Understanding Data Manipulation with Pandas: Extracting Ranges from Duplicated Rows

As data analysts and scientists, we frequently encounter datasets that contain duplicated rows, making it challenging to extract specific ranges of data. In this article, we’ll delve into the world of Pandas and explore how to select ranges of data in a DataFrame using duplicated rows.

Introduction to Pandas and DataFrames

Pandas is a powerful Python library used for data manipulation and analysis. The core component of Pandas is the DataFrame, which is similar to an Excel spreadsheet or a table in a relational database. A DataFrame consists of rows and columns, with each column representing a variable or feature, and each row representing an observation.

In this article, we’ll focus on using Pandas to extract ranges from duplicated rows in a DataFrame.

Problem Statement

Suppose you have a DataFrame containing data that has been concatenated into a single sequence. The data follows a specific pattern, repeating throughout the index of the DataFrame, where ‘Staff’ marks the beginning of each range and ‘Total Staff’ marks the end. Your goal is to extract each occurrence of data between ‘Staff’ and ‘Total Staff’.

Problem Analysis

The loc function in Pandas doesn’t work with duplicated data because it uses a specific value to index into the DataFrame, which can lead to incorrect results.

To address this challenge, we’ll explore alternative methods using Pandas’ built-in functions, such as filtering and grouping.

Step 1: Identifying Duplicated Rows

Let’s start by identifying the duplicated rows in our DataFrame. We can use the duplicated function from Pandas to achieve this.

import pandas as pd

# Create a sample DataFrame with duplicated rows
data = {
    'Column': ['Staff', 'Total Staff', 'Staff', 'Total Staff', 'Staff', 'Total Staff']
}
df = pd.DataFrame(data)

print(df)

Output:

As you can see, the first and third rows have duplicated values.

Step 2: Creating a Delimiter Column

To identify the ranges of data between ‘Staff’ and ‘Total Staff’, we need to create a delimiter column that separates each range.

We’ll use the cumsum function to create a new column that increments by 1 for each unique value in the ‘Column’ column.

# Create a delimiter column using cumsum
delim = (df['Column'] == 'Total Staff').cumsum()

print(delim)

Output:

The delim column now contains the cumulative sum of the indices where ‘Total Staff’ appears.

Step 3: Creating a Grouping Column

Next, we’ll create another column that shifts the values in the delim column by 1 and fills missing values with 0 using the shift function.

# Create a grouping column by shifting the delimiter column
groups = delim.shift().fillna(0).astype(int)

print(groups)

Output:

The groups column now contains the shifted values, which will help us identify the ranges of data between ‘Staff’ and ‘Total Staff’.

Step 4: Extracting Ranges

Now that we have the delim and groups columns, we can extract the ranges of data using a loop.

# Extract the ranges of data
for ii in range(groups[-1] + 1):
    section = df[groups == ii]

    print(section)

Output:

As you can see, the section variable now contains the ranges of data between ‘Staff’ and ‘Total Staff’.

Conclusion

In this article, we explored how to select ranges of data in a Pandas DataFrame using duplicated rows. We used various functions, such as duplicated, cumsum, shift, and fillna, to create delimiter and grouping columns that helped us identify the ranges.

By following these steps, you can extract specific ranges of data from your DataFrame even when dealing with duplicated rows.

Additional Resources

For further learning, we recommend checking out the following resources:

• Pandas Documentation
• Python Pandas Tutorial

We hope this article has been informative and helpful in your data analysis journey. Happy learning!

Last modified on 2023-07-05