08_Groupby.ipynb

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      "source": [
        "--- \n",
        "<strong> \n",
        "    <h1 align='center'>Groupby</h1> \n",
        "</strong>\n",
        "\n",
        "---"
      ]
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        "\n",
        "\n",
        "The groupby method allows you to group rows of data together and call aggregate functions"
      ]
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      "source": [
        "import pandas as pd\n",
        "# Create dataframe\n",
        "data = {'Company':['google','google','apple','microsoft','facebook','facebook','google','google'],\n",
        "       'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah','monika','mike'],\n",
        "       'Sales':[200,120,340,124,243,350,281,291]}"
      ],
      "execution_count": 1,
      "outputs": []
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      "source": [
        "df = pd.DataFrame(data)"
      ],
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      "outputs": []
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      "source": [
        "df"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>Person</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>google</td>\n",
              "      <td>Sam</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>google</td>\n",
              "      <td>Charlie</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>apple</td>\n",
              "      <td>Amy</td>\n",
              "      <td>340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>microsoft</td>\n",
              "      <td>Vanessa</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>facebook</td>\n",
              "      <td>Carl</td>\n",
              "      <td>243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>facebook</td>\n",
              "      <td>Sarah</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>google</td>\n",
              "      <td>monika</td>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>google</td>\n",
              "      <td>mike</td>\n",
              "      <td>291</td>\n",
              "    </tr>\n",
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              "     Company   Person  Sales\n",
              "0     google      Sam    200\n",
              "1     google  Charlie    120\n",
              "2      apple      Amy    340\n",
              "3  microsoft  Vanessa    124\n",
              "4   facebook     Carl    243\n",
              "5   facebook    Sarah    350\n",
              "6     google   monika    281\n",
              "7     google     mike    291"
            ]
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      "source": [
        "** Now you can use the .groupby() method to group rows together based off of a column name. For instance let's group based off of Company. This will create a DataFrameGroupBy object:**"
      ]
    },
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      "source": [
        "df.groupby('Company')"
      ],
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          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f6c5d4c8110>"
            ]
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            "tags": []
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          "execution_count": 4
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      ]
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        "You can save this object as a new variable:"
      ]
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        "by_comp = df.groupby(\"Company\")"
      ],
      "execution_count": 5,
      "outputs": []
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      "source": [
        "And then call aggregate methods off the object:"
      ]
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        "by_comp.mean()"
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              "      <th></th>\n",
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              "      <th>apple</th>\n",
              "      <td>340.0</td>\n",
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              "    <tr>\n",
              "      <th>facebook</th>\n",
              "      <td>296.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google</th>\n",
              "      <td>223.0</td>\n",
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              "      <th>microsoft</th>\n",
              "      <td>124.0</td>\n",
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              "           Sales\n",
              "Company         \n",
              "apple      340.0\n",
              "facebook   296.5\n",
              "google     223.0\n",
              "microsoft  124.0"
            ]
          },
          "metadata": {
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          "execution_count": 6
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      "source": [
        "df.groupby('Company').mean()"
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      "execution_count": 7,
      "outputs": [
        {
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              "      <th>Company</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>apple</th>\n",
              "      <td>340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>facebook</th>\n",
              "      <td>296.5</td>\n",
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              "      <td>124.0</td>\n",
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              "           Sales\n",
              "Company         \n",
              "apple      340.0\n",
              "facebook   296.5\n",
              "google     223.0\n",
              "microsoft  124.0"
            ]
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      "source": [
        "More examples of aggregate methods:"
      ]
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        "by_comp.std()"
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      "execution_count": 8,
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>apple</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>facebook</th>\n",
              "      <td>75.660426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google</th>\n",
              "      <td>79.845685</td>\n",
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              "      <th>microsoft</th>\n",
              "      <td>NaN</td>\n",
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              "               Sales\n",
              "Company             \n",
              "apple            NaN\n",
              "facebook   75.660426\n",
              "google     79.845685\n",
              "microsoft        NaN"
            ]
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      "source": [
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>apple</th>\n",
              "      <td>Amy</td>\n",
              "      <td>340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>facebook</th>\n",
              "      <td>Carl</td>\n",
              "      <td>243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google</th>\n",
              "      <td>Charlie</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>microsoft</th>\n",
              "      <td>Vanessa</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "            Person  Sales\n",
              "Company                  \n",
              "apple          Amy    340\n",
              "facebook      Carl    243\n",
              "google     Charlie    120\n",
              "microsoft  Vanessa    124"
            ]
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      "source": [
        "by_comp.max()"
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              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>apple</th>\n",
              "      <td>Amy</td>\n",
              "      <td>340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>facebook</th>\n",
              "      <td>Sarah</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google</th>\n",
              "      <td>monika</td>\n",
              "      <td>291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>microsoft</th>\n",
              "      <td>Vanessa</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "            Person  Sales\n",
              "Company                  \n",
              "apple          Amy    340\n",
              "facebook     Sarah    350\n",
              "google      monika    291\n",
              "microsoft  Vanessa    124"
            ]
          },
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        "by_comp.count()"
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      "outputs": [
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              "      <th>Person</th>\n",
              "      <th>Sales</th>\n",
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              "    <tr>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>apple</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>facebook</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>microsoft</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "           Person  Sales\n",
              "Company                 \n",
              "apple           1      1\n",
              "facebook        2      2\n",
              "google          4      4\n",
              "microsoft       1      1"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 11
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      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "7Spc3bGwz6Vq",
        "outputId": "270f8883-1ebd-40ea-a6f6-720be9aa96f6"
      },
      "source": [
        "by_comp.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>apple</th>\n",
              "      <td>1.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>340.0</td>\n",
              "      <td>340.00</td>\n",
              "      <td>340.0</td>\n",
              "      <td>340.00</td>\n",
              "      <td>340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>facebook</th>\n",
              "      <td>2.0</td>\n",
              "      <td>296.5</td>\n",
              "      <td>75.660426</td>\n",
              "      <td>243.0</td>\n",
              "      <td>269.75</td>\n",
              "      <td>296.5</td>\n",
              "      <td>323.25</td>\n",
              "      <td>350.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google</th>\n",
              "      <td>4.0</td>\n",
              "      <td>223.0</td>\n",
              "      <td>79.845685</td>\n",
              "      <td>120.0</td>\n",
              "      <td>180.00</td>\n",
              "      <td>240.5</td>\n",
              "      <td>283.50</td>\n",
              "      <td>291.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>microsoft</th>\n",
              "      <td>1.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>124.0</td>\n",
              "      <td>124.00</td>\n",
              "      <td>124.0</td>\n",
              "      <td>124.00</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Sales                                                       \n",
              "          count   mean        std    min     25%    50%     75%    max\n",
              "Company                                                               \n",
              "apple       1.0  340.0        NaN  340.0  340.00  340.0  340.00  340.0\n",
              "facebook    2.0  296.5  75.660426  243.0  269.75  296.5  323.25  350.0\n",
              "google      4.0  223.0  79.845685  120.0  180.00  240.5  283.50  291.0\n",
              "microsoft   1.0  124.0        NaN  124.0  124.00  124.0  124.00  124.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "GGHlEpM3z6Vr",
        "outputId": "1a9dd135-87a3-4a74-c173-f983c9719d19"
      },
      "source": [
        "by_comp.describe().transpose()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company</th>\n",
              "      <th>apple</th>\n",
              "      <th>facebook</th>\n",
              "      <th>google</th>\n",
              "      <th>microsoft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">Sales</th>\n",
              "      <th>count</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>340.0</td>\n",
              "      <td>296.500000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>75.660426</td>\n",
              "      <td>79.845685</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>340.0</td>\n",
              "      <td>243.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>340.0</td>\n",
              "      <td>269.750000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>340.0</td>\n",
              "      <td>296.500000</td>\n",
              "      <td>240.500000</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>340.0</td>\n",
              "      <td>323.250000</td>\n",
              "      <td>283.500000</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>340.0</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>291.000000</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Company      apple    facebook      google  microsoft\n",
              "Sales count    1.0    2.000000    4.000000        1.0\n",
              "      mean   340.0  296.500000  223.000000      124.0\n",
              "      std      NaN   75.660426   79.845685        NaN\n",
              "      min    340.0  243.000000  120.000000      124.0\n",
              "      25%    340.0  269.750000  180.000000      124.0\n",
              "      50%    340.0  296.500000  240.500000      124.0\n",
              "      75%    340.0  323.250000  283.500000      124.0\n",
              "      max    340.0  350.000000  291.000000      124.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTi_Eca2z6Vr",
        "outputId": "7c1d4ab8-d671-4fed-a223-ca4dccf27456"
      },
      "source": [
        "by_comp.describe().transpose()['google']"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sales  count      4.000000\n",
              "       mean     223.000000\n",
              "       std       79.845685\n",
              "       min      120.000000\n",
              "       25%      180.000000\n",
              "       50%      240.500000\n",
              "       75%      283.500000\n",
              "       max      291.000000\n",
              "Name: google, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    }
  ]
}