Descriptive Statistics - min(), max()

A. min() - Find out the minimum and maximum out of a given set of data.
Example:-
import pandas as pd
df = pd.DataFrame({2016:{'q1':34500,'q2':56000,'q3':47000,'q4':49000},2017:\{'q1':44900,'q2':46100,'q3':57000,'q4':59000},2018:{'q1':54500,'q2':51000}})
print(df)
Output:-

2016	2017	2018
q1	34500	44900	54500.0
q2	56000	46100	51000.0
q3	47000	57000	NaN
q4	49000	59000	NaN

1. print(df.min())
Output:-

2016    34500.0
2017    44900.0
2018    51000.0

Explanation:

min() finds the minimum among the indexes for each column and axis 0 by default.

2. print(df.min(axis=1))

Output:-

q1    34500.0
q2    46100.0
q3    47000.0
q4    49000.0
Explanation:
min(axis=1) finds the minimum among the columns for each indexes.


import pandas as pd

df2 = pd.DataFrame({2016:{'q1':34500,'q2':56000,'q3':47000,'q4':49000},2017:{'q1':'A','q2':'B','q3':'C','q4':'D'},2018:{'q1':54500,'q2':51000}})

print(df)

2016
2017
2018

q1
34500
A
54500.0
q2
56000
B
51000.0
q3
47000
C
NaN
q4
49000
D
NaN



3. print(df2.min())

Output:-
2016    34500
2017        A
2018    51000
Explanation:-
min() calculates the minimum skipping the NaN(Not a number) values because default 
value of skipna is True.

4.print(df2.min(skipna=False))
Output:-
2016    34500
2017        A
2018      NaN
Explanation:- 
skipna=False means not to skip NaN values while finding the minimum.


5. print(df2.min(numeric_only=True))
Output:-
2016    34500.0
2018    51000.0
Explanation:-

With numeric_only=True min() function skipped string values of column 2017


B. max() - Finds maximum value among a series of value.
Do it yourself
print(df.max())
print(df.max(axis=1))
print(df2.max(skipna=False))
print(df2.max(numeric_only=True))
print(df2.max(axis=1,skipna=False,numeric_only=True))

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