I'm confused about the syntax regarding the following line of code:
x_values = dataframe[['Brains']]
The dataframe object consists of 2 columns (Brains and Bodies)
Brains Bodies
42 34
32 23
When I print x_values I get something like this:
Brains
0 42
1 32
I'm aware of the pandas documentation as far as attributes and methods of the dataframe object are concerned, but the double bracket syntax is confusing me.
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.
[ ] and [[ ]] are the concept of NumPy.
Try to understand the basics of np.array creating and use reshape and check with ndim, you'll understand.
Check my answer here.