The difference between double brace `[[…]]` and single brace `[..]` indexing in Pandas

Question

I\'m confused about the syntax regarding the following line of code:

x_values = dataframe[[\'Brains\']]

The dataframe object consists of 2

Answer 1

Other solutions demonstrate the difference between a series and a dataframe. For the Mathematically minded, you may wish to consider the dimensions of your input and output. Here's a summary:

Object                                Series          DataFrame
Dimensions (obj.ndim)                      1                  2
Syntax arg dim                             0                  1
Syntax                             df['col']        df[['col']]
Max indexing dim                           1                  2
Label indexing              df['col'].loc[x]   df.loc[x, 'col']
Label indexing (scalar)      df['col'].at[x]    df.at[x, 'col']
Integer indexing           df['col'].iloc[x]  df.iloc[x, 'col']
Integer indexing (scalar)   df['col'].iat[x]   dfi.at[x, 'col']

When you specify a scalar or list argument to pd.DataFrame.__getitem__, for which [] is syntactic sugar, the dimension of your argument is one less than the dimension of your result. So a scalar (0-dimensional) gives a 1-dimensional series. A list (1-dimensional) gives a 2-dimensional dataframe. This makes sense since the additional dimension is the dataframe index, i.e. rows. This is the case even if your dataframe happens to have no rows.