pandas how to convert all the string value to float

Question

I want to convert all the string value in Pandas DataFrame into float, and I can define a short function to do this, but it's not a Pythonic way to do that. My DataFrame looks like this:

>>> df = pd.DataFrame(np.array([['1', '2', '3'], ['4', '5', '6']]))
>>> df
   0  1  2
0  1  2  3
1  4  5  6
>>> df.dtypes
0    object
1    object
2    object
dtype: object
>>> type(df[0][0])
<type 'str'>

I just wonder whether are there some built-in functions of Pandas DataFrame to convert all the string value to float. If you know the built-in function on the Pandas doc, please post the link.

Anand S Kumar · Accepted Answer · 2015-09-26 03:12:40Z

12

Assuming all values can be correctly converted to float, you can use DataFrame.astype() function to convert the type of complete dataframe to float. Example -

df = df.astype(float)

Demo -

In [5]: df = pd.DataFrame(np.array([['1', '2', '3'], ['4', '5', '6']]))

In [6]: df.astype(float)
Out[6]:
   0  1  2
0  1  2  3
1  4  5  6

In [7]: df = df.astype(float)

In [8]: df.dtypes
Out[8]:
0    float64
1    float64
2    float64
dtype: object

.astype() function also has a raise_on_error argument (which defaults to True) which you can set to False to make it ignore errors . In such cases, the original value is used in the DataFrame -

In [10]: df = pd.DataFrame([['1', '2', '3'], ['4', '5', '6'],['blah','bloh','bleh']])

In [11]: df.astype(float,raise_on_error=False)
Out[11]:
      0     1     2
0     1     2     3
1     4     5     6
2  blah  bloh  bleh

To convert just a series/column to float, again assuming all values can be converted, you can use [Series.astype()][2] . Example -

df['somecol'] = df['somecol'].astype(<type>)

edited Sep 26, 2015 at 3:12

answered Sep 26, 2015 at 2:19

Anand S Kumar

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1 Comment

ChesuCR Over a year ago

Since the version 0.20.0 raise_on_error was replaced by errors

unutbu · Accepted Answer · 2015-09-26 02:45:39Z

6

Another option is to use df.convert_objects(numeric=True). It attempts to convert numeric strings to numbers, with unconvertible values becoming NaN:

import pandas as pd

df = pd.DataFrame([['1', '2', '3'], ['4', '5', 'foo'], ['bar', 'baz', 'quux']])
df = df.convert_objects(convert_numeric=True)
print(df)

yields

    0   1   2
0   1   2   3
1   4   5 NaN
2 NaN NaN NaN

In contrast, df.astype(float) would raise ValueError: could not convert string to float: quux since in the above DataFrame some strings (such as 'quux') is not numeric.

Note: in future versions of pandas (after 0.16.2) the function argument will be numeric=True instead of convert_numeric=True.

answered Sep 26, 2015 at 2:45

unutbu

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