Python 在 Pandas 中将 float64 列转换为 int64

Convert float64 column to int64 in Pandas

提问by MCG Code

I tried to convert a column from data type float64to int64using:

我试图将一列从数据类型转换float64为int64使用：

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

but got an error:

但出现错误：

NameError: name 'int64' is not defined

NameError: 名称 'int64' 未定义

The column has number of people but was formatted as 7500000.0, any idea how I can simply change this float64into int64?

该列有人数，但格式为7500000.0，知道如何将其简单地更改float64为int64吗？

回答by jezrael

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

pandas 0.24+用于转换具有缺失值的数字的解决方案：

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

ValueError：无法将非有限值（NA 或 inf）转换为整数

#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:

我认为您需要强制转换为numpy.int64：

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

Sample:

样本：

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 typeof NaNis float:

如果某些NaNS IN列需要他们取代一些int（例如0）通过fillna，因为type的NaN是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

还要检查文档 - 缺少数据转换规则

EDIT:

编辑：

Convert values with NaNs is buggy:

用NaNs转换值是错误的：

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

回答by MSeifert

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

您可能需要传入字符串'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:

有一些替代方法可以指定 64 位整数：

>>> 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.int64directly on your column (but it returns a numpy.array):

或者np.int64直接在您的列上使用（但它返回 a numpy.array）：

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

回答by sparrow

This seems to be a little buggy in Pandas 0.23.4?

这在 Pandas 0.23.4 中似乎有点问题？

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

如果有 np.nan 值，那么这将按预期抛出错误：

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:

但是，如果使用“忽略”，则不会像我期望的那样将任何值从 float 更改为 int：

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

It worked if I first converted np.nan:

如果我首先转换 np.nan，它会起作用：

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:

现在我不知道如何让空值代替零，因为这会将所有内容再次转换回浮点数：

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

回答by Muhammad Bin Ali

consider using

考虑使用

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

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

nanwill be changed to NaN

nan将更改为 NaN