« Pandas
Update data based on cond (condition) if cond=True then by NaN or by other
Parameters
cond : Condition to check , if True then value at other is replaced. If False then nothing is changed.
other : If cond is True then data given here is replaced.
inplace: Default is False , if it is set True then original DataFrame is changed.
axis : integer , default None , Alignment towards Axis ( if required )
level : Level of alignment if required.
error : default is 'raise' , It can take value 'raise' or 'ignore'
try_cast : default False,
Difference between MASK & WHERE
MASK: Data is updated as NaN (if
other is not given ) if
cond ( condition ) is True.
WHERE : Data is updated as NaN (if
other is not given ) if
cond ( condition ) is False.
DataFrame.where()
Examples
Update where MATH column is more than 80
import pandas as pd
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
'ID':[1,2,3,4,5,6],
'MATH':[80,40,70,70,82,30],
'ENGLISH':[81,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_data=my_data.mask(my_data['MATH'] > 80,-5)
print(my_data)
Output
NAME ID MATH ENGLISH
0 Ravi 1 80 81
1 Raju 2 40 70
2 Alex 3 70 40
3 Ron 4 70 50
4 -5 -5 -5 -5
5 Jack 6 30 30
You can check that the all data of 4th row is replaced by -5.
We want to replace only cell data not the full row data.
my_data['MATH']=my_data['MATH'].mask(my_data['MATH'] > 80,-5)
print(my_data)
Output
NAME ID MATH ENGLISH
0 Ravi 1 80 81
1 Raju 2 40 70
2 Alex 3 70 40
3 Ron 4 70 50
4 King 5 -5 60
5 Jack 6 30 30
Multiple conditions with MASK
import pandas as pd
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
'ID':[1,2,3,4,5,6],
'MATH':[80,40,73,70,82,30],
'ENGLISH':[81,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_cond= (my_data['MATH'] >70) & (my_data['MATH'] <75)
replace=-7
my_data['MATH'].mask(my_cond,replace,inplace=True)
print(my_data)
Output
NAME ID MATH ENGLISH
0 Ravi 1 80 81
1 Raju 2 40 70
2 Alex 3 -7 40
3 Ron 4 70 50
4 King 5 82 60
5 Jack 6 30 30
inplace
By default inplace=Flase , this will not change the original DataFrame. By making it to True that is inplace=True we can change the original DataFrame.
my_data['MATH'].mask(my_data['MATH'] > 80,-5,inplace=True)
Output: Now the original DataFrame will change.
NAME ID MATH ENGLISH
0 Ravi 1 80 81
1 Raju 2 40 70
2 Alex 3 70 40
3 Ron 4 70 50
4 King 5 -5 60
5 Jack 6 30 30
Replace data based multiple condition like CASE THEN ( SQL ) by using np.where
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« where
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iloc - rows and columns by integers »
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