Pandas (2).ipynb

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      "metadata": {
        "id": "-VrSbGiua8oF"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ser=pd.Series()\n",
        "print(\"Series of pandas:\",ser)\n",
        "data=np.array(['g','o','o','o','d','m','o','r','n','i','n','g'])\n",
        "ser=pd.Series(data)\n",
        "print(\"Series of pandas:\",ser)"
      ],
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        },
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          "text": [
            "Series of pandas: Series([], dtype: float64)\n",
            "Series of pandas: 0     g\n",
            "1     o\n",
            "2     o\n",
            "3     o\n",
            "4     d\n",
            "5     m\n",
            "6     o\n",
            "7     r\n",
            "8     n\n",
            "9     i\n",
            "10    n\n",
            "11    g\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-035038587d49>:1: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
            "  ser=pd.Series()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.DataFrame()\n",
        "print(df)\n",
        "list1=['hi','bye','hello']\n",
        "df=pd.DataFrame(list1)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "ESHHyJSufk10",
        "outputId": "9af361b9-2df6-4f95-aff3-1d358f4e0995"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: []\n",
            "Index: []\n",
            "       0\n",
            "0     hi\n",
            "1    bye\n",
            "2  hello\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas import DataFrame\n",
        "list={'product':['mobile','laptop','desktop','AC','TV'],\n",
        "      'price':[20000,45000,22000,28000,19000],\n",
        "      'year':[2014,2015,2016,2017,2018]}\n",
        "df=DataFrame(list,columns=['product','price','year'])\n",
        "print(\"The original dataframe\\n: \",df)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vjsvjbVUichx",
        "outputId": "5d4ff0a3-79a3-4279-833a-4dfa07454530"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The original dataframe\n",
            ":     product  price  year\n",
            "0   mobile  20000  2014\n",
            "1   laptop  45000  2015\n",
            "2  desktop  22000  2016\n",
            "3       AC  28000  2017\n",
            "4       TV  19000  2018\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Descriptive Statistics for price column\n"
      ],
      "metadata": {
        "id": "DUBZ6oNmjgaK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Descriptive Statistics for Price \\n\")\n",
        "stats=df[\"price\"].describe()\n",
        "print(stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aILybboDjnL2",
        "outputId": "3908661d-e80d-410b-c29f-9196e781c481"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Descriptive Statistics for Price \n",
            "\n",
            "count        5.000000\n",
            "mean     26800.000000\n",
            "std      10756.393448\n",
            "min      19000.000000\n",
            "25%      20000.000000\n",
            "50%      22000.000000\n",
            "75%      28000.000000\n",
            "max      45000.000000\n",
            "Name: price, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Descriptive Statistics for Year \\n\")\n",
        "stats=df[\"year\"].describe()\n",
        "print(stats)"
      ],
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          "base_uri": "https://localhost:8080/"
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      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "\n",
            " Descriptive Statistics for Year \n",
            "\n",
            "count       5.000000\n",
            "mean     2016.000000\n",
            "std         1.581139\n",
            "min      2014.000000\n",
            "25%      2015.000000\n",
            "50%      2016.000000\n",
            "75%      2017.000000\n",
            "max      2018.000000\n",
            "Name: year, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Descriptive Statistics for whole DataFrame \\n\")\n",
        "stats=df.describe(include='all')\n",
        "print(stats)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mj4hk9SllLBR",
        "outputId": "159eb367-bd1f-47bd-c0d3-fcac5af807fe"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Descriptive Statistics for whole DataFrame \n",
            "\n",
            "       product         price         year\n",
            "count        5      5.000000     5.000000\n",
            "unique       5           NaN          NaN\n",
            "top     mobile           NaN          NaN\n",
            "freq         1           NaN          NaN\n",
            "mean       NaN  26800.000000  2016.000000\n",
            "std        NaN  10756.393448     1.581139\n",
            "min        NaN  19000.000000  2014.000000\n",
            "25%        NaN  20000.000000  2015.000000\n",
            "50%        NaN  22000.000000  2016.000000\n",
            "75%        NaN  28000.000000  2017.000000\n",
            "max        NaN  45000.000000  2018.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Descriptive Statistics for individual column"
      ],
      "metadata": {
        "id": "izv5r-WJm0sM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Descriptive Statistics for data individually \\n\")\n",
        "stats=df[\"price\"].mean()\n",
        "print(stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LJjwpNOmaft",
        "outputId": "b4ff1cf1-47f2-4c81-91dd-17ee22585346"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Descriptive Statistics for data individually \n",
            "\n",
            "26800.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Descriptive Statistics for data individually \\n\")\n",
        "stats=df[\"price\"].count()\n",
        "print(stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNASmOnanHBP",
        "outputId": "a0cd68bf-0227-4b56-f585-92d0c0a5c487"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Descriptive Statistics for data individually \n",
            "\n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Descriptive Statistics for data individually \\n\")\n",
        "stats=df[\"price\"].max()\n",
        "print(stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPganxfhnKQy",
        "outputId": "ed95506d-0964-4fbb-fc30-637af776617b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Descriptive Statistics for data individually \n",
            "\n",
            "45000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Descriptive Statistics for data individually \\n\")\n",
        "stats=df[\"price\"].std()\n",
        "print(stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lka91Y5anMjZ",
        "outputId": "2117c6ab-08c2-4353-b732-533c7f88f8f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Descriptive Statistics for data individually \n",
            "\n",
            "10756.393447619885\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Aggregation"
      ],
      "metadata": {
        "id": "d8uVKvLQobRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.DataFrame([[9,4,8,9],[8,10,7,6],[7,6,8,9]],\n",
        "                columns=[\"Maths\",\"English\",\"Science\",\"History\"])\n",
        "print(df)\n",
        "df.sum()\n",
        "df.agg(['sum','min','max'])"
      ],
      "metadata": {
        "colab": {
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Maths  English  Science  History\n",
            "0      9        4        8        9\n",
            "1      8       10        7        6\n",
            "2      7        6        8        9\n"
          ]
        },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Maths  English  Science  History\n",
              "sum     24       20       23       24\n",
              "min      7        4        7        6\n",
              "max      9       10        8        9"
            ],
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              "        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-fd05c5b7-7234-47ab-8ec1-48b4be23209f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.DataFrame([[9,4,8,9],[8,10,7,6],[7,6,8,9]],\n",
        "                columns=[\"Maths\",\"English\",\"Science\",\"History\"])\n",
        "print(df)\n",
        "g=df.groupby(['Maths','Science'])\n",
        "g.first()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "K0x0r1cFrw0T",
        "outputId": "22a5b269-19fc-43ce-c3ca-f8161d975405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Maths  English  Science  History\n",
            "0      9        4        8        9\n",
            "1      8       10        7        6\n",
            "2      7        6        8        9\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               English  History\n",
              "Maths Science                  \n",
              "7     8              6        9\n",
              "8     7             10        6\n",
              "9     8              4        9"
            ],
            "text/html": [
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              "      <th>English</th>\n",
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              "      <th>7</th>\n",
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              "\n",
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              "        document.querySelector('#df-d07321d8-3816-4f0d-a826-db16257efd17 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-d07321d8-3816-4f0d-a826-db16257efd17');\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",
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              "\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
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              "    height: 32px;\n",
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              "    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",
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              "  }\n",
              "\n",
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              "    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",
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              "      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-5e55f7c0-d59e-4132-9dcb-3e43bafed40d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "TtyifHWUZ21u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from google.colab import files\n",
        "#upload=files.upload()\n",
        "dataset=pd.read_csv('/content/people.csv')\n",
        "dataset.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TtPeZ0BvXrqz",
        "outputId": "4da0e3a9-6c04-4ccf-f1e0-dcd594ead7f6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Density  BodyFat  Age  Weight  Height  Neck  Chest  Abdomen    Hip  Thigh  \\\n",
              "0   1.0708     12.3   23  154.25   67.75  36.2   93.1     85.2   94.5   59.0   \n",
              "1   1.0853      6.1   22  173.25   72.25  38.5   93.6     83.0   98.7   58.7   \n",
              "2   1.0414     25.3   22  154.00   66.25  34.0   95.8     87.9   99.2   59.6   \n",
              "3   1.0751     10.4   26  184.75   72.25  37.4  101.8     86.4  101.2   60.1   \n",
              "4   1.0340     28.7   24  184.25   71.25  34.4   97.3    100.0  101.9   63.2   \n",
              "\n",
              "   Knee  Ankle  Biceps  Forearm  Wrist  \n",
              "0  37.3   21.9    32.0     27.4   17.1  \n",
              "1  37.3   23.4    30.5     28.9   18.2  \n",
              "2  38.9   24.0    28.8     25.2   16.6  \n",
              "3  37.3   22.8    32.4     29.4   18.2  \n",
              "4  42.2   24.0    32.2     27.7   17.7  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-71cb5eb8-63af-40a0-bfd0-66c49bcfa1d4\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Density</th>\n",
              "      <th>BodyFat</th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Neck</th>\n",
              "      <th>Chest</th>\n",
              "      <th>Abdomen</th>\n",
              "      <th>Hip</th>\n",
              "      <th>Thigh</th>\n",
              "      <th>Knee</th>\n",
              "      <th>Ankle</th>\n",
              "      <th>Biceps</th>\n",
              "      <th>Forearm</th>\n",
              "      <th>Wrist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0708</td>\n",
              "      <td>12.3</td>\n",
              "      <td>23</td>\n",
              "      <td>154.25</td>\n",
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              "      <td>93.1</td>\n",
              "      <td>85.2</td>\n",
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              "      <td>32.0</td>\n",
              "      <td>27.4</td>\n",
              "      <td>17.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0853</td>\n",
              "      <td>6.1</td>\n",
              "      <td>22</td>\n",
              "      <td>173.25</td>\n",
              "      <td>72.25</td>\n",
              "      <td>38.5</td>\n",
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              "      <td>83.0</td>\n",
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              "      <td>30.5</td>\n",
              "      <td>28.9</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0414</td>\n",
              "      <td>25.3</td>\n",
              "      <td>22</td>\n",
              "      <td>154.00</td>\n",
              "      <td>66.25</td>\n",
              "      <td>34.0</td>\n",
              "      <td>95.8</td>\n",
              "      <td>87.9</td>\n",
              "      <td>99.2</td>\n",
              "      <td>59.6</td>\n",
              "      <td>38.9</td>\n",
              "      <td>24.0</td>\n",
              "      <td>28.8</td>\n",
              "      <td>25.2</td>\n",
              "      <td>16.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0751</td>\n",
              "      <td>10.4</td>\n",
              "      <td>26</td>\n",
              "      <td>184.75</td>\n",
              "      <td>72.25</td>\n",
              "      <td>37.4</td>\n",
              "      <td>101.8</td>\n",
              "      <td>86.4</td>\n",
              "      <td>101.2</td>\n",
              "      <td>60.1</td>\n",
              "      <td>37.3</td>\n",
              "      <td>22.8</td>\n",
              "      <td>32.4</td>\n",
              "      <td>29.4</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0340</td>\n",
              "      <td>28.7</td>\n",
              "      <td>24</td>\n",
              "      <td>184.25</td>\n",
              "      <td>71.25</td>\n",
              "      <td>34.4</td>\n",
              "      <td>97.3</td>\n",
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              "      <td>63.2</td>\n",
              "      <td>42.2</td>\n",
              "      <td>24.0</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.7</td>\n",
              "      <td>17.7</td>\n",
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              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "  <style>\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 252,\n  \"fields\": [\n    {\n      \"column\": \"Density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019031434171520773,\n        \"min\": 0.995,\n        \"max\": 1.1089,\n        \"samples\": [\n          1.0547,\n          1.0236,\n          1.0873\n        ],\n        \"num_unique_values\": 218,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyFat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.368740413029707,\n        \"min\": 0.0,\n        \"max\": 47.5,\n        \"samples\": [\n          19.1,\n          13.6,\n          12.1\n        ],\n        \"num_unique_values\": 176,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        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    }\n    },\n    {\n      \"column\": \"Neck\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.430913234019508,\n        \"min\": 31.1,\n        \"max\": 51.2,\n        \"samples\": [\n          39.9,\n          38.8,\n          35.9\n        ],\n        \"num_unique_values\": 90,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.43047553192002,\n        \"min\": 79.3,\n        \"max\": 136.2,\n        \"samples\": [\n          92.8,\n          92.0,\n          103.9\n        ],\n        \"num_unique_values\": 174,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Abdomen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.783076801381705,\n        \"min\": 69.4,\n        \"max\": 148.1,\n        \"samples\": [\n          95.9,\n          79.5,\n          103.1\n        ],\n        \"num_unique_values\": 185,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.164057666842289,\n        \"min\": 85.0,\n        \"max\": 147.7,\n        \"samples\": [\n          100.2,\n          87.6,\n          93.2\n        ],\n        \"num_unique_values\": 152,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Thigh\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.249952028401044,\n        \"min\": 47.2,\n        \"max\": 87.3,\n        \"samples\": [\n          50.7,\n          54.8,\n          87.3\n        ],\n        \"num_unique_values\": 139,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Knee\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.411804587018756,\n        \"min\": 33.0,\n        \"max\": 49.1,\n        \"samples\": [\n          37.6,\n          34.5,\n          39.5\n        ],\n        \"num_unique_values\": 90,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ankle\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6948933981786372,\n        \"min\": 19.1,\n        \"max\": 33.9,\n        \"samples\": [\n          21.9,\n          22.9,\n          24.8\n        ],\n        \"num_unique_values\": 61,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biceps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0212737512508645,\n        \"min\": 24.8,\n        \"max\": 45.0,\n        \"samples\": [\n          45.0,\n          30.6,\n          32.9\n        ],\n        \"num_unique_values\": 104,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Forearm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0206911650269284,\n        \"min\": 21.0,\n        \"max\": 34.9,\n        \"samples\": [\n          27.7,\n          25.5,\n          30.2\n        ],\n        \"num_unique_values\": 77,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Wrist\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9335849289587035,\n        \"min\": 15.8,\n        \"max\": 21.4,\n        \"samples\": [\n          17.5,\n          17.4,\n          16.1\n        ],\n        \"num_unique_values\": 44,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "dataset=pd.read_csv('/content/people.csv')\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nRco8mXda-Wv",
        "outputId": "addd5614-9d37-48d6-80e7-5bf130f0cf29"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Density  BodyFat  Age  Weight  Height  Neck  Chest  Abdomen    Hip  Thigh  \\\n",
              "0   1.0708     12.3   23  154.25   67.75  36.2   93.1     85.2   94.5   59.0   \n",
              "1   1.0853      6.1   22  173.25   72.25  38.5   93.6     83.0   98.7   58.7   \n",
              "2   1.0414     25.3   22  154.00   66.25  34.0   95.8     87.9   99.2   59.6   \n",
              "3   1.0751     10.4   26  184.75   72.25  37.4  101.8     86.4  101.2   60.1   \n",
              "4   1.0340     28.7   24  184.25   71.25  34.4   97.3    100.0  101.9   63.2   \n",
              "\n",
              "   Knee  Ankle  Biceps  Forearm  Wrist  \n",
              "0  37.3   21.9    32.0     27.4   17.1  \n",
              "1  37.3   23.4    30.5     28.9   18.2  \n",
              "2  38.9   24.0    28.8     25.2   16.6  \n",
              "3  37.3   22.8    32.4     29.4   18.2  \n",
              "4  42.2   24.0    32.2     27.7   17.7  "
            ],
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              "      <th></th>\n",
              "      <th>Density</th>\n",
              "      <th>BodyFat</th>\n",
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              "      <th>Weight</th>\n",
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              "      <th>0</th>\n",
              "      <td>1.0708</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0853</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0414</td>\n",
              "      <td>25.3</td>\n",
              "      <td>22</td>\n",
              "      <td>154.00</td>\n",
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              "      <td>16.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0751</td>\n",
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              "      <td>26</td>\n",
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              "      <td>60.1</td>\n",
              "      <td>37.3</td>\n",
              "      <td>22.8</td>\n",
              "      <td>32.4</td>\n",
              "      <td>29.4</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0340</td>\n",
              "      <td>28.7</td>\n",
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              "      <td>63.2</td>\n",
              "      <td>42.2</td>\n",
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              "\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",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-482a45ea-c38a-4d2b-9821-d73d93dc7294 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-482a45ea-c38a-4d2b-9821-d73d93dc7294');\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",
              "          '<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",
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              "\n",
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              "  .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",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
              "      border-color: transparent;\n",
              "      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-6e9b4a9d-5503-49cf-ac81-f9a97126fc73 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 252,\n  \"fields\": [\n    {\n      \"column\": \"Density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019031434171520773,\n        \"min\": 0.995,\n        \"max\": 1.1089,\n        \"samples\": [\n          1.0547,\n          1.0236,\n          1.0873\n        ],\n        \"num_unique_values\": 218,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyFat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.368740413029707,\n        \"min\": 0.0,\n        \"max\": 47.5,\n        \"samples\": [\n          19.1,\n          13.6,\n          12.1\n        ],\n        \"num_unique_values\": 176,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 22,\n        \"max\": 81,\n        \"samples\": [\n          52,\n          72,\n          60\n        ],\n        \"num_unique_values\": 51,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.38915988536906,\n        \"min\": 118.5,\n        \"max\": 363.15,\n        \"samples\": [\n          226.75,\n          192.25,\n          195.75\n        ],\n        \"num_unique_values\": 197,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.6628557876803174,\n        \"min\": 29.5,\n        \"max\": 77.75,\n        \"samples\": [\n          68.5,\n          77.75,\n          73.25\n        ],\n        \"num_unique_values\": 48,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neck\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.430913234019508,\n        \"min\": 31.1,\n        \"max\": 51.2,\n        \"samples\": [\n          39.9,\n          38.8,\n          35.9\n        ],\n        \"num_unique_values\": 90,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.43047553192002,\n        \"min\": 79.3,\n        \"max\": 136.2,\n        \"samples\": [\n          92.8,\n          92.0,\n          103.9\n        ],\n        \"num_unique_values\": 174,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Abdomen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.783076801381705,\n        \"min\": 69.4,\n        \"max\": 148.1,\n        \"samples\": [\n          95.9,\n          79.5,\n          103.1\n        ],\n        \"num_unique_values\": 185,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.164057666842289,\n        \"min\": 85.0,\n        \"max\": 147.7,\n        \"samples\": [\n          100.2,\n          87.6,\n          93.2\n        ],\n        \"num_unique_values\": 152,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Thigh\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.249952028401044,\n        \"min\": 47.2,\n        \"max\": 87.3,\n        \"samples\": [\n          50.7,\n          54.8,\n          87.3\n        ],\n        \"num_unique_values\": 139,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Knee\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.411804587018756,\n        \"min\": 33.0,\n        \"max\": 49.1,\n        \"samples\": [\n          37.6,\n          34.5,\n          39.5\n        ],\n        \"num_unique_values\": 90,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ankle\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6948933981786372,\n        \"min\": 19.1,\n        \"max\": 33.9,\n        \"samples\": [\n          21.9,\n          22.9,\n          24.8\n        ],\n        \"num_unique_values\": 61,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Biceps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0212737512508645,\n        \"min\": 24.8,\n        \"max\": 45.0,\n        \"samples\": [\n          45.0,\n          30.6,\n          32.9\n        ],\n        \"num_unique_values\": 104,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Forearm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0206911650269284,\n        \"min\": 21.0,\n        \"max\": 34.9,\n        \"samples\": [\n          27.7,\n          25.5,\n          30.2\n        ],\n        \"num_unique_values\": 77,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Wrist\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9335849289587035,\n        \"min\": 15.8,\n        \"max\": 21.4,\n        \"samples\": [\n          17.5,\n          17.4,\n          16.1\n        ],\n        \"num_unique_values\": 44,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "meann=dataset['Age'].mean()\n",
        "print(meann)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jGKDrV8_dg4V",
        "outputId": "08b21e33-5fe7-4e9f-e58e-8736b80cac28"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44.88492063492063\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mediann=dataset['Age'].median()\n",
        "print(mediann)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "qx28sm4gk0aO",
        "outputId": "933a2aaf-8af5-4585-a91b-bddca1b2669a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "43.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modes=dataset['Age'].mode()\n",
        "print(modes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FP48zC00lXx7",
        "outputId": "2b2713c6-8650-427b-83a2-3462674a15eb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    40\n",
            "Name: Age, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "standev=dataset['Age'].std()\n",
        "print(standev)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0sKNJRX4e9qh",
        "outputId": "412e7f04-4766-4318-8296-2815a8555214"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12.602039722717864\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "minval=dataset['Age'].min()\n",
        "print(minval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "85V_9Av-fOFt",
        "outputId": "55814e9e-504b-4488-fc00-f71d2b21fc82"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxval=dataset['Age'].max()\n",
        "print(maxval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fTRoaYK_fWOz",
        "outputId": "8bdda8af-a3cd-4831-9de8-4e75aa97d19f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "81\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c=dataset[\"Age\"].count()\n",
        "print(c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "u5K62dSUfiU5",
        "outputId": "f829e4ba-eb51-4644-8c13-a00b7818f824"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "252\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "des=dataset['Age'].describe()\n",
        "print(des)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "4fx51rr9fpuT",
        "outputId": "18c14e85-84d5-45fd-df32-24bdf96bb85c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    252.000000\n",
            "mean      44.884921\n",
            "std       12.602040\n",
            "min       22.000000\n",
            "25%       35.750000\n",
            "50%       43.000000\n",
            "75%       54.000000\n",
            "max       81.000000\n",
            "Name: Age, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Percent_change"
      ],
      "metadata": {
        "id": "6l8KvOuwuTxE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "s=pd.Series([1,2,3,4,5,4])\n",
        "print(s.pct_change())\n",
        "\n",
        "df=pd.DataFrame(np.random.randn(5,2))\n",
        "print(df.pct_change())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "cxalsM4xpfxc",
        "outputId": "82bd8650-6475-4c5d-9d50-55021c7f6447"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0         NaN\n",
            "1    1.000000\n",
            "2    0.500000\n",
            "3    0.333333\n",
            "4    0.250000\n",
            "5   -0.200000\n",
            "dtype: float64\n",
            "          0         1\n",
            "0       NaN       NaN\n",
            "1 -2.371864 -1.495392\n",
            "2 -1.560283 -0.752225\n",
            "3  0.246324 -1.642156\n",
            "4  2.536503 -3.699633\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Covariance**"
      ],
      "metadata": {
        "id": "YXnVA9w7xjtn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "s1=pd.Series(np.random.randn(10))\n",
        "s2=pd.Series(np.random.randn(10))\n",
        "print(s1.cov(s2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lOCgoOS1qIxY",
        "outputId": "1378e1c2-60f4-4bf0-ebee-7a6cefd60747"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.03134350788480705\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Correlation**"
      ],
      "metadata": {
        "id": "62hhWr6Wxogc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "frame=pd.DataFrame(np.random.randn(10,5),columns=['a','b','c','d','e'])\n",
        "print(frame['a'].corr(frame['b']))\n",
        "print(frame.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JpvSbCtrwUDj",
        "outputId": "ead8af23-6864-46f0-a6d6-7500c0f1ed74"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.16816796464729283\n",
            "          a         b         c         d         e\n",
            "a  1.000000 -0.168168  0.170127 -0.604875 -0.406350\n",
            "b -0.168168  1.000000 -0.758021 -0.093981  0.548160\n",
            "c  0.170127 -0.758021  1.000000 -0.024711 -0.437581\n",
            "d -0.604875 -0.093981 -0.024711  1.000000  0.125510\n",
            "e -0.406350  0.548160 -0.437581  0.125510  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Ranking**"
      ],
      "metadata": {
        "id": "g9TJXbfNxrlF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "s=pd.Series(np.random.randn(5),index=list('abcde'))\n",
        "s['d']=s['b']\n",
        "print(s.rank())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gsGpJSgjxvit",
        "outputId": "10837fc7-3f15-4445-b39b-e84305b13997"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a    5.0\n",
            "b    1.5\n",
            "c    4.0\n",
            "d    1.5\n",
            "e    3.0\n",
            "dtype: float64\n"
          ]
        }
      ]
    }
  ]
}