Extracting and Dropping values

To extract a subset of values, we can use .loc[] or .iloc[] with row and column indices and labels respectively.

The .loc[] method is used to access a group of rows and columns by labels or a boolean array.

`.loc[row_labels, col_labels]`

The .loc operator selects rows and columns in a DataFrame by their row and column label(s), respectively. The row labels (commonly referred to as the indices) are the bold text on the far left of a DataFrame, while the column labels are the column names found at the top of a DataFrame.

To grab data with .loc, we must specify the row and column label(s) where the data exists. The row labels are the first argument to the .loc function; the column labels are the second. For example, we can select the the row labeled 0 and the column labeled Candidate from the elections DataFrame.

elections.loc[0, 'Candidate']

To select multiple rows and columns, we can use Python slice notation. Here, we select the rows from labels 0 to 3 and the columns from labels "Year" to "Popular vote".

elections.loc[0:3, 'Year':'Popular vote']

Suppose that instead, we wanted every column value for the first four rows in the elections DataFrame. The shorthand : is useful for this.

elections.loc[0:3, :]

There are a couple of things we should note. Firstly, unlike conventional Python, Pandas allows us to slice string values (in our example, the column labels). Secondly, slicing with .loc is inclusive. Notice how our resulting DataFrame includes every row and column between and including the slice labels we specified.

Equivalently, we can use a list to obtain multiple rows and columns in our elections DataFrame. elections.loc[[0, 1, 2, 3], [‘Year’, ‘Candidate’, ‘Party’, ‘Popular vote’]]

Lastly, we can interchange list and slicing notation. elections.loc[[0, 1, 2, 3], :]

`.iloc[row_indices, col_indices]`

The .iloc[] method is used to access a group of rows and columns by integer position.

Caution

If you find yourself needing to use .iloc then stop and think if you are about to implement a loop. If so, there is probably a better way to do it.

Slicing with .iloc works similarily to .loc, however, .iloc uses the index positions of rows and columns rather the labels (think to yourself: loc uses labels; iloc uses indices). The arguments to the .iloc function also behave similarly -– single values, lists, indices, and any combination of these are permitted.

Let’s begin reproducing our results from above. We’ll begin by selecting for the first presidential candidate in our elections DataFrame:

# elections.loc[0, "Candidate"] - Previous approach
elections.iloc[0, 1]

Notice how the first argument to both .loc and .iloc are the same. This is because the row with a label of 0 is conveniently in the 0th index (equivalently, the first position) of the elections DataFrame. Generally, this is true of any DataFrame where the row labels are incremented in ascending order from 0.

However, when we select the first four rows and columns using .iloc, we notice something.

# elections.loc[0:3, 'Year':'Popular vote'] - Previous approach
elections.iloc[0:4, 0:4]

Slicing is no longer inclusive in .iloc -– it’s exclusive. In other words, the right-end of a slice is not included when using .iloc. This is one of the subtleties of pandas syntax; you will get used to it with practice.

#elections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']] - Previous Approach
elections.iloc[[0, 1, 2, 3], [0, 1, 2, 3]]

This discussion begs the question: when should we use .loc vs .iloc? In most cases, .loc is generally safer to use. You can imagine .iloc may return incorrect values when applied to a dataset where the ordering of data can change.

Dropping rows and columns

To drop rows and columns in a DataFrame, we can use the drop() method.

For example, to drop the first row from the election DataFrame, we can use the following code:

elections.head()

	Year	Candidate	Party	Popular vote	Result	%
0	1824	Andrew Jackson	Democratic-Republican	151271	loss	57.210122
1	1824	John Quincy Adams	Democratic-Republican	113142	win	42.789878
2	1828	Andrew Jackson	Democratic	642806	win	56.203927
3	1828	John Quincy Adams	National Republican	500897	loss	43.796073
4	1832	Andrew Jackson	Democratic	702735	win	54.574789

elections.drop(columns=['Popular vote'])

	Year	Candidate	Party	Result	%
0	1824	Andrew Jackson	Democratic-Republican	loss	57.210122
1	1824	John Quincy Adams	Democratic-Republican	win	42.789878
2	1828	Andrew Jackson	Democratic	win	56.203927
3	1828	John Quincy Adams	National Republican	loss	43.796073
4	1832	Andrew Jackson	Democratic	win	54.574789
...	...	...	...	...	...
177	2016	Jill Stein	Green	loss	1.073699
178	2020	Joseph Biden	Democratic	win	51.311515
179	2020	Donald Trump	Republican	loss	46.858542
180	2020	Jo Jorgensen	Libertarian	loss	1.177979
181	2020	Howard Hawkins	Green	loss	0.255731

182 rows × 5 columns

# Drop the first row
elections.drop(index=0)

# Drop the first two rows
elections.drop(index=[0, 1])

	Year	Candidate	Party	Popular vote	Result	%
2	1828	Andrew Jackson	Democratic	642806	win	56.203927
3	1828	John Quincy Adams	National Republican	500897	loss	43.796073
4	1832	Andrew Jackson	Democratic	702735	win	54.574789
5	1832	Henry Clay	National Republican	484205	loss	37.603628
6	1832	William Wirt	Anti-Masonic	100715	loss	7.821583
...	...	...	...	...	...	...
177	2016	Jill Stein	Green	1457226	loss	1.073699
178	2020	Joseph Biden	Democratic	81268924	win	51.311515
179	2020	Donald Trump	Republican	74216154	loss	46.858542
180	2020	Jo Jorgensen	Libertarian	1865724	loss	1.177979
181	2020	Howard Hawkins	Green	405035	loss	0.255731

180 rows × 6 columns