Faster For Loop to Manipulate Data in Pandas

As a data analyst or scientist working with pandas dataframes, you’ve likely encountered situations where your code takes longer than desired to run. One common culprit is the for loop, especially when working with series containing lists. In this article, we’ll explore techniques to optimize your code and achieve faster processing times.

Understanding the Problem

The original poster’s question revolves around finding alternative methods to manipulate data in pandas that are faster than using traditional for loops. They’ve already tried list comprehensions and series operators but need to perform more complicated operations with nested for loops.

Let’s examine the provided example and understand what’s happening:

for row in DF.iterrows():
    removelist = []
    
    # Nested for loop to compare adjacent elements in each history list
    for i in xrange(0, len(row[1]['history'])-1):
        if ((row[1]['history'][i]['title'] == row[1]['history'][i+1]['title']) & 
            (row[1]['history'][i]['dept'] == row[1]['history'][i+1]['dept']) & 
            (row[1]['history'][i]['office'] == row[1]['history'][i+1]['office']) & 
            (row[1]['history'][i]['employment'] == row[1]['history'][i+1]['employment'])):
            removelist.append(i)

newlist = [v for i, v in enumerate(row[1]['history']) if i not in removelist]

This nested for loop is used to identify consecutive duplicates in each history list. However, as the poster noted, this approach can be cumbersome and slow.

Optimizing Data Structure

One key optimization is to restructure your data to use pandas structures at the bottom level of your structure. For example:

import pandas as pd

# Original data
john_history = [{'title': 'a', 'dept': 'cs'}, {'title': 'cj', 'dept': 'sales'}]
jill_history = [{'title': 'boss', 'dept': 'cs'}, {'title': 'boss', 'dept': 'cs'}, {'title': 'junior', 'dept': 'cs'}]

# Restructured data
john_history = pd.DataFrame({'title': ['a', 'cj'], 'dept': ['cs', 'sales']})
jill_history = pd.DataFrame({'title': ['boss', 'boss', 'junior'], 'dept': ['cs', 'cs', 'cs']})

people = pd.concat([john_history, jill_history])

By using pandas DataFrames at the bottom level of your structure, you can take advantage of optimized operations and faster performance.

Leveraging Groupby and Droplevel

One elegant solution is to use the groupby function in combination with droplevel to achieve the desired result:

def drop_consecutive_duplicates(df):
    df2 = df.shift()
    return df.drop(df[(df.dept == df2.dept) & (df.title == df2.title)].index)

people.groupby('name').apply(drop_consecutive_duplicates)

This approach creates a new DataFrame df2 by shifting the original DataFrame, and then uses this to filter out consecutive duplicates. The groupby function groups the data by name, and apply applies the drop_consecutive_duplicates function to each group.

Using Selection and Filtering

Another technique is to use selection and filtering to achieve the desired result:

selection_array = (df.history == df2.history) & (df.title == df2.title)
unduplicated_consecutive = df[~selection_array]
print(unduplicated_consecutive)

# One-liner:
df[~((df.history == df2.history) & (df.title == df2.title))]

# Or:
df[(df.history != df2.history) | (df.title != df2.title)]

This approach uses boolean indexing to select rows where the history and title are different, effectively filtering out consecutive duplicates.

Conclusion

By restructuring your data, leveraging groupby and droplevel, and using selection and filtering techniques, you can optimize your code and achieve faster processing times. Remember to always prioritize pandas primitives over iterating over DataFrames for optimal performance.

Last modified on 2023-12-19