Remove low frequency values from pandas.dataframe

Question

How can I remove values from a column in pandas.DataFrame, that occurs rarely, i.e. with a low frequency? Example:

In [4]: df[col_1].value_counts()  Out[4]: 0       189096         1       110500         2        77218         3        61372               ...         2065         1         2067         1         1569         1         dtype: int64 

So, my question is: how to remove values like 2065, 2067, 1569 and others? And how can I do this for ALL columns, that contain .value_counts() like this?

UPDATE: About 'low' I mean values like 2065. This value occurs in col_1 1 (one) times and I want to remove values like this.

Answer 1

I see there are two ways you might want to do this.

For the entire DataFrame

This method removes the values that occur infrequently in the entire DataFrame. We can do it without loops, using built-in functions to speed things up.

import pandas as pd import numpy as np  df = pd.DataFrame(np.random.randint(0, high=9, size=(100,2)),          columns = ['A', 'B'])  threshold = 10 # Anything that occurs less than this will be removed. value_counts = df.stack().value_counts() # Entire DataFrame  to_remove = value_counts[value_counts <= threshold].index df.replace(to_remove, np.nan, inplace=True) 

Column-by-column

This method removes the entries that occur infrequently in each column.

import pandas as pd import numpy as np  df = pd.DataFrame(np.random.randint(0, high=9, size=(100,2)),          columns = ['A', 'B'])  threshold = 10 # Anything that occurs less than this will be removed. for col in df.columns:     value_counts = df[col].value_counts() # Specific column      to_remove = value_counts[value_counts <= threshold].index     df[col].replace(to_remove, np.nan, inplace=True)