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Commit 36716840 authored by Akash Manujaya Liyanaarachchi's avatar Akash Manujaya Liyanaarachchi :smirk:
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FR6 completed but padding changing with different column sizes

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%% Cell type:markdown id: tags:
# UFCFVQ-15-M Programming for Data Science (Spring 2023)
%% Cell type:markdown id: tags:
<p style="color:red; font-weight:bold; font-size:xx-small">OVERALL COURSEWORK MARK: ___%</p>
%% Cell type:markdown id: tags:
### GitLab link submission, README.md file and Git commit messages
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
%% Cell type:markdown id: tags:
# Python Programming (Task B)
## Student Id:
%% Cell type:markdown id: tags:
### Requirement FR1 - Develop a function to find the geometric mean
%% Cell type:code id: tags:
``` python
def geometric_mean(numbers):
def geometric_mean(num_lst):
"""
Calculate the geometric mean of a list of numbers.
Parameters:
numbers (list): A list of positive integers or floats.
Returns:
float: The geometric mean of the numbers in the list.
:param num_lst: A list of positive integers or floats.
:returns float: The geometric mean of the numbers in the list.
"""
# Initialize the product of the numbers
product = 1
# Length of the list
num_elements = len(numbers)
# Iterate through the list of numbers and multiply each number
for number in numbers:
for number in num_lst:
product *= number
# Calculate the nth root of the product
geometric_mean_result = product ** (1 / num_elements)
return geometric_mean_result
# Calculate the nth root of the product and return it
return product ** (1 / len(num_lst))
# Test the function with the provided list of numbers
test_number_list = [64, 9, 90, 28, 46, 95, 34, 28, 86, 62, 14, 77, 99, 80,
99, 56, 79, 37, 74, 6, 67, 32, 5, 94, 53, 62, 19, 44,
16, 74, 92, 60, 74, 80, 10, 43, 51, 41, 91, 41, 27, 40,
48, 27, 13, 41, 13, 28, 17, 64]
result = geometric_mean(test_number_list)
print(f"The geometric mean of the list is: {result}")
```
%% Output
The geometric mean of the list is: 40.40574036744275
%% Cell type:markdown id: tags:
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
%% Cell type:markdown id: tags:
### Requirement FR2 - Develop a function to read a single column from a CSV file
%% Cell type:code id: tags:
``` python
def read_csv_column(file, index, convert_to_numbers=True):
"""
:param file: The path of the CSV file.
:param index: The index of the column to read (0-based).
:param convert_to_numbers: True if the column values should be converted to numbers, False otherwise.
:return: A tuple containing a list of the specified column's data values and the column name.
"""
try:
# Open the CSV file
with open(file, 'r') as csvfile:
# Initialize the data list
csv_column_data = []
# Read the file content line by line
lines = csvfile.readlines()
# Get the header row and extract the column name
header = lines.pop(0).strip().split(',')
col_name = header[index]
# Iterate through the remaining lines
for line in lines:
# Split the line by commas
row = line.strip().split(',')
# Get the value from the specified column
value = row[index]
# Convert the value to a number if the flag is set to True
if convert_to_numbers:
try:
value = float(value)
except ValueError:
pass # If the conversion fails, keep the value as a string
# Add the value to the data list
csv_column_data.append(value)
return col_name, csv_column_data
except FileNotFoundError:
print(f"Error: File {file} not found.")
return None, None
# Test the function using the 'task1.csv' file and a specified column index
file_path = 'task1.csv'
column_index = 0
column_name, data = read_csv_column(file_path, column_index)
print(f"Column name: {column_name}")
print(f"Data: {data}")
```
%% Output
Column name: age
Data: [16.0, 27.0, 26.0, 25.0, 29.0, 29.0, 22.0, 35.0, 44.0, 31.0, 76.0, 40.0, 31.0, 23.0, 39.0, 25.0, 54.0, 24.0, 57.0, 21.0, 42.0, 21.0, 36.0, 26.0, 49.0, 54.0, 26.0, 48.0, 33.0, 21.0, 41.0, 48.0, 36.0, 41.0, 29.0, 27.0, 45.0, 32.0, 35.0, 36.0, 35.0, 40.0, 18.0, 34.0, 39.0, 21.0, 62.0, 43.0, 44.0, 29.0, 35.0, 50.0, 18.0, 25.0, 31.0, 29.0, 49.0, 23.0, 45.0, 26.0, 35.0, 34.0, 46.0, 29.0, 39.0, 28.0, 51.0, 67.0, 53.0, 25.0, 30.0, 24.0, 35.0, 43.0, 24.0, 29.0, 38.0, 31.0, 36.0, 23.0, 38.0, 53.0, 24.0, 26.0, 28.0, 34.0, 28.0, 40.0, 51.0, 44.0, 25.0, 56.0, 37.0, 58.0, 39.0, 37.0, 35.0, 26.0, 47.0, 31.0, 60.0, 32.0, 45.0, 42.0, 17.0, 22.0, 33.0, 18.0, 39.0, 59.0, 33.0, 58.0, 58.0, 47.0, 41.0, 64.0, 45.0, 53.0, 24.0, 48.0, 29.0, 40.0, 34.0, 21.0, 26.0, 45.0, 25.0, 17.0, 24.0, 42.0, 29.0, 42.0, 30.0, 29.0, 39.0, 63.0, 49.0, 41.0, 27.0, 30.0, 60.0, 77.0, 19.0, 37.0, 54.0, 29.0, 30.0, 24.0, 21.0, 44.0, 40.0, 32.0, 22.0, 43.0, 52.0, 27.0, 34.0, 20.0, 25.0, 24.0, 20.0, 46.0, 42.0, 43.0, 41.0, 59.0, 25.0, 42.0, 64.0, 22.0, 24.0, 63.0, 56.0, 60.0, 54.0, 37.0, 22.0, 39.0, 45.0, 57.0, 42.0, 19.0, 26.0, 34.0, 69.0, 64.0, 35.0, 40.0, 19.0, 27.0, 37.0, 17.0, 39.0, 74.0, 42.0, 47.0, 43.0, 46.0, 44.0, 31.0, 47.0, 41.0, 43.0, 40.0, 32.0, 31.0, 20.0, 20.0, 33.0, 22.0, 41.0, 41.0, 32.0, 16.0, 29.0, 42.0, 29.0, 47.0, 53.0, 18.0, 47.0, 34.0, 36.0, 63.0, 36.0, 27.0, 28.0, 33.0, 32.0, 42.0, 31.0, 17.0, 28.0, 24.0, 71.0, 51.0, 28.0, 53.0, 54.0, 45.0, 33.0, 48.0, 34.0, 23.0, 35.0, 33.0, 32.0, 52.0, 30.0, 23.0, 35.0, 42.0, 37.0, 56.0, 36.0, 27.0, 30.0, 31.0, 46.0, 51.0, 72.0, 28.0, 63.0, 28.0, 33.0, 24.0, 27.0, 24.0, 28.0, 28.0, 17.0, 46.0, 52.0, 39.0, 49.0, 30.0, 51.0, 16.0, 18.0, 22.0, 40.0, 61.0, 52.0, 51.0, 36.0, 36.0, 59.0, 17.0, 18.0, 41.0, 33.0, 25.0, 23.0, 47.0, 58.0, 47.0, 34.0, 28.0, 37.0, 87.0, 39.0, 27.0, 35.0, 36.0, 24.0, 26.0, 34.0, 51.0, 49.0, 41.0, 54.0, 36.0, 26.0, 35.0, 22.0, 27.0, 42.0, 32.0, 32.0, 25.0, 26.0, 53.0, 26.0, 40.0, 55.0, 29.0, 31.0, 19.0, 57.0, 40.0, 35.0, 35.0, 39.0, 37.0, 36.0, 62.0, 43.0, 32.0, 34.0, 37.0, 37.0, 33.0, 35.0, 40.0, 21.0, 30.0, 23.0, 26.0, 39.0, 33.0, 34.0, 37.0, 26.0, 24.0, 25.0, 31.0, 49.0, 59.0, 50.0, 37.0, 28.0, 26.0, 23.0, 32.0, 24.0, 42.0, 34.0, 68.0, 31.0, 83.0, 35.0, 29.0, 50.0, 56.0, 43.0, 38.0, 27.0, 36.0, 55.0, 36.0, 68.0, 61.0, 46.0, 47.0, 26.0, 37.0, 22.0, 18.0, 39.0, 49.0, 23.0, 47.0, 32.0, 45.0, 51.0, 31.0, 54.0, 31.0, 23.0, 29.0, 28.0, 31.0, 24.0, 27.0, 57.0, 39.0, 38.0, 34.0, 39.0, 20.0, 35.0, 36.0, 38.0, 33.0, 57.0, 38.0, 72.0, 37.0, 47.0, 43.0, 37.0, 75.0, 21.0, 20.0, 29.0, 37.0, 41.0, 22.0, 23.0, 64.0, 34.0, 49.0, 32.0, 25.0, 39.0, 53.0, 27.0, 36.0, 20.0, 39.0, 19.0, 34.0, 36.0, 34.0, 31.0, 45.0, 34.0, 31.0, 28.0, 57.0, 29.0, 50.0, 40.0, 35.0, 53.0, 59.0, 18.0, 28.0, 52.0, 38.0, 48.0]
%% Cell type:markdown id: tags:
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
%% Cell type:markdown id: tags:
### Requirement FR3 - Develop a function to read CSV data from a file into memory
%% Cell type:code id: tags:
``` python
def read_all_csv_data(file_name, conversion_indicators) -> dict:
def read_all_csv_data(file_name, conversion_indicators):
"""
This function will return all columns data as a dictionary
:param file_name: CSV file path
:param conversion_indicators: indicator to check column values should be converted from strings to numbers
:return: dictionary
"""
#initializing the data dictionary
dict_data = {}
try:
with open(file_name, 'r') as csv_file:
#get the number of columns with headers
header = csv_file.readline().strip().split(',')
num_columns = len(header)
for index in range(num_columns):
convert = conversion_indicators[index]
col_name, col_data = read_csv_column(file_name, index, convert)
#add data to the dictionary
dict_data[col_name] = col_data
return dict_data
except FileNotFoundError:
print(f"Error: File {file_name} not found.")
return None
file_path = 'task1.csv'
conversion_flags = [True, True, True, True, True, True, True, True, True, True]
data_dict = read_all_csv_data(file_path, conversion_flags)
all_csv_data = read_all_csv_data(file_path, conversion_flags)
print(data_dict)
print(all_csv_data)
```
%% Output
{'age': [16.0, 27.0, 26.0, 25.0, 29.0, 29.0, 22.0, 35.0, 44.0, 31.0, 76.0, 40.0, 31.0, 23.0, 39.0, 25.0, 54.0, 24.0, 57.0, 21.0, 42.0, 21.0, 36.0, 26.0, 49.0, 54.0, 26.0, 48.0, 33.0, 21.0, 41.0, 48.0, 36.0, 41.0, 29.0, 27.0, 45.0, 32.0, 35.0, 36.0, 35.0, 40.0, 18.0, 34.0, 39.0, 21.0, 62.0, 43.0, 44.0, 29.0, 35.0, 50.0, 18.0, 25.0, 31.0, 29.0, 49.0, 23.0, 45.0, 26.0, 35.0, 34.0, 46.0, 29.0, 39.0, 28.0, 51.0, 67.0, 53.0, 25.0, 30.0, 24.0, 35.0, 43.0, 24.0, 29.0, 38.0, 31.0, 36.0, 23.0, 38.0, 53.0, 24.0, 26.0, 28.0, 34.0, 28.0, 40.0, 51.0, 44.0, 25.0, 56.0, 37.0, 58.0, 39.0, 37.0, 35.0, 26.0, 47.0, 31.0, 60.0, 32.0, 45.0, 42.0, 17.0, 22.0, 33.0, 18.0, 39.0, 59.0, 33.0, 58.0, 58.0, 47.0, 41.0, 64.0, 45.0, 53.0, 24.0, 48.0, 29.0, 40.0, 34.0, 21.0, 26.0, 45.0, 25.0, 17.0, 24.0, 42.0, 29.0, 42.0, 30.0, 29.0, 39.0, 63.0, 49.0, 41.0, 27.0, 30.0, 60.0, 77.0, 19.0, 37.0, 54.0, 29.0, 30.0, 24.0, 21.0, 44.0, 40.0, 32.0, 22.0, 43.0, 52.0, 27.0, 34.0, 20.0, 25.0, 24.0, 20.0, 46.0, 42.0, 43.0, 41.0, 59.0, 25.0, 42.0, 64.0, 22.0, 24.0, 63.0, 56.0, 60.0, 54.0, 37.0, 22.0, 39.0, 45.0, 57.0, 42.0, 19.0, 26.0, 34.0, 69.0, 64.0, 35.0, 40.0, 19.0, 27.0, 37.0, 17.0, 39.0, 74.0, 42.0, 47.0, 43.0, 46.0, 44.0, 31.0, 47.0, 41.0, 43.0, 40.0, 32.0, 31.0, 20.0, 20.0, 33.0, 22.0, 41.0, 41.0, 32.0, 16.0, 29.0, 42.0, 29.0, 47.0, 53.0, 18.0, 47.0, 34.0, 36.0, 63.0, 36.0, 27.0, 28.0, 33.0, 32.0, 42.0, 31.0, 17.0, 28.0, 24.0, 71.0, 51.0, 28.0, 53.0, 54.0, 45.0, 33.0, 48.0, 34.0, 23.0, 35.0, 33.0, 32.0, 52.0, 30.0, 23.0, 35.0, 42.0, 37.0, 56.0, 36.0, 27.0, 30.0, 31.0, 46.0, 51.0, 72.0, 28.0, 63.0, 28.0, 33.0, 24.0, 27.0, 24.0, 28.0, 28.0, 17.0, 46.0, 52.0, 39.0, 49.0, 30.0, 51.0, 16.0, 18.0, 22.0, 40.0, 61.0, 52.0, 51.0, 36.0, 36.0, 59.0, 17.0, 18.0, 41.0, 33.0, 25.0, 23.0, 47.0, 58.0, 47.0, 34.0, 28.0, 37.0, 87.0, 39.0, 27.0, 35.0, 36.0, 24.0, 26.0, 34.0, 51.0, 49.0, 41.0, 54.0, 36.0, 26.0, 35.0, 22.0, 27.0, 42.0, 32.0, 32.0, 25.0, 26.0, 53.0, 26.0, 40.0, 55.0, 29.0, 31.0, 19.0, 57.0, 40.0, 35.0, 35.0, 39.0, 37.0, 36.0, 62.0, 43.0, 32.0, 34.0, 37.0, 37.0, 33.0, 35.0, 40.0, 21.0, 30.0, 23.0, 26.0, 39.0, 33.0, 34.0, 37.0, 26.0, 24.0, 25.0, 31.0, 49.0, 59.0, 50.0, 37.0, 28.0, 26.0, 23.0, 32.0, 24.0, 42.0, 34.0, 68.0, 31.0, 83.0, 35.0, 29.0, 50.0, 56.0, 43.0, 38.0, 27.0, 36.0, 55.0, 36.0, 68.0, 61.0, 46.0, 47.0, 26.0, 37.0, 22.0, 18.0, 39.0, 49.0, 23.0, 47.0, 32.0, 45.0, 51.0, 31.0, 54.0, 31.0, 23.0, 29.0, 28.0, 31.0, 24.0, 27.0, 57.0, 39.0, 38.0, 34.0, 39.0, 20.0, 35.0, 36.0, 38.0, 33.0, 57.0, 38.0, 72.0, 37.0, 47.0, 43.0, 37.0, 75.0, 21.0, 20.0, 29.0, 37.0, 41.0, 22.0, 23.0, 64.0, 34.0, 49.0, 32.0, 25.0, 39.0, 53.0, 27.0, 36.0, 20.0, 39.0, 19.0, 34.0, 36.0, 34.0, 31.0, 45.0, 34.0, 31.0, 28.0, 57.0, 29.0, 50.0, 40.0, 35.0, 53.0, 59.0, 18.0, 28.0, 52.0, 38.0, 48.0], 'pop': [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, 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%% Cell type:markdown id: tags:
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
%% Cell type:markdown id: tags:
### Requirement FR4 - Develop a function to calculate the Spearman’s Rank Correlation Coefficient for two named columns
%% Cell type:markdown id: tags:
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
%% Cell type:code id: tags:
``` python
def rank_list(lst):
"""
This function returns a list of ranks for the input list, taking duplicates into account.
:param lst: A list of data that needed to be ranked
:return list: A of ranked values
"""
ranks = {}
for i, val in enumerate(sorted(lst), 1):
ranks.setdefault(val, []).append(i)
# get the average rank of each item using dictionary comprehension
avg_ranks = {v: sum(r) / len(r) for v, r in ranks.items()}
ranked_list = [avg_ranks[val] for val in lst]
return ranked_list
return [avg_ranks[val] for val in lst]
def sm_correlation_coefficient(data1, data2):
"""
This function will accept two list as parameter and will return Spear-man's Rank Correlation Coefficient
:param data1: A column data as a list from csv file
:param data2: A column data as a list from csv file
:return: float: The Spear-man's Rank Correlation Coefficient
"""
# raise an error if list sizes are not equal
if len(data1) != len(data2):
raise ValueError("Both lists must have the same length.")
ranked_data1 = rank_list(data1)
ranked_data2 = rank_list(data2)
# get the sum of squared differences of each item in both lists
sum_sqr_diff = 0
for i in range(len(ranked_data1)):
diff = ranked_data1[i] - ranked_data2[i] # differences of each list item
sqr = diff ** 2 # squared differences
sum_sqr_diff += sqr # get the sum of each squared differences
# Calculate the Spear-man's Rank Correlation Coefficient using the formula
n = len(data1)
correlation = 1 - (6 * sum_sqr_diff) / (n * (n ** 2 - 1))
correlation_coefficient = 1 - (6 * sum_sqr_diff) / (n * (n ** 2 - 1))
return correlation
return correlation_coefficient
# Read two columns of data from the CSV file
file_path = 'task1.csv'
column1_name, column1_data = read_csv_column(file_path, 0, True)
column2_name, column2_data = read_csv_column(file_path, 1, True)
# Calculate the Spear-man's Rank Correlation Coefficient for the two columns
try:
coefficient = sm_correlation_coefficient(column1_data, column2_data)
print(f"Spear-man's Rank Correlation Coefficient for `{column1_name}` and `{column2_name}`: {coefficient}")
except ValueError as e:
print(f"Error occured: {e}")
```
%% Output
Spear-man's Rank Correlation Coefficient for `age` and `pop`: 0.008388522728803749
%% Cell type:markdown id: tags:
### Requirement FR5 - Develop a function to generate a set of Spearman’s Rank Correlation Coefficients for a given data file
%% Cell type:code id: tags:
``` python
def generate_correlation_coefficients(csv_file_path):
def generate_all_correlation_coefficients(csv_file_path):
# Read the data from the CSV file
con_flags = [True, True, True, True, True, True, True, True, True, True]
column_data = read_all_csv_data(csv_file_path, con_flags)
conversion_indicators = [True, True, True, True, True, True, True, True, True, True]
all_column_data = read_all_csv_data(csv_file_path, conversion_indicators)
# Get the column names
column_names = list(column_data.keys())
column_names = list(all_column_data.keys())
# Initialize an empty list to store the correlation coefficients
correlation_coefficients = []
all_correlation_coefficients = []
# Iterate through all pairs of columns
for i in range(len(column_names)):
for j in range(len(column_names)):
if i == j:
continue # skip the comparison of the same column with itself
col1_name = column_names[i]
col1_data = column_data[col1_name]
col1_data = all_column_data[col1_name]
col2_name = column_names[j]
col2_data = column_data[col2_name]
col2_data = all_column_data[col2_name]
# Calculate the Spearman's Rank Correlation Coefficient for the current pair of columns
try:
coefficient = sm_correlation_coefficient(col1_data, col2_data)
correlation_coefficients.append((col1_name, col2_name, coefficient))
sm_cor_coefficient = sm_correlation_coefficient(col1_data, col2_data)
all_correlation_coefficients.append((col1_name, col2_name, sm_cor_coefficient))
except ValueError as e:
print(f"Error occured: {e}")
print(f"A value error occurred: {e}")
except Exception as ex:
print(f"An error occurred: {ex}")
return correlation_coefficients
return all_correlation_coefficients
# Test the generate_correlation_coefficients function
file_path = 'task1.csv'
result = generate_correlation_coefficients(file_path)
result = generate_all_correlation_coefficients(file_path)
print(result)
```
%% Output
[('age', 'pop', 0.008388522728803749), ('age', 'share_white', 0.2207405157324991), ('age', 'share_black', -0.10335098740738657), ('age', 'share_hispanic', -0.14877212526226957), ('age', 'personal_income', 0.061421540828312526), ('age', 'household_income', 0.06422180808910982), ('age', 'poverty_rate', -0.12141944634635382), ('age', 'unemployment_rate', -0.09615083822147863), ('age', 'uni_education_25+', 0.028563528332461674), ('pop', 'age', 0.008388522728803749), ('pop', 'share_white', 0.07665516130573191), ('pop', 'share_black', -0.13387812319718395), ('pop', 'share_hispanic', 0.1278109846392408), ('pop', 'personal_income', 0.2209602832327533), ('pop', 'household_income', 0.31369475803834), ('pop', 'poverty_rate', -0.28695045876503955), ('pop', 'unemployment_rate', -0.15865296638662674), ('pop', 'uni_education_25+', 0.12468582770619929), ('share_white', 'age', 0.2207405157324991), ('share_white', 'pop', 0.07665516130573191), ('share_white', 'share_black', -0.4916674832288337), ('share_white', 'share_hispanic', -0.520439020566343), ('share_white', 'personal_income', 0.40540064010308285), ('share_white', 'household_income', 0.3733566278105559), ('share_white', 'poverty_rate', -0.47754993992143846), ('share_white', 'unemployment_rate', -0.3814682441014612), ('share_white', 'uni_education_25+', 0.3622678768934545), ('share_black', 'age', -0.10335098740738657), ('share_black', 'pop', -0.13387812319718395), ('share_black', 'share_white', -0.4916674832288337), ('share_black', 'share_hispanic', -0.17341998282769766), ('share_black', 'personal_income', -0.2556799656064015), ('share_black', 'household_income', -0.3599164228207896), ('share_black', 'poverty_rate', 0.3433366996108673), ('share_black', 'unemployment_rate', 0.33669382348293975), ('share_black', 'uni_education_25+', -0.11787955748863044), ('share_hispanic', 'age', -0.14877212526226957), ('share_hispanic', 'pop', 0.1278109846392408), ('share_hispanic', 'share_white', -0.520439020566343), ('share_hispanic', 'share_black', -0.17341998282769766), ('share_hispanic', 'personal_income', -0.12611993542626387), ('share_hispanic', 'household_income', -0.022889166383932125), ('share_hispanic', 'poverty_rate', 0.16351308316318214), ('share_hispanic', 'unemployment_rate', 0.04988391548091631), ('share_hispanic', 'uni_education_25+', -0.17818345947177416), ('personal_income', 'age', 0.061421540828312526), ('personal_income', 'pop', 0.2209602832327533), ('personal_income', 'share_white', 0.40540064010308285), ('personal_income', 'share_black', -0.2556799656064015), ('personal_income', 'share_hispanic', -0.12611993542626387), ('personal_income', 'household_income', 0.8582992010347475), ('personal_income', 'poverty_rate', -0.8161829964810254), ('personal_income', 'unemployment_rate', -0.544004851604116), ('personal_income', 'uni_education_25+', 0.6920090613903686), ('household_income', 'age', 0.06422180808910982), ('household_income', 'pop', 0.31369475803834), ('household_income', 'share_white', 0.3733566278105559), ('household_income', 'share_black', -0.3599164228207896), ('household_income', 'share_hispanic', -0.022889166383932125), ('household_income', 'personal_income', 0.8582992010347475), ('household_income', 'poverty_rate', -0.8737056162737573), ('household_income', 'unemployment_rate', -0.5471233331414411), ('household_income', 'uni_education_25+', 0.6216765103235913), ('poverty_rate', 'age', -0.12141944634635382), ('poverty_rate', 'pop', -0.28695045876503955), ('poverty_rate', 'share_white', -0.47754993992143846), ('poverty_rate', 'share_black', 0.3433366996108673), ('poverty_rate', 'share_hispanic', 0.16351308316318214), ('poverty_rate', 'personal_income', -0.8161829964810254), ('poverty_rate', 'household_income', -0.8737056162737573), ('poverty_rate', 'unemployment_rate', 0.5513979748441693), ('poverty_rate', 'uni_education_25+', -0.5824024626698399), ('unemployment_rate', 'age', -0.09615083822147863), ('unemployment_rate', 'pop', -0.15865296638662674), ('unemployment_rate', 'share_white', -0.3814682441014612), ('unemployment_rate', 'share_black', 0.33669382348293975), ('unemployment_rate', 'share_hispanic', 0.04988391548091631), ('unemployment_rate', 'personal_income', -0.544004851604116), ('unemployment_rate', 'household_income', -0.5471233331414411), ('unemployment_rate', 'poverty_rate', 0.5513979748441693), ('unemployment_rate', 'uni_education_25+', -0.5366899652267001), ('uni_education_25+', 'age', 0.028563528332461674), ('uni_education_25+', 'pop', 0.12468582770619929), ('uni_education_25+', 'share_white', 0.3622678768934545), ('uni_education_25+', 'share_black', -0.11787955748863044), ('uni_education_25+', 'share_hispanic', -0.17818345947177416), ('uni_education_25+', 'personal_income', 0.6920090613903686), ('uni_education_25+', 'household_income', 0.6216765103235913), ('uni_education_25+', 'poverty_rate', -0.5824024626698399), ('uni_education_25+', 'unemployment_rate', -0.5366899652267001)]
%% Cell type:markdown id: tags:
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
%% Cell type:markdown id: tags:
### Requirement FR6 - Develop a function to print a custom table
%% Cell type:code id: tags:
``` python
# replace with your code
def print_table(correlations, border_char, columns):
# Find the length of the longest string in the list and add constant to define cell length
longest_string_length = len(max(columns, key=len))
max_cell_length = longest_string_length + 5
# print top boarder
# Initialize a variable to hold the sum of string lengths starts with 1 to hold the cell gap.
sum_col_lengths = 1
# Loop over each string in the list and add its length to the running total
for name in columns:
sum_col_lengths += len(name)
sum_col_lengths += len(columns) * 7
print(" " * max_cell_length + "$" * sum_col_lengths)
# print the column names
print(" " * max_cell_length, end="")
for name in columns:
print(border_char + " " * 3 + name + " " * 3 , end="")
print("$")
# print header separator
print("$" * (max_cell_length + sum_col_lengths))
# print the correlation coefficients
for i, value in enumerate(columns):
string_len_diff = longest_string_length - len(value) + 5
space_count = int(string_len_diff / 2)
if len(value) % 2 == 0:
print(border_char + space_count * " " + value + (space_count - 1) * " ", end="")
else:
print(border_char + space_count * " " + value + space_count * " ", end="")
for col_name in columns:
if value == col_name:
cell_space = int(((len(col_name) + 7 ) / 2) -1) * " "
print(border_char + cell_space + "-" + cell_space, end="")
else:
for element in correlations:
if element[0] == value and element[1] == col_name:
correlation_coefficient = round(element[2], 4)
cell_space = (len(col_name)) // 2 * " "
if correlation_coefficient > 0:
print(border_char + " " +cell_space + str(correlation_coefficient) + cell_space, end="")
else:
print(border_char +cell_space + str(correlation_coefficient) + cell_space, end="")
print("$")
# print footer
# print header separator
print("$" * (max_cell_length + sum_col_lengths))
file_path = 'task1.csv'
columns_to_include = ['age','pop', 'share_white', 'share_black']
print_table(generate_all_correlation_coefficients(file_path), '$', columns_to_include)
```
%% Output
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$ age $ pop $ share_white $ share_black $
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$ age $ - $ 0.0084 $ 0.2207 $ -0.1034 $
$ pop $ 0.0084 $ - $ 0.0767 $ -0.1339 $
$ share_white $ 0.2207 $ 0.0767 $ - $ -0.4917 $
$ share_black $ -0.1034 $ -0.1339 $ -0.4917 $ - $
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
%% Cell type:markdown id: tags:
# Coding Standards
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
%% Cell type:markdown id: tags:
# Process Development Report for Task B
%% Cell type:markdown id: tags:
add markdown text here
%% Cell type:markdown id: tags:
<p style="color:red; font-weight:bold; font-size:xx-small">MARK: __%</p>
<p style="color:red; font-weight:bold; font-size:xx-small">FEEDBACK: </p>
......
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