intro_to_pandas.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/dmiranda2791/ml-google-crashcourse-exercises/blob/master/intro_to_pandas.ipynb)"
      ]
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      "metadata": {
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      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "metadata": {
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        "id": "hMqWDc_m6rUC",
        "cellView": "both",
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      "cell_type": "code",
      "source": [
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "rHLcriKWLRe4"
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      "cell_type": "markdown",
      "source": [
        "# Quick Introduction to pandas"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "QvJBqX8_Bctk"
      },
      "cell_type": "markdown",
      "source": [
        "**Learning Objectives:**\n",
        "  * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n",
        "  * Access and manipulate data within a `DataFrame` and `Series`\n",
        "  * Import CSV data into a *pandas* `DataFrame`\n",
        "  * Reindex a `DataFrame` to shuffle data"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "TIFJ83ZTBctl"
      },
      "cell_type": "markdown",
      "source": [
        "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n",
        "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials."
      ]
    },
    {
      "metadata": {
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        "id": "s_JOISVgmn9v"
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      "cell_type": "markdown",
      "source": [
        "## Basic Concepts\n",
        "\n",
        "The following line imports the *pandas* API and prints the API version:"
      ]
    },
    {
      "metadata": {
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        "outputId": "c37c448d-0418-4cd4-a414-52125457b668"
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      "cell_type": "code",
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import pandas as pd\n",
        "pd.__version__"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "u'0.22.0'"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "daQreKXIUslr"
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      "cell_type": "markdown",
      "source": [
        "The primary data structures in *pandas* are implemented as two classes:\n",
        "\n",
        "  * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n",
        "  * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n",
        "\n",
        "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)."
      ]
    },
    {
      "metadata": {
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        "id": "fjnAk1xcU0yc"
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      "cell_type": "markdown",
      "source": [
        "One way to create a `Series` is to construct a `Series` object. For example:"
      ]
    },
    {
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        "outputId": "e0a4076f-4370-4e88-8f83-61d86f8fbf61"
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      "cell_type": "code",
      "source": [
        "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    San Francisco\n",
              "1         San Jose\n",
              "2       Sacramento\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:"
      ]
    },
    {
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        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
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        "outputId": "ba6156de-c6c2-468c-d6ad-bdf33d3364c3"
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      "cell_type": "code",
      "source": [
        "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n",
        "population = pd.Series([852469, 1015785, 485199])\n",
        "\n",
        "pd.DataFrame({ 'City name': city_names, 'Population': population })"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>City name</th>\n",
              "      <th>Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramento</td>\n",
              "      <td>485199</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "       City name  Population\n",
              "0  San Francisco      852469\n",
              "1       San Jose     1015785\n",
              "2     Sacramento      485199"
            ]
          },
          "metadata": {
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          "execution_count": 3
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      "metadata": {
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        "id": "oa5wfZT7VHJl"
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      "cell_type": "markdown",
      "source": [
        "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:"
      ]
    },
    {
      "metadata": {
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        "id": "av6RYOraVG1V",
        "colab": {
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        "outputId": "bab0e63b-7091-4867-fd1a-ead5524e1005"
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      "cell_type": "code",
      "source": [
        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
        "california_housing_dataframe.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.562108</td>\n",
              "      <td>35.625225</td>\n",
              "      <td>28.589353</td>\n",
              "      <td>2643.664412</td>\n",
              "      <td>539.410824</td>\n",
              "      <td>1429.573941</td>\n",
              "      <td>501.221941</td>\n",
              "      <td>3.883578</td>\n",
              "      <td>207300.912353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.005166</td>\n",
              "      <td>2.137340</td>\n",
              "      <td>12.586937</td>\n",
              "      <td>2179.947071</td>\n",
              "      <td>421.499452</td>\n",
              "      <td>1147.852959</td>\n",
              "      <td>384.520841</td>\n",
              "      <td>1.908157</td>\n",
              "      <td>115983.764387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.790000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>790.000000</td>\n",
              "      <td>282.000000</td>\n",
              "      <td>2.566375</td>\n",
              "      <td>119400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>1167.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.544600</td>\n",
              "      <td>180400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.000000</td>\n",
              "      <td>37.720000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3151.250000</td>\n",
              "      <td>648.250000</td>\n",
              "      <td>1721.000000</td>\n",
              "      <td>605.250000</td>\n",
              "      <td>4.767000</td>\n",
              "      <td>265000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>37937.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
              "count  17000.000000  17000.000000        17000.000000  17000.000000   \n",
              "mean    -119.562108     35.625225           28.589353   2643.664412   \n",
              "std        2.005166      2.137340           12.586937   2179.947071   \n",
              "min     -124.350000     32.540000            1.000000      2.000000   \n",
              "25%     -121.790000     33.930000           18.000000   1462.000000   \n",
              "50%     -118.490000     34.250000           29.000000   2127.000000   \n",
              "75%     -118.000000     37.720000           37.000000   3151.250000   \n",
              "max     -114.310000     41.950000           52.000000  37937.000000   \n",
              "\n",
              "       total_bedrooms    population    households  median_income  \\\n",
              "count    17000.000000  17000.000000  17000.000000   17000.000000   \n",
              "mean       539.410824   1429.573941    501.221941       3.883578   \n",
              "std        421.499452   1147.852959    384.520841       1.908157   \n",
              "min          1.000000      3.000000      1.000000       0.499900   \n",
              "25%        297.000000    790.000000    282.000000       2.566375   \n",
              "50%        434.000000   1167.000000    409.000000       3.544600   \n",
              "75%        648.250000   1721.000000    605.250000       4.767000   \n",
              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
              "\n",
              "       median_house_value  \n",
              "count        17000.000000  \n",
              "mean        207300.912353  \n",
              "std         115983.764387  \n",
              "min          14999.000000  \n",
              "25%         119400.000000  \n",
              "50%         180400.000000  \n",
              "75%         265000.000000  \n",
              "max         500001.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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    {
      "metadata": {
        "colab_type": "text",
        "id": "WrkBjfz5kEQu"
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      "cell_type": "markdown",
      "source": [
        "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
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        },
        "outputId": "796ed0f8-743c-4c9c-9088-c08e02245c07"
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2    -114.56     33.69                17.0        720.0           174.0   \n",
              "3    -114.57     33.64                14.0       1501.0           337.0   \n",
              "4    -114.57     33.57                20.0       1454.0           326.0   \n",
              "\n",
              "   population  households  median_income  median_house_value  \n",
              "0      1015.0       472.0         1.4936             66900.0  \n",
              "1      1129.0       463.0         1.8200             80100.0  \n",
              "2       333.0       117.0         1.6509             85700.0  \n",
              "3       515.0       226.0         3.1917             73400.0  \n",
              "4       624.0       262.0         1.9250             65500.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "w9-Es5Y6laGd"
      },
      "cell_type": "markdown",
      "source": [
        "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "nqndFVXVlbPN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "outputId": "9a6c3d7e-553d-42d1-a712-96e43bdae778"
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe.hist('housing_median_age')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5778f4b390>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wQNnZ2ZKkvLw89evXryGOCQCAn4Uaz6g//PBDFRcXa8aMGb5to0aN0owZM9SmTRuFhIRo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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f578fe4dbd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "XtYZ7114n3b-"
      },
      "cell_type": "markdown",
      "source": [
        "## Accessing Data\n",
        "\n",
        "You can access `DataFrame` data using familiar Python dict/list operations:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "_TFm7-looBFF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "25ff8ee0-0a89-4d82-80c2-5aee9efe0bfa"
      },
      "cell_type": "code",
      "source": [
        "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n",
        "print(type(cities['City name']))\n",
        "cities['City name']"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.series.Series'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    San Francisco\n",
              "1         San Jose\n",
              "2       Sacramento\n",
              "Name: City name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "V5L6xacLoxyv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "65b04524-142d-496d-8737-89afb91a4ef8"
      },
      "cell_type": "code",
      "source": [
        "print(type(cities['City name'][1]))\n",
        "cities['City name'][1]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<type 'str'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'San Jose'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "gcYX1tBPugZl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "e7b7ca70-89f9-407b-8916-f03cff15c568"
      },
      "cell_type": "code",
      "source": [
        "print(type(cities[0:2]))\n",
        "cities[0:2]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ],
          "name": "stdout"
        },
        {
          "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>City name</th>\n",
              "      <th>Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population\n",
              "0  San Francisco      852469\n",
              "1       San Jose     1015785"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "65g1ZdGVjXsQ"
      },
      "cell_type": "markdown",
      "source": [
        "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "RM1iaD-ka3Y1"
      },
      "cell_type": "markdown",
      "source": [
        "## Manipulating Data\n",
        "\n",
        "You may apply Python's basic arithmetic operations to `Series`. For example:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "XWmyCFJ5bOv-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "4d2cb3ad-6e42-49d9-b6fe-45634c1ea072"
      },
      "cell_type": "code",
      "source": [
        "population / 1000."
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     852.469\n",
              "1    1015.785\n",
              "2     485.199\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "TQzIVnbnmWGM"
      },
      "cell_type": "markdown",
      "source": [
        "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "ko6pLK6JmkYP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "e7fd7d3c-b036-41ac-b617-0a3f86838b02"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "np.log(population)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    13.655892\n",
              "1    13.831172\n",
              "2    13.092314\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "xmxFuQmurr6d"
      },
      "cell_type": "markdown",
      "source": [
        "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n",
        "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n",
        "\n",
        "The example below creates a new `Series` that indicates whether `population` is over one million:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Fc1DvPAbstjI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "f7bc158d-df20-48ad-ede1-1fa65316693c"
      },
      "cell_type": "code",
      "source": [
        "population.apply(lambda val: val > 1000000)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    False\n",
              "1     True\n",
              "2    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "ZeYYLoV9b9fB"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "0gCEX99Hb8LR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "5c7bd792-d7c8-4a14-8eb5-19f2056bdd0b"
      },
      "cell_type": "code",
      "source": [
        "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n",
        "cities['Population density'] = cities['Population'] / cities['Area square miles']\n",
        "cities"
      ],
      "execution_count": 13,
      "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramento</td>\n",
              "      <td>485199</td>\n",
              "      <td>97.92</td>\n",
              "      <td>4955.055147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Area square miles  Population density\n",
              "0  San Francisco      852469              46.87        18187.945381\n",
              "1       San Jose     1015785             176.53         5754.177760\n",
              "2     Sacramento      485199              97.92         4955.055147"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "6qh63m-ayb-c"
      },
      "cell_type": "markdown",
      "source": [
        "## Exercise #1\n",
        "\n",
        "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n",
        "\n",
        "  * The city is named after a saint.\n",
        "  * The city has an area greater than 50 square miles.\n",
        "\n",
        "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n",
        "\n",
        "**Hint:** \"San\" in Spanish means \"saint.\""
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "zCOn8ftSyddH",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cities['newSerie'] = cities['City name'].apply(lambda val: val.startswith('San')) & cities['Area square miles'].apply(lambda val: val > 50)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "YHIWvc9Ms-Ll"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for a solution."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "T5OlrqtdtCIb",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n",
        "cities"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "f-xAOJeMiXFB"
      },
      "cell_type": "markdown",
      "source": [
        "## Indexes\n",
        "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n",
        "\n",
        "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "2684gsWNinq9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "651775e5-ee76-4fb8-a52f-8bdac601c229"
      },
      "cell_type": "code",
      "source": [
        "city_names.index"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=3, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "F_qPe2TBjfWd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7ab6eee3-e786-40ba-c05a-818dc2ee8c0a"
      },
      "cell_type": "code",
      "source": [
        "cities.index"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=3, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "hp2oWY9Slo_h"
      },
      "cell_type": "markdown",
      "source": [
        "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "sN0zUzSAj-U1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "b4be9845-f75e-461e-d62c-3b2e695541a7"
      },
      "cell_type": "code",
      "source": [
        "cities.reindex([2, 0, 1])"
      ],
      "execution_count": 23,
      "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "      <th>newSerie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramento</td>\n",
              "      <td>485199</td>\n",
              "      <td>97.92</td>\n",
              "      <td>4955.055147</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Area square miles  Population density  newSerie\n",
              "2     Sacramento      485199              97.92         4955.055147     False\n",
              "0  San Francisco      852469              46.87        18187.945381     False\n",
              "1       San Jose     1015785             176.53         5754.177760      True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "-GQFz8NZuS06"
      },
      "cell_type": "markdown",
      "source": [
        "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n",
        "Try running the following cell multiple times!"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "mF8GC0k8uYhz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "8338538d-fc7c-4c20-9bc9-72e85ce0de8f"
      },
      "cell_type": "code",
      "source": [
        "cities.reindex(np.random.permutation(cities.index))"
      ],
      "execution_count": 24,
      "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "      <th>newSerie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramento</td>\n",
              "      <td>485199</td>\n",
              "      <td>97.92</td>\n",
              "      <td>4955.055147</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Area square miles  Population density  newSerie\n",
              "0  San Francisco      852469              46.87        18187.945381     False\n",
              "2     Sacramento      485199              97.92         4955.055147     False\n",
              "1       San Jose     1015785             176.53         5754.177760      True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "fSso35fQmGKb"
      },
      "cell_type": "markdown",
      "source": [
        "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "8UngIdVhz8C0"
      },
      "cell_type": "markdown",
      "source": [
        "## Exercise #2\n",
        "\n",
        "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "PN55GrDX0jzO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "f9848bd6-cc4d-46fb-da82-ef1196526bed"
      },
      "cell_type": "code",
      "source": [
        "cities.reindex([1,0, 2, 3])"
      ],
      "execution_count": 25,
      "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>City name</th>\n",
              "      <th>Population</th>\n",
              "      <th>Area square miles</th>\n",
              "      <th>Population density</th>\n",
              "      <th>newSerie</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>San Jose</td>\n",
              "      <td>1015785.0</td>\n",
              "      <td>176.53</td>\n",
              "      <td>5754.177760</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>852469.0</td>\n",
              "      <td>46.87</td>\n",
              "      <td>18187.945381</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramento</td>\n",
              "      <td>485199.0</td>\n",
              "      <td>97.92</td>\n",
              "      <td>4955.055147</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       City name  Population  Area square miles  Population density newSerie\n",
              "1       San Jose   1015785.0             176.53         5754.177760     True\n",
              "0  San Francisco    852469.0              46.87        18187.945381    False\n",
              "2     Sacramento    485199.0              97.92         4955.055147    False\n",
              "3            NaN         NaN                NaN                 NaN      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "TJffr5_Jwqvd"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for the solution."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "8oSvi2QWwuDH"
      },
      "cell_type": "markdown",
      "source": [
        "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "yBdkucKCwy4x",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cities.reindex([0, 4, 5, 2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "2l82PhPbwz7g"
      },
      "cell_type": "markdown",
      "source": [
        "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n",
        "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n",
        "in which the index values are browser names).\n",
        "\n",
        "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n",
        "sanitizing the input."
      ]
    }
  ]
}