6

For example I have a dataframe like this.

     Date          Open          High           Low         Close  \
0  2009-08-25  20246.789063  20476.250000  20143.509766  20435.240234   

      Adj Close      Volume  
0  20435.240234  1531430000

Using attribute or explicit naming both give me the same output:

sum(data.Date==data['Date']) == data.shape[0]

True

However I cannot access columns that are named with white space, like 'Adj Close' with df.columnname, but can do with df['columnname'].

Is using df['columnname'] strictly better than using df.columnname ?

wjandrea
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chrisckwong821
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    `df['columnname']` is better than `df.`, sometime you will find the` '_'` or even when you have a name called 'max', you can only using df['max'] to get it, base on my understanding , `.` is for pipeline. :) – BENY Sep 06 '17 at 02:57

1 Answers1

16

Using . as a column accessor is a convenience. There are many limitations beyond having spaces in the name. For example, if your column is named the same as an existing dataframe attribute or method, you won't be able to use it with a .. A non-exhaustive list is mean, sum, index, values, to_dict, etc. You also cannot reference columns with numeric headers via the . accessor.

So, yes, ['col'] is strictly better than .col because it is more consistent and reliable.

piRSquared
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