I have a problem with the type of one of my column in a pandas dataframe. Basically the column is saved in a csv file as a string, and I wanna use it as a tuple to be able to convert it in a list of numbers. Following there is a very simple csv:
ID,LABELS
1,"(1.0,2.0,2.0,3.0,3.0,1.0,4.0)"
2,"(1.0,2.0,2.0,3.0,3.0,1.0,4.0)"
If a load it with the function "read_csv" I get a list of strings. I have tried to convert to a list, but I get the list version of a string:
df.LABELS.apply(lambda x: list(x))
returns:
['(','1','.','0',.,.,.,.,.,'4','.','0',')']
Any idea on how to be able to do it?
Thank you.
values. tolist() you can convert pandas DataFrame Column to List. df['Courses'] returns the DataFrame column as a Series and then use values. tolist() to convert the column values to list.
You can also convert multiple columns to string by sending dict of column name -> data type to astype() method.
to_numeric() The best way to convert one or more columns of a DataFrame to numeric values is to use pandas. to_numeric() . This function will try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.
Use str.strip
and str.split
:
df['LABELS'] = df['LABELS'].str.strip('()').str.split(',')
But if no NaN
s here, list comprehension
working nice too:
df['LABELS'] = [x.strip('()').split(',') for x in df['LABELS']]
You can use ast.literal_eval
, which will give you a tuple:
import ast
df.LABELS = df.LABELS.apply(ast.literal_eval)
If you do want a list, use:
df.LABELS.apply(lambda s: list(ast.literal_eval(s)))
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