Grouping Duplicates and Combining String Columns via Pandas

When working with data that includes duplicate rows, it can be challenging to determine which row to keep. In this scenario, we are dealing with a pandas DataFrame where one of the columns contains duplicate values generated using if-conditions on other columns.

In this article, we will explore how to group duplicates and combine string columns in a pandas DataFrame.

Introduction

The problem arises from trying to identify unique rows in a DataFrame that has duplicate values in some columns. The question asked about removing these duplicate values and combining the remaining columns into single values for each city-state pair.

One common approach is using the groupby function with lambda functions, which can fill missing values using forward and backward filling. Another method involves dropping duplicate rows by selecting specific columns before grouping.

Using Groupby with Lambda Functions

The original solution provided in the Stack Overflow post uses the groupby function along with a lambda function to fill missing values. This approach allows for easy combination of string columns into single values.

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame
data = {
    'city': ['Orlando', 'Orlando', 'Orlando'],
    'state': ['fl', 'fl', 'fl'],
    'school_lvl': [1, 2, 3],
    'schl_name': ['Union Park', '', ''],
    'elem_name': ['', 'Legacy', '']
}
df = pd.DataFrame(data)

# Print the original DataFrame
print(df)

# Define a function to fill missing values using forward and backward filling
def fill_missing_values(x):
    return x.ffill().bfill()

# Group by city-state pairs, apply the lambda function, and then drop duplicates
c = df.columns[:2].tolist() + df.columns[-3:].tolist()
print(c)
['city', 'state', 'elem_name', 'middle_name', 'highschoo_name']

df = df.groupby(['city', 'state']).apply(fill_missing_values).drop_duplicates(c)
print(df)

In this code snippet, the groupby function is used to group rows by city-state pairs. The lambda function fill_missing_values applies forward and backward filling to missing values in each row.

After grouping, the drop_duplicates function removes duplicate rows based on the selected columns (c). This results in a new DataFrame where string columns have been combined into single values for each city-state pair.

Dropping Duplicate Rows Simpler

Another approach is to remove specific columns before grouping and then dropping duplicates.

# Import necessary libraries
import pandas as pd

# Create a sample DataFrame
data = {
    'city': ['Orlando', 'Orlando', 'Orlando'],
    'state': ['fl', 'fl', 'fl'],
    'school_lvl': [1, 2, 3],
    'schl_name': ['Union Park', '', ''],
    'elem_name': ['', 'Legacy', '']
}
df = pd.DataFrame(data)

# Print the original DataFrame
print(df)

# Drop specific columns before grouping and then dropping duplicates
c = df.columns[:2].tolist() + df.columns[-3:].tolist()
print(c)
['city', 'state', 'elem_name', 'middle_name', 'highschoo_name']

df = df.drop(['school_lvl', 'schl_name'], axis=1)
df = df.groupby(['city', 'state']).apply(lambda x: x.ffill().bfill()).drop_duplicates()
print(df)

In this code snippet, the drop function is used to remove specific columns ('school_lvl', 'schl_name') before grouping and then dropping duplicates. This approach can be more straightforward in some cases but may lead to loss of information if not carefully considered.

Choosing the Right Approach

When deciding between using groupby with lambda functions or removing columns and then grouping, consider the following factors:

• Data complexity: If your DataFrame contains complex relationships between columns, using groupby with lambda functions might provide a more elegant solution.
• Column importance: If some columns are crucial for identifying unique rows, you may need to preserve them while dropping others. Removing columns before grouping can lead to loss of information if not handled carefully.
• Data size and performance: For large DataFrames, using groupby with lambda functions might be more memory-efficient, as it avoids the overhead of removing columns.

Ultimately, choose the approach that best aligns with your data’s specific needs and the goals of your project.

Last modified on 2024-02-18