pandas 将对象类型的数据框列转换为浮动
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Convert dataframe columns of object type to float
提问by avik
I want to convert all the non float type columns of my dataframe to float ,is there any way i can do it .It would be great if i can do it in One Go . Below is the type
我想将我的数据框的所有非浮点类型列转换为浮点，有什么办法可以做到。如果我可以一次性完成，那就太好了。下面是类型
longitude - float64
latitude - float64
housing_median_age - float64
total_rooms - float64
total_bedrooms - object
population - float64
households - float64
median_income - float64
rooms_per_household - float64
category_<1H OCEAN - uint8
category_INLAND - uint8
category_ISLAND - uint8
category_NEAR BAY - uint8
category_NEAR OCEAN - uint8
Below is the snippet of my code
下面是我的代码片段
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
df = pd.DataFrame(housing)
df['ocean_proximity'] = pd.Categorical(df['ocean_proximity']) #type casting
dfDummies = pd.get_dummies(df['ocean_proximity'], prefix = 'category' )
df = pd.concat([df, dfDummies], axis=1)
print df.head()
housingdata = df
hf = housingdata.drop(['median_house_value','ocean_proximity'], axis=1)
hl = housingdata[['median_house_value']]
hf.fillna(hf.mean,inplace = True)
hl.fillna(hf.mean,inplace = True)
回答by jpp
A quick and easy method, if you don't need specific control over downcasting or error-handling, is to use df = df.astype(float).
如果您不需要对向下转换或错误处理进行特定控制，则一种快速简便的方法是使用df = df.astype(float).
For more control, you can use pd.DataFrame.select_dtypesto select columns by dtype. Then use pd.to_numericon a subset of columns.
为了获得更多控制，您可以使用pd.DataFrame.select_dtypesdtype 来选择列。然后pd.to_numeric在列的子集上使用。
Setup
设置
df = pd.DataFrame([['656', 341.341, 4535],
['545', 4325.132, 562]],
columns=['col1', 'col2', 'col3'])
print(df.dtypes)
col1 object
col2 float64
col3 int64
dtype: object
Solution
解决方案
cols = df.select_dtypes(exclude=['float']).columns
df[cols] = df[cols].apply(pd.to_numeric, downcast='float', errors='coerce')
Result
结果
print(df.dtypes)
col1 float32
col2 float64
col3 float32
dtype: object
print(df)
col1 col2 col3
0 656.0 341.341 4535.0
1 545.0 4325.132 562.0
