calculate the z score by group. in python

To calculate the z score by group in Python, we first need to group our data by some categorical variable. We can use the groupby() function from the pandas library to achieve this.

Assuming we have a dataframe df with some numeric data column data_col and a categorical column group_col that defines the groupings, we can calculate the z score by group as follows:

main.py
import numpy as np
import scipy.stats as stats
import pandas as pd

# define the data and grouping columns
data_col = 'some_numeric_data_column'
group_col = 'some_categorical_group_column'

# calculate the mean and standard deviation for each group
grouped = df.groupby(group_col)
means = grouped[data_col].mean()
stds = grouped[data_col].std()

# calculate the z scores for each observation
z_scores = (df[data_col] - means[df[group_col]].values) / stds[df[group_col]].values

# alternatively, we can use the zscore function in the stats module
z_scores_v2 = grouped[data_col].apply(stats.zscore)

# add the z scores to the dataframe
df['z_score'] = z_scores
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In the above code, we first group the data by group_col using the groupby() function. We then calculate the mean and standard deviation for each group using the mean() and std() functions applied to the data_col.

Next, we calculate the z score for each observation by subtracting the group mean and dividing by the group standard deviation. We use the .values attribute to obtain the array version of the the means and stds, which can be indexed by the group_col column of the original dataframe to get the group-specific means and stds for each observation.

Finally, we add the z scores to the dataframe as a new column 'z_score'. Alternatively, we can use the zscore() function in the stats module applied to each group using the apply() function. This returns a series of the same shape as the original group.

Note that when calculating the z-score, we are assuming that the data is normally distributed or follows a similar distribution.