Pandas Technique: Summary Statistics

Summary statistics is a part of descriptive statistics that summarizes and provides the gist of information about the sample data. Statisticians commonly try to describe and characterize the observations by finding: a measure of location, or central tendency, such as the arithmetic mean.

import pandas as pd
 import numpy as np

# read dataset
 df = pd.read_csv('Srt_dta.csv')
 df

df['Height(cm)'].mean()

'2011-12-11'

df['Date of Birth'].max()

'2018-02-27'

agg() is used to pass a function or list of function to be applied on a series or even each element of series separately. In case of list of function, multiple results are returned by agg() method.

def pct30(column):
  return column.quantile(0.3)
 
 df['Weight(kg)'].agg(pct30)

21.0

df[['Height(cm)', 'Weight(kg)']].agg(pct30)

Height(cm) 45.4
 Weight(kg) 21.0
 dtype: float64

def pct40(column):
  return column.quantile(0.4)
 
 df['Height(cm)'].agg([pct30, pct40])

pct30 45.4
 pct40 47.2
 Name: Height(cm), dtype: float64

df['Weight(kg)'].cumsum()
 # another method
 # .cummax()
 # .cumprod()
 # .cummin()

0 25
 1 48
 2 70
 3 87
 4 116
 5 118
 6 192
 Name: Weight(kg), dtype: int64