Pandas: Converting to numeric, creating NaNs when necessary

Question

Say I have a column in a dataframe that has some numbers and some non-numbers

>> df['foo'] 0       0.0 1     103.8 2     751.1 3       0.0 4       0.0 5         - 6         - 7       0.0 8         - 9       0.0 Name: foo, Length: 9, dtype: object 

How can I convert this column to np.float, and have everything else that is not float convert it to NaN?

When I try:

>> df['foo'].astype(np.float) 

or

>> df['foo'].apply(np.float) 

I get ValueError: could not convert string to float: -

Answer 1

In pandas 0.17.0 convert_objects raises a warning:

FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.

You could use pd.to_numeric method and apply it for the dataframe with arg coerce.

df1 = df.apply(pd.to_numeric, args=('coerce',)) 

or maybe more appropriately:

df1 = df.apply(pd.to_numeric, errors='coerce') 

EDIT

The above method is only valid for pandas version >= 0.17.0, from docs what's new in pandas 0.17.0:

pd.to_numeric is a new function to coerce strings to numbers (possibly with coercion) (GH11133)