Paste a list of numbers to instantly compute mean, median, mode, standard deviation, IQR, skewness, and more.
Descriptive statistics with NumPy and SciPy
import numpy as np
from scipy import stats
data = [2, 4, 4, 4, 5, 5, 7, 9]
arr = np.array(data)
print(f"Count: {len(arr)}")
print(f"Mean: {arr.mean():.4f}")
print(f"Median: {np.median(arr):.4f}")
print(f"Std Dev: {arr.std():.4f}") # population std
print(f"Variance: {arr.var():.4f}")
print(f"Min/Max: {arr.min()} / {arr.max()}")
print(f"Q1/Q3: {np.percentile(arr, 25)} / {np.percentile(arr, 75)}")
print(f"IQR: {stats.iqr(arr):.4f}")
print(f"Skewness: {stats.skew(arr):.4f}")
print(f"Mode: {stats.mode(arr).mode[0]}")
Pandas describe()
import pandas as pd
s = pd.Series([2, 4, 4, 4, 5, 5, 7, 9])
print(s.describe())
# count 8.000000
# mean 4.500000
# std 1.924501 # note: Pandas uses sample std (ddof=1)
# min 2.000000
# 25% 4.000000
# 50% 4.500000
# 75% 5.500000
# max 9.000000