I use Pandas 'ver 0.12.0' with Python 2.7 and have a dataframe as below:
df = pd.DataFrame({'id' : [123,512,'zhub1', 12354.3, 129, 753, 295, 610], 'colour': ['black', 'white','white','white', 'black', 'black', 'white', 'white'], 'shape': ['round', 'triangular', 'triangular','triangular','square', 'triangular','round','triangular'] }, columns= ['id','colour', 'shape']) The id Series consists of some integers and strings. Its dtype by default is object. I want to convert all contents of id to strings. I tried astype(str), which produces the output below.
df['id'].astype(str) 0 1 1 5 2 z 3 1 4 1 5 7 6 2 7 6 1) How can I convert all elements of id to String?
2) I will eventually use id for indexing for dataframes. Would having String indices in a dataframe slow things down, compared to having an integer index?
10 Answers
A new answer to reflect the most current practices: as of now (v1.2.4), neither astype('str') nor astype(str) work.
As per the documentation, a Series can be converted to the string datatype in the following ways:
df['id'] = df['id'].astype("string") df['id'] = pandas.Series(df['id'], dtype="string") df['id'] = pandas.Series(df['id'], dtype=pandas.StringDtype) You can convert all elements of id to str using apply
df.id.apply(str) 0 123 1 512 2 zhub1 3 12354.3 4 129 5 753 6 295 7 610 Edit by OP:
I think the issue was related to the Python version (2.7.), this worked:
df['id'].astype(basestring) 0 123 1 512 2 zhub1 3 12354.3 4 129 5 753 6 295 7 610 Name: id, dtype: object You must assign it, like this:-
df['id']= df['id'].astype(str) Personally none of the above worked for me. What did:
new_str = [str(x) for x in old_obj][0] You can use:
df.loc[:,'id'] = df.loc[:, 'id'].astype(str) This is why they recommend this solution: Pandas doc
TD;LR
To reflect some of the answers:
df['id'] = df['id'].astype("string") This will break on the given example because it will try to convert to StringArray which can not handle any number in the 'string'.
df['id']= df['id'].astype(str) For me this solution throw some warning:
> SettingWithCopyWarning: > A value is trying to be set on a copy of a > slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead There are two possibilities:
- Use
.astype("str").astype("string"). As seen here - Use
.astype(pd.StringDtype()). From the official documentation
For me it worked:
df['id'].convert_dtypes() see the documentation here:
Your problem can easily be solved by converting it to the object first. After it is converted to object, just use "astype" to convert it to str.
obj = lambda x:x[1:] df['id']=df['id'].apply(obj).astype('str') use pandas string methods ie df['id'].str.cat()
for me .to_string() worked
df['id']=df['id'].to_string() 
