How do you compute descriptive statistics on a Pandas DataFrame?

Descriptive statistics summarise the central tendency, spread, and shape of a dataset. Pandas df.describe() is the starting point for any exploratory analysis, but knowing the individual methods gives you more precise control.

import pandas as pd
import numpy as np

df = pd.read_csv('housing.csv')

# --- df.describe() ---
# Numeric columns: count, mean, std, min, 25%, 50%, 75%, max
df.describe()
# Include object columns too
df.describe(include='all')

# --- Individual statistics ---
df['price'].mean()      # arithmetic mean
df['price'].median()    # 50th percentile — robust to outliers
df['price'].mode()[0]   # most frequent value (returns Series)
df['price'].std()       # standard deviation (ddof=1 by default)
df['price'].var()       # variance
df['price'].skew()      # skewness: >0 right-skewed, <0 left-skewed
df['price'].kurt()      # excess kurtosis (0 = normal dist)
df['price'].quantile(0.90)  # 90th percentile
df['price'].quantile([0.25, 0.5, 0.75])  # multiple quantiles

# IQR — interquartile range (robust measure of spread)
Q1, Q3 = df['price'].quantile(0.25), df['price'].quantile(0.75)
IQR = Q3 - Q1

# Outlier detection via IQR fence
lower = Q1 - 1.5 * IQR
upper = Q3 + 1.5 * IQR
outliers = df[(df['price'] < lower) | (df['price'] > upper)]
print(f'{len(outliers)} outliers detected ({len(outliers)/len(df)*100:.1f}%)')

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