How to calculate statistics with NumPy

Statistical summaries turn a NumPy array into a small set of numbers that describe center, spread, and grouped behavior. They help Python scripts check measurements, scores, sensor readings, or table-shaped numeric data before plotting or exporting results.

NumPy reduction functions flatten an array when no axis is supplied. Passing axis=0 summarizes each column, while axis=1 summarizes each row, so the axis choice should match how records and fields are arranged.

Spread calculations need one extra decision before the numbers are reported. std() uses ddof=0 by default for a population-style standard deviation, ddof=1 switches to the common sample calculation, and NaN-aware functions such as np.nanmean() avoid one missing value turning the whole summary into NaN.

1. Create a script that calculates full-array, axis-based, percentile, and missing-value statistics.

   statistics-calculate.py

   import numpy as np
    
   np.set_printoptions(precision=2, suppress=True)
    
   scores = np.array(
       [
           [72.0, 75.0, 79.0],
           [80.0, 82.0, 88.0],
           [68.0, 74.0, 77.0],
           [88.0, 93.0, 96.0],
       ]
   )
    
   scores_with_missing = scores.copy()
   scores_with_missing[1, 2] = np.nan
    
   print("dataset shape:", scores.shape)
   print("overall mean:", scores.mean())
   print("overall median:", np.median(scores))
   print("population std:", round(float(scores.std()), 2))
   print("sample std:", round(float(scores.std(ddof=1)), 2))
   print("column means:", scores.mean(axis=0))
   print("column percentiles:")
   print(np.percentile(scores, [25, 50, 75], axis=0))
   print("row means with NaN ignored:", np.nanmean(scores_with_missing, axis=1))
   print("ordinary mean with NaN:", np.mean(scores_with_missing))

   axis=0 summarizes each column. axis=1 summarizes each row.

2. Run the script and compare the scalar, column, percentile, and NaN-aware summaries.

   $ python statistics-calculate.py
   dataset shape: (4, 3)
   overall mean: 81.0
   overall median: 79.5
   population std: 8.29
   sample std: 8.66
   column means: [77. 81. 85.]
   column percentiles:
   [[71.   74.75 78.5 ]
    [76.   78.5  83.5 ]
    [82.   84.75 90.  ]]
   row means with NaN ignored: [75.33 81.   73.   92.33]
   ordinary mean with NaN: nan

   The percentile rows represent 25, 50, and 75 percentiles for each input column. population std uses ddof=0, sample std uses ddof=1, and np.nanmean() ignores the single missing value.