Bitcoin-analysis-using-pandas

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":9788951,"sourceType":"datasetVersion","datasetId":5998033}],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:17:29.217117Z","iopub.execute_input":"2025-01-08T13:17:29.217415Z","iopub.status.idle":"2025-01-08T13:17:29.532456Z","shell.execute_reply.started":"2025-01-08T13:17:29.217395Z","shell.execute_reply":"2025-01-08T13:17:29.531277Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/bitcoin-dataset/bitcoin_price_Training - Training.csv\n","output_type":"stream"}],"execution_count":1},{"cell_type":"markdown","source":"# Reading the data and performing common analysis","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:17:29.533834Z","iopub.execute_input":"2025-01-08T13:17:29.534234Z","iopub.status.idle":"2025-01-08T13:17:29.538124Z","shell.execute_reply.started":"2025-01-08T13:17:29.534214Z","shell.execute_reply":"2025-01-08T13:17:29.537073Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"filename = \"/kaggle/input/bitcoin-dataset/bitcoin_price_Training - Training.csv\"\nbitcoin = pd.read_csv(filename)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:25:41.147382Z","iopub.execute_input":"2025-01-08T13:25:41.147703Z","iopub.status.idle":"2025-01-08T13:25:41.160535Z","shell.execute_reply.started":"2025-01-08T13:25:41.147680Z","shell.execute_reply":"2025-01-08T13:25:41.159662Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"bitcoin.head(10)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:25:43.699246Z","iopub.execute_input":"2025-01-08T13:25:43.699694Z","iopub.status.idle":"2025-01-08T13:25:43.714670Z","shell.execute_reply.started":"2025-01-08T13:25:43.699670Z","shell.execute_reply":"2025-01-08T13:25:43.713740Z"}},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"           Date     Open     High      Low    Close         Volume  \\\n0  Jul 31, 2017  2763.24  2889.62  2720.61  2875.34    860,575,000   \n1  Jul 30, 2017  2724.39  2758.53  2644.85  2757.18    705,943,000   \n2  Jul 29, 2017  2807.02  2808.76  2692.80  2726.45    803,746,000   \n3  Jul 28, 2017  2679.73  2897.45  2679.73  2809.01  1,380,100,000   \n4  Jul 27, 2017  2538.71  2693.32  2529.34  2671.78    789,104,000   \n5  Jul 26, 2017  2577.77  2610.76  2450.80  2529.45    937,404,000   \n6  Jul 25, 2017  2757.50  2768.08  2480.96  2576.48  1,460,090,000   \n7  Jul 24, 2017  2732.70  2777.26  2699.19  2754.86    866,474,000   \n8  Jul 23, 2017  2808.10  2832.18  2653.94  2730.40  1,072,840,000   \n9  Jul 22, 2017  2668.63  2862.42  2657.71  2810.12  1,177,130,000   \n\n       Market Cap  \n0  45,535,800,000  \n1  44,890,700,000  \n2  46,246,700,000  \n3  44,144,400,000  \n4  41,816,500,000  \n5  42,455,000,000  \n6  45,410,100,000  \n7  44,995,600,000  \n8  46,231,100,000  \n9  43,929,600,000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>Market Cap</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Jul 31, 2017</td>\n      <td>2763.24</td>\n      <td>2889.62</td>\n      <td>2720.61</td>\n      <td>2875.34</td>\n      <td>860,575,000</td>\n      <td>45,535,800,000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Jul 30, 2017</td>\n      <td>2724.39</td>\n      <td>2758.53</td>\n      <td>2644.85</td>\n      <td>2757.18</td>\n      <td>705,943,000</td>\n      <td>44,890,700,000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Jul 29, 2017</td>\n      <td>2807.02</td>\n      <td>2808.76</td>\n      <td>2692.80</td>\n      <td>2726.45</td>\n      <td>803,746,000</td>\n      <td>46,246,700,000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Jul 28, 2017</td>\n      <td>2679.73</td>\n      <td>2897.45</td>\n      <td>2679.73</td>\n      <td>2809.01</td>\n      <td>1,380,100,000</td>\n      <td>44,144,400,000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Jul 27, 2017</td>\n      <td>2538.71</td>\n      <td>2693.32</td>\n      <td>2529.34</td>\n      <td>2671.78</td>\n      <td>789,104,000</td>\n      <td>41,816,500,000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Jul 26, 2017</td>\n      <td>2577.77</td>\n      <td>2610.76</td>\n      <td>2450.80</td>\n      <td>2529.45</td>\n      <td>937,404,000</td>\n      <td>42,455,000,000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Jul 25, 2017</td>\n      <td>2757.50</td>\n      <td>2768.08</td>\n      <td>2480.96</td>\n      <td>2576.48</td>\n      <td>1,460,090,000</td>\n      <td>45,410,100,000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Jul 24, 2017</td>\n      <td>2732.70</td>\n      <td>2777.26</td>\n      <td>2699.19</td>\n      <td>2754.86</td>\n      <td>866,474,000</td>\n      <td>44,995,600,000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Jul 23, 2017</td>\n      <td>2808.10</td>\n      <td>2832.18</td>\n      <td>2653.94</td>\n      <td>2730.40</td>\n      <td>1,072,840,000</td>\n      <td>46,231,100,000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Jul 22, 2017</td>\n      <td>2668.63</td>\n      <td>2862.42</td>\n      <td>2657.71</td>\n      <td>2810.12</td>\n      <td>1,177,130,000</td>\n      <td>43,929,600,000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"bitcoin.tail()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:18:15.441315Z","iopub.execute_input":"2025-01-08T13:18:15.441663Z","iopub.status.idle":"2025-01-08T13:18:15.451638Z","shell.execute_reply.started":"2025-01-08T13:18:15.441643Z","shell.execute_reply":"2025-01-08T13:18:15.450545Z"}},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"              Date    Open    High     Low   Close Volume     Market Cap\n1551  May 02, 2013  116.38  125.60   92.28  105.21      -  1,292,190,000\n1552  May 01, 2013  139.00  139.89  107.72  116.99      -  1,542,820,000\n1553  Apr 30, 2013  144.00  146.93  134.05  139.00      -  1,597,780,000\n1554  Apr 29, 2013  134.44  147.49  134.00  144.54      -  1,491,160,000\n1555  Apr 28, 2013  135.30  135.98  132.10  134.21      -  1,500,520,000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>Market Cap</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1551</th>\n      <td>May 02, 2013</td>\n      <td>116.38</td>\n      <td>125.60</td>\n      <td>92.28</td>\n      <td>105.21</td>\n      <td>-</td>\n      <td>1,292,190,000</td>\n    </tr>\n    <tr>\n      <th>1552</th>\n      <td>May 01, 2013</td>\n      <td>139.00</td>\n      <td>139.89</td>\n      <td>107.72</td>\n      <td>116.99</td>\n      <td>-</td>\n      <td>1,542,820,000</td>\n    </tr>\n    <tr>\n      <th>1553</th>\n      <td>Apr 30, 2013</td>\n      <td>144.00</td>\n      <td>146.93</td>\n      <td>134.05</td>\n      <td>139.00</td>\n      <td>-</td>\n      <td>1,597,780,000</td>\n    </tr>\n    <tr>\n      <th>1554</th>\n      <td>Apr 29, 2013</td>\n      <td>134.44</td>\n      <td>147.49</td>\n      <td>134.00</td>\n      <td>144.54</td>\n      <td>-</td>\n      <td>1,491,160,000</td>\n    </tr>\n    <tr>\n      <th>1555</th>\n      <td>Apr 28, 2013</td>\n      <td>135.30</td>\n      <td>135.98</td>\n      <td>132.10</td>\n      <td>134.21</td>\n      <td>-</td>\n      <td>1,500,520,000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"bitcoin.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:19:50.960416Z","iopub.execute_input":"2025-01-08T13:19:50.960840Z","iopub.status.idle":"2025-01-08T13:19:50.966589Z","shell.execute_reply.started":"2025-01-08T13:19:50.960818Z","shell.execute_reply":"2025-01-08T13:19:50.965541Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"(1556, 7)"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"bitcoin.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:19:57.927260Z","iopub.execute_input":"2025-01-08T13:19:57.927702Z","iopub.status.idle":"2025-01-08T13:19:57.964640Z","shell.execute_reply.started":"2025-01-08T13:19:57.927678Z","shell.execute_reply":"2025-01-08T13:19:57.963052Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1556 entries, 0 to 1555\nData columns (total 7 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   Date        1556 non-null   object \n 1   Open        1556 non-null   float64\n 2   High        1556 non-null   float64\n 3   Low         1556 non-null   float64\n 4   Close       1556 non-null   float64\n 5   Volume      1556 non-null   object \n 6   Market Cap  1556 non-null   object \ndtypes: float64(4), object(3)\nmemory usage: 85.2+ KB\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"bitcoin.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:20:16.308222Z","iopub.execute_input":"2025-01-08T13:20:16.308643Z","iopub.status.idle":"2025-01-08T13:20:16.330165Z","shell.execute_reply.started":"2025-01-08T13:20:16.308619Z","shell.execute_reply":"2025-01-08T13:20:16.329200Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"              Open         High          Low        Close\ncount  1556.000000  1556.000000  1556.000000  1556.000000\nmean    582.625328   597.992847   567.851446   584.239396\nstd     523.137312   542.992855   505.877401   525.904442\nmin      68.500000    74.560000    65.530000    68.430000\n25%     254.287500   260.327500   248.835000   254.320000\n50%     438.600000   447.560000   430.570000   438.855000\n75%     662.437500   674.525000   646.735000   663.402500\nmax    2953.220000  2999.910000  2840.530000  2958.110000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1556.000000</td>\n      <td>1556.000000</td>\n      <td>1556.000000</td>\n      <td>1556.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>582.625328</td>\n      <td>597.992847</td>\n      <td>567.851446</td>\n      <td>584.239396</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>523.137312</td>\n      <td>542.992855</td>\n      <td>505.877401</td>\n      <td>525.904442</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>68.500000</td>\n      <td>74.560000</td>\n      <td>65.530000</td>\n      <td>68.430000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>254.287500</td>\n      <td>260.327500</td>\n      <td>248.835000</td>\n      <td>254.320000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>438.600000</td>\n      <td>447.560000</td>\n      <td>430.570000</td>\n      <td>438.855000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>662.437500</td>\n      <td>674.525000</td>\n      <td>646.735000</td>\n      <td>663.402500</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2953.220000</td>\n      <td>2999.910000</td>\n      <td>2840.530000</td>\n      <td>2958.110000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"bitcoin.duplicated().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:21:22.548897Z","iopub.execute_input":"2025-01-08T13:21:22.549163Z","iopub.status.idle":"2025-01-08T13:21:22.558705Z","shell.execute_reply.started":"2025-01-08T13:21:22.549146Z","shell.execute_reply":"2025-01-08T13:21:22.557785Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}],"execution_count":12},{"cell_type":"markdown","source":"It seems like the Data column is of object (string) datatype. Let's convert them to timestamp","metadata":{}},{"cell_type":"code","source":"bitcoin['Date'] = bitcoin['Date'].astype(dtype = 'datetime64[ns]')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:25:58.189753Z","iopub.execute_input":"2025-01-08T13:25:58.190180Z","iopub.status.idle":"2025-01-08T13:25:58.201838Z","shell.execute_reply.started":"2025-01-08T13:25:58.190146Z","shell.execute_reply":"2025-01-08T13:25:58.200786Z"}},"outputs":[],"execution_count":22},{"cell_type":"code","source":"bitcoin.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:25:59.287743Z","iopub.execute_input":"2025-01-08T13:25:59.288058Z","iopub.status.idle":"2025-01-08T13:25:59.300164Z","shell.execute_reply.started":"2025-01-08T13:25:59.288039Z","shell.execute_reply":"2025-01-08T13:25:59.299298Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1556 entries, 0 to 1555\nData columns (total 7 columns):\n #   Column      Non-Null Count  Dtype         \n---  ------      --------------  -----         \n 0   Date        1556 non-null   datetime64[ns]\n 1   Open        1556 non-null   float64       \n 2   High        1556 non-null   float64       \n 3   Low         1556 non-null   float64       \n 4   Close       1556 non-null   float64       \n 5   Volume      1556 non-null   object        \n 6   Market Cap  1556 non-null   object        \ndtypes: datetime64[ns](1), float64(4), object(2)\nmemory usage: 85.2+ KB\n","output_type":"stream"}],"execution_count":23},{"cell_type":"code","source":"bitcoin.sort_index(ascending = False).reset_index().drop(columns = ['index'], axis = 1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-08T13:42:54.231234Z","iopub.execute_input":"2025-01-08T13:42:54.231573Z","iopub.status.idle":"2025-01-08T13:42:54.248470Z","shell.execute_reply.started":"2025-01-08T13:42:54.231554Z","shell.execute_reply":"2025-01-08T13:42:54.247660Z"}},"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"           Date     Open     High      Low    Close         Volume  \\\n0    2013-04-28   135.30   135.98   132.10   134.21              -   \n1    2013-04-29   134.44   147.49   134.00   144.54              -   \n2    2013-04-30   144.00   146.93   134.05   139.00              -   \n3    2013-05-01   139.00   139.89   107.72   116.99              -   \n4    2013-05-02   116.38   125.60    92.28   105.21              -   \n...         ...      ...      ...      ...      ...            ...   \n1551 2017-07-27  2538.71  2693.32  2529.34  2671.78    789,104,000   \n1552 2017-07-28  2679.73  2897.45  2679.73  2809.01  1,380,100,000   \n1553 2017-07-29  2807.02  2808.76  2692.80  2726.45    803,746,000   \n1554 2017-07-30  2724.39  2758.53  2644.85  2757.18    705,943,000   \n1555 2017-07-31  2763.24  2889.62  2720.61  2875.34    860,575,000   \n\n          Market Cap  \n0      1,500,520,000  \n1      1,491,160,000  \n2      1,597,780,000  \n3      1,542,820,000  \n4      1,292,190,000  \n...              ...  \n1551  41,816,500,000  \n1552  44,144,400,000  \n1553  46,246,700,000  \n1554  44,890,700,000  \n1555  45,535,800,000  \n\n[1556 rows x 7 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>Market Cap</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-04-28</td>\n      <td>135.30</td>\n      <td>135.98</td>\n      <td>132.10</td>\n      <td>134.21</td>\n      <td>-</td>\n      <td>1,500,520,000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-04-29</td>\n      <td>134.44</td>\n      <td>147.49</td>\n      <td>134.00</td>\n      <td>144.54</td>\n      <td>-</td>\n      <td>1,491,160,000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-04-30</td>\n      <td>144.00</td>\n      <td>146.93</td>\n      <td>134.05</td>\n      <td>139.00</td>\n      <td>-</td>\n      <td>1,597,780,000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-05-01</td>\n      <td>139.00</td>\n      <td>139.89</td>\n      <td>107.72</td>\n      <td>116.99</td>\n      <td>-</td>\n      <td>1,542,820,000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-05-02</td>\n      <td>116.38</td>\n      <td>125.60</td>\n      <td>92.28</td>\n      <td>105.21</td>\n      <td>-</td>\n      <td>1,292,190,000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1551</th>\n      <td>2017-07-27</td>\n      <td>2538.71</td>\n      <td>2693.32</td>\n      <td>2529.34</td>\n      <td>2671.78</td>\n      <td>789,104,000</td>\n      <td>41,816,500,000</td>\n    </tr>\n    <tr>\n      <th>1552</th>\n      <td>2017-07-28</td>\n      <td>2679.73</td>\n      <td>2897.45</td>\n      <td>2679.73</td>\n      <td>2809.01</td>\n      <td>1,380,100,000</td>\n      <td>44,144,400,000</td>\n    </tr>\n    <tr>\n      <th>1553</th>\n      <td>2017-07-29</td>\n      <td>2807.02</td>\n      <td>2808.76</td>\n      <td>2692.80</td>\n      <td>2726.45</td>\n      <td>803,746,000</td>\n      <td>46,246,700,000</td>\n    </tr>\n    <tr>\n      <th>1554</th>\n      <td>2017-07-30</td>\n      <td>2724.39</td>\n      <td>2758.53</td>\n      <td>2644.85</td>\n      <td>2757.18</td>\n      <td>705,943,000</td>\n      <td>44,890,700,000</td>\n    </tr>\n    <tr>\n      <th>1555</th>\n      <td>2017-07-31</td>\n      <td>2763.24</td>\n      <td>2889.62</td>\n      <td>2720.61</td>\n      <td>2875.34</td>\n      <td>860,575,000</td>\n      <td>45,535,800,000</td>\n    </tr>\n  </tbody>\n</table>\n<p>1556 rows × 7 columns</p>\n</div>"},"metadata":{}}],"execution_count":38},{"cell_type":"markdown","source":"It looks like there are no values entered at the column - volume. But in our analysis we are not going to use the column series. ","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}