Python Pandas DataFrame count() For Counting rows and columns by using axis & levels

We can count number of in rows or columns by using count().

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.count())

Output

NAME       6
ID         6
MATH       6
ENGLISH    6

Using axis

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

( The last line is only changed )

print(my_data.count(axis=0))

Output is here

NAME       6
ID         6
MATH       6
ENGLISH    6

Now let us use axis=1

print(my_data.count(axis=1))

Output

Handling NA data

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.count(axis=1))

Output

count() has not considered np.nan so the third row is 3.

lavel option

We can specify the level option and get the data

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)
my_data.set_index(['NAME','ID']).count(level='NAME')

Output

	MATH	ENGLISH
NAME		
Alex	1	0
Jack	1	1
King	1	1
Raju	1	1
Ravi	1	1
Ron	1	1

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