Data Frame Splits and Finding the Next Value in a Sequence

In this article, we’ll explore how to efficiently find the next value in a sequence when a portion of a data frame matches a given list of names. We’ll delve into the details of data frame splits, indexing, and string manipulation techniques.

Introduction to Data Frame Splits

Data frames are a powerful tool for data analysis in Python’s Pandas library. When working with large datasets, splitting the data frame into smaller, manageable chunks can improve performance and memory efficiency. In this article, we’ll focus on creating small frames from a larger data frame while maintaining overlaps.

Creating Small Frames with Overlaps

To split a data frame into smaller frames with overlaps, you can use the following code:

list_of_dfs = [df.loc[i:i + short_frame-1,:].reset_index(drop=True) for i in range(0, len(df), short_frame - 2) if i < len(df) - 2]

This code creates a list of smaller data frames by iterating over the original data frame and extracting every short_frame number of rows. The -2 in the if statement ensures that we don’t create an incomplete last chunk.

Matching Names with Extract()

To find the next value in a sequence when a portion of the data frame matches a given list of names, you can use the extract() method to search for specific patterns in the staff column. Here’s how it works:

idx = df['Staff'].str.extract(f'({"|".join(to_find_list)})', expand=False).dropna().index

This code extracts the index positions where the specified names appear in the staff column using regular expressions. The expand=False parameter prevents the extraction from creating additional columns, and dropna() removes any rows with missing values.

Finding the Next Value

Now that we have the index positions of the matched names, we can use these indices to find the next value in the sequence. Here’s how it works:

out = df.loc[[x+3 for x in idx if x <= len(df)]]

This code creates a new data frame out by selecting rows from the original data frame that are three positions after each index position in the matched names list.

Alternative Approach: Extracting Staff Names

Alternatively, you can extract the staff names directly and then select the next value:

out = df.loc[[x+3 for x in idx], 'Staff']

This code achieves the same result as before but is more efficient since it only extracts the relevant columns.

Performance Considerations

When working with large datasets, performance can be a significant concern. To optimize your code, consider the following tips:

• Use short_frame values that are close to the square root of the data frame size for optimal overlap.
• Use str.extract() instead of other string manipulation methods when dealing with pattern matching.
• Avoid using loc[] extensively by creating intermediate data frames or using vectorized operations.

Example Code and Output

Here’s the complete code example:

import pandas as pd
from io import StringIO

to_find_list = ['Amelia','Elijah','Amelia']

short_frame = 3

csvfile = StringIO(
"""Date Staff
1990-05-01 00:00:00 Mason
1990-06-01 00:00:00 Amelia
1990-07-01 00:00:00 Elijah
1990-08-01 00:00:00 Amelia
1990-09-01 00:00:00 James
1990-10-01 00:00:00 Benjamin
1990-11-01 00:00:00 Isabella
1990-12-01 00:00:00 Lucas
1991-01-01 00:00:00 Mason""")

df = pd.read_csv(csvfile, sep='\t', engine='python')

list_of_dfs = [df.loc[i:i + short_frame-1,:].reset_index(drop=True) for i in range(0, len(df), short_frame - 2) if i < len(df) - 2]

idx = df['Staff'].str.extract(f'({"|".join(to_find_list)})', expand=False).dropna().index

out = df.loc[[x+3 for x in idx], 'Staff']

print(out)

Output:

4      James
5    Benjamin
6    Isabella
Name: Staff, dtype: object

In this article, we’ve explored how to efficiently find the next value in a sequence when a portion of a data frame matches a given list of names. By using data frame splits, indexing, and string manipulation techniques, you can optimize your code for performance while achieving accurate results.

Last modified on 2023-10-14