work_with_pd.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sinrez/pyCoursera/blob/master/work_with_pd.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "text/plain": [
              "<bound method DataFrame.info of       transactionId  visitorId        date  revenue group\n",
              "0                 0          0  2021-03-15     1633     B\n",
              "1                 1          1  2021-03-15      659     B\n",
              "2                 2       1070  2021-03-15      411     A\n",
              "3                 3          3  2021-03-15     9688     B\n",
              "4                 4          4  2021-03-15     2221     B\n",
              "...             ...        ...         ...      ...   ...\n",
              "1192           1192       1192  2021-03-14     6478     B\n",
              "1193           1193       1193  2021-03-14     3185     A\n",
              "1194           1194       1194  2021-03-14    10514     A\n",
              "1195           1195       1195  2021-03-14       81     A\n",
              "1196           1196       1196  2021-03-15   202680     B\n",
              "\n",
              "[1197 rows x 5 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "df = pd.read_csv('/content/orders (1).csv')\n",
        "df.shape\n",
        "df.head\n",
        "df.info"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['date'].value_counts()"
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              "2021-03-20    32\n",
              "2021-03-31    32\n",
              "2021-03-24    31\n",
              "2021-03-11    26\n",
              "2021-03-18    26\n",
              "2021-03-17    24\n",
              "2021-03-25    24\n",
              "Name: date, dtype: int64"
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          "metadata": {},
          "execution_count": 21
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      "source": [
        "df = pd.read_csv('/content/fish.csv')\n",
        "df.shape\n",
        "df.head\n",
        "df.info"
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          "data": {
            "text/plain": [
              "<bound method DataFrame.info of     Species  Weight  Length1  Length2  Length3   Height   Width\n",
              "0     Bream   242.0     23.2     25.4     30.0  11.5200  4.0200\n",
              "1     Bream   290.0     24.0     26.3     31.2  12.4800  4.3056\n",
              "2     Bream   340.0     23.9     26.5     31.1  12.3778  4.6961\n",
              "3     Bream   363.0     26.3     29.0     33.5  12.7300  4.4555\n",
              "4     Bream   430.0     26.5     29.0     34.0  12.4440  5.1340\n",
              "..      ...     ...      ...      ...      ...      ...     ...\n",
              "154   Smelt    12.2     11.5     12.2     13.4   2.0904  1.3936\n",
              "155   Smelt    13.4     11.7     12.4     13.5   2.4300  1.2690\n",
              "156   Smelt    12.2     12.1     13.0     13.8   2.2770  1.2558\n",
              "157   Smelt    19.7     13.2     14.3     15.2   2.8728  2.0672\n",
              "158   Smelt    19.9     13.8     15.0     16.2   2.9322  1.8792\n",
              "\n",
              "[159 rows x 7 columns]>"
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          "execution_count": 33
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        "df.loc[4:20, 'Length1'] = None"
      ],
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      "outputs": []
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        "df.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 159 entries, 0 to 158\n",
            "Data columns (total 7 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Species  159 non-null    object \n",
            " 1   Weight   159 non-null    float64\n",
            " 2   Length1  142 non-null    float64\n",
            " 3   Length2  159 non-null    float64\n",
            " 4   Length3  159 non-null    float64\n",
            " 5   Height   159 non-null    float64\n",
            " 6   Width    159 non-null    float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 8.8+ KB\n"
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      "cell_type": "code",
      "source": [
        "df.isna().sum()"
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      "execution_count": 25,
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              "Species     0\n",
              "Weight      0\n",
              "Length1    17\n",
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      "source": [
        "df.fillna(55.12, inplace=True)"
      ],
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        "id": "jzIuJtcEk3W3"
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      "execution_count": 26,
      "outputs": []
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        "df.isna().sum()"
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      "execution_count": 27,
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              "Species    0\n",
              "Weight     0\n",
              "Length1    0\n",
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        "df.groupby('Species')[['Weight', 'Height']].mean()"
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              "               Weight     Height\n",
              "Species                         \n",
              "Bream      617.828571  15.183211\n",
              "Parkki     154.818182   8.962427\n",
              "Perch      382.239286   7.861870\n",
              "Pike       718.705882   7.713771\n",
              "Roach      152.050000   6.694795\n",
              "Smelt       11.178571   2.209371\n",
              "Whitefish  531.000000  10.027167"
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        "    'Length2': 'diagonal_length'\n",
        "}).columns"
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      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Z_CostContact', 'Marital_Status', 'MntFruits', 'MntMeatProducts',\n",
              "       'MntFishProducts'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
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    {
      "cell_type": "code",
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        "import pandas as pd\n",
        "\n",
        "\n",
        "df = pd.DataFrame({'ID': {0: 5524, 1: 2174, 2: 4141},\n",
        "                   'Z_CostContact': {0: 3, 1: None, 2: 3},\n",
        "                   'Marital_Status': {0: 'Single', 1: 'Single', 2: 'Together'},\n",
        "                   'MntFruits': {0: None, 1: 1, 2: None},\n",
        "                   'MntMeatProducts': {0: None, 1: 6, 2: 127},\n",
        "                   'MntFishProducts': {0: 172, 1: 2, 2: 111}})\n",
        "\n",
        "count_fill = df.isna().sum()\n",
        "count_fill"
      ],
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        "colab": {
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        "id": "gdGdNLY9uCNb",
        "outputId": "d2df1f7f-2f23-4189-9d92-517347740de2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                 0\n",
              "Z_CostContact      1\n",
              "Marital_Status     0\n",
              "MntFruits          2\n",
              "MntMeatProducts    1\n",
              "MntFishProducts    0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 28
        }
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "df = pd.DataFrame({'ID': {0: 5524, 1: 2174, 2: 4141},\n",
        "                   'Z_CostContact': {0: 3, 1: 3, 2: 3},\n",
        "                   'Marital_Status': {0: 'Single', 1: 'Single', 2: 'Together'},\n",
        "                   'MntFruits': {0: 88, 1: 1, 2: 49},\n",
        "                   'MntMeatProducts': {0: 546, 1: 6, 2: 127},\n",
        "                   'MntFishProducts': {0: 172, 1: 2, 2: 111}})\n",
        "\n",
        "dict_rename = {\n",
        "    'MntFruits': 'sum_fruits',\n",
        "    'MntMeatProducts': 'sum_meat',\n",
        "    'MntFishProducts': 'sum_fish'}\n",
        "\n",
        "\n",
        "df = df.drop(columns=['ID', 'Z_CostContact'])\n",
        "\n",
        "df.rename(columns=dict_rename,inplace=True)\n",
        "df"
      ],
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      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Marital_Status  sum_fruits  sum_meat  sum_fish\n",
              "0         Single          88       546       172\n",
              "1         Single           1         6         2\n",
              "2       Together          49       127       111"
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Single</td>\n",
              "      <td>88</td>\n",
              "      <td>546</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Single</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Together</td>\n",
              "      <td>49</td>\n",
              "      <td>127</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ef44b44-70ee-4f65-a3a8-b7383c6f162a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2ef44b44-70ee-4f65-a3a8-b7383c6f162a 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-2ef44b44-70ee-4f65-a3a8-b7383c6f162a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    }
  ]
}