« Pandas
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
0 4
1 4
2 4
3 4
4 4
5 4
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
0 4
1 4
2 3
3 4
4 4
5 4
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
« Pandas
Plotting graphs
Filtering of Data
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