# Undersampling with pandas#

If you want to create a classifier, but you have an unequal number of observations for each class, then you have to be extra careful with how you estimate your model. For example, if you have a highly imbalanced dataset and optimise for accuracy, then a model might simply predict the majority class in all cases.

A common way of dealing with this issue is undersampling, which means you remove observations from the majority class$es$ such that you have an equal number of observations for each class.

To do this in pandas you can use the snippet below. You should consider using it if you are facing imbalanced classes.

import pandas as pd

def downsample(df:pd.DataFrame, label_col_name:str) -> pd.DataFrame:
# find the number of observations in the smallest group
nmin = df[label_col_name].value_counts().min()
return (df
# split the dataframe per group
.groupby(label_col_name)
# sample nmin observations from each group
.apply(lambda x: x.sample(nmin))
# recombine the dataframes
.reset_index(drop=True)
)