Creating Partially Filled Columns in Pandas Using the Assign Method

Creating a Partially Filled Column in Pandas

When working with data frames in pandas, it’s common to have columns that are partially filled or contain missing values. In this article, we’ll explore how to create a partially filled column in pandas.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to easily create and manipulate data frames. However, when working with partial fill columns, pandas provides several options that can be used to achieve the desired result.

In this article, we’ll explore one way to create a partially filled column in pandas using the assign method.

Background

Before diving into the solution, let’s take a look at some background information on how pandas handles data frames. When creating a new data frame, each column is created by default with missing values (NaN) unless specified otherwise.

# Import necessary libraries
import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame(np.arange(1, 51).reshape((5, -1)).T)

In the example above, we create a sample data frame df with five rows and multiple columns. By default, each column is created with missing values (NaN).

Solution

One way to create a partially filled column in pandas is by using the assign method.

# Create a new DataFrame 'df' and add a new column 'foo'
df = df.assign(foo='')

In this example, we first create an empty string for each row in the foo column. This approach assumes that the missing values are represented as strings. If you prefer to use NaN instead, you can modify the code to:

# Create a new DataFrame 'df' and add a new column 'foo'
df = df.assign(foo=np.nan)

However, there’s an issue with this approach. When updating values from other data frames using the update method, NaN will be converted to float. This can lead to unexpected behavior in some cases.

Alternative Approach

To avoid the conversion of NaN to float, we can use a different approach that involves creating a list of updates and then applying it to the original DataFrame.

# Create lists for each source data frame
updates = [s1, s2]

# Iterate over the updates and apply them to the DataFrame
for src in sources:
    df['foo'].update(other=src)

This approach can be more efficient than using the assign method, especially when working with large data frames.

Conclusion

Creating a partially filled column in pandas can be achieved through various methods. In this article, we explored two approaches: using the assign method and creating a list of updates to apply to the DataFrame. Both methods have their advantages and disadvantages, and the choice between them depends on your specific use case and requirements.

Code

import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame(np.arange(1, 51).reshape((5, -1)).T)

# Define the source data frames
s1 = pd.Series([11, 12, 13, 14, 15, 16], index=[0, 1, 2, 3, 7, 9])
s2 = pd.Series([27, 28], index=[4, 6])

# Create lists for each source data frame
sources = [s1, s2]

# Create a new DataFrame 'df' and add a new column 'foo'
df = df.assign(foo=np.nan)

# Iterate over the updates and apply them to the DataFrame
for src in sources:
    df['foo'].update(other=src)

Note: This code uses the assign method, which can lead to NaN being converted to float.

Last modified on 2024-02-02