Convert float64 column to int64 in Pandas

Question

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?

Answer 1

Solution for pandas 0.24+ for converting numeric with missing values:

df = pd.DataFrame({'column name':[7500000.0,7500000.0, np.nan]}) print (df['column name']) 0    7500000.0 1    7500000.0 2          NaN Name: column name, dtype: float64  df['column name'] = df['column name'].astype(np.int64) 

ValueError: Cannot convert non-finite values (NA or inf) to integer

#http://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html df['column name'] = df['column name'].astype('Int64') print (df['column name']) 0    7500000 1    7500000 2        NaN Name: column name, dtype: Int64 

---

I think you need cast to numpy.int64:

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

Sample:

df = pd.DataFrame({'column name':[7500000.0,7500000.0]}) print (df['column name']) 0    7500000.0 1    7500000.0 Name: column name, dtype: float64  df['column name'] = df['column name'].astype(np.int64) #same as #df['column name'] = df['column name'].astype(pd.np.int64) print (df['column name']) 0    7500000 1    7500000 Name: column name, dtype: int64 

If some NaNs in columns need replace them to some int (e.g. 0) by fillna, because type of NaN is float:

df = pd.DataFrame({'column name':[7500000.0,np.nan]})  df['column name'] = df['column name'].fillna(0).astype(np.int64) print (df['column name']) 0    7500000 1          0 Name: column name, dtype: int64 

Also check documentation - missing data casting rules

EDIT:

Convert values with NaNs is buggy:

df = pd.DataFrame({'column name':[7500000.0,np.nan]})  df['column name'] = df['column name'].values.astype(np.int64) print (df['column name']) 0                7500000 1   -9223372036854775808 Name: column name, dtype: int64 

Answer 2

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)