{"id":200653,"date":"2024-12-31T06:42:34","date_gmt":"2024-12-31T06:42:34","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/how-to-change-column-value-based-on-another-column-in-pandas\/"},"modified":"2024-12-31T06:42:34","modified_gmt":"2024-12-31T06:42:34","slug":"how-to-change-column-value-based-on-another-column-in-pandas","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/how-to-change-column-value-based-on-another-column-in-pandas\/","title":{"rendered":"How to change column value based on another column in pandas?"},"content":{"rendered":"

When working with data in Pandas, you may encounter situations where you need to modify values in one column based on the values in another column. This can be done easily using Pandas’ powerful capabilities. <\/p>\n

One common scenario is when you need to update a column based on conditions from another column. For example, let’s say you have a DataFrame with ‘Sales’ and ‘Target’ columns, and you want to mark ‘Sales’ as ‘Achieved’ if it is greater than or equal to ‘Target’, and ‘Not Achieved’ otherwise. <\/p>\n

In such cases, you can use Pandas’ loc method along with boolean indexing to update the values in the ‘Sales’ column based on the values in the ‘Target’ column. Here’s how you can do it:<\/p>\n

“`python
\nimport pandas as pd<\/p>\n

# Create a sample DataFrame
\ndata = {‘Sales’: [100, 150, 80, 120],
\n ‘Target’: [90, 100, 70, 110]}
\ndf = pd.DataFrame(data)<\/p>\n

# Update the ‘Sales’ column based on the ‘Target’ column
\ndf.loc[df[‘Sales’] >= df[‘Target’], ‘Sales’] = ‘Achieved’
\ndf.loc[df[‘Sales’] < df['Target'], 'Sales'] = 'Not Achieved'\n\n\nprint(df)
\n“`<\/p>\n

This code snippet will update the ‘Sales’ column values to ‘Achieved’ or ‘Not Achieved’ based on the condition specified.<\/p>\n

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