https://github.com/tinaland101/carcinoma-regression-project

The goal of this project is to analyze and visualize the results of a clinical study involving the treatment of squamous cell carcinoma (SCC) in mice. As a senior data analyst, you are tasked with preparing a technical report for Pymaceuticals, Inc.
https://github.com/tinaland101/carcinoma-regression-project

linear-regression mathplotlib numpy pandas pyhton

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The goal of this project is to analyze and visualize the results of a clinical study involving the treatment of squamous cell carcinoma (SCC) in mice. As a senior data analyst, you are tasked with preparing a technical report for Pymaceuticals, Inc.

• Host: GitHub
• URL: https://github.com/tinaland101/carcinoma-regression-project
• Owner: tinaland101
• Created: 2024-12-14T21:38:48.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-09T03:23:54.000Z (over 1 year ago)
• Last Synced: 2025-01-09T04:31:25.229Z (over 1 year ago)
• Topics: linear-regression, mathplotlib, numpy, pandas, pyhton
• Language: Jupyter Notebook
• Homepage:
• Size: 401 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Carcinoma Regression project

# create variable for reading cvs from computer

import pandas as pd

import matplotlib.pyplot as plt

import scipy.stats as st

# Paths to your data files

mouse_metadata_path = "/Users/christinaland/Downloads/Starter_Code-9/Pymaceuticals/data/Mouse_metadata.csv"

study_results_path = "/Users/christinaland/Downloads/Starter_Code-9/Pymaceuticals/data/Study_results.csv"

# Read the CSV files into DataFrames

mouse_metadata = pd.read_csv(mouse_metadata_path)

study_results = pd.read_csv(study_results_path)

# Merge the data on 'Mouse ID'

merged_data = pd.merge(mouse_metadata, study_results, on="Mouse ID")

# Specify the desired column order

desired_column_order = [

    'Mouse ID', 'Timepoint', 'Tumor Volume (mm3)', 'Metastatic Sites',

    'Drug Regimen', 'Sex', 'Age_months', 'Weight (g)'

]

# Reorganize the columns

data_merged_col = merged_data[desired_column_order]

# Sort by 'Timepoint' in ascending order

data_merged_col_sorted = data_merged_col.sort_values(by="Timepoint")

# Display the sorted data

print(data_merged_col_sorted.head())

# number of mice

mouse_sum = data_merged_col["Mouse ID"].nunique()

print(mouse_sum)

hould be uniquely identified by Mouse ID and Timepoint

# Get the duplicate mice by ID number that shows up for Mouse ID and Timepoint

duplicate_mice = data_merged_col[data_merged_col.duplicated(subset=["Mouse ID", "Timepoint"], keep=False)]

duplicate_mice_ids = duplicate_mice["Mouse ID"].unique()

print(duplicate_mice_ids, duplicate_mice_ids.dtype)

duplicates_df = data_merged_col[data_merged_cleaned.duplicated(subset=['Mouse ID', 'Timepoint'])]

print(duplicates)

data_merged = pd.merge(mouse_metadata, study_results, on="Mouse ID")

duplicates = data_merged[data_merged.duplicated(subset=["Mouse ID", "Timepoint"], keep="first")]

data_cleaned = data_merged.drop_duplicates(subset="Mouse ID", keep="first")

print("\nCleaned DataFrame:")

print(data_cleaned.head())

unique_mice_count = data_cleaned['Mouse ID'].nunique()

print(unique_mice_count)

# Group the merged data by 'Drug Regimen'

grouped_data = data_merged.groupby('Drug Regimen')['Tumor Volume (mm3)']

# Calculate summary statistics

mean_tumor_volume = grouped_data.mean()

median_tumor_volume = grouped_data.median()

variance_tumor_volume = grouped_data.var()

std_dev_tumor_volume = grouped_data.std()

sem_tumor_volume = grouped_data.sem()

# Create a summary DataFrame by combining all the calculated statistics

summary_stats = pd.DataFrame({

    'Mean Tumor Volume': mean_tumor_volume,

    'Median Tumor Volume': median_tumor_volume,

    'Tumor Volume Variance': variance_tumor_volume,

    'Tumor Volume Std. Dev.': std_dev_tumor_volume,

    'Tumor Volume Std. Err.': sem_tumor_volume

})

# Display the summary statistics table

print(summary_stats)

import pandas as pd

# Count the number of rows (Mouse ID/Timepoints) for each drug regimen

rows_per_drug_regimen = data_merged.groupby('Drug Regimen').size()

# Sort the values in descending order

rows_per_drug_regimen = rows_per_drug_regimen.sort_values(ascending=False)

# Create the bar plot using Pandas plot function

rows_per_drug_regimen.plot(kind='bar', figsize=(10, 6), color='skyblue')

# Adding labels and title

plt.title('Total Number of Rows (Mouse ID/Timepoints) per Drug Regimen')

plt.xlabel('Drug Regimen')

plt.ylabel('Number of Rows (Mouse ID/Timepoints)')

# Rotate the x-axis labels for better readability

plt.xticks(rotation=45)

# Show the plot

plt.tight_layout()  # Ensure the labels fit in the plot

plt.show()

import matplotlib.pyplot as plt

# Count the number of rows (Mouse ID/Timepoints) for each drug regimen

rows_per_drug_regimen = data_merged.groupby('Drug Regimen').size()

# Sort the values in descending order

rows_per_drug_regimen = rows_per_drug_regimen.sort_values(ascending=False)

# Generate the bar plot using Pandas

rows_per_drug_regimen.plot(kind='bar', color='blue', figsize=(10, 6))

plt.ylabel('# of Observed Mouse Timepoints')

plt.xlabel('Drug Regimen')

plt.xticks(rotation=45)

plt.tight_layout()  # Ensure the labels fit in the plot

plt.show()

import pandas as pd

 

 #Get the unique mice and their gender

unique_mice_gender = data_merged.drop_duplicates(subset='Mouse ID')['Sex']

# Count the number of unique female and male mice

gender_counts = unique_mice_gender.value_counts()

# Create a pie chart using Pandas

gender_counts.plot(kind='pie', autopct='%1.1f%%', figsize=(7, 7), startangle=90, colors=['lightblue', 'pink'])

# Show the plot

plt.ylabel('')  # Hide the y-label (it is redundant for a pie chart)

plt.tight_layout()  # Ensure the labels fit within the plot space

plt.show()

import matplotlib.pyplot as plt

# Assuming 'data_merged' is already created and merged from the previous steps

# Count the number of female and male mice in the study

gender_count = data_merged['Sex'].value_counts()

# Create the pie chart using Matplotlib's pyplot methods

plt.figure(figsize=(8, 8))  # Set the figure size

plt.pie(gender_count, labels=gender_count.index, autopct='%1.1f%%', startangle=90, colors=['lightblue', 'salmon'])

# Show the plot

plt.show()

import pandas as pd

# Assuming 'data_merged' is already loaded and merged with all columns.

# Group by Drug Regimen and get the final tumor volume for each mouse

last_timepoints = data_merged.groupby('Mouse ID')['Timepoint'].max()

last_timepoints_df = pd.merge(last_timepoints, data_merged, on=['Mouse ID', 'Timepoint'], how='left')

# Filter for the treatment regimens of interest

treatment_regimens = ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']

filtered_data = last_timepoints_df[last_timepoints_df['Drug Regimen'].isin(treatment_regimens)]

# Create a dictionary to hold potential outliers for each treatment regimen

outliers = {}

# Loop through each treatment regimen to find outliers

for regimen in treatment_regimens:

    # Filter the data for the current regimen

    regimen_data = filtered_data[filtered_data['Drug Regimen'] == regimen]['Tumor Volume (mm3)']

    

    # Calculate the quartiles and IQR

    Q1 = regimen_data.quantile(0.25)

    Q3 = regimen_data.quantile(0.75)

    IQR = Q3 - Q1

    

    # Calculate the lower and upper bounds for outliers

    lower_bound = Q1 - 1.5 * IQR

    upper_bound = Q3 + 1.5 * IQR

    

    # Identify potential outliers

    potential_outliers = regimen_data[(regimen_data < lower_bound) | (regimen_data > upper_bound)]

    

    # Store the outliers for this regimen

    outliers[regimen] = potential_outliers

# Display the potential outliers for each regimen

for regimen, outlier_data in outliers.items():

    print(f"{regimen}'s potential outliers: {outlier_data}\n")

import pandas as pd

import matplotlib.pyplot as plt

# Assuming 'data_merged' is already loaded and merged with all columns.

# Group by Drug Regimen and get the final tumor volume for each mouse

last_timepoints = data_merged.groupby('Mouse ID')['Timepoint'].max()

last_timepoints_df = pd.merge(last_timepoints, data_merged, on=['Mouse ID', 'Timepoint'], how='left')

# Filter for the treatment regimens of interest

treatment_regimens = ['Capomulin', 'Ramicane', 'Infubinol', 'Ceftamin']

filtered_data = last_timepoints_df[last_timepoints_df['Drug Regimen'].isin(treatment_regimens)]

# Create a dictionary to hold potential outliers for each treatment regimen

outliers = {}

# Create a list to hold tumor volume data for each regimen (for the box plot)

tumor_volumes = []

# Loop through each treatment regimen to find outliers and prepare data for the box plot

for regimen in treatment_regimens:

    # Filter the data for the current regimen

    regimen_data = filtered_data[filtered_data['Drug Regimen'] == regimen]['Tumor Volume (mm3)']

    

    # Calculate the quartiles and IQR

    Q1 = regimen_data.quantile(0.25)

    Q3 = regimen_data.quantile(0.75)

    IQR = Q3 - Q1

    

    # Calculate the lower and upper bounds for outliers

    lower_bound = Q1 - 1.5 * IQR

    upper_bound = Q3 + 1.5 * IQR

    

    # Identify potential outliers

    potential_outliers = regimen_data[(regimen_data < lower_bound) | (regimen_data > upper_bound)]

    

    # Store the outliers for this regimen

    outliers[regimen] = potential_outliers

    

    # Append tumor volume data for box plot

    tumor_volumes.append(regimen_data)

# Display the potential outliers for each regimen

for regimen, outlier_data in outliers.items():

    print(f"{regimen}'s potential outliers: {outlier_data}\n")

# Create the box plot

plt.figure(figsize=(10, 6))

plt.boxplot(tumor_volumes, labels=treatment_regimens, patch_artist=True)

# Adding labels and title

plt.title('Distribution of Tumor Volumes for Each Treatment Group')

plt.xlabel('Drug Regimen')

plt.ylabel('Tumor Volume (mm3)')

# Show the plot

plt.tight_layout()

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Assuming 'data_merged' is already loaded and merged with all columns.

# Filter the data for a single mouse treated with Capomulin

mouse_id = 'l509'  # Example Mouse ID for a mouse treated with Capomulin

capomulin_data = data_merged[(data_merged['Drug Regimen'] == 'Capomulin') & (data_merged['Mouse ID'] == mouse_id)]

# Create the plot of Tumor Volume vs. Timepoint (line plot without grid lines and points)

plt.figure(figsize=(8, 6))

# Plot with a solid line, but no points (no markers)

plt.plot(capomulin_data['Timepoint'], capomulin_data['Tumor Volume (mm3)'], color='b', linestyle='-', marker='', linewidth=2)

# Add titles and labels

plt.title(f'Tumor Volume vs. Timepoint for Mouse {mouse_id} (Capomulin Treatment)', fontsize=14)

plt.xlabel('Timepoint (Days)', fontsize=12)

plt.ylabel('Tumor Volume (mm3)', fontsize=12)

# Remove the grid

plt.grid(False)

# Display the plot

plt.tight_layout()

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

# Assuming 'data_merged' is already loaded and merged with all columns.

# Filter the data for the Capomulin regimen

capomulin_data = data_merged[data_merged['Drug Regimen'] == 'Capomulin']

# Group by 'Mouse ID' and calculate the average tumor volume for each mouse

average_tumor_volume = capomulin_data.groupby('Mouse ID')['Tumor Volume (mm3)'].mean()

# Merge the average tumor volume with the mouse weights (using 'Mouse ID')

mouse_weights = capomulin_data[['Mouse ID', 'Weight (g)']].drop_duplicates()

merged_data = pd.merge(average_tumor_volume, mouse_weights, on='Mouse ID')

# Create a scatter plot of mouse weight vs. average tumor volume

plt.figure(figsize=(8, 6))

plt.scatter(merged_data['Weight (g)'], merged_data['Tumor Volume (mm3)'], color='b', edgecolor='black', alpha=0.7)

# Add titles and labels

plt.title('Mouse Weight vs. Average Tumor Volume for Capomulin Regimen', fontsize=14)

plt.xlabel('Weight (g)', fontsize=12)

plt.ylabel('Average Tumor Volume (mm3)', fontsize=12)

# Display the plot

plt.tight_layout()

plt.show()

import pandas as pd

import matplotlib.pyplot as plt

from scipy import stats

# Assuming 'data_merged' is already loaded and merged with all columns.

# Filter the data for the Capomulin regimen

capomulin_data = data_merged[data_merged['Drug Regimen'] == 'Capomulin']

# Group by 'Mouse ID' and calculate the average tumor volume for each mouse

average_tumor_volume = capomulin_data.groupby('Mouse ID')['Tumor Volume (mm3)'].mean()

# Merge the average tumor volume with the mouse weights (using 'Mouse ID')

mouse_weights = capomulin_data[['Mouse ID', 'Weight (g)']].drop_duplicates()

merged_data = pd.merge(average_tumor_volume, mouse_weights, on='Mouse ID')

# Calculate the Pearson correlation coefficient

correlation = merged_data['Weight (g)'].corr(merged_data['Tumor Volume (mm3)'])

print(f"Pearson correlation coefficient: {correlation}")

# Perform linear regression to get the slope and intercept

slope, intercept, r_value, p_value, std_err = stats.linregress(merged_data['Weight (g)'], merged_data['Tumor Volume (mm3)'])

# Create the regression line

regression_line = slope * merged_data['Weight (g)'] + intercept

# Create the scatter plot

plt.figure(figsize=(8, 6))

plt.scatter(merged_data['Weight (g)'], merged_data['Tumor Volume (mm3)'], color='b', edgecolor='black', alpha=0.7)

# Plot the regression line

plt.plot(merged_data['Weight (g)'], regression_line, color='r', linewidth=2)

# Add titles and labels

plt.title('Mouse Weight vs. Average Tumor Volume for Capomulin Regimen', fontsize=14)

plt.xlabel('Weight (g)', fontsize=12)

plt.ylabel('Average Tumor Volume (mm3)', fontsize=12)

# Display the plot

plt.tight_layout()

plt.show()

# Print the linear regression results

print(f"Linear Regression Model: y = {slope:.2f}x + {intercept:.2f}")

print(f"R-squared: {r_value**2:.2f}")