p3_10_minutes_to_pandas.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1_n1RZx2maSeC9w2iy2CsMb8hq2arG6kW","timestamp":1718900050857},{"file_id":"1p3KvO-qrP-LgmE9KCpg8wcm9BN6IXm2f","timestamp":1677157287710}]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"exkLCPgyK_Vl"},"source":["###Introduction\n"]},{"cell_type":"markdown","metadata":{"id":"r_8A02PcLHwi"},"source":["This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the <a href=\"https://pandas.pydata.org/docs/user_guide/cookbook.html#cookbook\" target=\"_blank\">Cookbook<a/>."]},{"cell_type":"markdown","metadata":{"id":"4m0Li2ySLMa-"},"source":["Customarily, we import as follows:"]},{"cell_type":"code","metadata":{"id":"LKHxbN0QLGJE"},"source":["import numpy as np\n","import pandas as pd"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3nqdCLY3LZKo"},"source":["## Object Creation\n"]},{"cell_type":"markdown","metadata":{"id":"29g0yoUMLdoi"},"source":["See the <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/user_guide/dsintro.html#dsintro\">Intro to data structures section</a>.\n","\n","Creating a <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.Series.html#pandas.Series\">`Series`</a> by passing a list of values, letting pandas create a default integer index:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-f6TSrLHLmCc","executionInfo":{"status":"ok","timestamp":1677345619614,"user_tz":0,"elapsed":225,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"2e8e0e0d-54f9-4665-a1ba-59b993738ac1"},"source":["s = pd.Series([1, 3, 5, np.nan, 6, 8])\n","s"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    1.0\n","1    3.0\n","2    5.0\n","3    NaN\n","4    6.0\n","5    8.0\n","dtype: float64"]},"metadata":{},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"aCUh8QIML479"},"source":["Creating a <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame\">`DataFrame`</a> by passing a NumPy array, with a datetime index using <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.date_range.html#pandas.date_range\">`date_range()`</a> and labeled columns:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-TGGcxdnMFaZ","executionInfo":{"status":"ok","timestamp":1683032731766,"user_tz":-60,"elapsed":283,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"580e368a-6b67-4e48-de99-3bd4dbb83d91"},"source":["dates = pd.date_range(\"20130101\", periods=6)\n","dates"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n","               '2013-01-05', '2013-01-06'],\n","              dtype='datetime64[ns]', freq='D')"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"KQQ7ziMuMsQ6","executionInfo":{"status":"ok","timestamp":1683032744765,"user_tz":-60,"elapsed":657,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"b86541b4-a658-437d-e6da-48b2a5a91abf"},"source":["df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list(\"ABCD\"))\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D\n","2013-01-01 -0.524103  0.913936 -0.648058  2.680350\n","2013-01-02 -1.354008 -1.750013 -0.009807  0.872537\n","2013-01-03  0.977944 -0.449545 -0.545343 -1.075350\n","2013-01-04 -0.051502 -1.190048  0.502404  0.515585\n","2013-01-05  0.503904 -1.634828  0.749093 -0.481287\n","2013-01-06 -0.300019 -0.068817 -1.336835  0.070851"],"text/html":["\n","  <div id=\"df-00ff2355-9c56-4c72-a545-0b3a27c594f3\">\n","    <div class=\"colab-df-container\">\n","      <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","    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   </tr>\n","    <tr>\n","      <th>2013-01-05</th>\n","      <td>0.503904</td>\n","      <td>-1.634828</td>\n","      <td>0.749093</td>\n","      <td>-0.481287</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-06</th>\n","      <td>-0.300019</td>\n","      <td>-0.068817</td>\n","      <td>-1.336835</td>\n","      <td>0.070851</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00ff2355-9c56-4c72-a545-0b3a27c594f3')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-00ff2355-9c56-4c72-a545-0b3a27c594f3');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"mFhlx4qGMvJ4"},"source":["Creating a <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame\">`DataFrame`</a> by passing a dictionary of objects that can be converted into a series-like structure:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"ME0CZ45cM3FD","executionInfo":{"status":"ok","timestamp":1683032766780,"user_tz":-60,"elapsed":367,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"b7081881-af99-46b6-dac5-711c8c810bee"},"source":["df2 = pd.DataFrame(\n","    {\n","        \"A\": 1.0,\n","        \"B\": pd.Timestamp(\"20130102\"),\n","        \"C\": pd.Series(1, index=list(range(4)), dtype=\"float32\"),\n","        \"D\": np.array([3] * 4, dtype=\"int32\"),\n","        \"E\": pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n","        \"F\": \"foo\",\n","    }\n",")\n","df2"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     A          B    C  D      E    F\n","0  1.0 2013-01-02  1.0  3   test  foo\n","1  1.0 2013-01-02  1.0  3  train  foo\n","2  1.0 2013-01-02  1.0  3   test  foo\n","3  1.0 2013-01-02  1.0  3  train  foo"],"text/html":["\n","  <div id=\"df-1af863ab-e817-467d-88b1-ce3040afc7cf\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","      <th>E</th>\n","      <th>F</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>2013-01-02</td>\n","      <td>1.0</td>\n","      <td>3</td>\n","      <td>test</td>\n","      <td>foo</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>2013-01-02</td>\n","      <td>1.0</td>\n","      <td>3</td>\n","      <td>train</td>\n","      <td>foo</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>2013-01-02</td>\n","      <td>1.0</td>\n","      <td>3</td>\n","      <td>test</td>\n","      <td>foo</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>2013-01-02</td>\n","      <td>1.0</td>\n","      <td>3</td>\n","      <td>train</td>\n","      <td>foo</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1af863ab-e817-467d-88b1-ce3040afc7cf')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-1af863ab-e817-467d-88b1-ce3040afc7cf');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"QO1AgckJOH62"},"source":["The columns of the resulting <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame\">`DataFrame`</a> have different <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/user_guide/basics.html#basics-dtypes\">`dtypes`</a>:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j7Quvns4OMzO","executionInfo":{"status":"ok","timestamp":1683032778208,"user_tz":-60,"elapsed":196,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"97f3b54c-3211-4ed5-90e5-457e0a8447f7"},"source":["df2.dtypes"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["A           float64\n","B    datetime64[ns]\n","C           float32\n","D             int32\n","E          category\n","F            object\n","dtype: object"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"HVONmnIoOpLL"},"source":["If you’re using an interactive tool with automatic completion for column names, here’s a subset of the attributes that will be completed:\n","```\n","df2.<TAB>\n","df2.A                  df2.bool\n","df2.abs                df2.boxplot\n","df2.add                df2.C\n","df2.add_prefix         df2.clip\n","df2.add_suffix         df2.columns\n","df2.align              df2.copy\n","df2.all                df2.count\n","df2.any                df2.combine\n","df2.append             df2.D\n","df2.apply              df2.describe\n","df2.applymap           df2.diff\n","df2.B                  df2.duplicated\n","```\n","As you can see, the columns `A`, `B`, `C`, and `D` are automatically tab completed. `E` and `F` are there as well; the rest of the attributes have been truncated for brevity.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"b2H2SnfKPDh7"},"source":["## Viewing data"]},{"cell_type":"markdown","metadata":{"id":"qgJj9v3NPOVv"},"source":["See the <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/user_guide/basics.html#basics\">Basics section</a>.\n","\n","Use <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html#pandas.DataFrame.head\">`DataFrame.head()`</a> and <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.tail.html#pandas.DataFrame.tail\">`DataFrame.tail()`</a> to view the top and bottom rows of the frame respectively:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"nM7nF6eJPTti","executionInfo":{"status":"ok","timestamp":1683032816012,"user_tz":-60,"elapsed":227,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"7bb3eec0-5c33-4c67-a208-60dd9cc02107"},"source":["df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D\n","2013-01-01 -0.524103  0.913936 -0.648058  2.680350\n","2013-01-02 -1.354008 -1.750013 -0.009807  0.872537\n","2013-01-03  0.977944 -0.449545 -0.545343 -1.075350\n","2013-01-04 -0.051502 -1.190048  0.502404  0.515585\n","2013-01-05  0.503904 -1.634828  0.749093 -0.481287"],"text/html":["\n","  <div id=\"df-69c8c572-7106-4cb3-9ed9-ff150ff0b736\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2013-01-01</th>\n","      <td>-0.524103</td>\n","      <td>0.913936</td>\n","      <td>-0.648058</td>\n","      <td>2.680350</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-02</th>\n","      <td>-1.354008</td>\n","      <td>-1.750013</td>\n","      <td>-0.009807</td>\n","      <td>0.872537</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-03</th>\n","      <td>0.977944</td>\n","      <td>-0.449545</td>\n","      <td>-0.545343</td>\n","      <td>-1.075350</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-04</th>\n","      <td>-0.051502</td>\n","      <td>-1.190048</td>\n","      <td>0.502404</td>\n","      <td>0.515585</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-05</th>\n","      <td>0.503904</td>\n","      <td>-1.634828</td>\n","      <td>0.749093</td>\n","      <td>-0.481287</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-69c8c572-7106-4cb3-9ed9-ff150ff0b736')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 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Note that this can be an expensive operation when your <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame\">`DataFrame`</a> has columns with different data types, which comes down to a fundamental difference between pandas and NumPy: **NumPy arrays have one dtype for the entire array, while pandas DataFrames have one dtype per column**. When you call <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy\">`DataFrame.to_numpy()`</a>, pandas will find the NumPy dtype that can hold all of the dtypes in the DataFrame. This may end up being `object`, which requires casting every value to a Python object.\n","\n","For `df`, our <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame\">`DataFrame`</a> of all floating-point values, and <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy\">`DataFrame.to_numpy()`</a> is fast and doesn’t require copying data:"],"metadata":{"id":"ZxWsnW6dhxgv"}},{"cell_type":"code","source":["df.to_numpy()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WXGypmdcigW0","executionInfo":{"status":"ok","timestamp":1683032993948,"user_tz":-60,"elapsed":589,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"8040ce96-e564-4f12-e6f1-43f75d501e69"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.52410284,  0.91393585, -0.64805804,  2.68035011],\n","       [-1.35400797, -1.75001349, -0.00980681,  0.87253736],\n","       [ 0.97794425, -0.44954492, -0.54534324, -1.07534963],\n","       [-0.05150191, -1.19004796,  0.50240362,  0.51558494],\n","       [ 0.50390391, -1.63482808,  0.74909289, -0.481287  ],\n","       [-0.3000188 , -0.06881716, -1.33683529,  0.07085129]])"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["For `df2`, the <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame\">`DataFrame`</a> with multiple dtypes, <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy\">`DataFrame.to_numpy()`</a> is relatively expensive:\n","\n"],"metadata":{"id":"W6RtN0dkimTH"}},{"cell_type":"code","source":["df2.to_numpy()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P0XNc3kPi1bO","executionInfo":{"status":"ok","timestamp":1683032996523,"user_tz":-60,"elapsed":205,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"741c1477-f639-4f3f-c2b9-dfe2ba12144b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],\n","       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'],\n","       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],\n","       [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']],\n","      dtype=object)"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","source":["NOTE: <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy\">`DataFrame.to_numpy()`</a> does not include the index or column labels in the output."],"metadata":{"id":"d4RIbLjzi7jY"}},{"cell_type":"markdown","metadata":{"id":"SaYf9MG6Ri1A"},"source":["<a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html#pandas.DataFrame.describe\">`describe()`</a> shows a quick statistic summary of your data:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"MgE_lSKiRpMi","executionInfo":{"status":"ok","timestamp":1683033000043,"user_tz":-60,"elapsed":188,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"6765c1fc-ce49-4a4a-ee76-4d1ea7b28f23"},"source":["df.describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              A         B         C         D\n","count  6.000000  6.000000  6.000000  6.000000\n","mean  -0.124631 -0.696553 -0.214758  0.430448\n","std    0.814590  1.027854  0.779927  1.303284\n","min   -1.354008 -1.750013 -1.336835 -1.075350\n","25%   -0.468082 -1.523633 -0.622379 -0.343252\n","50%   -0.175760 -0.819796 -0.277575  0.293218\n","75%    0.365052 -0.163999  0.374351  0.783299\n","max    0.977944  0.913936  0.749093  2.680350"],"text/html":["\n","  <div id=\"df-48d2e4d9-2bef-4114-9125-bafbf4b9a19d\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>6.000000</td>\n","      <td>6.000000</td>\n","      <td>6.000000</td>\n","      <td>6.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>-0.124631</td>\n","      <td>-0.696553</td>\n","      <td>-0.214758</td>\n","      <td>0.430448</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.814590</td>\n","      <td>1.027854</td>\n","      <td>0.779927</td>\n","      <td>1.303284</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-1.354008</td>\n","      <td>-1.750013</td>\n","      <td>-1.336835</td>\n","      <td>-1.075350</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-0.468082</td>\n","      <td>-1.523633</td>\n","      <td>-0.622379</td>\n","      <td>-0.343252</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>-0.175760</td>\n","      <td>-0.819796</td>\n","      <td>-0.277575</td>\n","      <td>0.293218</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.365052</td>\n","      <td>-0.163999</td>\n","      <td>0.374351</td>\n","      <td>0.783299</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.977944</td>\n","      <td>0.913936</td>\n","      <td>0.749093</td>\n","      <td>2.680350</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48d2e4d9-2bef-4114-9125-bafbf4b9a19d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 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<td>-1.336835</td>\n","    </tr>\n","    <tr>\n","      <th>D</th>\n","      <td>2.680350</td>\n","      <td>0.872537</td>\n","      <td>-1.075350</td>\n","      <td>0.515585</td>\n","      <td>-0.481287</td>\n","      <td>0.070851</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d4e77c3-c706-46b5-8c2e-e0b8acc16da7')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 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<td>0.977944</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-04</th>\n","      <td>0.515585</td>\n","      <td>0.502404</td>\n","      <td>-1.190048</td>\n","      <td>-0.051502</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-05</th>\n","      <td>-0.481287</td>\n","      <td>0.749093</td>\n","      <td>-1.634828</td>\n","      <td>0.503904</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-06</th>\n","      <td>0.070851</td>\n","      <td>-1.336835</td>\n","      <td>-0.068817</td>\n","      <td>-0.300019</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-add74eba-3c05-4d62-91c1-226f30bb6433')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" 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values:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"j_7cuOtwSYf1","executionInfo":{"status":"ok","timestamp":1683033038049,"user_tz":-60,"elapsed":198,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"4ac4cb26-172b-4ea4-dad0-a2db7ad6fc4c"},"source":["df.sort_values(by='B')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D\n","2013-01-02 -1.354008 -1.750013 -0.009807  0.872537\n","2013-01-05  0.503904 -1.634828  0.749093 -0.481287\n","2013-01-04 -0.051502 -1.190048  0.502404  0.515585\n","2013-01-03  0.977944 -0.449545 -0.545343 -1.075350\n","2013-01-06 -0.300019 -0.068817 -1.336835  0.070851\n","2013-01-01 -0.524103  0.913936 -0.648058  2.680350"],"text/html":["\n","  <div id=\"df-ac489b2b-c353-4015-a5b5-628af64905a7\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    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<td>0.515585</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-03</th>\n","      <td>0.977944</td>\n","      <td>-0.449545</td>\n","      <td>-0.545343</td>\n","      <td>-1.075350</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-06</th>\n","      <td>-0.300019</td>\n","      <td>-0.068817</td>\n","      <td>-1.336835</td>\n","      <td>0.070851</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-01</th>\n","      <td>-0.524103</td>\n","      <td>0.913936</td>\n","      <td>-0.648058</td>\n","      <td>2.680350</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac489b2b-c353-4015-a5b5-628af64905a7')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"Irh3gEM7Sjw6"},"source":["## Selection"]},{"cell_type":"markdown","source":["NOTE: While standard Python / NumPy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.at.html#pandas.DataFrame.at\">`DataFrame.at()`</a>, <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iat.html#pandas.DataFrame.iat\">`DataFrame.iat()`</a>, <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html#pandas.DataFrame.loc\">`DataFrame.loc()`</a> and <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html#pandas.DataFrame.iloc\">`DataFrame.iloc()`</a>.\n","\n","See the indexing documentation <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/user_guide/indexing.html#indexing\">Indexing and Selecting Data</a> and <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/user_guide/advanced.html#advanced\">MultiIndex / Advanced Indexing</a>.\n","\n"],"metadata":{"id":"ipv6fGmvkTzV"}},{"cell_type":"markdown","metadata":{"id":"nHaQJaVhTHSl"},"source":["### Getting"]},{"cell_type":"markdown","metadata":{"id":"lVl2W8bQSs02"},"source":["Selecting a single column, which yields a <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.Series.html#pandas.Series\">`Series`</a>, equivalent to `df.A`:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l1d7nm3QS0qg","executionInfo":{"status":"ok","timestamp":1683033087263,"user_tz":-60,"elapsed":189,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"148de886-e085-4f93-984f-86539dcca10e"},"source":["df[\"A\"]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2013-01-01   -0.524103\n","2013-01-02   -1.354008\n","2013-01-03    0.977944\n","2013-01-04   -0.051502\n","2013-01-05    0.503904\n","2013-01-06   -0.300019\n","Freq: D, Name: A, dtype: float64"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"zN6UA4ZAS4SV"},"source":["Selecting via `[]` (`__getitem__`), which slices the rows:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"tkCiLvw3S3ll","executionInfo":{"status":"ok","timestamp":1683033101869,"user_tz":-60,"elapsed":203,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"20e65ed9-fe86-47ce-b4a2-27f01abc2e68"},"source":["df[0:3]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D\n","2013-01-01 -0.524103  0.913936 -0.648058  2.680350\n","2013-01-02 -1.354008 -1.750013 -0.009807  0.872537\n","2013-01-03  0.977944 -0.449545 -0.545343 -1.075350"],"text/html":["\n","  <div id=\"df-75790645-9862-4c25-bd6b-5f5ea92dc0ec\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html#pandas.DataFrame.loc\">`DataFrame.loc()`</a> or <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.at.html#pandas.DataFrame.at\">`DataFrame.at()`</a>.\n","\n","For getting a cross section using a label:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eIAarsmaTSCi","executionInfo":{"status":"ok","timestamp":1683033140318,"user_tz":-60,"elapsed":206,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"e14c61ec-813e-4041-86ac-2fefe9c5377a"},"source":["df.loc[dates[0]]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["A   -0.524103\n","B    0.913936\n","C   -0.648058\n","D    2.680350\n","Name: 2013-01-01 00:00:00, dtype: float64"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","metadata":{"id":"a4kPW5AVTf5o"},"source":["Selecting on a multi-axis by label:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"m8hHZWCbTjIC","executionInfo":{"status":"ok","timestamp":1683033166416,"user_tz":-60,"elapsed":284,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"244ddcb8-ad3c-4a10-d518-5bfd647d5ef0"},"source":["df.loc[:, [\"A\", \"B\"]]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B\n","2013-01-01 -0.524103  0.913936\n","2013-01-02 -1.354008 -1.750013\n","2013-01-03  0.977944 -0.449545\n","2013-01-04 -0.051502 -1.190048\n","2013-01-05  0.503904 -1.634828\n","2013-01-06 -0.300019 -0.068817"],"text/html":["\n","  <div id=\"df-088f0252-29bc-4429-9261-aa03dc612351\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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<td>-0.068817</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-088f0252-29bc-4429-9261-aa03dc612351')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":20}]},{"cell_type":"markdown","source":["Showing label slicing, both endpoints are included:"],"metadata":{"id":"AYZHmpTvnLX_"}},{"cell_type":"code","source":["df.loc[\"20130102\":\"20130104\", [\"A\", \"B\"]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"Bji30wMhnMfq","executionInfo":{"status":"ok","timestamp":1683033178805,"user_tz":-60,"elapsed":211,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"8f4eaec4-963f-4760-83fd-fe8b488e6817"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B\n","2013-01-02 -1.354008 -1.750013\n","2013-01-03  0.977944 -0.449545\n","2013-01-04 -0.051502 -1.190048"],"text/html":["\n","  <div id=\"df-e75f35a1-8a88-4871-a470-ea749869a602\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2013-01-02</th>\n","      <td>-1.354008</td>\n","      <td>-1.750013</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-03</th>\n","      <td>0.977944</td>\n","      <td>-0.449545</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-04</th>\n","      <td>-0.051502</td>\n","      <td>-1.190048</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e75f35a1-8a88-4871-a470-ea749869a602')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e75f35a1-8a88-4871-a470-ea749869a602 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","source":["Reduction in the dimensions of the returned object:"],"metadata":{"id":"yyPla7wjnTGf"}},{"cell_type":"code","source":["df.loc[\"20130102\", [\"A\", \"B\"]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HQNcB0xjnUbR","executionInfo":{"status":"ok","timestamp":1683033190460,"user_tz":-60,"elapsed":181,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"f07f3d2d-12bd-4e54-e69b-78512bdefe71"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["A   -1.354008\n","B   -1.750013\n","Name: 2013-01-02 00:00:00, dtype: float64"]},"metadata":{},"execution_count":22}]},{"cell_type":"markdown","source":["For getting a scalar value:"],"metadata":{"id":"pR_2DTRtnaah"}},{"cell_type":"code","source":["df.loc[dates[0], \"A\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VTtQq99Unbol","executionInfo":{"status":"ok","timestamp":1683033204552,"user_tz":-60,"elapsed":183,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"5460fb27-a127-4be6-e83b-31e8d49c3e04"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-0.5241028394428331"]},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","source":["For getting fast access to a scalar (equivalent to the prior method):"],"metadata":{"id":"msUtRC5_nhFg"}},{"cell_type":"code","source":["df.at[dates[0], \"A\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TNDwK-dAniQb","executionInfo":{"status":"ok","timestamp":1683033207053,"user_tz":-60,"elapsed":216,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"582dce7e-8b0d-4e63-c43b-6fb09a1a18a5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-0.5241028394428331"]},"metadata":{},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"7gY3VNbhT4w8"},"source":["### Selection by position"]},{"cell_type":"markdown","source":["See more in <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/user_guide/indexing.html#indexing-integer\">Selection by Position</a> using <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html#pandas.DataFrame.iloc\">`DataFrame.iloc()`</a> or <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.at.html#pandas.DataFrame.at\">`DataFrame.at()`</a>.\n","\n","Select via the position of the passed integers:"],"metadata":{"id":"-2hN_bmKw8yy"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AUrNqRvKT81K","executionInfo":{"status":"ok","timestamp":1683033228842,"user_tz":-60,"elapsed":248,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"17951567-52e3-4e7c-cee3-7f5f365491cd"},"source":["df.iloc[3]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["A   -0.051502\n","B   -1.190048\n","C    0.502404\n","D    0.515585\n","Name: 2013-01-04 00:00:00, dtype: float64"]},"metadata":{},"execution_count":25}]},{"cell_type":"markdown","metadata":{"id":"XOoPOUzDUE4Z"},"source":["By integer slices, acting similar to NumPy/Python:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"qDgEXEQnUIxE","executionInfo":{"status":"ok","timestamp":1683033243010,"user_tz":-60,"elapsed":201,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"3302a102-ca3d-4fc8-c0e6-722b8726d909"},"source":["df.iloc[3:5, 0:2]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B\n","2013-01-04 -0.051502 -1.190048\n","2013-01-05  0.503904 -1.634828"],"text/html":["\n","  <div id=\"df-73476089-5594-4b72-a28e-a06598fe5c27\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr 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indexing"]},{"cell_type":"markdown","metadata":{"id":"SgC1QGqrUvPf"},"source":["Using a single column’s values to select data:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"vMxGqWCFUrEL","executionInfo":{"status":"ok","timestamp":1683033302058,"user_tz":-60,"elapsed":199,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"2c76794f-0c65-4a3a-9b0a-ae02a1e90fa7"},"source":["df[df[\"A\"] > 0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D\n","2013-01-03  0.977944 -0.449545 -0.545343 -1.075350\n","2013-01-05  0.503904 -1.634828  0.749093 -0.481287"],"text/html":["\n","  <div id=\"df-152b1439-56ff-4e04-9e05-127bb3ceeb62\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe 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periods=6))\n","s1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JtRM1lKjzXC6","executionInfo":{"status":"ok","timestamp":1683033356653,"user_tz":-60,"elapsed":449,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"2453b808-b259-4ff4-f4fb-91807576d2b6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2013-01-02    1\n","2013-01-03    2\n","2013-01-04    3\n","2013-01-05    4\n","2013-01-06    5\n","2013-01-07    6\n","Freq: D, dtype: int64"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["df[\"F\"] = s1"],"metadata":{"id":"_p1dI4f4zebk"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Setting values by label:"],"metadata":{"id":"2XvBpwSUzkIX"}},{"cell_type":"code","source":["df.at[dates[0], \"A\"] = 0"],"metadata":{"id":"yiZSgu-wziEv"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Setting values by position:"],"metadata":{"id":"owxsv0CfzrRJ"}},{"cell_type":"code","source":["df.iat[0, 1] = 0"],"metadata":{"id":"cqUU_vA2ztl_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Setting by assigning with a NumPy array:"],"metadata":{"id":"LzqB_Vdkzxu3"}},{"cell_type":"code","source":["df.loc[:, \"D\"] = np.array([5] * len(df))"],"metadata":{"id":"LC79kyj5z0Ho"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["The result of the prior setting operations:"],"metadata":{"id":"Ns0SHn9Nz6qD"}},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"J5VHRC-bz7uV","executionInfo":{"status":"ok","timestamp":1683033481010,"user_tz":-60,"elapsed":213,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"1290640b-3aae-45a2-8160-3d2c07a7e95c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D\n","2013-01-01  0.000000  0.000000 -0.648058  2.680350\n","2013-01-02 -1.354008 -1.750013 -0.009807  0.872537\n","2013-01-03  0.977944 -0.449545 -0.545343 -1.075350\n","2013-01-04 -0.051502 -1.190048  0.502404  0.515585\n","2013-01-05  0.503904 -1.634828  0.749093 -0.481287\n","2013-01-06 -0.300019 -0.068817 -1.336835  0.070851"],"text/html":["\n","  <div id=\"df-06d2b71c-c308-441f-b913-d56dde0dad0f\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  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   <td>0.070851</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06d2b71c-c308-441f-b913-d56dde0dad0f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  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-df2\n","df2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"CxqdBrMQ0Nv8","executionInfo":{"status":"ok","timestamp":1683033488823,"user_tz":-60,"elapsed":916,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"697a1c9f-fff5-4241-9174-87548b2b1dc3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D\n","2013-01-01  0.000000  0.000000 -0.648058 -2.680350\n","2013-01-02 -1.354008 -1.750013 -0.009807 -0.872537\n","2013-01-03 -0.977944 -0.449545 -0.545343 -1.075350\n","2013-01-04 -0.051502 -1.190048 -0.502404 -0.515585\n","2013-01-05 -0.503904 -1.634828 -0.749093 -0.481287\n","2013-01-06 -0.300019 -0.068817 -1.336835 -0.070851"],"text/html":["\n","  <div id=\"df-e043c03e-4473-4967-994d-1d90a6897ea2\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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<th>2013-01-04</th>\n","      <td>-0.051502</td>\n","      <td>-1.190048</td>\n","      <td>-0.502404</td>\n","      <td>-0.515585</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-05</th>\n","      <td>-0.503904</td>\n","      <td>-1.634828</td>\n","      <td>-0.749093</td>\n","      <td>-0.481287</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-06</th>\n","      <td>-0.300019</td>\n","      <td>-0.068817</td>\n","      <td>-1.336835</td>\n","      <td>-0.070851</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e043c03e-4473-4967-994d-1d90a6897ea2')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 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See the [Missing Data](https://pandas.pydata.org/docs/user_guide/missing_data.html#missing-data) section.\n","\n","Reindexing allows you to change/add/delete the index on a specified axis. This returns a copy of the data:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"7cMJ8FInWfuP","executionInfo":{"status":"ok","timestamp":1683033583458,"user_tz":-60,"elapsed":194,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"6ddd24af-7c34-4611-9fe7-01f64c8cb092"},"source":["df1 = df.reindex(index = dates[0:4], columns = list(df.columns) + ['E'])\n","df1.loc[dates[0] : dates[1], 'E'] = 1\n","df1"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D    E\n","2013-01-01  0.000000  0.000000 -0.648058  2.680350  1.0\n","2013-01-02 -1.354008 -1.750013 -0.009807  0.872537  1.0\n","2013-01-03  0.977944 -0.449545 -0.545343 -1.075350  NaN\n","2013-01-04 -0.051502 -1.190048  0.502404  0.515585  NaN"],"text/html":["\n","  <div id=\"df-f89f621a-3e25-424d-a3b1-143ffdb7ca71\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","      <th>E</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2013-01-01</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>-0.648058</td>\n","      <td>2.680350</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-02</th>\n","      <td>-1.354008</td>\n","      <td>-1.750013</td>\n","      <td>-0.009807</td>\n","      <td>0.872537</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-03</th>\n","      <td>0.977944</td>\n","      <td>-0.449545</td>\n","      <td>-0.545343</td>\n","      <td>-1.075350</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-04</th>\n","      <td>-0.051502</td>\n","      <td>-1.190048</td>\n","      <td>0.502404</td>\n","      <td>0.515585</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f89f621a-3e25-424d-a3b1-143ffdb7ca71')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-f89f621a-3e25-424d-a3b1-143ffdb7ca71 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"ngkcJsvTXF_9"},"source":["[`DataFrame.fillna()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.fillna.html#pandas.DataFrame.fillna) fills missing data:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"vQCm-C51XIW-","executionInfo":{"status":"ok","timestamp":1683033775214,"user_tz":-60,"elapsed":186,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"aa7f7d17-6996-4078-8506-bfda024d28d0"},"source":["df1.fillna(value=5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D    E\n","2013-01-01  0.000000  0.000000 -0.648058  2.680350  1.0\n","2013-01-02 -1.354008 -1.750013 -0.009807  0.872537  1.0\n","2013-01-03  0.977944 -0.449545 -0.545343 -1.075350  5.0\n","2013-01-04 -0.051502 -1.190048  0.502404  0.515585  5.0"],"text/html":["\n","  <div id=\"df-57891ac3-49cf-46ef-9006-383e67f2f7a8\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","  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<td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-04</th>\n","      <td>-0.051502</td>\n","      <td>-1.190048</td>\n","      <td>0.502404</td>\n","      <td>0.515585</td>\n","      <td>5.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57891ac3-49cf-46ef-9006-383e67f2f7a8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":43}]},{"cell_type":"markdown","metadata":{"id":"FQZ6G8GAXQcE"},"source":["[`isna()`](https://pandas.pydata.org/docs/reference/api/pandas.isna.html#pandas.isna) gets the boolean mask where values are `nan`:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"IQHuiz0kEbGi","executionInfo":{"status":"ok","timestamp":1683033915959,"user_tz":-60,"elapsed":256,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"adcd030c-9bd4-44dd-860a-ea556e016633"},"source":["pd.isna(df)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                A      B      C      D\n","2013-01-01  False  False  False  False\n","2013-01-02  False  False  False  False\n","2013-01-03  False  False  False  False\n","2013-01-04  False  False  False  False\n","2013-01-05  False  False  False  False\n","2013-01-06  False  False  False  False"],"text/html":["\n","  <div id=\"df-0e67c87f-90ec-4fdc-856b-13d907dc9ed0\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    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   <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-05</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-06</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e67c87f-90ec-4fdc-856b-13d907dc9ed0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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In addition, pandas automatically\n","broadcasts along the specified dimension"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cdahzzBWFTym","executionInfo":{"status":"ok","timestamp":1683033943197,"user_tz":-60,"elapsed":198,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"7750118d-c829-44e2-d08a-782238614484"},"source":["s = pd.Series([1, 3, 5, np.nan, 6, 8], index = dates).shift(2)\n","s"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2013-01-01    NaN\n","2013-01-02    NaN\n","2013-01-03    1.0\n","2013-01-04    3.0\n","2013-01-05    5.0\n","2013-01-06    NaN\n","Freq: D, dtype: float64"]},"metadata":{},"execution_count":47}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"ISLvLvN1F5xs","executionInfo":{"status":"ok","timestamp":1683033947692,"user_tz":-60,"elapsed":202,"user":{"displayName":"Marc 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<td>-6.634828</td>\n","      <td>-4.250907</td>\n","      <td>-5.481287</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-06</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b87208cc-b2da-479c-b3e3-2980287f5b5b')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 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data:"],"metadata":{"id":"Q7o2R8QrLKuG"}},{"cell_type":"code","source":["df.apply(np.cumsum)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"aEFEaXMULTf_","executionInfo":{"status":"ok","timestamp":1683042975706,"user_tz":-60,"elapsed":2,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"84655d86-e369-40ff-bbd7-6d181dee41ef"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   A         B         C         D\n","2013-01-01  0.000000  0.000000 -0.648058  2.680350\n","2013-01-02 -1.354008 -1.750013 -0.657865  3.552887\n","2013-01-03 -0.376064 -2.199558 -1.203208  2.477538\n","2013-01-04 -0.427566 -3.389606 -0.700804  2.993123\n","2013-01-05  0.076338 -5.024434  0.048288  2.511836\n","2013-01-06 -0.223681 -5.093252 -1.288547  2.582687"],"text/html":["\n","  <div id=\"df-e741780c-16dd-4952-b53f-9ee3f9909d63\">\n","    <div class=\"colab-df-container\">\n","      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<td>-1.203208</td>\n","      <td>2.477538</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-04</th>\n","      <td>-0.427566</td>\n","      <td>-3.389606</td>\n","      <td>-0.700804</td>\n","      <td>2.993123</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-05</th>\n","      <td>0.076338</td>\n","      <td>-5.024434</td>\n","      <td>0.048288</td>\n","      <td>2.511836</td>\n","    </tr>\n","    <tr>\n","      <th>2013-01-06</th>\n","      <td>-0.223681</td>\n","      <td>-5.093252</td>\n","      <td>-1.288547</td>\n","      <td>2.582687</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e741780c-16dd-4952-b53f-9ee3f9909d63')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":49}]},{"cell_type":"code","source":["df.apply(lambda x: x.max() - x.min())"],"metadata":{"id":"9q03tP3QLXDO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Histogramming\n","\n","See more at [Histogramming and Discretization](https://pandas.pydata.org/docs/user_guide/basics.html#basics-discretization)."],"metadata":{"id":"DP5b-E4qLahT"}},{"cell_type":"code","source":["s = pd.Series(np.random.randint(0, 7, size=10))\n","s"],"metadata":{"id":"g9Zmh-6ZLlmn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["s.value_counts()"],"metadata":{"id":"6KQ_XLUtLsFz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### String Methods\n","\n","Series is equipped with a set of string processing methods in the `str` attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in `str` generally uses [regular expressions](https://docs.python.org/3/library/re.html) by default (and in some cases always uses them). See more at [Vectorized String Methods](https://pandas.pydata.org/docs/user_guide/text.html#text-string-methods)."],"metadata":{"id":"_eNG0cphLtfN"}},{"cell_type":"code","source":["s = pd.Series([\"A\", \"B\", \"C\", \"Aaba\", \"Baca\", np.nan, \"CABA\", \"dog\", \"cat\"])\n","s.str.lower()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ci--jcmwL_na","executionInfo":{"status":"ok","timestamp":1683043146014,"user_tz":-60,"elapsed":190,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"2d13b71f-f2d1-40b2-f176-6a3ebd9b4539"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0       a\n","1       b\n","2       c\n","3    aaba\n","4    baca\n","5     NaN\n","6    caba\n","7     dog\n","8     cat\n","dtype: object"]},"metadata":{},"execution_count":50}]},{"cell_type":"markdown","metadata":{"id":"nuPJ-c8p921j"},"source":["## Merge"]},{"cell_type":"markdown","metadata":{"id":"kbKhn8BC995O"},"source":["### Concat\n","\n","pandas provides various facilities for easily combining together Series and DataFrame objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.\n","\n","See the [Merging section](https://pandas.pydata.org/docs/user_guide/merging.html#merging).\n","\n","Concatenating pandas objects together along an axis with [`concat()`](https://pandas.pydata.org/docs/reference/api/pandas.concat.html#pandas.concat):"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"oldvT8Wb-ari","executionInfo":{"status":"ok","timestamp":1683046755392,"user_tz":-60,"elapsed":193,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"10a78ccf-2287-4145-d5b3-f061e37d0619"},"source":["df = pd.DataFrame(np.random.randn(10, 4))\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          0         1         2         3\n","0 -0.969243 -0.085699  1.123780  0.031939\n","1  1.497772  0.719339 -0.487569 -0.364224\n","2 -2.156861  0.642053  0.314071 -0.031905\n","3 -0.809775  0.101392 -1.243223  1.834187\n","4 -0.284252  0.111199  0.459905  1.166347\n","5 -0.538575  0.460858  0.138357 -1.084389\n","6  2.206184 -0.374048 -0.397699 -0.412602\n","7  0.303301 -1.769001 -0.800805 -0.434386\n","8  0.036679 -0.901170 -0.446103 -1.636565\n","9 -0.023259  0.499429 -0.015833 -0.597605"],"text/html":["\n","  <div id=\"df-f5e12c03-f9da-4d5d-9e4e-1fc916894c46\">\n","    <div class=\"colab-df-container\">\n","      <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>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-0.969243</td>\n","      <td>-0.085699</td>\n","      <td>1.123780</td>\n","      <td>0.031939</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.497772</td>\n","      <td>0.719339</td>\n","      <td>-0.487569</td>\n","      <td>-0.364224</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-2.156861</td>\n","      <td>0.642053</td>\n","      <td>0.314071</td>\n","      <td>-0.031905</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-0.809775</td>\n","      <td>0.101392</td>\n","      <td>-1.243223</td>\n","      <td>1.834187</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-0.284252</td>\n","      <td>0.111199</td>\n","      <td>0.459905</td>\n","      <td>1.166347</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>-0.538575</td>\n","      <td>0.460858</td>\n","      <td>0.138357</td>\n","      <td>-1.084389</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2.206184</td>\n","      <td>-0.374048</td>\n","      <td>-0.397699</td>\n","      <td>-0.412602</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.303301</td>\n","      <td>-1.769001</td>\n","      <td>-0.800805</td>\n","      <td>-0.434386</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.036679</td>\n","      <td>-0.901170</td>\n","      <td>-0.446103</td>\n","      <td>-1.636565</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>-0.023259</td>\n","      <td>0.499429</td>\n","      <td>-0.015833</td>\n","      <td>-0.597605</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5e12c03-f9da-4d5d-9e4e-1fc916894c46')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      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google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-f5e12c03-f9da-4d5d-9e4e-1fc916894c46');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":51}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":359},"id":"0qLkrJ8vARaE","executionInfo":{"status":"ok","timestamp":1606804421130,"user_tz":-300,"elapsed":1077,"user":{"displayName":"Mr Zohaib","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgxQElLKyfTWGRBbBXJlJlAQ_81Yh6nbwUebhYdNg=s64","userId":"05982278651474616930"}},"outputId":"13a71b07-b319-4f71-8984-ea35f0415da0"},"source":["# break it onto pieces\n","pieces = [df[:3], df[3:7], df[7:]]\n","\n","pd.concat(pieces)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"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>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.102795</td>\n","      <td>-0.909272</td>\n","      <td>1.576964</td>\n","      <td>-0.830619</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.060983</td>\n","      <td>-0.190275</td>\n","      <td>-0.061194</td>\n","      <td>-1.224478</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.018114</td>\n","      <td>-0.585449</td>\n","      <td>-1.186918</td>\n","      <td>1.190355</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-0.132577</td>\n","      <td>-1.077505</td>\n","      <td>-0.444223</td>\n","      <td>0.182243</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.385212</td>\n","      <td>-1.229749</td>\n","      <td>1.151778</td>\n","      <td>-0.290500</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.274542</td>\n","      <td>-1.689567</td>\n","      <td>-1.202591</td>\n","      <td>-1.391290</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>-0.423694</td>\n","      <td>1.036054</td>\n","      <td>1.143841</td>\n","      <td>-1.501955</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.435165</td>\n","      <td>-0.257491</td>\n","      <td>1.393511</td>\n","      <td>0.827656</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.435526</td>\n","      <td>0.732379</td>\n","      <td>0.766487</td>\n","      <td>-1.109552</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>-1.471742</td>\n","      <td>-1.185060</td>\n","      <td>0.577455</td>\n","      <td>-0.149555</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          0         1         2         3\n","0  0.102795 -0.909272  1.576964 -0.830619\n","1 -0.060983 -0.190275 -0.061194 -1.224478\n","2  0.018114 -0.585449 -1.186918  1.190355\n","3 -0.132577 -1.077505 -0.444223  0.182243\n","4  0.385212 -1.229749  1.151778 -0.290500\n","5  0.274542 -1.689567 -1.202591 -1.391290\n","6 -0.423694  1.036054  1.143841 -1.501955\n","7  0.435165 -0.257491  1.393511  0.827656\n","8  0.435526  0.732379  0.766487 -1.109552\n","9 -1.471742 -1.185060  0.577455 -0.149555"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","source":["NOTE: Adding a column to a [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame) is relatively fast. However, adding a row requires a copy, and may be expensive. We recommend passing a pre-built list of records to the [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame) constructor instead of building a [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame) by iteratively appending records to it."],"metadata":{"id":"iZ0Ll-LPbd3W"}},{"cell_type":"markdown","source":["### Join\n","\n","[`merge()`](https://pandas.pydata.org/docs/reference/api/pandas.merge.html#pandas.merge) enables SQL style join types along specific columns. See the [Database style joining](https://pandas.pydata.org/docs/user_guide/merging.html#merging-join) section."],"metadata":{"id":"T4Q3Z0_3aAIS"}},{"cell_type":"code","source":["left = pd.DataFrame({\"key\": [\"foo\", \"foo\"], \"lval\": [1, 2]})\n","right = pd.DataFrame({\"key\": [\"foo\", \"foo\"], \"rval\": [4, 5]})\n","print(left)\n","print(right)\n","pd.merge(left, right, on=\"key\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"CDC3F5KYaS7f","executionInfo":{"status":"ok","timestamp":1683046978738,"user_tz":-60,"elapsed":260,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"b594781f-1567-42af-fb6b-47676cc9ea0c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["   key  lval\n","0  foo     1\n","1  foo     2\n","   key  rval\n","0  foo     4\n","1  foo     5\n"]},{"output_type":"execute_result","data":{"text/plain":["   key  lval  rval\n","0  foo     1     4\n","1  foo     1     5\n","2  foo     2     4\n","3  foo     2     5"],"text/html":["\n","  <div id=\"df-3c162e7d-a4e9-42e7-b256-c9e99714cbf6\">\n","    <div class=\"colab-df-container\">\n","      <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>key</th>\n","      <th>lval</th>\n","      <th>rval</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>foo</td>\n","      <td>1</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>foo</td>\n","      <td>1</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>foo</td>\n","      <td>2</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>foo</td>\n","      <td>2</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c162e7d-a4e9-42e7-b256-c9e99714cbf6')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-3c162e7d-a4e9-42e7-b256-c9e99714cbf6 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":54}]},{"cell_type":"markdown","metadata":{"id":"TS9OW23pBP2X"},"source":["Adding a column to a `DataFrame` is relatively fast. However adding a row requires a copy, and may be expensive. It is recorecommended passing a pre-build list of records to the `DataFrame` constructor instead of buildinga `DataFrame` by iteratively appending records to it."]},{"cell_type":"markdown","source":["Another example that can be given is:"],"metadata":{"id":"Fy-ys5kea3P1"}},{"cell_type":"code","source":["left = pd.DataFrame({\"key\": [\"foo\", \"bar\"], \"lval\": [1, 2]})\n","right = pd.DataFrame({\"key\": [\"foo\", \"bar\"], \"rval\": [4, 5]})\n","print(left)\n","print(right)\n","pd.merge(left, right, on=\"key\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":216},"id":"EYkJw-ura-GQ","executionInfo":{"status":"ok","timestamp":1683047153135,"user_tz":-60,"elapsed":441,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"59faf316-4350-4bd0-bc12-e9af22b62861"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["   key  lval\n","0  foo     1\n","1  bar     2\n","   key  rval\n","0  foo     4\n","1  bar     5\n"]},{"output_type":"execute_result","data":{"text/plain":["   key  lval  rval\n","0  foo     1     4\n","1  bar     2     5"],"text/html":["\n","  <div id=\"df-90a6f4df-4aac-4e77-a28f-ffc59801c8f2\">\n","    <div class=\"colab-df-container\">\n","      <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>key</th>\n","      <th>lval</th>\n","      <th>rval</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>foo</td>\n","      <td>1</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>bar</td>\n","      <td>2</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90a6f4df-4aac-4e77-a28f-ffc59801c8f2')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-90a6f4df-4aac-4e77-a28f-ffc59801c8f2 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":55}]},{"cell_type":"markdown","metadata":{"id":"ulumIOwwD7ea"},"source":["## Grouping\n","\n","By 'group by' we are referring to a process invloving one or more of the following steps:\n","\n","\n","*   **Splitting** the data into group base on some criteria\n","*   **Applying** a function to each group independently\n","\n","*   **Combining** the results into a data structure\n","\n","See the [Grouping section](https://pandas.pydata.org/docs/user_guide/groupby.html#groupby).\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"VsWP5stGEtjH","executionInfo":{"status":"ok","timestamp":1683049711818,"user_tz":-60,"elapsed":213,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"149f4b0f-5eab-4f61-b47c-5d36985bfd18"},"source":["df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',\n","                         'foo', 'bar', 'foo', 'bar'],\n","                   'B': ['one', 'one', 'two', 'three',\n","                         'two', 'two', 'one', 'three'],\n","                   'C': np.random.randn(8),\n","                   'D': np.random.randn(8)})\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     A      B         C         D\n","0  foo    one -0.848117  0.290586\n","1  bar    one  1.763683 -0.199249\n","2  foo    two -0.115365 -0.528739\n","3  bar  three  0.657117 -0.188029\n","4  foo    two -0.142749 -0.648729\n","5  bar    two  0.696670 -1.187473\n","6  foo    one -0.617058 -0.621837\n","7  bar  three -1.294211 -0.675882"],"text/html":["\n","  <div id=\"df-d0ab84cd-f9aa-4699-b4c2-e9e82e0f92ac\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>foo</td>\n","      <td>one</td>\n","      <td>-0.848117</td>\n","      <td>0.290586</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>bar</td>\n","      <td>one</td>\n","      <td>1.763683</td>\n","      <td>-0.199249</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>foo</td>\n","      <td>two</td>\n","      <td>-0.115365</td>\n","      <td>-0.528739</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>bar</td>\n","      <td>three</td>\n","      <td>0.657117</td>\n","      <td>-0.188029</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>foo</td>\n","      <td>two</td>\n","      <td>-0.142749</td>\n","      <td>-0.648729</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>bar</td>\n","      <td>two</td>\n","      <td>0.696670</td>\n","      <td>-1.187473</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>foo</td>\n","      <td>one</td>\n","      <td>-0.617058</td>\n","      <td>-0.621837</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>bar</td>\n","      <td>three</td>\n","      <td>-1.294211</td>\n","      <td>-0.675882</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0ab84cd-f9aa-4699-b4c2-e9e82e0f92ac')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":59}]},{"cell_type":"markdown","metadata":{"id":"Vi5aIZy_FheS"},"source":["Grouping and then applying the `sum()` funcion to the resulting group:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"M1rPBWjfFnRt","executionInfo":{"status":"ok","timestamp":1683049758510,"user_tz":-60,"elapsed":395,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"9e15c55a-29a8-4d5e-f7cc-28561ccd875b"},"source":["df.groupby('A')[[\"C\", \"D\"]].sum()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            C         D\n","A                      \n","bar  1.823258 -2.250633\n","foo -1.723289 -1.508719"],"text/html":["\n","  <div id=\"df-358a9fed-aa62-4d70-ab72-158553d170e8\">\n","    <div class=\"colab-df-container\">\n","      <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>C</th>\n","      <th>D</th>\n","    </tr>\n","    <tr>\n","      <th>A</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>bar</th>\n","      <td>1.823258</td>\n","      <td>-2.250633</td>\n","    </tr>\n","    <tr>\n","      <th>foo</th>\n","      <td>-1.723289</td>\n","      <td>-1.508719</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-358a9fed-aa62-4d70-ab72-158553d170e8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-358a9fed-aa62-4d70-ab72-158553d170e8 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-358a9fed-aa62-4d70-ab72-158553d170e8');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":63}]},{"cell_type":"markdown","metadata":{"id":"jW8gpuWFGk74"},"source":["Grouping by multiple columns forms a hierarchical index, and again we can apply the `sum()` function:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"af4RVR0xHOS_","executionInfo":{"status":"ok","timestamp":1683049856162,"user_tz":-60,"elapsed":261,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"44fd7d8c-fa11-4f09-bbf6-27375c658506"},"source":["df.groupby([\"A\", \"B\"]).sum()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                  C         D\n","A   B                        \n","bar one    1.763683 -0.199249\n","    three -0.637095 -0.863911\n","    two    0.696670 -1.187473\n","foo one   -1.465175 -0.331251\n","    two   -0.258114 -1.177468"],"text/html":["\n","  <div id=\"df-e51747c4-1c06-4a78-9682-e92b9f410d23\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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<tr>\n","      <th>two</th>\n","      <td>-0.258114</td>\n","      <td>-1.177468</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e51747c4-1c06-4a78-9682-e92b9f410d23')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-e51747c4-1c06-4a78-9682-e92b9f410d23');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":65}]},{"cell_type":"markdown","source":["## Reshaping\n","\n","See the sections on [Hierarchical Indexing](https://pandas.pydata.org/docs/user_guide/advanced.html#advanced-hierarchical) and [Reshaping](https://pandas.pydata.org/docs/user_guide/reshaping.html#reshaping-stacking)."],"metadata":{"id":"ZAzp6zt7lvPk"}},{"cell_type":"markdown","source":["### Stack"],"metadata":{"id":"OPgRTWEJmPAZ"}},{"cell_type":"code","source":["tuples = list(\n","    zip(\n","        [\"bar\", \"bar\", \"baz\", \"baz\", \"foo\", \"foo\", \"qux\", \"qux\"],\n","        [\"one\", \"two\", \"one\", \"two\", \"one\", \"two\", \"one\", \"two\"],\n","    )\n",")\n","\n","index = pd.MultiIndex.from_tuples(tuples, names=[\"first\", \"second\"])\n","\n","df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=[\"A\", \"B\"])\n","df2 = df[:4]\n","df2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"NG-9iKV7mEJu","executionInfo":{"status":"ok","timestamp":1683050036300,"user_tz":-60,"elapsed":309,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"2aea5aba-1799-46fa-a2e7-3541005cba37"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                     A         B\n","first second                    \n","bar   one    -0.066512 -1.932089\n","      two     0.738722  2.306815\n","baz   one     0.503809 -0.561800\n","      two     2.477166  1.416968"],"text/html":["\n","  <div id=\"df-ebb136dc-7c9b-4b74-87c6-417bfc2bf1bd\">\n","    <div class=\"colab-df-container\">\n","      <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></th>\n","      <th>A</th>\n","      <th>B</th>\n","    </tr>\n","    <tr>\n","      <th>first</th>\n","      <th>second</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">bar</th>\n","      <th>one</th>\n","      <td>-0.066512</td>\n","      <td>-1.932089</td>\n","    </tr>\n","    <tr>\n","      <th>two</th>\n","      <td>0.738722</td>\n","      <td>2.306815</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">baz</th>\n","      <th>one</th>\n","      <td>0.503809</td>\n","      <td>-0.561800</td>\n","    </tr>\n","    <tr>\n","      <th>two</th>\n","      <td>2.477166</td>\n","      <td>1.416968</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebb136dc-7c9b-4b74-87c6-417bfc2bf1bd')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-ebb136dc-7c9b-4b74-87c6-417bfc2bf1bd');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":67}]},{"cell_type":"markdown","source":["The [`stack()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.stack.html#pandas.DataFrame.stack) method “compresses” a level in the DataFrame’s columns:\n","\n"],"metadata":{"id":"aVLdutDGmalo"}},{"cell_type":"code","source":["stacked = df2.stack()\n","stacked"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2_TTGLgEmkNP","executionInfo":{"status":"ok","timestamp":1683050095597,"user_tz":-60,"elapsed":456,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"75ce7c0d-7eef-45a1-ea09-5edcb47e96c9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["first  second   \n","bar    one     A   -0.066512\n","               B   -1.932089\n","       two     A    0.738722\n","               B    2.306815\n","baz    one     A    0.503809\n","               B   -0.561800\n","       two     A    2.477166\n","               B    1.416968\n","dtype: float64"]},"metadata":{},"execution_count":68}]},{"cell_type":"markdown","source":["With a “stacked” DataFrame or Series (having a [MultiIndex](https://pandas.pydata.org/docs/reference/api/pandas.MultiIndex.html#pandas.MultiIndex) as the `index`), the inverse operation of [`stack()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.stack.html#pandas.DataFrame.stack) is [`unstack()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.unstack.html#pandas.DataFrame.unstack), which by default unstacks the **last level**:"],"metadata":{"id":"yYUQc9DJmpie"}},{"cell_type":"code","source":["stacked.unstack()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"eWHGOsoQnD3y","executionInfo":{"status":"ok","timestamp":1683050247886,"user_tz":-60,"elapsed":341,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"71f71b02-c2f8-472e-fe7f-f6ac8218ee49"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                     A         B\n","first second                    \n","bar   one    -0.066512 -1.932089\n","      two     0.738722  2.306815\n","baz   one     0.503809 -0.561800\n","      two     2.477166  1.416968"],"text/html":["\n","  <div id=\"df-a6ff8dc7-ec41-4d09-8064-c780b83301bb\">\n","    <div class=\"colab-df-container\">\n","      <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></th>\n","      <th>A</th>\n","      <th>B</th>\n","    </tr>\n","    <tr>\n","      <th>first</th>\n","      <th>second</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">bar</th>\n","      <th>one</th>\n","      <td>-0.066512</td>\n","      <td>-1.932089</td>\n","    </tr>\n","    <tr>\n","      <th>two</th>\n","      <td>0.738722</td>\n","      <td>2.306815</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">baz</th>\n","      <th>one</th>\n","      <td>0.503809</td>\n","      <td>-0.561800</td>\n","    </tr>\n","    <tr>\n","      <th>two</th>\n","      <td>2.477166</td>\n","      <td>1.416968</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6ff8dc7-ec41-4d09-8064-c780b83301bb')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":69}]},{"cell_type":"code","source":["stacked.unstack(1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"cp3U2yHcnIqg","executionInfo":{"status":"ok","timestamp":1683050253442,"user_tz":-60,"elapsed":235,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"6a097308-ac2a-4fbe-b753-7fd148dd78e2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["second        one       two\n","first                      \n","bar   A -0.066512  0.738722\n","      B -1.932089  2.306815\n","baz   A  0.503809  2.477166\n","      B -0.561800  1.416968"],"text/html":["\n","  <div id=\"df-f7f4accd-a9b0-495e-8243-b2930e76456a\">\n","    <div class=\"colab-df-container\">\n","      <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>second</th>\n","      <th>one</th>\n","      <th>two</th>\n","    </tr>\n","    <tr>\n","      <th>first</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">bar</th>\n","      <th>A</th>\n","      <td>-0.066512</td>\n","      <td>0.738722</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>-1.932089</td>\n","      <td>2.306815</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">baz</th>\n","      <th>A</th>\n","      <td>0.503809</td>\n","      <td>2.477166</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>-0.561800</td>\n","      <td>1.416968</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7f4accd-a9b0-495e-8243-b2930e76456a')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-f7f4accd-a9b0-495e-8243-b2930e76456a button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":["stacked.unstack(0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"kLAp8MuknJgR","executionInfo":{"status":"ok","timestamp":1683050256779,"user_tz":-60,"elapsed":238,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"fb49cc27-8fc8-48cc-cdf7-42958d6a48b4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["first          bar       baz\n","second                      \n","one    A -0.066512  0.503809\n","       B -1.932089 -0.561800\n","two    A  0.738722  2.477166\n","       B  2.306815  1.416968"],"text/html":["\n","  <div id=\"df-fe40a0a2-1b0d-4cbc-a2b9-5b910e54f957\">\n","    <div class=\"colab-df-container\">\n","      <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>first</th>\n","      <th>bar</th>\n","      <th>baz</th>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">one</th>\n","      <th>A</th>\n","      <td>-0.066512</td>\n","      <td>0.503809</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>-1.932089</td>\n","      <td>-0.561800</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">two</th>\n","      <th>A</th>\n","      <td>0.738722</td>\n","      <td>2.477166</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>2.306815</td>\n","      <td>1.416968</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe40a0a2-1b0d-4cbc-a2b9-5b910e54f957')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-fe40a0a2-1b0d-4cbc-a2b9-5b910e54f957 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-fe40a0a2-1b0d-4cbc-a2b9-5b910e54f957');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":71}]},{"cell_type":"markdown","source":["### Pivot tables\n","\n","See the section on [Pivot Tables](https://pandas.pydata.org/docs/user_guide/reshaping.html#reshaping-pivot)."],"metadata":{"id":"YqXELQdHnRmT"}},{"cell_type":"code","source":["df = pd.DataFrame(\n","    {\n","        \"A\": [\"one\", \"one\", \"two\", \"three\"] * 3,\n","        \"B\": [\"A\", \"B\", \"C\"] * 4,\n","        \"C\": [\"foo\", \"foo\", \"foo\", \"bar\", \"bar\", \"bar\"] * 2,\n","        \"D\": np.random.randn(12),\n","        \"E\": np.random.randn(12),\n","    }\n",")\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":426},"id":"e9Z_RKTjnUD_","executionInfo":{"status":"ok","timestamp":1683050322055,"user_tz":-60,"elapsed":3,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"14dcfeea-2bc8-4137-b311-6c125b657a87"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        A  B    C         D         E\n","0     one  A  foo -0.608827 -0.172586\n","1     one  B  foo -1.014371  0.415117\n","2     two  C  foo -0.837770 -0.597578\n","3   three  A  bar  1.555443 -1.772584\n","4     one  B  bar  1.239417  0.566768\n","5     one  C  bar -0.690746 -0.347901\n","6     two  A  foo  1.130389 -0.317614\n","7   three  B  foo -0.355813  0.687121\n","8     one  C  foo -1.306915  0.784907\n","9     one  A  bar -0.134371 -0.687810\n","10    two  B  bar -1.430566 -0.524227\n","11  three  C  bar  0.032900  1.493735"],"text/html":["\n","  <div id=\"df-5eea9244-167c-4453-a2a5-efd5bf908b38\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","      <th>E</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>one</td>\n","      <td>A</td>\n","      <td>foo</td>\n","      <td>-0.608827</td>\n","      <td>-0.172586</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>one</td>\n","      <td>B</td>\n","      <td>foo</td>\n","      <td>-1.014371</td>\n","      <td>0.415117</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>two</td>\n","      <td>C</td>\n","      <td>foo</td>\n","      <td>-0.837770</td>\n","      <td>-0.597578</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>three</td>\n","      <td>A</td>\n","      <td>bar</td>\n","      <td>1.555443</td>\n","      <td>-1.772584</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>one</td>\n","      <td>B</td>\n","      <td>bar</td>\n","      <td>1.239417</td>\n","      <td>0.566768</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>one</td>\n","      <td>C</td>\n","      <td>bar</td>\n","      <td>-0.690746</td>\n","      <td>-0.347901</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>two</td>\n","      <td>A</td>\n","      <td>foo</td>\n","      <td>1.130389</td>\n","      <td>-0.317614</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>three</td>\n","      <td>B</td>\n","      <td>foo</td>\n","      <td>-0.355813</td>\n","      <td>0.687121</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>one</td>\n","      <td>C</td>\n","      <td>foo</td>\n","      <td>-1.306915</td>\n","      <td>0.784907</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>one</td>\n","      <td>A</td>\n","      <td>bar</td>\n","      <td>-0.134371</td>\n","      <td>-0.687810</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>two</td>\n","      <td>B</td>\n","      <td>bar</td>\n","      <td>-1.430566</td>\n","      <td>-0.524227</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>three</td>\n","      <td>C</td>\n","      <td>bar</td>\n","      <td>0.032900</td>\n","      <td>1.493735</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":72}]},{"cell_type":"markdown","source":["[`pivot_table()`](https://pandas.pydata.org/docs/reference/api/pandas.pivot_table.html#pandas.pivot_table) pivots a [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame) specifying the `values`, `index`, and `columns`\n","\n"],"metadata":{"id":"FO9FwVylniBI"}},{"cell_type":"code","source":["pd.pivot_table(df, values=\"D\", index=[\"A\", \"B\"], columns=[\"C\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"Zh4MUAFnn2nh","executionInfo":{"status":"ok","timestamp":1683050458825,"user_tz":-60,"elapsed":159,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"2a57a2d0-954b-411a-af94-e14472aaaff8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["C             bar       foo\n","A     B                    \n","one   A -0.134371 -0.608827\n","      B  1.239417 -1.014371\n","      C -0.690746 -1.306915\n","three A  1.555443       NaN\n","      B       NaN -0.355813\n","      C  0.032900       NaN\n","two   A       NaN  1.130389\n","      B -1.430566       NaN\n","      C       NaN -0.837770"],"text/html":["\n","  <div id=\"df-08d890ac-6a34-489f-b1c5-a12765c9f3cb\">\n","    <div class=\"colab-df-container\">\n","      <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>C</th>\n","      <th>bar</th>\n","      <th>foo</th>\n","    </tr>\n","    <tr>\n","      <th>A</th>\n","      <th>B</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">one</th>\n","      <th>A</th>\n","      <td>-0.134371</td>\n","      <td>-0.608827</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>1.239417</td>\n","      <td>-1.014371</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>-0.690746</td>\n","      <td>-1.306915</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">three</th>\n","      <th>A</th>\n","      <td>1.555443</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>NaN</td>\n","      <td>-0.355813</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>0.032900</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">two</th>\n","      <th>A</th>\n","      <td>NaN</td>\n","      <td>1.130389</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>-1.430566</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>NaN</td>\n","      <td>-0.837770</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08d890ac-6a34-489f-b1c5-a12765c9f3cb')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-08d890ac-6a34-489f-b1c5-a12765c9f3cb');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":73}]},{"cell_type":"markdown","source":["## Time series\n","\n","pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the [Time Series](https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries) section."],"metadata":{"id":"e15ssNmI3sCk"}},{"cell_type":"code","source":["rng = pd.date_range(\"1/1/2012\", periods=100, freq=\"S\")\n","ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)\n","ts.resample(\"5Min\").sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WojUs_8b4EYY","executionInfo":{"status":"ok","timestamp":1683054714914,"user_tz":-60,"elapsed":201,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"d5a26f4b-e9b5-4d98-f012-d75daf497a1f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2012-01-01    23852\n","Freq: 5T, dtype: int64"]},"metadata":{},"execution_count":74}]},{"cell_type":"markdown","source":["[`Series.tz_localize()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.tz_localize.html#pandas.Series.tz_localize) localizes a time series to a time zone:"],"metadata":{"id":"Hxwdchsp4RjK"}},{"cell_type":"code","source":["rng = pd.date_range(\"3/6/2012 00:00\", periods=5, freq=\"D\")\n","ts = pd.Series(np.random.randn(len(rng)), rng)\n","print(ts, \"\\n\")\n","ts_utc = ts.tz_localize(\"UTC\")\n","ts_utc"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cHJ75aBT4Ysr","executionInfo":{"status":"ok","timestamp":1683054854444,"user_tz":-60,"elapsed":321,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"d99275ce-9f32-4947-d2e9-1fe3375c4ee1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2012-03-06    0.406571\n","2012-03-07    2.276888\n","2012-03-08    0.443980\n","2012-03-09   -0.598940\n","2012-03-10   -1.531285\n","Freq: D, dtype: float64 \n","\n"]},{"output_type":"execute_result","data":{"text/plain":["2012-03-06 00:00:00+00:00    0.406571\n","2012-03-07 00:00:00+00:00    2.276888\n","2012-03-08 00:00:00+00:00    0.443980\n","2012-03-09 00:00:00+00:00   -0.598940\n","2012-03-10 00:00:00+00:00   -1.531285\n","Freq: D, dtype: float64"]},"metadata":{},"execution_count":79}]},{"cell_type":"markdown","source":["Converting between time span representations:"],"metadata":{"id":"gD455NQT4z1S"}},{"cell_type":"code","source":["rng = pd.date_range(\"1/1/2012\", periods=5, freq=\"M\")\n","ts = pd.Series(np.random.randn(len(rng)), index=rng)\n","ps = ts.to_period()\n","ps"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6ByC55m441XE","executionInfo":{"status":"ok","timestamp":1683054922550,"user_tz":-60,"elapsed":3,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"774d3024-e167-4efb-d889-27393909f4e3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2012-01   -1.318314\n","2012-02   -0.986512\n","2012-03    0.186429\n","2012-04   -0.298471\n","2012-05    0.211679\n","Freq: M, dtype: float64"]},"metadata":{},"execution_count":80}]},{"cell_type":"code","source":["ps.to_timestamp()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cj1T_ySB4_zH","executionInfo":{"status":"ok","timestamp":1683054928568,"user_tz":-60,"elapsed":273,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"ae235b95-f2bb-4d0d-e979-57ce7279e321"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2012-01-01   -1.318314\n","2012-02-01   -0.986512\n","2012-03-01    0.186429\n","2012-04-01   -0.298471\n","2012-05-01    0.211679\n","Freq: MS, dtype: float64"]},"metadata":{},"execution_count":81}]},{"cell_type":"markdown","source":["Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:"],"metadata":{"id":"XnWJymgk5I20"}},{"cell_type":"code","source":["prng = pd.period_range(\"1990Q1\", \"2000Q4\", freq=\"Q-NOV\")\n","ts = pd.Series(np.random.randn(len(prng)), prng)\n","ts.index = (prng.asfreq(\"M\", \"e\") + 1).asfreq(\"H\", \"s\") + 9\n","ts.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6NpIopTU5KNQ","executionInfo":{"status":"ok","timestamp":1683055002175,"user_tz":-60,"elapsed":175,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"cac217f5-af27-4d44-b25b-5250927458f9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1990-03-01 09:00   -0.647442\n","1990-06-01 09:00    0.654381\n","1990-09-01 09:00    1.104538\n","1990-12-01 09:00   -0.587950\n","1991-03-01 09:00    0.213284\n","Freq: H, dtype: float64"]},"metadata":{},"execution_count":82}]},{"cell_type":"markdown","source":["## Categoricals\n","\n","pandas can include categorical data in a [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame). For full docs, see the [categorical introduction](https://pandas.pydata.org/docs/user_guide/categorical.html#categorical) and the [API documentation](https://pandas.pydata.org/docs/reference/arrays.html#api-arrays-categorical)."],"metadata":{"id":"JNLZHB6f5ZUq"}},{"cell_type":"code","source":["df = pd.DataFrame(\n","    {\"id\": [1, 2, 3, 4, 5, 6], \"raw_grade\": [\"a\", \"b\", \"b\", \"a\", \"a\", \"e\"]}\n",")\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"mApkNjzF5sRk","executionInfo":{"status":"ok","timestamp":1683055182802,"user_tz":-60,"elapsed":309,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"fc43f7c8-ab01-4e6d-e112-08b2c17c3833"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   id raw_grade\n","0   1         a\n","1   2         b\n","2   3         b\n","3   4         a\n","4   5         a\n","5   6         e"],"text/html":["\n","  <div id=\"df-29075b9a-c896-45a9-b344-94e8bc7c921e\">\n","    <div class=\"colab-df-container\">\n","      <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>id</th>\n","      <th>raw_grade</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>a</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>b</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>b</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>a</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>a</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>e</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29075b9a-c896-45a9-b344-94e8bc7c921e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-29075b9a-c896-45a9-b344-94e8bc7c921e button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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df[\"raw_grade\"].astype(\"category\")\n","df[\"grade\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Lkam_pMR6DJP","executionInfo":{"status":"ok","timestamp":1683055223428,"user_tz":-60,"elapsed":325,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"06caa441-87d1-4f2d-bc72-8b90fdb4ce24"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    a\n","1    b\n","2    b\n","3    a\n","4    a\n","5    e\n","Name: grade, dtype: category\n","Categories (3, object): ['a', 'b', 'e']"]},"metadata":{},"execution_count":85}]},{"cell_type":"markdown","source":["Rename the categories to more meaningful names:"],"metadata":{"id":"UrkfwFMH6LkM"}},{"cell_type":"code","source":["new_categories = [\"very good\", \"good\", \"very bad\"]\n","df[\"grade\"] = df[\"grade\"].cat.rename_categories(new_categories)\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"xLrZuwAh6ObR","executionInfo":{"status":"ok","timestamp":1683055263486,"user_tz":-60,"elapsed":185,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"7b08af30-ac6c-44f7-c206-84635ca9d903"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   id raw_grade      grade\n","0   1         a  very good\n","1   2         b       good\n","2   3         b       good\n","3   4         a  very good\n","4   5         a  very good\n","5   6         e   very bad"],"text/html":["\n","  <div id=\"df-72d3cdbd-7a20-46e4-b1d1-4f9eb1e78705\">\n","    <div class=\"colab-df-container\">\n","      <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","    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bad</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72d3cdbd-7a20-46e4-b1d1-4f9eb1e78705')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      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default):"],"metadata":{"id":"OvX2V0zH6VPw"}},{"cell_type":"code","source":["df[\"grade\"] = df[\"grade\"].cat.set_categories(\n","    [\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"]\n",")\n","df[\"grade\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NDbg7qXA6osC","executionInfo":{"status":"ok","timestamp":1683055365307,"user_tz":-60,"elapsed":263,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"81b07c1e-3d41-4599-af42-cd5861f0c590"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    very good\n","1         good\n","2         good\n","3    very good\n","4    very good\n","5     very bad\n","Name: grade, dtype: category\n","Categories (5, object): ['very bad', 'bad', 'medium', 'good', 'very good']"]},"metadata":{},"execution_count":88}]},{"cell_type":"markdown","source":["Sorting is per order in the categories, not lexical order:"],"metadata":{"id":"E3TJfoED6xBx"}},{"cell_type":"code","source":["df.sort_values(by=\"grade\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"ZfPlZRgj6ymT","executionInfo":{"status":"ok","timestamp":1683055424787,"user_tz":-60,"elapsed":428,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"f7e9cf72-d0ba-4b43-afa7-eb913515214a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   id raw_grade      grade\n","5   6         e   very bad\n","1   2         b       good\n","2   3         b       good\n","0   1         a  very good\n","3   4         a  very good\n","4   5         a  very good"],"text/html":["\n","  <div id=\"df-aa05a750-6c90-4689-a195-6b456a89e0f1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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categories:"],"metadata":{"id":"PSUHrkdM68uv"}},{"cell_type":"code","source":["df.groupby(\"grade\").size()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fhKbknQq7AL2","executionInfo":{"status":"ok","timestamp":1683055459854,"user_tz":-60,"elapsed":852,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"f0e6366e-9713-45c9-a627-3b9362f0a5e5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["grade\n","very bad     1\n","bad          0\n","medium       0\n","good         2\n","very good    3\n","dtype: int64"]},"metadata":{},"execution_count":91}]},{"cell_type":"markdown","source":["## Plotting\n","\n","See the [Plotting](https://pandas.pydata.org/docs/user_guide/visualization.html#visualization) docs.\n","\n","We use the standard convention for referencing the matplotlib API:"],"metadata":{"id":"GTIkhd1e7JYH"}},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","plt.close(\"all\")\n","\n","ts = pd.Series(np.random.randn(1000), index=pd.date_range(\"1/1/2000\", periods=1000))\n","ts = ts.cumsum()\n","ts.plot();"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"TR9e_l8E7ZUU","executionInfo":{"status":"ok","timestamp":1683055580366,"user_tz":-60,"elapsed":894,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"d909f068-3f95-457d-f47f-dd612b074d71"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["If running under Jupyter Notebook, the plot will appear on [`plot()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html#pandas.Series.plot). Otherwise use [`matplotlib.pyplot.show`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.show.html) to show it or [`matplotlib.pyplot.savefig`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.savefig.html) to write it to a file.\n","\n","On a DataFrame, the [`plot()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot) method is a convenience to plot all of the columns with labels:"],"metadata":{"id":"FatFDTec7kQV"}},{"cell_type":"code","source":["df = pd.DataFrame(\n","    np.random.randn(1000, 4), index=ts.index, columns=[\"A\", \"B\", \"C\", \"D\"]\n",")\n","\n","df = df.cumsum()\n","plt.figure();\n","df.plot();\n","plt.legend(loc='best');"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":463},"id":"ikizbGQ78Ekf","executionInfo":{"status":"ok","timestamp":1683055744274,"user_tz":-60,"elapsed":1082,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"06dd0e36-d7f1-499d-f96b-47f04a24e158"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Importing and exporting data"],"metadata":{"id":"AqN2l5fZ8My7"}},{"cell_type":"markdown","source":["### CSV\n","\n","[Writing to a csv file](https://pandas.pydata.org/docs/user_guide/io.html#io-store-in-csv): using [`DataFrame.to_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html#pandas.DataFrame.to_csv)"],"metadata":{"id":"Wf4VLZVv8RXZ"}},{"cell_type":"code","source":["df.to_csv(\"foo.csv\")"],"metadata":{"id":"cVJnX0hl8enJ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["[Reading from a csv file](https://pandas.pydata.org/docs/user_guide/io.html#io-read-csv-table): using [`read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html#pandas.read_csv)"],"metadata":{"id":"NmDOwbEy8jT5"}},{"cell_type":"code","source":["pd.read_csv(\"foo.csv\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"CQIQtl8g8xWx","executionInfo":{"status":"ok","timestamp":1683055933102,"user_tz":-60,"elapsed":238,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"f9b04636-f01e-4ebd-fbfd-47f520794a89"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Unnamed: 0          A          B          C          D\n","0    2000-01-01   0.579490  -0.408378  -0.285932  -0.035712\n","1    2000-01-02  -0.685348  -1.771663   0.022815   0.165348\n","2    2000-01-03  -0.603863  -1.653470   1.439071  -0.196666\n","3    2000-01-04  -0.945835   0.690462   1.444210  -0.567836\n","4    2000-01-05   0.118966   1.217380   2.285591   0.851422\n","..          ...        ...        ...        ...        ...\n","995  2002-09-22  35.731926 -14.358713 -37.621386  17.392921\n","996  2002-09-23  37.620032 -14.009780 -35.553711  17.012057\n","997  2002-09-24  37.009100 -15.271335 -36.368402  17.318600\n","998  2002-09-25  38.827031 -13.718650 -36.715253  17.809859\n","999  2002-09-26  39.557892 -14.276855 -37.119307  17.743718\n","\n","[1000 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-0171f54f-700c-4e6d-9e9c-ba68d4bab93f\">\n","    <div class=\"colab-df-container\">\n","      <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>Unnamed: 0</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2000-01-01</td>\n","      <td>0.579490</td>\n","      <td>-0.408378</td>\n","      <td>-0.285932</td>\n","      <td>-0.035712</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2000-01-02</td>\n","      <td>-0.685348</td>\n","      <td>-1.771663</td>\n","      <td>0.022815</td>\n","      <td>0.165348</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2000-01-03</td>\n","      <td>-0.603863</td>\n","      <td>-1.653470</td>\n","      <td>1.439071</td>\n","      <td>-0.196666</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2000-01-04</td>\n","      <td>-0.945835</td>\n","      <td>0.690462</td>\n","      <td>1.444210</td>\n","      <td>-0.567836</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2000-01-05</td>\n","      <td>0.118966</td>\n","      <td>1.217380</td>\n","      <td>2.285591</td>\n","      <td>0.851422</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>995</th>\n","      <td>2002-09-22</td>\n","      <td>35.731926</td>\n","      <td>-14.358713</td>\n","      <td>-37.621386</td>\n","      <td>17.392921</td>\n","    </tr>\n","    <tr>\n","      <th>996</th>\n","      <td>2002-09-23</td>\n","      <td>37.620032</td>\n","      <td>-14.009780</td>\n","      <td>-35.553711</td>\n","      <td>17.012057</td>\n","    </tr>\n","    <tr>\n","      <th>997</th>\n","      <td>2002-09-24</td>\n","      <td>37.009100</td>\n","      <td>-15.271335</td>\n","      <td>-36.368402</td>\n","      <td>17.318600</td>\n","    </tr>\n","    <tr>\n","      <th>998</th>\n","      <td>2002-09-25</td>\n","      <td>38.827031</td>\n","      <td>-13.718650</td>\n","      <td>-36.715253</td>\n","      <td>17.809859</td>\n","    </tr>\n","    <tr>\n","      <th>999</th>\n","      <td>2002-09-26</td>\n","      <td>39.557892</td>\n","      <td>-14.276855</td>\n","      <td>-37.119307</td>\n","      <td>17.743718</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1000 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0171f54f-700c-4e6d-9e9c-ba68d4bab93f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-0171f54f-700c-4e6d-9e9c-ba68d4bab93f button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-0171f54f-700c-4e6d-9e9c-ba68d4bab93f');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":97}]},{"cell_type":"markdown","source":["### HDF5\n","\n","Reading and writing to [HDFStores](https://pandas.pydata.org/docs/user_guide/io.html#io-hdf5).\n","\n","Writing to a HDF5 Store using [`DataFrame.to_hdf()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_hdf.html#pandas.DataFrame.to_hdf):"],"metadata":{"id":"EAky-7nK84KU"}},{"cell_type":"code","source":["df.to_hdf(\"foo.h5\", \"df\")"],"metadata":{"id":"fRNcBUOb9JMU"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Reading from a HDF5 Store using [`read_hdf()`](https://pandas.pydata.org/docs/reference/api/pandas.read_hdf.html#pandas.read_hdf):"],"metadata":{"id":"P-pgFSE09M6-"}},{"cell_type":"code","source":["pd.read_hdf(\"foo.h5\", \"df\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"G5RpQCFL9VuM","executionInfo":{"status":"ok","timestamp":1683056067935,"user_tz":-60,"elapsed":159,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"1806c4d2-92e5-4cc5-c085-e75b7cdc5c26"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    A          B          C          D\n","2000-01-01   0.579490  -0.408378  -0.285932  -0.035712\n","2000-01-02  -0.685348  -1.771663   0.022815   0.165348\n","2000-01-03  -0.603863  -1.653470   1.439071  -0.196666\n","2000-01-04  -0.945835   0.690462   1.444210  -0.567836\n","2000-01-05   0.118966   1.217380   2.285591   0.851422\n","...               ...        ...        ...        ...\n","2002-09-22  35.731926 -14.358713 -37.621386  17.392921\n","2002-09-23  37.620032 -14.009780 -35.553711  17.012057\n","2002-09-24  37.009100 -15.271335 -36.368402  17.318600\n","2002-09-25  38.827031 -13.718650 -36.715253  17.809859\n","2002-09-26  39.557892 -14.276855 -37.119307  17.743718\n","\n","[1000 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-53f975a2-bebe-4b70-b4b1-467492c0f8b6\">\n","    <div class=\"colab-df-container\">\n","      <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>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-01-01</th>\n","      <td>0.579490</td>\n","      <td>-0.408378</td>\n","      <td>-0.285932</td>\n","      <td>-0.035712</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-02</th>\n","      <td>-0.685348</td>\n","      <td>-1.771663</td>\n","      <td>0.022815</td>\n","      <td>0.165348</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-03</th>\n","      <td>-0.603863</td>\n","      <td>-1.653470</td>\n","      <td>1.439071</td>\n","      <td>-0.196666</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-04</th>\n","      <td>-0.945835</td>\n","      <td>0.690462</td>\n","      <td>1.444210</td>\n","      <td>-0.567836</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-05</th>\n","      <td>0.118966</td>\n","      <td>1.217380</td>\n","      <td>2.285591</td>\n","      <td>0.851422</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2002-09-22</th>\n","      <td>35.731926</td>\n","      <td>-14.358713</td>\n","      <td>-37.621386</td>\n","      <td>17.392921</td>\n","    </tr>\n","    <tr>\n","      <th>2002-09-23</th>\n","      <td>37.620032</td>\n","      <td>-14.009780</td>\n","      <td>-35.553711</td>\n","      <td>17.012057</td>\n","    </tr>\n","    <tr>\n","      <th>2002-09-24</th>\n","      <td>37.009100</td>\n","      <td>-15.271335</td>\n","      <td>-36.368402</td>\n","      <td>17.318600</td>\n","    </tr>\n","    <tr>\n","      <th>2002-09-25</th>\n","      <td>38.827031</td>\n","      <td>-13.718650</td>\n","      <td>-36.715253</td>\n","      <td>17.809859</td>\n","    </tr>\n","    <tr>\n","      <th>2002-09-26</th>\n","      <td>39.557892</td>\n","      <td>-14.276855</td>\n","      <td>-37.119307</td>\n","      <td>17.743718</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1000 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53f975a2-bebe-4b70-b4b1-467492c0f8b6')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-53f975a2-bebe-4b70-b4b1-467492c0f8b6');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":99}]},{"cell_type":"markdown","source":["### Excel\n","\n","Reading and writing to [Excel](https://pandas.pydata.org/docs/user_guide/io.html#io-excel).\n","\n","Writing to an excel file using [`DataFrame.to_excel()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_excel.html#pandas.DataFrame.to_excel):"],"metadata":{"id":"eZwSYwiV9ZAj"}},{"cell_type":"code","source":["df.to_excel(\"foo.xlsx\", sheet_name=\"Sheet1\")"],"metadata":{"id":"O0jdwLa89meJ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Reading from an excel file using [`read_excel()`](https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html#pandas.read_excel):"],"metadata":{"id":"AGyQVEyv9pvD"}},{"cell_type":"code","source":["pd.read_excel(\"foo.xlsx\", \"Sheet1\", index_col=None, na_values=[\"NA\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"HGo5hOyf9wZA","executionInfo":{"status":"ok","timestamp":1683056182447,"user_tz":-60,"elapsed":976,"user":{"displayName":"Marc Cohen","userId":"07430968821203788943"}},"outputId":"64aac6e7-f11c-4c9e-fb82-5016a607c10f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Unnamed: 0          A          B          C          D\n","0   2000-01-01   0.579490  -0.408378  -0.285932  -0.035712\n","1   2000-01-02  -0.685348  -1.771663   0.022815   0.165348\n","2   2000-01-03  -0.603863  -1.653470   1.439071  -0.196666\n","3   2000-01-04  -0.945835   0.690462   1.444210  -0.567836\n","4   2000-01-05   0.118966   1.217380   2.285591   0.851422\n","..         ...        ...        ...        ...        ...\n","995 2002-09-22  35.731926 -14.358713 -37.621386  17.392921\n","996 2002-09-23  37.620032 -14.009780 -35.553711  17.012057\n","997 2002-09-24  37.009100 -15.271335 -36.368402  17.318600\n","998 2002-09-25  38.827031 -13.718650 -36.715253  17.809859\n","999 2002-09-26  39.557892 -14.276855 -37.119307  17.743718\n","\n","[1000 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-77cddf8d-a63d-4c65-a871-ac9470ddb1a5\">\n","    <div class=\"colab-df-container\">\n","      <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>Unnamed: 0</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2000-01-01</td>\n","      <td>0.579490</td>\n","      <td>-0.408378</td>\n","      <td>-0.285932</td>\n","      <td>-0.035712</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2000-01-02</td>\n","      <td>-0.685348</td>\n","      <td>-1.771663</td>\n","      <td>0.022815</td>\n","      <td>0.165348</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2000-01-03</td>\n","      <td>-0.603863</td>\n","      <td>-1.653470</td>\n","      <td>1.439071</td>\n","      <td>-0.196666</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2000-01-04</td>\n","      <td>-0.945835</td>\n","      <td>0.690462</td>\n","      <td>1.444210</td>\n","      <td>-0.567836</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2000-01-05</td>\n","      <td>0.118966</td>\n","      <td>1.217380</td>\n","      <td>2.285591</td>\n","      <td>0.851422</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>995</th>\n","      <td>2002-09-22</td>\n","      <td>35.731926</td>\n","      <td>-14.358713</td>\n","      <td>-37.621386</td>\n","      <td>17.392921</td>\n","    </tr>\n","    <tr>\n","      <th>996</th>\n","      <td>2002-09-23</td>\n","      <td>37.620032</td>\n","      <td>-14.009780</td>\n","      <td>-35.553711</td>\n","      <td>17.012057</td>\n","    </tr>\n","    <tr>\n","      <th>997</th>\n","      <td>2002-09-24</td>\n","      <td>37.009100</td>\n","      <td>-15.271335</td>\n","      <td>-36.368402</td>\n","      <td>17.318600</td>\n","    </tr>\n","    <tr>\n","      <th>998</th>\n","      <td>2002-09-25</td>\n","      <td>38.827031</td>\n","      <td>-13.718650</td>\n","      <td>-36.715253</td>\n","      <td>17.809859</td>\n","    </tr>\n","    <tr>\n","      <th>999</th>\n","      <td>2002-09-26</td>\n","      <td>39.557892</td>\n","      <td>-14.276855</td>\n","      <td>-37.119307</td>\n","      <td>17.743718</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1000 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77cddf8d-a63d-4c65-a871-ac9470ddb1a5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      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Use a.empty, a.bool(), a.item(), a.any() or a.all()."]}]},{"cell_type":"markdown","source":["See [Comparisons](https://pandas.pydata.org/docs/user_guide/basics.html#basics-compare) and [Gotchas](https://pandas.pydata.org/docs/user_guide/gotchas.html#gotchas) for an explanation and what to do."],"metadata":{"id":"9RLEJJVV-NHL"}}]}