Slicing Only Columns Using .loc[]

cereal.loc[:, 'calories':'fiber']

	calories	protein	fat	sodium	fiber
0	70	4	1	130	10.0
1	120	3	5	15	2.0
2	70	4	1	260	9.0
...	...	...	...	...	...
74	100	3	1	230	3.0
75	100	3	1	200	3.0
76	110	2	1	200	1.0

77 rows × 5 columns

What happens now if we wanted all the rows of the dataframe but only the columns calories to fiber?

We can use : in the row postion of the .loc[] call to indicate we want all the rows. So here we write cereal.loc[:, 'calories':'fiber'].

So Far

• .loc[] is used to slice columns and rows by label and within an interval.

• We always specify row indexing first, then columns.

cereal.loc['row name start':'row name end', 'column name start':'column name end']

• If we aren’t slicing any columns, but we are slicing rows we can shorten that to

df.loc['row name start':'row name end']

• However, the reverse is not true. If we want all the rows with only specific columns, we specify we want all the row first with just a colon : followed by interval of the columns:

df.loc[:, 'column name start':'column name end']

• We can read : as “to”.

• If the indices are labeled with numbers, we do not need “quotations” when calling them. This is only when the labels are using letters.

Let’s talk about what we have covered so far.

• .loc[] is used to slice columns and rows by label and within an interval.

• We always specify row indexing first, then columns.

• If we are not slicing any columns, but we are slicing rows we only need to specify the row labels.

• However, the reverse is not true. If we want all the rows with only specific columns, we specify rows first and therefore we would need to make it clear with a colon first that we are slicing all the rows followed by the column labels.

• We can read : as “to”.

• And finally, if the row index is labeled with numbers, we do not need “quotations” when slicing.

Let’s apply what we learned!