Day43 - Pivot_Table.ipynb

{
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      "source": [
        "# **Pivot Table in Python**"
      ]
    },
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      "source": [
        "In Python, you can create pivot tables using libraries like pandas or NumPy, which are commonly used for data manipulation and analysis. \n",
        "\n",
        "\n",
        "Now, let's create a simple pivot table:"
      ]
    },
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      "execution_count": 2,
      "metadata": {
        "id": "UOf2gXJwDAa_"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Sample data\n",
        "data = {\n",
        "    'Date': ['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'],\n",
        "    'Product': ['A', 'B', 'A', 'B'],\n",
        "    'Sales': [100, 200, 150, 250],\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create a pandas DataFrame\n",
        "df = pd.DataFrame(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create a pivot table\n",
        "pivot_table = pd.pivot_table(df, values='Sales', index='Date', columns='Product', aggfunc='sum')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Product       A    B\n",
            "Date                \n",
            "2023-01-01  100  200\n",
            "2023-01-02  150  250\n"
          ]
        }
      ],
      "source": [
        "print(pivot_table)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this example, we first create a DataFrame using sample data. Then, we use the `pd.pivot_table` function to create a pivot table. Here's what each argument does:\n",
        "\n",
        "- `df`: The DataFrame containing the data.\n",
        "- `values`: The column for which you want to aggregate values (in this case, 'Sales').\n",
        "- `index`: The column that you want to use as the rows in the pivot table (in this case, 'Date').\n",
        "- `columns`: The column that you want to use as the columns in the pivot table (in this case, 'Product').\n",
        "- `aggfunc`: The aggregation function to apply when there are multiple values that need to be combined. You can use functions like 'sum', 'mean', 'max', etc., depending on your requirements."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## **Real-world Examples**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "6_wf4anbDLOZ"
      },
      "outputs": [],
      "source": [
        "df = sns.load_dataset('tips')"
      ]
    },
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              "      <th></th>\n",
              "      <th>total_bill</th>\n",
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              "      <td>16.99</td>\n",
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            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9332a2d6-9cd1-416a-8ac1-1bc5a035a747 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "smoker        Yes         No\n",
              "sex                         \n",
              "Male    22.284500  19.791237\n",
              "Female  17.977879  18.105185"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.pivot_table(index='sex',columns='smoker',values='total_bill')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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          "height": 143
        },
        "id": "D1UlFURcIzrf",
        "outputId": "749eab85-9d4d-46b3-e772-02e74eb5b052"
      },
      "outputs": [
        {
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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            "text/plain": [
              "smoker       Yes        No\n",
              "sex                       \n",
              "Male    9.911845  8.726566\n",
              "Female  9.189751  7.286455"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "## aggfunc# aggfunc\n",
        "df.pivot_table(index='sex',columns='smoker',values='total_bill',aggfunc='std')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "CEq0nW8KIzth",
        "outputId": "c26782c2-ce30-4900-e489-94d0656f0c3f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-7-dd5735ad60ca>:2: FutureWarning: pivot_table dropped a column because it failed to aggregate. This behavior is deprecated and will raise in a future version of pandas. Select only the columns that can be aggregated.\n",
            "  df.pivot_table(index='sex',columns='smoker')['size']\n"
          ]
        },
        {
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              "      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-534e7e6c-f4d2-4d0e-93dd-1e71bbe261ac 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-534e7e6c-f4d2-4d0e-93dd-1e71bbe261ac');\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",
              "\n",
              "\n",
              "<div id=\"df-0717bdc6-5689-4f9c-bace-41ae91f2dfd4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0717bdc6-5689-4f9c-bace-41ae91f2dfd4')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0717bdc6-5689-4f9c-bace-41ae91f2dfd4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "smoker       Yes        No\n",
              "sex                       \n",
              "Male    2.500000  2.711340\n",
              "Female  2.242424  2.592593"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# all cols together\n",
        "df.pivot_table(index='sex',columns='smoker')['size']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "clxzlFyQDTDa",
        "outputId": "47080bd5-abbb-4635-cf44-b1b42b5dfd68"
      },
      "outputs": [
        {
          "data": {
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              "      <td>2.300000</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "        document.querySelector('#df-649a51fd-1049-4a00-8154-19edc653d570 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
              "\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4d426c90-26ba-4934-aff4-5e6dc9266436 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   size                                               tip  \\\n",
              "day                Thur              Fri              Sat       Sun  Thur   \n",
              "time              Lunch Dinner     Lunch Dinner    Dinner    Dinner Lunch   \n",
              "sex    smoker                                                               \n",
              "Male   Yes     2.300000    NaN  1.666667    2.4  2.629630  2.600000  5.00   \n",
              "       No      2.500000    NaN       NaN    2.0  2.656250  2.883721  6.70   \n",
              "Female Yes     2.428571    NaN  2.000000    2.0  2.200000  2.500000  5.00   \n",
              "       No      2.500000    2.0  3.000000    2.0  2.307692  3.071429  5.17   \n",
              "\n",
              "                                                total_bill                \\\n",
              "day                    Fri           Sat    Sun       Thur           Fri   \n",
              "time          Dinner Lunch Dinner Dinner Dinner      Lunch Dinner  Lunch   \n",
              "sex    smoker                                                              \n",
              "Male   Yes       NaN  2.20   4.73  10.00    6.5     191.71   0.00  34.16   \n",
              "       No        NaN   NaN   3.50   9.00    6.0     369.73   0.00   0.00   \n",
              "Female Yes       NaN  3.48   4.30   6.50    4.0     134.53   0.00  39.78   \n",
              "       No        3.0  3.00   3.25   4.67    5.2     381.58  18.78  15.98   \n",
              "\n",
              "                                                   \n",
              "day                     Sat           Sun          \n",
              "time           Dinner Lunch  Dinner Lunch  Dinner  \n",
              "sex    smoker                                      \n",
              "Male   Yes     129.46   0.0  589.62   0.0  392.12  \n",
              "       No       34.95   0.0  637.73   0.0  877.34  \n",
              "Female Yes      48.80   0.0  304.00   0.0   66.16  \n",
              "       No       22.75   0.0  247.05   0.0  291.54  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# multidimensional\n",
        "df.pivot_table(index=['sex','smoker'],columns=['day','time'],aggfunc={'size':'mean','tip':'max','total_bill':'sum'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "VZyCyRcTMROw",
        "outputId": "28c5030f-b659-4521-e6ab-ab857d7ce573"
      },
      "outputs": [
        {
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              "      <th>smoker</th>\n",
              "      <th>Yes</th>\n",
              "      <th>No</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
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            ],
            "text/plain": [
              "smoker      Yes       No      All\n",
              "sex                              \n",
              "Male    1337.07  1919.75  3256.82\n",
              "Female   593.27   977.68  1570.95\n",
              "All     1930.34  2897.43  4827.77"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# margins\n",
        "df.pivot_table(index='sex',columns='smoker',values='total_bill',aggfunc='sum',margins=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### **Plotting graph**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "k79ZDRueMT3A"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('Data\\Day43\\expense_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>Date</th>\n",
              "      <th>Account</th>\n",
              "      <th>Category</th>\n",
              "      <th>Subcategory</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3/2/2022 10:11</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Food</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>50.0</td>\n",
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              "      <td>NaN</td>\n",
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              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>3/2/2022 10:11</td>\n",
              "      <td>CUB - online payment</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3/1/2022 19:50</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Food</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>78.0</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>INR</td>\n",
              "      <td>78.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3/1/2022 18:56</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Transportation</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Metro</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Expense</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>INR</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3/1/2022 18:22</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Food</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Snacks</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Expense</td>\n",
              "      <td>NaN</td>\n",
              "      <td>67.0</td>\n",
              "      <td>INR</td>\n",
              "      <td>67.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Date               Account        Category  Subcategory  \\\n",
              "0  3/2/2022 10:11  CUB - online payment            Food          NaN   \n",
              "1  3/2/2022 10:11  CUB - online payment           Other          NaN   \n",
              "2  3/1/2022 19:50  CUB - online payment            Food          NaN   \n",
              "3  3/1/2022 18:56  CUB - online payment  Transportation          NaN   \n",
              "4  3/1/2022 18:22  CUB - online payment            Food          NaN   \n",
              "\n",
              "               Note    INR Income/Expense  Note.1  Amount Currency  Account.1  \n",
              "0           Brownie   50.0        Expense     NaN    50.0      INR       50.0  \n",
              "1  To lended people  300.0        Expense     NaN   300.0      INR      300.0  \n",
              "2            Dinner   78.0        Expense     NaN    78.0      INR       78.0  \n",
              "3             Metro   30.0        Expense     NaN    30.0      INR       30.0  \n",
              "4            Snacks   67.0        Expense     NaN    67.0      INR       67.0  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Category\n",
              "Food                156\n",
              "Other                60\n",
              "Transportation       31\n",
              "Apparel               7\n",
              "Household             6\n",
              "Allowance             6\n",
              "Social Life           5\n",
              "Education             1\n",
              "Salary                1\n",
              "Self-development      1\n",
              "Beauty                1\n",
              "Gift                  1\n",
              "Petty cash            1\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Category'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 277 entries, 0 to 276\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Date            277 non-null    object \n",
            " 1   Account         277 non-null    object \n",
            " 2   Category        277 non-null    object \n",
            " 3   Subcategory     0 non-null      float64\n",
            " 4   Note            273 non-null    object \n",
            " 5   INR             277 non-null    float64\n",
            " 6   Income/Expense  277 non-null    object \n",
            " 7   Note.1          0 non-null      float64\n",
            " 8   Amount          277 non-null    float64\n",
            " 9   Currency        277 non-null    object \n",
            " 10  Account.1       277 non-null    float64\n",
            "dtypes: float64(5), object(6)\n",
            "memory usage: 23.9+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "df['month'] = df['Date'].dt.month_name()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "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>Date</th>\n",
              "      <th>Account</th>\n",
              "      <th>Category</th>\n",
              "      <th>Subcategory</th>\n",
              "      <th>Note</th>\n",
              "      <th>INR</th>\n",
              "      <th>Income/Expense</th>\n",
              "      <th>Note.1</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Currency</th>\n",
              "      <th>Account.1</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-03-02 10:11:00</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Food</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brownie</td>\n",
              "      <td>50.0</td>\n",
              "      <td>Expense</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50.0</td>\n",
              "      <td>INR</td>\n",
              "      <td>50.0</td>\n",
              "      <td>March</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-03-02 10:11:00</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Other</td>\n",
              "      <td>NaN</td>\n",
              "      <td>To lended people</td>\n",
              "      <td>300.0</td>\n",
              "      <td>Expense</td>\n",
              "      <td>NaN</td>\n",
              "      <td>300.0</td>\n",
              "      <td>INR</td>\n",
              "      <td>300.0</td>\n",
              "      <td>March</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-03-01 19:50:00</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Food</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>78.0</td>\n",
              "      <td>Expense</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78.0</td>\n",
              "      <td>INR</td>\n",
              "      <td>78.0</td>\n",
              "      <td>March</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-03-01 18:56:00</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Transportation</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Metro</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Expense</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>INR</td>\n",
              "      <td>30.0</td>\n",
              "      <td>March</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-03-01 18:22:00</td>\n",
              "      <td>CUB - online payment</td>\n",
              "      <td>Food</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Snacks</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Expense</td>\n",
              "      <td>NaN</td>\n",
              "      <td>67.0</td>\n",
              "      <td>INR</td>\n",
              "      <td>67.0</td>\n",
              "      <td>March</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Date               Account        Category  Subcategory  \\\n",
              "0 2022-03-02 10:11:00  CUB - online payment            Food          NaN   \n",
              "1 2022-03-02 10:11:00  CUB - online payment           Other          NaN   \n",
              "2 2022-03-01 19:50:00  CUB - online payment            Food          NaN   \n",
              "3 2022-03-01 18:56:00  CUB - online payment  Transportation          NaN   \n",
              "4 2022-03-01 18:22:00  CUB - online payment            Food          NaN   \n",
              "\n",
              "               Note    INR Income/Expense  Note.1  Amount Currency  Account.1  \\\n",
              "0           Brownie   50.0        Expense     NaN    50.0      INR       50.0   \n",
              "1  To lended people  300.0        Expense     NaN   300.0      INR      300.0   \n",
              "2            Dinner   78.0        Expense     NaN    78.0      INR       78.0   \n",
              "3             Metro   30.0        Expense     NaN    30.0      INR       30.0   \n",
              "4            Snacks   67.0        Expense     NaN    67.0      INR       67.0   \n",
              "\n",
              "   month  \n",
              "0  March  \n",
              "1  March  \n",
              "2  March  \n",
              "3  March  \n",
              "4  March  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='month'>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.pivot_table(index='month',columns='Income/Expense',values='INR',aggfunc='sum',fill_value=0).plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='month'>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.pivot_table(index='month',columns='Account',values='INR',aggfunc='sum',fill_value=0).plot()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}