[python] Convert float64 column to int64 in Pandas

I tried to convert a column from data type float64 to int64 using:

df['column name'].astype(int64)

but got an error:

NameError: name 'int64' is not defined

The column has number of people but was formatted as 7500000.0, any idea how I can simply change this float64 into int64?

consider using

df['column name'].astype('Int64')

nan will be changed to NaN

This seems to be a little buggy in Pandas 0.23.4?

If there are np.nan values then this will throw an error as expected:

df['col'] = df['col'].astype(np.int64)

But doesn't change any values from float to int as I would expect if "ignore" is used:

df['col'] = df['col'].astype(np.int64,errors='ignore') 

It worked if I first converted np.nan:

df['col'] = df['col'].fillna(0).astype(np.int64)
df['col'] = df['col'].astype(np.int64)

Now I can't figure out how to get null values back in place of the zeroes since this will convert everything back to float again:

df['col']  = df['col'].replace(0,np.nan)

You can need to pass in the string 'int64':

>>> import pandas as pd
>>> df = pd.DataFrame({'a': [1.0, 2.0]})  # some test dataframe

>>> df['a'].astype('int64')
0    1
1    2
Name: a, dtype: int64

There are some alternative ways to specify 64-bit integers:

>>> df['a'].astype('i8')      # integer with 8 bytes (64 bit)
0    1
1    2
Name: a, dtype: int64

>>> import numpy as np
>>> df['a'].astype(np.int64)  # native numpy 64 bit integer
0    1
1    2
Name: a, dtype: int64

Or use np.int64 directly on your column (but it returns a numpy.array):

>>> np.int64(df['a'])
array([1, 2], dtype=int64)