# How to Update Column Values in Pandas Based on Criteria From Another Column

As a data scientist or software engineer, you may often come across a situation where you need to update certain column values in a Pandas DataFrame based on certain criteria from another column. This can be done using conditional statements and the `apply`

function in Pandas. In this article, we will discuss how to update column values in Pandas based on criteria from another column, with step-by-step instructions and code examples.

## Table of Contents

- What is Pandas?
- Updating Column Values Based on Criteria
- Updating Multiple Columns Based on Criteria
- Common Errors and Solutions
- Conclusion

## What is Pandas?

Pandas is an open-source data analysis and manipulation library for Python. It provides data structures for efficiently storing and manipulating large datasets, as well as tools for data cleaning, transformation, and analysis. Pandas is widely used in data science and machine learning for tasks such as data preprocessing, exploratory data analysis, and feature engineering.

## Updating Column Values Based on Criteria

Suppose you have a Pandas DataFrame with two columns, `column1`

and `column2`

. You want to update the values in `column2`

based on certain criteria from `column1`

. For example, you may want to update the values in `column2`

to be the same as the values in `column1`

if the values in `column1`

meet a certain condition.

To do this, you can use the `apply`

function in Pandas along with a lambda function that checks the condition and returns the updated value. Here’s an example code snippet that updates the values in `column2`

to be the same as the values in `column1`

if the values in `column1`

are greater than 10:

```
import pandas as pd
# create a sample dataframe
df = pd.DataFrame({'column1': [5, 10, 15, 20], 'column2': [0, 0, 0, 0]})
# update column2 based on criteria from column1
df['column2'] = df.apply(lambda row: row['column1'] if row['column1'] > 10 else row['column2'], axis=1)
print(df)
```

Output:

```
column1 column2
0 5 0
1 10 0
2 15 15
3 20 20
```

In this example, we first create a sample DataFrame with two columns, `column1`

and `column2`

, and initialize the values in `column2`

to 0. We then update the values in `column2`

based on the condition that the values in `column1`

are greater than 10. We do this by applying a lambda function to each row of the DataFrame using the `apply`

function. The lambda function checks if the value in `column1`

is greater than 10, and if so, returns the value in `column1`

. Otherwise, it returns the value in `column2`

. We specify `axis=1`

to apply the function to each row of the DataFrame.

## Updating Multiple Columns Based on Criteria

In some cases, you may need to update multiple columns in a Pandas DataFrame based on criteria from another column. You can do this by modifying the lambda function to return a tuple of updated values, and then assigning the tuple to the corresponding columns.

Here’s an example code snippet that updates the values in `column2`

and `column3`

to be the same as the values in `column1`

if the values in `column1`

are greater than 10:

```
import pandas as pd
# create a sample dataframe
df = pd.DataFrame({'column1': [5, 10, 15, 20], 'column2': [0, 0, 0, 0], 'column3': [0, 0, 0, 0]})
# update column2 and column3 based on criteria from column1
df[['column2', 'column3']] = df.apply(lambda row: (row['column1'], row['column1']) if row['column1'] > 10 else (row['column2'], row['column3']), axis=1).tolist()
print(df)
```

Output:

```
column1 column2 column3
0 5 0 0
1 10 0 0
2 15 15 15
3 20 20 20
```

In this example, we first create a sample DataFrame with three columns, `column1`

, `column2`

, and `column3`

, and initialize the values in `column2`

and `column3`

to 0. We then update the values in `column2`

and `column3`

based on the condition that the values in `column1`

are greater than 10. We do this by applying a lambda function to each row of the DataFrame using the `apply`

function. The lambda function checks if the value in `column1`

is greater than 10, and if so, returns a list of the value in `column1`

for both `column2`

and `column3`

. Otherwise, it returns a list of the values in `column2`

and `column3`

. We assign the resulting list to the corresponding columns using the double bracket notation (`[['column2', 'column3']]`

).

## Common Errors and Solutions

### Error 1: Incorrect Axis Specification

When using the `apply`

function, specifying the axis incorrectly can lead to unexpected results or errors. Ensure that `axis=1`

is used when applying the function row-wise.

```
# Incorrect usage
df['column2'] = df.apply(lambda row: row['column1'] if row['column1'] > 10 else row['column2'], axis=0).tolist()
# Correct usage
df['column2'] = df.apply(lambda row: row['column1'] if row['column1'] > 10 else row['column2'], axis=1).tolist()
```

### Error 2: Mismatched DataFrame Shapes

**Error**: Assigning a single value to multiple columns without matching the DataFrame shapes.

**Solution**: Make sure the shapes match when updating multiple columns; use tuples or lists accordingly.

## Conclusion

In this article, we discussed how to update column values in Pandas based on criteria from another column. We showed how to use the `apply`

function along with a lambda function to update a single column, and how to modify the lambda function to update multiple columns. By following these steps, you can easily update column values in Pandas based on any criteria from another column.

#### About Saturn Cloud

Saturn Cloud is your all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, and more. Join today and get 150 hours of free compute per month.