pandas 数据框.更改浮动格式.保持类型为"float"

问题描述

我正在尝试更改pd数据框列的格式而不更改数据类型.这是我所拥有的:df = pd.DataFrame({'Age': [24.0, 32.0}])

I'm trying to change a format of pd data frame column without changing the type of data.Here is what I have: df = pd.DataFrame({'Age': [24.0, 32.0}])

我想用24 32类型或24.00 32.00表示Age，并将它们保持为浮点数.这是我可以做的:

I'd like to represent Age in 24 32 type or 24.00 32.00 and keep them as floats.Here is what I can do:

df['Age'].map('{:,.2f}'.format)

但是此行将数据类型更改为对象.我也在尝试申请:`

But this line changes the type of data to object. I was also trying to apply: `

df = df.style.format({'Age': '{:,.2f}'.format})`

但是其中有问题.请帮助找出正确的方法.

but there is something wrong in it. Please help to figure out the right way.

推荐答案

您的dataFrame本身为float类型.

Your dataFrame itself a type float.

数据框:

>>> df
    Age
0  24.0
1  32.0

检查DataFrame类型:

Check DataFrame type:

>>> df.dtypes
Age    float64
dtype: object

检查dtype是否为DataFrame列类型:

check dtype for DataFrame column type:

>>> df.Age
0    24.0
1    32.0
Name: Age, dtype: float64

甚至检查如下:

>>> df['Age'].dtype.kind
'f'

您使用正确的方法将四舍五入到两位数的零位，但是再次将其转换为浮点数将使它们保持为浮点数而保持为单个零.

The way you are using to round up double digit zeros that's correct but converting them again to float will get them remain in single zero as being float.

>>> df['Age'].map('{:,.2f}'.format)
0    24.00
1    32.00
Name: Age, dtype: object

---

由于您希望保持类似int值24, 32或24.00 & 32.00这样的模拟，如果仅对浮点数的显示感兴趣，则可以执行pd.set_option('display.float_format','{:.0f}'.format)，这实际上不会影响您的数据.

---

As you are interested keeping either mimic like int values 24, 32 or 24.00 & 32.00, if you are only interested in the display of floats then you can do pd.set_option('display.float_format','{:.0f}'.format), which doesn't actually affect your data.

>>> pd.set_option('display.float_format','{:.0f}'.format)
>>> df
   Age
0   24
1   32

>>> df.dtypes
Age    float64
dtype: object

对于浮动格式

>>> pd.set_option('display.float_format','{:.2f}'.format)
>>> df
    Age
0 24.00
1 32.00
>>> df.dtypes
Age    float64
dtype: object

替代方式

设置显示精度选项:

Alternative way

Set the display precision option:

>>> pd.set_option('precision', 0)
>>> df
   Age
0   24
1   32

>>> df.dtypes
Age    float64
dtype: object

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