07_Operations.ipynb

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        "___\n",
        "\n",
        "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
        "___"
      ]
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        "# Operations\n",
        "\n",
        "There are lots of operations with pandas that will be really useful to you, but don't fall into any distinct category. Let's show them here in this lecture:"
      ]
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              "   col1  col2 col3\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"col1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"col2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106,\n        \"min\": 444,\n        \"max\": 666,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          444,\n          555,\n          666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"col3\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"def\",\n          \"xyz\",\n          \"abc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame({'col1':[1,2,3,4],'col2':[444,555,666,444],'col3':['abc','def','ghi','xyz']})\n",
        "df.head()"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltJoP7XaieK4"
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      "source": [
        "### Info on Unique Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lfnk1fJGieK4",
        "outputId": "e40f8b2a-adfa-4899-b205-b00dd9e914dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([444, 555, 666])"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['col2'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Laogd6rgieK5",
        "outputId": "22e006fd-4971-4d20-d540-064f7b49135d"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['col2'].nunique()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AcSUOnqbieK5",
        "outputId": "be9f38b7-e586-4013-df3e-f32433938a17"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "444    2\n",
              "555    1\n",
              "666    1\n",
              "Name: col2, dtype: int64"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "df['col2'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjVJtILBieK5"
      },
      "source": [
        "### Selecting Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hLKRuBhcieK5"
      },
      "outputs": [],
      "source": [
        "#Select from DataFrame using criteria from multiple columns\n",
        "newdf = df[(df['col1']>2) & (df['col2']==444)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TlfXlaMlieK5",
        "outputId": "d2a2fbdc-0ac4-41db-e8c2-4600f325f2af"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>col1</th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
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              "  </thead>\n",
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              "      <th>3</th>\n",
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            "text/plain": [
              "   col1  col2 col3\n",
              "3     4   444  xyz"
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          "execution_count": 57,
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        "newdf"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8WARvTMieK6"
      },
      "source": [
        "### Applying Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "YYUxcENvieK6"
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      "outputs": [],
      "source": [
        "def times2(x):\n",
        "    return x*2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7tvD1abPieK6",
        "outputId": "424370f7-e475-41a7-c9e9-68c06d4b6baa"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    2\n",
              "1    4\n",
              "2    6\n",
              "3    8\n",
              "Name: col1, dtype: int64"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['col1'].apply(times2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "An9UcovcieK6",
        "outputId": "ed6372f9-9ef1-4ac1-89bd-76f797c50aaf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    3\n",
              "1    3\n",
              "2    3\n",
              "3    3\n",
              "Name: col3, dtype: int64"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['col3'].apply(len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fVq47AFaieK6",
        "outputId": "cd8aa62f-b687-4bd7-b55c-c6a4b68b6ab4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "df['col1'].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BhAIHUmieK7"
      },
      "source": [
        "** Permanently Removing a Column**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3rHblQN3ieK7"
      },
      "outputs": [],
      "source": [
        "del df['col1']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-3YLqYOzieK7",
        "outputId": "ee2f1f59-1855-4314-bb16-65d4e513cf6e"
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      "outputs": [
        {
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            "text/plain": [
              "   col2 col3\n",
              "0   444  abc\n",
              "1   555  def\n",
              "2   666  ghi\n",
              "3   444  xyz"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qI2hUmF0ieK7"
      },
      "source": [
        "** Get column and index names: **"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XXVgQWypieK7",
        "outputId": "b66250e2-92be-4e23-e684-8ce13047b02e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['col2', 'col3'], dtype='object')"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8ijwV0b1ieK7",
        "outputId": "b54f502a-7b04-46d9-c8e4-bfeeba08e4b6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=4, step=1)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQCa0qlxieK7"
      },
      "source": [
        "** Sorting and Ordering a DataFrame:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9cAkyuI6ieK7",
        "outputId": "dab428f0-bf04-4184-ea69-8bacc7169b50"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>444</td>\n",
              "      <td>abc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>555</td>\n",
              "      <td>def</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>666</td>\n",
              "      <td>ghi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>444</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col2 col3\n",
              "0   444  abc\n",
              "1   555  def\n",
              "2   666  ghi\n",
              "3   444  xyz"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zpBIqwnlieK8",
        "outputId": "c55800e1-dcb3-405c-d8cf-472b1c6c8e8a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>444</td>\n",
              "      <td>abc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>444</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>555</td>\n",
              "      <td>def</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>666</td>\n",
              "      <td>ghi</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col2 col3\n",
              "0   444  abc\n",
              "3   444  xyz\n",
              "1   555  def\n",
              "2   666  ghi"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.sort_values(by='col2') #inplace=False by default"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXxZ09j4ieK8"
      },
      "source": [
        "** Find Null Values or Check for Null Values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kX72K7bTieK8",
        "outputId": "4da7063f-855a-4591-9f7b-d12a716fcefa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    col2   col3\n",
              "0  False  False\n",
              "1  False  False\n",
              "2  False  False\n",
              "3  False  False"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OTSxIjDiieK8",
        "outputId": "a137db3c-30bf-4065-e9d0-615c96cc5892"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>444</td>\n",
              "      <td>abc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>555</td>\n",
              "      <td>def</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>666</td>\n",
              "      <td>ghi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>444</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col2 col3\n",
              "0   444  abc\n",
              "1   555  def\n",
              "2   666  ghi\n",
              "3   444  xyz"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Drop rows with NaN Values\n",
        "df.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5v01CfKieK8"
      },
      "source": [
        "** Filling in NaN values with something else: **"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "PKlGXz46ieK8"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QYmxz4FFieK8",
        "outputId": "076c5572-e4b2-4c0b-dda2-5786cf1d6b68"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>col1</th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>abc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>555.0</td>\n",
              "      <td>def</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>ghi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>444.0</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col1   col2 col3\n",
              "0   1.0    NaN  abc\n",
              "1   2.0  555.0  def\n",
              "2   3.0  666.0  ghi\n",
              "3   NaN  444.0  xyz"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.DataFrame({'col1':[1,2,3,np.nan],\n",
        "                   'col2':[np.nan,555,666,444],\n",
        "                   'col3':['abc','def','ghi','xyz']})\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cu_-4tGuieK8",
        "outputId": "c379294f-31f0-4a0a-d073-caf0aa576a72"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>col1</th>\n",
              "      <th>col2</th>\n",
              "      <th>col3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>FILL</td>\n",
              "      <td>abc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>555</td>\n",
              "      <td>def</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>666</td>\n",
              "      <td>ghi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FILL</td>\n",
              "      <td>444</td>\n",
              "      <td>xyz</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   col1  col2 col3\n",
              "0     1  FILL  abc\n",
              "1     2   555  def\n",
              "2     3   666  ghi\n",
              "3  FILL   444  xyz"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.fillna('FILL')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "-jJJlEAIieK9"
      },
      "outputs": [],
      "source": [
        "data = {'A':['foo','foo','foo','bar','bar','bar'],\n",
        "     'B':['one','one','two','two','one','one'],\n",
        "       'C':['x','y','x','y','x','y'],\n",
        "       'D':[1,3,2,5,4,1]}\n",
        "\n",
        "df = pd.DataFrame(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ge7xmr6TieK9",
        "outputId": "db9481c3-3953-4a32-a718-2c85088d5661"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\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>x</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>foo</td>\n",
              "      <td>one</td>\n",
              "      <td>y</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>foo</td>\n",
              "      <td>two</td>\n",
              "      <td>x</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bar</td>\n",
              "      <td>two</td>\n",
              "      <td>y</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bar</td>\n",
              "      <td>one</td>\n",
              "      <td>x</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>bar</td>\n",
              "      <td>one</td>\n",
              "      <td>y</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A    B  C  D\n",
              "0  foo  one  x  1\n",
              "1  foo  one  y  3\n",
              "2  foo  two  x  2\n",
              "3  bar  two  y  5\n",
              "4  bar  one  x  4\n",
              "5  bar  one  y  1"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DfeZ57laieK9",
        "outputId": "fbdd074e-d534-4a39-a083-29c45a846d86"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C</th>\n",
              "      <th>x</th>\n",
              "      <th>y</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=\"2\" valign=\"top\">bar</th>\n",
              "      <th>one</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
              "      <th>one</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "C          x    y\n",
              "A   B            \n",
              "bar one  4.0  1.0\n",
              "    two  NaN  5.0\n",
              "foo one  1.0  3.0\n",
              "    two  2.0  NaN"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.pivot_table(values='D',index=['A', 'B'],columns=['C'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSZwCy_aieK9"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.1"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}