Mean or average in python

The (arithmetic) mean is calculated using the formula

\begin{equation*} \bar{x} = \frac{\sum_i x_i}{\sum_i 1}. \end{equation*}

In python, we can use any either numpy, pandas or statistics.

Below, I show their use case for the given data:

If we have a set of values, e.g.

100
50
30
65
76
93
53
28
61
7

NOTE that the data in our example is a column array.

print(f"Values: \n {values}")

Values: 
 [[100], [50], [30], [65], [76], [93], [53], [28], [61], [7]]

import numpy as np
print(f"Numpy Mean: {np.array(values).mean()}")

Numpy Mean: 56.3

import pandas as pd
print(f"Pandas Mean: {pd.DataFrame(values).mean()}")

Pandas Mean: 0    56.3
dtype: float64

import statistics
import numpy as np
values = np.array(values).flatten().astype(float)
print(f"Statistics Mean: {statistics.mean(values)}")

Statistics Mean: 56.3

Concluding remarks

numpy and pandas excel in calculating the mean from a column array data, and the result is a float despite the original data is of int datatype. On the other hand, the mean from the statistics module requires a flatten array, and by default doesn't cast the result to a float.