将列从float转换为int时收到KeyError

Question

created a pandas data frame using read_csv.

I then changed the column name of the 0th column from 'Unnamed' to 'Experiment_Number' .

The values in this column are floating point numbers and I've been trying to convert them to integers using:

df['Experiment_Number'] = df['Experiment_Number'].astype(int)

I get this error:

KeyError: 'Experiment_Number'

I've been trying every way since yesterday, for example also

df['Experiment_Number'] = df.astype({'Experiment_Number': int})

and many other variations.

Can someone please help, I'm new using pandas and this close to giving up on this :(

Any help will be appreciated

Answer 1

I had used this for renaming the column before:

 df.columns.values[0] = 'Experiment_Number' 

This should have worked. The fact that it didn't can only mean there were special characters/unprintable characters in your column names.

I can offer another possible suggestion, using df.rename :

df = df.rename(columns={df.columns[0] : 'Experiment_Number'})

Answer 2

You can convert the type during your read_csv() call then rename it afterward. As in

df = pandas.read_csv(filename, 
                     dtype = {'Unnamed': 'float'}, # inform read_csv this field is float
                     converters = {'Unnamed': int}) # apply the int() function
df.rename(columns = {'Unnamed' : 'Experiment_Number'}, inplace=True)

The dtype is not strictly necessary, because the converter will override it in this case, but it is wise to get in the habit of always providing a dtype for every field of your input. It is annoying, for example, how pandas treats integers as floats by default. Also, you may later remove the converters option without worry, if you specified dtype.