Convert float64 column to int64 in Pandas
dataframe, pandas, python
Solution
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 `NaN`s 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 `NaN`s 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
Problem
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`?