Lab3.ipynb

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      "name": "python"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4acJ2gRuLeE6"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Variant 5\n",
        "# 2, 4, 6, 7, 9, 10, 15, 16, 17, 18, 20, 21, 22, 23, 25, 26, 27"
      ],
      "metadata": {
        "id": "bCXHtLOYNFGu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('NationalNames.csv').dropna()"
      ],
      "metadata": {
        "id": "7utKaJkXMiwX"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 2\n",
        "df.tail(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "836QK31xMpCI",
        "outputId": "d3aaf72f-bf88-469e-c2e6-a65fdb8bac3b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'Name', 'Year', 'Gender', 'Count'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 4\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Hpnyp6QHNTn-",
        "outputId": "47b60c6d-b0ad-490c-e5ef-26f71feff1a0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Id           Year          Count\n",
              "count  708268.000000  708268.000000  708268.000000\n",
              "mean   354134.500000    1936.367717     252.725649\n",
              "std    204459.504567      22.582218    2047.597817\n",
              "min         1.000000    1880.000000       5.000000\n",
              "25%    177067.750000    1920.000000       7.000000\n",
              "50%    354134.500000    1938.000000      13.000000\n",
              "75%    531201.250000    1956.000000      38.000000\n",
              "max    708268.000000    1970.000000   99680.000000"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Year</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>708268.000000</td>\n",
              "      <td>708268.000000</td>\n",
              "      <td>708268.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>354134.500000</td>\n",
              "      <td>1936.367717</td>\n",
              "      <td>252.725649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>204459.504567</td>\n",
              "      <td>22.582218</td>\n",
              "      <td>2047.597817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1880.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>177067.750000</td>\n",
              "      <td>1920.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>354134.500000</td>\n",
              "      <td>1938.000000</td>\n",
              "      <td>13.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>531201.250000</td>\n",
              "      <td>1956.000000</td>\n",
              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>708268.000000</td>\n",
              "      <td>1970.000000</td>\n",
              "      <td>99680.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6214a77-c36b-4dd1-b166-49c524bebdb9')\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "                                                     [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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          "metadata": {},
          "execution_count": 4
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 6\n",
        "df.drop_duplicates(subset = 'Name').groupby(\"Gender\").count()['Count']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZ8ax4w5Nkns",
        "outputId": "47a3fb00-8201-416d-ccce-87f53b767015"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "F    20454\n",
              "M    10972\n",
              "Name: Count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 7\n",
        "df.query(\"Gender == 'M' and Year == 1970\").sort_values(by=['Count'], ascending=False)[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYGssY5tN05g",
        "outputId": "d1338c88-3350-4bd0-8f14-cd07dcc58963"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42931    Mary\n",
              "Name: Name, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 9\n",
        "df.query(f\"Count == {df['Count'].min()}\")['Count'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOCLtjSvOrp0",
        "outputId": "5603ff88-403e-4ac5-ee7d-6fdcf4abd2ed"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "464400.0"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 10\n",
        "df.groupby('Year')['Name'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "_zWd-dphPcaL",
        "outputId": "8694b585-9011-49a2-d038-c78beeb17b07"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-12c13c8ed4a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Task 11\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\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[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m    909\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 911\u001b[0;31m         raise AttributeError(\n\u001b[0m\u001b[1;32m    912\u001b[0m             \u001b[0;34mf\"'{type(self).__name__}' object has no attribute '{attr}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m         )\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'SeriesGroupBy' object has no attribute 'drop_duplicates'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 15\n",
        "df.groupby('Year')['Count'].sum().to_frame()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
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        "outputId": "3fff6a6e-39f3-4f70-9986-4891841dc16f"
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      "execution_count": 179,
      "outputs": [
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              "          Count\n",
              "Year           \n",
              "1880   201484.0\n",
              "1881   192699.0\n",
              "1882   221538.0\n",
              "1883   216950.0\n",
              "1884   243467.0\n",
              "...         ...\n",
              "1966  3475832.0\n",
              "1967  3395291.0\n",
              "1968  3379031.0\n",
              "1969  3476679.0\n",
              "1970  3582033.0\n",
              "\n",
              "[91 rows x 1 columns]"
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a46bebf8-d9a2-4407-b383-f2a452882f2c 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-a46bebf8-d9a2-4407-b383-f2a452882f2c');\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": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 16\n",
        "df.groupby('Year')['Count'].sum().to_frame().idxmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0TKdEYfQthh",
        "outputId": "b21171a9-2a7b-4c1e-971e-db545aaf54a4"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Count    1957\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 17\n",
        "df.groupby(['Year', 'Gender'])['Count'].sum().to_frame()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "xFPb1saUQyNn",
        "outputId": "12242a0b-d5b3-4ffe-b90c-7ee5c31e0ea8"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Count\n",
              "Year Gender           \n",
              "1880 F         90993.0\n",
              "     M        110491.0\n",
              "1881 F         91954.0\n",
              "     M        100745.0\n",
              "1882 F        107850.0\n",
              "...                ...\n",
              "1968 M       1738928.0\n",
              "1969 F       1686947.0\n",
              "     M       1789732.0\n",
              "1970 F       1748147.0\n",
              "     M       1833886.0\n",
              "\n",
              "[182 rows x 1 columns]"
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              "      <th rowspan=\"2\" valign=\"top\">1970</th>\n",
              "      <th>F</th>\n",
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              "      <th>M</th>\n",
              "      <td>1833886.0</td>\n",
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              "<p>182 rows × 1 columns</p>\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 18\n",
        "year_to_male = df.query('Gender == \"M\"').groupby('Year')['Count'].sum()\n",
        "year_to_female = df.query('Gender == \"F\"').groupby('Year')['Count'].sum()\n",
        "(year_to_female > year_to_male).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_Ak7pBBSS__",
        "outputId": "f89012a5-4db2-4083-f866-ba7096f8603d"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 20\n",
        "male = df.query('Gender == \"M\"')['Name'].drop_duplicates()\n",
        "female = df.query('Gender == \"F\"')['Name'].drop_duplicates()\n",
        "male.isin(female).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHvD3AXfUSgw",
        "outputId": "f9199d26-a623-44d8-a327-10e14666aae5"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3913"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 21\n",
        "df.query('Name == \"Barbara\" and Gender == \"M\"')['Count'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6-tBDyOWbIj",
        "outputId": "c3b0b55d-30f4-46a1-ac22-2b95fef50c7a"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3863.0"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 22\n",
        "df['Year'].max() - df['Year'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QuMRHAJWcE5",
        "outputId": "fb03ff37-6666-4672-e2e5-cb9511e047a4"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "90"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 23\n",
        "male = df.query('Gender == \"M\"')['Name'].drop_duplicates()\n",
        "female = df.query('Gender == \"F\"')['Name'].drop_duplicates()\n",
        "nosex_names = male[male.isin(female)]\n",
        "years_count = df.Year.unique().size\n",
        "df[df.Name.isin(nosex_names)].groupby('Name').filter(lambda d: d['Year'].unique().size == years_count).groupby('Name')['Count'].max().sort_values(ascending=False).to_frame()"
      ],
      "metadata": {
        "id": "cKwMY6XoYiw9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "2c91432d-0ebb-4344-a398-320dd359ce14"
      },
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Count\n",
              "Name            \n",
              "Linda    99680.0\n",
              "James    94755.0\n",
              "Michael  92709.0\n",
              "Robert   91642.0\n",
              "John     88318.0\n",
              "...          ...\n",
              "Bailey      50.0\n",
              "Nevada      48.0\n",
              "Lonie       44.0\n",
              "Norah       37.0\n",
              "Isa         30.0\n",
              "\n",
              "[839 rows x 1 columns]"
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              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 25\n",
        "john = df.query('Name == \"John\"').groupby('Year')['Count'].sum()\n",
        "mary = df.query('Name == \"Mary\"').groupby('Year')['Count'].sum()\n",
        "john.plot()\n",
        "mary.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "6V4fGBy-Zlk1",
        "outputId": "c6d0e7a2-3b8f-4150-c753-9142bae631dc"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Year'>"
            ]
          },
          "metadata": {},
          "execution_count": 187
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 26\n",
        "john = df.query('Name == \"John\" and Gender == \"F\"').groupby('Year')['Count'].sum()\n",
        "mary = df.query('Name == \"Mary\" and Gender == \"M\"').groupby('Year')['Count'].sum()\n",
        "john.plot()\n",
        "mary.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "_zA31fhnZuph",
        "outputId": "87cbff6c-a695-4a1a-effd-d9e780e16fab"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Year'>"
            ]
          },
          "metadata": {},
          "execution_count": 188
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 27\n",
        "df.groupby(['Year']).apply(lambda d: d[d.Count == d.Count.max()].iloc[0][['Name', 'Count']]).reset_index()"
      ],
      "metadata": {
        "id": "GQDFgOQ_a7HT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "a16cf349-0aa2-4401-b296-f355d02a8003"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Year     Name    Count\n",
              "0   1880     John   9655.0\n",
              "1   1881     John   8769.0\n",
              "2   1882     John   9557.0\n",
              "3   1883     John   8894.0\n",
              "4   1884     John   9388.0\n",
              "..   ...      ...      ...\n",
              "86  1966  Michael  80002.0\n",
              "87  1967  Michael  82454.0\n",
              "88  1968  Michael  82019.0\n",
              "89  1969  Michael  85227.0\n",
              "90  1970  Michael  85321.0\n",
              "\n",
              "[91 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dd51ebfd-2ea4-41b8-ab58-ced6d86a505b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Year</th>\n",
              "      <th>Name</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1880</td>\n",
              "      <td>John</td>\n",
              "      <td>9655.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1881</td>\n",
              "      <td>John</td>\n",
              "      <td>8769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1882</td>\n",
              "      <td>John</td>\n",
              "      <td>9557.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1883</td>\n",
              "      <td>John</td>\n",
              "      <td>8894.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1884</td>\n",
              "      <td>John</td>\n",
              "      <td>9388.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>1966</td>\n",
              "      <td>Michael</td>\n",
              "      <td>80002.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>1967</td>\n",
              "      <td>Michael</td>\n",
              "      <td>82454.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>1968</td>\n",
              "      <td>Michael</td>\n",
              "      <td>82019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>1969</td>\n",
              "      <td>Michael</td>\n",
              "      <td>85227.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>1970</td>\n",
              "      <td>Michael</td>\n",
              "      <td>85321.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>91 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd51ebfd-2ea4-41b8-ab58-ced6d86a505b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dd51ebfd-2ea4-41b8-ab58-ced6d86a505b 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-dd51ebfd-2ea4-41b8-ab58-ced6d86a505b');\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": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "woxHwUOsihHi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}