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:-
201620172018
q1345004490054500.0
q2560004610051000.0
q34700057000NaN
q44900059000NaN

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)




201620172018



q134500A54500.0


q256000B51000.0


q347000CNaN


q449000DNaN






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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