Pandas DataFrame: when using read_csv, rows with blanks are converting entire column to “object” data type

Question

I have a CSV file that looks as follows:

name,id,weight
a,12345,196.5
b,83748, 
 ,83748,200.0
c, ,155.5

Note, there are several missing values indicated by a single space.

When I load this CSV file into a Pandas DataFrame and check the data types using dtypes , it says that every column is of type "object". Even after I convert the spaces to NaN, it still says everything is an "object".

How do I get the data types to be read in correctly, despite the spaces? Could this possibly be an issue with the Anaconda platform?

Answer 1

调用read_csv() ，请确保使用na_values=' ' （或您实际使用的NaN值read_csv()进行调用。