Using pandas, how to convert multiple dateframe column of datatype "object" to float.
df = pd.DataFrame()
df["A"] = ["123.45","34","-9","4","5"]
df["B"] = ["-9.07","5.4","3","1.0","4.5557"]
df["C"] = ["34","34.98","-9.654","45","6"]
df["D"] = ["AAA","AVF","ERD","DFE","SFE"]
using this gives AttributeError: 'list' object has no attribute 'apply':
[df["A"],df["B"],df["C"]] = [df["A"],df["B"],df["C"]].apply(pd.to_numeric, errors='coerce')
df = df.apply(pd.to_numeric, errors='coerce')
In [119]: df
Out[119]:
A B C
0 123.45 -9.0700 34.000
1 34.00 5.4000 34.980
2 -9.00 3.0000 -9.654
3 4.00 1.0000 45.000
4 5.00 4.5557 6.000
In [120]: df.dtypes
Out[120]:
A float64
B float64
C float64
dtype: object
UPDATE:
In [128]: df[df.columns.drop('D')] = df[df.columns.drop('D')].apply(pd.to_numeric, errors='coerce')
In [129]: df
Out[129]:
A B C D
0 123.45 -9.0700 34.000 AAA
1 34.00 5.4000 34.980 AVF
2 -9.00 3.0000 -9.654 ERD
3 4.00 1.0000 45.000 DFE
4 5.00 4.5557 6.000 SFE
In [130]: df.dtypes
Out[130]:
A float64
B float64
C float64
D object
dtype: object
UPDATE2:
In [143]: df[['A','B','C']] = df[['A','B','C']].apply(pd.to_numeric, errors='coerce')
In [144]: df
Out[144]:
A B C D
0 123.45 -9.0700 34.000 AAA
1 34.00 5.4000 34.980 AVF
2 -9.00 3.0000 -9.654 ERD
3 4.00 1.0000 45.000 DFE
4 5.00 4.5557 6.000 SFE
In [145]: df.dtypes
Out[145]:
A float64
B float64
C float64
D object
dtype: object
