{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"# UFCFVQ-15-M Programming for Data Science (Spring 2023)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">OVERALL COURSEWORK MARK: ___%</p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"### GitLab link submission, README.md file and Git commit messages\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"# Python Programming (Task B)\n",
"\n",
"## Student Id: 22074847"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"### Requirement FR1 - Develop a function to find the geometric mean"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"deletable": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The geometric mean is: 40.40574036744275\n"
]
}
],
"source": [
"def geometric_mean(num_lst):\n",
" \"\"\"\n",
" Calculate the geometric mean of a list of numbers.\n",
" :param num_lst: A list of positive integers or floats.\n",
" :returns float: The geometric mean of the numbers in the list.\n",
" \"\"\"\n",
" try:\n",
" # product of numbers\n",
" prod = 1\n",
"\n",
" # loop through the numbers and multiply each number\n",
" for number in num_lst:\n",
" prod *= number\n",
"\n",
" # Return the nth root of the product\n",
" return prod ** (1 / len(num_lst))\n",
" except (ValueError, Exception) as ex:\n",
" print(f\"Error occurred: {ex}\")\n",
"\n",
"# Test the function with the provided list of numbers\n",
"test_number_list = [64, 9, 90, 28, 46, 95, 34, 28, 86, 62, 14, 77, 99, 80,\n",
" 99, 56, 79, 37, 74, 6, 67, 32, 5, 94, 53, 62, 19, 44,\n",
" 16, 74, 92, 60, 74, 80, 10, 43, 51, 41, 91, 41, 27, 40,\n",
" 48, 27, 13, 41, 13, 28, 17, 64]\n",
"\n",
"result = geometric_mean(test_number_list)\n",
"print(f\"The geometric mean is: {result}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"### Requirement FR2 - Develop a function to read a single column from a CSV file"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"deletable": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column: pop\n",
"Data: [3779.0, 2769.0, 4079.0, 4343.0, 6809.0, 4682.0, 5027.0, 5238.0, 4832.0, 3795.0, 2786.0, 9429.0, 3413.0, 5573.0, 6315.0, 1997.0, 3651.0, 5286.0, 5740.0, 4303.0, 5213.0, 4149.0, 6460.0, 3348.0, 5824.0, 4521.0, 3202.0, 6172.0, 4160.0, 7886.0, 4924.0, 4228.0, 6018.0, 5601.0, 2742.0, 3305.0, 4838.0, 2584.0, 6747.0, 3245.0, 4947.0, 5887.0, 5004.0, 4465.0, 8623.0, 3013.0, 3580.0, 5876.0, 5203.0, 4601.0, 2664.0, 8874.0, 2182.0, 6701.0, 9680.0, 2860.0, 5057.0, 4179.0, 3880.0, 3078.0, 3770.0, 4689.0, 1392.0, 10220.0, 4828.0, 3568.0, 3035.0, 2422.0, 5102.0, 2560.0, 2621.0, 4049.0, 2367.0, 5222.0, 5304.0, 3357.0, 3548.0, 3886.0, 7687.0, 18168.0, 6987.0, 3292.0, 6457.0, 10229.0, 4377.0, 3520.0, 11284.0, 3121.0, 4941.0, 4293.0, 4917.0, 8205.0, 3531.0, 11794.0, 2314.0, 3887.0, 4904.0, 1625.0, 1812.0, 1970.0, 5787.0, 2023.0, 3481.0, 5059.0, 2291.0, 3864.0, 4934.0, 5100.0, 4596.0, 4487.0, 4745.0, 2991.0, 4968.0, 3403.0, 4066.0, 4225.0, 4791.0, 4643.0, 6056.0, 4232.0, 6074.0, 5169.0, 5029.0, 8222.0, 3536.0, 4044.0, 3085.0, 3751.0, 6461.0, 4542.0, 5072.0, 2156.0, 6106.0, 2733.0, 5335.0, 3721.0, 6604.0, 3700.0, 7499.0, 5994.0, 8023.0, 3708.0, 5206.0, 5950.0, 3070.0, 2886.0, 4775.0, 6844.0, 5206.0, 3300.0, 5269.0, 4681.0, 5921.0, 3082.0, 7528.0, 9699.0, 4356.0, 6613.0, 6493.0, 4074.0, 4863.0, 4720.0, 4284.0, 4726.0, 4412.0, 4411.0, 3544.0, 2674.0, 1911.0, 3396.0, 5383.0, 2650.0, 4040.0, 6460.0, 6971.0, 5823.0, 12351.0, 7338.0, 4239.0, 3422.0, 3255.0, 4424.0, 3754.0, 6466.0, 2143.0, 3358.0, 7538.0, 2929.0, 1325.0, 2665.0, 2537.0, 1404.0, 732.0, 4049.0, 3745.0, 4447.0, 8668.0, 2892.0, 4696.0, 3636.0, 4911.0, 3524.0, 2712.0, 5737.0, 6851.0, 1862.0, 8196.0, 2986.0, 7471.0, 678.0, 6941.0, 8810.0, 5938.0, 5437.0, 4296.0, 2993.0, 8033.0, 1354.0, 4523.0, 1781.0, 6740.0, 4748.0, 6084.0, 7883.0, 6993.0, 6384.0, 4498.0, 4419.0, 2875.0, 3436.0, 4923.0, 4293.0, 6202.0, 4302.0, 2979.0, 5506.0, 4579.0, 3307.0, 3042.0, 5683.0, 3522.0, 3238.0, 3404.0, 4020.0, 5651.0, 2619.0, 2732.0, 5977.0, 6081.0, 6230.0, 4227.0, 5947.0, 3660.0, 4966.0, 4759.0, 13987.0, 4716.0, 2680.0, 3748.0, 2808.0, 7956.0, 3859.0, 6257.0, 5153.0, 5442.0, 1363.0, 4792.0, 5808.0, 4264.0, 26826.0, 3111.0, 4082.0, 3535.0, 3219.0, 3803.0, 5863.0, 3817.0, 4294.0, 3014.0, 4378.0, 8081.0, 3045.0, 13561.0, 6142.0, 4702.0, 7753.0, 3276.0, 4045.0, 3088.0, 1683.0, 5733.0, 4945.0, 3641.0, 4667.0, 5006.0, 4978.0, 3715.0, 7864.0, 4502.0, 3947.0, 4630.0, 4192.0, 4371.0, 1471.0, 2391.0, 4283.0, 7340.0, 2917.0, 4083.0, 5199.0, 3954.0, 6876.0, 3441.0, 3140.0, 5502.0, 4517.0, 5372.0, 5731.0, 3021.0, 2485.0, 3172.0, 2911.0, 5726.0, 8064.0, 5329.0, 7585.0, 2816.0, 4189.0, 4466.0, 6530.0, 4480.0, 4865.0, 4129.0, 5456.0, 2377.0, 5169.0, 5062.0, 2150.0, 4775.0, 6415.0, 7408.0, 1977.0, 6888.0, 3776.0, 5931.0, 3687.0, 3574.0, 4829.0, 8988.0, 4542.0, 8810.0, 1345.0, 5747.0, 3040.0, 7323.0, 9738.0, 3902.0, 8960.0, 7556.0, 7099.0, 6309.0, 2802.0, 4866.0, 7621.0, 6185.0, 4908.0, 1990.0, 4836.0, 4321.0, 5410.0, 4017.0, 3679.0, 1271.0, 6657.0, 6033.0, 5378.0, 1610.0, 4284.0, 2186.0, 3921.0, 6016.0, 7456.0, 3721.0, 5375.0, 4430.0, 2666.0, 4315.0, 2355.0, 5158.0, 2923.0, 3300.0, 4932.0, 6118.0, 4279.0, 3999.0, 5917.0, 6747.0, 6001.0, 3755.0, 4993.0, 4579.0, 2459.0, 5449.0, 8776.0, 4026.0, 5447.0, 5543.0, 7914.0, 5791.0, 3362.0, 2747.0, 7010.0, 403.0, 7371.0, 3081.0, 3681.0, 2868.0, 1548.0, 2758.0, 7590.0, 6145.0, 1753.0, 1925.0, 3501.0, 2326.0, 3737.0, 3630.0, 5246.0, 7671.0, 1548.0, 2150.0, 11719.0, 3416.0, 3394.0, 2646.0, 4347.0, 6462.0, 2886.0, 2024.0, 3565.0, 6145.0, 4177.0, 4980.0, 2032.0, 10818.0, 2657.0, 5459.0, 5065.0, 3862.0, 4164.0, 4996.0, 4110.0, 4074.0, 3507.0, 4547.0, 5065.0, 1640.0, 4154.0, 3850.0, 2544.0, 5177.0]\n"
]
}
],
"source": [
"def read_column(file, index, convert_to_numbers=True):\n",
" \"\"\"\n",
" :param file: The path of the CSV file.\n",
" :param index: The index of the column to read (0-based).\n",
" :param convert_to_numbers: True if the column values should be converted to numbers, False otherwise.\n",
" :return: A tuple containing a list of the specified column's data values and the column name.\n",
" \"\"\"\n",
"\n",
" try:\n",
" with open(file, 'r') as csvfile:\n",
" csv_column_data = []\n",
"\n",
" # Read the file content line by line\n",
" lines = csvfile.readlines()\n",
"\n",
" # Get the header row and extract the column name\n",
" header = lines.pop(0).strip().split(',')\n",
" col_name = header[index]\n",
"\n",
" # Iterate through the remaining lines\n",
" for line in lines:\n",
" # Split the line by commas and get the specified column\n",
" row = line.strip().split(',')\n",
" value = row[index]\n",
"\n",
" # Convert to a number if required\n",
" if convert_to_numbers:\n",
" try:\n",
" value = float(value)\n",
" except ValueError:\n",
" pass # If fails, keep the value as a string\n",
"\n",
" # Add the value to the data list\n",
" csv_column_data.append(value)\n",
"\n",
" return col_name, csv_column_data\n",
" except FileNotFoundError:\n",
" print(f\"Error: File {file} not found.\")\n",
" return None, None\n",
" except Exception as e:\n",
" print(f\"Something went wrong: {e}\")\n",
" return None, None\n",
"\n",
"# Test the function\n",
"column_name, data = read_column('task1.csv', 1)\n",
"\n",
"print(f\"Column: {column_name}\")\n",
"print(f\"Data: {data}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"### Requirement FR3 - Develop a function to read CSV data from a file into memory"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"deletable": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"def read_all_data(file_name, conversion_indicators):\n",
" \"\"\"\n",
" This function will return all columns data as a dictionary\n",
" :param file_name: CSV file path\n",
" :param conversion_indicators: indicator to check column values needs be converted from strings to numbers\n",
" :return: dictionary\n",
" \"\"\"\n",
" #initializing the data dictionary\n",
" dict_data = {}\n",
" try:\n",
" with open(file_name, 'r') as csv_file:\n",
" #get the number of columns with headers\n",
" header = csv_file.readline().strip().split(',')\n",
" num_columns = len(header)\n",
"\n",
" for index in range(num_columns):\n",
" convert = conversion_indicators[index]\n",
" col_name, col_data = read_column(file_name, index, convert)\n",
"\n",
" #add data to the dictionary\n",
" dict_data[col_name] = col_data\n",
"\n",
" return dict_data\n",
" except (FileNotFoundError, Exception) as ex:\n",
" print(f\"Error occurred: {ex}.\")\n",
" return None\n",
"\n",
"file_path = 'task1.csv'\n",
"conversion_flags = [True, True, True, True, True, True, True, True, True, True]\n",
"all_csv_data = read_all_data(file_path, conversion_flags)\n",
"\n",
"print(all_csv_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"### Requirement FR4 - Develop a function to calculate the Spearman’s Rank Correlation Coefficient for two named columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
},
{
"cell_type": "code",
"execution_count": 73,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Spear-man's Rank Correlation Coefficient for `age` and `pop`: 0.0084\n"
]
}
],
"source": [
"def arithmatic_mean(numbers):\n",
" \"\"\"\n",
" This function will take a list of number and will return arithmatic mean\n",
" :param numbers: list of numbers\n",
" :return: float\n",
" \"\"\"\n",
"\n",
" try:\n",
"\n",
" # Handle exception for divide by zero error\n",
" if len(numbers) == 0:\n",
" raise ValueError(\"Your list should have at least one number\")\n",
"\n",
" # initialize sum\n",
" total = 0\n",
"\n",
" for num in numbers:\n",
" total += num\n",
"\n",
" return total / len(numbers)\n",
" except (ZeroDivisionError, Exception) as ex:\n",
" print(f\"Error occurred: {ex}\")\n",
"\n",
"def rank_list(lst):\n",
" \"\"\"\n",
" Returns a list of ranks for the input list, taking duplicates into account.\n",
" :param lst: A list of numbers\n",
" :return list: A list of ranked values\n",
" \"\"\"\n",
" ranks = {}\n",
"\n",
" try:\n",
" for i, val in enumerate(sorted(lst), 1):\n",
" #create a new list for each key if it doesn't already exist and append the rank of the corresponding value to it.\n",
" ranks.setdefault(val, []).append(i)\n",
"\n",
" avg_ranks = {}\n",
" for index, rank in ranks.items():\n",
" avg_ranks[index] = arithmatic_mean(rank)\n",
"\n",
" return [avg_ranks[val] for val in lst]\n",
" except Exception as ex:\n",
" print(f\"Error occurred: {ex}\")\n",
"\n",
"\n",
"def sm_correlation_coefficient(data1, data2):\n",
" \"\"\"\n",
" This function will accept two list as parameter and will return Spear-man's Rank Correlation Coefficient\n",
" :param data1: A column data as a list from csv file\n",
" :param data2: A column data as a list from csv file\n",
" :return: float: The Spear-man's Rank Correlation Coefficient\n",
" \"\"\"\n",
"\n",
" try:\n",
" # raise an error if list sizes are not equal\n",
" if len(data1) != len(data2):\n",
" raise ValueError(\"Both lists must have the same length.\")\n",
"\n",
" ranked_data1 = rank_list(data1)\n",
" ranked_data2 = rank_list(data2)\n",
"\n",
" # get the sum of squared differences of each item in both lists\n",
" sum_sqr_diff = 0\n",
" for i in range(len(ranked_data1)):\n",
" diff = ranked_data1[i] - ranked_data2[i] # differences of each list item\n",
" sqr = diff ** 2 # squared differences\n",
" sum_sqr_diff += sqr # get the sum of each squared differences\n",
"\n",
" # Calculate the Spear-man's Rank Correlation Coefficient using the formula\n",
" n = len(data1)\n",
" correlation_coefficient = 1 - (6 * sum_sqr_diff) / (n * (n ** 2 - 1))\n",
"\n",
" rounded_value = round(correlation_coefficient, 4)\n",
" return float(format(rounded_value, \".4f\"))\n",
" except(ValueError, Exception) as ex:\n",
" print(f\"Error occurred: {ex}\")\n",
"\n",
"\n",
"# Read two columns of data from the CSV file\n",
"file_path = 'task1.csv'\n",
"column1_name, column1_data = read_column(file_path, 0, True)\n",
"column2_name, column2_data = read_column(file_path, 1, True)\n",
"\n",
"# Calculate the Spear-man's Rank Correlation Coefficient for the two columns\n",
"try:\n",
" coefficient = sm_correlation_coefficient(column1_data, column2_data)\n",
" print(f\"Spear-man's Rank Correlation Coefficient for `{column1_name}` and `{column2_name}`: {coefficient}\")\n",
"except (ValueError, Exception) as e:\n",
" print(f\"Error occurred: {e}\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"### Requirement FR5 - Develop a function to generate a set of Spearman’s Rank Correlation Coefficients for a given data file "
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"deletable": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('age', 'pop', 0.0084), ('age', 'share_white', 0.2207), ('age', 'share_black', -0.1034), ('age', 'share_hispanic', -0.1488), ('age', 'personal_income', 0.0614), ('age', 'household_income', 0.0642), ('age', 'poverty_rate', -0.1214), ('age', 'unemployment_rate', -0.0962), ('age', 'uni_education_25+', 0.0286), ('pop', 'age', 0.0084), ('pop', 'share_white', 0.0767), ('pop', 'share_black', -0.1339), ('pop', 'share_hispanic', 0.1278), ('pop', 'personal_income', 0.221), ('pop', 'household_income', 0.3137), ('pop', 'poverty_rate', -0.287), ('pop', 'unemployment_rate', -0.1587), ('pop', 'uni_education_25+', 0.1247), ('share_white', 'age', 0.2207), ('share_white', 'pop', 0.0767), ('share_white', 'share_black', -0.4917), ('share_white', 'share_hispanic', -0.5204), ('share_white', 'personal_income', 0.4054), ('share_white', 'household_income', 0.3734), ('share_white', 'poverty_rate', -0.4775), ('share_white', 'unemployment_rate', -0.3815), ('share_white', 'uni_education_25+', 0.3623), ('share_black', 'age', -0.1034), ('share_black', 'pop', -0.1339), ('share_black', 'share_white', -0.4917), ('share_black', 'share_hispanic', -0.1734), ('share_black', 'personal_income', -0.2557), ('share_black', 'household_income', -0.3599), ('share_black', 'poverty_rate', 0.3433), ('share_black', 'unemployment_rate', 0.3367), ('share_black', 'uni_education_25+', -0.1179), ('share_hispanic', 'age', -0.1488), ('share_hispanic', 'pop', 0.1278), ('share_hispanic', 'share_white', -0.5204), ('share_hispanic', 'share_black', -0.1734), ('share_hispanic', 'personal_income', -0.1261), ('share_hispanic', 'household_income', -0.0229), ('share_hispanic', 'poverty_rate', 0.1635), ('share_hispanic', 'unemployment_rate', 0.0499), ('share_hispanic', 'uni_education_25+', -0.1782), ('personal_income', 'age', 0.0614), ('personal_income', 'pop', 0.221), ('personal_income', 'share_white', 0.4054), ('personal_income', 'share_black', -0.2557), ('personal_income', 'share_hispanic', -0.1261), ('personal_income', 'household_income', 0.8583), ('personal_income', 'poverty_rate', -0.8162), ('personal_income', 'unemployment_rate', -0.544), ('personal_income', 'uni_education_25+', 0.692), ('household_income', 'age', 0.0642), ('household_income', 'pop', 0.3137), ('household_income', 'share_white', 0.3734), ('household_income', 'share_black', -0.3599), ('household_income', 'share_hispanic', -0.0229), ('household_income', 'personal_income', 0.8583), ('household_income', 'poverty_rate', -0.8737), ('household_income', 'unemployment_rate', -0.5471), ('household_income', 'uni_education_25+', 0.6217), ('poverty_rate', 'age', -0.1214), ('poverty_rate', 'pop', -0.287), ('poverty_rate', 'share_white', -0.4775), ('poverty_rate', 'share_black', 0.3433), ('poverty_rate', 'share_hispanic', 0.1635), ('poverty_rate', 'personal_income', -0.8162), ('poverty_rate', 'household_income', -0.8737), ('poverty_rate', 'unemployment_rate', 0.5514), ('poverty_rate', 'uni_education_25+', -0.5824), ('unemployment_rate', 'age', -0.0962), ('unemployment_rate', 'pop', -0.1587), ('unemployment_rate', 'share_white', -0.3815), ('unemployment_rate', 'share_black', 0.3367), ('unemployment_rate', 'share_hispanic', 0.0499), ('unemployment_rate', 'personal_income', -0.544), ('unemployment_rate', 'household_income', -0.5471), ('unemployment_rate', 'poverty_rate', 0.5514), ('unemployment_rate', 'uni_education_25+', -0.5367), ('uni_education_25+', 'age', 0.0286), ('uni_education_25+', 'pop', 0.1247), ('uni_education_25+', 'share_white', 0.3623), ('uni_education_25+', 'share_black', -0.1179), ('uni_education_25+', 'share_hispanic', -0.1782), ('uni_education_25+', 'personal_income', 0.692), ('uni_education_25+', 'household_income', 0.6217), ('uni_education_25+', 'poverty_rate', -0.5824), ('uni_education_25+', 'unemployment_rate', -0.5367)]\n"
]
}
],
"source": [
"def generate_all_correlation_coefficients(csv_file):\n",
" \"\"\"\n",
" This function takes the csv file and generate all possible correlations with each and every column in the csv file\n",
" :param csv_file: The CSV file path\n",
" :return: A list of tuples containing all possible correlations with each and every column.\n",
" \"\"\"\n",
" # Read the data from the CSV file\n",
" con_indicators = [True, True, True, True, True, True, True, True, True, True]\n",
" all_column_data = read_all_data(csv_file, con_indicators)\n",
"\n",
" # Get the column names\n",
" column_names = list(all_column_data.keys())\n",
"\n",
" # Initialize an empty list to store the correlation coefficients\n",
" all_correlation_coefficients = []\n",
"\n",
" try:\n",
" # Iterate through all pairs of columns\n",
" for row_name in range(len(column_names)):\n",
" for col_name in range(len(column_names)):\n",
" # skip the comparison of the same column with itself\n",
" if row_name == col_name:\n",
" continue\n",
"\n",
" column1_name = column_names[row_name]\n",
" column1_data = all_column_data[column1_name]\n",
"\n",
" column2_name = column_names[col_name]\n",
" column2_data = all_column_data[column2_name]\n",
"\n",
" # Calculate the Correlation Coefficient for the current pair of columns\n",
" try:\n",
" sm_cor_coefficient = sm_correlation_coefficient(column1_data, column2_data)\n",
" all_correlation_coefficients.append((column1_name, column2_name, sm_cor_coefficient))\n",
" except (ValueError, Exception) as ex:\n",
" print(f\"An error occurred: {ex}\")\n",
"\n",
" return all_correlation_coefficients\n",
" except (ValueError, IndexError, Exception) as ex:\n",
" print(f\"Error occurred: {ex}\")\n",
"\n",
"# Test the function\n",
"result = generate_all_correlation_coefficients('task1.csv')\n",
"print(result)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"### Requirement FR6 - Develop a function to print a custom table"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"deletable": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n",
" $ age $ pop $ share_white $ share_black $\n",
"$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n",
"$ age $ - $ 0.0084 $ 0.2207 $ -0.1034 $\n",
"$ pop $ 0.0084 $ - $ 0.0767 $ -0.1339 $\n",
"$ share_white $ 0.2207 $ 0.0767 $ - $ -0.4917 $\n",
"$ share_black $ -0.1034 $ -0.1339 $ -0.4917 $ - $\n",
"$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n"
]
}
],
"source": [
"def print_table(correlations, border_char, columns):\n",
" # check border character length\n",
" if len(border_char) > 1 or not len(border_char):\n",
" raise ValueError(\"Border character should be one single character\")\n",
"\n",
"\n",
" # Find the length of the longest string in the list and add constant to define cell length\n",
" longest_string_length = len(max(columns, key=len))\n",
" max_cell_length = longest_string_length + 5\n",
"\n",
" try:\n",
" # print top boarder\n",
" # Initialize a variable to hold the sum of string lengths starts with 1 to hold the cell gap.\n",
" sum_col_lengths = 1\n",
"\n",
" # Loop over each string in the list and add its length to the running total\n",
" for name in columns:\n",
" sum_col_lengths += len(name)\n",
"\n",
" sum_col_lengths += len(columns) * 7\n",
" print(\" \" * max_cell_length + border_char * sum_col_lengths)\n",
"\n",
" # print the column names\n",
" print(\" \" * max_cell_length, end=\"\")\n",
" for name in columns:\n",
" print(border_char + \" \" * 3 + name + \" \" * 3 , end=\"\")\n",
" print(border_char)\n",
"\n",
" # print header separator\n",
" print(border_char * (max_cell_length + sum_col_lengths))\n",
"\n",
" # print the correlation coefficients\n",
" for i, value in enumerate(columns):\n",
" string_len_diff = longest_string_length - len(value) + 5\n",
" space_count = int(string_len_diff / 2)\n",
"\n",
" if len(value) % 2 == 0:\n",
" print(border_char + space_count * \" \" + value + (space_count - 1) * \" \", end=\"\")\n",
" else:\n",
" print(border_char + space_count * \" \" + value + space_count * \" \", end=\"\")\n",
"\n",
" for col_name in columns:\n",
" if value == col_name:\n",
" cell_space = int(((len(col_name) + 7 ) / 2) -1) * \" \"\n",
" print(border_char + cell_space + \"-\" + cell_space, end=\"\")\n",
"\n",
" else:\n",
" for element in correlations:\n",
" if element[0] == value and element[1] == col_name:\n",
" correlation_coefficient = round(element[2], 4)\n",
" cell_space = (len(col_name)) // 2 * \" \"\n",
" if correlation_coefficient > 0:\n",
" print(border_char + \" \" +cell_space + str(correlation_coefficient) + cell_space, end=\"\")\n",
" else:\n",
" print(border_char +cell_space + str(correlation_coefficient) + cell_space, end=\"\")\n",
"\n",
"\n",
" print(border_char)\n",
"\n",
" # print footer\n",
" print(border_char * (max_cell_length + sum_col_lengths))\n",
" except Exception as ex:\n",
" print(f\"Error occurred: {ex}\")\n",
"\n",
"\n",
"file_path = 'task1.csv'\n",
"columns_to_include = ['age','pop', 'share_white', 'share_black']\n",
"print_table(generate_all_correlation_coefficients(file_path), '$', columns_to_include)"
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"# Coding Standards\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"# Process Development Report for Task B\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Development Process Reflective Report\n",
"\n",
"## Problem Analysis\n",
"\n",
"Spearman's Rank Correlation Coefficient needed to be determined for all possible pairs of columns in a given dataset. The correlation coefficients also needed to be displayed in a special table, which required some more work. The objective was to create a set of procedures for doing these things.\n",
"\n",
"## Approach and Challenges\n",
"\n",
"As the first main task, I have to read a CSV file without using any library. I used python inbuild read file function to read through reach line of the csv file and appltying the relavant logic to get the given column data. To convert values to a number, I used exception handling because if there is a value that cannot be converted to number, that value should be keep as it is.\n",
"\n",
"Following that, I began the development process by writing code to determine the Spearman's Rank Correlation Coefficient between two data sets. A function to rank the data, taking into account duplicates, had to be developed initially in order to reduce code redundancy. Managing the duplicates and guaranteeing accurate rankings was the main difficulty and I had to check the ranking with MS Excel as well.\n",
"\n",
"Then, I moved to create a function that can generate a collection of correlation coefficients for a certain dataset. This was accomplished by comparing every pair of columns in the dataset using the correlation coefficient function that had been constructed earlier. During this step, I ran into the difficulty of dealing with repeated and reversed pairs, which had to be addressed to prevent output redundancy.\n",
"\n",
"Subsequently, a custom table generation function was developed to display the correlation coefficients for a subset of column pairs. The table needed to be formatted with proper padding for readability. The primary challenge in this stage was ensuring the table's cell alignment and properly displaying the compared columns for better comprehension. I used pre-defined padding for both left and right in evey column, based on the character value in longest column name. After may attepms, I got the table as in the requirement. However I have notice that even that table works perfectly with odd number length of the column names but if you have even and odd number length column names at the same time, the table get messed up little bit\n",
"\n",
"## Reflection\n",
"\n",
"The final solution used functions to efficiently calculate the Spearman's Rank Correlation Coefficient for every pair of columns in a dataset and present the results in a bespoke table. The project showed the need of modifying code based on output and overcoming problems to fulfil output.\n",
"\n",
"This taught me:\n",
"\n",
"1. Create functions that are flexible and can be easily combined with others for more advanced task.\n",
"2. Consider special cases, like dealing with repeated values in the ranking process and preventing unnecessary repetition in the output.\n",
"3. Focus on proper formatting and clear presentation, as shown by the function that generates a custom table for displaying results.\n",
"\n",
"While solving complicated problems, iterative improvements, feedback-driven modifications, and readability and presentation are crucial."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false
},
"source": [
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">MARK: __%</p>\n",
"<p style=\"color:red; font-weight:bold; font-size:xx-small\">FEEDBACK: </p>"
]
}
],
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