I'm new to pandas and trying to figure out how to convert multiple columns which are formatted as strings to float64's. Currently I'm doing the below, but it seems like apply() or applymap() should be able to accomplish this task even more efficiently...unfortunately I'm a bit too much of a rookie to figure out how. Currently the values are percentages formatted as strings like '15.5%'
for column in ['field1', 'field2', 'field3']:
data[column] = data[column].str.rstrip('%').astype('float64') / 100
Starting in 0.11.1 (coming out this week), replace has a new option to replace with a regex, so this becomes possible
In [14]: df = DataFrame('10.0%',index=range(100),columns=range(10))
In [15]: df.replace('%','',regex=True).astype('float')/100
Out[15]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 0 to 99
Data columns (total 10 columns):
0 100 non-null values
1 100 non-null values
2 100 non-null values
3 100 non-null values
4 100 non-null values
5 100 non-null values
6 100 non-null values
7 100 non-null values
8 100 non-null values
9 100 non-null values
dtypes: float64(10)
And a bit faster
In [16]: %timeit df.replace('%','',regex=True).astype('float')/100
1000 loops, best of 3: 1.16 ms per loop
In [18]: %timeit df.applymap(lambda x: float(x[:-1]))/100
1000 loops, best of 3: 1.67 ms per loop
df.applymap(lambda x:float(x.rstrip('%'))/100)
在接受的答案中回答评论:对于特定的列,请确保不要在原地进行。
df['Column1'] = df['Column1'].replace('%','',regex=True).astype('float')/100
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