import pandas as pd # Loading in the data pulls_one = pd.read_csv('datasets/pulls_2011-2013.csv') pulls_two = pd.read_csv('datasets/pulls_2014-2018.csv') pull_files = pd.read_csv('datasets/pull_files.csv')

# Append pulls_one to pulls_two pulls = pd.concat([pulls_one, pulls_two], ignore_index=True) # Convert the date for the pulls object pulls['date'] = pd.to_datetime(pulls['date'], utc=True)

# Merge the two DataFrames data = pulls.merge(pull_files, on='pid')

%matplotlib inline # Create a column that will store the month data['month'] = data['date'].dt.month # Create a column that will store the year data['year'] = data['date'].dt.year # Group by the month and year and count the pull requests counts = data.groupby(['year', 'month'])['pid'].count() # Plot the results counts.plot(kind='bar', figsize = (12,4))

##### Required for matplotlib %matplotlib inline # Group by the submitter by_user = data.groupby('user')['pid'].count() # Plot the histogram by_user.hist(bins=50)

# Identify the last 10 pull requests last_10 = pulls_two.nlargest(10, 'pid') # Join the two data sets joined_pr = last_10.merge(pull_files, on='pid') # Identify the unique files files = set(joined_pr['file']) # Print the results files

# This is the file we are interested in: file = 'src/compiler/scala/reflect/reify/phases/Calculate.scala' # Identify the commits that changed the file file_pr = data[data['file'] == file] # Count the number of changes made by each developer author_counts = file_pr.groupby('user')['pid'].count() # Print the top 3 developers author_counts.sort_values(ascending=False).head(3)

file = 'src/compiler/scala/reflect/reify/phases/Calculate.scala' # Select the pull requests that changed the target file file_pr = pull_files[pull_files['file'] == file] # Merge the obtained results with the pulls DataFrame joined_pr = file_pr.merge(pulls, on='pid') # Find the users of the last 10 most recent pull requests users_last_10 = set(joined_pr.nlargest(10, 'date')['user']) # Printing the results users_last_10

%matplotlib inline # The developers we are interested in authors = ['xeno-by', 'soc'] # Get all the developers' pull requests by_author = pulls[pulls['user'].isin(authors)] # Count the number of pull requests submitted each year counts = by_author.groupby(['user', by_author['date'].dt.year]).agg({'pid': 'count'}).reset_index() # Convert the table to a wide format counts_wide = counts.pivot_table(index='date', columns='user', values='pid', fill_value=0) # Plot the results counts_wide.plot(kind='bar')

authors = ['xeno-by', 'soc'] file = 'src/compiler/scala/reflect/reify/phases/Calculate.scala' # Select the pull requests submitted by the authors, from the `data` DataFrame by_author = data[data['user'].isin(authors)] # Select the pull requests that affect the file by_file = by_author[by_author['file'] == file] # Group and count the number of PRs done by each user each year grouped = by_file.groupby(['user', by_file['date'].dt.year]).count()['pid'].reset_index() # Transform the data into a wide format by_file_wide = grouped.pivot_table(index='date', columns='user', values='pid', fill_value=0) # Plot the results by_file_wide.plot(kind='bar')