The difference between double brace `[[...]]` and single brace `[..]` indexing in Pandas
Consider this:
Source DF:
In [79]: df
Out[79]:
Brains Bodies
0 42 34
1 32 23
Selecting one column - results in Pandas.Series:
In [80]: df['Brains']
Out[80]:
0 42
1 32
Name: Brains, dtype: int64
In [81]: type(df['Brains'])
Out[81]: pandas.core.series.Series
Selecting subset of DataFrame - results in DataFrame:
In [82]: df[['Brains']]
Out[82]:
Brains
0 42
1 32
In [83]: type(df[['Brains']])
Out[83]: pandas.core.frame.DataFrame
Conclusion: the second approach allows us to select multiple columns from the DataFrame. The first one just for selecting single column...
Demo:
In [84]: df = pd.DataFrame(np.random.rand(5,6), columns=list('abcdef'))
In [85]: df
Out[85]:
a b c d e f
0 0.065196 0.257422 0.273534 0.831993 0.487693 0.660252
1 0.641677 0.462979 0.207757 0.597599 0.117029 0.429324
2 0.345314 0.053551 0.634602 0.143417 0.946373 0.770590
3 0.860276 0.223166 0.001615 0.212880 0.907163 0.437295
4 0.670969 0.218909 0.382810 0.275696 0.012626 0.347549
In [86]: df[['e','a','c']]
Out[86]:
e a c
0 0.487693 0.065196 0.273534
1 0.117029 0.641677 0.207757
2 0.946373 0.345314 0.634602
3 0.907163 0.860276 0.001615
4 0.012626 0.670969 0.382810
and if we specify only one column in the list we will get a DataFrame with one column:
In [87]: df[['e']]
Out[87]:
e
0 0.487693
1 0.117029
2 0.946373
3 0.907163
4 0.012626
There is no special syntax in Python for [[
and ]]
. Rather, a list is being created, and then that list is being passed as an argument to the DataFrame indexing function.
As per @MaxU's answer, if you pass a single string to a DataFrame a series that represents that one column is returned. If you pass a list of strings, then a DataFrame that contains the given columns is returned.
So, when you do the following
# Print "Brains" column as Series
print(df['Brains'])
# Return a DataFrame with only one column called "Brains"
print(df[['Brains']])
It is equivalent to the following
# Print "Brains" column as Series
column_to_get = 'Brains'
print(df[column_to_get])
# Return a DataFrame with only one column called "Brains"
subset_of_columns_to_get = ['Brains']
print(df[subset_of_columns_to_get])
In both cases, the DataFrame is being indexed with the []
operator.
Python uses the []
operator for both indexing and for constructing list literals, and ultimately I believe this is your confusion. The outer [
and ]
in df[['Brains']]
is performing the indexing, and the inner is creating a list.
>>> some_list = ['Brains']
>>> some_list_of_lists = [['Brains']]
>>> ['Brains'] == [['Brains']][0]
True
>>> 'Brains' == [['Brains']][0][0] == [['Brains'][0]][0]
True
What I am illustrating above is that at no point does Python ever see [[
and interpret it specially. In the last convoluted example ([['Brains'][0]][0]
) there is no special ][
operator or ]][
operator... what happens is
- A single-element list is created (
['Brains']
) - The first element of that list is indexed (
['Brains'][0]
=>'Brains'
) - That is placed into another list (
[['Brains'][0]]
=>['Brains']
) - And then the first element of that list is indexed (
[['Brains'][0]][0]
=>'Brains'
)