Menu
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?
244
As we can see in the output, the Series. to_string() function has successfully rendered a string representation to the given object. Example #2: Use Series. to_string() function to render a string representation of the given series object.
Change data type of a series in PandasUse a numpy. dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy. dtype or Python type to cast one or more of the DataFrame's columns to column-specific types.
There are two ways for changing any data type into a String in Python : Using the str() function. Using the __str__() function.
If you want to change the data type for all columns in the DataFrame to the string type, you can use df. applymap(str) or df.
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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
