Reshaping a Pandas DataFrame and Creating New Columns Based on a Column

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In this article, we will explore how to reshape a Pandas DataFrame from wide format to long format, and create new columns based on an existing column. We will use the pivot_table function provided by Pandas to achieve this.

Introduction

The Pandas library in Python provides data structures and functions designed to make working with structured data (e.g., tabular) easy. When dealing with large datasets, it’s often necessary to reshape or transform the data into a more suitable format for analysis or further processing. In this article, we will focus on reshaping a DataFrame from wide format to long format using the pivot_table function.

Problem Statement

Let’s start by understanding the problem statement provided in the question:

df1 = pd.DataFrame(columns=['Serial','Seq_Sp','PT','FirstPT','DiffAngle','R1'], 
                    data=[['1001W','2_1',15.13,15.07,1.9,7.4], ['1001W','2_2',16.02,15.80,0.0,0.05],
                          ['1001W','2_3',14.3,15.3,6,0.32],['1001W','2_4',14.18,15.07,2.2,0.16],
                          ['6279W','2_1',15.13,15.13,2.3,0.31],['6279W','2_2',13.01,15.04,1.3,0.04],
                          ['6279W','2_3',14.13,17.04,2.3,0.31],['6279W','2_4',14.01,17.23,3.1,1.17]])

df2 = pd.DataFrame(columns=['Serial','PT_2_1','FirstPT_2_1','DiffAngle_2_1','R1_2_1','PT_2_2','FirstPT_2_2','DiffAngle_2_2',
                           'R1_2_2','PT_2_3','FirstPT_2_3','DiffAngle_2_3','R1_2_3','PT_2_4','FirstPT_2_4','DiffAngle_2_4',
                           'R1_2_4'],
                   data=[
                       ['1001W',15.13,15.07,1.9,7.4,16.02,15.80,0.0,0.05, 14.3,15.3,6,0.32 ,14.18,15.07,2.2,0.16],
                       ['6279W',15.13,15.13,2.3,0.31,13.01,15.04,1.3,0.04,14.13,17.04,2.3,0.31,14.01,17.23,3.1,1.17]
                   ])

We are given two DataFrames: df1 and df2. The difference between them lies in their structure:

• df1 has a wide format with columns for serial numbers, sequence numbers (Seq_Sp), measurement values (e.g., PT, FirstPT, DiffAngle, R1), and another set of measurements (PT_2_x, FirstPT_2_x, DiffAngle_2_x, R1_2_x).
• df2 has a long format with two columns: the serial number and a vector of measurement values for both sets of sequence numbers.

Solution

To reshape df1 into df2, we can use the pivot_table function. This function creates a new DataFrame from an existing one by grouping the data along certain axes. In this case, we will group by the serial number (index) and the sequence numbers (columns='Seq_Sp'). The resulting DataFrame will have unique serial numbers as indices and a vector of measurement values for both sets of sequence numbers.

Here is the code that achieves this:

df2 = df1.pivot_table(index='Serial', columns='Seq_Sp')

However, we also need to rename the resulting column names. The map function can be used to achieve this.

df2.columns = df2.columns.map('_'.join).str.strip('_')

This code joins each column name with an underscore (_) and then strips the leading and trailing underscores using str.strip('_'). This ensures that the resulting column names have a clean format, without any unnecessary characters.

Explanation

Let’s break down what happens in the code above:

1. df1.pivot_table(index='Serial', columns='Seq_Sp'): This line creates a new DataFrame from the original one by grouping the data along certain axes.

   • The index parameter specifies that we want to group by the serial number column ('Serial'). This means that each unique value in this column will become a row in the resulting DataFrame.
   • The columns parameter specifies that we want to group by the sequence numbers column ('Seq_Sp'). This means that each unique value in this column will become a column in the resulting DataFrame.

2. df2.columns = df2.columns.map('_'.join).str.strip('_'): This line renames the columns of the resulting DataFrame.

   • The map function applies a transformation to each element in the list of column names (i.e., df2.columns). In this case, we use _.join to join each element with an underscore (_).
   • The str.strip('_') method removes leading and trailing underscores from each string.

Example Use Cases

Here are some example use cases for reshaping a DataFrame using the pivot_table function:

• Analyzing medical data: Suppose you have a dataset of patient measurements, with columns for different types of data (e.g., blood pressure, heart rate). You can reshape this data into long format to analyze it more easily.
• Marketing analysis: Imagine you have a dataset of customer interactions, with columns for different types of metrics (e.g., click-through rates, conversion rates). You can reshape this data into long format to better understand the relationships between these metrics.

Conclusion

In conclusion, reshaping a DataFrame from wide format to long format using the pivot_table function is a useful technique in data analysis. By grouping the data along certain axes and renaming the resulting column names, we can create a more suitable format for further analysis or processing.

Last modified on 2024-03-10