pandas_merge.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gna2bulugHS"
      },
      "source": [
        "# An Introduction to Combining Data with Pandas' `merge`, `join`, and `concat`\n",
        "\n",
        "This notebook contains code examples to use with the article of the same name, along with light commentary on each of the examples. \n",
        "\n",
        "Based on: https://realpython.com/pandas-merge-join-and-concat/\n",
        "\n",
        "\n",
        "![join_diagram.webp](data:image/webp;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pII4VBBmugHU"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oj6Fm_dtugHV"
      },
      "source": [
        "## Importing Data\n",
        "\n",
        "Here you will import the temperature and precipitation climate normals datasets into DataFrames. Calling `.head()` on the DataFrame will give you a 5 row preview of your data, and the `shape` attribute will give you the dimensions of the data in the form `(rows, columns)`. These are great sanity checks to run before doing too much with the data. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "FqnNE6fMugHV",
        "outputId": "fb5bf41d-fe2c-48e4-fc5d-78065b054cec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION            STATION_NAME ELEVATION  LATITUDE   LONGITUDE  \\\n",
              "0  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "1  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "2  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "3  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "4  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "\n",
              "       DATE  DLY-CLDD-BASE45  DLY-CLDD-BASE50  DLY-CLDD-BASE55  \\\n",
              "0  20100101                6                2            -7777   \n",
              "1  20100102                6                2                1   \n",
              "2  20100103                6                2                1   \n",
              "3  20100104                6                2                1   \n",
              "4  20100105                6                2                1   \n",
              "\n",
              "   DLY-CLDD-BASE57  ...  DLY-CLDD-NORMAL  DLY-CLDD-BASE70  DLY-CLDD-BASE72  \\\n",
              "0            -7777  ...                0                0                0   \n",
              "1            -7777  ...                0                0                0   \n",
              "2            -7777  ...                0                0                0   \n",
              "3            -7777  ...                0                0                0   \n",
              "4            -7777  ...                0                0                0   \n",
              "\n",
              "   DLY-HTDD-BASE40  DLY-HTDD-BASE45  DLY-HTDD-BASE50  DLY-HTDD-BASE55  \\\n",
              "0            -7777                1                2                6   \n",
              "1            -7777                1                2                6   \n",
              "2            -7777                1                2                5   \n",
              "3            -7777                1                2                5   \n",
              "4            -7777            -7777                2                5   \n",
              "\n",
              "   DLY-HTDD-BASE57  DLY-HTDD-BASE60  DLY-HTDD-NORMAL  \n",
              "0                7               10               15  \n",
              "1                7               10               15  \n",
              "2                7               10               15  \n",
              "3                7               10               15  \n",
              "4                7               10               15  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf608aea-35b9-4057-95c6-5b305f00ce6a\">\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>STATION</th>\n",
              "      <th>STATION_NAME</th>\n",
              "      <th>ELEVATION</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>DATE</th>\n",
              "      <th>DLY-CLDD-BASE45</th>\n",
              "      <th>DLY-CLDD-BASE50</th>\n",
              "      <th>DLY-CLDD-BASE55</th>\n",
              "      <th>DLY-CLDD-BASE57</th>\n",
              "      <th>...</th>\n",
              "      <th>DLY-CLDD-NORMAL</th>\n",
              "      <th>DLY-CLDD-BASE70</th>\n",
              "      <th>DLY-CLDD-BASE72</th>\n",
              "      <th>DLY-HTDD-BASE40</th>\n",
              "      <th>DLY-HTDD-BASE45</th>\n",
              "      <th>DLY-HTDD-BASE50</th>\n",
              "      <th>DLY-HTDD-BASE55</th>\n",
              "      <th>DLY-HTDD-BASE57</th>\n",
              "      <th>DLY-HTDD-BASE60</th>\n",
              "      <th>DLY-HTDD-NORMAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100101</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>-7777</td>\n",
              "      <td>-7777</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-7777</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
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              "      <td>602</td>\n",
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              "      <td>-116.03694</td>\n",
              "      <td>20100102</td>\n",
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              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100103</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-7777</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-7777</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100104</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-7777</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-7777</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100105</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-7777</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-7777</td>\n",
              "      <td>-7777</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf608aea-35b9-4057-95c6-5b305f00ce6a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cf608aea-35b9-4057-95c6-5b305f00ce6a');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "climate_temp = pd.read_csv(\"https://github.com/raw/armandoordonez/eda_couse/main/climate_temp.csv\")\n",
        "climate_temp.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "zZ4d--ITugHW",
        "outputId": "19c04d02-b8c4-419c-f4fa-539d3577a01d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127020, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "climate_temp.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "5KKCN7zuugHX",
        "outputId": "ad0db0e8-2ab7-4e3a-dc38-c1e71c5ce2fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION            STATION_NAME      DATE  DLY-PRCP-25PCTL  \\\n",
              "0  GHCND:USC00049099  TWENTYNINE PALMS CA US  20100101            -6.66   \n",
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              "3  GHCND:USC00049099  TWENTYNINE PALMS CA US  20100104            -6.66   \n",
              "4  GHCND:USC00049099  TWENTYNINE PALMS CA US  20100105            -6.66   \n",
              "\n",
              "   DLY-SNWD-25PCTL  DLY-SNOW-25PCTL  DLY-PRCP-50PCTL  DLY-SNWD-50PCTL  \\\n",
              "0             -666            -66.6            -6.66             -666   \n",
              "1             -666            -66.6            -6.66             -666   \n",
              "2             -666            -66.6            -6.66             -666   \n",
              "3            -9999          -9999.0            -6.66            -9999   \n",
              "4            -9999          -9999.0            -6.66            -9999   \n",
              "\n",
              "   DLY-SNOW-50PCTL  DLY-PRCP-75PCTL  ...  DLY-PRCP-PCTALL-GE100HI  \\\n",
              "0            -66.6            -6.66  ...                        3   \n",
              "1            -66.6            -6.66  ...                        3   \n",
              "2            -66.6            -6.66  ...                        3   \n",
              "3          -9999.0            -6.66  ...                        3   \n",
              "4          -9999.0            -6.66  ...                        3   \n",
              "\n",
              "   DLY-SNWD-PCTALL-GE001WI  DLY-SNWD-PCTALL-GE010WI  DLY-SNWD-PCTALL-GE003WI  \\\n",
              "0                    -9999                        0                    -9999   \n",
              "1                    -9999                        0                    -9999   \n",
              "2                    -9999                        0                    -9999   \n",
              "3                        0                        0                        0   \n",
              "4                        0                        0                        0   \n",
              "\n",
              "   DLY-SNWD-PCTALL-GE005WI  DLY-SNOW-PCTALL-GE001TI  DLY-SNOW-PCTALL-GE010TI  \\\n",
              "0                    -9999                    -9999                    -9999   \n",
              "1                    -9999                    -9999                    -9999   \n",
              "2                    -9999                    -9999                    -9999   \n",
              "3                        0                        0                        0   \n",
              "4                        0                        0                        0   \n",
              "\n",
              "   DLY-SNOW-PCTALL-GE100TI  DLY-SNOW-PCTALL-GE030TI  DLY-SNOW-PCTALL-GE050TI  \n",
              "0                        0                    -9999                    -9999  \n",
              "1                        0                    -9999                    -9999  \n",
              "2                        0                    -9999                    -9999  \n",
              "3                        0                        0                        0  \n",
              "4                        0                        0                        0  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "climate_precip = pd.read_csv(\"https://github.com/raw/armandoordonez/eda_couse/main/climate_precip.csv\")\n",
        "climate_precip.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "x6YiZL5wugHX",
        "outputId": "33e3605d-3d2b-4341-9a15-11d574bf5c1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(151110, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "climate_precip.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrDPqVenugHX"
      },
      "source": [
        "## merge()\n",
        "\n",
        "In this section, you will learn about `merge()` functionality in Pandas.\n",
        "\n",
        "### Inner Join\n",
        "\n",
        "Here, you will use a plain `merge()` call to do an inner join and learn how this can result in a smaller, more focused dataset. First, you will create a new DataFrame object that contains the precipitation data from one station."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        "id": "4TB5sPqAugHX",
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          "height": 525
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              "                STATION            STATION_NAME      DATE  DLY-PRCP-25PCTL  \\\n",
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              "\n",
              "      DLY-SNWD-PCTALL-GE001WI  DLY-SNWD-PCTALL-GE010WI  \\\n",
              "1460                        4                        0   \n",
              "1461                        4                        0   \n",
              "1462                        4                        0   \n",
              "1463                        4                        0   \n",
              "1464                        4                        0   \n",
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              "      DLY-SNWD-PCTALL-GE003WI  DLY-SNWD-PCTALL-GE005WI  \\\n",
              "1460                        3                        3   \n",
              "1461                        3                        3   \n",
              "1462                        3                        3   \n",
              "1463                        3                        2   \n",
              "1464                        3                        2   \n",
              "\n",
              "      DLY-SNOW-PCTALL-GE001TI  DLY-SNOW-PCTALL-GE010TI  \\\n",
              "1460                        9                        6   \n",
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              "1460                        0                    -9999   \n",
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              "1463                        0                    -9999   \n",
              "1464                        0                    -9999   \n",
              "\n",
              "      DLY-SNOW-PCTALL-GE050TI  \n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "precip_one_station = climate_precip.query(\"STATION == 'GHCND:USC00045721'\")\n",
        "precip_one_station.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "yZ1kn3qvugHY",
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        "colab": {
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        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(365, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "precip_one_station.shape"
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    {
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        "colab": {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION            STATION_NAME      DATE  DLY-PRCP-25PCTL  \\\n",
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              "      <th>DATE</th>\n",
              "      <th>DLY-PRCP-25PCTL</th>\n",
              "      <th>DLY-SNWD-25PCTL</th>\n",
              "      <th>DLY-SNOW-25PCTL</th>\n",
              "      <th>DLY-PRCP-50PCTL</th>\n",
              "      <th>DLY-SNWD-50PCTL</th>\n",
              "      <th>DLY-SNOW-50PCTL</th>\n",
              "      <th>DLY-PRCP-75PCTL</th>\n",
              "      <th>...</th>\n",
              "      <th>DLY-CLDD-NORMAL</th>\n",
              "      <th>DLY-CLDD-BASE70</th>\n",
              "      <th>DLY-CLDD-BASE72</th>\n",
              "      <th>DLY-HTDD-BASE40</th>\n",
              "      <th>DLY-HTDD-BASE45</th>\n",
              "      <th>DLY-HTDD-BASE50</th>\n",
              "      <th>DLY-HTDD-BASE55</th>\n",
              "      <th>DLY-HTDD-BASE57</th>\n",
              "      <th>DLY-HTDD-BASE60</th>\n",
              "      <th>DLY-HTDD-NORMAL</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GHCND:USC00045721</td>\n",
              "      <td>MITCHELL CAVERNS CA US</td>\n",
              "      <td>20100101</td>\n",
              "      <td>0.04</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.44</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GHCND:USC00045721</td>\n",
              "      <td>MITCHELL CAVERNS CA US</td>\n",
              "      <td>20100102</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-666</td>\n",
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              "      <td>6</td>\n",
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              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GHCND:USC00045721</td>\n",
              "      <td>MITCHELL CAVERNS CA US</td>\n",
              "      <td>20100103</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.45</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GHCND:USC00045721</td>\n",
              "      <td>MITCHELL CAVERNS CA US</td>\n",
              "      <td>20100104</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.45</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GHCND:USC00045721</td>\n",
              "      <td>MITCHELL CAVERNS CA US</td>\n",
              "      <td>20100105</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.46</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 47 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97c1eda9-bb2d-4f81-9e9b-1456427776a3')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "inner_merged = pd.merge(precip_one_station, climate_temp)\n",
        "inner_merged.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfHpyTEjugHY"
      },
      "source": [
        "How many rows do you think this merged DataFrame has?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "GBDfgFn2ugHY",
        "outputId": "631e6d55-bda1-4f6b-c45e-abda823a61e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(365, 47)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "inner_merged.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RdiD7wDugHZ"
      },
      "source": [
        "You get 365 rows because any non-matching rows are discarded in an inner join, which is the default merge method for a `merge()` call, and `precip_one_station` had only 365 rows.\n",
        "\n",
        "What if you want to merge both full datasets, but specify which columns to join on? In this case, you will use the `on` parameter:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "BQj9A7RFugHZ",
        "outputId": "eee4d5cd-db25-4ab8-9284-fcad9a109c2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION          STATION_NAME_x ELEVATION  LATITUDE   LONGITUDE  \\\n",
              "0  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "1  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "2  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "3  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "4  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "\n",
              "       DATE  DLY-CLDD-BASE45  DLY-CLDD-BASE50  DLY-CLDD-BASE55  \\\n",
              "0  20100101                6                2            -7777   \n",
              "1  20100102                6                2                1   \n",
              "2  20100103                6                2                1   \n",
              "3  20100104                6                2                1   \n",
              "4  20100105                6                2                1   \n",
              "\n",
              "   DLY-CLDD-BASE57  ...  DLY-PRCP-PCTALL-GE100HI  DLY-SNWD-PCTALL-GE001WI  \\\n",
              "0            -7777  ...                        3                    -9999   \n",
              "1            -7777  ...                        3                    -9999   \n",
              "2            -7777  ...                        3                    -9999   \n",
              "3            -7777  ...                        3                        0   \n",
              "4            -7777  ...                        3                        0   \n",
              "\n",
              "   DLY-SNWD-PCTALL-GE010WI  DLY-SNWD-PCTALL-GE003WI  DLY-SNWD-PCTALL-GE005WI  \\\n",
              "0                        0                    -9999                    -9999   \n",
              "1                        0                    -9999                    -9999   \n",
              "2                        0                    -9999                    -9999   \n",
              "3                        0                        0                        0   \n",
              "4                        0                        0                        0   \n",
              "\n",
              "   DLY-SNOW-PCTALL-GE001TI  DLY-SNOW-PCTALL-GE010TI  DLY-SNOW-PCTALL-GE100TI  \\\n",
              "0                    -9999                    -9999                        0   \n",
              "1                    -9999                    -9999                        0   \n",
              "2                    -9999                    -9999                        0   \n",
              "3                        0                        0                        0   \n",
              "4                        0                        0                        0   \n",
              "\n",
              "   DLY-SNOW-PCTALL-GE030TI  DLY-SNOW-PCTALL-GE050TI  \n",
              "0                    -9999                    -9999  \n",
              "1                    -9999                    -9999  \n",
              "2                    -9999                    -9999  \n",
              "3                        0                        0  \n",
              "4                        0                        0  \n",
              "\n",
              "[5 rows x 48 columns]"
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              "      <th>2</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100103</td>\n",
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              "      <td>GHCND:USC00049099</td>\n",
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              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "inner_merged_total = pd.merge(\n",
        "    climate_temp, climate_precip, on=[\"STATION\",\"DATE\"]\n",
        ")\n",
        "inner_merged_total.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "wyVS9apgugHZ",
        "outputId": "0b9ad49d-043a-4fe1-8a5f-ec7ac1a69e9f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(123005, 48)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "inner_merged_total.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GwqrgW-ugHa"
      },
      "source": [
        "You can specify a single _key column_ with a string, or multiple key columns with a list, as in the above example. This results in a DataFrame with 123005 rows and 48 columns. \n",
        "\n",
        "Why 48 columns instead of 47? Because you specified the keys columns to join on, Pandas doesn't try to merge all mergeable columns. This can result in \"duplicate\" column names, which may or may not have different values. \"Duplicate\" is in quotes because the columns will actually have new names, by default they are appended with `_x` and `_y`. You can also use the `suffixes` parameter to control what is appended to the column names.\n",
        "\n",
        "### Outer Join\n",
        "With the outer join, you will retain rows that don't have matches as well. For this example, you will use the smaller precipitation DataFrame `precip_one_station` with the full `climate_temp` DataFrame and join with `STATION` and `DATE` columns as the key columns. Take a second and think about how many rows you expect the new DataFrame to have."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "J5F-eCS0ugHa",
        "outputId": "d15b4afd-9749-4b1c-ef5d-9db3949aa57c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION          STATION_NAME_x      DATE  DLY-PRCP-25PCTL  \\\n",
              "0  GHCND:USC00045721  MITCHELL CAVERNS CA US  20100101             0.04   \n",
              "1  GHCND:USC00045721  MITCHELL CAVERNS CA US  20100102             0.05   \n",
              "2  GHCND:USC00045721  MITCHELL CAVERNS CA US  20100103             0.05   \n",
              "3  GHCND:USC00045721  MITCHELL CAVERNS CA US  20100104             0.05   \n",
              "4  GHCND:USC00045721  MITCHELL CAVERNS CA US  20100105             0.05   \n",
              "\n",
              "   DLY-SNWD-25PCTL  DLY-SNOW-25PCTL  DLY-PRCP-50PCTL  DLY-SNWD-50PCTL  \\\n",
              "0           -666.0            -66.6             0.16           -666.0   \n",
              "1           -666.0            -66.6             0.16           -666.0   \n",
              "2           -666.0            -66.6             0.16           -666.0   \n",
              "3           -666.0            -66.6             0.16           -666.0   \n",
              "4           -666.0            -66.6             0.17           -666.0   \n",
              "\n",
              "   DLY-SNOW-50PCTL  DLY-PRCP-75PCTL  ...  DLY-CLDD-NORMAL  DLY-CLDD-BASE70  \\\n",
              "0            -66.6             0.44  ...                0                0   \n",
              "1            -66.6             0.44  ...                0                0   \n",
              "2            -66.6             0.45  ...                0                0   \n",
              "3            -66.6             0.45  ...                0                0   \n",
              "4            -66.6             0.46  ...                0                0   \n",
              "\n",
              "   DLY-CLDD-BASE72  DLY-HTDD-BASE40  DLY-HTDD-BASE45  DLY-HTDD-BASE50  \\\n",
              "0                0                1                3                6   \n",
              "1                0                1                3                6   \n",
              "2                0                1                2                5   \n",
              "3                0                1                2                5   \n",
              "4                0                1                2                5   \n",
              "\n",
              "   DLY-HTDD-BASE55  DLY-HTDD-BASE57  DLY-HTDD-BASE60  DLY-HTDD-NORMAL  \n",
              "0               10               12               14               19  \n",
              "1               10               11               14               19  \n",
              "2                9               11               14               19  \n",
              "3                9               11               14               19  \n",
              "4                9               11               14               19  \n",
              "\n",
              "[5 rows x 48 columns]"
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <td>-666.0</td>\n",
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              "      <td>0.45</td>\n",
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              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GHCND:USC00045721</td>\n",
              "      <td>MITCHELL CAVERNS CA US</td>\n",
              "      <td>20100105</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-666.0</td>\n",
              "      <td>-66.6</td>\n",
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              "      <td>14</td>\n",
              "      <td>19</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 48 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b834a10d-add2-4bf2-baf3-bc6d7962ba05')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b834a10d-add2-4bf2-baf3-bc6d7962ba05 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-b834a10d-add2-4bf2-baf3-bc6d7962ba05');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "outer_merged = pd.merge(\n",
        "    precip_one_station, climate_temp, how=\"outer\", on=[\"STATION\", \"DATE\"]\n",
        ")\n",
        "outer_merged.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "MhHG4YZEugHa",
        "outputId": "aeb62fed-cbe7-40dc-a823-bf66065f878e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127020, 48)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "outer_merged.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPJ0CKRMugHb"
      },
      "source": [
        "If you remember from when you checked the `.shape` attribute of `climate_temp`, you'll see that the number of rows in `outer_merged` matches that. With an outer join, you can expect to have the same number of rows as the larger DataFrame, since none are lost like they are in an inner join. \n",
        "\n",
        "### Left Join\n",
        "Also known as a left outer join. In this join, you will retain rows that don't have matches only on the left (or first) DataFrame to be merged."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "IRSxju8qugHb",
        "outputId": "07d798f7-db1a-4c0c-84b1-145189145f78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION          STATION_NAME_x ELEVATION  LATITUDE   LONGITUDE  \\\n",
              "0  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "1  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "2  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
              "3  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806  -116.03694   \n",
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          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "left_merged = pd.merge(\n",
        "    climate_temp, precip_one_station, how=\"left\", on=[\"STATION\", \"DATE\"]\n",
        ")\n",
        "left_merged.head()"
      ]
    },
    {
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      "execution_count": 15,
      "metadata": {
        "id": "hoSM8uj7ugHb",
        "outputId": "fccf2efe-32fd-4ce9-d9a2-0205a93157ab",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127020, 48)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
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      "source": [
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      ]
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    {
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        "id": "uYTXf-C6ugHb"
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      "source": [
        "Here, you see that the number of rows in the resulting DataFrame matches that of the rows in the `climate_temp` DataFrame. What if we switched the positions of the two DataFrames that we are merging?"
      ]
    },
    {
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      "execution_count": 16,
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        "id": "ELw4gCKeugHb",
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              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GHCND:USC00045721</td>\n",
              "      <td>MITCHELL CAVERNS CA US</td>\n",
              "      <td>20100104</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.45</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GHCND:USC00045721</td>\n",
              "      <td>MITCHELL CAVERNS CA US</td>\n",
              "      <td>20100105</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-666</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>0.46</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 48 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0eb446b-be96-4c64-89e5-5a3bb95a4a1e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .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",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-f0eb446b-be96-4c64-89e5-5a3bb95a4a1e 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-f0eb446b-be96-4c64-89e5-5a3bb95a4a1e');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "left_merged_reversed = pd.merge(\n",
        "    precip_one_station, climate_temp, how=\"left\", on=[\"STATION\", \"DATE\"]\n",
        ")\n",
        "left_merged_reversed.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "roGihDU6ugHc",
        "outputId": "e5aaed96-1941-443b-d40e-1a2abe260886",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(365, 48)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "left_merged_reversed.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HoP85CvfugHc"
      },
      "source": [
        "### Right Join\n",
        "This works the same as the left join, however non-matching rows are only retained in the _right_ DataFrame. In the next example, you will recreate the `left_merged` DataFrame but with a right join."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "UyaUCC1ZugHc",
        "outputId": "9a6a740c-1948-45b1-d054-f6ee46111181",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION STATION_NAME_x      DATE  DLY-PRCP-25PCTL  \\\n",
              "0  GHCND:USC00049099            NaN  20100101              NaN   \n",
              "1  GHCND:USC00049099            NaN  20100102              NaN   \n",
              "2  GHCND:USC00049099            NaN  20100103              NaN   \n",
              "3  GHCND:USC00049099            NaN  20100104              NaN   \n",
              "4  GHCND:USC00049099            NaN  20100105              NaN   \n",
              "\n",
              "   DLY-SNWD-25PCTL  DLY-SNOW-25PCTL  DLY-PRCP-50PCTL  DLY-SNWD-50PCTL  \\\n",
              "0              NaN              NaN              NaN              NaN   \n",
              "1              NaN              NaN              NaN              NaN   \n",
              "2              NaN              NaN              NaN              NaN   \n",
              "3              NaN              NaN              NaN              NaN   \n",
              "4              NaN              NaN              NaN              NaN   \n",
              "\n",
              "   DLY-SNOW-50PCTL  DLY-PRCP-75PCTL  ...  DLY-CLDD-NORMAL  DLY-CLDD-BASE70  \\\n",
              "0              NaN              NaN  ...                0                0   \n",
              "1              NaN              NaN  ...                0                0   \n",
              "2              NaN              NaN  ...                0                0   \n",
              "3              NaN              NaN  ...                0                0   \n",
              "4              NaN              NaN  ...                0                0   \n",
              "\n",
              "   DLY-CLDD-BASE72  DLY-HTDD-BASE40  DLY-HTDD-BASE45  DLY-HTDD-BASE50  \\\n",
              "0                0            -7777                1                2   \n",
              "1                0            -7777                1                2   \n",
              "2                0            -7777                1                2   \n",
              "3                0            -7777                1                2   \n",
              "4                0            -7777            -7777                2   \n",
              "\n",
              "   DLY-HTDD-BASE55  DLY-HTDD-BASE57  DLY-HTDD-BASE60  DLY-HTDD-NORMAL  \n",
              "0                6                7               10               15  \n",
              "1                6                7               10               15  \n",
              "2                5                7               10               15  \n",
              "3                5                7               10               15  \n",
              "4                5                7               10               15  \n",
              "\n",
              "[5 rows x 48 columns]"
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      <td>GHCND:USC00049099</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>10</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 48 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f87d7e8e-5781-4fbc-84a1-91ad8ea028d9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f87d7e8e-5781-4fbc-84a1-91ad8ea028d9 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-f87d7e8e-5781-4fbc-84a1-91ad8ea028d9');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "right_merged = pd.merge(\n",
        "    precip_one_station, climate_temp, how=\"right\", on=[\"STATION\", \"DATE\"]\n",
        ")\n",
        "right_merged.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "cNZV-PzkugHc",
        "outputId": "7ba6cc9b-b163-43b0-8494-6cfddf9b606f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127020, 48)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "right_merged.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPoybTZAugHc"
      },
      "source": [
        "Here, you simply flipped the positions of the input DataFrames and specified a right join. When you inspect `right_merged`, you might notice that it's not exactly the same as `left_merged`. The only difference between the two is the order of the columns: the first input's columns will always be the first in the newly formed DataFrame.\n",
        "\n",
        "## .join()\n",
        "`.join()` uses `merge()` under the hood, but provides a much more simplified interface to `merge()` and by default joins on indexes. Here is an introductory example using the `lsuffix` and `rsuffix` parameters to handle overlapping column names."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "RHnpiZGQugHd",
        "outputId": "2f6001e6-1313-4140-f2bc-51853a034b87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           STATION_left       STATION_NAME_left  DATE_left  DLY-PRCP-25PCTL  \\\n",
              "1460  GHCND:USC00045721  MITCHELL CAVERNS CA US   20100101             0.04   \n",
              "1461  GHCND:USC00045721  MITCHELL CAVERNS CA US   20100102             0.05   \n",
              "1462  GHCND:USC00045721  MITCHELL CAVERNS CA US   20100103             0.05   \n",
              "1463  GHCND:USC00045721  MITCHELL CAVERNS CA US   20100104             0.05   \n",
              "1464  GHCND:USC00045721  MITCHELL CAVERNS CA US   20100105             0.05   \n",
              "...                 ...                     ...        ...              ...   \n",
              "1820  GHCND:USC00045721  MITCHELL CAVERNS CA US   20101227             0.04   \n",
              "1821  GHCND:USC00045721  MITCHELL CAVERNS CA US   20101228             0.04   \n",
              "1822  GHCND:USC00045721  MITCHELL CAVERNS CA US   20101229             0.04   \n",
              "1823  GHCND:USC00045721  MITCHELL CAVERNS CA US   20101230             0.04   \n",
              "1824  GHCND:USC00045721  MITCHELL CAVERNS CA US   20101231             0.04   \n",
              "\n",
              "      DLY-SNWD-25PCTL  DLY-SNOW-25PCTL  DLY-PRCP-50PCTL  DLY-SNWD-50PCTL  \\\n",
              "1460             -666            -66.6             0.16             -666   \n",
              "1461             -666            -66.6             0.16             -666   \n",
              "1462             -666            -66.6             0.16             -666   \n",
              "1463             -666            -66.6             0.16             -666   \n",
              "1464             -666            -66.6             0.17             -666   \n",
              "...               ...              ...              ...              ...   \n",
              "1820             -666            -66.6             0.15             -666   \n",
              "1821             -666            -66.6             0.15             -666   \n",
              "1822             -666            -66.6             0.15             -666   \n",
              "1823             -666            -66.6             0.15             -666   \n",
              "1824             -666            -66.6             0.15             -666   \n",
              "\n",
              "      DLY-SNOW-50PCTL  DLY-PRCP-75PCTL  ...  DLY-CLDD-NORMAL  DLY-CLDD-BASE70  \\\n",
              "1460            -66.6             0.44  ...                0                0   \n",
              "1461            -66.6             0.44  ...                0                0   \n",
              "1462            -66.6             0.45  ...                0                0   \n",
              "1463            -66.6             0.45  ...                0                0   \n",
              "1464            -66.6             0.46  ...                0                0   \n",
              "...               ...              ...  ...              ...              ...   \n",
              "1820            -66.6             0.44  ...            -7777                0   \n",
              "1821            -66.6             0.43  ...            -7777                0   \n",
              "1822            -66.6             0.43  ...            -7777                0   \n",
              "1823            -66.6             0.43  ...            -7777                0   \n",
              "1824            -66.6             0.44  ...            -7777                0   \n",
              "\n",
              "      DLY-CLDD-BASE72  DLY-HTDD-BASE40  DLY-HTDD-BASE45  DLY-HTDD-BASE50  \\\n",
              "1460                0                1                3                6   \n",
              "1461                0                1                3                6   \n",
              "1462                0                1                2                5   \n",
              "1463                0                1                2                5   \n",
              "1464                0                1                2                5   \n",
              "...               ...              ...              ...              ...   \n",
              "1820                0                1                3                6   \n",
              "1821                0                1                3                6   \n",
              "1822                0                1                3                6   \n",
              "1823                0                1                3                6   \n",
              "1824                0                1                3                6   \n",
              "\n",
              "      DLY-HTDD-BASE55  DLY-HTDD-BASE57  DLY-HTDD-BASE60  DLY-HTDD-NORMAL  \n",
              "1460               10               12               14               19  \n",
              "1461               10               11               14               19  \n",
              "1462                9               11               14               19  \n",
              "1463                9               11               14               19  \n",
              "1464                9               11               14               19  \n",
              "...               ...              ...              ...              ...  \n",
              "1820               10               12               15               20  \n",
              "1821               10               12               15               20  \n",
              "1822               10               12               15               20  \n",
              "1823               10               12               15               20  \n",
              "1824               10               12               15               19  \n",
              "\n",
              "[365 rows x 50 columns]"
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              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
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              "<p>365 rows × 50 columns</p>\n",
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          "execution_count": 20
        }
      ],
      "source": [
        "precip_one_station.join(climate_temp, lsuffix=\"_left\", rsuffix=\"_right\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MF9sEN6QugHd"
      },
      "source": [
        "If you inspect the data, you'll see that overlapping columns are kept, just renamed to be unique. If we flip this around and instead call `.join()` on the larger DataFrame, you'll notice that the DataFrame is larger, but data that doesn't exist in the smaller DataFrame (`precip_one_station`) is filled in with `NaN` (_Not a Number_) values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "GA4oz9HWugHd",
        "outputId": "a5040f76-3cf9-455c-9ac8-cffe9e5b3ad0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION_left       STATION_NAME_left ELEVATION  LATITUDE  \\\n",
              "0       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "1       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "2       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "3       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "4       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "...                   ...                     ...       ...       ...   \n",
              "127015  GHCND:USC00046006  MOUNT WILSON CBS CA US    1740.4   34.2308   \n",
              "127016  GHCND:USC00046006  MOUNT WILSON CBS CA US    1740.4   34.2308   \n",
              "127017  GHCND:USC00046006  MOUNT WILSON CBS CA US    1740.4   34.2308   \n",
              "127018  GHCND:USC00046006  MOUNT WILSON CBS CA US    1740.4   34.2308   \n",
              "127019  GHCND:USC00046006  MOUNT WILSON CBS CA US    1740.4   34.2308   \n",
              "\n",
              "         LONGITUDE  DATE_left  DLY-CLDD-BASE45  DLY-CLDD-BASE50  \\\n",
              "0       -116.03694   20100101                6                2   \n",
              "1       -116.03694   20100102                6                2   \n",
              "2       -116.03694   20100103                6                2   \n",
              "3       -116.03694   20100104                6                2   \n",
              "4       -116.03694   20100105                6                2   \n",
              "...            ...        ...              ...              ...   \n",
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              "127018   -118.0711   20101230                4                2   \n",
              "127019   -118.0711   20101231                4                2   \n",
              "\n",
              "        DLY-CLDD-BASE55  DLY-CLDD-BASE57  ...  DLY-PRCP-PCTALL-GE100HI  \\\n",
              "0                 -7777            -7777  ...                      NaN   \n",
              "1                     1            -7777  ...                      NaN   \n",
              "2                     1            -7777  ...                      NaN   \n",
              "3                     1            -7777  ...                      NaN   \n",
              "4                     1            -7777  ...                      NaN   \n",
              "...                 ...              ...  ...                      ...   \n",
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              "\n",
              "        DLY-SNWD-PCTALL-GE001WI  DLY-SNWD-PCTALL-GE010WI  \\\n",
              "0                           NaN                      NaN   \n",
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              "2                           NaN                      NaN   \n",
              "3                           NaN                      NaN   \n",
              "4                           NaN                      NaN   \n",
              "...                         ...                      ...   \n",
              "127015                      NaN                      NaN   \n",
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              "\n",
              "        DLY-SNWD-PCTALL-GE003WI  DLY-SNWD-PCTALL-GE005WI  \\\n",
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              "...                         ...                      ...   \n",
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              "\n",
              "        DLY-SNOW-PCTALL-GE001TI  DLY-SNOW-PCTALL-GE010TI  \\\n",
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              "...                         ...                      ...   \n",
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              "...                         ...                      ...   \n",
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              "\n",
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              "3                           NaN  \n",
              "4                           NaN  \n",
              "...                         ...  \n",
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          "metadata": {},
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      ],
      "source": [
        "climate_temp.join(precip_one_station, lsuffix=\"_left\", rsuffix=\"_right\")"
      ]
    },
    {
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        "id": "VELPkDiVugHd"
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        "If you must use `.join()` and want to merge the columns, you must set them to be indexes first. First take a look at this previously used `merge()` operation:"
      ]
    },
    {
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              "             STATION          STATION_NAME_x ELEVATION  LATITUDE   LONGITUDE  \\\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "inner_merged_total = pd.merge(\n",
        "    climate_temp, climate_precip, on=[\"STATION\", \"DATE\"]\n",
        ")\n",
        "inner_merged_total.head()"
      ]
    },
    {
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      "execution_count": 23,
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              "             STATION          STATION_NAME_x ELEVATION  LATITUDE   LONGITUDE  \\\n",
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              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
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              "      <td>-7777</td>\n",
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              "      <td>-9999</td>\n",
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              "      <td>-9999</td>\n",
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              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
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              "    <tr>\n",
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              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100103</td>\n",
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              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "      <td>0</td>\n",
              "      <td>-9999</td>\n",
              "      <td>-9999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100104</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 48 columns</p>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "inner_joined_total = climate_temp.join(\n",
        "    climate_precip.set_index([\"STATION\", \"DATE\"]),\n",
        "    on=[\"STATION\", \"DATE\"],\n",
        "    how=\"inner\",\n",
        "    lsuffix=\"_x\",\n",
        "    rsuffix=\"_y\",\n",
        ")\n",
        "inner_joined_total.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "op6Wah3-ugHe"
      },
      "source": [
        "Because `.join()` works on indexes, if we want to recreate `merge()` before, then we must set indexes on the join columns we specify. In this example, you used the `.set_index()` method to set your indexes to the key columns within the join. \n",
        "\n",
        "Below you will see an almost-bare `.join()` call. Because there are overlapping columns, you will have to specify a suffix with `lsuffix`, `rsuffix`, or both, but this example will demonstrate the more typical behavior of `.join()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "yfsHG9JCugHe",
        "outputId": "6c2a3e56-0624-4737-90b6-81b74d591053",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "             STATION_left       STATION_NAME_left ELEVATION  LATITUDE  \\\n",
              "0       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "1       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "2       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "3       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "4       GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "...                   ...                     ...       ...       ...   \n",
              "127015  GHCND:USC00046006  MOUNT WILSON CBS CA US    1740.4   34.2308   \n",
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              "127019  GHCND:USC00046006  MOUNT WILSON CBS CA US    1740.4   34.2308   \n",
              "\n",
              "         LONGITUDE  DATE_left  DLY-CLDD-BASE45  DLY-CLDD-BASE50  \\\n",
              "0       -116.03694   20100101                6                2   \n",
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              "127018   -118.0711   20101230                4                2   \n",
              "127019   -118.0711   20101231                4                2   \n",
              "\n",
              "        DLY-CLDD-BASE55  DLY-CLDD-BASE57  ...  DLY-PRCP-PCTALL-GE100HI  \\\n",
              "0                 -7777            -7777  ...                        3   \n",
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              "127019                1            -7777  ...                       16   \n",
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              "        DLY-SNWD-PCTALL-GE001WI  DLY-SNWD-PCTALL-GE010WI  \\\n",
              "0                         -9999                        0   \n",
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              "127019                        0                        0   \n",
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              "127016                        0                        0   \n",
              "127017                        0                        0   \n",
              "127018                        0                        0   \n",
              "127019                        0                        0   \n",
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              "127019                        0                        0   \n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>127019</th>\n",
              "      <td>GHCND:USC00046006</td>\n",
              "      <td>MOUNT WILSON CBS CA US</td>\n",
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              "<p>127020 rows × 50 columns</p>\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "climate_temp.join(climate_precip, lsuffix=\"_left\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fU1orKHbugHe"
      },
      "source": [
        "## concat()\n",
        "\n",
        "First, you will see a basic concatenation along axis 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "SgnnL_PbugHe",
        "outputId": "2b6ea120-0511-4bb9-c724-2cb9eeed473d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "1460                        4                        0   \n",
              "1461                        4                        0   \n",
              "1462                        4                        0   \n",
              "1463                        4                        0   \n",
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              "1460                        3                        3   \n",
              "1461                        3                        3   \n",
              "1462                        3                        3   \n",
              "1463                        3                        2   \n",
              "1464                        3                        2   \n",
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          "metadata": {},
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      ],
      "source": [
        "double_precip = pd.concat([precip_one_station, precip_one_station])\n",
        "double_precip.head()"
      ]
    },
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      "cell_type": "markdown",
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        "To reset the index, use the `ignore_index` optional parameter."
      ]
    },
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          "metadata": {},
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      ],
      "source": [
        "reindexed = pd.concat(\n",
        "    [precip_one_station, precip_one_station], ignore_index=True\n",
        ")\n",
        "reindexed.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "When axis labels for the axis you are **not** concatenating along don't match (for example, column labels when concatenating along rows), then all columns are preserved and missing data is filled in with `NaN`. "
      ]
    },
    {
      "cell_type": "code",
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              "      <th>DLY-SNWD-50PCTL</th>\n",
              "      <th>DLY-SNOW-50PCTL</th>\n",
              "      <th>DLY-PRCP-75PCTL</th>\n",
              "      <th>...</th>\n",
              "      <th>DLY-CLDD-NORMAL</th>\n",
              "      <th>DLY-CLDD-BASE70</th>\n",
              "      <th>DLY-CLDD-BASE72</th>\n",
              "      <th>DLY-HTDD-BASE40</th>\n",
              "      <th>DLY-HTDD-BASE45</th>\n",
              "      <th>DLY-HTDD-BASE50</th>\n",
              "      <th>DLY-HTDD-BASE55</th>\n",
              "      <th>DLY-HTDD-BASE57</th>\n",
              "      <th>DLY-HTDD-BASE60</th>\n",
              "      <th>DLY-HTDD-NORMAL</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>20100101</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-666.0</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-666.0</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>20100102</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-666.0</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-666.0</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>20100103</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-666.0</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-666.0</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>20100104</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>20100105</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>-6.66</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\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",
              "<p>5 rows × 47 columns</p>\n",
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              "                                                     [key], {});\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "outer_joined = pd.concat([climate_precip, climate_temp])\n",
        "outer_joined.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
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        "colab": {
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          "height": 206
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION            STATION_NAME      DATE\n",
              "0  GHCND:USC00049099  TWENTYNINE PALMS CA US  20100101\n",
              "1  GHCND:USC00049099  TWENTYNINE PALMS CA US  20100102\n",
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              "      <th>4</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          "metadata": {},
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      ],
      "source": [
        "inner_joined = pd.concat([climate_temp, climate_precip], join=\"inner\")\n",
        "inner_joined.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Oyk2YtzuugHf",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(278130, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "inner_joined.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YUWrQd-ugHf"
      },
      "source": [
        "To illustrate how this would work with rows, concatenate along columns instead:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "8clkDYYbugHf",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             STATION            STATION_NAME      DATE\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c78216b-5e8e-401f-b3f8-a9b9e66fa11d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const element = document.querySelector('#df-6c78216b-5e8e-401f-b3f8-a9b9e66fa11d');\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "inner_joined_cols = pd.concat(\n",
        "    [climate_temp, climate_precip], axis=\"columns\", join=\"inner\"\n",
        ")\n",
        "inner_joined.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "SNZ-bIoyugHg",
        "outputId": "1c507993-d01d-48f2-a882-5122ecdfc794",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127020, 50)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "inner_joined_cols.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRFlUsw8ugHg"
      },
      "source": [
        "You can also use the `keys` parameter to set hierarchical axis labels which can be used, for example, to preserve original labels while maintaining labels that tell you which dataset each row or column came from."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "GY5a_PpfugHg",
        "outputId": "40b50802-ef1a-4214-ff4e-4fff17cec68f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  STATION            STATION_NAME ELEVATION  LATITUDE  \\\n",
              "temp 0  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "     1  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "     2  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "     3  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "     4  GHCND:USC00049099  TWENTYNINE PALMS CA US       602  34.12806   \n",
              "\n",
              "         LONGITUDE      DATE  DLY-CLDD-BASE45  DLY-CLDD-BASE50  \\\n",
              "temp 0  -116.03694  20100101              6.0              2.0   \n",
              "     1  -116.03694  20100102              6.0              2.0   \n",
              "     2  -116.03694  20100103              6.0              2.0   \n",
              "     3  -116.03694  20100104              6.0              2.0   \n",
              "     4  -116.03694  20100105              6.0              2.0   \n",
              "\n",
              "        DLY-CLDD-BASE55  DLY-CLDD-BASE57  ...  DLY-PRCP-PCTALL-GE100HI  \\\n",
              "temp 0          -7777.0          -7777.0  ...                      NaN   \n",
              "     1              1.0          -7777.0  ...                      NaN   \n",
              "     2              1.0          -7777.0  ...                      NaN   \n",
              "     3              1.0          -7777.0  ...                      NaN   \n",
              "     4              1.0          -7777.0  ...                      NaN   \n",
              "\n",
              "        DLY-SNWD-PCTALL-GE001WI  DLY-SNWD-PCTALL-GE010WI  \\\n",
              "temp 0                      NaN                      NaN   \n",
              "     1                      NaN                      NaN   \n",
              "     2                      NaN                      NaN   \n",
              "     3                      NaN                      NaN   \n",
              "     4                      NaN                      NaN   \n",
              "\n",
              "        DLY-SNWD-PCTALL-GE003WI  DLY-SNWD-PCTALL-GE005WI  \\\n",
              "temp 0                      NaN                      NaN   \n",
              "     1                      NaN                      NaN   \n",
              "     2                      NaN                      NaN   \n",
              "     3                      NaN                      NaN   \n",
              "     4                      NaN                      NaN   \n",
              "\n",
              "        DLY-SNOW-PCTALL-GE001TI  DLY-SNOW-PCTALL-GE010TI  \\\n",
              "temp 0                      NaN                      NaN   \n",
              "     1                      NaN                      NaN   \n",
              "     2                      NaN                      NaN   \n",
              "     3                      NaN                      NaN   \n",
              "     4                      NaN                      NaN   \n",
              "\n",
              "        DLY-SNOW-PCTALL-GE100TI  DLY-SNOW-PCTALL-GE030TI  \\\n",
              "temp 0                      NaN                      NaN   \n",
              "     1                      NaN                      NaN   \n",
              "     2                      NaN                      NaN   \n",
              "     3                      NaN                      NaN   \n",
              "     4                      NaN                      NaN   \n",
              "\n",
              "        DLY-SNOW-PCTALL-GE050TI  \n",
              "temp 0                      NaN  \n",
              "     1                      NaN  \n",
              "     2                      NaN  \n",
              "     3                      NaN  \n",
              "     4                      NaN  \n",
              "\n",
              "[5 rows x 47 columns]"
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100103</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
              "      <td>-116.03694</td>\n",
              "      <td>20100104</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GHCND:USC00049099</td>\n",
              "      <td>TWENTYNINE PALMS CA US</td>\n",
              "      <td>602</td>\n",
              "      <td>34.12806</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>NaN</td>\n",
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              "<p>5 rows × 47 columns</p>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-00023e86-60ec-4c3f-a5bb-d85f9f73fa3a 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-00023e86-60ec-4c3f-a5bb-d85f9f73fa3a');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "hierarchical_keys = pd.concat(\n",
        "    [climate_temp, climate_precip], keys=[\"temp\", \"precip\"]\n",
        ")\n",
        "hierarchical_keys.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "krMk4hs4ugHg",
        "outputId": "c2b9f6be-dfbe-478a-c643-a37f81cf7db9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         STATION            STATION_NAME ELEVATION LATITUDE  \\\n",
              "precip 151105  GHCND:USC00046006  MOUNT WILSON CBS CA US       NaN      NaN   \n",
              "       151106  GHCND:USC00046006  MOUNT WILSON CBS CA US       NaN      NaN   \n",
              "       151107  GHCND:USC00046006  MOUNT WILSON CBS CA US       NaN      NaN   \n",
              "       151108  GHCND:USC00046006  MOUNT WILSON CBS CA US       NaN      NaN   \n",
              "       151109  GHCND:USC00046006  MOUNT WILSON CBS CA US       NaN      NaN   \n",
              "\n",
              "              LONGITUDE      DATE  DLY-CLDD-BASE45  DLY-CLDD-BASE50  \\\n",
              "precip 151105       NaN  20101227              NaN              NaN   \n",
              "       151106       NaN  20101228              NaN              NaN   \n",
              "       151107       NaN  20101229              NaN              NaN   \n",
              "       151108       NaN  20101230              NaN              NaN   \n",
              "       151109       NaN  20101231              NaN              NaN   \n",
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          "metadata": {},
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