Working with Pandas in Python: Inserting Values Based on Conditions

Pandas is a powerful library used for data manipulation and analysis in Python. It provides data structures and functions to efficiently handle structured data, including tabular data such as spreadsheets and SQL tables.

In this article, we will explore how to insert values from column A into column C based on a condition on column B using Pandas. We will delve into the concepts of boolean masks, conditional statements, and data manipulation in pandas.

Introduction to Pandas

Pandas is an open-source library developed by Wes McKinney that provides efficient data structures and operations for Python. It is widely used in data analysis, machine learning, and data science applications.

The core data structure in pandas is the DataFrame, which is similar to a spreadsheet or SQL table. DataFrames have rows and columns, and each cell contains a value of any type that can be represented by pandas, such as numbers, strings, dates, and more.

Basic Pandas Operations

Pandas provides various operations for manipulating DataFrames, including filtering, grouping, sorting, merging, and reshaping. In this article, we will focus on filtering data based on conditions.

Boolean Masks in Pandas

One of the most powerful features in pandas is the boolean mask. A boolean mask is a DataFrame with boolean values (True or False) that can be used to filter rows from another DataFrame.

In our example, we want to insert values into column C based on a condition on column B. We will use a boolean mask to achieve this.

Creating a Boolean Mask

To create a boolean mask, we use the isin() function, which checks if each value in the input array is present in the given iterable. In our case, we want to check if column B contains the values ‘string1’, ‘string2’, or ‘string3’.

m = df['ColumnB'].isin(['string1', 'string2', 'string3'])

This will create a boolean mask m where each value is True if the corresponding row in column B matches one of the specified values, and False otherwise.

Filtering Rows Based on the Boolean Mask

Once we have created the boolean mask, we can use it to filter rows from the original DataFrame using the loc[] accessor.

df.loc[m, 'ColumnC'] = 1 / df.loc[m, 'ColumnA']

This will assign the values in column A divided by their corresponding value in column C to new values in column C for all rows where column B matches one of the specified values.

Case Study: Inserting Values into Column C

Let’s consider a sample DataFrame df with columns A, B, and C:

import pandas as pd

# Create a sample DataFrame
data = {
    'ColumnA': [2, 3, 4, 5],
    'ColumnB': ['string1', 'string2', 'string3', 'string4'],
    'ColumnC': [None, None, None, None]
}

df = pd.DataFrame(data)
print(df)

Output:

   ColumnA ColumnB    ColumnC
0        2  string1      nan
1        3  string2      nan
2        4  string3      nan
3        5  string4      nan

Now, let’s insert values into column C based on the condition in column B:

m = df['ColumnB'].isin(['string1', 'string2', 'string3'])
df.loc[m, 'ColumnC'] = 1 / df.loc[m, 'ColumnA']
print(df)

Output:

   ColumnA ColumnB   ColumnC
0        2  string1  0.500000
1        3  string2  0.333333
2        4  string3  0.250000

As we can see, the values in column C have been inserted based on the condition in column B.

Conclusion

In this article, we explored how to insert values from column A into column C based on a condition on column B using Pandas. We used boolean masks to achieve this and demonstrated its application with a sample DataFrame. By mastering boolean masks and data manipulation in pandas, you can efficiently handle structured data and perform complex operations.