Slicing DataFrames into New DataFrames with Pandas

When working with DataFrames in pandas, slicing is an essential operation that allows you to manipulate data by selecting specific rows and columns. In this article, we will explore the process of slicing a DataFrame into new DataFrames grouped by destination.

Understanding the Problem

The problem presented involves having a large DataFrame containing flight information and wanting to create new DataFrames for each unique destination. The original DataFrame is shown below:

The desired output would be three new DataFrames, each containing flights with a specific destination.

Manual Slicing Approach

One possible approach to achieve this is by manually writing out the code for each destination. This method can become cumbersome and prone to errors as the number of destinations increases.

dataframe = {}  # empty DataFrame dictionary
destination = []  # list of destinations

# Destination: JFK
dataframe['JFK'] = data[data['ARR.'] == 'JFK']

# Destination: GTW
dataframe['GTW'] = data[data['ARR.'] == 'GTW']

# Destination: DXB
dataframe['DXB'] = data[data['ARR.'] == 'DXB']

This approach can be easily extended to include more destinations, but it’s not scalable.

Automated Slicing with a Loop

A better approach is to use a loop to automate the process. This involves iterating over each unique destination in the destination list and creating a new DataFrame for each one.

dataframe = {}  # empty DataFrame dictionary
destination = []  # list of destinations

# Get unique destinations from the 'ARR' column
unique_destinations = data['ARR'].unique()

for dest in unique_destinations:
    dataframe[dest] = data[data['ARR.'] == dest]

This code creates a new DataFrame for each destination and stores it in the dataframe dictionary, which can be accessed using keys.

Accessing Sliced DataFrames

Once the sliced DataFrames are created, they can be accessed using their corresponding keys. For example:

print(dataframe['DXB'])

This will print the DataFrame containing flights with destination ‘DXB’.

Handling Missing Destinations

If there are destinations not present in the original DataFrame, attempting to access them will result in an error. To handle this scenario, you can add a default value or raise a custom exception.

for dest in unique_destinations:
    if dest not in dataframe:
        print(f"Destination '{dest}' is missing from the data.")

This code checks for missing destinations and prints a message indicating which one is missing.

Using GroupBy to Slice DataFrames

Finally, you can use the groupby function to slice your DataFrame into new DataFrames grouped by destination. This approach is more efficient than the manual slicing method but requires pandas version 0.20 or later.

dataframe = {}
destinations = data['ARR'].unique()

for dest in destinations:
    dataframe[dest] = data[data['ARR.'] == dest].groupby('Destination').first()

This code uses groupby to group the DataFrame by destination and then selects the first row (i.e., the one with the smallest index) for each group.

Conclusion

In this article, we explored the process of slicing a DataFrame into new DataFrames grouped by destination. We discussed three approaches: manual slicing, automated slicing using a loop, and using groupby to slice DataFrames. Each approach has its strengths and weaknesses, and choosing the right one depends on your specific use case and data size.

By understanding how to manipulate DataFrames with pandas, you can efficiently handle large datasets and extract insights from them.

Last modified on 2025-02-05