Descriptive Statistics in Python
Calculate mean, median, standard deviation, and percentiles in Python using NumPy and SciPy — with examples.
Published:
Tags: statistics Python NumPy, Python descriptive statistics, NumPy mean standard deviation
Descriptive Statistics in Python NumPy and SciPy make descriptive statistics in Python fast and concise. , , , and cover the core calculations. For an instant full summary, returns all key statistics in one call. --- See our complete guide to health calculators and the statistics calculator guide for the mathematical foundation of these operations. Setting Up: NumPy, SciPy, and pandas Current stable versions (as of early 2026): NumPy 2.0.x (Python 3.9+ required) SciPy 1.14.x pandas 2.2.x NumPy's official documentation and SciPy's stats module reference are the authoritative API references. Basic Descriptive Statistics with NumPy The parameter: use for sample standard deviation (divides by n−1, Bessel's correction). Use for population standard deviation (divides by n). Use when your data…
Frequently Asked Questions
How do I calculate mean and standard deviation in Python?
Use NumPy: `np.mean(data)` for mean and `np.std(data, ddof=1)` for sample standard deviation (ddof=1 applies Bessel's correction). For the population standard deviation use `ddof=0`. Python's built-in `statistics` module also provides `statistics.mean()` and `statistics.stdev()` without needing NumPy.
What is NumPy and why use it for statistics?
NumPy (Numerical Python) is a library that provides fast array operations implemented in C. Operations like mean, standard deviation, and percentiles on a 1-million-element NumPy array are 10–100× faster than equivalent Python loops. NumPy 1.26 requires Python 3.9+ and is the foundation of the scientific Python ecosystem.
How do I use scipy.stats for descriptive statistics?
scipy.stats.describe(data) returns nobs (count), minmax, mean, variance, skewness, and kurtosis in one call. It also provides distribution-fitting functions, hypothesis tests (t-test, chi-square, ANOVA), and probability distribution functions that go beyond NumPy's basic statistics.
How do I handle missing values in Python statistics?
NumPy uses np.nan_mean(), np.nanmedian(), np.nanstd() variants that skip NaN values. Pandas provides .mean(skipna=True) by default. For scipy, use scipy.stats.describe() which handles NaN through a mask or clean the data first with data = data[~np.isnan(data)].
What is pandas describe() for statistics?
df.describe() returns a summary table with count, mean, std, min, 25th percentile, 50th percentile (median), 75th percentile, and max for all numeric columns in a DataFrame. It's the fastest way to get a complete descriptive statistics overview of a dataset and accepts a `percentiles` parameter for custom percentile values.
All articles · theproductguy.in