Python: Pandas Series – Why use loc?

Explicit is better than implicit.

df[boolean_mask] selects rows where boolean_mask is True, but there is a corner case when you might not want it to: when df has boolean-valued column labels:

In [229]: df = pd.DataFrame({True:[1,2,3],False:[3,4,5]}); df
Out[229]: 
   False  True 
0      3      1
1      4      2
2      5      3

You might want to use df[[True]] to select the True column. Instead it raises a ValueError:

In [230]: df[[True]]
ValueError: Item wrong length 1 instead of 3.

Versus using loc:

In [231]: df.loc[[True]]
Out[231]: 
   False  True 
0      3      1

In contrast, the following does not raise ValueError even though the structure of df2 is almost the same as df1 above:

In [258]: df2 = pd.DataFrame({'A':[1,2,3],'B':[3,4,5]}); df2
Out[258]: 
   A  B
0  1  3
1  2  4
2  3  5

In [259]: df2[['B']]
Out[259]: 
   B
0  3
1  4
2  5

Thus, df[boolean_mask] does not always behave the same as df.loc[boolean_mask]. Even though this is arguably an unlikely use case, I would recommend always using df.loc[boolean_mask] instead of df[boolean_mask] because the meaning of df.loc‘s syntax is explicit. With df.loc[indexer] you know automatically that df.loc is selecting rows. In contrast, it is not clear if df[indexer] will select rows or columns (or raise ValueError) without knowing details about indexer and df.

df.loc[row_indexer, column_index] can select rows and columns. df[indexer] can only select rows or columns depending on the type of values in indexer and the type of column values df has (again, are they boolean?).

In [237]: df2.loc[[True,False,True], 'B']
Out[237]: 
0    3
2    5
Name: B, dtype: int64

When a slice is passed to df.loc the end-points are included in the range. When a slice is passed to df[...], the slice is interpreted as a half-open interval:

In [239]: df2.loc[1:2]
Out[239]: 
   A  B
1  2  4
2  3  5

In [271]: df2[1:2]
Out[271]: 
   A  B
1  2  4