How to Remove Values Above Threshold in Pandas

To remove values above a certain threshold in pandas, you can use different methods depending on your needs. Here are some possible solutions:

Clip Method

You can use the `clip` method on a DataFrame object to limit the values in each column to a given range.

To remove values above 50 in the age column, you can use:

df['age'] = df['age'].clip(upper=50)

Where method

You can use the `where` method on a DataFrame object to replace values that do not satisfy a given condition with NaN or another value.

To remove values above 170 in the height column, you can use:

df['height'] = df['height'].where(df['height'] <= 170, np.nan)

You can also specify a different value to replace the values that do not meet the condition, such as 0 or the mean of the column.

Boolean Indexing

You can use the boolean indexing on a DataFrame object to filter out rows that have values above a certain threshold in a specific column.

To remove rows that have values above 60 in the weight column, you can use:

df = df[df['weight'] <= 60]

Logical operators

You can also combine multiple conditions using logical operators such as `&` (and), `|` (or), and `~` (not).

I hope this helps you understand how to use pandas to remove values above a threshold.

See also:

clip documentation

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