Pandas_Assignment_Data_Analysing.ipynb

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      "name": "Pandas Assignment - Data Analysing.ipynb",
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        "### Assignment\n",
        "\n",
        "# Demographic Data Analysing\n",
        "\n",
        "In this challenge you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database. Here is a sample of what the data looks like:\n",
        "\n",
        "|    |   age | workclass        |   fnlwgt | education   |   education-num | marital-status     | occupation        | relationship   | race   | sex    |   capital-gain |   capital-loss |   hours-per-week | native-country   | salary   |\n",
        "|---:|------:|:-----------------|---------:|:------------|----------------:|:-------------------|:------------------|:---------------|:-------|:-------|---------------:|---------------:|-----------------:|:-----------------|:---------|\n",
        "|  0 |    39 | State-gov        |    77516 | Bachelors   |              13 | Never-married      | Adm-clerical      | Not-in-family  | White  | Male   |           2174 |              0 |               40 | United-States    | <=50K    |\n",
        "|  1 |    50 | Self-emp-not-inc |    83311 | Bachelors   |              13 | Married-civ-spouse | Exec-managerial   | Husband        | White  | Male   |              0 |              0 |               13 | United-States    | <=50K    |\n",
        "|  2 |    38 | Private          |   215646 | HS-grad     |               9 | Divorced           | Handlers-cleaners | Not-in-family  | White  | Male   |              0 |              0 |               40 | United-States    | <=50K    |\n",
        "|  3 |    53 | Private          |   234721 | 11th        |               7 | Married-civ-spouse | Handlers-cleaners | Husband        | Black  | Male   |              0 |              0 |               40 | United-States    | <=50K    |\n",
        "|  4 |    28 | Private          |   338409 | Bachelors   |              13 | Married-civ-spouse | Prof-specialty    | Wife           | Black  | Female |              0 |              0 |               40 | Cuba             | <=50K    |\n",
        "\n",
        "\n",
        "You must use Pandas to answer the following questions:\n",
        "* How many people of each race are represented in this dataset? This should be a Pandas series with race names as the index labels. (`race` column)\n",
        "* What is the average age of men?\n",
        "* What is the percentage of people who have a Bachelor's degree?\n",
        "* What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?\n",
        "* What percentage of people without advanced education make more than 50K?\n",
        "* What is the minimum number of hours a person works per week?\n",
        "* What percentage of the people who work the minimum number of hours per week have a salary of more than 50K?\n",
        "* What country has the highest percentage of people that earn >50K and what is that percentage?\n",
        "* Identify the most popular occupation for those who earn >50K in India. \n",
        "\n",
        "Use the starter code in the file `demographic_data_anaylizer`. Update the code so all variables set to \"None\" are set to the appropriate calculation or code. Round all decimals to the nearest tenth.\n",
        "\n",
        "Unit tests are written for you under `test_module.py`.\n",
        "\n",
        "### Development\n",
        "\n",
        "For development, you can use `main.py` to test your functions. Click the \"run\" button and `main.py` will run.\n",
        "\n",
        "### Testing \n",
        "\n",
        "We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the \"run\" button.\n",
        "\n",
        "### Submitting\n",
        "\n",
        "Copy your project's URL and submit it to freeCodeCamp.\n",
        "\n",
        "### Dataset Source\n",
        "\n",
        "Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science."
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        "# Solution\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "df = pd.read_csv('/content/drive/My Drive/adult_data.csv')\n",
        "df.head()\n"
      ],
      "execution_count": null,
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          "output_type": "execute_result",
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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>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age         workclass  fnlwgt  ... hours-per-week  native-country salary\n",
              "0   39         State-gov   77516  ...             40   United-States  <=50K\n",
              "1   50  Self-emp-not-inc   83311  ...             13   United-States  <=50K\n",
              "2   38           Private  215646  ...             40   United-States  <=50K\n",
              "3   53           Private  234721  ...             40   United-States  <=50K\n",
              "4   28           Private  338409  ...             40            Cuba  <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
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          "execution_count": 69
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      "source": [
        "# Some Statistical Overview of our Data\n",
        "df.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>fnlwgt</th>\n",
              "      <th>education-num</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>32561.000000</td>\n",
              "      <td>3.256100e+04</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.581647</td>\n",
              "      <td>1.897784e+05</td>\n",
              "      <td>10.080679</td>\n",
              "      <td>1077.648844</td>\n",
              "      <td>87.303830</td>\n",
              "      <td>40.437456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.640433</td>\n",
              "      <td>1.055500e+05</td>\n",
              "      <td>2.572720</td>\n",
              "      <td>7385.292085</td>\n",
              "      <td>402.960219</td>\n",
              "      <td>12.347429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.228500e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.178270e+05</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>1.783560e+05</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>2.370510e+05</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>1.484705e+06</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>99999.000000</td>\n",
              "      <td>4356.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age        fnlwgt  ...  capital-loss  hours-per-week\n",
              "count  32561.000000  3.256100e+04  ...  32561.000000    32561.000000\n",
              "mean      38.581647  1.897784e+05  ...     87.303830       40.437456\n",
              "std       13.640433  1.055500e+05  ...    402.960219       12.347429\n",
              "min       17.000000  1.228500e+04  ...      0.000000        1.000000\n",
              "25%       28.000000  1.178270e+05  ...      0.000000       40.000000\n",
              "50%       37.000000  1.783560e+05  ...      0.000000       40.000000\n",
              "75%       48.000000  2.370510e+05  ...      0.000000       45.000000\n",
              "max       90.000000  1.484705e+06  ...   4356.000000       99.000000\n",
              "\n",
              "[8 rows x 6 columns]"
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        },
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      "source": [
        "# Checking to see if there are any `Null` values in our Dataset\n",
        "df.isnull().sum().max()"
      ],
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        "df.columns"
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          "data": {
            "text/plain": [
              "Index(['age', 'workclass', 'fnlwgt', 'education', 'education-num',\n",
              "       'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
              "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
              "       'salary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
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        },
        "outputId": "04103fd3-41ec-4a3c-f09a-5f12fabe63b0"
      },
      "source": [
        "# 1. How many people of each race are represented in each dataset?\n",
        "# This should be a Pandas series with race names as the index labels (`race` column)\n",
        "df['race'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "White                 27816\n",
              "Black                  3124\n",
              "Asian-Pac-Islander     1039\n",
              "Amer-Indian-Eskimo      311\n",
              "Other                   271\n",
              "Name: race, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
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        "outputId": "50839179-e578-46c4-9e5f-f9607cf4e7be"
      },
      "source": [
        "df['race'].value_counts().plot(kind='bar', figsize=(14, 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb8fd45c0>"
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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTYLl8hn3qoJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "c828379c-0bf6-4001-dfd5-05b5ce833c21"
      },
      "source": [
        "# 2. What is the average age of men (and women)?\n",
        "s = round(df.groupby('sex').age.mean())\n",
        "s"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "Female    37.0\n",
              "Male      39.0\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yh7DDsxlHHhg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "789e8723-08ed-4f0c-e7ad-2e6dd46786df"
      },
      "source": [
        "# Average age of men - rounded up\n",
        "avg_age = round(df.loc[df['sex'] == 'Male', 'age'].mean(),1)\n",
        "print('Average age of men:', avg_age, 'years.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average age of men: 39.4 years.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aW3QDENp8nNL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a4877878-479a-4476-bfbb-0b9fc2a1f20b"
      },
      "source": [
        "# 3. What is the percentage of people who have a Bachelor's degree?\n",
        "bach = round(df['education'].value_counts(normalize=True) * 100,1)\n",
        "bach_per = bach['Bachelors']\n",
        "print('Bachelors:', bach_per, '%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Bachelors: 16.4 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sVxVUOj-hx25",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "61dbe65f-0ec1-4e57-ef58-7bcc80b26e9b"
      },
      "source": [
        "# 4. What percentage of people with advanced education (Bachelors, Masters, or Doctorate) make more than 50K?\n",
        "\n",
        "high_edu = df[df['education'].isin(['Bachelors', 'Masters', 'Doctorate'])]\n",
        "low_edu = df[~df['education'].isin(['Bachelors', 'Masters', 'Doctorate'])]\n",
        "\n",
        "# print('advanced education holders:', high_edu)\n",
        "# print('non-advanced education holders:', low_edu)\n",
        "\n",
        "# Percentage with salary of >50K\n",
        "high_earn = round(len(high_edu[high_edu['salary'] == '>50K']) / len(high_edu) * 100, 1)\n",
        "low_earn = round(len(low_edu[low_edu['salary'] == '>50K']) / len(low_edu) * 100, 1)\n",
        "\n",
        "print('\\n % of >50k earners with advanced education:', high_earn, '%')\n",
        "print('% of >50k earners with low education:', low_earn, '%')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " % of >50k earners with advanced education: 46.5 %\n",
            "% of >50k earners with low education: 17.4 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-F2NPUms-uSm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bd5e1b19-f643-406a-b3e6-53dc20458b75"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32561, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLNs_-vmhIhU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9d0bb98b-9d3d-4994-94c4-64cb2e560b55"
      },
      "source": [
        "# 5. What is the minimum number of hours a person works per week?\n",
        "min_hours = df['hours-per-week'].min()\n",
        "min_hours\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZeSIsp__t3Wg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c72a4e69-83ce-4597-ad77-dc276787cf34"
      },
      "source": [
        "# 6. What percentage of the people who work the minimum number of hours per week have a salary of more than 50K?\n",
        "\n",
        "per_work = len(df[df['hours-per-week'] == min_hours])\n",
        "high_sal = round(len(df[(df['hours-per-week'] == min_hours) & (df['salary'] == '>50K')]) / per_work * 100, 1)\n",
        "\n",
        "print(\"% of people who work the minimum number of hours per week and have a salary of '>50K':\", high_sal)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "% of people who work the minimum number of hours per week and have a salary of '>50K': 10.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nuiw0E8uc3d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "eaccfd3e-5a5e-4797-baea-971d532be84a"
      },
      "source": [
        "# 7. # What country has the highest percentage of people that earn >50K and what is the percentage?\n",
        "\n",
        "highest_earning_country = (df.loc[df['salary'] == \">50K\", 'native-country'].value_counts() / df['native-country'].value_counts()).fillna(0).sort_values(ascending=False).index[0]\n",
        "highest_earning_country_percentage = round(len(df[(df['native-country'] == highest_earning_country) & (df['salary'] == '>50K')]) / len(df[df['native-country'] == highest_earning_country]) * 100, 1)\n",
        "\n",
        "print('Country with highest % of people earning >50K:', highest_earning_country)\n",
        "print('% of people that earn >50K:', highest_earning_country_percentage)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Country with highest % of people earning >50K: Iran\n",
            "% of people that earn >50K: 41.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5ycVz3UFyHy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1cb36ad0-59c2-4678-d128-fdf75427850d"
      },
      "source": [
        "# 8. Identify the most popular occupation for those who earn >50K in India.\n",
        "top_occupation = df[(df['salary'] == \">50K\") & (df['native-country'] == \"India\")][\"occupation\"].value_counts().index[0]\n",
        "\n",
        "print('Most popular occupation for those who earn >50k in India:', top_occupation)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Most popular occupation for those who earn >50k in India: Prof-specialty\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woAcstrbGjjP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}