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)

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.

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]

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.