exploring_data1.ipynb

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  "metadata": {
    "colab": {
      "name": "exploring_data1.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "code",
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        "id": "q-UuO5auQuPr",
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sk2uiWnaQ0ta",
        "colab_type": "code",
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVmAIX-fRQTX",
        "colab_type": "text"
      },
      "source": [
        "head()\n",
        "\n",
        "tail()\n",
        "\n",
        "shape\n",
        "\n",
        "info()\n",
        "\n",
        "describe()\n",
        "\n",
        "dtypes\n",
        "\n",
        "select_dtypes()\n",
        "\n",
        "columns\n",
        "\n",
        "\n",
        "rename()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsI_uLEaN8ip",
        "colab_type": "code",
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmWg_9DsN7jX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gn__00SfNN8d",
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          "height": 300
        },
        "outputId": "e73eae36-2714-4dc4-8897-bdaa538e7ce3"
      },
      "source": [
        "df2 = pd.DataFrame({'name':pd.Series(['riya','julie','marie','sandy','garim','darsh','ranisha','john']),'age':pd.Series([15,16,15,15,16,15,15,15]),'eng_marks':pd.Series([78,45,90,88,65,94,52,63]),'science_marks':pd.Series([87,54,100,78,55,44,92,73])})\n",
        "df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>eng_marks</th>\n",
              "      <th>science_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>riya</td>\n",
              "      <td>15</td>\n",
              "      <td>78</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>julie</td>\n",
              "      <td>16</td>\n",
              "      <td>45</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>marie</td>\n",
              "      <td>15</td>\n",
              "      <td>90</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sandy</td>\n",
              "      <td>15</td>\n",
              "      <td>88</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>garim</td>\n",
              "      <td>16</td>\n",
              "      <td>65</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>darsh</td>\n",
              "      <td>15</td>\n",
              "      <td>94</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ranisha</td>\n",
              "      <td>15</td>\n",
              "      <td>52</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>john</td>\n",
              "      <td>15</td>\n",
              "      <td>63</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name  age  eng_marks  science_marks\n",
              "0     riya   15         78             87\n",
              "1    julie   16         45             54\n",
              "2    marie   15         90            100\n",
              "3    sandy   15         88             78\n",
              "4    garim   16         65             55\n",
              "5    darsh   15         94             44\n",
              "6  ranisha   15         52             92\n",
              "7     john   15         63             73"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
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    {
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      "metadata": {
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          "height": 112
        },
        "outputId": "db085aec-0ae1-410e-df3a-79480737cdf2"
      },
      "source": [
        "# head()\n",
        "# displays first 5 rows\n",
        "\n",
        "df2.head(2)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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>name</th>\n",
              "      <th>age</th>\n",
              "      <th>eng_marks</th>\n",
              "      <th>science_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>riya</td>\n",
              "      <td>15</td>\n",
              "      <td>78</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>julie</td>\n",
              "      <td>16</td>\n",
              "      <td>45</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "    name  age  eng_marks  science_marks\n",
              "0   riya   15         78             87\n",
              "1  julie   16         45             54"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j46WIfmQS8M8",
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          "height": 238
        },
        "outputId": "de69456e-fc75-4f13-a8a6-0933036975dc"
      },
      "source": [
        "# tail()\n",
        "# displays last 5 rows of data\n",
        "df2.tail(6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>eng_marks</th>\n",
              "      <th>science_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>marie</td>\n",
              "      <td>15</td>\n",
              "      <td>90</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sandy</td>\n",
              "      <td>15</td>\n",
              "      <td>88</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>garim</td>\n",
              "      <td>16</td>\n",
              "      <td>65</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>darsh</td>\n",
              "      <td>15</td>\n",
              "      <td>94</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ranisha</td>\n",
              "      <td>15</td>\n",
              "      <td>52</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>john</td>\n",
              "      <td>15</td>\n",
              "      <td>63</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "      name  age  eng_marks  science_marks\n",
              "2    marie   15         90            100\n",
              "3    sandy   15         88             78\n",
              "4    garim   16         65             55\n",
              "5    darsh   15         94             44\n",
              "6  ranisha   15         52             92\n",
              "7     john   15         63             73"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTklYcO7TBXw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "241ba45a-cb51-43a9-9330-620e48f65ff6"
      },
      "source": [
        "# shape\n",
        "# it displays the dimentionality(rows,columns)\n",
        "\n",
        "df2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtGSY_AGXUbl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7daf16ed-ce85-4437-c3be-8cf1e6392fd6"
      },
      "source": [
        "# ndim\n",
        "# displays the dimensions i.e return 1 for series, 2 for df\n",
        "df2.ndim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5_3vbf6RNPk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c933f1c2-b08f-4606-cad0-e179021bf92c"
      },
      "source": [
        "type(df2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smMyy8ruQ-gj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "outputId": "e12e8c12-1d9e-4ef5-acaf-69b450fd989c"
      },
      "source": [
        "df3 = df2['name']\n",
        "df3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       riya\n",
              "1      julie\n",
              "2      marie\n",
              "3      sandy\n",
              "4      garim\n",
              "5      darsh\n",
              "6    ranisha\n",
              "7       john\n",
              "Name: name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzSQnJLjRFD_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f82dc573-e215-4e74-d68b-64a6e4ff964a"
      },
      "source": [
        "type(df3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "VJHfpUcsRJAP",
        "colab_type": "code",
        "colab": {
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          "height": 35
        },
        "outputId": "f51b605f-4608-4ff1-bedd-01b14e513b3a"
      },
      "source": [
        "df3.ndim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwnQOq96XWLp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e681a66a-92c3-4176-d2f8-7b9c5001d3aa"
      },
      "source": [
        "# size\n",
        "# displays size of df(rows*columns)\n",
        "df2.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlGWBwd1TtRY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "outputId": "06c35a1b-e943-4d1e-9537-aa36a8e7b95e"
      },
      "source": [
        "# info()\n",
        "# column wise dtype,null/non null entries\n",
        "df2.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8 entries, 0 to 7\n",
            "Data columns (total 4 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   name           8 non-null      object\n",
            " 1   age            8 non-null      int64 \n",
            " 2   eng_marks      8 non-null      int64 \n",
            " 3   science_marks  8 non-null      int64 \n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 384.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0cLlHedT3sG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "ad1354d7-c811-4441-ea63-99b66c1f5db9"
      },
      "source": [
        "# describe()\n",
        "# statistical summary column wise(mean,min max........)\n",
        "df2.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>eng_marks</th>\n",
              "      <th>science_marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.00000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>15.25000</td>\n",
              "      <td>71.875000</td>\n",
              "      <td>72.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.46291</td>\n",
              "      <td>18.357073</td>\n",
              "      <td>20.131266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.00000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>44.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>15.00000</td>\n",
              "      <td>60.250000</td>\n",
              "      <td>54.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15.00000</td>\n",
              "      <td>71.500000</td>\n",
              "      <td>75.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.25000</td>\n",
              "      <td>88.500000</td>\n",
              "      <td>88.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>16.00000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            age  eng_marks  science_marks\n",
              "count   8.00000   8.000000       8.000000\n",
              "mean   15.25000  71.875000      72.875000\n",
              "std     0.46291  18.357073      20.131266\n",
              "min    15.00000  45.000000      44.000000\n",
              "25%    15.00000  60.250000      54.750000\n",
              "50%    15.00000  71.500000      75.500000\n",
              "75%    15.25000  88.500000      88.250000\n",
              "max    16.00000  94.000000     100.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-E32_FOT6Dy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "outputId": "3765eea2-1237-423a-84d0-407166cab05c"
      },
      "source": [
        "# dtypes\n",
        "# tells us about the data types that are present in a data frame\n",
        "df2.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name             object\n",
              "age               int64\n",
              "eng_marks         int64\n",
              "science_marks     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFHaDtcQWXt6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "fd410434-3b4b-4735-9a0f-6bee4d357b11"
      },
      "source": [
        "# select_dtypes\n",
        "# includes or excludes columns with particular dtypes\n",
        "df2.select_dtypes(exclude=int,include=object)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>riya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>julie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>marie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sandy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>garim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>darsh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ranisha</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>john</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name\n",
              "0     riya\n",
              "1    julie\n",
              "2    marie\n",
              "3    sandy\n",
              "4    garim\n",
              "5    darsh\n",
              "6  ranisha\n",
              "7     john"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbWVuvMMUKaX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e9e2588a-9a5c-421a-fadb-ade897320394"
      },
      "source": [
        "# columns\n",
        "# we can see the names of all the columns that are present\n",
        "df2.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['name', 'age', 'eng_marks', 'science_marks'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3ftSIZ2eRs1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# rename\n",
        "# syntax::  df.rename(columns={},inplace=)\n",
        "df2.rename(columns={'eng_marks':'english','science_marks':'science'},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lT8fz_6BAooY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "320a174b-4f6e-4f57-803d-9e138f7f3ab7"
      },
      "source": [
        "df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>english</th>\n",
              "      <th>science</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>riya</td>\n",
              "      <td>15</td>\n",
              "      <td>78</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>julie</td>\n",
              "      <td>16</td>\n",
              "      <td>45</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>marie</td>\n",
              "      <td>15</td>\n",
              "      <td>90</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sandy</td>\n",
              "      <td>15</td>\n",
              "      <td>88</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>garim</td>\n",
              "      <td>16</td>\n",
              "      <td>65</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>darsh</td>\n",
              "      <td>15</td>\n",
              "      <td>94</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ranisha</td>\n",
              "      <td>15</td>\n",
              "      <td>52</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>john</td>\n",
              "      <td>15</td>\n",
              "      <td>63</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name  age  english  science\n",
              "0     riya   15       78       87\n",
              "1    julie   16       45       54\n",
              "2    marie   15       90      100\n",
              "3    sandy   15       88       78\n",
              "4    garim   16       65       55\n",
              "5    darsh   15       94       44\n",
              "6  ranisha   15       52       92\n",
              "7     john   15       63       73"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAfDXYc0Aokg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcfZPNYtAoiJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_V_e8_lpAodn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKuCmtNTAoYy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}