Slicing and Selecting Using .iloc[]

Slicing Dataframe

cereal = pd.read_csv('data/cereal.csv')
cereal.head(10)

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.00	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.00	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.00	0.33	59.425505
3	All-Bran with Extra Fiber	K	Cold	50	...	3	1.00	0.50	93.704912
4	Almond Delight	R	Cold	110	...	3	1.00	0.75	34.384843
5	Apple Cinnamon Cheerios	G	Cold	110	...	1	1.00	0.75	29.509541
6	Apple Jacks	K	Cold	110	...	2	1.00	1.00	33.174094
7	Basic 4	G	Cold	130	...	3	1.33	0.75	37.038562
8	Bran Chex	R	Cold	90	...	1	1.00	0.67	49.120253
9	Bran Flakes	P	Cold	90	...	3	1.00	0.67	53.313813

10 rows × 16 columns

Up until point, we have been manipulating our dataframe with column and row labels using .loc[].

Slicing can also be done by the location position of each row with .iloc[].

.iloc[] is very similar to .loc[], however, the “i” in iloc refers to the index integer position.

We are going to return to our cereal dataset and take a look at the first 10 rows.

Let’s say we want the rows from All-Bran to Apple Cinnamon Cheerios, but we want to slice based on their position instead of their label.

Using Python’s counting method of starting at zero, we conclude that All-Bran to be at position to 2.

We get Apple Cinnamon Cheerios position to be 5 in the same way.

We are lucky with this dataframe because our index labels match the position of the rows, and this makes things a little bit simpler.

cereal.loc[2:5]

	name	mfr	type	calories	...	shelf	weight	cups	rating
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505
3	All-Bran with Extra Fiber	K	Cold	50	...	3	1.0	0.50	93.704912
4	Almond Delight	R	Cold	110	...	3	1.0	0.75	34.384843
5	Apple Cinnamon Cheerios	G	Cold	110	...	1	1.0	0.75	29.509541

4 rows × 16 columns

cereal.iloc[2:5]

	name	mfr	type	calories	...	shelf	weight	cups	rating
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505
3	All-Bran with Extra Fiber	K	Cold	50	...	3	1.0	0.50	93.704912
4	Almond Delight	R	Cold	110	...	3	1.0	0.75	34.384843

3 rows × 16 columns

We can use the same coding structure we learned with .loc[] but this time using row positions instead of labels with .iloc[].

But wait! Something is missing here!

Why doesn’t Apple Cinnamon Cheerios appear in the dataframe?

cereal.iloc[2:6]

	name	mfr	type	calories	...	shelf	weight	cups	rating
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505
3	All-Bran with Extra Fiber	K	Cold	50	...	3	1.0	0.50	93.704912
4	Almond Delight	R	Cold	110	...	3	1.0	0.75	34.384843
5	Apple Cinnamon Cheerios	G	Cold	110	...	1	1.0	0.75	29.509541

4 rows × 16 columns

The reason for this is that when we use slicing with indices, it will take all the indices including the lower bound but EXCLUDING the upper bound.

If we want to include Apple Cinnamon Cheerios we would have to go 1 index position further.

If we think about this a bit it actually make some sense. Think about the calculation 6 - 2 = 4 . We get 4 items remaining which is the amount of cereals we want in our in new dataframe.

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.0	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.0	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505
3	All-Bran with Extra Fiber	K	Cold	50	...	3	1.0	0.50	93.704912
4	Almond Delight	R	Cold	110	...	3	1.0	0.75	34.384843

5 rows × 16 columns

cereal.iloc[:, 0:6]

	name	mfr	type	calories	protein	fat
0	100% Bran	N	Cold	70	4	1
1	100% Natural Bran	Q	Cold	120	3	5
2	All-Bran	K	Cold	70	4	1
3	All-Bran with Extra Fiber	K	Cold	50	4	0
4	Almond Delight	R	Cold	110	2	2
...	...	...	...	...	...	...
72	Triples	G	Cold	110	2	1
73	Trix	G	Cold	110	1	1
74	Wheat Chex	R	Cold	100	3	1
75	Wheaties	G	Cold	100	3	1
76	Wheaties Honey Gold	G	Cold	110	2	1

77 rows × 6 columns

The same concept can be applied to the columns of the dataframe.

Let’s say we want all the rows but we only want the columns starting at name and ending (and including) at column fat.

Using the logic we learned in the last set of slides, we would use the following code.

We would need to specify all rows using : as we did when we used .loc[].

The column name is at index position 0 (we do not include the index label as a column) and fat is at index position 5.

Since we want to include the 5th column we need to use the 6th position to make sure we get all the columns BEFORE the upper bound.

Let’s say we want the rows All-Bran to Apple Cinnamon Cheerios and name to fat.

Rows

Lower Bound: All-Bran located at position 2.
Upper Bound:Apple Cinnamon Cheerios is located at position 5.

Columns

Lower Bound: name is located at position 0.
Upper Bound:fat is located at position 5.

So the code we have to use to do this is the following:

cereal.iloc[2:6, 0:6]

	name	mfr	type	calories	protein	fat
2	All-Bran	K	Cold	70	4	1
3	All-Bran with Extra Fiber	K	Cold	50	4	0
4	Almond Delight	R	Cold	110	2	2
5	Apple Cinnamon Cheerios	G	Cold	110	2	2

Let’s say we want the rows All-Bran to Apple Cinnamon Cheerios and name to fat.

For rows, the lower bound All-Bran is located at position 2 and the upper bound Apple Cinnamon Cheerios is located at position 5.

Now the column’s lower bound name is located at position 0 and the upper bound fat is located at position 5.

The same would apply if we only wanted certain rows with certain columns.

Both of our upper bound have been compensated with an added 1 to make sure they are included in the new dataframe.

So the code we have to use to do this is the following:

cereal.iloc[2:6, 0:6]

cereal.head(3)

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.0	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.0	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505

3 rows × 16 columns

cereal.iloc[0:3]

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.0	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.0	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505

3 rows × 16 columns

cereal.iloc[:3]

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.0	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.0	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505

3 rows × 16 columns

There are multiple different way of writing code when you are selecting items from the beginning or end of your data.

Perhaps you only want the first 3 rows of your data.

We can use .head(3) or we can use .iloc[].

Since we are indicating the beginning of the dataframe, we can omit the upper bound 0 just like we did when we learned slicing with .loc[].

cereal.iloc[74:77]

	name	mfr	type	calories	...	shelf	weight	cups	rating
74	Wheat Chex	R	Cold	100	...	1	1.0	0.67	49.787445
75	Wheaties	G	Cold	100	...	1	1.0	1.00	51.592193
76	Wheaties Honey Gold	G	Cold	110	...	1	1.0	0.75	36.187559

3 rows × 16 columns

cereal.iloc[-3:]

	name	mfr	type	calories	...	shelf	weight	cups	rating
74	Wheat Chex	R	Cold	100	...	1	1.0	0.67	49.787445
75	Wheaties	G	Cold	100	...	1	1.0	1.00	51.592193
76	Wheaties Honey Gold	G	Cold	110	...	1	1.0	0.75	36.187559

3 rows × 16 columns

The same logic can be applied for end of a dataframe. This time we want the last 3 rows.

Since this dataframe has 76 rows we could use our lower bound as 74 and upper bound as 77 (76 +1).

A better and easier way to write this, where you don’t even need to know the number of rows in the dataframe would be to specify you are counting your rows from the bottom with a negative in front of the number of rows you want.

This example takes the first 3 rows of the bottom of the dataframe.

Since we are collecting data to the end of the dataframe, we do not need to include the ending row index number.

Selecting with .iloc[]

cereal.head(10)

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.00	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.00	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.00	0.33	59.425505
3	All-Bran with Extra Fiber	K	Cold	50	...	3	1.00	0.50	93.704912
4	Almond Delight	R	Cold	110	...	3	1.00	0.75	34.384843
5	Apple Cinnamon Cheerios	G	Cold	110	...	1	1.00	0.75	29.509541
6	Apple Jacks	K	Cold	110	...	2	1.00	1.00	33.174094
7	Basic 4	G	Cold	130	...	3	1.33	0.75	37.038562
8	Bran Chex	R	Cold	90	...	1	1.00	0.67	49.120253
9	Bran Flakes	P	Cold	90	...	3	1.00	0.67	53.313813

10 rows × 16 columns

Row Row Position Almond Delight Position 4 Basic 4 Position 7 Apple Jacks Position 6

Columns Column Position name Position 0 calories Position 3 fat Position 5 type Position 2

Selecting using .iloc[] is done identically to .loc[], however, the items within each set of square brackets MUST be integers, and not in quotation marks.

Let’s say we want the rows Almond Delight, Basic 4 and Apple Jacks with the columns name, calories, fat and type and in that specific order.

Row Row Position Almond Delight Position 4 Basic 4 Position 7 Apple Jacks Position 6

Columns Column Position name Position 0 calories Position 3 fat Position 5 type Position 2

cereal.iloc[[4, 7, 6], [0, 3, 5, 2]]

	name	calories	fat	type
4	Almond Delight	110	2	Cold
7	Basic 4	130	2	Cold
6	Apple Jacks	110	0	Cold

Almond Delight takes row position 4, Basic 4 takes row position 7 and Apple Jacks is located at position 6. The desired columns name, calories, fat, and type take column index positions 0,5,3, and 2 respectively.

Now let’s put those position into square backing within df.iloc[].

Nice work! Let’s apply what we learned!