Access and Modify DataFrames Values

Let's find out how to access and modify pandas DataFrame values.

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Try it yourself

Try executing the code below to see the result.

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import pandas as pd
df = pd.DataFrame([
  ['Bugs', True, 72.3],
  ['Daffy', False, 30.7],
  ['Tweety', True, 23.5],
  ['Elmer', False, 103.9],
], columns=['Customer', 'Member', 'Amount'])
df[df['Member']]['Amount'] *= 0.9
print(df)

Explanation

The change is not reflected in df. The reason is that pandas does a lot of work under the hood to avoid copying data. However, in some cases, it can’t. In those cases, we get a copy of the data.

The warning is helpful. Unfortunately, lots of developers ignore it.

discount.py:11: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs...

The existence of the warning above indicates that many developers face this same issue. Let’s apply the warning suggestion to our code:

Solution

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import pandas as pd
df = pd.DataFrame([
  ['Bugs', True, 72.3],
  ['Daffy', False, 30.7],
  ['Tweety', True, 23.5],
  ['Elmer', False, 103.9],
], columns=['Customer', 'Member', 'Amount'])
df.loc[df['Member'], 'Amount'] *= 0.9
print(df)

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