGsLimppsq2aIpWq9AGMZesui64AAB9jSURBVNhz9X/SqvRKOvAZXHN4J0uBw985tLJ9TdLQIYBG/vETADbsPBChd80wtFtLplw7vNYLR49oKH3SVemBLSLS3j7eHtiamSEqilWRJ50mlAKH2aS4ceaK8M6888SotuxZa3aGtTnt80pkRCJXaqorxOOF4lqVHngLuMTevgSYkv7hKYqFIf4qOPHgdAPPpANKm6ZFHBbFnv3ZMiv3iTNTYSYq0OcjXo9ErJVaV4jHBu6vSr9QRPwVYO8EJgKviMhlwBrg3MwMUVEsG3ga5XfQwmXDDCdtiqfQ8htzNwS2D2Q5mCdX8IhQFVqqqY6RSlV6gBPTOxxFcceY9GrKzvzaFx6d2cX1eIoiOAsKZzsaM1dQE4rmQlFyBMsGnj4J3rZpUUKl0VLBzfTz9wsHc9WLcwHLlfDPHy0PHLtqZHqiTfMdj0jKeeJzHRXgSk7gS7MGnm2cHh6/+u+3ge13bjiOPh1qLv93LqM1MTUXipIj+Hzp9QOvaY7u3jKwPfnyYRx3SLVL7Z6y6lqZKrzjpzZV5Hl34SaufnFO7I5pRgW4khNUGRPXYmBt5ZierQLbnVs2CLKLp9O/vS5hmVBqhwC/7bUFvLuo5nPbqABXai3llVXc+9Ziduw/SKXP1OoK6bHYX14dul8QEro/x8UXXIlNgTf7GviO/QeZVbKDPXZqhmsmza3R+6sNXKm1vLtwM89/WcL2/QdZsmkP5TnsH/30jNWB7UgPoob1anfkY22jNkRiDr5/WtD+B99t4etV2xnWo1WEM9KLauBK2ti6tyxt1yr5YT83vWyFHWzefYBV2/azYVd2Q8zThd8UNKJX66D2bAujXKPAk56ixhf/cyYPvbc04fMiLaDOXVtzb1QqwJW08OLXazjqwY+YMn9D7M5xsGLrvsD2QbugcU1UzakJvHYelpLtwdWFVIAnhtfjCSp2nSyfLdvGk5+sTPi8ygh/r9fmrE91SHGjAlxJC795cxEAb83fmPZrr9pmCfN88dDwl3Pr0KxBUHskgaC44/Wk/tAzKWjwkRZQV27bX2PBWCrAlbTQp70lXD9aujWlL4Uf5xX22gtE2c7Slwp/OX9QYNvvA960gS5BpYLX40nZhLL/YHKC1hjD2X//MuLx+9/+LqB4ZBIV4Epa+G5TdTKm9xdvTvl6bg+BmSU7Ur5utujtYv65cFjXoP1+HZuF9VEikw4NfPW25Ipk7yqtYNGGPRGPT/pmLaP+9Gmyw4obFeBK2vnLxytSjpDLN2tCA5fc2j2Lg0uWvXjZ0TU1nLygwONJWYBf9sKspM4rr6wdHlEqwJW0s3jjHn716rexO0Zgx/6D1CvIXZ9vN9o3Lwpr69yyITPvrM4H16xhYU0OKedJRyTm1pCSdvHiljHSGW3rJx3mxGioAFdSxi367PW5yXmj/ObNhQy+fxp/+XhF2LHZvxmd1DVrA4Ve969am6bhgl2JD4+kbkJxZqVMBLdFymt+1IvnLx0a1JZp19d4Cjr8U0S2isgiR9u9IrJBRObbP2MzOkqlVhPpdfLLlT+4tn+30d12+NB7S3nx67UAzFsbXqeydQYr52ST/103gndvPC7bw8g5PCIY90qOcROt2EY09tmRteMHd+Lz237EOUd24tierejaqlFQv12lFW6np414NPDngTEu7Y8aYwbaP++kd1hKLvHX6Za2PPrwNkHtW/dUv57uPmD9I18zaQ5j/zyDe99aHHYdN1/c43vnfx3Vfp2acXj7/HCRrFEk9bWSgw7lIxFzx6/tDJLjB3ekU4uGPHzOAAq9Hto2DVYySpP0comXmALcGPMZkLvL/0pGOXCwKiB4q3yGJkXVrnHFTax/5jfnbWDAfR/wzsJNvLPQ8lB5/suSuK5/+XHd0ztgJW8QJEIp9fhxvj1W+gx7yioo+SG2Z0rJ9lIAWjSqF9TesF6wa2h5ZZYFeBSuE5EFtomlRaROInKFiMwWkdnbtm1L4XZKbWTSN2sC211bNWLBPSfz6lXHANWBKZ98b9W7Dk30cyAO7aRHiKdGLjPt5uN5+Yph2R5G3uARUjahvLuo2uX1YKWPC57+mpF//CTmeWcN6ggQ881p7ppwU2A6SVaAPwn0BAYCm4A/RepojHnKGDPEGDOkuDj/X4frAk9MXxGwb+8vrxbCt445FBEJLNj5FzcPOhY5Tx3QIbC9s/RgzHsVeoQJQztz8+jeaRl7NjmkbROOrqEkR3UBSYMJxUl5pS/g2x1rcXTbvnIGdm7ueuzGEw/hItvHvzDD3lRJCXBjzBZjTJUxxgc8DRyV3mEptZmH3/+eC57+BoBHP1wWaPe/PhbYuT4+XGJp3s7MbP/7tjrUfmfpQeav2xVYEHKjYf0CJo7vz42jD0nfBJS8QJCE3fTKKqp4ZdY61/MWbtgd2F66OXKQjjGGGct/COrv5OaTenPXuMMTGleyJCXARaS9Y/dMYFGkvkp+4QzQeWJ6tavfmCPaBbb9GvjkmWs5WOljVol7drate8s544kvuOz5WUz7botrn8YZrhiv5C6eJEzgD723lFtfW0D3O94Jyxp41b+rK+rs2O/+dmiM4ZXZ64DoWro/ZXBVGpJtRSMeN8LJwFfAoSKyXkQuA/4gIgtFZAHwI+DmjI5SqRWc/9TXAY8TsDRxP49NGBjYdlbO6f2bd4O0bifbbC+Vb1bv4PJ/zQ60P3regLSNWcljREg0TubrVdX+GL985Vsa1fPSzvbFP3VAtV560bMzXc9/Z+FmbnttYcz7+L8DmU5QFlO9Mcac79L8bAbGotRiDlb6+GrVdr5atT3s2B/O7h9UpDe04owbXo+weKP7K2hNJcNXchu/mmCMQeIoS1flM7RpUp8lm6z9+gUe9h+solOLhmzeU8Yrs2OngX130abA9tMXD4k8NhG8HqHSl9mQe30/VeIikokD4NwhnYP248kQV+UzvPDVGtdjLW3XrJ7FjVyPKwpYi5gAxlRvR2PCU18FmfOWbt4LQEUCQtYfzwBwRIz0xgUeybgGrqH0Sly0iJCno9Ab/s2pV+D+b/XKlcfEda/6BV5W/W4sH/1yZNzjU+oe/mLQ8YrISGsxq+Pw+wZ4b9EmZiyvji6OVYy6wCNpKTgRDRXgSlwsjhD+PrRbeAKfjs0buPSEIV2tcIGf9G/PeSFae+A+9/0YAE8OFzBWaoY37epPC9an5msd+p82+vA2FBWGi8a5Iekd3DJMOinweqio8mU0oZUKcCUuXp/nnpzqt6cf4dreKKRAb7umRXg8QsnEcfz1gsEMdcncBtBIvU6UOFll5/L2m0KSxWnl+OfPhtC+WQPKKnyUHgx2b23duDrqckjXFjGzRxZ4hH99tYbr/jMvpfFFQwW4woffbeEPMYq6jjy0OgjrcYfHSa827smAXr7yGH4+vDsL7z2Zo7q15Hdn9Q06HqkclaIkSjz/S9G04J8d2w2AHq0bMeqwtnyz2lqov++t74L6OfOmvHr1sTHvud12RZy6cFOMnsmj6k4d541567n5ZSsxz42jD6F+Qfhr4RcrfuDJT1ZSr8DDsgdOAayw+TXbI9sO+3ZsRl+7wswrV4Xbvt0yGB7lYo5RlFjEowtEK8Dwy5N707VVQ8Yf2QmAZVusUmgvz17HQ2f3D/TzBxT/9YJBYdfIFqqB12HW7SgNCG+AzbvLALh7yiK63T6VGcu3UVZRxU+fsaIunRrIwM7NOX1gx6TvPa6f5XN719jqiLWLjukaqbuiRCSW/J5VsiNiWtfHJwykSVEhlw7vTtOiyCYRY0wg6vgn/TtE7BeJVCtURUIFeB3muD9MD9q/e4qV4vVftnvfRc/OjBiEkyotG9WjZOI4Lj++B6f0taI4vbpwqSTBZ8siJ8nbsf8g5/z9K66eNMf1uJsS4g/CadOkOjVsRYreJDNWuOfGTxUV4EqAT12+CP6w4Uzi1+xVfivJMO27LewqPUi326eGeaT4S5+5FQiJxJvXDgesVA+vzFrHpc/N5OVZVqERZ7rkRNixP7nSbbFQG7gSxHuLghdc/L6zBR7h2Z8NdTslZc4Z0omPlm5lcNeIWYkVJSrPfVECwGl//YKSieMC7RURbN/z7z4pYi4T/9oNwK2vLQBg+veWcnP1yJ5Jjc9tbSkdqAaexyzdvIcH3v6OtXby+UjcOfawwPb9by9x7bPid2M5IUPVccb0bU/JxHG0aaL1IZXkWBUhGKfMpaDCtT/qSfOG9WiVRIm+P7z3fexOLlS41I1NByrA85gxj83gmc9Xc/zDlq37Fy/M4uH3LXfBheutPCTdWjXkiuOrtQp/EVZndkFFqe2s2Gp5jgwJeYsLLRpy3pDO/PrHh5EsY/vF/72Ydddozj/KCliL5gWTCirA85RQF7+9ZRV8uGQrT0xfSWWVj1P/+jlAwGwxYWhwZOQTPx0c2PZHRypKbcWf0iHUJ/xASPX4VAssPHHB4NidbIqb1OcmuxBJpkLqk61K31JEponIcvu3Gi9rESu27uOEhz8JanMm4el117uB7atPsLTvieP7B/V3eoRodKRSGzm5T9vAtl9uh4rJ8opgzfeGE+MrDDLxrH6u7fFkPXTi92iZVZKZssLJVqW/HfjIGHMI8JG9r9QSfvXfb4P2+3ZsGvEVzpkt7e3rRwDVWQBfu/oYpt4wIkOjVJTU+JvjLdFfHce5Lrl0856w70JRjPwlfg5pGx5h7M8bngiFdmK3N+ZtYHYGhHiyVelPB16wt18AzkjzuJQUmL+u2mWqbdP6LNqwh39+vtq1b+eWDQPbfTs2o2TiuEAWwCO7tuSIDs1cz1OUbFPgDRdfVY7UsD/756xAOLufei7nuHFk1xbcNibYVr55T1nCYyx05MZ/e0H6Q+qTtYG3Ncb4R7MZaBupo1alr3n8iaQOa9eELXbVm0nfrA3rN+uu0VqyTMkrnNHC2/aF+14XxinAAbrYyo0/0OzMQYlHHjuzGmYi0jjlRUxjZYmJaKHXqvQ1z2l2dNl7Nx0f9bWvuEniblSKUptxCnA3P+9Eon3H9G3HH87uz5/PH8Qb1xzLQyHrRPHgtJl3aOaeZjkVkhXgW/yFje3fW9M3JCVV9pZV0KO1Zcd2pm09UgNllDzHKcCbh6R7TfRt0+sRzh3SmUKvh0FdWkQsVBIvbjnGUyXZK74FXGJvXwJMSc9wlHSwt6ySxnbIrzNZ1NJN1UUZ/OHCipJPOIVso3rBAntfeWVo9xolUQ+WeIj5SLKr0o8EWovIeuAeYCLwil2hfg1wbtpHpiTM+p2ljHhoOiJwbE+rMHDbptVmkm6tG/Hvy46m0Cs0iZJ5TVFyFf//9TertgeC0vKZZKvSA5yY5rEoKeJP+2oMLLAjLUWE7377Yy56diaTLx+W8mugotRm/IE75z31dVD7y1cMo0+MIsSZ4pwjO/G1XSQi3agLQp5gjGGNI+fJ6QOrcxY3rFfAa3FUEFGUXKfUNpP0bts4UJgB4OgerbI1JB4+Z0DGrq3qWA6xu7SC6Uur14u37a12k7rp5flBfY8/RD1+lPznL+cHV8fZuLuMS5+byejDqz2bJ/3i6JoeVo2hAjxHMMYw4qGPufT5WWzfV85b325k6IMf8tePlwMwZX5w4YXD22fndVFRapJTB4RXx5n+/bZAZGaTogKG92pd08OqMVSA5wiH/uY99tqvh7PX7OSGyVal6z9+sIwlDu8SgHn/d1JQhKWi1DVmLLcq4My8c3SWR5JZVIDnAHPW7OSgI5/wlf8OLg91yuMzAtuFXqFFo3o1NjZFqS3cOubQsLYG9TJTSKG2oIuYtZh95ZX0vef9uPufNqADv/nJ4bE7Kkoe8rNjuyVdcCFXUQ28FvDRki10u30q1/1nbpA5xLlgCbDkt2O4YVSviNf507kDtKqNUmcpylDZstqMCvA4mfbdFj5asiVqH2MMO0KynznZX17JRpfggstemA1Y2cpOeXwGlVU+Kqp83PXGwqB+Dep5+cXxPQDLtzQ0W1oiiXoUJd/weIQrT+iR7WHUKGpCicGKrXsZ/chngf2/nD/IdeUbLE+Qm16ez9vXjwgqjOrnlMdnsHZHKcsfPIVCrwdjDPsPhtfs27S7jGc/X82esurQ379feCQATYsKg4q2DuvRkjP/9mXS81OUfKJxvbol0urWbJPAKbwBrp88j+Vb93HLSb0DbVU+w76ySn79qpU8/tr/zOWt60bQrIEV1lt6sJKrX5zL2h1WoM1V/57DM5cM4ZC73g0qqODnshdmBQUhOAV2KIO6tODL20fRvpmaThSlrlWPqluzTYAp8zdw40vzXY/9+aPl3DCqVyCh/NAHPwwynazZXsrIh6cz7+6T2b6vnCMf+DDo/I+WbqX7He8EtT00vh9rtpfyt09WBgnvh8a7l3Zy0qF5+tNUKkqu8MQFg2nnosBcH2W9KF+oUwK82+1TARjXvz1T7eoY8+8+ieYNw93uQoX3yt+Npeed1UJ32O8/ZliPlhGrbOwsreCrlds5/+mvXY+Hct7QLgD87ZOVgbafD+8eaFcUxZ1x/dsHtneVVitSI/I4gMdPzq967dx/kK17y9iw60BY9rGKKh/XTprLGU98EVRSbKpD6A787TT2O9JM7imrCAh6J16PsPJ3Y3nSrsP3w77yIOHtz/XbunF19r9Q4T37N+FBBRcN68o7NxwX1n7JMV25+9Q+7pNWFMWVhg4Tyta94RV58o2c0sC37S2nntdD0wYFvPWtFToeqik77cVvL9jI1IW2pu2oExnKkQ9MY+n9pwDwmzcWhR0fc4RVUsnrEU7p1z7sOMBfzx/MaLtK9pw1Oxn/ZPDC4pe3j6J14/osvPdknvxkJSN6tWZYj1Z4QiqEvHb1sXy4ZEuYh4miKLEpdihQkQp55xMpCXARKQH2AlVApTFmSDoG5caiDbv5yV8+B+CWk3rzyLRlMc+5+eVvIx5rUr8gEJpeVuGj2+1Tg0wrAH8+fxAn92lL/ZAUrJ1bNmDdjmpt/5S+7Ti+d3XyqMPaVVe0vvy47tw1rlqTblJUyK1RhPORXVto5RxFSZIzBnXkl3Yl+kJv+gso1DbSoYH/yBjzQxquExFjDBc++01gP5rw/m7jnkDe3xN6F/PpsuBCyqt/PzZQGWP5lr28+PUaXvhqDRBsWjm5T1tO7d/etYrG9F+OpNJnKCp0Dxzwr4Sf3KdtkPBWFCWzOGteDunWMkrP/CAnTCi/eGE2u0orgtoOb9+UY3u2YmfpQV6fuyHQ/r8FG+naqiH/+GxVQHiv/v1Y3lu0mU4tGgYJ5EPaNuEXx/UICHA/14zsGVVLLvB6iBX0teDek2kQQcAripI5GtcvoE3T+nSsA95ZqQpwA3wgIgb4hzHmqdAOInIFcAVAly7JeVS0b265CP3s2G4M7NycRRt2c8fYwwNP23OHdKZegYdLnp3J/vJKrpk0N0jzFolsu+7YvAHjB3dixbZ9fGvbyQ91mECSpamWLFOUrDDrrtFkoPxkrUSMCQ8kiftkkY7GmA0i0gaYBlxvjPksUv8hQ4aY2bNnJ32/WLh5jzxz8ZDA4mI0jDE8MX0Fz3+5hpl3nhi2uKgoipItRGSO2xpjShq4MWaD/XuriLwBHAVEFOA1yfG9i6nnFX50WJu4+osI1406hOtGHZLhkSmKoqSHpP3ARaSRiDTxbwMnA+E+eDXIb08/IrD9r58fxTOXDA1a1FAURcknUtHA2wJv2IuCBcB/jDHvpWVUSXLxMd04bUAHNX8oilInSFqAG2NWAZkrt5wkbmHxiqIo+UjOh9IriqLUVVSAK4qi5CgqwBVFUXIUFeCKoig5igpwRVGUHEUFuKIoSo6SUih9wjcT2Qasidkx+7QGMpphMcvo/HIbnV/uk+gcuxpjikMba1SA5woiMjuTuc2zjc4vt9H55T7pmqOaUBRFUXIUFeCKoig5igpwd8LymucZOr/cRueX+6RljmoDVxRFyVFUA1cURclRVIAriqLkKCrAFUVRcpQ6LcBF8rf0qYikWrA6J8jXv6GINLR/5+v88rrqd0393eqcABeRw0XkGACThyu4InKMiDwNDM32WDKBiIwQkSdF5BrIr7+hiHhEpKWIfAD8GvJrfgAiMkxEXgIeFpG+2R5PuhGRo+zv320iEhY5mW7qjAAXkWb2B/sScL+IPCgivbI9rnQiIpdjuSfNBeaJiDfLQ0orIjIYeBKYA4wVkUdFZGCWh5U2jDE+oBJoBvQQkdGQP1q4iJyD9fd7GygCbrHbc35+IuIVkd9jff++AAYD94hI20zet84IcCyNRowxA4ArgVZAt6yOKP10Ae4yxjxpjCkzxlRle0Bp5ihgljHmGeAXQCmWIG+d3WGllT7AFmAGcKqINMgjLfwQ4H/GmBeBR8EypeTJ/DzAWuBcY8zzwE3AMKBBpm+at4hIdxHxf4BPA3cDGGNWAs2BftkaWzqw51ff3m4J9AVmisgoEXlfRO4UkbPs4zmn5YjIuSJyi4gcazfNBRqLSDtjzGbgY6AYGJG1QaaAY37DHM1rgEXAMsAHjBGRdlkZYIo45neM3fQ9cJaI3Ap8BXQAnhCRnMx7YpuDetu7PmCyMWaZiNQ3xmwE1mMlrcoYeSnARaSbiLwLPAO8KCKHGmPWGGM2ioi/6vEBYGX2Rpk8IfP7j4gcbozZAWwHJgFnAE8Am4C7RWRALmk59uvo3cBtdtM/RORUYD9QApxgt38K7AI62eflxEPKZX5P+x+0wECgoTHmM6y5/QV4QEQKcnx+pwGvAzcCxwMXG2PGANuAs3PpISUizUVkKjANOFdEGhtjqowxuwCMMeUi0gToDmzM5FjyRoCH/HP/CvjGGHMiMB3L5n2EfcxvVugIrLPPrfWfQ5T5fYz1Be8O3IP1VrHJGPOWMeY54B3g9BofcArYpp9DgV8aYx4B7gOuAwqwvhADRaSPMaYSS6s70z4vJx5SLvO7B7jB1uY2AvtF5DngUixNfIExpjLH53cz0NsY8xFQhvV3A5gC9Md6OOcKjYD3gevt7eNc+hwNLLaVxsYickgmBlLrBVcCFEGQ+9x3AMaYv2LZTn8qIm2MMVX24uUOY8w8Ebka+D8RaZ6VUcdPpPk9ARyJZdf/AUsrH+84rw3wZc0NMzlE5GIROcHxd9gCtBCRAmPMq1hvSydhPbDKgAfsfh2BWbXdbTLG/F4HFmO9ORUDPwb2AAOAh4FBItKt5kcdPzHm9xrW/M63Ne2VwNl2v0FYf89ajWN+TY0xG7AWK1/BGvvRItLB7uf/P2wOrBORS4FZWG9WaSfnBbiInCQi07Dcks61tbIdWP/0A0RkAJZNsQvWwiVAD2CoiEwHTgNe8r/+1DYSmF9noLMx5k5grYhMFJGvgZZYX55ah1i0t/8OlwA/xbKJNsZ6GPUDGtvd/wxcCGwxxtwH7LJfYycAz9ifS60iwfn9FTgf+BYYZYy50RizG5gP3GqMKanxCcQgifmdgfUG/AHW9+9r4BzgTmPM3hqfQAwizO9JEWltOwmUAh8CLYBRAI7/w9OxHCeOB84zxvw3I4M0xuTsD9AL+Mb+sAYBk4FrgCbA/2G5K30ODAH+A9xgn/dTLCE4OttzSPP8brbPawocBpyc7TlEmZvX/t0beNHfhuVm9k8sDeY9rC9AQ/v4K445FgLF2Z5HBuZ3o73tATzZnkea5/df4Bp7uzHQL9vzSGJ+fwFeD+l7M9YbYVOgsd02ATg70+Os1a+dbvjt1cbymT0amGOMmWIf+xD4E/BfY8z9ItLDGLPKPvYFUG5f5iVjzKSaH31s0jS/vcaYpcDSGp9ADMTyTb8f8IrIO1j/9FVg2U5F5Dqsxdc/YT2UJgDtgZeBCmxzkDGmAmsBrFaRhvl9bff11fzoY5Pi/A5i+fBjjNkHLKzxCcQgjvndCGwUkROMMZ/apz2NJcA/ArqIyEBjzEs1Md6cMqHY9qT1WB8wWP8AE+wFPLAWuVZi+5gCq+3zrgAuw3JDw9RS/+g0zq9WLnaJyAlYX+AWwAqseVYAPxKRoyDwt7kPeNgY8y+s1+2LRWQe1vxr3Zfej86vTszPB9xr//gZh/VmPB/rrWJTjQ06268qCbzSNAbexHJDmgscZrc/hmVa+AJ4EcvuNhVoax+/CWsRYWi251CX52eP9TjgIsf+34CrgZ9hvWmApVS0A17Fsulj7/fI9vh1fjo/x/xeAbrZbacDx2dlzNn+0BL8gLvYvycCL9vbXqyFuhH2fmfgeaC+vd8w2+PW+QXm1xCoT7V98afA7+3t+cD19vYQrKCIrI9Z56fzq83zyykTijFmrb35GNBdRH5srFe23caYz+1jV2GFWFfa55TW/EiTow7Mr9QYU26qTVgnUW3HvhQ4XETexnrjmJuNMaaCzq/uzU8ku8FVObeICWCM2SwizwJ3Au8ba3HhKOAuLO+En5taaueOh3yfn71QZIC2wFt2816s+fYFVhvL1zYn0fnVnfkZWyXPFjlZE1NEPMYYn4i8irXiXY7lj7ncWHlOcpo6MD8B6mEFHb0B/BwrDcD1xpg92RxbOtD55Ta5NL9c1cB9YiW8bwOMBH5rjHkvu6NKH3VgfkZEBmHZGLsDzxljns3ysNKGzi+3yaX55aQGDiAiv8JKYnSbMaY8Vv9cow7MrxNwEfCIzi/30PnVDnJZgHtMLQ12SAf5Pj9FUVInZwW4oihKXSen3AgVRVGUalSAK4qi5CgqwBVFUXIUFeCKoig5igpwRVGUHEUFuKIoSo7y/7tN7NeMDhIQAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bbOoKd8JBhFX"
+ },
+ "source": [
+ "This is also the matplotlib version of the same method that we showed where `df['AA']` showcases the `AA` column of the stock market data and the matplotlib function `plt.legend()` indicates the type of data you're working with for a specific column. This would be very useful if we're plotting many columns on the same graph later on. Also, calling `plt.show()` directly displays the plot that matplotlib has created.\n",
+ "\n",
+ "```\n",
+ "plt.plot(df['AA'])\n",
+ "plt.legend(['AA'])\n",
+ "plt.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "59q8X0V3DYhi"
+ },
+ "source": [
+ "Above instead of plotting the whole dataframe we plotted a single column `AA`. We did this by declaring the `y` parameter as a string `AA`. This way we can plot any column in our dataframe by simply declaring its name as the y parameter.\n",
+ "\n",
+ "**Note**: We didn't have to declare the x-axis, this is because ```df.plot``` automatically assumes that the `index` values (in our case the datetime) to be the x-axis. But what if we wanted a different x-axis? Lets see how we can do that below."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H4Lo0hBtEsdd"
+ },
+ "source": [
+ "### Changing the x-axis [`x`]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iZyvqZ-wE01g"
+ },
+ "source": [
+ "For this example we are going to simple use another column as our x-axis (for e.g, `GE`)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "R5yTtKn5BqfZ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 297
+ },
+ "outputId": "0c7a07a3-dc70-4145-9ef5-e622caf05bc6"
+ },
+ "source": [
+ "df.plot(x = 'GE', # new code\n",
+ " y = 'AA'\n",
+ " ) "
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 16
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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hWyq/7MoguaCKr2bFcWUvH/m8az/bRlFVHVufHXvany27rJZhb60H4M1pfc56U+XzgSLgCgqnQG55LScLq7FUCbY+O9ZkdldSXU+PxrznD9YkkZRfxfd3DGBMpBcACY0LmqMjPbl9aEiH7+XnYkOUjyNT+/rxzsrj8gy4JaXV9Uz9bCv+Lra8NKUnvfyczR7XhF4v8eJfCczfnWmyfWyUV5vnDA13p7S6vl0Br2vQMX93BuOivGVr3KY4/j0/75Vj+KEe9oyMMPQI2JtegrWlhUm5fleo1Gg5llfJyYIqkguqSC6sIj6tVN4/vZ/ZfjMXPYqAKyicAvN3ZyIEbHhqdKtH86KqOjzsrdibXsK8zSnMHBgoizdApI8jT1zRnVsHB3fKmjU2yJWVj43kr/0Gy/22ZuBfbU4hs6SWzJJaJn+8ldev6cWsISFmj9XpJWZ+vVOOTRtjXOoPUFPfIP9+7y97UQnB0geHmfUtB8Nsu7i6ntlDDLPv9OLm7Jrr+gWQXFjFvoxS3r0uGq1ezxdrTvLxuhPyMTEB7d94zDHpoy1klRri9NaWKsI8HdBoDfH2D26IuWSrQBUBV1DoIvUNeubvzmBMpFcrEatv0FOhacDWypInFx7E38WWFyf3NDnGylLVYeWlOZpK3JvCMzq9xIHMMtYm5vPn3iwKK00NQb/clNKmgE//fFubVZQt48+Juc0+3k0z6bt+3MOi+4fiaNO6uOinHemEedgzvDGHvMm5cMszY0gvruHWb3dx9/BQ+oe48d+lh/lxR7rJ+Qezysktr8XX2dbs+FrSoNOjarwRbnlmDP4utmj1esa9vwlHGzXX9r00Z9+gZKEoKHSZ1UfzKKysY9bg4Fb7mhYcF8ZnklZcw3vXx7SKJ58qTX7i+RUanvnzIIPeXMuML7bzxcaTrcQbDDHgtoqD2iuBb9lx/vttaQDYNs5iP54ZS1J+FQv2ZLY8lUNZZezPKGPWkGBUKkFBpYY3lxuyblzs1Dy76BBhHvaysVVL8W5i8b7ONfjSaHXc98s+MkpqePKK7gS62aFSCX7ekU5WaS0vXBV1xhZEL0QUAVdQ6CI/70gn0M2Wkd1b93gtrjIIeEl1PXcOC2VwmHurY06VpmjLdV/uYGF8FkVV9cyZ1rvN4weEuKK2MC9e71wXbXb7o+MiWoV1/j3UvADZw9eJqTGG5hJpxa0Lj37akY6dlQUz4gLYmVLMwDnr5H0P/LqP3PJa3r3eENIwF75p+pyDwzq2nS2v1TL7292sO5bPa9f04uHGp5ryGi2frE9mRIQHIyIu7T68nWlqbCOE2C2EOCiEOCKEeLVx+w9CiFQhxIHGn75nf7gKCueX5Qm57Eot4ZZBwWatVIurDTPhME97npl4Zu1Tp0T7clWf5owNG7UKe6vm2f3VMX5E+TQX1rxxbevmyACZJTU88+chAFzt1Hx/R3PF5f2jw1sdP3NgIHHBrhzPq2R4N8MNyd/FVo45N1FaXc/fB3O4NtafX3amc7ORdwrAlhNFeDna8NmGZMprtG02aL51UHCHvuFrjuYT8+pqdqeVMCXaDy9HG1Yk5LLsUC6v/nOECo2W585T96JzSWee7eqAsZIkVQkh1MBWIcSKxn1PS5L059kbnoLChUN5jZYHfjWUmrdl0ORgbWjm+/71Z37hzNFGTaWmAQ8HK56b1IOn/jjIf/8+Iu/PKK7mWGPBEBgWS43RaHV8uemkiRvi3OnRlFQ1l7cbj1mj1WGjtmDu9Gg2JRVy23e7Gd44o62qa+BITgV/7c+msq6BURGeLD+cS32Dni0nCg0LqdG+WAjB3wdz5GvmVWjIq9AQ89rqNj9nalE1+zJK2/RZ2ZVSzP/9FC+//udgDv8YvQcYbjodZeFcCnQo4JIhw7+q8aW68ec8NHZSUDi/GIcM3NrwJYkNcuXAyxPOStx1X0apoXP9pChGRBgWCI3zt2u1plWOR3Mq6OnnhCRJrDmaz2v/HiWrtJYp0b7MHhLC84sPcd8v5kvb96aXMuOL7XKe9tYThVhZqBgY4oZGq5OLih5bcACAWwYF8WtjrnheuYZXp/Zi9pBgDmWVmwh4Z9iaXMTOlGLuHx3OdXEBBLvbm3ymG+cZZvaDQt34z+SeCAEqIeR/LVQGz5nLgU7FwIUQFkKIA0ABsEaSpF2Nu+YIIQ4JIT4UQphNDBVC3COEiBdCxBcWKg1NFS5efF2a0wXzGg2azHG2Fs0+WXcCVzs1t5pZPE17azL3jTKEP768tR/OtmrmrkgkpbCK27/fwz0/78XOyoLf/m8Qn97cj4Ghbix7ZITJNYztal9ckgCAAH7Zmc6vuzIYEOqKrZUFNmoL+rdoqdYk3gAL7x3CbUNDEEJQZNS8YubA1k8tkxsrM5uwUAnmzYrjip7efLI+mVHvbuTqT7by1aaTLNmfxY3zduDeePMcEeFBnwBnevs709PPiR6+TkT6ONLNy7FT6ZmXAp1aHpckSQf0FUK4AEuEEL2B54E8wAqYh6FL/Wtmzp3XuJ/+/fsrM3eFixYvRxvWPjGS8R9s5pW/j+DhaEV9g54XJ/fE2fbsenUfyipjw/FCnr4yktxyDbd9t1ve9+vdgwBDmX6Iux1X9vIhq7SWN5YlMuHDzdiqLXhpSk9mDwmWUxDBEKowxvipoikU8/7qJI7nG36faFQxGZ9eijn2v3SF7Jqo0eq460dDqOP2oSEm6Yj9glzYl9G6aOe5iVFM6OXDhF4+ZJXWsLwxrj13RbN/zF3DQ/lpR5rJ08flSpfymyRJKhNCbAAmSpL0XuPmOiHE98BTZ3x0CgoXGOGeDvT2d2JlY26zi52aJydEnnUB/3hdMs62anr7O3Pdl9uxVAk8HKwpqqrD28maE/mV7Ekr5flJURRV1XN1jB/rEgsIcLXlmYlReDq2fkBuuQjblCb4y87m1L4m8QZwb6y+nPDhJpPz7h4eSkygC5P7+Jo8fZQ0WseOjvRkZ0oxJwur5H3+rnbsyyiTPVIArurjw90jQuXXAa523DMyHFc7Kw42Lrr6Otvw7dZUoNkZ8XKmQwEXQngC2kbxtgWuAN4WQvhKkpQrDM8q1wKKa7vCZcFVfXw5nF1BlI8jX8/uf9ZbnR3OLmdtYj4xAc7c+3M8Pk42/HjnQCxUguFvb2Dj8UJyyjSoLQR9A1248n+bGRLuzvx7Brd73e7ejnw1K457f96LlYUKW7UFpdX1/Ocv83/Kfi62HMoqk7sLgcEfvLu3I34utibiLUkShZV1jIvyYt2xAgB+vmsgagsVN83b2WrREeCd62JahT6+2nSSuSuOMbybB1/OisPB2pKUwirWJuYzNNyj09/hpUpnZuC+wI9CCAsMMfOFkiT9K4RY3yjuAjgA3HcWx6mgcN6prdfx7KJD/H0wh8l9fHn3+mjsrM5uMbNWp+eVxkyTg1nlxAQ48+3tA2Qvku7eDmw4XsDJgmrCPBx4eP5+Sqrr8Xbs3E1lx8libNQqIrwcsVFbtNk04pq+fgS62jJ4riGve+bAIObvzuBYXiXPLDLMjh8f353p/fz5a382Sw5kk1JYbXL+8G4eSBJ4OFhRVFWPjVrFqO6erDqSz5IHhjJvcwq2agvUFoIFezKJ8nXin8bv+oMbY7C2NDwhhHk6cM9lskjZEZ3JQjkEtPKNlCTp9O3CFBQuErJKa7jnp70k5lXwzMRI7h8Vfk4Wyt5Zecwk3lyr1bHmaD4zG61qx0R68d22VMZFebPySB5Nk+Aevm03WjBm+8kiBoS4NQqqhVyub4yPkw0vTelJ3Btr5W0tbXEBPlybxIdrk8y+z9IDOVhbqnhzWh92vTCe3PJaHpm/n1VH8hkX5dVY5GO6MHyioIrpsf68e32M2Zx7BcULRUGhQ3acLObB3/ah1en57rYBjGnHre9MsuxQLl9vMcR7VcLgA56UX8XSA9mygNuoLWjQS7x+bW/8XGzxd7Xl9X+P4u7Qcfu1wso6kvKrmNjbl4/XnSC5oJLRjV3pjcmr0NDfSLzbY0JPb9YfK0ClEvi72JoslC6Mz2JhfBajunuyyah5RFOIxRxzZ/RRxLsdFAFXUDBDXYOODccK2JRUxML4TAJdbfnu9gGEnaNH98TcCpP+lE1NHGYODOLVqb0Ag3HWgj2Z+Dnb4ulozctX96SqroG5yxPZk1bK2Chvk8+TWVJDalENqUVVpBbVyLPoJidArU4iu8y0urIrhHnYs/F4IT7ONnx5axzxaSW88s/RVscZi3d7hHvay2ETBfMoAq6gYERpdT3PLT7EqiP5JtvTimvQG3WtWXk4j25eDnTzOjuCPumjLa22qQTMnd5Hfv3e6uPkVRjCDjq9hL5x4bBBL/HFxpNU1zWQWlRNYm4lxdV1GDfdMU4ZnNEvgEX7srCzsqCm/tRbnqUUVTOquyf/u7EvrvZWsocKwJe3xrEnrUTOIOkM5pwOFUxRBFxBwYgftqe1Eu8mmmLeLzT2hJw5MJDXr+nNb7sz+GxDMm9Nj5bDK6uO5GGjtmCUGcOrjpAkiSnRvoR62PPJ+mR5+9QYP3R6iZyyWvZllDJvc4q8b/wHm8gsqZFbt4HBWMqY/93YlxAPe0Ld7XG2U/PqP0f4flsai/ZlAbQp3tf29eOvA52rpuwX5IqLnZrkgkq+3GQo2R8R4cHPO9PYllzMgBBX9qSZ5pB/fks/KjVanl2UYLK9Lb9xhWZEy154Z5P+/ftL8fHxHR+ooHCeqGvQsSWpiBMFVfTyc+KDNUlyl5itz47B28mGiBcNVkDPTIzk7wM5ctHL3w8NIzrAhdSiasa8txFrSxXH35h0WuMZ/OY6eZbdFioBE3v7EOphT4i7PesSC1h5JA9Ha0sq6wy50qEe9lzR05vMkhqySmvJKq2htKb9Qphr+/qx/WQxGq2Oig5yroPc7MgoqWlzv5WFit7+TmaLd9pjYKgb8/9vMGuO5vPrrnRmDwnhip7eHZ94iSGE2CtJUv9W2xUBV1Bom592pPHy0iNt7vdztiGvQkN0gAt/PTgMgJDnlgHQ29+Jfx8e0ea55tibXsLH65L5+KZYLC0Evf67Cmjuh2mOlDevapWDnV5cw9WfbjUpdrGyVBHgakugqx0BrrbsTS81Mb86Vf55aDi/7krndzP+4O1hb2WBr4styQVV7R7nZGMp30Bu7B/I221Y4V7KtCXgSghFQaEN/j6Y0654xwQ488CYbtz78165t+WgN5uzNQ5nV6DV6U3K1zti0b5sNiUV8uJfCbLnx82DgphzbW9eWNK6f+Wo7p4m4q3R6rj+yx0kZDc3bIgOcGberP5UarSsPppPoJsdU2P8KKjUmPh1nwrvzIhGL0ldFu+/HhxGTIAzm5IKuf37Pe0eW6Fp4NWpvZi/O0P2VkkprEIvSXTz6ly65KWKIuAKCmbQ6yUemb+/3WMqNQ0s3JOJh4MVkxp9uvMrTDvjqLqYK95k7Wq8APjCVT04mlvB/N2ZTI/1Z3Fjb8zYIBfevd50NvrJ+hMm4g1QUatl7PsbTWLcV0f74tXJYp/e/k4czq4wu6+piKcj7hwWyqAwN15eepj8ijqu/Wwbyx4ZzvOLDXHva/r6MTjMnazSGhbGG9rD9Q10QaPVcSyvkiM55Xg72VBYVcc3W1J4Y1kiHg7WbH12zCXb77IzKB15FBTMoFIJ1j85qtV2Z1u13PQ3paiadccKmDkwCGtLC7YnF7U6/ooPN/HNlhQ6E6osr9XKHit3Dmv2BHGwtmR7cjEAz0yMYtVjI1FbCBxt1CYi/N3WVD7bcJKWpBXXmIj35qfHtFmENCjUja9mxcmv7x0Zxj8PDZefMKDtpspgSHNMe2syax4fyQNGzSG+25bKvT/vNbnBTf54q1y8k5BVztzliXy24SSR3o58eWsc3b0dOJZXSZinPbOHhODpaM2x3EreWJYIGJpHG/u2XI4oAq6g0AZhng6EezZ7Ud8+NIR/Hx5uYsoEhuyNN/49ys3f7Gp5CVIKq3ljWWK7C3xg6JJz3RfbAbCzsqBWa4j5BrsbMjG2nSwi3NMeH2cbIn0cuS4ugIOZZUiShF4vseVEIa/92zrn2hxTPtlCWU09KYWtY88nC6u59+dmj/AxUV4IIRhrVLxknOkCpl3sX7vGkESDi3YAACAASURBVKMe4e3IMxOj+PHOgR2Ox0Il6OHrxPie3vz14DBuGxrCf/8+zKJ92dw/Opzlj4ygt78zXo7W1DdWijalb76xLJG96eY7+1wOKCEUBYV2mDkwSJ7xPX1lJKlF1bTQrw5zm5c+OMykKUFLVh/J4+k/D8n2qE9OiOT1RjFOL66hoELDxuOFTItt7q4eE+DC/N2ZPDR/P/vTS8lpx5+8JRWaBvq+tsbsPmP/boCb5u3k6Ssj5c7y5jhRUMV/JvdAksBCCCRJIj69lK83p7D6aHNK5qL7h+Jsq+ZITjmP/n5A3t7H35nPbulHVV0DLy89zOJ92UT5OPLN7AH0CWjuqnPzoCC+afyuF90/lCcXHmBtYgEzvtjB0deuPOu+NBcil98nVlDoAkWNMekxkZ7YW1vKKYWbnx7Dowv2s7+dtLinr4zkjmEhlNZoOZxdTlmNlrLaekprtJRVG/79bltr8X/936M8NaE77602+IoMfNOw0LhkfzZJ+ZW42Vux5YQhXLPsUC7RAc5dEnAXOzXX9vVn/bGCDp8MAN5dddzktYeDNbteGMfKw3lytWjTTe7d1ceJ8HLgSE5zzPyavn7878a+ctjGxU6NvZUFoyI9OVlQjVOjqD/8237Siqt5ZFwED43phpWlaYDAzd4KawsVoZ72vLA4gbWJzSX4761K4uWre3b6O7hUUARcQaEdftqRBsCG44V8sCaJ7NJa3O2tqNBo2xVvMAhfS/Ezxt6q7cW3JvFuibEwgqFIxslGzaGscrPHt+SXuwYxPMKDD9YktSnedwwL4fttaW1eY90To/hiY7LJGANcDU2O6xv0rcZobakyibl/tiGZWq2OJ66I5NutKczfncn25CLcHaz47f8GMzjM3ez7frYhmcq6Bg5llXOyoIr7R4fL/T2/25ZKlI8jNwww36v0UkURcAWFNiiuqjNZ/GvyDOkf7NrpZgIxgS4czGwW+umx/jw3KQpnOzXWlhZIkkSPl1ei0ZrP8e6Ippl4ZxkQ6sq3W1Plz9KSvf8Zj7uDNX8fyGFCL2/mTo+muKrOxImwZUNiK0sVWaW1jOzuSVlNPYeyyrFUCd6c1odnFh1iYXwWx/Or+OuBoQghOJxdjl6C5xYdkp0WnW3VLH9khNw0wpiM4ho+Xn+CP/caKkbvHRnGvaPCcbVT88uOdLlY6ZlFh3Czt2L8ZVToowi4gkIbGDcdGBDiyrge3ry1wmDvOvPrna2OT54ziUkfbcFGbcFbM/pwyze7TMQb4OoYP7yMGkA06KVTFu+ucl1cAEsP5Mjx9ZYMCHGVBTTEw152Evxhe1q7170+LoA7hoXQzcsRSZJYGJ9JsLs9g8PcGRPlxYA5azmYWUbkSytJfG0i390+gJ92pJs8nRRX13PNZ9vIKm020+ob6MLh7HKTRdN7R4bx/FU95NeBbnbYWVkQ5G7H4n3Z3P1TPGlvTe7yd3OxomShKCi0wZLGfGswpPVtM5MmaMy3W1M5UVDFTQMDifJx4re7mzvi7HvpCtLemtzKivaTNmbCTQS72xHoZnsKo2/Nn3uzeK6dvO2DWeXUN1Z7hrjbk1ZUw+akQhM/FmOsLVUcePkKCivrWNBYyCOE4MYBQXIYxNPRmqOvXQkY3BPDX1iOhUrw4JhuxLVojGws3gAHMstk8Xa2VTNzYBAPj4swOSbIzY7Smno+uKEvSx8cxve3D+js13FJ0KGACyFshBC7hRAHhRBHhBCvNm4PFULsEkIkCyEWCCE6NiBWULiAyCvXsOVEIcfyWhepnCys4qBRXPn7bWkdhiuaGu++uOQwA+asNWlu4Gpn3lnvxoFBPDK2m9l9k6N9SS+uIbOkWdi8zPS27AotM2iMqW/QczjH8JlDPezIq9Aw26h5chNvTe/DsxOjOPjfCagtVKw+mi/7lpvDzsqSJCNPmJ4vryKvXEN2qXnr2iif5upKHycbvry1HyseHcGb03rjYG0aNAhytyOztBa9XiIm0OWcebVfKHQmhFIHjJUkqUoIoQa2CiFWAE8AH0qS9LsQ4kvgLuCLszhWBYUzRnVdg9weLMjNjvVPjsLSqOT9wzWmi4i705pzjQeHubEzpf3c45LqetYYpdCFPr+cQDdb7K0scbZV89ykKGKDXPF3sWV6vwA+NjPLXWZUjdlEQWVzml+Iux1pxc0Lkf4utqfl5w2GG1W/INc20x5bputtMGrGMOWTLUzs5cPE3j6tStytLFWcfPMqhsxdR0Flnfzdy+dG+/LouAjCPB3kBg7fbEnhzeWJ3PeLIdPF0caSSG9Huvs4yr04q+saqG/QU6HR4mJ3+c0hO9NSTQKaMv7VjT8SMBa4uXH7j8ArKAKucJFwwshAKaOkhhWH87g6xk/e9q8Z8QRDXPaDG/oy9K31XX7PzJJaHG0sOZZXyV/7s4kNMg0hNPWZ7Aybnx7DyHc3ACCEodGyOcHvDLcODuK6uECu/Wwb/xzMIdzTnhUJrfO+tz03Vhbv43mVJBdUyU8ZV/XxYXdqKe+tTuK91UmEedoTF+RKcmEV9Q16Pru5HyEe9ux8fhxhLyyXr+nrbMOGp0abLYe/e0QY18cFklRQybG8So7nVZCUV8W/B3P4bVfzIvI1ff0uS/GGTi5iNjY03gt0Az4DTgJlkiQ1fYtZgH8bpysoXHAUVpoWrDzz5yF2pRazJ7WU4/ltO/QFudmZiPfnt/TjgV/3tXl8S5qyV0abedTvrHgDLIg3HNvD14nE3AoGh7qdsoAPDHXnWG5zGOl/a1vH5cdGeZFdWou3ozWWFipeWnqY3anNTyHLWwh+SmG1SVPj0e9tZOVjI3h3pWlaZW65hqiXVnLyzavMtk5ztlMzIMSNASFu8jZJksir0HA8r5IKTQMTe/l0/UNfInTJTlYI4QIsAV4CfpAkqVvj9kBghSRJvc2ccw9wD0BQUFBcevrl7V2gcGGg00ukFFbx6YZklnayWYE50t6aLNvHdoWTb15FcVUdP+5IY09aqYkYdoanJnTH0UbNf/9u2y3xTPD4+O7sSSthV2oxWp2Es62a0ZGeDAhxo1LTwNsrj53SdUdEePDpzf2IebU5JfFyyh7pKmfMD1wI8TJQCzwL+EiS1CCEGAK8IknSle2dq/iBK1xoJBdUMf6DTWfkWk2Nh5sYG+XF1Bg/HltwoNWxe14cz4A5nWsUDIbFy4IWTw1ngsfGRxDl40QPX0cqNQ1M+WQraguBVtf8QWICXfh6dhyrDuexID6TE/lVJt7kE3v5EOxux1dGHYLaYkJPb1Yfzeefh4bTJ8CZ+gY93f9jaJBx/I2JSg/MNjhlARdCeAJaSZLKhBC2wGrgbeA2YJHRIuYhSZI+b+9aioArXEjo9RLbThYx69vWmRbX9PWjuKqerR2kDl7MzBwYyAOju8mty/R6iS82nWRwmDtejtaMeGeD2fPG9/DmtqHBPLHwoEkoKjbIhWmx/kyJ9kOSJO74YY/ZCtHJfXz57JZ+Jttq63XYtlOZerlzOgIejWGR0gJD2uFCSZJeE0KEAb8DbsB+4FZJktqdIigCrnChsDA+kw/XJMl2pqfKvw8PZ8onW+XX/zcilE1JhUzq7ct3W1PlKsGOmBzd+UXI0ZGelNVoZV+W02F0pCc/3GHeMfC7ralmHQ69HK0prq5H1yIncd2To6it1/Hj9jT+aKyaNMe6J0cR7nl2mkFfqpxyRx5Jkg4BsWa2pwAde0UqKFxglNXU8/LSw0R4ORLp48jG44Um+93trYj0ccTFTo2zrRVlNfVsOF7QqmJywT2D6e3vzJxpvSmsrON/a0/w9ZZU4oJd6eblQPxL43li4cEOhfn2oSFyY+HO8MMdA3n6j4OtBNzfxZaiqro2W6+Z49EWhTHGjOzuIf/uaGPJFT28Wbw/2ySU4+loLc/Cx73ffiiqp68TD4/tpoj3GUQppVe47PgjPguNVs/bM6Kp1GhbCXhxdT3bTxa3e43E1ybKj/y3DApGkiSW7M8mvbiGveml7E0vxcfJxkQE26KjUnVj/m9EKFmlNa1muHcMC+Havv5c89k2k+2Tevuw4nDbVrAeZrxHAJILKpn00Rb5dWyQq0kufBMts3na4t+Hh9Pb37njA7uIJElsOVFEQnY50Y0x9fJaLSXV9RzNqWBfRqmcKx8b5MKSB4ad8TGcTxQBV7gsqNBoaWjMovhpZxoDQ9yI9HHk+cWdawlmzLHXJ7bKWxZCEBfsSnqjWDSl9y2M7/zMujPYWlly89etG0f8GZ9l1kGwpXj3D3aVDaQARryzgbVPjJIbJDSxL70MrU5i5WMj+HBNEquOGIqSPrghhigfJ677cruJ0Vd73DEspNPHdoWDmWW8/u9Rk8/THlmltUiS1GY3oosRRcAVLnl+2pHGa/8cxcHGkpkDg8gsqeW2ISHc8s3OdisqZw8J5pq+fsz4Yoe8bUa/gFbi/dHaEyZl82DoQ3k6eDhYt2quALTpItiZWPuO58fiZm/F9V/uMFlcHP/BJj6ZGWtSyJRVWoNKQE5ZrSzeMwcGERPowifrTnRJkL/floafsy0DQ906PrgdJEmioLKOpPxKswvP5vj05lhGRHjibGveyuBiRxFwhUsanV6SO8s7WFvK/tEfrT2BTpJ4e0YfMkpqTHpJ3j40hB+2p2GpUhEbaFoteaMZv+nj+a29VE63pL2+Qddm6qCFSrRaQOwMQ+a2XT368Pz9xKeV8MSESH7ankZ8eikqIbj/l+YipfwKTYdxboD3r4/hyT8OAob1BFsrC2YNCTY5pq5Bx8rDeYyM8MTVvuMqyg3HCnh84QHKajp3Y1x0/1CEMIi+uQKhSwVFwBUuWV75+4hJfPmekWGymFfWNcjZECfyK00EvOkcPxcbVCpBdICzPGMN8bAzeQ+9XmJ4N89WlYini16izbzvUxHvlsQFu7K3Rejhxx3p/LjDtNDO2Mp1vZHvSW9/J27sH0iFpqFV04pgdzs+uqkvj/5+gOLqer6cFic/tUiSxOMLDNu3nCjCRq1iRr8AnpkYhbOtGo1WR2pRNUVVdYafynqKquo6lWNuzIzG/qJgeHKYO71Pl86/WFAEXOGSpaDSNEWwSbybaMqGKKisI8rHET8XW0Lc7Qn1sKNfsCu9/JzRaHUm4QbPFot+ZbVaXliSIL+eFutvYkN7qgS42nIsr+2S/lPBwdqSqsZQS0vx7iqHsys4nN26CnTW4GAifRwJ9Wg2w5qz/CixQS54O9lw47ydJlWnGq2eX3dlMCTcnaLKOj7dkCy3seuIWYODqWvQsTA+iwgvBx4b351D2WV8tSmF2CAXsktrKaisky0KbhoQSHSAc6di4Hq9xOqj+fwRn8ngMHdmDgpq5YR4IXDhjUhB4Qzx1oxo4oLd2mxgEPLcMnr4OtHNy4Hf7xls1hDJuGdlNy+HVn/8xp3dv7ilH4vPgHgDPDcpitu/33PK5z88thufrE/GykJF0pxmK9cKjZbcMg05ZbVkl9VyMLOs3ZztllzZyxs3eytWHM6Twxm3Dg4iq7SWjccLGRLujqONmhyjEFKlpgFHG0vSi6tbWQY8NymK8lotc5cfI7uslsFhbrx8dTDejtZ4OFrj4WDNq38fYfH+bPxdbHn6ykimxvihMgqLpBXVsDuthA/XJpHW2ITCuN3d2CgvluzPYv7uDEPbtf6BTIv1bzN0syethOcXJ5DcaHi27lgBc5Yn4myrZskDQwm7gNIgFQFXuOTYnlzEH3uz2HC8oN2Y6bgoL9YdKyCrpIbaq6JwabG/uKqOzzecZGR3T3amFBPu2TyrXJeYz90/xSNJEOZhzy93Dzolh0JzuNqpT0u850zrjVvjzWhIuGl/SScbNU4+aiIbPbcdbSw7FPA7hoXw7MQodHqJ77amMm9zismi6ejuXuRVaNhyoognFx6kolbLm8sTm88fGoqdlSW/7mpegPV0tOb1a3rzwZrjJOVX0dvfibnT+zAozA2VEKiNrH0n9PImOsCZmwYGyaGYzJIaVh3JY9WRPPakGZ4mkguquG9UOD5O1rzyj+GmPTXGj49nxlKh0fLPwRwW7MnktX+P8taKY4yN8qJep+fd66JNWrmtTcwnuaCK167pxc0Dg3h84UH+OZhDea2Wk4XVXRbwTUmFfLMlhbdnROPncmaaczShCLjCJUVaUTU3f9M6zc4c644V4GhtyY93DcTXufUf1rOLDlFV18CTV3QnKb+SME8H1iXmc9ePptXEH8+M5ek/D5psmzkwkPm7M0/pM5R2cqHOHP2CXHCyUcvNJYZ1M98guKiqju+2ptIvyFB0lGxkr2vMzufH4eNsQ3xaCff9speiqnrc7a0MXQIaufsnw/fh4WBFUVU9zy1OwHjdcOagQP7zVwK/7Gx2WyysrOO+X/YS5mHP3Ol9sFGrmL87g3t/3kv/EFc+u6UfTjaGzJGJvX0BQ276ysN5rDySx+Fsw8JxD18nHh/fnSM55aw+mk9skAuvNBp8WVmqGBHhwb0/x/P8pB7cMiiYWwYFk5hbwe+7M+R4f9wba3l8fHeu7x+An4stM/oF8NWmFGrqdaQUVbPqSB6jIz358Ia+8qz96T8OklpUjau9FW52VoZ/7dW42VvT29+JKB8nauobmLv8GD/vNLxPbnntGRfwLptZnQ5KKb3C2SK/QsN321L5alPzYte9o8KYHhvAlf/b3OZ5Sx4Y2sqXu7Zex7OLDvG3UU/MR8dF8MCYcCL/s7LDsYzv4YWPs42JYH1+Sz90eomH5+/vysc6bRJemYCjjWkKnUarI+ql5s/RN9CF9OJqszeOppz3n3ak8fLSI63yyI2JDXIxCV10REyAM442atnp0Liq087Kgj/uG4JeDyuP5LLycB4nG+1p+wW5MLG3D1f28pEbT9TUN3DFB5vbzP7p7e/EovuHyo2kI15cIS/QDuvmzrbkYoSA4d08CHC1bXXztbeyYP/LE7CyNDwZPPTbPtkzXm1huFs1GYB5O1nzxa1xPLnwIGnF1UR6O3Isr5Ktz44hwNV0EbyznHIpvYLCxcBbK47Ji4fDurnzwx0DKaqqa9cq9qUpPU3EW5IkliXkyvFYYz5ad4I/4jueUQ8MdWNtYoHJtt/uHsTQboaKzIm9fYh4cUWnP9fpEBfs2kq8AVQt4vjtearM/Hon940Kl7/H9opmuiLeYOjBGeRmx53DQvF3teXvAzmygNfU65j8scFjxkIlGBzmxu1DQ5jQywdvo6bQYEhJLKio45+Hh/PI/P2yAZmLnZrHx3fH1sqCZ/48xDdbUnlgdDiTPtoii3fynElYWqjILKnhj/hM/jqQY3bx2MpSRUZJDZuSClmXmG8Syzd2bgTIr6jjui+24+tsyxe3xPFi4yK3l6PpuM8EioArXBI0PUbbW1syNcaP277bzY6UYowfMJusTJs4kl2ORqvDRm3BkZxyWTCaMiiM87BvGxLcKsXOHC0X6Zxt1dhaWbAiIRdrtYoXFh8+3Y9qwhvX9ua6OENx0dID2RzOLufpK6OIfnUVvfyczJ6TVWqoFn3hqijeXN6+n/f+jDLu/Xmv2X2DQt3Y1YGP+cRePoR72ZukaRoT4eXA0dwK5m1Jwc3OiqevjOTDNUkm6YsL7x3SqgFyhUbLsdxKVh3J49uthoXme0aGyeI9LdafV67uRZ1Ox6xvdmNlqSI20IX7f9knC/ShVybIbfQC3ex4YkIkT0yIBAwt93r9d5X8GU4WNtsOR3g5cOfwUHR6SX7vlozq7om9tSWPLzhArdZQ9NQ0ez+TKCEUhUuGwso6Zn+3m8TcCkLc7ZgWG8A1ff34dmsqyxJyKalunZ726tRezF2RKBtVCWFoV9bSSvW3uwfx8Pz9FLe4Rrinvfxof75Ie2sye9JKuP5LQ8Xom9P6MH93BkLA3w8Nb3X85xuTeWflcRbcM5gb5+3k7Rl9yCnT8FEbVZ5tYau2kMXJzsqiU9WZffyd+efh4eSU1bZa9N341GhCPOzJLKnB1d6KvPJabvxqJ2oLFR/cEENBZZ1Zb3VzrHxsBE42am75Zhf5FRq+nt2ftYn5st3AlmfGyDa6LSmv1Zo0mrBQCQaFujGuhzdxwa6sS8znz71ZspNlbJALE3v5MDzCgwd/3WfSp7TJUgFOr2HFGWvocDooAq5wtsguq+XWb3aRV67ho5v6ckVPb5OUvw3HC7ijg8wOK0sV+166AgdrS8prtMzfk8FbjYuBn93cjwd/24dKwKc396O4up6P1p4wW+5uzMyBgcQEuNDLz5l6nY7HFhww6TJ/JjAXe35gdDifbzwpi6IxUz/dihCC6bH+p9XRx1Dp2PFxkd6Ocpu6xQ8MpbCyjjeXJ5JeXEOUjyOONpbsTS/FVm3BXSPC+L8RodhbWTJ3RWK73e47wsnGknqdnieviOTLTSflm++i+4cQF9x2Wf+xvAom/m8LtmoL3r4umlHdm0vx3111rM2nifaI8HJgzROjTu2DoAi4wiVMckEVs77dRVVdAz/cMcDkj7O2XscvO9OZY5TWZg5zbnm/787gucWG+OXMgUH8ezCH/S9fIT92Gz9mt8XbM/pw44Ag+XX3F1dQr9Nz+9AQ8is07ToFngqO1pYmKX7X9vXjjWl95CKU7LJahr21Xl64Ox38XWxbrRWEedpz+9AQro72kzM29HqJse9vpKCyjtggF7YlFxPh5cBLU3oysrsnYPhv+MGa4yxPyMPFTs3UGD9+6kTIqqt8eWs/4oLdSMqvNPqpYkykJw+NbdtatwmtTk9ibgVanUSDTk+DXkKr09Ogk2jQ6w3bm/7VSXKR19onRtLNy/GUx60sYipckhzOLmf2d7tRCcGCe4bQszHuW1xVx50/xnM4u7zd0vOnr4zkvlHhrfwyKjVaWbzBUNwRF+Iqi7deL1FV18AN/QPadBwcEeGBRqs3yWap1xlCNS9N6cmKw7lnXMC9nKzRlOjoF+TKrtQS/jqQw18Hcljx6Ah6+DqxqvH9np0Yxf2/7Ou0Z8s1ff0Y18ObR4yyaFqe+9j4CB4dF9Gq2OnPfVlyWOFwdgWvTu3FLYOC5O8SDEVSn98SR0JWOe+uPn5WxLt/sCsvLDlsEkpzsVNTU6/DVm3BQ2M7vobaQkV0QMuKAfPUNej4YM1xhoR7nJZ4t0eHAt7YsPgnwBuQgHmSJH0khHgF+D+gyUz5BUmSlp+VUSoomGFXSjF3/RiPs62aX+4eZFK+balS4WBt0a54O9lYEu7pYNbsqGWfzOSCKgaEGBbSNFodD/66j/XHC9oMIfx69yB2nCw2G6L49+HhhL9w5v5UBoa6yYun3942gCA3O4SAJfuzeX91Etlltby4JIHFDwxj5eE8onwciQ5wYdtzY6lv0GNlqaK8Vsux3AqS8ivJq9DIYYJ7RoZRU9/ALzszWHogh35BLlzZy0fOMzemZXaLRqvj2s+2yYuGtw4O4qkJkWYrXsFQnLPySC6VmtNzcjSHrdqC6nodE3p6093b0fDj44CngzUj392Ah0PHhlpdZdmhXIqq6rk+LuCMX7uJziyLNgBPSpLUExgMPCiE6Nm470NJkvo2/ijirXDOWH8sn9nf7cbbyZo/7x9iIt4AznZqxkR6mT33k5mxbHxqNMHu9tz3y16iXlpBv9fXMPzt9Uz4cBN3fL+b/IrWse2/9ueQXVbL7G93s/54AaMaH//Nccs3u7CyVNHd23CDeO2aXoBh0XPWt50rNOosu1NL+HimoWnW6Pc2klNea4hx9wtgcJihkMfN3orCyjr2pJdwZS8f+dymzAhnWzUONpbsSCnm840nsVGreGdGNCcLqthyorkv6L6MMrPiDfDBmiT5aUOnl4h6aaUs3h/cEMMb1/ZpU7zBkKr5+caTHa4ruNp13hq2SZhrtToScysYHenFncNDGR7hIaf1FVbW4elovrHFqSJJhgyVCC8HRkR03NTjVOlMS7VcILfx90ohRCLgf9ZGpKDQAduSi7jzB8NaypBwd3acLCbEw55Qd3sTf4vvzKR4OTbOukM87Fl0/1Dm784gu6yWmvoGaup0LDmQTVK++arEWq2OYW+tRwj4+CaDf3ZmSQ0vLz3MhhZdfcAgaE00GWmdLKwmJtCFhfdG4+9qS8+X24+hd5ZDRrncw9/eIMdclycYik1GR3qx+mgekgST+viYnLs3vYRP1yez4XghjtaWPDA6nDuHheLuYM1//z6Cl5M10QHOpBfXUN7C5zzM056HxnRjQIgba47mMzLCcFNTCUP3oPJaLW/PiO6UgVSDTo8kgb5FR7hQD3uC3OzIKq3hZKH5gqO2aGmMNTjMdPGyul6HRqtvszPRqbI7tYQjORXMnd7nrDaQ6FIMXAgRgqE/5i5gGPCQEGI2EI9hln56FmcKCp3AQiWws7LAUiX4bVeGScWjs62aEHc7Qj3smTe7P1E+jjQ0zgbBYKz0yO/7WfvEKKwsVdw2NASAqroGbvxqR6uQSICrLZP7+JrYmUoSTIk2lHcHutnx0pSebDjesU82wFMTujO9XwDrEvN5aWnbGSCT+/iyLKFzTY4Bvmlxs9qcVIROD4FutiTlV7HxeAF1DXpC3O2I9HZEkiS2nyzm0/XJ7EgpxtVOzVMTujNrSIiccaHTS9RqdUyL9eex8d0BKKjQcPv3ezjamBqXWlTNEwsPEuHlwJvT+8g3UCEEL07uSVfIKjXE1LPLanGwtmRKtC/XxQUQ6eNIfHopOWW1zNucInc96gyO1pYEutnJ463UNJg8BTQVDp1pAf9uWyqudmqmxZ7duW6nBVwI4QAsAh6TJKlCCPEF8DqGuPjrwPvAnWbOuwe4ByAoKKjlbgWFLjM4zJ2jr00EoL5BT2ZpDWlF1aQWVZNWXE18Wil/Hcihb6ALvf2dOZbXbAfb3duB167pDRgyCracKGTe5pQ2O/Nkldaa9aK+8audTOvnz+97MjnYhe7w761O4r3VSR0eZyze5rI9OqKpXpQnVAAAIABJREFUm7xjY/bJgBA33l11nLuGh7IusYBPNyRzILMML0dr/jO5BzcPCsLOylQOSmsMs1d7o+1eTjYsf3QEv+/O4OW/j1Df2ED5REEV/xzMYUDIqXfdMS50MbgaWpKYW8F1X+5o56z2ef3a3ixLyJUF/HB2uUn+d1O45kyGUDKKa1h9NJ8HRoe36t50pumUgAsh1BjE+1dJkhYDSJKUb7T/a+Bfc+dKkjQPmAeGNMLTHbCCgjFWlirCPR1MOp3f/v1u8is0jOzuyTN/HuSPvVm42VvxxBUGw6IDGWW8uCSB5Qm5Jo/jMQHO3DEsVC4WsbZU8fSVkVzZy4d9GaU8+ntzEcnutBKzTX67yt3DQ1vNno15akJ3evs7n7I7YVNKoaONmga9xO97MvlqcwoBrrYmVZwt0er0PL7gABYqwQAzrdBuGhjEhF4+VNc1YGtlgb2VJTbq06s0fHRcBK72VhRWGNqmrTGqmu0K3bwcSC2qRqeX5P+W43t4seF4IYdzypnUx/D0tCethP8sOYwQhiYUZ4q3ViZiIQSzh4ScsWu2RWeyUATwLZAoSdIHRtt9G+PjANOAM1sjrKBwCsSnlbDxeCHOtmqu/mQr9To902L9iQlwYVdqCe+sPEaFxnz/yINZ5SaVfnUNeixUgk/XJ7PAyAflhzsGdFlQ35kRzTOLWjdQbk+8gU7N1ttDJQxhpT2NNxsPBytentKTqX39TCxbm9DrJVYdyWPJ/my2nCjinRnR9A00nzbnZm+FWyfaoXWWQWHuDGpcdF16IFu+YS5+YCjTP9/e3qkmmHNWbPKnScqvoriqjrkrjvHn3iz8XWz5elZ/2RTrdPl9dwbLE/LwdbZp5dlyNujMDHwYMAtIEEI0/d/9AjBTCNEXQwglDbj3rIxQQaGT7MsolR+3mxbb3OytWLI/m8X7Ot9oIcrHUc6eePWf1s0gOhLv+0eHs+pIHilGJfZrEg2zSVu1Be4OVsweEtyhD0lHHHplAtGvrG73GL1ksKedPSSYq/r4MiTcnacWHuS7bamGzBNrSxxsLHG0tkRtoTK5ofTycyLEw172izlb6PUSX21OYfG+LBbcO4RfdqabLABbmbnRgCHPPjG3Ug6DeDtZMyXaj0hvR0I87GnQ6Xnt36Mcy6vk0XERSEBWSQ1j399ETX0DD4wO56Gx3VqFjk6HnMby+kfHdVwUdCboTBbKVsDcMqqSNqhw3pEkiTVH87nHjOGSEIZFxtlDghkb5UWQmx0D31wnx23NseyR4fTyc6a2XscLSxJOqT1aU+NkY9YczWdgqBsDQ9z4fGNyK8fCrvLfq3vKftkt+WZ2f2KDXIh7Y628rZefM1aWKlYfMfhpN+HvYosQzQuIxhzJqeCGr3bw1ITunapS7CrLE3L5atNJDhq1rFsYn2ki3gBTPtlq8vqNa3vj72LL6EhP8ivqGDx3HQC7Xhjf6j1srSyIDXLhip7evPjXYQ5mljE4zI03ru19VoprnGwMktoUpjnbKJWYChc1by4375fx/vUxjIr0lLML/tqfzWcbknl6QiSL92fLBkPGTI/1p5efoZze1sqCD2/s26GAb3tuLGuO5MkdYNoj0NWOJfuz0UutXQs7S6CbLZkltYyI8MCcDcbzk6IY39MbgL3/Gc8t3+witcjQymz4/7d33lFRXV8bfi4MvQrSkaKAYAEr9t5LTLH8YmKKMb2a+pleTWIS03tiEk0xajQxicbee28IgghK773PzP3+mGFkmBmaIKDnWSuLmdvmcCPvnLvP3vsN7sjYMA++uK0PS/clcigxl9ySSib19MTcLI+0gnIszCRKtE2pHKwV2FiYM7NfpyaNtT52xWbpiTeg6z1TF0v2JJBdXMGAQBfdl1gnF0OjhIKyKk4m5dPR3oppn+/Bxc6Kj//Xixt7eTdLal9ZpYp5Sw/T178DT40L4URSPjvOZWFhLumEvKURvVAE7ZqjF/N4fPlxXZbGuG4efDa7t94j/2db41hc85FcYWZyFn7ohTG4O1qTV1LJb4cuGTiu18bf1ZYvb+9DVw8HzM0kfjlwkZfXRuHpaM20Xt58W4eb+i/zBhDRyYlPtsTR09dJb5G0sTw1LoQhQR0N2q7mllRy23cHSMguYcld/Rlao6gkKrWApfsS+e9MOu4OVrw3IxwnGwvGfqgxwLA0N2PFAwMNDC+uFLVa5qud8Xy4OVZXKTuhuwcbo+pftOxga8GQoI7YWyn4/bB+f/ZRXd1Ycld/nV/mhjPpPPjLUSRJY4D89PiuOForyCyquOL4tCxrzDmqTR2qsTCXmN7Hl3enh1/R9WsjmlkJrkkKyqp4csUJtsVkcv/wziyYGKpneLvjXKYuZr1gUijdvBx54vfj5JVWMaqrG5/d1odz6UXc+u1+qlQyz03syl/HDYt5arrFVPPe9HA+3BxLbmklr0/rzq39O7FgtSbssv3Zkfg425BZWM6BhFw8HKzwdrZBLcuMeH8HC2/uwe0D/HXXCliwrs7f01JhxrQIb0orlaw/rd8/xZjrTk1qivi3d/ZjeHDHOmegz67SZO58MDOCGc1cBl5WqeKBX46yKzaLGyK8eevGHsxfcdxoIVSwuz2PjArSLSzbWyn4dHYvMgorNF0njbS/nTskgFdv0FS9nkkp4LNtcTw6Kpievponqzf/PavXw/u2AX68fXPPBo+/SqXmwIUc7lhyyGDfh7MiGBPmocujb05EMyvBNUd0WiEP/nKUlLwy3rqpB3MG+uvtj88q1ltw/GL7ef55dCh+rnYUlOWz/VwWvd/YRDdvJ52rynsbDGfco7q68ePcSDILy4l8WxNv9e1gw8x+vowJc2f+ihM8v+Y0O85lsiU6kzsH+eOj9T50d7RmWoS37loqtYylucbdBSAuo4hxH5m2fKumUqkmOa+U3+8f1Mi7pFnI/e2+gdz23QHu+uEQHWwtCPZwIMTDnhAPB4LdNa9d7a1YeSSJVUeTuWdIYLOLN0BiTgm747KQJI0gZ5dUMHdIoFEBj8ss1ssKKq5Q6ipwTfHj3kSdgPfwceKbOy5r3t8nUw0MGAYYSZGsTYVSxZ64bP47k86W6Aw9o+xPZ/cmOa+U9zacw85K0SLiXRdCwAXtkj+PJ/P8mtM42Viw4oGBBv2dVWqZMYv1qyN7eDsR0NGOtY8Moai8iiMX8zhwIUfPR7MmP8+L5I4lh3R50DUr+Hp1ckaSJFztrfhpbiSfbYvjk61x2FiY88ioIJPjNjeT8HWxIUkr4NvPXV7MfHJsCMv2JxqYRgA8MLwzz00Mrfum1IGLnSUr7h/En8eTOZdRTFxGEWtPpFJUI6XS1c6SwvIqhgS58sLkpn9WXYR5ObL5yeEs2ZPA6mPJLD90qf6Tapzr4WiFpbmZnrNSTW7q5c0/J1PJK60kr6SKvNJKcksq+edUqkGVrYOVghvCvY1ep5p/Tqby/JrTFFcocbBW0M3LkYMJufg427Bh/jBNfr1KzZ/HUnjjn7OEejo0W0piQxACLmhXVCrVvLXuLMv2X2RAoAuf3dbbwGtQrZaZ9vkeg3MXz4rQvXaw1jS7Gh7sxs5zWVQq1VzIvpz298mtvXTZENUz6FPJlysujyRe7hphbiYxf2wIw4I7UqmU6y3L9nOx1c3AFWaaFLljL4/Dxc6SqRFejFm8E09Ha96fGU64jzNOjWjeVBdOthbcPSRQ916WZTK0RTOxGUXEZRSjVMu8NCVMr9VrcxPk7sA7t4Tz9Piu/HbwkkHWSW2mhHux7lQaqx4cRHxmMTd+sdfksdXtc6txtFYYzfvv7u3IK1O76YXbjFFSoaS4QsndgwN4enwIt39/EHsrBcvmRerCVgpzMz6YGcFdPx7ipi/28vWcvrp89pZGCLig3ZBWUMbDvx7j+KV87h/emecmdDUqNCuPJBGVqp9lYiahs/8CzRfBn8eT+b/Vmp7ftYtVai4oDl20Xc8fEyC9sNzgc+tyeamJn4stx7TmwNUl3NnFFbjYWepynp8eH8KwYNPdDpsDSZLwdLLG08laZ6xwNelob8XjY4J5fEww722I4csd8TwzPoSHRgaxLz5bF2dep10oTMwuYVfs5VDL9D6+dLC1oIOdJfFZxaw5lsI9QwKZHdkJZ1tLHKwVfLI1Ti+t8/ExwdzavxPezoZZK8aY2a8Tyw9d4t9TaaQVlHEquYDv7uynV/kLENHJmb8eHsI9Sw8zZ8lB3r65Z4tl79Sk5b5mBYJm5ExKAVM/3UNsehFf3t6HFyabniX2D3TRi992cdN0HuziZk+FUsXPBy4y6oMdOvEGQ2d2eysFD43swhRt06rMWguY+xY0oPu/CfxcbCksV1JQWqWbrVcvkFZnNTR3e9O2zj1DNU8GH22Jo6xKxbBgNxLfncJbN/XQHaNUy/x5IoVOLjZEvzGRxbMieGlqN27p48Op5AKsFGbMjuxEsDYjKPTlDXrivfPZkTw1LqTB4g2ap6uFN/cku7iCjVEZPD46iHHaNM3aBHS048+HhhAZ6MKzf5xi0YYY1HX0o28OxAxc0OaRZZmX/jqDuZnE2keH1FuAcT6zmJ2xWZibSbpqO1mGH/cm8M3OC7rZs4W5pFu8rMmG+cM4nJjHy38Z7w6x9ekRjRKB2lQ3U7qUW6o3Az9wIYcPNp1jSrhXnb3Gr0WqHXhu6e2js38DmDPQnyB3e+5fdoSZX++jSiWz7J5IbCw1aaKJ2SXc/v1BCsqqWHpPJMEemn8bNWfqoMkQaWpsurzGk1t9TypOthb8NDeSV9ZG8dWOeBKzS/jk1t4t4kgPQsAF7YCNURmcSMpn0fSe9Yp3dZpYNy9Hfry7P4Ed7Vi6L5FvdyUYGAUYE2+AiR/vNnn9yAAXg8fnxuJXQ8CHBmnysg8n5vLvqTT8XW15t4V7SLc1vth+nk+3xjGrny/v3mKYPz2wsyurHxrMvKVHGBLkqhPRmPRC5nx/CJVazfL7BupSBQFu6u2Dq70ld/1wiJt7+3JLn6Zl1GQUlvPQr8dwc7CirFLFS3+d4Z/HhhrtIwOaJ6kNUemMDnWnQqlizbEUzFac4NPZvY06P10pQsAFbRqlSs37G2Po4mbH9Ab8Ef51PIVRXd34cFYvfjt0iTuWHKzTAOCW3j6sqafa8oYITWYDaLoQbj+XyX+n03huYmiT+khXz8ATsouZ3NMTS3MzXU/ze4cG1pnTfa3x/e4LvL/xHDf18uadW8JNLioGeziw45mRyGis9NafTmPNsRRsrcxZft8g3cy7mvzSSp774xR+Lra8rnVDagzJeaUs3ZfI74eSUMkyfz48hIs5Jdz/81GW7EngwRFdjJ63/VymwZPbutNphHg48MTY5m9HIARc0KZZfSyZ+KwSvp7TxyDmrVLLSKD3R59TUsn2c1mMeH87heVKPB2tCfe15VStkm2AR0Z1oazSsCLT0txMZz4Mho/jc7W55XcMDKCoXMnSfYk8MKIzXk4NC6vYWyno6ePEXydSiQx01fusDzbFUlqpuqKUwfaCLMss3hRLDx9HPpgZYXKGqlLLHE7MZf1pjQl0VlEFVgozRoe688LkML3+3tXXXbD6NNnFFax5aIheSKY+jl7M44c9Cbp+MZN7evHQiC509XQgyN0eNwcrdp7LMingN/f24ePNsaQWlPPl7X1IzS8jJb+M/oHNW81ajRBwQZulvErFR5vj6NXJWc/HsXpf6Msb6OJmx9anR5JbUsmSPZfzuQd2dqV/gAsL10cbzRgBdMa91WyYP4xQT0cSs0sY+cEO3fbaNmIAy+6JpKevE1/tiOenfYn8tC8R0LjczxnoX29Bx9whATy18iTP/nFSb3tXDwe+3BFPgKsds/q3fBZDayJJElPDvfjnlCYfvaYdnkotcyhBI9obojSibW1hxqiu7kzu6cXoUHfsTAjzb4cusSEqnRcnh+mFVUyhVKn570w6S/YkcCIpHwdrBfcOC+SuQQF6ax0rDieRVVTB69NMz+gtzM2wsTRnSJArk69CQysh4II2y7L9iaQXlvPR/3rpxYSrxVvzWs1fx1N44c/TlGqbME3s7kmIhz0L10c36vMu5ZQy6ZPdJp3mAUZ2deOjWb10YvPgiM5YKsx4U+uA8/7Gc7y/8Rw+zjY8N7ErY8I8DGaAReVVfKnNjqhtD3YuQ9PG9rnVp+jh40RHe0vcr0Jf6avJtpgMfjt4idJKFaeTCyivUtP7zc3cOcif0koVfxxNNjhn0fSeTA33Nina1cRmFPHGP2cZHuLGvKGBdR5bUFbFisOXWLrvIin5ZQS42vL6tO7M6Otr8DmF5VUs3nSOyAAXJvXwNHFFuJBVTHxWCXfUqgpuKYSAC9okBWVVfLE9nhEhbgzq4qq3fcT723Xv5w4JYP6KEwwIdKFfQAe+2B7Phqh0Npi2mzRJ7Za0Eb5OeDvb8N+Zy71HfqjRLAk0s8h5QwOZNzSQC1nFPLnyJCeT8knJL9Plkk/s7sm0Xt6MDnVHqZa5+8fDBqYDxgwfJn+6m1v6+PDhrF6N/2XaMKbK4f8+mYqtkb7jHWwtmBbho8s8MUV5lYrHfjuOg7WCxTMjTMbTL+aU8OPeRFYeSaK0UsWAQBdem9YdFzsLzM3MjPY+/2LbeXJLK/lparc6F5i3aPu+jzWRatjcCAEXtEm+2RlPQVkVz03sCmjimkm5Zdz94yFdL4pb+3firXXRTOzuya2RnZpsO+ZgrcDS3MyghP1kcoFeu9OtT4+os3Kvs5s9ax8ZQkFpFb8euqjrq7KhVg9uYyTmlHDXIH+WatPpqnlSayZ8LbH7uVGcTSvE1tIcW0tzbCwUBHS0vWJjhYXrojmXUcTSeyJN5tErVWqmfrpHZzUHcORiHseTjhntUOnlZI29lYI47Reun0vd1mubz2YQ5uWIb4fms2irCyHggjZHbEYRP+xN4MZe3nTzcmTDmXSeXnlC16e6mt8PJzE7shNv3dSTLi9cmb9ITkklNhbmjOyqmfH/tDdRr7T+i9v6NDh90MnWgodHBnHfsM5sOJPOnrhsNp1NN5oNYyaBtYU5q44mG/Tl2Dh/uMEC3bVAJxfbZv+9NkWl8/OBi9w3LLDOHHqFuRlfzunDxZxSyqtUlFepKKtScS69WDd7rklagf76ScQbm3hlajfmDPQ3yO3OKa7g6MW8FjG/MEW97WQlSeoELAM80NinfSvL8ieSJLkAK4AANJZqs2RZzjN1HRDtZAX1cyQxl3t+OoylwpwHR3RmzbEUnaN4bR4e2YVnJ3RFkiSDNqG1qZ1Z0t3bkUXTw1mw5hTdvByZ0N2TIUEddY/PWUUV/Hk8WdfAalYzlUWnFZQx5N1tKMzNCNAa6dZuXVuT/c+PbnB2y/VKWkEZkz7ZTacOtqx+aHCTimb2xGUzZ8lBlt83UBeyu5RTyvAa4bramJtJLJ0bqeuxvupIEs/+cYp/Hh3aoMXTxnAl7WSVwNOyLB+TJMkBOCpJ0mbgbmCrLMvvSpK0AFgA/F9zDlpwfbH5bAaP/naMCqUaO0uZt9aZXoR8aUoY9w7rDMDJpHx+2GtavMeGebDnfBZoJ/ATunvw6ezeWCnM+fexYUbPcXOw4v7hxlPFrgQHawvuGOhPdkkllUo1lUo1zjaWRh3ufZxt6GDbfKbB1yIqtcz8309QqVTz6ezeSJJG0NWyxgeyoRWzSXmaxeSazj6nUjTtFeaPDebx0cFsOpvBxqh0/jqRgixrPnvOkoNM7+PLezPC2RKdgaejNT18HJv99zRFQzwx04A07esiSZKiAR/gRmCk9rClwA6EgAuayIrDl3h+zWmqW0fUDpfU5IYIb514AwS42vHi5DCGBHVk9ncHyC+twtXOkoU392DZ/ot6j8bT+/iyaHrPFu22Vxf2Vgpev7GHwfbqZk7V2Fmas/7xYS1qJnwtsGTPBQ5q7elG1Uj9rOb3+wcysAGdAZNyS1GYSbqnnUqlmvc2nCPU04EZfX154c/TrDqajLkkseahwXT3dmJ3XBbzlh5h9bFkVh/TZM7MGeh3VatoG/WvWJKkAKA3cBDw0Io7QDqaEIuxc+6XJOmIJElHsrIMm7YLBF/vjOf/VmvEO8zLka9u76NrImWMPn7OyLLM59viOJWcj5OtBfcO60yYlyMTumlSvBZND+fBX46xLz4H0BgcS5KmMGjOkoNG/SRbk+oQzWOjg/jk1l7sWzCm2drIXktUqdREpxXyx9FkXvs7irfXm/bQnBbhTZ8G2sFlFlVgb63Qubf/dvAil3JLUZhLjF68kzXHUpgzwA93Ryse+uUY+WWVjAnzYP/z+k3N0vLLW7yBVU0avIgpSZI9sBqYL8tyYc1vGVmWZUmSjI5aluVvgW9BEwO/suEKriXUapldcVm8+18M4b5O3DesMyn5ZTz067E6z+vj14GPt2gMFC7mlPL+zMutYKvzqO9dpr/WUlOvD1zI5WxaIbKscW1pCwR0tGNQZ1fWnkhlxzMj6+1TfT1QVqkiJr2QM6mFnE0tICq1kJj0Il22iI2FOd29HQnxcGBAoAs9fJwI9rDHStGwpxZZlskuriQus4jyKhX5pVVsP5dJZKALn247D8DZ1EKm9/HlibHB+HawZVb/AqZ/tY9Hfj3Gr/cOxMvJhlOvjefmL/YSn1XC1phM5i09zPd39W+R3ie1aZCAS5JkgUa8f5VleY12c4YkSV6yLKdJkuQFZJq+gkBwmZIKJX8cTeanfYkkZJcwobsH0/v4GuRhm0KplnV+iDWbFFWp1AZtYU0x5dM9ONlYcPLV8Y3/BVqI//XvxPwVJzhwIYfBQR3rP+EaI7u4gr+OpxCVWsiZlALis4p1ITUnGwt6+Dhy9+AAuns70t3bkcCO9g0SSVmWSSsoJy5T40QUn1VMXEYx57OK9ezRHKwUmJlJxGYUkVtSyZSeXjw5LoQg98vZR929nVg0PZwnfj/BwnVnef3GHjhaW7DlqRGUV6n59eBF3loXzXe7L5gst29O6hVwSTPVXgJEy7L8YY1dfwN3Ae9qf65tkREKrhkyCst5fs1ptsVovuurH+I2RmU0yJEc4M+HB+v5JFa7sG8/l8lzf5wydZqOmm44xkrkW5OJPTxxXKtxW78eBXzpvkQ+0858qzE3kxjcxZXBXToS2NEWf1c7/F2N54yr1DJJuaWczyzWiHVmEfGZxZzPLNZbU+lga0GwuwOTe3oR5GZPsIc9we4OeDha6eLXdRlF39jLh9PJBXy/J4Gl+y/yxJhgumm/VOYNDeRIYh4fboplTKi7QZOt5qYhaYRDgd3AaaA6D+sFNHHwlYAfcBFNGqHhUnoNRBrh9c1DvxzVq2o0RbC7PSO7uvHdbkMD2g62lrqimGkR3jw2Ooi31kWzM7bu9RWFmUS4rxNF5UoKy6vIKNS0ll3/+DC6eV+9rIH6eHXtGZYfSuLgC2P0eoNcDyhVas6kFnIxp4SLOaUkZpeQqH1du8jK3cGKAK2YlyvVxGUUcSG7RK8Yx9PRmiB3e91/wdqfrk3oIGlsrEEv/gdoJiJyjScFLydrYtKLCPd1YvVDg022nm0MptII6xXw5kQI+PVNWkEZ7204x5/1tG9tKB3tLckuvvyH7WxrofdIXE30GxN1ZdiyLDN68U4StEU6G+cPp6tny86SGsPZ1EImf7qbV2/oxtwhdffyuJ4oLK/iUk6pTtATs7U/c0qwsjAj2N1BT6yD3O1xbOG2vLIsU16lRkYmJr2Is6mFRKUWcjatkJi0QiqUarY8NUIvBNNUhIAL2gzJeaUMXaQpkNjy1HCC3B1QqWX+OZmqFx6pyfAQN26L7MSvBy+xOy67QZ/zytRuOquuajZFpeti7b/dO6BNhiqmfb6Hiio1G+YPu66MHa4llCo1+WVVTeoXb4wrKeQRCJoV3w62JL47RW/byiNJvPDnaaPHPz4mmAGBLty/7Eid+eG12RqTYSDgFgozFGYS397Zt02KN2gWM1/88wynUwoI93Wu/wRBm0NhbtZs4l0XwtRYcNWpVKp5978Yfj6gadz01Y54nl9zmhEhbvx+/0BmR/px/OVxuuM/3RrH7d8f1In32LDLJQcjQty410Tb0L3nc1DVyskd1dWdc29NYnTo1ekW1xSqqweLy5X1HCm43hECLriqZBVVcNt3B/h6Zzxp+WW8sz6aRRtimBbhzbd39GNgZ1feuaUnHews6V5rcXF2ZCcS3pmst+i4MzaL7030QHlwRBeMZZldjfzcK+FoYh7mZhK9/MTsW1A3IoQiaFFyiivIKakkxMOBMykF3L/sCKkF5dhbKUjMKWH96XTuGOjP69O66xWvFJZXEZWq38TqzRt7kJxXxqfaHPC6WPPw4AZX4bUlLuaUsC0mkx7ejlfcXlVw7SP+hQhaDKVKzR1LDhGdXoiPsw1pBeW6kEZxhZL1p9N5bHQQT40L0VusO5mUz41f7NW9f3ZCV+4f3plPtsYZ5AlX8+9jQ+nh48QvBy7y0l9nWLD6FJueHNGo8Z5JKeCplSe4a3AAt0VenZ4WsiwTl1nMhjPp/HcmnWht58VXb+jW4p8taP8IARe0GD/sTdC1gk3OKzPYf9sAPxRmZlQo1VhbmJNVVMH7G2NYeeSypdaJV8YhyzB68Q6Scg2vARAZ4MLxpHz+PpnKt7s0vpixGcWUVirrncUeTszlsd+O42ijIC2/nNIqFS/+eYZ953N4cEQXuns7NntZuyzLnEkpZEOUxqT3QpYmpbGffwdemhLGhO6e12QfcEHzIwRc0Ggyi8pxs7fipi/3MTbUncfGGDawT8ot5cPNsYwN8yAus8jA+xHgt4OXABgT5s7BhFw+3hxLUYWSwV1cdU2obv5yny5n2xSHEnONtmNtSAjiSGIe6YXlpGujNV/c1ocLWcUs3hzLutNpWCnM+O+JYXRuoJlDQ1h1NFlXNaqpJNX8fqseHCTSBgWNQgi4oFG89neUzoEdNOEOTydrbunjq1u99UMWAAAgAElEQVQcLKtU8fya05hLEv0DOhh1Oqnm8dFBPLb8uJ5IV4s3UK94VzOlpxe7YrN0Vln7Foyu5wwND43swrDgjvywJ4F/TqXyyG/6jbQqlGpGL96pGeeY4GapqhvZ1Y1JPTz570y6njemEG9BYxGFPAID1GqZj7fG4e9iy5gwd50rzcdbYvl4i/EFxKfHhfDYmGAOXMjh1m8PGD0m3NeJUzU8JmsT0cmZkw1sRlUXt/bvxLvTwxt93vrTaTxcTyfE6lj7lZBZVI6VwpzzmUVM/2o/AJ/c2osbe/lc0XUF1y6ikEfQIJJyS3n9nyi2RGsaTpmbSQzs7ELnjva6vO2ahHk5YmkuseZ4Co42FnUKdF37nhkfwokk0/sbyv9NDOXOQf6NOketlvnl4EVeWXvZyn5smLvuHtRk6md7uPD25Hrj4ukF5by3MYbbB/jR198F0DRbenz5cdadTmNmX1/enxnBDRHe7I7LYlIP0/3PBQJTiBm4QEeFUsXI93cYGLkaY8tTw3lyxUncHKzo6unAV1o3mYdHduHOQQGUVioZvXin0XP7+nfgq9v7EPn21mYbew8fR5P2aPXxzn/RfLPzQpM/e/dzo/QWHTefzeDZP06SX1rFp7N7My3Cm+i0QiZ9slt3jMJMYudzoxj9wQ7+178Tbxhx6REIqjE1AxeFPAJAMzsc1UDxfmFyKEHuDphJoJZlnhwbwl5tzPnLHfFYKczo7GZPwjuT+fXeAXrnvjK1G388OEjnQdgcjAhxa7J4A9hpFztfntq01L0ibcWkLMssXHeW+5YdIb+0CmsLM7bHZBKwYJ2eeIOmp/mQd7dRoVRTVqnikpFFXoGgPkQIRQBATkkFqQ0Q76jXJ6CWZb7aEc9JbUjkYEKOnu9gtfu7JElkFl2+5pqHB9O7kzP74nO4/fuDzTLuKeFevHmFs9db+vjw0ZZY3vz3bIPP2fHMSHw72Oh5ayZkl/Dd7gRCPR2ISS+ivErdoM6Lq44ms+poMjueGUlAR7sm/Q6C6xMh4AKgYSl3oCl2+V+tRco7lhyif8DlqscnV5zA08kaW0tzfjlwichAFx4e2YWt0Rnc8uW+Jo9x2T2R3PnDId37B0d0YcGkUKPHllepqFCqcbLRtBRVqtSk5JeRkF1CQnYJPX2c6BegiU37drDlqbEh7IrL4nBinsG1xnXzYFyYB1tjNMYTPs42vPTXGbKLK/i/iaGMCnXX3Btt5WhMepHuXA9HKzIKKwjzcuSXeZHYWSnYF5/NrthsdsZmYSZBZKArHWwt8HK2bvK9EVyfiBi4QMfP+xNZdTRZt9g4LLgj47p58N6GcxRXtE5jJSuFGf6utvw4NxJHawW/H0pi4fpo9i4YjY+zDVUqNRdzSujiZs+7/8Xw/Z4EvQZWH8yMIL2gjO92J+g58Izs6sZPcyMNPq9KpSavtJLIhZr4/H3DAnlhchiHEnJZfSxZV2Tk28EGS3MzLmSXMKufL4umh7M/PodX/45ifHcPbu3vR1RqIY8tP0aopyM/z4vUZfMIBI2lyVkokiT9AEwFMmVZ7qHd9hpwH1Btg/KCLMvrm2+4gtbgjkEB/K+/H1/uOM/HW+LYcz67wb23q+nq4aAzFq7Nsnsicba1oLBMyZwldYdQ1jw8mHAfJ/44msyW6EyGvLsN0CwY3jssEEmSiEkvZOLHu+u8zjOrTupeT+zuSb+ADry1Lpp+/sb7pFiYm3G0xiw8s6iCoYu2k5Jfhp3lZbPcnc+OYltMJm/+e5aVR5JxtLagk4stY7t54O1sg7OtBY8tP4arnRW/3DtA9yQgEDQnDbFUGw4UA8tqCXixLMsfNObDxAy8fSDLMp1fWI+NhTllVSpkWTMTXjwrgooqNU/XEMXG8PWcPjz06zHqe+g7/OJY3BysWHsihSd+N27w0BDenxGOpcLM6DXqyuee+fU+XSjF09GacF8nJvbwZGIPT95eH83KI8l61l2189vNzSTOvjGBV/6KYsWRJJ4ZH8Kjow2rVQWChtLkGbgsy7skSQpoiUEJ2iaSJGEuSXR2s2NkiDufbz9PTx8nqlTGxfuuQf4s3X85R7xa+EFTaTky1J25Px7mwV9MF8m4O1iRWVTBT3P7Y2+lYO6Ph9h+rm6fy5r4ONvw4IjO+HSwYdF/5ziXoSnfX3EkCR9nGz6d3Yv80ioKy6uwsTCvsxjn5andyCysINzXCXdH/bj0wM6u/HLgku79z/Mi8XKyZuyHuwD4+H+9mL/iBInZpbx9S0+qVGo+2BSLUi0zf2xIg38fgaAhXMki5qOSJN0JHAGelmXZcPUHkCTpfuB+AD8/vyv4OMHVoKCsih/3JqBUaxounUnRLMwduZjHkYtG/xfzi7aniW8HG764rQ9WFmZM/Hg3L0/txoTuHnyyJa5eB/jMogpenBzGwYRc7v7xcKPG/M+jQ+nm7agr5R8d6sG59CIe/OUoFVUqfrt3QKPcwU254GQWlXM6RTPT7tXJmW/v6KsT+HHdPNh8NkPnf/jh5nNISMRmasJJH2+JY0J3T8K82o6BsqD901QB/wp4E5C1PxcD9xg7UJblb4FvQRNCaeLnCeogKbeUlPwyvVS+pvLSX2f452RqncfYWym4IcKbzWfTmdTDi6yiCmb09WVsN43LzaINMZibSUyL8Oa73RdYdTSZ2ZGdiM0o5qiJLwGAn/YlkpJvvOOgt5M1i2f14nRKPm+vj9Ft/+7OfvT0vTybLiyv4onlx3Wz9zdv6tEo8a6NSi3z9c543t94TrdtRl9f3rqpB9YWl2Pi393ZjwqlJtzkZGPBluhM/F1t6eJmz5hQd4LdHQhuBnNbgaAmTRJwWZZ13YkkSfoO+LfZRiRoFP+cTOWx5ccB2PTkcEIaKFZqtcyqo0ks23+RWf06UaFUMbCzKxuj0rlzkD/L9huWzVfz3xPD6ORiy/9N7IqjtYVeWblaLbP2eAqhng4UVyjZFasR0uWHkuodk+ZLyIUPZ/XiuT9Osee8ZgG1p48Ty+6JpIOdpa49ra2lOR6O1uSVVrLycBI7Y7NYdzrN4Jqx6cYXVBvKO+ujDRx/fDvY6Il3NVYKzbYDz4/B3EzCUiHq5AQtS5MEXJIkL1mWq/9abgbONN+QBA3l31OXxRtg7YkUnp1gPC+6NltjMvm/1RoT4Vf/1vQAsbYwo1Kp5qbePhSVK00WoXy6NY5VRzXpdHaW5szo68vS/Re5d2gggW52pBaUk1pQzqgPdjT6dzpwIZfB727j5t4+OFgpKKpQ4u1szaRPdvPrfQOYNzSQeUMDeXrlSVYfu9yW1RS2lvpCeyalgN1x2eyLz+ZCVgnujlYsvKmnnk1bTaq/EO8a5I9SLbPicBIfb4mjUwdbpvf1NXqOjaWhuAsELUFD0giXAyOBjpIkJQOvAiMlSeqFJoSSCDzQgmMUmKB2/w6/ekwAVGqZsioVWUUV/HrQcIZdXqXG39WWnOLKOisIq8UboKRSpVvANOVN2RRqfv7GKM0D35jFO3liTDB2VubYWppjb6XgpSlh7L+Qw9oTxsM+3+y6wDe7TPc5Sckv48PN5/j+rv5G9+/UPkEs3X8RB2sFoV4O+LvY0dWz6WEZgaC5EIU87ZRHfj1mEDL4YGYEM0zMCmVZJvB5Taq+uZmEpbmZLlOkuTj92ngu5pTiZGNBdFoh9/98tFmvXxMXO0tySyoNOgMeTsxl5tf7G3ydx8cE88DwzthZaeYysiwjSRIJ2SUsXBfNlugMbCzMWXJXPwYHdWz230MgaAiinew1RG5JpdF47zOrTtLZzU7PzLegrIrYjCLe33B5Ee6Ogf48MiqIjvaWTP9qH+G+zkzo7klGYTnzVzQt7/rZCV05ejGP3XHZpBeWs+6U4fhu6ePDmmN19wZxsrHgsdFBvLUu2uj+x0cH4eZozVfbz2NraU5KfhmO1hY42VpQUqE0Kt4vTA7lvmGduZRbytyfDusszEATDlp+6BIutpZkFVeQX1pJN29HYtKKsFKY8eyErswbGmg05i0QtDZiBt4O2R6TydyfjKfahXo68PltfRj7oaaVq4W5RJVK//9x4rtTDM7LKCxngLa9a5C7vZ5TTHNjbibplbs3hrFh7thZKfRCJncO8ueNG3vw68GLvPin/nLMjL6+PDO+K55OmnS/0kolQxdtJ9zXiRcnh3EoMZcjiXmUVChxd7TC1lLBoYRcwrwceHJcCO4Ooj+JoPURM/BrgJziCj7aEktuSaXJY2LSi3TiDXDPkEAGdnFlrja3+i4TZgcDavTmbknxBuoU7wBXWxLraK1qzGTBz8WWXbFZ7K9hxVbN3YMDdOINkFOsuXeZhRUEezgQ7OHA7QMaZwAhELQVhIC3I55aeVK3qFYXHWwtmDPQnyfGBKMwN+ONfy63Sa3uXV2TEm2jKgdrBZ/e2pt5Sw/TxAlyvbx1Uw9eWXuGwI523NLHVy+/GjAQ75emhFFSoaJ/YAe2x2Ty3e4EXpoSxvGkfF2YxlS4BTRPJAAXc0pIzCll4bqzKFVqPrutdzP/ZgLB1UcIeDuiIeK9/L6BDOqiKei5kFXM2+tj9EyF1xxPYf7YEJ5cecKgqKaoXGkyNGOMD2dFoJb1G0aZYmJ3T4YEufLy2jMMDHTloZFd9FrD1uaBEZ15Ykywrs3tulNpfLdbk+VSl2DXZm98DpvPpvPbwUu6L6XFMyPo0owu8wJBayEEvAWRZRlZhtSCMswkCW9nmyZfq76Y8dgwdx4fE6wrAy8qrzJpaTb8/e1NHkdNbuzlQ/+FW3TvnW0tKCpXGh3rhqh0NkSl4+NsQ2mVykC8LRVmugZR0yK8eX5SmN7+R5fXbTZcG0tzMxxtFNz1wyEUZhJjwzzYdFbzRdbHRCdCgaC9IQS8hZBlmVnf7NczCJg3NJAbe3mb7LVRF5ui0vXe+7nY8uKUMEaHumNhbljxV1NYG8KUnl68PzOcxOxSNkal4+5ohaO1BYnZJSzeHKs77v0Z4Tz7xyn6+DnT5QX9DsLf3dmP/32zn8dGBzGumwfTPt9r8Dkp+WVGy+VtLc2pVKpxsbNkz/lsSiuVRKUWkphdgsJcqreDYW1m9vNlwaRQ/juTTv8AFwI72jH72wOo1DKBwvVGcI0gslBaiA1n0nnwF+N50HMG+jG5pxdmkkSYl2O9vaKPJOYyQ5se5+Nsw9whAZzPLGbdqTRemhrGrH6dkKTLudBxGUWM+2hXg8fq4WjFzmdHGU2Vq1Kpmf/7Cbq42zM61J2bvjAU5aZQ3c61vErFq2s1bVdN8crUbqw+lszZtEI9IZ8d2UlXoj+umwcnk/LJLKoANLHvDfOH611HqbV6Uxj5whMI2jKmslCEgLcQReVV9Hxtk962R0cFUaFU6WK5NfFwtOKDmREMC3bT215epWLoou1kF1eY/Kz4tyfrOvEBHL2Yy/Sv6i5mGRrUUddr5NCLY+pMl5NlmXEf7bri7JQJ3T14enxXvX4tq48m8/LaM1Sp1HT1dNB1PwTNl9V7M8IZoi2gKSitIjq9kJ4+TrrCm+rxHU7M4+ud8WyLycTGwpzHxwTz0MguVzRegaCtINIIrzIO1haceGUcvd7YrNv2+fbznHxlvFEBzyisICatyEDA71hysE7xBthzPpsRIZfP++2g8dnsI6O6cNegAFzsLAl68T9A43xTl3hnFpYTWSPFsLH09nNmfDdPBndxJaKTJnRUWqnkt4OX2B+fw9aYy2mBM/t2oqLqInHaL4rhIW6sPZHCsv2J5JdWUVB2+b/Obnb8cHd/TiYV8NWO8xy7lI+LnSVPjg3hzkH+dLAT9mWCax8h4C2ALMu8+NcZfjt4yWDf7UsOGDlDQ16pJkc5MbsES4UZ3s423NTbx6jRbk2eWnGCoy+P071ffSxZb7+3kzUvTe3G5J5egEZA7a0U3D7QT69qszYNLUuvaeAA8NDILvi52PL2umiOX8rnYk4pyXmllFWp2HAmnZ/2JRq9zgcbz1FUw3tz+aFLeDha4WxjiZOtxrKsp40FdlYKfj98iaGLtlOpVOPbwYbXp3VnVr9OopGU4LpCCHgLYUy8AeYM8GfBmtMG2z0crVi2/yJ2VgpdbvTsyE7Ea8u+J/Xw5L8z6QbnPTk2hPuGB+rev7NeP8XOWCm4raWCM69PqHP82cUVDe4pUi3eHe0t+eaOfvTVZnnc3NuHnbFZfLgpll8PXuJXE/fk9WnduX2AHyUVKjKKyjmTUsBTK0/y4939dY7vtRkd6s53uy8wo68vU3p6ibi24LpECLgJqlRq/j6RyvmsYjrYWnDHwIAGz+4kSWLb0yMYvXgn84YG8tKUMPbF5/DcH6cYHerOk2ND+GjL5cyOl6aEselsBonZJTrx7uJmp9dD+78z6VhbmDE61J1KpZot0ZlYW5gxo5+medWaY8msOJzEwYRc3Tk7nx2Jv2vDMy7OpRcRm1GEl5O1btG0MWQXV3IuvUgn4NYW5uw9n23S5Bg0It8voAPmZhJOtpqeJn4utjy/5jS74rJMCvjwEDeGh7gZ3ScQXC8IATfBq39H6c2iE7JLuC3Sn56+Gm/I+KxiTiUXUKlUMyrUnROX8vF3tdV5LS7Rtlb9aV8iS/Yk8N6McG4f6Mdr/0SxLUa/HNxYYUp8jYZL1ax7fBidO9qx+lgKW6IzKa9S69zaa3JDhDf/nEzlx72JvDate4N/558PJOr5PTYGC3OJbl6OvPjXaSwVZrquiN3qsBAbEOjCPydT+fN4Cp3d7Lgh3JsbIrwIcndgQGdXdsdlN2ksAsH1ghBwE0SlFOi9X34oieWHknhvRjhWtZzOJQlkWVPCfvyV8YAmfgyXC3DqMx4A+OPBQbg5WLEtJpPXteXvVgozJvXwZFiwG13c7Bn/0U5iM4xngyjMJB4e2YWnxndlozbW3FABr1CqGiXe47p58PjoYOb+dIiSChVf39GXAYEu3Lv0CM/9cRI7S3Mm9fQyGi6aPzaYJ8YEI0kSuSWVbDiTzj8nU/l0WxyfbI0j1NMBKwtzzmcWk5pfdkUFUALBtYwIHJpg7pBAvfc7nx3JgEAX3vjnrEEhjrXWSiuvtIoTSfkAONs2Lgvi8dFB9Atwwd/VjvHdPXXbtzw1go9v7Y2LvSXD3tumE+87BvrT0d4SV222xbhuHmx+agRPje8KQKU25/mTLXH1fvahhFy6vrShQeP8+H+92Dh/OH38OjDzm31UKNX8cm8kI0LcsLYwZ8GkUNQyrDySxPSv9umda6Uw4/PbejN/bIgub93FzpLbBvix/P6BHHx+DK/e0A1bS3NOau+jKSNlgUDQMEeeH4CpQKYsyz2021yAFUAAGkeeWaZc6dsr8Vn6s1w7KwVdPR04mJDLufQiNswfxsSPd/PaDd34/XASMVrvxZu+2EuErxMnkwuMXdaAMC9HHh8dxMQel0Xbx9mGR0cF4ediSycXW2LSC3XdBKs5m1ZIdnEloZ4OfDa7t4HZwGeze/PY8uN8tCWWMWHuutBONUqVmt3ns/lsaxzHLuU3aKyhng4G/cLXPDSE4golKfllONtY6Hqp7IvPoUJbGg+aPt8/z4usswrV3dGauUMCmTskkOS8UnbFZjNMmCgIBCZpSAjlJ+BzYFmNbQuArbIsvytJ0gLt+/9r/uG1Hjf28uazbed173/cm8Cy/Re5c5A/Y8LcWX86DXsrBWdSCxnfzUMn4IBR8X5pShg3RHhz/FI+I7u61WsQ8MwEzUxalmUmfrzbYH91I6pnxnfVifeaY8l0drMnp7iCt9Zd7kC44nCSTsAzi8r5cns8/55KJbvYdFtaY8TUMgj+/LbeBHa0I+wVw9l7B1tLevg46RppeTpaN6qFgG8HW24b4Neo8QkE1xv1Crgsy7skSQqotflGND6ZAEuBHVxjAh7k7sD0Pr66nOovtscDYG+l4JW1USw/pIkX9/HrgLez8UIYNwcr7CzNeX9mBP0DXAD0ZtoN4Y+jyXXur77u8kOXeN5IvBng5wMXefOmHsiyzORP9tRbGNRQHv3tuNHtIR72BLnbs/705bTHIHfR/U8gaG6aGgP3qOFKnw54mDpQkqT7JUk6IknSkays+tuhthXKq1TsjtOM95Y+Piya3hOAL3fE68QbNJZc/QNcdKlzNenq4cCOZ0fpRLYpbD57uRXsnIF+PDKqC1YKMxysFPz58GCcbC3YFZvFS39pnGisLcx4b0a4wXV+PXiR1/85qyfe3U04sTeWT27tpXv92ezevHljD514WynMeOPG7nwu+m8LBM3OFWehyLIsS5JksqGKLMvfAt+CphfKlX7e1WLHuUwyiypYdk8kw0PcUKrU/N9qwxnu/VpD3M4d7fT6a4d5OTaLaFV37hvfzYPjl/KJSi1kXDcP3ryxB55O1sSkF/Lwr8cIdrdn1YODsLdSIEkSI0LcKCyr0jW1qmk1Zmdpzk/3ROq+WP49lWpyNl0fXk7W3BDujVqWGRasCQ31eHUjoJl1fz2nr5h9CwQtRFMFPEOSJC9ZltMkSfICDH2u2jFqtcyPexNxtbNksNYcoXaln6W5GbELJwGaop9VNUId39zRlwndGxcqAU0JfXGFkoKyKo4k5rHpbDpRqZrmTpvOZtDR3oovb+/DpB6euiyORf/FYG1hxo9z++NgrelqmFFYzkebY/n98OVCICcbC4orlHg6WrPk7n4EuNrxx9FkftiTwNm0QoOxmJtJPDUuhOi0Qv6tZVAc6unA4C4d+WFvAg8M74yZmcTNvX35fvcFvZz29Y8Pw1IhEp0EgpaiqQL+N3AX8K7259pmG1EbYGNUOgcTcnnnlp464a7dtXHHsyN1ry3Mzbiltw9rjqfQz79Dk8QbYOQHO0zue3pcCHcOCsDJViPS5VUqzM0kkvLK6B/ggpeTDQVlVXy9M56vdsQbnB/Q0Y5wHydu6u3NysPJ/HE0iUIj9mrVqNSygd1ZNeczi3ULmhui0nmthmVbNbcP8BPiLRC0MA1JI1yOZsGyoyRJycCraIR7pSRJ84CLwKyWHOTVZqW2N/WaY8nMjjSeCVG7uOTD//Xinek9MavRl7uxTOjuwcYoTcx7dKg7SbmlxGUWE9jRjjsHBfDZtji+33O5k+FDI7uQUVhO/wAXPt4Sy8dGcr7fvrkn/+vfiWOX8vh+9wVmfL0fc0kyagJRF89O6MqAQBc6u9ljqTDThUkOXMg1enxBWVWjri8QCBpPQ7JQZpvYNaaZx9JmuKg11j2cmEdMeiGhno5IksTv9w9k7YlU7hkSYPQ8K8WVdcL75o5+nE0txNfFBkdrC6LTCvl21wX+PJ5CxBubDI7/fvcFqlQyO85lklZQbvSalgozbv5yL6eSC3C2tWBCN0+UarVRd/ea51Tbm+1dMBofI5WQ0yK8+ftkqu7913P6kl9ayYI1pxnV1Y0PZkY09tcXCASNRBg6GCFgwTq997FvTbqq4YAKpYrX/o7Sa2ZlDH9XW+YNDWRIUEfG1PK/vGOgPyuOJFGpVOPnYou/qy0lFUqDoh0HKwVPjA3mtgF+rD2RytRwLxysLVCpZT2TiJrkl1bq9TlfeHMPzCWJBWtOM7KrG1/P6VtvnrtAIGg4wtDBCGq1jFktkTp2ybCg1MK86WGRxpJeUM7cnw4TnVbI2DAPPUf5mjwwojPPTQilSqXW62wIcPaNCdhaKpjWy5uPNscSn1XM7rhsvJysCfGw50JWCUq1zIy+vjw3savO0KFmuMiUeAOUVF7u/T0l3EuIt0DQSlyXAl5QWsUHm86x6mgS47t5UlyhZO/5bL6a04d7ftJ/Qujq4aDnN9lSHL+Ux81f6vcOMSXeAN/svMA3Oy8YbH/3lp7EpBexZE8CG8+ko1TLDAvuyOxIP3bGZnH8Uj4Rvk68Nq07veswc6gLT0dr+vg5cyG7hG5ejkK8BYJW4roU8BVHLvHzgYsAenHc2uINcC6jiOS8Unw72LbYeIx5WL44OYyF66MxN5MY3KVhrVV9O9hQUqlixlf7sLdScPfgACaHe7H6aDKfbI3DxdaSRdN7MrNvJ4Mnj8awdF8ixy7lM7CzC+9vPMeIECHeAkFrcF0KuKkFvEdHBfH59vN62/xcbPFyarl2pkm5pUYNiA9cyCFu4SROJuU32FwhOa+MN/89y9Cgjnxxex/+PpHC3B8PU1yh5O7BAcwfG4KTjcUVj7c6vfDAhVxGhLjxzR1CvAWC1uC6E/DU/DIOJRhPfasp3jP6+nIoIZedz45ssRDK3ydTeWbVSaP7tsZkEqw1Hga4tX8nNp/NoLxKxXszIpgS7oUsy1Qo1ZRXqfh65wW+3hnPiBA35g0N5NZvDxCdVsjgLq68Nq27nhP8lXA6pYBKlZowL0eGBrny9PiuQrwFglbiustCqZ1hYorEd6cYXeRsLv46nmLQmhW0fb3PGo99mypN/27XBRaujybc1wlvJxs2RKXj42zDS1PCmFijarO5qCtDRSAQND8iC6UR2FtpbktLiTdoZt8W5hJVKs0XaB8/Z36cG8lvBy+xJTqDmt+rdw7yp4ePE1N6emFnpf+/7LOtcSzerMlCiUkrIia9iMfHBPPQiC4t5tAuxFsgaBtccwK+8nASXdzt9boDyrKsm4V+cVsfHvntWJ3XKK4wXWLeHGQWlhv4Yv44N5LX/o7iz+MpTA33YtH0cAOxroksyyzeFKsX9hkd6s6LU8Lo5NJyC64CgaDtcE0J+IWsYp5brfGe/GBmBDP6+rJsfyKvrI3ixCvjqFSq6xXvliazsJzIt7fq3g8JcmXxzF7c/eMhjl/K5+lxITw6OqjOsIcsy7zzXwzf7tKkEQa52/PqDd0YFixc2gWC64lrSsBrzkafWXWSW3r78MraKEATDimtUYBiipg3J6JUN/+6gCzLrD2RystrL7d1fXRUEI+ODmLM4p3kllTy9Zw+TOzhVe+1Fm+K5dtdF3RVlHcNDmh0bxOBQND+uab+6qPT9HB7mvcAAAoQSURBVC2/Or+wXvd66qd7COhoV+f5G+YPw9rCXBcDbypVKjVrT6QQsGCdrrJz4bpo5q84QZG2A+D4bh5M6+XNztgsUgvKmBLu1SDxBsgrreTW/p3Y+swI7h3WWYi3QHCdck3MwFVqmUUbYog20te6mku5pcz/vW7TglDPyw41CdklKMykBseTZVlm7/kc/j6ZwqazGeSXarrxfbY1johOznpdBEHT33tTjWwT50bkZy+8uWeDjxUIBNcu7V7ASyqUdNe2Nq2Pv06kGmyrbuHq7mClt/2ZVSdJyy9j45PDdUYJpjibWsib/55l/4Ucg3374nPYfi4LH2cbnbvOgEAXDmpz0T+5tRdjwjyueNYvEAiuP9qVavxxNJn80krmDgnE3Ezi213xvL0+pt7z3rypBy//dcZg+8b5w+nqabzAJa+0ktSCchZtiOGtm4zPeLOKKvhw87k6uwZWaNuyVou3wkzioZFdWHhzT8Z+uJOicqUQb4FA0CTalXJUVy3WtO2qi/dnhDOzXydOJecb7Nu3YLSBKUNNSitUWJhL/HLgElPDvRnY2VW3r7xKxVvrzvLLgUsmz68mzMuRrh72nEjKJzGnFKVa5sFfjrLsngH4drBhZ2wWcwb6N+j3EQgEgppckYBLkpQIFAEqQGmsUqi5kGWZDrYW5JU2zOll17Oj8HPVxK97+jhx+wA/fj2oEdwnx4bUKd4AJZVKpvfxZV98Dvf8dBhPJ2vKKlUGxgnOthY8PjqYyT29+PtkCmpZ4/bu7qBp3VqdDlhQVsVXO+K5Y5A/c74/yKxv9mMmQV5JJZVKtbAfEwgEjaY5ZuCjZFmuv1XeFVBepSL05Q0NPn7j/OE68QaQJIknxgaz6mgyT48L4YERXeo8X5ZlSitVXMgqYUhQR5YfusSFrBK9YwZ3cWXR9HC9Rc77h5u+rpONBQsmhQKw6sFB/HU8haMX8yitVKEQlY0CgaAJtIsQSpVK3aDj/n1sKL4dbHC2tTTY5+5gTcwbExtcHh/q6cChxFwOJWoWG/1dbZkW4c0NEd5X3Biqo70V9w7rzL3DrugyAoHgOudKBVwGNkmSJAPfyLL8be0DJEm6H7gfwM/PuEFwfThYW+DnYsul3FKTxzw8sgs9fJzqvE5DxVuSJNY9PgylSk1BWRVlVSp8nG2uirGDQCAQNJQrDbwOlWW5DzAJeESSpOG1D5Bl+VtZlvvJstzPza1ppd4rjyQZiHfiu1P48vY+AAwPceOBOsIXTUVhboarvRW+HWyFeAsEgjbHFc3AZVlO0f7MlCTpTyAS2NUcA6vJnlpuNNFvTARgck8vEt+d0twfJxAIBO2CJs/AJUmykyTJofo1MB4wTLZuBuaPDdZ731JtUgUCgaA9cSUzcA/gT21oQQH8Jstyw1NFGoG/qx1jw9zp5GLL85PCWuIjBAKBoN3RZAGXZfkCENGMYzGJuZnE93f1vxofJRAIBO0GUT0iEAgE7RQh4AKBQNBOEQIuEAgE7RQh4AKBQNBOEQIuEAgE7RQh4AKBQNBOEQIuEAgE7RQh4AKBQNBOkWRZvnofJklZwMWr9oF10xFo0T7mTaAtjgnEuBpLWxxXWxwTiHE1FH9Zlg26AV5VAW9LSJJ0pCUdhJpCWxwTiHE1lrY4rrY4JhDjulJECEUgEAjaKULABQKBoJ1yPQu4gXtQG6AtjgnEuBpLWxxXWxwTiHFdEddtDFwgEAjaO9fzDFwgEAjaNULABQKBoJ1y3Qm4JEmJkiSdliTphCRJR1pxHD9IkpQpSdKZGttcJEnaLElSnPZnhzYyrtckSUrR3rMTkiRNvspj6iRJ0nZJks5KkhQlSdIT2u2ter/qGFdr3y9rSZIOSZJ0Ujuu17XbAyVJOihJ0nlJklZIkmTZBsb0kyRJCTXuVa+rNaZa4zOXJOm4JEn/at+32r1qDNedgGsZJctyr1bO8/wJmFhr2wJgqyzLwcBW7furzU8YjgvgI+096yXL8vqrPCYl8LQsy92AgcAjkiR1o/Xvl6lxQeverwpgtCzLEUAvYKIkSQOBRdpxBQF5wLw2MCaAZ2vcqxNXcUw1eQKIrvG+Ne9Vg7leBbzVkWV5F5Bba/ONwFLt66XATVd1UJgcV6siy3KaLMvHtK+L0Pyh+dDK96uOcbUqsoZi7VsL7X8yMBr4Q7v9qt6vOsbU6kiS5AtMAb7XvpdoxXvVGK5HAZeBTZIkHZUk6f7WHkwtPGRZTtO+TkdjHN1WeFSSpFPaEMtVD+1UI0lSANAbOEgbul+1xgWtfL+0IYETQCawGYgH8mVZVmoPSeYqf9nUHpMsy9X3aqH2Xn0kSZLV1RyTlo+B5wC19r0rrXyvGsr1KOBDZVnuA0xC88g7vLUHZAxZk9/ZJmYowFdAFzSPvmnA4tYYhCRJ9sBqYL4sy4U197Xm/TIyrla/X7Isq2RZ7gX4ApFA6NUeQ21qj0mSpB7A82jG1h9wAf7vao5JkqSpQKYsy0ev5uc2F9edgMuynKL9mQn8ieYfd1shQ5IkLwDtz8xWHg8AsixnaP/41MB3tMI9kyTJAo1I/irL8hrt5la/X8bG1RbuVzWyLOcD24FBgLMkSQrtLl8gpZXHNFEbhpJlWa4AfuTq36shwDRJkhKB39GETj6hjdyr+riuBFySJDtJkhyqXwPjgTN1n3VV+Ru4S/v6LmBtK45FR7VIarmZq3zPtDHJJUC0LMsf1tjVqvfL1LjawP1ykyTJWfvaBhiHJj6/HZihPeyq3i8TY4qp8QUsoYkzX9V7Jcvy87Is+8qyHADcCmyTZfl2WvFeNYbrqhJTkqTOaGbdAArgN1mWF7bSWJYDI9G0rcwAXgX+AlYCfmja7s6SZfmqLiiaGNdINOEAGUgEHqgRe74aYxoK7AZOczlO+QKaeHOr3a86xjWb1r1f4WgW3szRTNJWyrL8hvbf/+9oQhXHgTnamW9rjmkb4AZIwAngwRqLnVcVSZJGAs/Isjy1Ne9VY7iuBFwgEAiuJa6rEIpAIBBcSwgBFwgEgnaKEHCBQCBopwgBFwgEgnaKEHCBQCBopyjqP0QguLaQJMkD+AhNA6o8oBJ4T/t6LZBQ4/BnZFnectUHKRA0ACHggusKbcHIX8BSWZZv027zB6ahEfDdsixPbcUhCgQNRoRQBNcbo4FKWZa/rt4gy/JFWZY/a8UxCQRNQszABdcb3YFjdewfpu2YV810WZbjW3hMAkGTEAIuuK6RJOkLYCiaOPiziBCKoB0hQiiC640ooE/1G1mWHwHGoOnHIRC0K4SAC643tgHWkiQ9VGObbWsNRiC4EkQzK8F1h7aF6UfAACALKAG+RtN9sXYa4VuyLP9hcBGBoA0gBFwgEAjaKSKEIhAIBO0UIeACgUDQThECLhAIBO0UIeACgUDQThECLhAIBO0UIeACgUDQThECLhAIBO2U/weCJ/LEluoVUgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gmyW09dlE9ZE"
+ },
+ "source": [
+ "We have successfully changed the x-axis above from the index to GE. However, this plot doesn't make sense. This is because when we are plotting the x-axis the practice is to plot a **continous** variable (for example, date-time or incrementing Id numbers.)\n",
+ "\n",
+ "For the rest of this notebook we will keep the `index` as our default x-axis. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "x7JHLvujGEKI"
+ },
+ "source": [
+ "We have learned how to plot one, column but what if we want to plot more than one? What if we want to go for two? That's what we'll do next. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UqoPjEusGPTg"
+ },
+ "source": [
+ "### Plotting multiple columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qiby-TyRn75k"
+ },
+ "source": [
+ "To plot multiple coloumns, the syntax would be `df.plot(y = ['column 1', 'column 2'])` where you would comma separate each column of choice within the brackets for defining the y-axis inside the `df.plot()` function. In this case we're going to plot the stock market growth for `AA` and `MSFT`. To do so, we would call the function like so `df.plot(y = ['AA', 'MSFT'])`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8PIxVFXuE8nN",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 272
+ },
+ "outputId": "186dcb1f-f6a4-4456-c20a-5ca3c90d4443"
+ },
+ "source": [
+ "df.plot(y=['AA','MSFT']) # new code"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "u4Hz-DiaGZrU"
+ },
+ "source": [
+ "So in order to plot more than 1 column in our plot (with respect to a dataframe) there is a convention we will have to follow. The column names should be in the format `['column1','column2',....,'columnN']`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ksB42-VBZNGh"
+ },
+ "source": [
+ "The `matplotlib` implementation for this code is:\n",
+ "```\n",
+ "plt.plot(df['AA'])\n",
+ "plt.plot(df['MSFT'])\n",
+ "plt.legend(['AA','MSFT'])\n",
+ "plt.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KXcxoSZ6Epe9"
+ },
+ "source": [
+ "### Adding Labels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IHg8q6rnBLGb"
+ },
+ "source": [
+ "When working with visualizations it is often very important to make sure that, the person viewing our visualizations knows what he/she is looking at. In order do that we can simply add on 2 **parameters** to our `df.plot` function. These parameters are:\n",
+ "- `xlabel`\n",
+ "- `ylabel`\n",
+ "\n",
+ "`xlabel` is used as a title for the x-axis and `ylabel` is used as a title for the y-axis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1NeX2MtlPzZ0",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 286
+ },
+ "outputId": "f7cce4d2-06a6-4960-c757-0b1da4909d30"
+ },
+ "source": [
+ "df.plot(y= ['AA','MSFT'],\n",
+ " xlabel = 'Date from 1990 - 2012', # new code\n",
+ " ylabel = 'Stock Market Price' # new code\n",
+ " )\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 93
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SJx3fBPQB2AA"
+ },
+ "source": [
+ "Above we can see our `x-axis` and `y-axis` clearly labeled.\n",
+ "\n",
+ "\n",
+ "The code above is the same as\n",
+ "```\n",
+ "plt.plot(df['AA'])\n",
+ "plt.plot(df['MSFT'])\n",
+ "plt.xlabel('Date from 1990 - 2012')\n",
+ "plt.ylabel('Stock Market Price')\n",
+ "plt.legend(['AA','MSFT'])\n",
+ "plt.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_nBshCSqDf0b"
+ },
+ "source": [
+ "## 1 - Now Try This"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SHOzkg1NNHxU"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "Plot two columns of your choice (other than `AA`, `MSFT`) on the same graph. \n",
+ "- Add a `xlabel` and a `ylabel` to your graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9QiTKr3-0f5x"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/1.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VDH4-7OAErbo"
+ },
+ "source": [
+ "### Adding a Title"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mbNn4IFfB99B"
+ },
+ "source": [
+ "In addition to labels for the x and y axis. It is also important to add a `title` to our plot. This is so the viewer understands the purpose of a plot."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4l6qKMxSR4jl",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 302
+ },
+ "outputId": "be2d7c55-555d-4cbb-fafc-a093984bd116"
+ },
+ "source": [
+ "df.plot(y= ['AA','MSFT'],\n",
+ " xlabel = 'Date from 1990 - 2012',\n",
+ " ylabel = 'Stock Market Price',\n",
+ " title = 'Stock Market Index for AA and MSFT' # new code\n",
+ " )\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 94
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mNyvabczZH-8"
+ },
+ "source": [
+ "The `matplotlib` implementation for this code is:\n",
+ "```\n",
+ "plt.plot(df['AA'])\n",
+ "plt.plot(df['MSFT'])\n",
+ "plt.xlabel('Date from 1990 - 2012')\n",
+ "plt.ylabel('Stock Market Price')\n",
+ "plt.title('Stock Market Index for AA and MSFT')\n",
+ "plt.legend(['AA','MSFT'])\n",
+ "plt.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "muKdbboLEtqb"
+ },
+ "source": [
+ "### Using alpha to make the plots more transparent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DIRuquQSCPsm"
+ },
+ "source": [
+ "When we are working with data and visualizations, it is often useful to control the depth (or trasperency) of a plot. In simpler terms this means to we want the colors of our plot to be light or dark. If we want to further customize our plot we can add the parameter `alpha`. \n",
+ "\n",
+ "Use cases for `alpha` vary. For example, is some cases it can simply be used as a cosmetic tool to make our plot nicer. In some cases, such as overlapping points (which arisis with scatter plots), using `alpha` allows to visualize densely clustered points easily.\n",
+ "\n",
+ "Alpha has a range or `0.0` to `1.0`. The closer it is to `1.0` the darker and lighter if it is closer to `0.0`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MQgRAwhARyNb",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 302
+ },
+ "outputId": "8d5edada-046a-41b7-b85b-8179b4b5172d"
+ },
+ "source": [
+ "df.plot(y= ['AA','MSFT'],\n",
+ " xlabel = 'Date from 1990 - 2012',\n",
+ " ylabel = 'Stock Market Price',\n",
+ " title = 'Stock Market Index for AA and MSFT',\n",
+ " alpha = 0.8 # new code\n",
+ " )\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 99
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kj8dkt3zZCa9"
+ },
+ "source": [
+ "The `matplotlib` implementation for this code is:\n",
+ "```\n",
+ "plt.plot(df['AA'],alpha = 0.8)\n",
+ "plt.plot(df['MSFT'],alpha =0.8)\n",
+ "plt.xlabel('Date from 1990 - 2012')\n",
+ "plt.ylabel('Stock Market Price')\n",
+ "plt.title('Stock Market Index for AA and MSFT')\n",
+ "plt.legend(['AA','MSFT'])\n",
+ "plt.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MNVCsVPXE1LJ"
+ },
+ "source": [
+ "### Changing the size of the figure"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s72sSJzjDFXF"
+ },
+ "source": [
+ "The `figsize` parameter allows us to modify the size of our figure according to our needs. The format uses a `tuple` to declare the size of our graph in inches. (for e.g (20,7) means 20 x 7)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mEjI-2UNSEXm",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 449
+ },
+ "outputId": "6366cc30-1f3c-4b25-8498-b9e4fbcac7b2"
+ },
+ "source": [
+ "df.plot(y= ['AA','MSFT'],\n",
+ " xlabel = 'Date from 1990 - 2012',\n",
+ " ylabel = 'Stock Market Price',\n",
+ " title = 'Stock Market Index for AA and MSFT',\n",
+ " alpha = 0.8,\n",
+ " figsize = (10,7) # new code\n",
+ " )\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 102
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fJLu5jrnY6jq"
+ },
+ "source": [
+ "The matplotlib implementation for this code is:\n",
+ "```\n",
+ "fig = plt.figure(figsize=(10,7))\n",
+ "plt.plot(df['AA'],alpha = 0.8)\n",
+ "plt.plot(df['MSFT'],alpha =0.8)\n",
+ "plt.xlabel('Date from 1990 - 2012')\n",
+ "plt.ylabel('Stock Market Price')\n",
+ "plt.title('Stock Market Index for AA and MSFT')\n",
+ "plt.legend(['AA','MSFT'])\n",
+ "plt.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N6AiyUbgE8pl"
+ },
+ "source": [
+ "### Changing Colors, adding markers and linestyles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "esOLA6b8nHWg"
+ },
+ "source": [
+ "\n",
+ "By now, you probably noticed that the main function you are using in `pandas` is the ```plot``` function.\n",
+ "This function accepts arrays of x and y coordinates and optional arguments such as color and line style and figure size.\n",
+ "\n",
+ "```\n",
+ "df.plot(x,y,'g--')\n",
+ "```\n",
+ "\n",
+ "We can show the same plot more explicitly by adding a linestyle:\n",
+ "\n",
+ "```\n",
+ "df.plot(x,y,linestyle='--',color='g')\n",
+ "```\n",
+ "\n",
+ "Line plots can also have *markers* in order to highlight the actual data points. \n",
+ "\n",
+ "When pandas creates plots, they are a continuous line plot (**interpolating** between points), it can occasionally be unclear where the points are. Markers help us observe the *interpolation* in a clearer manner. \n",
+ "\n",
+ "*I used interpolation here because it's a more mathematical term when it comes to plot points. with respect to a given axis (in our case data). What it really means in our case is plotting y with respect to x and joining those points.*\n",
+ "\n",
+ "when we have multiple plots on the same graph we can follow the convention we used for plotting the `y`, i.e. `['column1','column2',...,'columnN']`. For colors, markers and linestyles we can similary do: `['ro-','bx--',...,'ColorMarkerLinestyle']`\n",
+ "\n",
+ "These are the possible **marker** options available:\n",
+ "\n",
+ "|character|\tdescription|\n",
+ "|-| -|\n",
+ "|'.'|\t|point marker|\n",
+ "|','|\tpixel marker|\n",
+ "|'o'|\tcircle marker|\n",
+ "|'v'|\ttriangle_down marker|\n",
+ "|'^'|\ttriangle_up marker|\n",
+ "|'<'|\ttriangle_left marker|\n",
+ "|'>'|\ttriangle_right marker|\n",
+ "|'1'|\ttri_down marker|\n",
+ "|'2'|\ttri_up marker|\n",
+ "|'3'|\ttri_left marker|\n",
+ "|'4'|\ttri_right marker|\n",
+ "|'s'|\tsquare marker|\n",
+ "|'p'|\tpentagon marker|\n",
+ "|'*'|\tstar marker|\n",
+ "|'h'|\thexagon1 marker|\n",
+ "|'H'|\thexagon2 marker|\n",
+ "|'+'|\tplus marker|\n",
+ "|'x'|\tx marker|\n",
+ "|'D'|\tdiamond marker|\n",
+ "|'d'|\tthin_diamond marker|\n",
+ "|'|'|\tvline marker|\n",
+ "|'_'|\thline marker|\n",
+ "\n",
+ "\n",
+ "These are the **Line Styles** available:\n",
+ "\n",
+ "|character|\tdescription|\n",
+ "|-|-|\n",
+ "|'-'\t|solid line style|\n",
+ "|'--'\t|dashed line style|\n",
+ "|'-.'|\tdash-dot line style|\n",
+ "|':'|\tdotted line style|\n",
+ "\n",
+ "\n",
+ "These are the available **colors**:\n",
+ "\n",
+ "|character|\tcolor|\n",
+ "|-|-|\n",
+ "|'b'|\tblue|\n",
+ "|'g'|\tgreen|\n",
+ "|'r'|\tred|\n",
+ "|'c'|\tcyan|\n",
+ "|'m'|\tmagenta|\n",
+ "|'y'|\tyellow|\n",
+ "|'k'|\tblack|\n",
+ "|'w'|\twhite|"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2CRbyKdcTkSO",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 596
+ },
+ "outputId": "34505693-a03b-4a4d-db6d-aca0f38362d4"
+ },
+ "source": [
+ "df.plot(y= ['AA','MSFT'],\n",
+ " xlabel = 'Date from 1990 - 2012',\n",
+ " ylabel = 'Stock Market Price',\n",
+ " title = 'Stock Market Index for AA and MSFT',\n",
+ " alpha = 0.8,\n",
+ " figsize = (20,10),\n",
+ " style=['ro-','bx--'] # new code\n",
+ " )\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 103
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UxeDzqG_Y0zE"
+ },
+ "source": [
+ "The `matplotlib` implementation for this code is:\n",
+ "\n",
+ "```\n",
+ "fig = plt.figure(figsize=(10,7))\n",
+ "plt.plot(df['AA'],'ro',alpha = 0.8)\n",
+ "plt.plot(df['MSFT'],'bx',alpha =0.8)\n",
+ "plt.xlabel('Date from 1990 - 2012')\n",
+ "plt.ylabel('Stock Market Price')\n",
+ "plt.title('Stock Market Index for AA and MSFT')\n",
+ "plt.legend(['AA','MSFT'])\n",
+ "plt.show()\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r6eLkixzNMLk"
+ },
+ "source": [
+ "## 2 - Now Try This \n",
+ "\n",
+ "Plot the column ```SPX``` as a `dashed line` and with the color `orange`. \n",
+ "- Set `figsize` as `10,7`. \n",
+ "- Don't forget to add the `xlabel`,`ylabel` and title. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Q9h5bT972rh8"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/2.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "l9P36ertG_05"
+ },
+ "source": [
+ "### Limiting our x-axis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jWe3lyjbQLd_"
+ },
+ "source": [
+ "In circumstances where our x-axis is not date time, it is very easy to limit our x-axis using x lim for example, if we have a dataframe as shown in the small example below, we can simple limit our x-axis by using `xlim` and declaring it as a **tuple** `(start_value,end_value)`.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "B-cEAn9pQtv2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 282
+ },
+ "outputId": "79435ed2-d39d-4b59-8e91-11a1cb2e5ee0"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "test_df = pd.DataFrame(data = {'y' : [0,1,4,9,16,25,36,49,64,81,100]})\n",
+ "test_df.plot(xlim = (2,8))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "C-kLTZ8PSWCh"
+ },
+ "source": [
+ "In the example above we can see that by declaring `xlim` as `(2,8)` we have successfully limited the axis (from 2 to 8 only).\n",
+ "\n",
+ "Therefore when have a simple numerical x-axis, it is easy to use `xlim` but what if we have a `datetime` index. We can try applying the same approach but it won't work. This is because datetime is special data type on its own. Luckily python has a built in library called `datetime` which lets us leverage datetimes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SlWl9RhOFtRh"
+ },
+ "source": [
+ "import datetime"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mb_mWukWSrkU",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 604
+ },
+ "outputId": "ee7dd45b-3440-46b3-f6bb-edb050a37c9b"
+ },
+ "source": [
+ "df.plot(y= ['AA','MSFT'],\n",
+ " xlabel = 'Date from 1990 - 2012',\n",
+ " ylabel = 'Stock Market Price',\n",
+ " title = 'Stock Market Index for AA and MSFT',\n",
+ " alpha = 0.8,\n",
+ " figsize = (20,10),\n",
+ " style=['ro','bx'],\n",
+ " xlim = (datetime.date(2006,1,1),datetime.date(2010,1,1)) # new code\n",
+ " )\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 30
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rtkWxYoDS4jg"
+ },
+ "source": [
+ "Above we are importing datetime in the first line. Next we plot our dataframe column. In the third line the first thing to notice is the ```datetime.date()``` method. This takes in three parameters ```Year, Month, Date```. and looks for the range on the x-axis. In our case from 2006-01-01 to 2009-01-01.\n",
+ "\n",
+ "\n",
+ "The matplotlib implementation for this code is:\n",
+ "```\n",
+ "fig = plt.figure(figsize=(10,7))\n",
+ "plt.plot(df['AA'],'ro',alpha = 0.8)\n",
+ "plt.plot(df['MSFT'],'bx',alpha =0.8)\n",
+ "plt.xlabel('Date from 1990 - 2012')\n",
+ "plt.ylabel('Stock Market Price')\n",
+ "plt.title('Stock Market Index for AA and MSFT')\n",
+ "plt.legend(['AA','MSFT'])\n",
+ "plt.xlim(datetime.date(2006,1,1),datetime.date(2010,1,1))\n",
+ "plt.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "emsLUVv-2wk_"
+ },
+ "source": [
+ "## 3 - Now Try This\n",
+ "\n",
+ "Plot the `SPX` index from `1998` to `2002`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_brcU_oe2zhA"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/3.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZwHuvFVcNxot"
+ },
+ "source": [
+ "# Types of Charts"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PkjeHp7TU1RH"
+ },
+ "source": [
+ "### The `kind` parameter\n",
+ "\n",
+ "We can see by default that every plot we have plotted so far is mostly a line plot. This means that the plots are connected to each other. Although this is the default setting, it is not the only one available. `df.plot` lets us plot a variety of plots if we add a parameter called `kind`. \n",
+ "\n",
+ "The available options with the `kind` parameter are:\n",
+ "\n",
+ "|string |type of plot|\n",
+ "|-|-|\n",
+ " | ‘line’ | line plot (default)|\n",
+ "| ‘bar’ | vertical bar plot|\n",
+ " | ‘barh’ | horizontal bar plot|\n",
+ " | ‘hist’ | histogram|\n",
+ " | ‘box’ | boxplot|\n",
+ " | ‘kde’ | Kernel Density Estimation plot|\n",
+ " | ‘density’ | same as ‘kde’|\n",
+ " | ‘area’ | area plot|\n",
+ " | ‘pie’ | pie plot|\n",
+ " | ‘scatter’ | scatter plot|\n",
+ " | ‘hexbin’ | hexbin plot|\n",
+ "\n",
+ "\n",
+ "\n",
+ "We will some of these types further in the exercises below.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mUjgGMCZHFQ9"
+ },
+ "source": [
+ "### Plotting Histograms\n",
+ "\n",
+ "\n",
+ "When working with dataframes on my occassions we might want to see the count of values in a column. For example if we have a column (shown as a list) `[1,1,1,1,1,1,2,2,2,2,2,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,4]`, we might want to check how many times these values have repeated. This is when a histogram is useful. A histogram allows us to see the **frequency** of a value.\n",
+ "\n",
+ "\n",
+ "So lets go ahead an plot a histogram of the dataframe we have been working with.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DrFEgxR-VClE",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 458
+ },
+ "outputId": "29db846b-8b09-4275-cf31-f3f9808e2fb3"
+ },
+ "source": [
+ "df.plot(kind = 'hist', # new code\n",
+ " bins = 30, # new code\n",
+ " y=['IBM','AA'],\n",
+ " xlabel = 'Date from 1990 - 2012', # This is unused\n",
+ " ylabel = 'Stock Market Price',\n",
+ " title = 'Stock Market Index for AA and MSFT',\n",
+ " alpha = 0.8,\n",
+ " figsize = (10,7) \n",
+ " )\n",
+ "\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eEVLrL5bUhY-"
+ },
+ "source": [
+ "Above we can see the the counts of values for `AA` and `MSFT` are visible. The only modification we made to our original code is that we added a new parameter inside our `df.plot` function, which is the `kind` parameter. We set the parameter has `hist` to plot a histogram"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NZxqOC5DTg9I"
+ },
+ "source": [
+ "Fir the matplotlib version, we would change the `plt.plot()` function to `plt.kind()` where `.kind()` indicates the type of plot we want to create specifically. In this case, if we wanted to create histograms, we would change the `.kind()` function to specify as `.hist()`, so long story short, it would be `plt.hist()`.\n",
+ "```\n",
+ "fig = plt.figure(figsize=(10,7))\n",
+ "plt.hist(df['AA'],bins = 30,alpha = 0.8)\n",
+ "plt.hist(df['MSFT'],bins = 30,alpha =0.8)\n",
+ "plt.xlabel('Date from 1990 - 2012')\n",
+ "plt.ylabel('Stock Market Price')\n",
+ "plt.title('Stock Market Index for AA and MSFT')\n",
+ "plt.legend(['AA','MSFT'])\n",
+ "plt.show()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qMMOFbHJDSqT"
+ },
+ "source": [
+ "## 4 - Now Try this"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8pzhknxc13B0"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "Plot the histogram for `AAPL` and `GE` together. Don't forget to add the labels.\n",
+ "- Set bins as 30\n",
+ "- What are your observations?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IJmZgrvq2qgP"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/4.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VHnjgZFKOPxH"
+ },
+ "source": [
+ "### Plotting Area Charts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "C7-3EptzOPDB",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 449
+ },
+ "outputId": "169bc937-14d7-4fa1-a65f-813a8ef46ca5"
+ },
+ "source": [
+ "df.plot(kind = 'area', # new code\n",
+ " y=['IBM','AA'],\n",
+ " xlabel = 'Date from 1990 - 2012',\n",
+ " ylabel = 'Stock Market Price',\n",
+ " title = 'Stock Market Index for AA and MSFT',\n",
+ " alpha = 0.8,\n",
+ " figsize = (10,7),\n",
+ " style=['r-','b--'] # new code\n",
+ " )\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 27
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
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+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lNQWhCUMFGix"
+ },
+ "source": [
+ "In the code above movies is our ```x-axis```, so we are measuring the quantities with respect to the movie names. This makes our ```y-axis```, ```num_of_oscars```.\n",
+ "\n",
+ "- To plot the bar plot we use the ```df.plot```, with the `kind` parameter as `bar`. We also declare the x and y inside.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1YIisg7ODUb1"
+ },
+ "source": [
+ "## 5 - Now Try This"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GC5d3kNN5c3B"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "In the next cell, I have added two list types by the name of ```cal_state``` and ```enrollment```. Your task is to do the following\n",
+ "- Check if the two lists are equal.\n",
+ "**Hint**: Use ```if``` and ```len()``` to comapare the two.\n",
+ "- If lists are not equal, find the error.\n",
+ "- Convert the two lists into a dataframe and name it `cal_state_enroll_df`.\n",
+ "- Plot the bar plot of ```cal_states``` vs ```enrollemnts``` from `cal_state_enroll_df`. \n",
+ "- Add a ```title```, `ylabel` and `xlabel`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WA4nvOGn75AS"
+ },
+ "source": [
+ "cal_states= ['Bakersfield','Channel Islands','Chico','Dominguez Hills','East Bay','Fresno','Fullerton''Humboldt','Long Beach','Los Angeles','Maritime Academy','Monterey Bay','Northridge','Pomona','Sacramento','San Bernardino','San Diego','San Francisco','San José','San Luis Obispo','San Marcos','Sonoma','Stanislaus','International Programs','CalState TEACH']\n",
+ "enrollment = [11199,7093,17019,17027,14705,24139,39868,6983,38074,26361,911,7123,38391,27914,31156,20311,35081,28880,33282,21242,14519,8649,10614,455,933]\n",
+ "\n",
+ "\n",
+ "# check if length of cal_states is = length of enrollments\n",
+ "if # Insert Code Here \n",
+ " print('These are not equal')\n",
+ " # Insert Code Here\n",
+ " print('There are equal, you may proceed')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dvNK0x4p76cw"
+ },
+ "source": [
+ "Once you have confirmed that the lasts are equal convert it into a DataFrame. Using the technique we learned in Week 2 convert the two lists into a new dataframe and name is `cal_state_enroll df`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "z1YM-dW976zK"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "q8cqiRYn3enX"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/5.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE\n",
+ "\n",
+ "____.plot( # add kind = bar\n",
+ " # add x-axis column \n",
+ " # add y-axis column \n",
+ " # add title\n",
+ " # add xlabel\n",
+ " # add ylabel\n",
+ ")\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T-GUkwZtIlfJ"
+ },
+ "source": [
+ "## Mapping the California Housing dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XTKupOfbLGpw"
+ },
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gT0NYImBIptJ"
+ },
+ "source": [
+ "For this exersice we will continue using `df.plot`, however given the dataset we have, we will use a new type of plot, the `scatter` plot. We can easily do this by setting `kind = scatter`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "__e38fg1I7y5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 411
+ },
+ "outputId": "3a6a1cb5-c330-47f5-da04-b85de719f98e"
+ },
+ "source": [
+ "url = 'https://raw.githubusercontent.com/bitprj/DigitalHistory/master/Week4-Introduction-to-data-visualization-and-graphs-with-matplotlib/data/housing/housing.csv'\n",
+ "\n",
+ "df = pd.read_csv(url)\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
longitude
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+ "
latitude
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+ "
housing_median_age
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+ "
total_rooms
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+ "
total_bedrooms
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+ "
population
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+ "
households
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+ "
median_income
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+ "
median_house_value
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+ "
ocean_proximity
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+ "
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+ " \n",
+ " \n",
+ "
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0
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558.0
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341300.0
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4
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37.85
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280.0
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565.0
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342200.0
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NEAR BAY
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+ "
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+ "
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+ "
...
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+ "
...
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+ "
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+ "
20640 rows × 10 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " longitude latitude ... median_house_value ocean_proximity\n",
+ "0 -122.23 37.88 ... 452600.0 NEAR BAY\n",
+ "1 -122.22 37.86 ... 358500.0 NEAR BAY\n",
+ "2 -122.24 37.85 ... 352100.0 NEAR BAY\n",
+ "3 -122.25 37.85 ... 341300.0 NEAR BAY\n",
+ "4 -122.25 37.85 ... 342200.0 NEAR BAY\n",
+ "... ... ... ... ... ...\n",
+ "20635 -121.09 39.48 ... 78100.0 INLAND\n",
+ "20636 -121.21 39.49 ... 77100.0 INLAND\n",
+ "20637 -121.22 39.43 ... 92300.0 INLAND\n",
+ "20638 -121.32 39.43 ... 84700.0 INLAND\n",
+ "20639 -121.24 39.37 ... 89400.0 INLAND\n",
+ "\n",
+ "[20640 rows x 10 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 2
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SKP8cofeKvN9",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 296
+ },
+ "outputId": "3ac5b0a5-c75c-4496-f511-8cebf347a268"
+ },
+ "source": [
+ "df.plot(kind='scatter',\n",
+ " x = 'longitude',\n",
+ " y = 'latitude')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2jv3WBssLO8J"
+ },
+ "source": [
+ "### Adding another paramter to map (`c`)\n",
+ "\n",
+ "In many cases when working with large datasets, we want to map multiple features/columns to our plot. When it comes to `scatter` plots we can use one paramater `c`. The `c` parameter basically means color or sequence. This means that it can take multiple colors depending on the type of input given (i.e The `housing_median_age` is a column with values ranging from `0` to `~50`. \n",
+ "\n",
+ "If we declare `c` as `housing_median_age`. Our graph will have a range of colors with each color (or shade) indicating a value from `0 to ~50`. Lets try it out below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "l7ptY0iHLfvQ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 595
+ },
+ "outputId": "8942e0ad-483a-4346-bc94-2e5372251126"
+ },
+ "source": [
+ "df.plot(kind='scatter',\n",
+ " x = 'longitude',\n",
+ " y = 'latitude',\n",
+ " alpha = 0.3,\n",
+ " xlabel = 'Longitude',\n",
+ " ylabel = 'Lattitude',\n",
+ " figsize = (20,10),\n",
+ " c = 'housing_median_age',\n",
+ " cmap = plt.get_cmap('jet'),\n",
+ " colorbar= True)\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9mHxUumwNSax"
+ },
+ "source": [
+ "## 6 - Now Try This \n",
+ "\n",
+ "Plot the California Housing dataset using the `c`paramter. \n",
+ "- Set `c` as `median_housing_value`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hncoFo1RGXIU"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/6.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "75KoagV2LMjS"
+ },
+ "source": [
+ "### Adding a Fourth paramter (`s`)\n",
+ "\n",
+ "So far we have successfully added 3 features/columns to a single plot before we wrap up, there is one last special feature available to us which lets us add another paramter to our plot. This paramter is called the `s` or scalar.\n",
+ "\n",
+ "In order for scalar to work the column we want to use must be an array like column. This means that the column must be:\n",
+ "- numerical\n",
+ "- array like ( e.g:`[1232,35124,1245,......,124542]`)\n",
+ "\n",
+ "A simple way to describe what `s` does is that it creates circles over the points depending on their value. The bigger the value the larger the circle and vice versa.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rQJdjlgqLLLe",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 391
+ },
+ "outputId": "fdaed9c5-ae61-4a8d-835e-af9412b9a232"
+ },
+ "source": [
+ "df.plot(kind='scatter',\n",
+ " x = 'longitude',\n",
+ " y = 'latitude',\n",
+ " alpha = 0.3,\n",
+ " xlabel = 'Longitude',\n",
+ " ylabel = 'Lattitude',\n",
+ " figsize = (20,10),\n",
+ " c = 'housing_median_age',\n",
+ " cmap = plt.get_cmap('jet'),\n",
+ " colorbar= True,\n",
+ " s = df['population']/100,\n",
+ " legend = True)\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 4
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "E9WUJ8-sNVSN"
+ },
+ "source": [
+ "## 7 - Now Try This\n",
+ "\n",
+ "Plot a column that inclues the `s` value. Choose a `numerical` column other than\n",
+ "- `longitude`\n",
+ "- `latitude`\n",
+ "- `population`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BrxwwFjZGYtO"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/7.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Sw_Cv2SRyB_1"
+ },
+ "source": [
+ "\n",
+ "## Submission\n",
+ "Run this code block to download your answers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pptJmIZIyBUd"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "!zip -r \"{student_id}.zip\" \"{student_id}\"\n",
+ "files.download(f\"{student_id}.zip\")"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QGRRJZpaFTi0"
+ },
+ "source": [
+ "# Glossary\n",
+ "\n",
+ "- **parameters**\n",
+ "- **continous**:\n",
+ "- **longitude**\n",
+ "- **latitude**\n",
+ "- **scalar**:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "k2bCYIA5FV31"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Digital_History/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/Practicum-Visualize-Trans-Atlantic-Slave-Trade.ipynb b/Digital_History/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/Practicum-Visualize-Trans-Atlantic-Slave-Trade.ipynb
new file mode 100644
index 0000000..6544465
--- /dev/null
+++ b/Digital_History/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/Practicum-Visualize-Trans-Atlantic-Slave-Trade.ipynb
@@ -0,0 +1,2738 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Practicum-Visualize-Trans-Atlantic-Slave-Trade.ipynb",
+ "provenance": [],
+ "collapsed_sections": [
+ "U30n5IXzIVFX",
+ "UFfKdw9eV6Wc",
+ "mdUGc-qWwzv1",
+ "9SwePQu6qsZl",
+ "s5I3a9h9gzmM",
+ "FFqqKsABeT7y",
+ "Ilf3rsfmBiLw",
+ "IhviGetnfp9B",
+ "oEEj1jeGAlhU",
+ "nwuNo5o2fzGe",
+ "aOi3ah9yDI-T",
+ "2cYXofF6t-hE",
+ "p4NlzzF0y-oK",
+ "jMCmsdSWHlfW",
+ "cvaJr4-BHlgG",
+ "84pXofoLHlhG",
+ "VByUUKTiIYM-",
+ "ObtE8-BHODkC",
+ "TI9Zcwf5Hlh2",
+ "s3AFqNpu2GJ4"
+ ],
+ "toc_visible": true,
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HTy4Z3Uogzl1"
+ },
+ "source": [
+ " \n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U30n5IXzIVFX"
+ },
+ "source": [
+ "#
Visualizing the Trans-Atlantic Slave Trade
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UFfKdw9eV6Wc"
+ },
+ "source": [
+ "# Table of Contents\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rAPAfOpTwqDJ"
+ },
+ "source": [
+ "- Recap\n",
+ "- About the Dataset\n",
+ " - The Transatlantic Slave Trade\n",
+ " - Facts about the dataset\n",
+ "- Labs and Methodology\n",
+ "- Goals\n",
+ "- **Part 1 - Getting Our Basic Data Analysis Set-Up**\n",
+ " - Import Libraries and unpack a file\n",
+ " - Load file\n",
+ " - Observing the Dataset using Pandas\n",
+ " - Important Facts About the Dataset\n",
+ " - Visualize Year of Arrival vs Number of Slaves arrived\n",
+ "- **Part 2 - Getting Started with Data Wrangling**\n",
+ " - Create a copy of the Original Dataset\n",
+ " - Changing Column Names\n",
+ " - Moving Column Positions - ```df.reindex()```\n",
+ " - Remove Voyage ID -```df.drop()```\n",
+ " - Using ```dropna()```\n",
+ " - Changing Column Type and Sorting - ```df.sort_values()```\n",
+ " - Finding Unique and similar strings \n",
+ " - Working with Strings - ```df['column_name'].str.replace()```\n",
+ "- **Part 3 - Micro Wrangling and Visualization**\n",
+ " - Between 1500 - 1600\n",
+ " - Between 1601 - 1700\n",
+ " - Between 1701 - 1800\n",
+ " - Between 1801 - 1900\n",
+ "- **Part 4 - Conclusion**\n",
+ "- Resources\n",
+ "- Appendix"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RE3H5y7yIRHZ"
+ },
+ "source": [
+ "# Recap\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KaDn0K67wuzJ"
+ },
+ "source": [
+ "By this time, you should have an understanding of how to implement the following:\n",
+ "- Loading a Dataset '.csv' as a dataframe using ```pd.read_csv```\n",
+ "- Observing the properties of the loaded dataset using functions such as:\n",
+ " - ```pd.head()```\n",
+ " - ```pd.describe()```\n",
+ " - ```pd.info()```\n",
+ "- Modifying the dataset by removing ```NaN``` values.\n",
+ "- A conceptual understanding of the term ```object``` in DataFrames. (really what it means is that the value is probably text/string)*\n",
+ "- Re-indexing columns\n",
+ "- Visualizing Data using ```matplotlib``` and ```pandas```:\n",
+ " - Scatter plots\n",
+ " - Barplots\n",
+ " - Line plots\n",
+ " - Histograms"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4uvgAANOIKwm"
+ },
+ "source": [
+ "# About the Dataset\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mdUGc-qWwzv1"
+ },
+ "source": [
+ "### The Trans-Atlantic Slave Trade\n",
+ " \n",
+ "\n",
+ "It is difficult and sad to believe that in the first decades of the twenty-first century that **just over two centuries ago**, the shipping of enslaved Africans across the Atlantic was morally indistinguishable from shipping textiles, wheat, or even sugar. Our reconstruction of a major part of this migration experience covers an era in which there was a massive technological change (steamers were among the last slave ships), as well as very dramatic shifts in perceptions of good and evil. \n",
+ "\n",
+ "Just as important to examine is the relationship between the Western and non-Western worlds that the trade both reflected and encapsulated. Slavery constituted the most important reason for contact between Europeans and Africans for nearly two centuries. The shipment of slaves from Africa was in part caused by the demographic disaster due to the meeting of Europeans and Native Americans, which greatly reduced the numbers of Native American laborers and raised the demand for labor drawn from elsewhere, particularly Africa. As Europeans colonized the Americas, a steady stream of European peoples migrated to the Americas between 1492 and the early nineteenth century. But what is often overlooked is that, before 1820, perhaps three times as many enslaved Africans crossed the Atlantic as Europeans. This was the largest transoceanic migration of a people until that day, and it provided the Americas with a crucial labor force for their own economic development. Thus, we must acknowledge the millions of Africans and their descendants who essentially built America, as well as the effects of their journey here.\n",
+ "\n",
+ "The details of the more than **36,000** voyages presented here greatly facilitate the study of cultural, demographic, and economic change in the Atlantic world from the late *sixteenth to the mid-nineteenth centuries*. Trends and cycles in the flow of African captives from specific coastal outlets should provide scholars with new, basic information useful in examining the relationships among slavery, warfare in both Africa and Europe—political instability, and climatic and ecological change, among other forces. \n",
+ "\n",
+ "### Facts about the dataset\n",
+ "\n",
+ "- The dataset covers approximately 36,110 trans-Atlantic voyages.\n",
+ "- The estimates suggest around 12,520,000 captives departed Africa to the Americas. \n",
+ "\n",
+ "- Not all 36,000 voyages in the database carried slaves from Africa.\n",
+ "- A total of 633 voyages (1.8%) never reached the African coast because they were lost at sea, captured, or affected by some other misfortune. \n",
+ "- The database also contains records of 34,106 voyages that disembarked slaves, or could have done so (in other words, for some of these we do not know the outcome of the voyage).\n",
+ "\n",
+ "- The latter group comprised mainly of ships captured in the nineteenth century which were taken to Sierra Leone and St. Helena as part of the attempt to suppress the trade. \n",
+ "\n",
+ "\n",
+ "This is a very insightful resource titled,'The Atlantic Slave Trade in Two Minutes. You can read it [here](http://www.slate.com/articles/life/the_history_of_american_slavery/2015/06/animated_interactive_of_the_history_of_the_atlantic_slave_trade.html)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fPyizd_JIHPU"
+ },
+ "source": [
+ "# Practicum and Methodology\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Aljt8kKWxCRe"
+ },
+ "source": [
+ "Congratulations, you have made it to the first practicum of this course. The purpose of these practicums is to help you apply the Data Science pipeline in a project-based environment. You will be using the tools taught to you in the previous modules and adopt and Question and Answer based approach when you work with the dataset.\n",
+ "\n",
+ "For this project, we start by asking questions which you will answer in code and simple explanations.\n",
+ "\n",
+ "**example** - change the name of 'column_x' to 'column y' \n",
+ "**answer**: ```df = df.rename(columns = {'column_x : 'column_y})```\n",
+ "\n",
+ "We have divided our approach into 4 parts:\n",
+ "\n",
+ "- The first part is the traditional set up. These are some things we should do before modifying the dataset.\n",
+ "- The second part involves cleaning the dataset and choosing columns that fit our methodology.\n",
+ "- The third part involves further splitting our cleaned dataframe into smaller dataframes and visualizing them.\n",
+ "- Finally, the fourth part involves summarizing our conclusion."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Tl_6Scu8IE7Q"
+ },
+ "source": [
+ "\n",
+ "# Grading\n",
+ "\n",
+ "This exercise has a total of 27 questions. Every question has 1 point. Some questions might have multiple parts but the weight of the question is the same.\n",
+ "\n",
+ "In order to work on the questions in this Practicum and submit them for grading, you'll need to run the code block below. It will ask for your student ID number and then create a folder that will house your answers for each question. At the very end of the notebook, there is a code section that will download this folder as a zip file to your computer. This zip file will be your final submission."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HmZvJAZIpGf6"
+ },
+ "source": [
+ "import os\n",
+ "import shutil\n",
+ "\n",
+ "!rm -rf sample_data\n",
+ "\n",
+ "student_id = input('Please Enter your Student ID: ') # Enter Student ID.\n",
+ "\n",
+ "while len(student_id) != 9:\n",
+ " student_id = int('Please Enter your Student ID: ') \n",
+ " \n",
+ "folder_location = f'{student_id}/Week_Six/Practicum'\n",
+ "if not os.path.exists(folder_location):\n",
+ " os.makedirs(folder_location)\n",
+ " print('Successfully Created Directory, Lets get started')\n",
+ "else:\n",
+ " print('Directory Already Exists')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ixk29-rMHvR9"
+ },
+ "source": [
+ "# Part 1 - Getting Our Basic Data Analysis Set-Up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0W--Kb7wgzl7"
+ },
+ "source": [
+ "Import the libraries you will be using for this project. These libraries are the ones we have used in the previous labs."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7q6R0hBZONBe"
+ },
+ "source": [
+ "## Q1 Load libraries and file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vs7AJxshHlc9"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/1.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "import _____ as pd # INSERT CODE HERE\n",
+ "import __________ as plt # INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DfmowYZZOVRn"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/1.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "url = 'https://rb.gy/cjfen3'\n",
+ "\n",
+ "trans_atlc_trade = __.read_csv(___) # INSERT CODE HERE "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9SwePQu6qsZl"
+ },
+ "source": [
+ "### Observing the Dataset using Pandas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mCNQqlwecmjv"
+ },
+ "source": [
+ "Now, the dataset is loaded as a dataframe `trans_atlc_trade`\n",
+ "\n",
+ "### ```head()```\n",
+ "Let's check what columns this file has by calling 'head()' function.\n",
+ "It returns first n rows, and it's useful to see the dataset at a quick glance.\n",
+ "\n",
+ "By default, the head() function returns the first 5 rows.\n",
+ "\n",
+ "You can specify the number of rows to display by calling `df.head(number)`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_Fwr655mcjr5"
+ },
+ "source": [
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s5I3a9h9gzmM"
+ },
+ "source": [
+ "### ```tail()```\n",
+ "\n",
+ "The ```tail()``` method prints the last 5 rows of our dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WVD4aPJbHldP"
+ },
+ "source": [
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FFqqKsABeT7y"
+ },
+ "source": [
+ "### ```info()```\n",
+ "This will return all of the column names and its types. This function is useful to get the idea of what the dataframe is like.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wxqUmTFdfkCi"
+ },
+ "source": [
+ "## INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ilf3rsfmBiLw"
+ },
+ "source": [
+ "### Observations:\n",
+ "\n",
+ "#### Questions:\n",
+ "- List down the number of unique ```Dtype``` in this dataset\n",
+ "- Is the dataset uneven? If so list down the column with the most missing rows?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sT2OXLFoJNTu"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IhviGetnfp9B"
+ },
+ "source": [
+ "### ```describe()```\n",
+ "describe() is used to view summary statistics of numeric columns. This will help you to have general idea of the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DuDPOCZ9frBI"
+ },
+ "source": [
+ "trans_atlc_trade._____() # Insert code here"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oEEj1jeGAlhU"
+ },
+ "source": [
+ "### Observations:\n",
+ "\n",
+ "#### Questions:\n",
+ "- Why is ```describe``` showing only 3 columns?Is it because of their types e.g(int,float,object)?\n",
+ "- What could be reason for the counts not being the same?\n",
+ "- Are the mean, standard deviation,..., max. important for Voyage ID?\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "86n5av1gJGmI"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nwuNo5o2fzGe"
+ },
+ "source": [
+ "### ```shape```\n",
+ "To see the size of the dataset, we can use shape function, which returns the number of rows and columns in a format of (#rows, #columns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fIhERH7KHldk"
+ },
+ "source": [
+ "trans_atlc_trade.shape"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aOi3ah9yDI-T"
+ },
+ "source": [
+ "#### Questions/Observations:\n",
+ "How many **rows** and **columns** are there?\n",
+ "#### Answer:\n",
+ "Note: These questions that are not numbered are not graded. You can optionally choose to answer to help you think about the code you are using."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8jaZnYnII4Tx"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AeidaHn6Hldo"
+ },
+ "source": [
+ "## Q2. Important Facts About the Dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XIFbkyfIDinJ"
+ },
+ "source": [
+ "The next thing we want to do is count the number of trips that have been unaccounted for. We'll know this by observing the ```Slaves arrived at 1st port``` This is simple, all we have to do is run two functions:\n",
+ "- The first one will be to check if the column has null values, ```isna()```.\n",
+ "- The second one will be sum the number of null rows in the column, ```sum()```."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lKl32c6JHldo"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/2.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "Unaccounted_trips = trans_atlc_trade['Slaves arrived at 1st port'].____()._____() # Insert Code Here\n",
+ "print(f'The total number of unaccounted trips is: {Unaccounted_trips}')\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2JPDPjr-EI60"
+ },
+ "source": [
+ "\n",
+ "**What about the total of slaves accounted for?** \n",
+ "In the following line of code, we will have to ```sum``` the the total number of slaves in every column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "v_Gai5XEHlds",
+ "scrolled": true
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/2.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "number_of_slaves_accounted = trans_atlc_trade['Slaves arrived at 1st port'].___() # Insert code here\n",
+ "print(f'The total number of slaves accounted for are: {number_of_slaves_accounted}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bYuDgHKIHldv"
+ },
+ "source": [
+ "Historical estimates suggest that the total number of slaves traded are estimated to be ~12.5 Million. This means that according to this dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xyymyjbgEduN"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/2.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "possible_unaccounted_slaves = 12500000 - ________________ # INSERT CODE HERE \n",
+ "possible_unaccounted_slaves "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "heW7wau-Hldv"
+ },
+ "source": [
+ "### Visualize Year of Arrival vs Number of Slaves arrived"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zdNAAj8EvEDs"
+ },
+ "source": [
+ "fig = plt.figure(figsize = (35,10))\n",
+ "\n",
+ "ax1 = fig.add_subplot(1,2,1)\n",
+ "ax2 = fig.add_subplot(1,2,2)\n",
+ "\n",
+ "trans_atlc_trade.plot(x = 'Year of arrival at port of disembarkation',\n",
+ " y = 'Slaves arrived at 1st port',\n",
+ " kind = 'scatter',\n",
+ " c = 'Slaves arrived at 1st port',\n",
+ " title = 'Year of arrival at port of disembarkation vs Slaves arrived at 1st port',\n",
+ " alpha = 0.3,\n",
+ " cmap = plt.get_cmap('ocean'),\n",
+ " colorbar = True,\n",
+ " ax = ax1,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "trans_atlc_trade.plot(x = 'Year of arrival at port of disembarkation',\n",
+ " y = 'Slaves arrived at 1st port',\n",
+ " kind = 'area',\n",
+ " title = 'Year of arrival at port of disembarkation vs Slaves arrived at 1st port',\n",
+ " ax = ax2,\n",
+ " )\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2cYXofF6t-hE"
+ },
+ "source": [
+ "### Questions/Observations\n",
+ "- Are the plots above useful? \n",
+ "- Can we get anything specific by observing them?\n",
+ "- Is there a visible trend?\n",
+ "- What are the possible issues with the plot above? \n",
+ "- Lastly, which one is more practical, the ```scatter``` or ```area```?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wFb2pufJJYAC"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nIFt7KCZHld-"
+ },
+ "source": [
+ "# Part 2 - Getting Started with Data Wrangling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_JW6MwRSFF3B"
+ },
+ "source": [
+ "Now that we have observed the basic features of our dataset raw, we will began cleaning it. This involves several steps that you will be working through.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NJhawspPHld-"
+ },
+ "source": [
+ "### Create a copy of the Original Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HJ6p5GksHld_"
+ },
+ "source": [
+ "df = trans_atlc_trade.copy(deep = True) # We have used deep = True to make sure the copy is not linked to the trans_atlc_trade dataframe.\n",
+ " # If we did not add it, any changes made to the new df would be made on the tran_atlc_trade too."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "--Q1q9kDgznD"
+ },
+ "source": [
+ "## Q3. Column List\n",
+ "\n",
+ "List down the names of all the columns in our dataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NvhxosPNF5LH"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/3.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "df._______ # Insert Code Here\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BJAtGmeEq2U3"
+ },
+ "source": [
+ "## Q4. Change column names"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J4KjruAfIDe2"
+ },
+ "source": [
+ "For this exercise, you will change the names of the previously existing columns to something that is more readable. \n",
+ "\n",
+ "\n",
+ "Using the columns above write down the name of the column in place of ```COLUMN_NAME_HERE```.\n",
+ "\n",
+ "\n",
+ "The last column name will be tricky to change, this is because it has a ```'``` here. In order to change that right before the ```'``` add a ```\\```."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aSzOUf_CHleE"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/4.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "df = df.rename(\n",
+ " columns={'COLUMN_NAME_HERE':'voyage_id', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'vessel_name', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'voyage_started', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'voyage_pit_stop', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'end_port', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'year_of_arrival', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'slaves_onboard', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'captain_names' # Insert Column Name you want to change\n",
+ " })\n",
+ "\n",
+ "\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "08LYIpGyHleI"
+ },
+ "source": [
+ "## Q5 Moving Column Positions - ```df.reindex()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "m6SuvgcJIsfL"
+ },
+ "source": [
+ "When we're looking at the renamed database, for our purposes, we don't want to work with the ```captain_names```. Next, we will use ```df.reindex()``` to change the order of our columns.\n",
+ "\n",
+ "You can see below a list below which has the ```column_names``` in the order we want and is without ```captains_name```.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "B0p8g8M6HleJ"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/5.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "\n",
+ "column_names = ['voyage_id',\"year_of_arrival\",\"vessel_name\", \"voyage_started\",\"voyage_pit_stop\", \"end_port\",\"slaves_onboard\"]\n",
+ "\n",
+ "df = df._____(columns=________) # Insert Code here\n",
+ "\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MfhTG28pHleN"
+ },
+ "source": [
+ "***Take a step and think:***\n",
+ "\n",
+ "**Is Voyage ID a good index and do we need it as a column?**\n",
+ "\n",
+ "No, but we need an index.\n",
+ "\n",
+ "**Can 'year_of_arrival' be an Index?**\n",
+ "\n",
+ "No, because there are repeating dates in the charts, there for we need a simple log counter.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qOYcbFRwHleN"
+ },
+ "source": [
+ "## Q6. Remove Voyage ID -```df.drop()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uocghp2yHleO"
+ },
+ "source": [
+ "Now that we have a new index from 0 to 15299, do we need ```voyage_id```?\n",
+ "\n",
+ "No, we don't because it doesn't help us find anything useful. Every Voyage ID is unique.\n",
+ "\n",
+ "So, drop this column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "90MwAELVHleO"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/6.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "df = __.drop(columns='_____') # INSERT CODE HERE\n",
+ "df "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d_Ds03fRHleS"
+ },
+ "source": [
+ "### Using ```dropna()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OB1gdak8HleT"
+ },
+ "source": [
+ "For this data set, we will be working with trips that were completely accounted for in all of the remaining features.\n",
+ "\n",
+ "The ```dropna()``` method is designed top drop every value in our dataframe whos cell might have a null or undefined value. They are usually shown as ```NaN```.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HUUV6ipxHleT"
+ },
+ "source": [
+ "df = df.dropna()\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-8OASxc-HleZ"
+ },
+ "source": [
+ "df.info()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "p4NlzzF0y-oK"
+ },
+ "source": [
+ "### Questions/Observations\n",
+ "How many rows are we left with?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Eg8eM2gTJ4I3"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ju-Bw99zHled"
+ },
+ "source": [
+ "## Q7. Sorting Column using ```year_of_arrival``` using - ```df.sort_values()```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KCQb6H5uHled"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/7.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "df = df.sort_values(by='_______', ascending=_____) # INSERT CODE HERE\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AZbNdBQKgznj"
+ },
+ "source": [
+ "## Q8. Reseting the Index "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AMV7309HHleh"
+ },
+ "source": [
+ "Reseting Index using ```df.reset_index``` with respect 'year_of_arrival' in Ascending Order. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rEIogWnUHlei"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/8.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "df.______(inplace=True, drop=True) # INSERT CODE HERE\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i_4uzK6tHlel"
+ },
+ "source": [
+ "### Finding Unique and similar strings \n",
+ "\n",
+ "First, we will list down all the unique names in these columns. Next, we will sort these in alphabetical order in order to make it easier to observe.\n",
+ "\n",
+ "```df['column_name].unique()``` and ```df.sort()```\n",
+ "\n",
+ "for simplicity i have just declared the first line as a variable ```a``` in order to print it\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zcZzcYc6Hlel"
+ },
+ "source": [
+ "a = df['voyage_started'].unique()\n",
+ "a.sort()\n",
+ "a"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Xo4FXypKHleq"
+ },
+ "source": [
+ "a = df['voyage_pit_stop'].unique()\n",
+ "a.sort()\n",
+ "a"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "m4mKFIA8Hlex"
+ },
+ "source": [
+ "a = df['end_port'].unique()\n",
+ "a.sort()\n",
+ "a"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Tilt8tICP_Yw"
+ },
+ "source": [
+ "As we can see above our object columns, ```voytage_started```, ```voyage_pit_stop``` and ```end_port``` have phrases such as ``` port unspecified``` and ```unspecified```. We need to get rid of these.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zKVGN7_cHle2"
+ },
+ "source": [
+ "## Q9. Working with Strings - ```df['column_name'].str.replace()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SESPapu0UDyS"
+ },
+ "source": [
+ "To replace unwanted parts of a string, we use the function ```df['columun_name'].str.replace('string to find','string to replace')```. This command looks for the string we have specified and replaces with what we want.\n",
+ "\n",
+ "For example:\n",
+ "If have an entry in the 'voyage_started' column, 'Virginia, port unspecified'. By running the command:\n",
+ "```df['voyage_started'].str.replace(', port unspecified', '')```\n",
+ "The string will be changed from ''Virginia, port unspecified' to 'Virginia'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NPxciPRzHle3"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/9.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "df['voyage_started'] = df['voyage_started'].str.replace('', '')\n",
+ "df['voyage_started'] = df['voyage_started'].str.replace('', '')\n",
+ "df['voyage_started'] = df['voyage_started'].str.replace('', '')\n",
+ "\n",
+ "df['voyage_pit_stop'] = df['voyage_pit_stop'].str.replace('', '')\n",
+ "df['voyage_pit_stop'] = df['voyage_pit_stop'].str.replace('', '')\n",
+ "df['voyage_pit_stop'] = df['voyage_pit_stop'].str.replace('.', '')\n",
+ "\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('.', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace(', south coast', '') # Insert string to replace\n",
+ "df['end_port'] = df['end_port'].str.replace(', west coast', '') \n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IOPDZL8vHle7"
+ },
+ "source": [
+ "df.dtypes"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "x0c0BG7SHle_"
+ },
+ "source": [
+ "#### Creating a Copy of our modified Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4jrvqhIAHle_"
+ },
+ "source": [
+ "modified_dataset = df.copy(deep = True)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wkw5pefkHlfC"
+ },
+ "source": [
+ "# Part 3 - Micro Wrangling and Visualization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_yO0iI-vrq8h"
+ },
+ "source": [
+ "
\n",
+ "\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pwDzGZeYHlfD"
+ },
+ "source": [
+ "We will start this part by dividing our dataset into multiple smaller dataframes. The approach we will be taking is separating dataframes based on the ```year_of_arrival``` dataset.\n",
+ "\n",
+ "For example, in the blocks below you will see code for 4 intervals:\n",
+ "- ```1500 to 1600```\n",
+ "- ```1601 to 1700```\n",
+ "- ```1701 to 1800```\n",
+ "- ```1801 to 1900```\n",
+ "\n",
+ "\n",
+ "To help you understand the procedure, we have worked through ```1500 to 1600``` . You will be required to do the same for the next 2 periods. The last one i.e. 1801 to 1900 is optional and we hope you will attempt it as a significant chunk of the voyages (specially the number of slaves transported) occurred in the early 19th century.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3ZkrSv8hHlfD"
+ },
+ "source": [
+ "# Between 1500 to 1600"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NC8OXsdVuj5c"
+ },
+ "source": [
+ "## Create a new dataframe for the given date range.\n",
+ "\n",
+ "Here, we are creating a new dataframe from the copy of our dataset ```modified_dataset``` from step 2. we are using the range ```1500``` and ```1600``` from the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Lp6kae0pHlfE"
+ },
+ "source": [
+ "dataset_between15_16 = modified_dataset.where((modified_dataset['year_of_arrival'] >= 1500) & (modified_dataset['year_of_arrival'] <= 1600))\n",
+ "dataset_between15_16"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JF5n5i1huunL"
+ },
+ "source": [
+ "## Dropping the Null values\n",
+ "\n",
+ "If you notice the column above, we can only see ```NaN``` values. This is because when we made a new dataframe. We haven't changed the shape of the dataset at all. In fact, we have only made the rows between our defined ranges ```True```. The rest of them have been converted into empty calls. Therefore, the next step will be to drop them.\n",
+ "\n",
+ "You can do this by simply running the line below. Another way to do this is by using the code ```dataset_between15_16.dropna(inplace = True)```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "a-tQvhW3HlfI"
+ },
+ "source": [
+ "dataset_between15_16 = dataset_between15_16.dropna()\n",
+ "dataset_between15_16"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T6C4yZSgHlfN"
+ },
+ "source": [
+ "## Total Number of Slaves Transported between 1501-1600 - Complete Records\n",
+ "\n",
+ "Let's check the number of slaves transported between 1501-1600.\n",
+ "\n",
+ "*Please remember we are looking at rows that donot have any empty cells. Look back to this [part](https://colab.research.google.com/github/bitprj/DigitalHistory/blob/master/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/Lab-Visualize-Trans-Atlantic-Slave-Trade.ipynb#scrollTo=d_Ds03fRHleS). We dropped a significant amount of rows over there because we had at least 1 or more ```NaN``` values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lpCOGYH2HlfN"
+ },
+ "source": [
+ "dataset_between15_16.slaves_onboard.sum()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yoLMT1SKHlfR"
+ },
+ "source": [
+ "## Visualizing Trips During 1501-1601\n",
+ "\n",
+ "Let's quickly visuallize our data. We will plot 4 plots: 2 bar and 2 scatter. All of them will use the columns ```year_of_arrrival``` or ```slaves_onboard```. Additionally, in two of the plots we will switch the x and y columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ejmvzk-2HlfR"
+ },
+ "source": [
+ "fig = plt.figure(figsize = (20,10))\n",
+ "\n",
+ "ax1 = fig.add_subplot(2,2,1)\n",
+ "ax2 = fig.add_subplot(2,2,2)\n",
+ "ax3 = fig.add_subplot(2,2,3)\n",
+ "ax4 = fig.add_subplot(2,2,4)\n",
+ "\n",
+ "\n",
+ "ax1.scatter(dataset_between15_16['year_of_arrival'],\n",
+ " dataset_between15_16['slaves_onboard'],\n",
+ " alpha = 0.4)\n",
+ "\n",
+ "ax2.scatter(dataset_between15_16['slaves_onboard'],\n",
+ " dataset_between15_16['year_of_arrival'],\n",
+ " alpha = 0.4)\n",
+ "\n",
+ "ax3.bar(\n",
+ " dataset_between15_16['year_of_arrival'],\n",
+ " dataset_between15_16['slaves_onboard'],\n",
+ " alpha = 0.4)\n",
+ "\n",
+ "ax4.set_ylim(1500,1600)\n",
+ "ax4.bar(\n",
+ " dataset_between15_16['slaves_onboard'], \n",
+ " dataset_between15_16['year_of_arrival'],\n",
+ " alpha = 0.4)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jMCmsdSWHlfW"
+ },
+ "source": [
+ "## 3.1 Choosing Graphs\n",
+ "\n",
+ "### Questions/Observations\n",
+ "- Which of these graphs seem useful and which ones are unnecessary?\n",
+ "**Write a 2 sentence explanation about why these two plots seem or might be useful.** **Did the visualization style influence your decision?**\n",
+ "Select the two graphs you think are more useful and add the following:\n",
+ "- Add ```title``` for both subplots.\n",
+ "- Add ```xlabel``` and ```ylabel```.\n",
+ "- Change the color for one of the plots\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mwmTuvvfKw96"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5eu2pWKrHlfX"
+ },
+ "source": [
+ "## Plot the ```vessel_name``` vs the ```slaves_onboard```.\n",
+ "\n",
+ "\n",
+ "Next, we'll use the ```pandas``` ```plot``` function and use the columns ```vessel_name``` as ```x``` and ```slaves_onboard``` as ```y```.\n",
+ "\n",
+ "Remember, ```vessel_name``` is a categorical value so we're plotting a bar chart of categorical vs numerical here. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "90m3tTRHHlfY"
+ },
+ "source": [
+ "dataset_between15_16.plot(x= 'vessel_name',\n",
+ " y = 'slaves_onboard',\n",
+ " kind = 'bar',\n",
+ " rot = 90)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7oOVxiLsHlfc"
+ },
+ "source": [
+ "## Plotting voyages carrying Less than 100 slaves per trip\n",
+ "\n",
+ "The plot above is crowded since there are a lot of ships that were used throughout the 16th century. Our next step will be to simplify the plotting a little bit and to actually be able to visualize the plots properly.\n",
+ "\n",
+ "Below we create a new variable for our plot and we name it ```temp_df```.\n",
+ "*You can name it anything you want.*\n",
+ "\n",
+ "We first make a dataframe that only contains rows where the number of slaves onboard were less than 100."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-_Oy0Cr_Hlfd"
+ },
+ "source": [
+ "temp_df = dataset_between15_16.where(dataset_between15_16['slaves_onboard'] < 100.0).dropna()\n",
+ "\n",
+ "temp_df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I1UkMU9wHlfg"
+ },
+ "source": [
+ "temp_df.plot(x='vessel_name',\n",
+ " y = 'slaves_onboard',\n",
+ " kind = 'bar',\n",
+ " rot = 90)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OjOjDcogHlfk"
+ },
+ "source": [
+ "## Plotting voyages carrying greater than 100 slaves per trip\n",
+ "\n",
+ "Next, we check for ships where the number of slaves carried was greater than 100.\n",
+ "\n",
+ "Notice we added a ```dropna``` at the end. This is the same as the step where we drop the null values from our dataframes but instead of writing it as a new line we have simply attactched at to our ```dataset_between15_16.where```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lrAOZa1hHlfm"
+ },
+ "source": [
+ "temp_df = dataset_between15_16.where(dataset_between15_16['slaves_onboard'] > 100.0).dropna()\n",
+ "\n",
+ "temp_df.plot(x='vessel_name',\n",
+ " y = 'slaves_onboard',\n",
+ " kind = 'bar',\n",
+ " rot = 90,\n",
+ " grid = True,\n",
+ " figsize = (20,10)\n",
+ " )\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pfb7xZN44OOn"
+ },
+ "source": [
+ "As we can see above, it is still a little congested. Therefore, we'll narrow down our search a little more.\n",
+ "\n",
+ "As you can see plotting using random numbers might not give us the best results.\n",
+ "\n",
+ "One thing we can do is select our values based on the ```mean```,```standard deviation```,```25%```,```50%```,```75%```\n",
+ "\n",
+ "So, let's check those values for our current dataframe, which is ```dataset_between15_16```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "73CIarliyZqf"
+ },
+ "source": [
+ "dataset_between15_16.describe()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qytcnc86yj7m"
+ },
+ "source": [
+ "We can see the values above. For this project we will be looking at the ```75%``` value for the ```slaves_onboard``` column, therefore:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "L9mAoEe4y56i"
+ },
+ "source": [
+ "num_of_slaves_3q = 202 # 3q means third quartile. The value is 201.5 but we are rounding up"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iC1mKd2BHlfu"
+ },
+ "source": [
+ "## Plotting voyages with respect to ```num_of_slaves_3q```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HNwE7zKBHlfv"
+ },
+ "source": [
+ "temp_df = dataset_between15_16.where(dataset_between15_16['slaves_onboard'] >num_of_slaves_3q).dropna()\n",
+ "\n",
+ "print(f'There are {temp_df.shape[0]} trips that carries more than {num_of_slaves_3q} slaves.') "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cnrQVyY4zlnt"
+ },
+ "source": [
+ "The second line written above is simply an ```f-string```. These are not important but are still useful when printing statements and let us print variables inside a string."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dfBf3GfyHlfz",
+ "scrolled": true
+ },
+ "source": [
+ "temp_df.plot(x= 'vessel_name',\n",
+ " y = 'slaves_onboard',\n",
+ " kind = 'bar',\n",
+ " rot = 90, # Adjusted accordingly, you can do the same\n",
+ " grid = True, # Adjusted accordingly, you can do the same\n",
+ " figsize = (20,10) # Adjusted accordingly, you can do the same\n",
+ " )\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZIZ8rFjhHlf3"
+ },
+ "source": [
+ "## Plotting the most used ```start_port```\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7IOuRHKwHlf4",
+ "scrolled": true
+ },
+ "source": [
+ "temp_df['voyage_started'].hist(bins = 20, # Adjusted accordingly, you can do the same \n",
+ " alpha = 0.5, # Adjusted accordingly, you can do the same\n",
+ " xrot = 45, # Adjusted accordingly, you can do the same\n",
+ " figsize = (10,10) # Adjusted accordingly, you can do the same\n",
+ " )"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ShDktMYRHlf8"
+ },
+ "source": [
+ "## Histogram - Check the most used ```voyage_pit_stop```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vYoFVQrIHlf9"
+ },
+ "source": [
+ "temp_df['voyage_pit_stop'].hist(bins=10, # Adjusted accordingly, you can do the same\n",
+ " alpha=0.7, # Adjusted accordingly, you can do the same\n",
+ " xrot = 0, # Adjusted accordingly, you can do the same\n",
+ " figsize = (10,10) # Adjusted accordingly, you can do the same\n",
+ " )"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ntI-QvHuHlgB"
+ },
+ "source": [
+ "## Histogram - Check the most used ```End_Port``` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ermZi3B7HlgC",
+ "scrolled": false
+ },
+ "source": [
+ "temp_df['end_port'].hist(bins=10, # Adjusted accordingly, you can do the same\n",
+ " alpha=0.7, # Adjusted accordingly, you can do the same\n",
+ " xrot = 0, # Adjusted accordingly, you can do the same\n",
+ " figsize = (10,10) # Adjusted accordingly, you can do the same\n",
+ " )"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cvaJr4-BHlgG"
+ },
+ "source": [
+ "### Questions/Observations\n",
+ "\n",
+ "- Where were most of the trips made? \n",
+ "- Where did they start from.\n",
+ "- Any other important observations?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tSX0_9DKLXEf"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1w9XvpNdHlgG"
+ },
+ "source": [
+ "# Between 1601 - 1700\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "br4un6S5r7QC"
+ },
+ "source": [
+ "## Q10. Create a new dataframe for the given date range."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CWt8CtQDHlgH"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/10.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "dataset_between16_17 = modified_dataset.____ #INSERT CODE HERE\n",
+ "\n",
+ "dataset_between16_17"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Eg7EEdhKgzpG"
+ },
+ "source": [
+ "## Q11. Drop null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cv3zqBdzHlgK"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/11.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "dataset_between16_17 = dataset_between16_17._____() # Insert Code here (drop nul values)\n",
+ "dataset_between16_17"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ltr9T0R2HlgQ"
+ },
+ "source": [
+ "## Q12. Total Number of Slaves Transported between 1601-1700 - Complete Records\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xzPsmzJVHlgQ"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/12.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "dataset_between16_17.slaves_onboard.___() # Insert Code Here - Sum of slaves "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6KPEwqj3HlgT"
+ },
+ "source": [
+ "## Q13. Visualizing Trips During 1601-1701"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kM5-QvjmHlgT"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/13.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "fig = # INSERT CODE HERE\n",
+ "\n",
+ "ax1 = # INSERT CODE HERE\n",
+ "ax2 = # INSERT CODE HERE\n",
+ "\n",
+ "ax1.scatter(# INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " )\n",
+ "\n",
+ "ax2.bar(\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " )\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QjxCnj6nHlgX"
+ },
+ "source": [
+ "## Q14. Plot the ```vessel_name``` vs the ```slaves_onboard```.\n",
+ "Next, we'll use the ```pandas``` ```plot``` function and use the columns ```vessel_name``` as ```x``` and ```slaves_onboard``` as ```y```.\n",
+ "\n",
+ "Remember, ```vessel_name``` is a categorical value so we're plotting a bar chart of categorical vs numerical here. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4dx1qSH-HlgY"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/14.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "fig = plt.figure(figsize = (50,20))\n",
+ "ax1 = fig.add_subplot(2,2,1)\n",
+ "\n",
+ "dataset_between16_17.plot(# INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " )"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KDVwLu8y1bys"
+ },
+ "source": [
+ "Note: The graph above will be more congested compared to the ```1500-1601``` plot. This is because there are more trips.\n",
+ "\n",
+ "As you can see, plotting using random numbers might not give us the best results.\n",
+ "\n",
+ "Remember, one thing we can do is select our values based on the ```mean```,```standard deviation```,```25%```,```50%```,```75%```\n",
+ "\n",
+ "Let's check those values for our current dataframe which is ```dataset_between16_17```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ESlgYy0f2HBw"
+ },
+ "source": [
+ "dataset_between16_17.describe()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CXctHsP72HCN"
+ },
+ "source": [
+ "## Q15. Plotting voyages with respect to ```num_of_slaves_3q```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5Lwe6AHz2HCD"
+ },
+ "source": [
+ "We can see the values above, for this project we will be looking at the ```75%``` value for the ```slaves_onboard``` column.\n",
+ "\n",
+ "Therefore:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gwkivLOU2HCF"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/15.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "slaves_onboard_3q = ### INSERT VALUE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sSCZq0Z62HCP"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/15.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "temp_df = ## \n",
+ "\n",
+ "print(f'There are {temp_df.shape[0]} trips that carries more than {slaves_onboard_3q} slaves.') "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "scrolled": true,
+ "id": "nNBh6mG02HCX"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/15.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "temp_df.plot(# INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " rot = 90, # Adjusted accordingly, you can do the same\n",
+ " grid = True, # Adjusted accordingly, you can do the same\n",
+ " figsize = (20,10) # Adjusted accordingly, you can do the same\n",
+ " )\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MGDHXfAnHlgf"
+ },
+ "source": [
+ "## Q16. Plotting the most used ```start_port```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cuTVq2HkHlgh"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/16.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5alh-9SgHlgj"
+ },
+ "source": [
+ "## Q17. Plotting the most used ```voyage_pit_stop``` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "m49Xm2SkHlgk"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/17.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6RZ4R9mFHlgo"
+ },
+ "source": [
+ "## Q18. Plotting the most used ```End_Port```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KqQSbPfOHlgo",
+ "scrolled": false
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/18.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SXYLnplNHlgs"
+ },
+ "source": [
+ "# Between 1701 - 1800"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pugWaMBHtEp1"
+ },
+ "source": [
+ "## Q19. Create a new dataframe for the given date range."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mpED3SzKHlgt"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/19.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "dataset_between17_18 = # INSERT CODE HERE\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bmGllGlLtLG1"
+ },
+ "source": [
+ "## Q20. Drop null values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "UztHQkTmHlgx"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/20.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "# INSERT CODE HERE\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8Pu1E16BHlg0"
+ },
+ "source": [
+ "## Q21. Total Number of Slaves Transported between 1701-1800 - Complete Records.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ICN8XRxYHlg0"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/21.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aoav5Z58Hlg2"
+ },
+ "source": [
+ "## Q22. Visualizing Trips During 1701-1800"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "c6LVgQ1wHlg3"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/22.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "fig = # INSERT CODE HERE\n",
+ "\n",
+ "ax1 = # INSERT CODE HERE\n",
+ "ax2 = f# INSERT CODE HERE\n",
+ "\n",
+ "ax1.scatter(# INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " )\n",
+ "\n",
+ "ax2.bar(\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " )\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "p65ASw2_Hlg6"
+ },
+ "source": [
+ "## Q23. Plot the ```vessel_name``` vs the ```slaves_onboard```.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bU5P2A0CHlg7"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/23.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RKuwKHwV3eRz"
+ },
+ "source": [
+ "Note: The graph above will also be more congested compared to the ```1500-1600``` and ```1601-1700``` plot. This is because the number of trips are more the previous century.\n",
+ "\n",
+ "Again, plotting using random numbers might not give us the best results.\n",
+ "\n",
+ "Recall we can simply select our values based on the ```mean```,```standard deviation```,```25%```,```50%```,```75%```\n",
+ "\n",
+ "Let's check those values for our current dataframe which is ```dataset_between17_18```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0yqxQFSE3eR3"
+ },
+ "source": [
+ "dataset_between17_18.describe()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WqV5upo63eSC"
+ },
+ "source": [
+ "## Q24. Plotting voyages with respect to ```num_of_slaves_3q```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1Usvkj2J3eSE"
+ },
+ "source": [
+ "We can see the values above, for this project we will be looking at the ```75%``` value for the ```slaves_onboard``` column.\n",
+ "\n",
+ "Therefore:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DXZybD783eSG"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/24.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "slaves_onboard_3q = ### INSERT VALUE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jmCxRfFi3eSQ"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/24.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "temp_df = ## \n",
+ "\n",
+ "print(f'There are {temp_df.shape[0]} trips that carries more than {slaves_onboard_3q} slaves.') "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "scrolled": true,
+ "id": "pp6Xm1cW3eSW"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/24.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "temp_df.plot(# INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " # INSERT CODE HERE\n",
+ " rot = 90, # Adjusted accordingly, you can do the same\n",
+ " grid = True, # Adjusted accordingly, you can do the same\n",
+ " figsize = (20,10) # Adjusted accordingly, you can do the same\n",
+ " )\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ejs7tofyHlg9"
+ },
+ "source": [
+ "## Q25. Plotting the most used ```start_port``` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5g_CwoUBHlg9"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/25.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wQXiGjGLHlhB"
+ },
+ "source": [
+ "## Q26. Plotting the most used ```voyage_pit_stop``` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yiLTrPGTHlhB"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/26.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VHcaOGz9HlhD"
+ },
+ "source": [
+ "## Q27. Plotting the most used ```End_Port```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2XHKgB-qHlhE",
+ "scrolled": false
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/27.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "\n",
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "84pXofoLHlhG"
+ },
+ "source": [
+ "# Extra\n",
+ "# Between 1801 - 1900"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_hntDtdsgzqd"
+ },
+ "source": [
+ "# Conclusion\n",
+ "\n",
+ "For this, you will write a summary of what steps you followed throughout this notebook, why they were important and your findings.\n",
+ "For example:\n",
+ "- The findings you observed when working through the 4 centuries of slave trade voyages.\n",
+ "- Are our findings reliable or do we need further research?\n",
+ "- Was ```vessel_name``` useful?\n",
+ "- What could we have found if we kept the captains name column?\n",
+ "- What else could we find with this dataset?\n",
+ "- What are our limitations?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "UZXmHxErL3o-"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "# \r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Sw_Cv2SRyB_1"
+ },
+ "source": [
+ "\n",
+ "## Submission\n",
+ "Run this code block to download your answers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pptJmIZIyBUd"
+ },
+ "source": [
+ "from google.colab import files\n",
+ "!zip -r \"{student_id}.zip\" \"{student_id}\"\n",
+ "files.download(f\"{student_id}.zip\")"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VByUUKTiIYM-"
+ },
+ "source": [
+ "# Appendix"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ObtE8-BHODkC"
+ },
+ "source": [
+ "## Connecting to Your Google Drive\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_3xzPhI_ByD5"
+ },
+ "source": [
+ "# Start by connecting google drive into google colab\n",
+ "\n",
+ "from google.colab import drive\n",
+ "\n",
+ "drive.mount('/content/gdrive')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_epZFcuXNVRv"
+ },
+ "source": [
+ "!ls \"/content/gdrive/My Drive/DigitalHistory\""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "y6r-8UBGNueF"
+ },
+ "source": [
+ "cd \"/content/gdrive/My Drive/DigitalHistory/tmp/trans-atlantic-slave-trade\""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Pp8mubdjVqY2"
+ },
+ "source": [
+ "ls"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZLhlkjd3Hlhr"
+ },
+ "source": [
+ "### Extracting ZipFiles"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rzDr3KIkOJdy"
+ },
+ "source": [
+ "\n",
+ "import zipfile\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zkIDSJWUQFCV"
+ },
+ "source": [
+ "file_location = 'data/trans-atlantic-slave-trade.csv.zip'\n",
+ "\n",
+ "zip_ref = zipfile.ZipFile(file_location,'r')\n",
+ "zip_ref.extractall('data/tmp/trans-atlantic-slave-trade')\n",
+ "zip_ref.close()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TI9Zcwf5Hlh2"
+ },
+ "source": [
+ "### Checking and Changing Column Types \n",
+ "```df.dtypes``` and ```df.astype()```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pGl1MUnOHlh3"
+ },
+ "source": [
+ "df.dtypes"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JIG8wtR2Hlh6"
+ },
+ "source": [
+ "df.year_of_arrival.astype(int)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Z4Wl6kePHlh9"
+ },
+ "source": [
+ "df.dtypes"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-V4Kbei5HliA"
+ },
+ "source": [
+ "df.year_of_arrival = df.year_of_arrival.astype(int)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fPgIGprYHliD"
+ },
+ "source": [
+ "df.dtypes"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vL0gW74bHliI"
+ },
+ "source": [
+ "**Extra**:\n",
+ "```df.slaves_onboard = df.slaves_onboard.astype(int)```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "obBSXVY2HliI"
+ },
+ "source": [
+ "df.slaves_onboard = df.slaves_onboard.astype(int)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bmm_3kqnHliK"
+ },
+ "source": [
+ "df.dtypes"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9dC_vb_KHliM"
+ },
+ "source": [
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s3AFqNpu2GJ4"
+ },
+ "source": [
+ "### GeoTagging Locations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lpBC6OsdEwl-"
+ },
+ "source": [
+ "!pip install geopandas\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3N9KNSl-15ll"
+ },
+ "source": [
+ "!pip install googlemaps"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0gYRsx8v2HfO"
+ },
+ "source": [
+ "from googlemaps import Client as GoogleMaps\n",
+ "import pandas as pd "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GYqCDWTt2KKz"
+ },
+ "source": [
+ "gmaps = GoogleMaps('')# ENTER KEY"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bLLkI_sD9hzv"
+ },
+ "source": [
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AKnGxe_w2Lzw"
+ },
+ "source": [
+ "addresses = df.filter(['Voyage itinerary imputed port where began (ptdepimp) place'], axis=1)\n",
+ "addresses.head()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TVEZc-Qs2PbV"
+ },
+ "source": [
+ "addresses['long'] = \"\"\n",
+ "addresses['lat'] = \"\""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sBJppNIY9ZBP"
+ },
+ "source": [
+ "addresses"
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Week3-Introduction-to-Open-Data-Importing-Data-and-Basic-Data-Wrangling/New Format_ Introduction to Pandas M2.pdf b/Week3-Introduction-to-Open-Data-Importing-Data-and-Basic-Data-Wrangling/New Format_ Introduction to Pandas M2.pdf
new file mode 100644
index 0000000..98e434c
Binary files /dev/null and b/Week3-Introduction-to-Open-Data-Importing-Data-and-Basic-Data-Wrangling/New Format_ Introduction to Pandas M2.pdf differ
diff --git a/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/README.md b/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/README.md
index 429c0e9..5c7f746 100644
--- a/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/README.md
+++ b/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/README.md
@@ -1,24 +1,24 @@
-
+
#
Visualizing the Transatlantic Slave Trade
## Lab Methodology
-Congratulations you have made it to the first lab of this course. The focus of these labs is to help you learn and apply a structured approach when working on a project. You will be using the tools such as ```pandas```, ```matplotlib``` and ```NumPy``` To explore the dataset.
-For this we will divide our approach into 4 parts:
-- The first part is the traditional set up every data scientist has to do when starting a project. These inlcude loading datasets, observing the basic numerical analysis of the dataset.
+Congratulations, you have made it to the first lab of this course. The focus of these labs is to help you learn and apply a structured approach when working on a project. You will be using the tools such as ```pandas```, ```matplotlib``` and ```NumPy``` to explore the dataset.
+For this, we will divide our approach into 4 parts:
+- The first part is the traditional set up every data scientist has to do when starting a project. This inlcudes loading datasets and observing the basic numerical analysis of the dataset.
- The second part involves cleaning the dataset and choosing columns that fit our methodology.
- The third part involves further splitting our cleaned dataframe into smaller dataframes and visualizing them.
- Finally, the fourth part involves summarizing our conclusion.
## Recap
-- By this time, you should have an understanding and practice on how to implement the following:
+- By this point, you should have an understanding and practice on how to implement the following:
- Loading a Dataset '.csv' as a dataframe using ```pd.read_csv```
- Observing the properties of the loaded dataset using functions such as:
- ```pd.head()```
- ```pd.describe()```
- ```pd.info()```
- Modifying the dataset by removing ```NaN``` values.
-- A conceptual understanding of the term ```object``` in DataFrames.
+- Using ```objects``` in DataFrames.
- Re-indexing columns
- Visualizing Data using ```matplotlib``` and ```pandas```:
- Scatter plots
@@ -30,13 +30,13 @@ For this we will divide our approach into 4 parts:
### The Trans-Atlantic Slave Trade
-It is difficult to believe in the first decades of the twenty-first century that just over two centuries ago, for those Europeans who thought about the issue, the shipping of enslaved Africans across the Atlantic was morally indistinguishable from shipping textiles, wheat, or even sugar. Our reconstruction of a major part of this migration experience covers an era in which there was a massive technological change (steamers were among the last slave ships), as well as very dramatic shifts in perceptions of good and evil. Just as important perhaps were the relations between the Western and non-Western worlds that the trade both reflected and encapsulated. Slaves constituted the most important reason for contact between Europeans and Africans for nearly two centuries. The shipment of slaves from Africa was related to the demographic disaster consequent to the meeting of Europeans and Amerindians, which greatly reduced the numbers of Amerindian laborers and raised the demand for labor drawn from elsewhere, particularly Africa. As Europeans colonized the Americas, a steady stream of European peoples migrated to the Americas between 1492 and the early nineteenth century. But what is often overlooked is that, before 1820, perhaps three times as many enslaved Africans crossed the Atlantic as Europeans. This was the largest transoceanic migration of a people until that day, and it provided the Americas with a crucial labor force for their own economic development. The slave trade is thus a vital part of the history of some millions of Africans and their descendants who helped shape the modern Americas culturally as well as in the material sense.
+It is difficult and sad to believe that in the first decades of the twenty-first century that just over two centuries ago, the shipping of enslaved Africans across the Atlantic was morally indistinguishable from shipping textiles, wheat, or even sugar. Our reconstruction of a major part of this migration experience covers an era in which there was a massive technological change (steamers were among the last slave ships), as well as very dramatic shifts in perceptions of good and evil. Just as important to examine is the relationship between the Western and non-Western worlds that the trade both reflected and encapsulated. Slavery constituted the most important reason for contact between Europeans and Africans for nearly two centuries. The shipment of slaves from Africa was in part caused by the demographic disaster due to the meeting of Europeans and Native Americans, which greatly reduced the numbers of Native American laborers and raised the demand for labor drawn from elsewhere, particularly Africa. As Europeans colonized the Americas, a steady stream of European peoples migrated to the Americas between 1492 and the early nineteenth century. But what is often overlooked is that, before 1820, perhaps three times as many enslaved Africans crossed the Atlantic as Europeans. This was the largest transoceanic migration of a people until that day, and it provided the Americas with a crucial labor force for their own economic development. Thus, we must acknowledge the millions of Africans and their descendants who essentially built America, as well as the effects of their journey here.
-The details of the more than 36,000 voyages presented here greatly facilitate the study of cultural, demographic, and economic change in the Atlantic world from the late sixteenth to the mid-nineteenth centuries. Trends and cycles in the flow of African captives from specific coastal outlets should provide scholars with new, basic information useful in examining the relationships among slaving, warfare—in both Africa and Europe—political instability, and climatic and ecological change, among other forces.
+The details of the more than 36,000 voyages presented here greatly facilitate the study of cultural, demographic, and economic change in the Atlantic world from the late sixteenth to the mid-nineteenth centuries. Trends and cycles in the flow of African captives from specific coastal outlets should provide scholars with new, basic information useful in examining the relationships among slavery, warfare—in both Africa and Europe—political instability, and climatic and ecological change, among other forces.
#### Facts about the dataset
-- The dataset approximately 36,110 trans-Atlantic voyages.
+- The dataset covers approximately 36,110 trans-Atlantic voyages.
- The estimates suggest around 12,520,000 captives departed Africa to the Americas.
- Not all 36,000 voyages in the database carried slaves from Africa.
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
![](\"https://github.com/ShayanRiyaz/DigitalHistory/blob/Shayan/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/assets/icons/bitproject.png?raw=1\"){kind=icon}
\r\n",
+ "![](\"https://github.com/ShayanRiyaz/DigitalHistory/blob/Shayan/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/assets/icons/data-science.jpg?raw=1\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "B-bG6hEs0W2A"
+ },
+ "source": [
+ "# ![](\"https://raw.githubusercontent.com/bitprj/DigitalHistory/master/Week4-Introduction-to-data-visualization-and-graphs-with-matplotlib/assets/wallstreet.png?raw=1\")
\n",
+ "\n",
+ "\n",
+ "This is a simple introductory dataset. The dataset contains a shape of 5472 rows and 10 columns, the first row is the header row, therefore the shape of our dataset is (5472,10). The first column is the datetime ranging from 1990 to 2012. The remaining 9 columns are numerical values that indicate the stock prices of companies such as:\n",
+ "- AA \n",
+ "- AAPL \n",
+ "- GE \n",
+ "- IBM \n",
+ "- JNJ \n",
+ "- MSFT \n",
+ "- PEP \n",
+ "- SPX \n",
+ "- XOM\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H0En23wfdMLc"
+ },
+ "source": [
+ "### California Housing Information \n",
+ "![](\"https://raw.githubusercontent.com/bitprj/DigitalHistory/master/Week4-Introduction-to-data-visualization-and-graphs-with-matplotlib/assets/california.png?raw=1\")
\n",
+ "This dataset serves as an excellent introduction to visualizing simple numerical data. The data contains information from the 1990 California census.\n",
+ "\n",
+ "The following is the data methodology described in the paper where this dataset was published.\n",
+ "\n",
+ "**Content**\n",
+ "The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. The columns are as follows, their names are self explanatory:\n",
+ "\n",
+ "- longitude\n",
+ "- latitude\n",
+ "- housing median age\n",
+ "- total_rooms\n",
+ "- total_bedrooms\n",
+ "- population\n",
+ "- households\n",
+ "- median_income\n",
+ "- median house value\n",
+ "- ocean_proximity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gMheundcWewc"
+ },
+ "source": [
+ "##Goals\n",
+ "- Building a plot step by step using matplotlib.\n",
+ "- Loading datasets using pandas and visualizing selected columns.\n",
+ "- Using the stock-market dataset to visualize trends in data from 1990-2016 for 6 listed companies.\n",
+ "- Breaking down the ```matplotlib``` function ```pyplot.plot```\n",
+ "- Introduction to Matplotlib\n",
+ " - Figures and subplots\n",
+ " - Colors, Markers, and Line Styles\n",
+ " - Ticks, Labels and Legends\n",
+ "- Plotting simple relational graphs such as:\n",
+ " - Bar plots\n",
+ " - Histograms\n",
+ " - Line plots\n",
+ " - Scatter plots\n",
+ "- Using our tutorial to map longitude and latitude data.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xlQspYFJxYHj"
+ },
+ "source": [
+ "# Grading\n",
+ "\n",
+ "In order to work on the NTT sections and submit them for grading, you'll need to run the code block below. It will ask for your student ID number and then create a folder that will house your answers for each question. At the very end of the notebook, there is a code section that will download this folder as a zip file to your computer. This zip file will be your final submission."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "glNwYzOyxYHl",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 52
+ },
+ "outputId": "b63d2882-4589-400d-9e47-5fa94fb1dd93"
+ },
+ "source": [
+ "import os\n",
+ "import shutil\n",
+ "\n",
+ "!rm -rf sample_data\n",
+ "\n",
+ "student_id = input('Please Enter your Student ID: ') # Enter Student ID.\n",
+ "\n",
+ "while len(student_id) != 9:\n",
+ " student_id = int('Please Enter your Student ID: ') \n",
+ " \n",
+ "folder_location = f'{student_id}/Week_Three/Now_Try_This'\n",
+ "if not os.path.exists(folder_location):\n",
+ " os.makedirs(folder_location)\n",
+ " print('Successfully Created Directory, Lets get started')\n",
+ "else:\n",
+ " print('Directory Already Exists')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Please Enter your Student ID: 123456789\n",
+ "Successfully Created Directory, Lets get started\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "keXK_L7B7oRI"
+ },
+ "source": [
+ "## Plotting with Pandas and matplotlib.\n",
+ "\n",
+ "By now you have been introduced to what Pandas primarily does, if you want a quick refresher here are some important points:\n",
+ "- A DataFrame is a collection or rows and columns (Just like any single Excel Sheet).\n",
+ "- Using pandas we can modify our DataFrames, this can involve dropping null(empty cells) values, changing column names, modifying column properties or sometimes trivial things as moving columns around.\n",
+ "- We can also load `.csv` files and convert them to DataFrames using `read_csv`.\n",
+ "\n",
+ "By using simple `pandas` operations we can get the most out of our dataset. However, pandas also has another super useful tool that comes with it. This tool allows us to directly plot columns from our data frames and visualize them as forms of graphs. This method is called ```plot``` and we will go though it in more detail throughout this notebook\n",
+ "\n",
+ "### Matplotlib\n",
+ "Although pandas completes most of our visualization needs. There are somecases where we want to be more precise with how we want to visualize our data. This is where `matplotlib` comes in. Infact, most of the plotting **options** that pandas has, come directly from the matplotlib. Although we won't strictly be focusing on matplotlib, we will explain both pandas and matplotlib imeplementations side by side for every **feature** we go through.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "T7UcLg97_4PW"
+ },
+ "source": [
+ "For this exercise and tutorial we will be using a publicaly available dataset. This is the stock price for 8 companies and 1 index. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0vqPgVtxAhls"
+ },
+ "source": [
+ "### Importing libraries "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VCR3sTSJAliI"
+ },
+ "source": [
+ "import pandas as pd\r\n",
+ "import matplotlib.pyplot as plt\r\n",
+ "import datetime"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lm1hE6J4AJ8f"
+ },
+ "source": [
+ "### Using Pandas to load `csv` files - \n",
+ "\n",
+ "\n",
+ "In order to get started with the visualizations we first need to load our dataset. As mentioned earlier we can do this simply by using the built in pandas method `read_csv`. A wayr to do this would be to store the data (the URL link) as a variable called `url`, then you would would call pandas' `pd.read_csv()` function to convert the actual data into a pandas dataframe and store that into a variable called `df`, and then you can finally call the `df` variable again in the notebook to see the informtion of the dataset as a Pandas dataframe.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NT8x3dQ1A03C",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 424
+ },
+ "outputId": "437d7ff7-8641-4978-be0b-8b71a67bcfde"
+ },
+ "source": [
+ "url = 'https://raw.githubusercontent.com/bitprj/DigitalHistory/master/Week4-Introduction-to-data-visualization-and-graphs-with-matplotlib/data/stock_px/stock_px.csv'\n",
+ "\n",
+ "df = pd.read_csv(url,index_col = 0,parse_dates = True)\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "![](\"https://github.com/ShayanRiyaz/DigitalHistory/blob/Shayan/Week5-Lab-Visualizing-the-Translatlantic-Slave-Trade/assets/ship.jpg?raw=1\")
\n",
+ "\n",
+ "It is difficult and sad to believe that in the first decades of the twenty-first century that **just over two centuries ago**, the shipping of enslaved Africans across the Atlantic was morally indistinguishable from shipping textiles, wheat, or even sugar. Our reconstruction of a major part of this migration experience covers an era in which there was a massive technological change (steamers were among the last slave ships), as well as very dramatic shifts in perceptions of good and evil. \n",
+ "\n",
+ "Just as important to examine is the relationship between the Western and non-Western worlds that the trade both reflected and encapsulated. Slavery constituted the most important reason for contact between Europeans and Africans for nearly two centuries. The shipment of slaves from Africa was in part caused by the demographic disaster due to the meeting of Europeans and Native Americans, which greatly reduced the numbers of Native American laborers and raised the demand for labor drawn from elsewhere, particularly Africa. As Europeans colonized the Americas, a steady stream of European peoples migrated to the Americas between 1492 and the early nineteenth century. But what is often overlooked is that, before 1820, perhaps three times as many enslaved Africans crossed the Atlantic as Europeans. This was the largest transoceanic migration of a people until that day, and it provided the Americas with a crucial labor force for their own economic development. Thus, we must acknowledge the millions of Africans and their descendants who essentially built America, as well as the effects of their journey here.\n",
+ "\n",
+ "The details of the more than **36,000** voyages presented here greatly facilitate the study of cultural, demographic, and economic change in the Atlantic world from the late *sixteenth to the mid-nineteenth centuries*. Trends and cycles in the flow of African captives from specific coastal outlets should provide scholars with new, basic information useful in examining the relationships among slavery, warfare in both Africa and Europe—political instability, and climatic and ecological change, among other forces. \n",
+ "\n",
+ "### Facts about the dataset\n",
+ "\n",
+ "- The dataset covers approximately 36,110 trans-Atlantic voyages.\n",
+ "- The estimates suggest around 12,520,000 captives departed Africa to the Americas. \n",
+ "\n",
+ "- Not all 36,000 voyages in the database carried slaves from Africa.\n",
+ "- A total of 633 voyages (1.8%) never reached the African coast because they were lost at sea, captured, or affected by some other misfortune. \n",
+ "- The database also contains records of 34,106 voyages that disembarked slaves, or could have done so (in other words, for some of these we do not know the outcome of the voyage).\n",
+ "\n",
+ "- The latter group comprised mainly of ships captured in the nineteenth century which were taken to Sierra Leone and St. Helena as part of the attempt to suppress the trade. \n",
+ "\n",
+ "\n",
+ "This is a very insightful resource titled,'The Atlantic Slave Trade in Two Minutes. You can read it [here](http://www.slate.com/articles/life/the_history_of_american_slavery/2015/06/animated_interactive_of_the_history_of_the_atlantic_slave_trade.html)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fPyizd_JIHPU"
+ },
+ "source": [
+ "# Practicum and Methodology\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Aljt8kKWxCRe"
+ },
+ "source": [
+ "Congratulations, you have made it to the first practicum of this course. The purpose of these practicums is to help you apply the Data Science pipeline in a project-based environment. You will be using the tools taught to you in the previous modules and adopt and Question and Answer based approach when you work with the dataset.\n",
+ "\n",
+ "For this project, we start by asking questions which you will answer in code and simple explanations.\n",
+ "\n",
+ "**example** - change the name of 'column_x' to 'column y' \n",
+ "**answer**: ```df = df.rename(columns = {'column_x : 'column_y})```\n",
+ "\n",
+ "We have divided our approach into 4 parts:\n",
+ "\n",
+ "- The first part is the traditional set up. These are some things we should do before modifying the dataset.\n",
+ "- The second part involves cleaning the dataset and choosing columns that fit our methodology.\n",
+ "- The third part involves further splitting our cleaned dataframe into smaller dataframes and visualizing them.\n",
+ "- Finally, the fourth part involves summarizing our conclusion."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Tl_6Scu8IE7Q"
+ },
+ "source": [
+ "\n",
+ "# Grading\n",
+ "\n",
+ "This exercise has a total of 27 questions. Every question has 1 point. Some questions might have multiple parts but the weight of the question is the same.\n",
+ "\n",
+ "In order to work on the questions in this Practicum and submit them for grading, you'll need to run the code block below. It will ask for your student ID number and then create a folder that will house your answers for each question. At the very end of the notebook, there is a code section that will download this folder as a zip file to your computer. This zip file will be your final submission."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HmZvJAZIpGf6"
+ },
+ "source": [
+ "import os\n",
+ "import shutil\n",
+ "\n",
+ "!rm -rf sample_data\n",
+ "\n",
+ "student_id = input('Please Enter your Student ID: ') # Enter Student ID.\n",
+ "\n",
+ "while len(student_id) != 9:\n",
+ " student_id = int('Please Enter your Student ID: ') \n",
+ " \n",
+ "folder_location = f'{student_id}/Week_Six/Practicum'\n",
+ "if not os.path.exists(folder_location):\n",
+ " os.makedirs(folder_location)\n",
+ " print('Successfully Created Directory, Lets get started')\n",
+ "else:\n",
+ " print('Directory Already Exists')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ixk29-rMHvR9"
+ },
+ "source": [
+ "# Part 1 - Getting Our Basic Data Analysis Set-Up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0W--Kb7wgzl7"
+ },
+ "source": [
+ "Import the libraries you will be using for this project. These libraries are the ones we have used in the previous labs."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7q6R0hBZONBe"
+ },
+ "source": [
+ "## Q1 Load libraries and file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vs7AJxshHlc9"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/1.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "import _____ as pd # INSERT CODE HERE\n",
+ "import __________ as plt # INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DfmowYZZOVRn"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/1.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "url = 'https://rb.gy/cjfen3'\n",
+ "\n",
+ "trans_atlc_trade = __.read_csv(___) # INSERT CODE HERE "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9SwePQu6qsZl"
+ },
+ "source": [
+ "### Observing the Dataset using Pandas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mCNQqlwecmjv"
+ },
+ "source": [
+ "Now, the dataset is loaded as a dataframe `trans_atlc_trade`\n",
+ "\n",
+ "### ```head()```\n",
+ "Let's check what columns this file has by calling 'head()' function.\n",
+ "It returns first n rows, and it's useful to see the dataset at a quick glance.\n",
+ "\n",
+ "By default, the head() function returns the first 5 rows.\n",
+ "\n",
+ "You can specify the number of rows to display by calling `df.head(number)`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_Fwr655mcjr5"
+ },
+ "source": [
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s5I3a9h9gzmM"
+ },
+ "source": [
+ "### ```tail()```\n",
+ "\n",
+ "The ```tail()``` method prints the last 5 rows of our dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WVD4aPJbHldP"
+ },
+ "source": [
+ "# INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FFqqKsABeT7y"
+ },
+ "source": [
+ "### ```info()```\n",
+ "This will return all of the column names and its types. This function is useful to get the idea of what the dataframe is like.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wxqUmTFdfkCi"
+ },
+ "source": [
+ "## INSERT CODE HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ilf3rsfmBiLw"
+ },
+ "source": [
+ "### Observations:\n",
+ "\n",
+ "#### Questions:\n",
+ "- List down the number of unique ```Dtype``` in this dataset\n",
+ "- Is the dataset uneven? If so list down the column with the most missing rows?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sT2OXLFoJNTu"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IhviGetnfp9B"
+ },
+ "source": [
+ "### ```describe()```\n",
+ "describe() is used to view summary statistics of numeric columns. This will help you to have general idea of the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DuDPOCZ9frBI"
+ },
+ "source": [
+ "trans_atlc_trade._____() # Insert code here"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oEEj1jeGAlhU"
+ },
+ "source": [
+ "### Observations:\n",
+ "\n",
+ "#### Questions:\n",
+ "- Why is ```describe``` showing only 3 columns?Is it because of their types e.g(int,float,object)?\n",
+ "- What could be reason for the counts not being the same?\n",
+ "- Are the mean, standard deviation,..., max. important for Voyage ID?\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "86n5av1gJGmI"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nwuNo5o2fzGe"
+ },
+ "source": [
+ "### ```shape```\n",
+ "To see the size of the dataset, we can use shape function, which returns the number of rows and columns in a format of (#rows, #columns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fIhERH7KHldk"
+ },
+ "source": [
+ "trans_atlc_trade.shape"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aOi3ah9yDI-T"
+ },
+ "source": [
+ "#### Questions/Observations:\n",
+ "How many **rows** and **columns** are there?\n",
+ "#### Answer:\n",
+ "Note: These questions that are not numbered are not graded. You can optionally choose to answer to help you think about the code you are using."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8jaZnYnII4Tx"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AeidaHn6Hldo"
+ },
+ "source": [
+ "## Q2. Important Facts About the Dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XIFbkyfIDinJ"
+ },
+ "source": [
+ "The next thing we want to do is count the number of trips that have been unaccounted for. We'll know this by observing the ```Slaves arrived at 1st port``` This is simple, all we have to do is run two functions:\n",
+ "- The first one will be to check if the column has null values, ```isna()```.\n",
+ "- The second one will be sum the number of null rows in the column, ```sum()```."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lKl32c6JHldo"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/2.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "Unaccounted_trips = trans_atlc_trade['Slaves arrived at 1st port'].____()._____() # Insert Code Here\n",
+ "print(f'The total number of unaccounted trips is: {Unaccounted_trips}')\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2JPDPjr-EI60"
+ },
+ "source": [
+ "\n",
+ "**What about the total of slaves accounted for?** \n",
+ "In the following line of code, we will have to ```sum``` the the total number of slaves in every column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "v_Gai5XEHlds",
+ "scrolled": true
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/2.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "number_of_slaves_accounted = trans_atlc_trade['Slaves arrived at 1st port'].___() # Insert code here\n",
+ "print(f'The total number of slaves accounted for are: {number_of_slaves_accounted}')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bYuDgHKIHldv"
+ },
+ "source": [
+ "Historical estimates suggest that the total number of slaves traded are estimated to be ~12.5 Million. This means that according to this dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xyymyjbgEduN"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/2.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "possible_unaccounted_slaves = 12500000 - ________________ # INSERT CODE HERE \n",
+ "possible_unaccounted_slaves "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "heW7wau-Hldv"
+ },
+ "source": [
+ "### Visualize Year of Arrival vs Number of Slaves arrived"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zdNAAj8EvEDs"
+ },
+ "source": [
+ "fig = plt.figure(figsize = (35,10))\n",
+ "\n",
+ "ax1 = fig.add_subplot(1,2,1)\n",
+ "ax2 = fig.add_subplot(1,2,2)\n",
+ "\n",
+ "trans_atlc_trade.plot(x = 'Year of arrival at port of disembarkation',\n",
+ " y = 'Slaves arrived at 1st port',\n",
+ " kind = 'scatter',\n",
+ " c = 'Slaves arrived at 1st port',\n",
+ " title = 'Year of arrival at port of disembarkation vs Slaves arrived at 1st port',\n",
+ " alpha = 0.3,\n",
+ " cmap = plt.get_cmap('ocean'),\n",
+ " colorbar = True,\n",
+ " ax = ax1,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "trans_atlc_trade.plot(x = 'Year of arrival at port of disembarkation',\n",
+ " y = 'Slaves arrived at 1st port',\n",
+ " kind = 'area',\n",
+ " title = 'Year of arrival at port of disembarkation vs Slaves arrived at 1st port',\n",
+ " ax = ax2,\n",
+ " )\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2cYXofF6t-hE"
+ },
+ "source": [
+ "### Questions/Observations\n",
+ "- Are the plots above useful? \n",
+ "- Can we get anything specific by observing them?\n",
+ "- Is there a visible trend?\n",
+ "- What are the possible issues with the plot above? \n",
+ "- Lastly, which one is more practical, the ```scatter``` or ```area```?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wFb2pufJJYAC"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nIFt7KCZHld-"
+ },
+ "source": [
+ "# Part 2 - Getting Started with Data Wrangling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_JW6MwRSFF3B"
+ },
+ "source": [
+ "Now that we have observed the basic features of our dataset raw, we will began cleaning it. This involves several steps that you will be working through.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NJhawspPHld-"
+ },
+ "source": [
+ "### Create a copy of the Original Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HJ6p5GksHld_"
+ },
+ "source": [
+ "df = trans_atlc_trade.copy(deep = True) # We have used deep = True to make sure the copy is not linked to the trans_atlc_trade dataframe.\n",
+ " # If we did not add it, any changes made to the new df would be made on the tran_atlc_trade too."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "--Q1q9kDgznD"
+ },
+ "source": [
+ "## Q3. Column List\n",
+ "\n",
+ "List down the names of all the columns in our dataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NvhxosPNF5LH"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/3.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "df._______ # Insert Code Here\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BJAtGmeEq2U3"
+ },
+ "source": [
+ "## Q4. Change column names"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J4KjruAfIDe2"
+ },
+ "source": [
+ "For this exercise, you will change the names of the previously existing columns to something that is more readable. \n",
+ "\n",
+ "\n",
+ "Using the columns above write down the name of the column in place of ```COLUMN_NAME_HERE```.\n",
+ "\n",
+ "\n",
+ "The last column name will be tricky to change, this is because it has a ```'``` here. In order to change that right before the ```'``` add a ```\\```."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aSzOUf_CHleE"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/4.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "df = df.rename(\n",
+ " columns={'COLUMN_NAME_HERE':'voyage_id', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'vessel_name', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'voyage_started', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'voyage_pit_stop', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'end_port', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'year_of_arrival', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'slaves_onboard', # Insert Column Name you want to change\n",
+ " 'COLUMN_NAME_HERE':'captain_names' # Insert Column Name you want to change\n",
+ " })\n",
+ "\n",
+ "\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "08LYIpGyHleI"
+ },
+ "source": [
+ "## Q5 Moving Column Positions - ```df.reindex()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "m6SuvgcJIsfL"
+ },
+ "source": [
+ "When we're looking at the renamed database, for our purposes, we don't want to work with the ```captain_names```. Next, we will use ```df.reindex()``` to change the order of our columns.\n",
+ "\n",
+ "You can see below a list below which has the ```column_names``` in the order we want and is without ```captains_name```.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "B0p8g8M6HleJ"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/5.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "\n",
+ "column_names = ['voyage_id',\"year_of_arrival\",\"vessel_name\", \"voyage_started\",\"voyage_pit_stop\", \"end_port\",\"slaves_onboard\"]\n",
+ "\n",
+ "df = df._____(columns=________) # Insert Code here\n",
+ "\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MfhTG28pHleN"
+ },
+ "source": [
+ "***Take a step and think:***\n",
+ "\n",
+ "**Is Voyage ID a good index and do we need it as a column?**\n",
+ "\n",
+ "No, but we need an index.\n",
+ "\n",
+ "**Can 'year_of_arrival' be an Index?**\n",
+ "\n",
+ "No, because there are repeating dates in the charts, there for we need a simple log counter.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qOYcbFRwHleN"
+ },
+ "source": [
+ "## Q6. Remove Voyage ID -```df.drop()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uocghp2yHleO"
+ },
+ "source": [
+ "Now that we have a new index from 0 to 15299, do we need ```voyage_id```?\n",
+ "\n",
+ "No, we don't because it doesn't help us find anything useful. Every Voyage ID is unique.\n",
+ "\n",
+ "So, drop this column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "90MwAELVHleO"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/6.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "df = __.drop(columns='_____') # INSERT CODE HERE\n",
+ "df "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d_Ds03fRHleS"
+ },
+ "source": [
+ "### Using ```dropna()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OB1gdak8HleT"
+ },
+ "source": [
+ "For this data set, we will be working with trips that were completely accounted for in all of the remaining features.\n",
+ "\n",
+ "The ```dropna()``` method is designed top drop every value in our dataframe whos cell might have a null or undefined value. They are usually shown as ```NaN```.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HUUV6ipxHleT"
+ },
+ "source": [
+ "df = df.dropna()\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-8OASxc-HleZ"
+ },
+ "source": [
+ "df.info()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "p4NlzzF0y-oK"
+ },
+ "source": [
+ "### Questions/Observations\n",
+ "How many rows are we left with?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Eg8eM2gTJ4I3"
+ },
+ "source": [
+ "# Answer Here\r\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ju-Bw99zHled"
+ },
+ "source": [
+ "## Q7. Sorting Column using ```year_of_arrival``` using - ```df.sort_values()```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KCQb6H5uHled"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/7.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "df = df.sort_values(by='_______', ascending=_____) # INSERT CODE HERE\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AZbNdBQKgznj"
+ },
+ "source": [
+ "## Q8. Reseting the Index "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AMV7309HHleh"
+ },
+ "source": [
+ "Reseting Index using ```df.reset_index``` with respect 'year_of_arrival' in Ascending Order. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rEIogWnUHlei"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/8.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "df.______(inplace=True, drop=True) # INSERT CODE HERE\n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i_4uzK6tHlel"
+ },
+ "source": [
+ "### Finding Unique and similar strings \n",
+ "\n",
+ "First, we will list down all the unique names in these columns. Next, we will sort these in alphabetical order in order to make it easier to observe.\n",
+ "\n",
+ "```df['column_name].unique()``` and ```df.sort()```\n",
+ "\n",
+ "for simplicity i have just declared the first line as a variable ```a``` in order to print it\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zcZzcYc6Hlel"
+ },
+ "source": [
+ "a = df['voyage_started'].unique()\n",
+ "a.sort()\n",
+ "a"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Xo4FXypKHleq"
+ },
+ "source": [
+ "a = df['voyage_pit_stop'].unique()\n",
+ "a.sort()\n",
+ "a"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "m4mKFIA8Hlex"
+ },
+ "source": [
+ "a = df['end_port'].unique()\n",
+ "a.sort()\n",
+ "a"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Tilt8tICP_Yw"
+ },
+ "source": [
+ "As we can see above our object columns, ```voytage_started```, ```voyage_pit_stop``` and ```end_port``` have phrases such as ``` port unspecified``` and ```unspecified```. We need to get rid of these.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zKVGN7_cHle2"
+ },
+ "source": [
+ "## Q9. Working with Strings - ```df['column_name'].str.replace()```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SESPapu0UDyS"
+ },
+ "source": [
+ "To replace unwanted parts of a string, we use the function ```df['columun_name'].str.replace('string to find','string to replace')```. This command looks for the string we have specified and replaces with what we want.\n",
+ "\n",
+ "For example:\n",
+ "If have an entry in the 'voyage_started' column, 'Virginia, port unspecified'. By running the command:\n",
+ "```df['voyage_started'].str.replace(', port unspecified', '')```\n",
+ "The string will be changed from ''Virginia, port unspecified' to 'Virginia'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NPxciPRzHle3"
+ },
+ "source": [
+ "#Once your have verified your answer please uncomment the line below and run it, this will save your code \n",
+ "#%%writefile -a {folder_location}/9.py\n",
+ "#Please note that if you uncomment and run multiple times, the program will keep appending to the file.\n",
+ "\n",
+ "\n",
+ "df['voyage_started'] = df['voyage_started'].str.replace('', '')\n",
+ "df['voyage_started'] = df['voyage_started'].str.replace('', '')\n",
+ "df['voyage_started'] = df['voyage_started'].str.replace('', '')\n",
+ "\n",
+ "df['voyage_pit_stop'] = df['voyage_pit_stop'].str.replace('', '')\n",
+ "df['voyage_pit_stop'] = df['voyage_pit_stop'].str.replace('', '')\n",
+ "df['voyage_pit_stop'] = df['voyage_pit_stop'].str.replace('.', '')\n",
+ "\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('.', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace('', '')\n",
+ "df['end_port'] = df['end_port'].str.replace(', south coast', '') # Insert string to replace\n",
+ "df['end_port'] = df['end_port'].str.replace(', west coast', '') \n",
+ "df"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IOPDZL8vHle7"
+ },
+ "source": [
+ "df.dtypes"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "x0c0BG7SHle_"
+ },
+ "source": [
+ "#### Creating a Copy of our modified Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4jrvqhIAHle_"
+ },
+ "source": [
+ "modified_dataset = df.copy(deep = True)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wkw5pefkHlfC"
+ },
+ "source": [
+ "# Part 3 - Micro Wrangling and Visualization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_yO0iI-vrq8h"
+ },
+ "source": [
+ "![](\"https://scx2.b-cdn.net/gfx/news/hires/2017/590c5b1f40cbd.gif\")
\n",
+ "
+