Descriptive Stats

import numpy as np
import math
import pandas as pd

x=[x1,x2,x3,...]

x_df=pd.DataFrame({'column_name':x})

procedure	numpy	pandas
frequency counts	`np.unique(x,return_counts=True)`	`x_df.value_counts()`
size	`np.size(x)`	`x_df.count()`
min	`np.amin(x)`	`x_df.min()`
max	`np.amax(x)`	`x_df.max()`
mean	`np.mean(x)`	`x_df.mean()`
mode	NA	`x_df.mode()`
median	`np.median(x)`	`x_df.median()`
sample variance	`np.var(x,ddof=1)`	`x_df.var()`
pop variance	`np.var(x)`	`x_df.var(ddof=0)`
sample std	`np.std(x,ddof=1)`	`x_df.std()`
pop std	`np.std(x)`	`x_df.std(ddof=0)`
weighted mean	`np.average(x,weights=freq)`	NA
weighted sample std	`(np.cov(x,fweights=freq,ddof=1))**(1/2)`	NA
weighted pop std	`(np.cov(x,fweights=freq))**(1/2)`	NA
percentiles	`np.quantile(x,p)`	`x_df.quantile(p)`
summary stats	NA	`x_df.describe()`
factorial, n!	`math.factorial(n)`	NA
n random integers from a to b	`np.random.randint(a,b+1,size=n)`	NA
weighted mean	`np.average(x,weights=y)`	NA
Weighted std	`(np.cov(x,aweights=y,ddof=0))**(1/2)`	NA
correlation coef.	`np.corrcoef(x,y)`	`x_df.corr()`

NA = Not Available

Summary statistics by grouping with categorical columns:

x_df.groupby(['level','gender']).agg({'test1_score': ['mean','std','size']})

Adapted from Python for Introductory Statistics, by Simon Aman (Truman College, City Colleges of Chicago), licensed under CC BY 4.0. Changes were made: reformatted as an accessible XYZ web edition with live in-browser code cells. License: CC-BY-4.0.