README.md

Use Pandas to clean SIS info

Problem Statement

You have a csv export from an SIS that you want to import into another system. There are lots of duplicate accounts, people with the same first, last, and birthdays. You would like to merge the rows. You decided to do this by identifying any duplicates, and choosing the one with the most columns filled out as the winner. If any of the remaining rows have non-null values for columns where the winning row is null, coelesce them across ("merge" them).

But wait, the target system has the following additional requirements:

• You need to add a role column, based on the gender, 'M' = 'Father', etc
• The export column names don't match, need to rename them
• The email address has to be unique across all rows when importing it
• Some rows have null emails, so label them as "[email protected]"

Oh, and parents like to share the same email address, so you'll have to build aliases for duplicate emails used, too.

How can use use pandas from pytohn to help us to this?

Install

pip3 install pandas
python3 remove_dups_coalesce.py

Customize

The source.py file has example data. In any real situation, read in the data using read_excel or read_csv.

remove_dups_coalesce.py

import pandas as pd
from collections import defaultdict
from source import data, groupby, map_column_names

# Create the source dataframe (via import)
source = pd.DataFrame(data)
original_tally = len(source.axes[0])  # how many 

# Map any of the columns names as needed (via import)
source.rename(columns=map_column_names, inplace=True)

# Add column to the source indicating how many columns have legit values
source['filled'] = source.apply(lambda x: x.count(), axis=1)

# Sort it so that the ones with the most filled-out columns is first
source_sorted = source.sort_values(by=['filled'], ascending=False)

# Next, create a truth table where source rows with no dups is false,
#   the first appearance of any dup is false
#   and any subsequent appearance of any dup is true
truth_table = source_sorted.duplicated(subset=groupby, keep='first')

# Then inverse that truth table ("~") so that we can pull out rows
#   which has the first appearance of any dups
#   and of any rows that aren't part of any dup
#   -> giving us no dups and those that were never dups
final = source_sorted.loc[~truth_table]
final_tally = len(final.axes[0])  # total after removing dups

# Do any (optional) transformations you might need here
#   For example, perhaps the "role" is gonna be determined by the gender:
#   (we have to make a copy of it so we can write new values to it)
#   here demonstrates how to use a defaultdict to map common values, with default value
final = final.copy()
gender_to_role = defaultdict(lambda: 'Unknown')
gender_to_role['M'] = 'Father'
gender_to_role['F'] = 'Mother'
final['role'] = final['gender'].map(gender_to_role)


# Crucially, coalesce any leftover rows that we lost when removing dups,
#   ensuring any null columns in our saved rows are populated
#   with values from removed row (if they have info)
#   :


# Save a column with source row index for reference, off-by-two because 0 indexed, plus header
#   (as we're about to lose the info when we set the index below)
final['source_row'] = [i + 2 for i in final.axes[0]]

# Use sql idea of indexes so that pandas can match up like rows
final_grouped = final.set_index(groupby)

# Get the rows that we dropped when we duplicated
remaining = source.loc[truth_table].groupby(groupby)

# Go through getting the nth items of each group...
for n in range(0, 10):
    rows = remaining.nth(n)
    if rows.empty:
        break  # ... breaking when there's no more ...

    # ... and use `combine_first` which will fill null columns with non-null columns found in rows
    final_grouped = final_grouped.combine_first(rows)

# Finally, ungroup it for output
final = final_grouped.reset_index()

# Add a column to see how many more values were filled out on this row
final['diff'] = final.apply(lambda x: x.count() - x['filled'] - 1, axis=1)

# Sort for convenience
final.sort_values(by=['source_row'], inplace=True)


# Convert repeated email addresses into aliases
#   using algorithm that tracks how many times we've come across the email
#   do it by setting up a function and saved state (tally)


tally = defaultdict(int)  # default = 0
def convert_email_function(value, values=None):
    if pd.isna(value): value = '[email protected]'  # if null, use this
    if ';' in value:  # list of emails with ; as delimiter
        value = value.split(';')[0].strip()  # take the first email
    if not '@' in value: return f'{value}@example.com'
    if values.count(value) > 1:  # there's more than one of these in the list
        handle, domain = value.split('@')
        ats = "+" * tally[value]
        tally[value] += 1
        return f'{handle}{ats}@{domain}'
    return value

# Create list of all email addresses found, adding blank email twice so it gets flagged as repeating
all_values = final['email'].values.tolist() + ['[email protected]'] * 2

# Use `apply` and `convert_email_function` to turn email addresses into valid ones
final['email'] = final['email'].apply(convert_email_function, values=all_values)

# Tada!
final.to_csv('merged.csv', index=False)

source.py

"""
Example data
"""

groupby = ['first_name', 'last_name', 'birthday']
map_column_names = {
    'First Name': 'first_name',
    'Last Name': 'last_name',
    'Gender': 'gender',
    'Day of Birth': 'birthday',
    'Country': 'country',
    'Address': 'address',
    'Email': 'email'
}

data = [
    {
        'First Name': 'Joe',
        'Last Name': 'Schmoe',
        'Day of Birth': '1900-01-01',
        'Gender': 'M',
        'value': 'V',
        'role': 'Self',
        'Address': '123 Road',
        'Country': 'USA',
        'more': 'more',
        'Email': None
    },
    {
        'First Name': 'Joe',
        'Last Name': 'Schmoe',
        'Day of Birth': '1900-01-01',
        'Gender': 'M',
        'extra': 'E',
        'role': 'Father',
        'Address': '123 Road Name, Someplace, Country 12345',
        'Email': None
    },
    {
        'First Name': 'Joe',
        'Last Name': 'Schmoe',
        'Day of Birth': '1900-01-01',
        'Gender': 'M',
        'role': 'Father',
        'Address': '123 Road Name, Someplace'
    },
   {
        'First Name': 'Jane',
        'Last Name': 'Smith',
        'Day of Birth': '1900-02-01',
        'Gender': 'F',
        'role': 'Self',
        'Address': '123 Lane',
        'Email': '[email protected];[email protected]'
    },
   {
        'First Name': 'Jane',
        'Last Name': 'Smith',
        'Day of Birth': '1900-02-01',
        'Gender': 'F',
        'role': 'Mother',
        'Address': '123 Lane Name, Somewhere, Country 34353'
    },
   {
        'First Name': 'Another',
        'Last Name': 'Person',
        'Day of Birth': '1900-03-01',
        'Gender': 'F',
        'role': 'Mother',
        'Address': '123 Another Road Name, Somewhere else, Country 12345',
        'Email': None
    }


]