Python Pandas DataFrame min() For minimum value of rows and columns by using axis , skipna,numeric

We can minimum number in rows or columns by using min().

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
print(my_data.min())

Output

NAME       Alex
ID            1
MATH         30
ENGLISH      30

What is the minimum mark in MATH ?

print(my_data['MATH'].max()) # 30

We can get the row or details of the record who got minimum mark in MATH

print(my_data[my_data['MATH'].min()==my_data['MATH']])

Output is here

   NAME  ID  MATH  ENGLISH
5  Jack   6    30       30

Using axis

We will use option axis=0 ( default ) by adding to above code.

( The last line is only changed )

print(my_data.min(axis=1))

Output is here

We are getting all the minimum values from the ID column. You can change the DataFrame and then check the minimum value.

level option

For MultiIndex (hierarchical) axis we can specify the level.

import pandas as pd 
my_dict=pd.MultiIndex.from_arrays(
         [[1,2,3,4,5,6],
         [80,40,70,70,70,30],
         [80,70,40,50,60,30]],
names=['id','math','eng'])
my_data = pd.Series([4, 2, 0, 8,3,4], name='marks', index=my_dict)
print(my_data.min(level='math'))

Output

Handling NA data using skipna option

We will use skipna=True to ignore the null or NA data. Let us check what happens if it is set to True ( skipna=True )

import numpy as np
import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,np.nan,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
print(my_data.min(skipna=True))

Output

NAME       Alex
ID            1
MATH         30
ENGLISH      30

numeric_only

Default value is None, we can set it to True ( numeric_only=True ) to include only float, int, boolean columns. We can included all by setting it to False ( numeric_only=False ) . Let us see the outputs .

print(my_data.min(numeric_only=False))

Output is here

NAME       Alex
ID            1
MATH         30
ENGLISH      30

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