diff --git a/02_Filtering_&_Sorting/Occupation_Improved/occu_exe.ipynb b/02_Filtering_&_Sorting/Occupation_Improved/occu_exe.ipynb new file mode 100644 index 000000000..c1aba9386 --- /dev/null +++ b/02_Filtering_&_Sorting/Occupation_Improved/occu_exe.ipynb @@ -0,0 +1,1091 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "v_EYz24qYOSV" + }, + "source": [ + "\n", + "# **Improved Occupation Exercise – Pandas Filtering & Sorting**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8dMSKDOMeBXK" + }, + "source": [ + "This notebook improves the original exercise by providing clearer instructions, additional tasks, and well-documented solutions. The dataset contains user demographics, occupations, and zip codes." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "collapsed": true, + "id": "qmpqFGcxZU2j", + "outputId": "73541045-12ea-4192-9e7f-eb5db3a66a9f" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"users\",\n \"rows\": 943,\n \"fields\": [\n {\n \"column\": \"user_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 272,\n \"min\": 1,\n \"max\": 943,\n \"num_unique_values\": 943,\n \"samples\": [\n 97,\n 266,\n 811\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12,\n \"min\": 7,\n \"max\": 73,\n \"num_unique_values\": 61,\n \"samples\": [\n 24,\n 57,\n 52\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"F\",\n \"M\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"occupation\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 21,\n \"samples\": [\n \"technician\",\n \"healthcare\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"zip_code\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 795,\n \"samples\": [\n \"90016\",\n \"15232\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "users" + }, + "text/html": [ + "\n", + "
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user_idagegenderoccupationzip_code
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\n" + ], + "text/plain": [ + " user_id age gender occupation zip_code\n", + "0 1 24 M technician 85711\n", + "1 2 53 F other 94043\n", + "2 3 23 M writer 32067\n", + "3 4 24 M technician 43537\n", + "4 5 33 F other 15213" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Load the dataset\n", + "users = pd.read_table(\n", + " \"https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user\",\n", + " sep='|'\n", + ")\n", + "\n", + "users.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-hGYygIoeboD" + }, + "source": [ + "## Original Tasks (Improved)\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + "**Task 1**\n", + "\n", + "Display the first 10 records of the dataset and describe the column names.\n", + "\n", + "**Task 2**\n", + "\n", + "Find the most common occupation in the dataset.\n", + "\n", + "**Task 3**\n", + "\n", + "Show the number of male and female users in the dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r-_xSntjfOEm" + }, + "source": [ + "## Additional Tasks Added for Clarity and Practice\n", + "\n", + "\n", + "---\n", + "\n", + "\n", + "**Additional Task 1**\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show the top 5 most common occupations and their counts.\n", + "\n", + "\n", + "\n", + "\n", + "**Additional Task 2**\n", + "\n", + "Calculate the proportion (percentage) of each occupation in the dataset.\n", + "\n", + "**Additional Task 3**\n", + "\n", + "Group the users by gender and occupation and show the count for each combination." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mKS56wipf7qL" + }, + "source": [ + "#**Solutions**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VgbFoJAghCn_", + "outputId": "66c53d14-23ee-431d-ab96-0f8ccf73e050" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['user_id', 'age', 'gender', 'occupation', 'zip_code'], dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Task 1\n", + "users.head(10)\n", + "users.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "i4GKrkbvhZWb", + "outputId": "86943e20-56b3-4e73-c0c3-f989f83de4df" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'student'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Task 2\n", + "users['occupation'].value_counts().idxmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "x4Tu2ZdJhZmU", + "outputId": "5a7619eb-fa47-4257-a9df-8606e618ef46" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "occupation\n", + "student 196\n", + "other 105\n", + "educator 95\n", + "administrator 79\n", + "engineer 67\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Additional Task 1\n", + "users['occupation'].value_counts().head(5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 774 + }, + "id": "tUswtzdtknne", + "outputId": "82f0d09d-8927-4cb4-ecf6-49b37cf90915" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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librarian5.41
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technician2.86
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retired1.48
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salesman1.27
none0.95
homemaker0.74
doctor0.74
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