Pandas ".convert_objects(convert_numeric=True)" deprecated [duplicate]

Question

I have this line in my code which converts my data to numeric...

data["S1Q2I"] = data["S1Q2I"].convert_objects(convert_numeric=True) 

The thing is that now the new pandas release (0.17.0) said that this function is deprecated.. This is the error:

FutureWarning: convert_objects is deprecated.   Use the data-type specific converters pd.to_datetime,  pd.to_timedelta and pd.to_numeric.  data["S3BD5Q2A"] = data["S3BD5Q2A"].convert_objects(convert_numeric=True) 

So, I went to the new documentation and I couldn't find any examples of how to use the new function to convert my data...

It only says this:

"DataFrame.convert_objects has been deprecated in favor of type-specific functions pd.to_datetime, pd.to_timestamp and pd.to_numeric (new in 0.17.0) (GH11133)."

Any help would be nice!

Answer 1

As explained by @EvanWright in the comments,

data['S1Q2I'] = pd.to_numeric(data['S1Q2I']) 

is now the prefered way of converting types. A detailed explanation in of the change can be found in the github PR GH11133.

Answer 2

You can effect a replacement using apply as done here. An example would be:

>>> import pandas as pd >>> a = pd.DataFrame([{"letter":"a", "number":"1"},{"letter":"b", "number":"2"}]) >>> a.dtypes letter    object number    object dtype: object >>> b = a.apply(pd.to_numeric, errors="ignore") >>> b.dtypes letter    object number     int64 dtype: object >>>  

But it sucks in two ways:

1. You have to use apply rather than a non-native dataframe method
2. You have to copy to another dataframe--can't be done in place. So much for use with "big data."

I'm not really loving the direction pandas is going. I haven't used R data.table much, but so far it seems superior.

I think a data table with native, in-place type conversion is pretty basic for a competitive data analysis framework.