str（）和astype（str）之间的区别？

I want to save the dataframe df to the .h5 file MainDataFile.h5 :

我想将数据帧df保存到.h5文件MainDataFile.h5:

df.to_hdf ("c:/Temp/MainDataFile.h5", "MainData", mode = "w", format = "table", data_columns=['_FirstDayOfPeriod','Category','ChannelId'])

and get the following error :

并得到以下错误:

*** Exception: cannot find the correct atom type -> > [dtype->object,items->Index(['Libellé_Article', 'Libellé_segment'], dtype='object')]

***异常:找不到正确的原子类型 - >> [dtype-> object,items-> Index(['Libellé_Article','Libellé_segment'],dtype ='object')]

If I modifify the column 'Libellé_Article' in this way :

如果我以这种方式修改“Libellé_Article”列:

df['Libellé_Article'] = str(df['Libellé_Article'])

there is no error anymore, whereas I still get the error message when doing :

没有错误,而我在执行时仍然收到错误消息:

df['Libellé_Article'] = df['Libellé_Article'].astype(str)

The problem is that using str() is blowing up my ram.

问题是使用str()会炸毁我的ram。

Any idea ?

任何的想法 ?

1 个解决方案

#1

str(df['Libellé_Article']) will convert the contents of the entire column in to single string. It will end up with a very big string. And thats the reason for blowing up your RAM

str(df ['Libellé_Article'])会将整个列的内容转换为单个字符串。它会以一个非常大的字符串结束。这就是炸毁RAM的原因

For example

>> df = pd.DataFrame([1,2,3], columns=['A'])
>> df['A']
0    1
1    2
2    3 
Name: A, dtype: int64

>> str(df['A'])
 '0    1\n1    2\n2    3\nName: A, dtype: int64'
>> df['A'].astype(str)
0    1
1    2
2    3
Name: A, dtype: object

So you should use .astype(str) only, if you want to convert your entire column to type string

因此,如果要将整个列转换为字符串类型,则应仅使用.astype(str)

#1