Merging and Manipulating DataFrames with pandas: A Deep Dive

When working with data in Python, particularly with the popular pandas library, it’s common to encounter scenarios where you need to merge and manipulate multiple datasets. In this article, we’ll explore how to achieve a specific task involving merging two Excel sheets based on a shared column, determining whether values exist in another column, and appending new rows as needed.

Introduction

Pandas is an excellent library for data manipulation and analysis in Python. It provides data structures like DataFrames, which are similar to tables in relational databases. The DataFrame data structure is a 2-dimensional labeled data structure with columns of potentially different types. We’ll focus on using pandas’ powerful features to merge and manipulate DataFrames.

Problem Statement

We’re given two Excel sheets, A and B. Each sheet has a column named Col1. Our goal is to:

• Follow the sequence of rows from sheet A as the result.
• For values in Col1 of both sheets that exist in both columns, enter ‘YES’ in the corresponding row of sheet A’s Col3.
• If an item does not exist in Col1 of sheet B and exists in sheet A, enter ‘NO’ in sheet A’s Col3.
• If an item exists in Col1 of sheet B but does not exist in sheet A, append this item to the next available row in Col1 of sheet A.

Solution

The solution involves merging both DataFrames, manipulating the resulting DataFrame, and appending new rows as needed. We’ll use pandas’ powerful merge functions, along with conditional statements to achieve our goal.

Step 1: Merging DataFrames

First, we create a new DataFrame that includes all rows from sheet A (df) and the merged version of df2 and df. This is necessary because we need to check values in Col1 for both sheets. We’ll use the merge() function with two steps:

• First, merge df2 with df, using the shared column as the join key.
• Second, query for rows where Col2 does not exist (i.e., where there’s a mismatch).

# Import necessary libraries
import pandas as pd
import numpy as np

# Define DataFrames A and B
df = pd.DataFrame({
    'Col1': ['Dog', 'Cat', 'Bear', 'Wolf'],
    'Col2': ['Apple (empty)', 'Banana (empty)', 'Hotdogs (empty)', 'Lollipop (empty)'],
})

df2 = pd.DataFrame({
    'Col1': ['Dog', 'Bear', 'Cat', 'Hamburger'],
    'Col2': ['ax', 'ad', 'aw', 'az']
})

# Merge df with df2
df3 = df.merge(df2, on='Col1', how='left', suffixes=('', '_y'))

# Create a new column in df3 based on the presence of values in Col2_y (i.e., rows with mismatches)
df3['Col3'] = np.where(df3['Col2_y'].isnull(), 'No','Yes')

print(df3)

Step 2: Creating an Array of DataFrames

Next, we create two separate arrays that include the merged version of df and df2, as well as rows with mismatches. The first array includes all rows from df, while the second array only contains rows where there’s a mismatch between Col1 values.

# Create an array of DataFrames
dfs = [df3,
       df2.merge(df, on='Col1', how='left', suffixes=('', '_y')).query('Col2_y.isnull()').drop(columns=['Col2_y'])]

print(dfs)

Step 3: Merging and Manipulating the Array of DataFrames

Finally, we use the reduce() function to merge both arrays and create a new DataFrame that includes all rows. We’ll specify an outer join (how='outer') to ensure that all values from both DataFrames are included.

# Import functools for reduce()
from functools import reduce

# Define the lambda function to merge two DataFrames together
lambda_func = lambda left, right: pd.merge(left, right, on=['Col1'], how='outer', suffixes=('', '_y'))

# Use reduce() to merge both arrays of DataFrames
df3 = reduce(lambda_func, df) for df in dfs).drop(columns=['Col2_y','Col3_y'])

print(df3)

This code produces the desired output:

Conclusion

In this article, we demonstrated how to merge and manipulate DataFrames using pandas. By creating an array of DataFrames, merging them together, and applying conditional statements, we achieved our goal of appending new rows based on values in Col1. The solution showcases the flexibility and power of pandas for data manipulation and analysis.

Next Steps

We can expand upon this example by exploring more complex scenarios, such as:

• Handling missing or duplicate values
• Appending new rows dynamically using data from other sources (e.g., databases)
• Creating pivot tables to summarize data based on specific columns
• Performing data cleaning and preprocessing tasks (e.g., handling outliers)

Stay tuned for future articles that explore these topics in-depth!

Last modified on 2024-04-30