Reading_files in pandas.ipynb

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      "source": [
        "import pandas as pd\n",
        "import warnings \n",
        "warnings.filterwarnings (\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To read tsv file in pandas we use pd.read_table(\"Name of file with extension\")\n",
        "\n",
        "You can use this method if you don't know the extension of any file. later you can add seperator as per the output displayed."
      ],
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      "source": [
        "df1=pd.read_table(\"chiporders.tsv\")\n",
        "df1.head()"
      ],
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              "   order_id  quantity                              item_name  \\\n",
              "0         1         1           Chips and Fresh Tomato Salsa   \n",
              "1         1         1                                   Izze   \n",
              "2         1         1                       Nantucket Nectar   \n",
              "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
              "4         2         2                           Chicken Bowl   \n",
              "\n",
              "                                  choice_description item_price  \n",
              "0                                                NaN     $2.39   \n",
              "1                                       [Clementine]     $3.39   \n",
              "2                                            [Apple]     $3.39   \n",
              "3                                                NaN     $2.39   \n",
              "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   "
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      "cell_type": "markdown",
      "source": [
        "We can also use pd.read_table(\"Name of file with extension\") for reading excel file by adding seperator as comma"
      ],
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              "  symboling,normalized-losses,make,fuel-type,body-style,drive-wheels,engine-location,width,height,engine-type,engine-size,horsepower,city-mpg,highway-mpg,price\n",
              "0  3,?,alfa-romero,gas,convertible,rwd,front,64.1...                                                                                                           \n",
              "1  3,?,alfa-romero,gas,convertible,rwd,front,64.1...                                                                                                           \n",
              "2  1,?,alfa-romero,gas,hatchback,rwd,front,65.5,5...                                                                                                           \n",
              "3  2,164,audi,gas,sedan,fwd,front,66.2,54.3,ohc,1...                                                                                                           \n",
              "4  2,164,audi,gas,sedan,4wd,front,66.4,54.3,ohc,1...                                                                                                           "
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      "source": [
        "Here in optput it is not displayed properly so we will now add seperator as comma."
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            "text/plain": [
              "   symboling normalized-losses         make fuel-type   body-style  \\\n",
              "0          3                 ?  alfa-romero       gas  convertible   \n",
              "1          3                 ?  alfa-romero       gas  convertible   \n",
              "2          1                 ?  alfa-romero       gas    hatchback   \n",
              "3          2               164         audi       gas        sedan   \n",
              "4          2               164         audi       gas        sedan   \n",
              "\n",
              "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
              "0          rwd           front   64.1    48.8        dohc          130   \n",
              "1          rwd           front   64.1    48.8        dohc          130   \n",
              "2          rwd           front   65.5    52.4        ohcv          152   \n",
              "3          fwd           front   66.2    54.3         ohc          109   \n",
              "4          4wd           front   66.4    54.3         ohc          136   \n",
              "\n",
              "  horsepower  city-mpg  highway-mpg  price  \n",
              "0        111        21           27  13495  \n",
              "1        111        21           27  16500  \n",
              "2        154        19           26  16500  \n",
              "3        102        24           30  13950  \n",
              "4        115        18           22  17450  "
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              "      <th>symboling</th>\n",
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              "      <th>engine-size</th>\n",
              "      <th>horsepower</th>\n",
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              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
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              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>64.1</td>\n",
              "      <td>48.8</td>\n",
              "      <td>dohc</td>\n",
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              "      <td>gas</td>\n",
              "      <td>convertible</td>\n",
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              "      <td>alfa-romero</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>65.5</td>\n",
              "      <td>52.4</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>152</td>\n",
              "      <td>154</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>164</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.2</td>\n",
              "      <td>54.3</td>\n",
              "      <td>ohc</td>\n",
              "      <td>109</td>\n",
              "      <td>102</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>164</td>\n",
              "      <td>audi</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>66.4</td>\n",
              "      <td>54.3</td>\n",
              "      <td>ohc</td>\n",
              "      <td>136</td>\n",
              "      <td>115</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To read csv file you can either use pd.read_csv(\"Name of file\")\n",
        "\n",
        "**OR**\n",
        "\n",
        "pd.read_table(\"Name of file with extension\",sep=\",\")\n"
      ],
      "metadata": {
        "id": "7yUd9juYnzUT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3=pd.read_csv(\"titanic.csv\")\n",
        "df3.head()"
      ],
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        "colab": {
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          "height": 320
        },
        "id": "DuZ5Zr-Mnb6A",
        "outputId": "b4b209bc-8cff-4e91-823c-95c1bab1de54"
      },
      "execution_count": null,
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "df3=pd.read_table(\"titanic.csv\",sep=\",\")\n",
        "df3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "C6FscQ2poWNZ",
        "outputId": "1cb1c91e-4fdc-44c2-9f66-0c105ed8662e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
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              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5a880bb-5378-4c57-bcac-489712f5246d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d5a880bb-5378-4c57-bcac-489712f5246d 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-d5a880bb-5378-4c57-bcac-489712f5246d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To read json file (it is basically list of dictionaries or only dictionary)\n",
        "\n",
        "we use pd.read_json(\"Name of file with extension\")"
      ],
      "metadata": {
        "id": "Eplbd2A7pkkd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4=pd.read_json(\"simple_records.json\")\n",
        "df4.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "onwqQQLZp_Ak",
        "outputId": "ddb2c537-ee2a-43ae-a915-6d69de25c4c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-3e9eccaef301>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf4\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"simple_records.json\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mread_json\u001b[0;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, encoding_errors, lines, chunksize, compression, nrows, storage_options)\u001b[0m\n\u001b[1;32m    755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 757\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mjson_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    758\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    913\u001b[0m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_lines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_object_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36m_get_object_parser\u001b[0;34m(self, json)\u001b[0m\n\u001b[1;32m    935\u001b[0m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    936\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"frame\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 937\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFrameParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    938\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    939\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"series\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1062\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1063\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1064\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_no_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1065\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1066\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/json/_json.py\u001b[0m in \u001b[0;36m_parse_no_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1319\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m             self.obj = DataFrame(\n\u001b[0;32m-> 1321\u001b[0;31m                 \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprecise_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprecise_float\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1322\u001b[0m             )\n\u001b[1;32m   1323\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"split\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Trailing data"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If error comes then use pd.read_json(\"Name of files with extension\",lines=True)"
      ],
      "metadata": {
        "id": "96X68aDUqpbH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4=pd.read_json(\"simple_records.json\",lines=True)\n",
        "df4.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VfwnO1RMqVwh",
        "outputId": "e08c917e-7728-4b01-87ee-8f5e5d775612"
      },
      "execution_count": null,
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              "      name  age grade\n",
              "0    Andew   12     A\n",
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              "     {\"name\": \"Andew\", \"age\": 12, \"grade\": \"A\"}\n",
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              "   {\"name\"     \"Andew\", \"age\"   12, \"grade\"   \"A\"}\n",
              "0  {\"name\"    \"Bhuvan\", \"age\"   18, \"grade\"   \"B\"}\n",
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      "source": [
        "colname=[\"name\",\"age\",\"grade\"]\n",
        "df6=pd.read_table(\"simple_records.json\",sep=\",\",header=None,names=colname)\n",
        "df6.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "F2e0zJjJrYzV",
        "outputId": "ec8606c0-59bb-4b9f-eb5c-21a155c0c587"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 name         age           grade\n",
              "0    {\"name\": \"Andew\"   \"age\": 12   \"grade\": \"A\"}\n",
              "1   {\"name\": \"Bhuvan\"   \"age\": 18   \"grade\": \"B\"}\n",
              "2  {\"name\": \"Clinton\"   \"age\": 11   \"grade\": \"A\"}\n",
              "3    {\"name\": \"Drake\"   \"age\": 12   \"grade\": \"C\"}\n",
              "4    {\"name\": \"Eisha\"   \"age\": 13   \"grade\": \"B\"}"
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              "      <td>{\"name\": \"Andew\"</td>\n",
              "      <td>\"age\": 12</td>\n",
              "      <td>\"grade\": \"A\"}</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{\"name\": \"Bhuvan\"</td>\n",
              "      <td>\"age\": 18</td>\n",
              "      <td>\"grade\": \"B\"}</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>{\"name\": \"Clinton\"</td>\n",
              "      <td>\"age\": 11</td>\n",
              "      <td>\"grade\": \"A\"}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>{\"name\": \"Drake\"</td>\n",
              "      <td>\"age\": 12</td>\n",
              "      <td>\"grade\": \"C\"}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>{\"name\": \"Eisha\"</td>\n",
              "      <td>\"age\": 13</td>\n",
              "      <td>\"grade\": \"B\"}</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reading file from Web**\n",
        "\n",
        "Here we don't know the extension so use pd.read_table method"
      ],
      "metadata": {
        "id": "754irRaH6q91"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url=\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
        "df7=pd.read_table(url)\n",
        "df7.head()"
      ],
      "metadata": {
        "id": "hR819Q-r0JQK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9c0a837f-7979-48ac-9868-c22969668f2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   5.1,3.5,1.4,0.2,Iris-setosa\n",
              "0  4.9,3.0,1.4,0.2,Iris-setosa\n",
              "1  4.7,3.2,1.3,0.2,Iris-setosa\n",
              "2  4.6,3.1,1.5,0.2,Iris-setosa\n",
              "3  5.0,3.6,1.4,0.2,Iris-setosa\n",
              "4  5.4,3.9,1.7,0.4,Iris-setosa"
            ],
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              "\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>5.1,3.5,1.4,0.2,Iris-setosa</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>4.9,3.0,1.4,0.2,Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.7,3.2,1.3,0.2,Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.6,3.1,1.5,0.2,Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0,3.6,1.4,0.2,Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.4,3.9,1.7,0.4,Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5349cf9-b85b-4126-afc2-ab8dcdf97fd4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e5349cf9-b85b-4126-afc2-ab8dcdf97fd4 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-e5349cf9-b85b-4126-afc2-ab8dcdf97fd4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can see that here the seperator is comma "
      ],
      "metadata": {
        "id": "6QV9p4rT7aHG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df7=pd.read_table(url,sep=\",\")\n",
        "df7.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "N0OI5AiI7MoJ",
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   5.1  3.5  1.4  0.2  Iris-setosa\n",
              "0  4.9  3.0  1.4  0.2  Iris-setosa\n",
              "1  4.7  3.2  1.3  0.2  Iris-setosa\n",
              "2  4.6  3.1  1.5  0.2  Iris-setosa\n",
              "3  5.0  3.6  1.4  0.2  Iris-setosa\n",
              "4  5.4  3.9  1.7  0.4  Iris-setosa"
            ],
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              "      <th></th>\n",
              "      <th>5.1</th>\n",
              "      <th>3.5</th>\n",
              "      <th>1.4</th>\n",
              "      <th>0.2</th>\n",
              "      <th>Iris-setosa</th>\n",
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              "      <th>0</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again here we need to mention the column names as pandas taking first row as the  column header. "
      ],
      "metadata": {
        "id": "Y8105ACD7lni"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colname=[\"sepal length\",\"sepal width\",\"petal length\",\"petal width\",\"class\"]\n",
        "df7=pd.read_table(url,sep=\",\",header=None,names=colname)\n",
        "df7.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u8ohP_Oo7i_O",
        "outputId": "15d1ecad-7a41-4dc4-db31-8f10b22f4fb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal length  sepal width  petal length  petal width        class\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ],
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              "      <th>sepal length</th>\n",
              "      <th>sepal width</th>\n",
              "      <th>petal length</th>\n",
              "      <th>petal width</th>\n",
              "      <th>class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "          const element = document.querySelector('#df-cb16734e-e965-4043-9b0c-b9c60ce5d80d');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**This way we can read the files in pandas**"
      ],
      "metadata": {
        "id": "jMqnXVbF8SmJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weblink=\"https://www.kaggle.com/datasets/kapturovalexander/nvidia-amd-intel-asus-msi-share-prices?select=AMD+%281980-2023%29.csv/AMD(1980-2023).csv\"\n",
        "df8=pd.read_table(weblink)\n",
        "df8.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PtwpZz8L8oIe",
        "outputId": "7163ce87-c81a-4547-cd7c-4dad1b108cd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     <!DOCTYPE html>\n",
              "0                                   <html lang=\"en\">\n",
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              "2    <title>NVIDIA, AMD, Intel, ASUS, MSI share p...\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <td>&lt;head&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>&lt;title&gt;NVIDIA, AMD, Intel, ASUS, MSI share p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&lt;meta charset=\"utf-8\" /&gt;</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>&lt;meta name=\"robots\" content=\"index, follow...</td>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cbf50427-3216-4d5f-bb4a-aeb99ddd1256 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-cbf50427-3216-4d5f-bb4a-aeb99ddd1256');\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",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 41
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "63T-D_Xa9NH6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}