pandas.ipynb

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      "metadata": {
        "id": "lzGDy9V6xhX4"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing the data from a CSV file to a pandas DataFrame"
      ],
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      }
    },
    {
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      "source": [
        "boston_df=pd.read_csv('/content/housing.csv')\n",
        "type(boston_df)"
      ],
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              "pandas.core.frame.DataFrame"
            ]
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          "execution_count": 12
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    {
      "cell_type": "code",
      "source": [
        "#csv file to pandas dataframe\n",
        "diabetes_df=pd.read_csv('/content/diabetes.csv')"
      ],
      "metadata": {
        "id": "rzPpPXcV2LMA"
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      "outputs": []
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      "source": [
        "type(diabetes_df)"
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              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 5
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prints the first five rows\n",
        "diabetes_df.head()"
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              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
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              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
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      "cell_type": "code",
      "source": [
        "#prints the last five rows\n",
        "diabetes_df.tail()"
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              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
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              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
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              "764                     0.340   27        0  \n",
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    {
      "cell_type": "code",
      "source": [
        "diabetes_df.info()"
      ],
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      "execution_count": null,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prints the entire dataframe\n",
        "print(diabetes_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnSDgyD8MZ29",
        "outputId": "5cc82df0-7bc1-4bd2-b4d0-28c137428c14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
            "0              6      148             72             35        0  33.6   \n",
            "1              1       85             66             29        0  26.6   \n",
            "2              8      183             64              0        0  23.3   \n",
            "3              1       89             66             23       94  28.1   \n",
            "4              0      137             40             35      168  43.1   \n",
            "..           ...      ...            ...            ...      ...   ...   \n",
            "763           10      101             76             48      180  32.9   \n",
            "764            2      122             70             27        0  36.8   \n",
            "765            5      121             72             23      112  26.2   \n",
            "766            1      126             60              0        0  30.1   \n",
            "767            1       93             70             31        0  30.4   \n",
            "\n",
            "     DiabetesPedigreeFunction  Age  Outcome  \n",
            "0                       0.627   50        1  \n",
            "1                       0.351   31        0  \n",
            "2                       0.672   32        1  \n",
            "3                       0.167   21        0  \n",
            "4                       2.288   33        1  \n",
            "..                        ...  ...      ...  \n",
            "763                     0.171   63        0  \n",
            "764                     0.340   27        0  \n",
            "765                     0.245   30        0  \n",
            "766                     0.349   47        1  \n",
            "767                     0.315   23        0  \n",
            "\n",
            "[768 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#In an m*n matrix shape returns (m,n)\n",
        "diabetes_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWrxFRNRMgS8",
        "outputId": "0c853a38-5264-4d87-a8fa-d28579263e95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exporting a DataFrame to a CSV file"
      ],
      "metadata": {
        "id": "EmOQFq6ZPnEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "boston_df.to_csv('boston.csv')"
      ],
      "metadata": {
        "id": "kcQIZIzvMi6O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading the data from an excel file to a pandas DataFrame\n",
        "\n",
        "pd.read_excel('file path')\n",
        "\n",
        "Exporting the pandas DataFrame to an excel file\n",
        "\n",
        "pd.to_excel('file name')"
      ],
      "metadata": {
        "id": "lkWKQ7Q-Q9_n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a dataframe with random values\n",
        "random_df=pd.DataFrame(np.random.rand(20,10))\n",
        "print(random_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKJF8G1DP8IL",
        "outputId": "b6cd8c08-246d-4c98-a4e2-69bcf1e2ccbd"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "           0         1         2         3         4         5         6  \\\n",
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            "6   0.842542  0.325463  0.320272  0.199546  0.138400  0.511595  0.188685   \n",
            "7   0.546153  0.852663  0.994272  0.281281  0.838482  0.869400  0.880230   \n",
            "8   0.454117  0.678763  0.875692  0.729720  0.295565  0.175168  0.990910   \n",
            "9   0.721248  0.852570  0.116496  0.304419  0.150091  0.718225  0.990521   \n",
            "10  0.049858  0.676567  0.529595  0.443124  0.495347  0.027235  0.674820   \n",
            "11  0.924891  0.226750  0.349518  0.361198  0.932418  0.889944  0.771213   \n",
            "12  0.302104  0.344690  0.270122  0.337490  0.299575  0.502231  0.228347   \n",
            "13  0.502429  0.715024  0.789998  0.036311  0.192244  0.355110  0.676762   \n",
            "14  0.164835  0.757990  0.205690  0.723043  0.414150  0.155369  0.125870   \n",
            "15  0.687743  0.350408  0.425653  0.622089  0.438660  0.696713  0.016613   \n",
            "16  0.872113  0.360560  0.464541  0.179109  0.415987  0.155794  0.521531   \n",
            "17  0.677957  0.427497  0.016479  0.076150  0.275641  0.673986  0.364830   \n",
            "18  0.880280  0.571778  0.894450  0.350655  0.807992  0.088517  0.348164   \n",
            "19  0.911374  0.202520  0.993893  0.105751  0.876718  0.033658  0.233593   \n",
            "\n",
            "           7         8         9  \n",
            "0   0.791349  0.690197  0.846738  \n",
            "1   0.145334  0.776187  0.540468  \n",
            "2   0.685186  0.672971  0.984489  \n",
            "3   0.906505  0.765320  0.717602  \n",
            "4   0.430916  0.767495  0.037674  \n",
            "5   0.067921  0.775000  0.322506  \n",
            "6   0.166147  0.525885  0.197631  \n",
            "7   0.599334  0.665130  0.922735  \n",
            "8   0.333356  0.781241  0.380908  \n",
            "9   0.786995  0.143062  0.778149  \n",
            "10  0.903768  0.658147  0.727494  \n",
            "11  0.459421  0.347739  0.893571  \n",
            "12  0.642994  0.892816  0.683343  \n",
            "13  0.878159  0.004102  0.750684  \n",
            "14  0.679545  0.280259  0.946731  \n",
            "15  0.047454  0.342430  0.974270  \n",
            "16  0.338586  0.183423  0.042618  \n",
            "17  0.148929  0.929318  0.029106  \n",
            "18  0.589438  0.312870  0.257283  \n",
            "19  0.510810  0.404887  0.291754  \n"
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        "random_df.head()"
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      "metadata": {
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          "height": 206
        },
        "id": "RzDvfnqWSG79",
        "outputId": "00058bf2-b16e-4854-b44c-0468d9221028"
      },
      "execution_count": null,
      "outputs": [
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              "          0         1         2         3         4         5         6  \\\n",
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    {
      "cell_type": "code",
      "source": [
        "random_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pW1i5kz5UxuG",
        "outputId": "21e190e9-df48-4f91-a7b9-d0c4a58761ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
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    {
      "cell_type": "code",
      "source": [
        "random_df=pd.DataFrame(np.random.rand(1,5))"
      ],
      "metadata": {
        "id": "R6AB248GU5Sk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_df.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-3yfScDVBfA",
        "outputId": "27978638-65e5-4444-9ca5-7154d3233943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1 entries, 0 to 0\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   0       1 non-null      float64\n",
            " 1   1       1 non-null      float64\n",
            " 2   2       1 non-null      float64\n",
            " 3   3       1 non-null      float64\n",
            " 4   4       1 non-null      float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 168.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#counting the values based on labels\n",
        "diabetes_df.value_counts('Outcome')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRDQCkozWIkv",
        "outputId": "d9c86354-b908-467c-f308-d88d7559fdef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Outcome\n",
              "0    500\n",
              "1    268\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#group the values based on the mean\n",
        "diabetes_df.groupby('Outcome').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "DiGZryD1YqTj",
        "outputId": "1e25d133-7626-44b4-a781-73a823e4150d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "Outcome                                                                      \n",
              "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
              "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
              "\n",
              "               BMI  DiabetesPedigreeFunction        Age  \n",
              "Outcome                                                  \n",
              "0        30.304200                  0.429734  31.190000  \n",
              "1        35.142537                  0.550500  37.067164  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Statistical measures"
      ],
      "metadata": {
        "id": "Rvqd9D_MZiQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSrccKIfZHIB",
        "outputId": "3378f750-8d7f-4a4a-873f-1589c2933154"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 768\n",
              "Glucose                     768\n",
              "BloodPressure               768\n",
              "SkinThickness               768\n",
              "Insulin                     768\n",
              "BMI                         768\n",
              "DiabetesPedigreeFunction    768\n",
              "Age                         768\n",
              "Outcome                     768\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_df.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESy5hW2iZlw-",
        "outputId": "57387685-647b-4581-e80e-c5f09fea4362"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   3.845052\n",
              "Glucose                     120.894531\n",
              "BloodPressure                69.105469\n",
              "SkinThickness                20.536458\n",
              "Insulin                      79.799479\n",
              "BMI                          31.992578\n",
              "DiabetesPedigreeFunction      0.471876\n",
              "Age                          33.240885\n",
              "Outcome                       0.348958\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#standard deviation\n",
        "diabetes_df.std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-tO0_SJLZqte",
        "outputId": "fe93c575-0849-4598-81e9-db0519c6d3f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   3.369578\n",
              "Glucose                      31.972618\n",
              "BloodPressure                19.355807\n",
              "SkinThickness                15.952218\n",
              "Insulin                     115.244002\n",
              "BMI                           7.884160\n",
              "DiabetesPedigreeFunction      0.331329\n",
              "Age                          11.760232\n",
              "Outcome                       0.476951\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(boston_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ek7jK8CZ3XY",
        "outputId": "008139fd-dea5-4897-fc97-f133426eb1a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00\n",
            "0     0.02731   0.00   7.070  0  0.4690  6.4210  78...                                              \n",
            "1     0.02729   0.00   7.070  0  0.4690  7.1850  61...                                              \n",
            "2     0.03237   0.00   2.180  0  0.4580  6.9980  45...                                              \n",
            "3     0.06905   0.00   2.180  0  0.4580  7.1470  54...                                              \n",
            "4     0.02985   0.00   2.180  0  0.4580  6.4300  58...                                              \n",
            "..                                                 ...                                              \n",
            "500   0.06263   0.00  11.930  0  0.5730  6.5930  69...                                              \n",
            "501   0.04527   0.00  11.930  0  0.5730  6.1200  76...                                              \n",
            "502   0.06076   0.00  11.930  0  0.5730  6.9760  91...                                              \n",
            "503   0.10959   0.00  11.930  0  0.5730  6.7940  89...                                              \n",
            "504   0.04741   0.00  11.930  0  0.5730  6.0300  80...                                              \n",
            "\n",
            "[505 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "boston_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXwgb8qIaPab",
        "outputId": "b0ab0f5d-bae6-434e-9fc7-2725744fd2a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(505, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing_df=pd.read_csv('/content/housing.csv')"
      ],
      "metadata": {
        "id": "VmT4IWiJaV5N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(housing_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pz5gB1stbMk6",
        "outputId": "3bb5d0de-48d1-42cb-9918-4440b09c41a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00\n",
            "0     0.02731   0.00   7.070  0  0.4690  6.4210  78...                                              \n",
            "1     0.02729   0.00   7.070  0  0.4690  7.1850  61...                                              \n",
            "2     0.03237   0.00   2.180  0  0.4580  6.9980  45...                                              \n",
            "3     0.06905   0.00   2.180  0  0.4580  7.1470  54...                                              \n",
            "4     0.02985   0.00   2.180  0  0.4580  6.4300  58...                                              \n",
            "..                                                 ...                                              \n",
            "500   0.06263   0.00  11.930  0  0.5730  6.5930  69...                                              \n",
            "501   0.04527   0.00  11.930  0  0.5730  6.1200  76...                                              \n",
            "502   0.06076   0.00  11.930  0  0.5730  6.9760  91...                                              \n",
            "503   0.10959   0.00  11.930  0  0.5730  6.7940  89...                                              \n",
            "504   0.04741   0.00  11.930  0  0.5730  6.0300  80...                                              \n",
            "\n",
            "[505 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_df.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9T_dOpUubPzV",
        "outputId": "2425d0e0-f4cb-4bb3-9acc-4ecf794ea5d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                  0.000\n",
              "Glucose                      0.000\n",
              "BloodPressure                0.000\n",
              "SkinThickness                0.000\n",
              "Insulin                      0.000\n",
              "BMI                          0.000\n",
              "DiabetesPedigreeFunction     0.078\n",
              "Age                         21.000\n",
              "Outcome                      0.000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_df.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bou7DUpUbURi",
        "outputId": "f85bd280-4156-48e3-9dff-6fcac617d1aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                  17.00\n",
              "Glucose                     199.00\n",
              "BloodPressure               122.00\n",
              "SkinThickness                99.00\n",
              "Insulin                     846.00\n",
              "BMI                          67.10\n",
              "DiabetesPedigreeFunction      2.42\n",
              "Age                          81.00\n",
              "Outcome                       1.00\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "boston_df.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6zdZFDabWlj",
        "outputId": "bd411d70-122d-42b6-a66b-d09ab39d63ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00    88.97620   0.00  18.100  0  0.6710  6.9680  91...\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#all the statistical measures about the dataframe\n",
        "diabetes_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "tbzjMGuSbZ_X",
        "outputId": "f31abe2e-e5c3-41b3-d73d-029fa2e45054"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c91be603-9025-4752-8404-b4eafb4f5eeb\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
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              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
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              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c91be603-9025-4752-8404-b4eafb4f5eeb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c91be603-9025-4752-8404-b4eafb4f5eeb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-e1MiI6ub5mD",
        "outputId": "c20dd49a-f4d1-4810-9f46-c762bd0088e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
              "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
              "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
              "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
              "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
              "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
              "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
              "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
              "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
              "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
              "\n",
              "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
              "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
              "Glucose                   0.331357  0.221071                  0.137337   \n",
              "BloodPressure             0.088933  0.281805                  0.041265   \n",
              "SkinThickness             0.436783  0.392573                  0.183928   \n",
              "Insulin                   1.000000  0.197859                  0.185071   \n",
              "BMI                       0.197859  1.000000                  0.140647   \n",
              "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
              "Age                      -0.042163  0.036242                  0.033561   \n",
              "Outcome                   0.130548  0.292695                  0.173844   \n",
              "\n",
              "                               Age   Outcome  \n",
              "Pregnancies               0.544341  0.221898  \n",
              "Glucose                   0.263514  0.466581  \n",
              "BloodPressure             0.239528  0.065068  \n",
              "SkinThickness            -0.113970  0.074752  \n",
              "Insulin                  -0.042163  0.130548  \n",
              "BMI                       0.036242  0.292695  \n",
              "DiabetesPedigreeFunction  0.033561  0.173844  \n",
              "Age                       1.000000  0.238356  \n",
              "Outcome                   0.238356  1.000000  "
            ],
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              "      <th>SkinThickness</th>\n",
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              "      <td>0.057328</td>\n",
              "      <td>0.207371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.436783</td>\n",
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              "      <th>Insulin</th>\n",
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              "      <td>0.292695</td>\n",
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              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>-0.033523</td>\n",
              "      <td>0.137337</td>\n",
              "      <td>0.041265</td>\n",
              "      <td>0.183928</td>\n",
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              "      <td>0.466581</td>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ac96903e-c33d-4eaf-8cc8-72a7e8a4de57 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-ac96903e-c33d-4eaf-8cc8-72a7e8a4de57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V387NzXLc8Je"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}