I am trying to update the multiple columns using pandas dataframe apply function. I am successfully able to do this for one column.
Existing code
def funcgetsh(input):
... hash_object = hashlib.sha1(input)
... return hash_object.hexdigest()
df["col_1"]=df["col_1"].apply(funcgetsh)
Wanted to know if there is any way to do the same thing for any number of columns like
df["col_1","col_2","col_3"]=df["col_1","col_2","col_3"].apply(funcgetsh)
Try modifying df["col_1","col_2","col_3"]=df["col_1","col_2","col_3"].apply(funcgetsh) to df[["col_1","col_2","col_3"]]=df[["col_1","col_2","col_3"]].apply(funcgetsh). See example below.
import pandas as pd
data1 = {"col_1": [1, 2, 3],
"col_2": [4, 5, 6],
'col_3': [7, 8, 9]}
df1 =pd.DataFrame(data1)
print(df1)
col_1 col_2 col_3
0 1 4 7
1 2 5 8
2 3 6 9
def times10(x):
return 10*x
df1[['col_1', 'col_2']] = df1[['col_1', 'col_2']].apply(times10)
print(df1)
col_1 col_2 col_3
0 10 40 7
1 20 50 8
2 30 60 9
This workaround should work for you, replace your function with the example.
import pandas as pd
data1 = {"col_1": [1, 2, 3],
"col_2": [4, 5, 6],
'col_3': [7, 8, 9]}
df1 =pd.DataFrame(data1)
# combine columns you want to apply the function to
working_data = df1[['col_1', 'col_2']]
# drop the original values from the columns being altered
# keep unaltered columns
df2 = df1.drop(['col_1', 'col_2'], axis = 1)
# your function here
def times10(x):
return 10*x
# apply function to the columns/values desired
working_data = working_data.apply(times10)
# merge post function columns/values with the original unaltered columns/values
final_df = pd.merge(working_data, df2, how = 'inner', left_index = True, right_index = True)
print(final_df)
col_1 col_2 col_3
0 10 40 7
1 20 50 8
2 30 60 9
