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148 changes: 131 additions & 17 deletions 03_05/03_05 Missing Values [Begin].ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@
},
{
"cell_type": "code",
"execution_count": 80,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -70,23 +70,42 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"(10127, 23)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10127"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"To fill in the missing values, we will use the following code:"
"data[\"CLIENTNUM\"].nunique()"
]
},
{
Expand All @@ -96,6 +115,26 @@
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10127, 23)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -105,18 +144,88 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"Index(['CLIENTNUM', 'Attrition_Flag', 'Customer_Age', 'Gender',\n",
" 'Dependent_count', 'Education_Level', 'Marital_Status',\n",
" 'Income_Category', 'Card_Category', 'Months_on_book',\n",
" 'Total_Relationship_Count', 'Months_Inactive_12_mon',\n",
" 'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal',\n",
" 'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt',\n",
" 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio',\n",
" 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1',\n",
" 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Datatypes"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CLIENTNUM int64\n",
"Attrition_Flag object\n",
"Customer_Age int64\n",
"Gender object\n",
"Dependent_count int64\n",
"Education_Level object\n",
"Marital_Status object\n",
"Income_Category object\n",
"Card_Category object\n",
"Months_on_book int64\n",
"Total_Relationship_Count int64\n",
"Months_Inactive_12_mon int64\n",
"Contacts_Count_12_mon int64\n",
"Credit_Limit float64\n",
"Total_Revolving_Bal int64\n",
"Avg_Open_To_Buy float64\n",
"Total_Amt_Chng_Q4_Q1 float64\n",
"Total_Trans_Amt int64\n",
"Total_Trans_Ct int64\n",
"Total_Ct_Chng_Q4_Q1 float64\n",
"Avg_Utilization_Ratio float64\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.dtypes"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -141,7 +250,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
"version": "3.10.4 (main, Jan 18 2023, 00:26:41) [GCC 9.4.0]"
},
"vscode": {
"interpreter": {
"hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
}
}
},
"nbformat": 4,
Expand Down