Introduction
Pandas Tutorial Part 4: Grouping and Sorting.
Maps allow us to transform data in a DataFrame or Series one value at a time for an entire column. However, often we want to group our data, and then do something specific to the group the data is in.
As you’ll learn, we do this with the groupby() operation. We’ll also cover some additional topics, such as more complex ways to index your DataFrames, along with how to sort your data.
For this exercise we will continue using the famous wine review dataframe. You can obtain this from Kaggle, following this link.
import pandas as pd
pd.set_option('max_rows', 5)
import numpy as np
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)Download the most comprehensive Python Pandas Cheat Sheet here
Python-Pandas-Cheat-Sheets-1-8-v1.0.2.pdf
Groupwise analysis
One function we’ve been using heavily thus far is the value_counts() function. We can replicate what value_counts() does by doing the following:
reviews.groupby('points').points.count()points
80 397
81 692
...
99 33
100 19
Name: points, Length: 21, dtype: int64
groupby() created a group of reviews which allotted the same point values to the given wines. Then, for each of these groups, we grabbed the points() column and counted how many times it appeared. value_counts() is just a shortcut to this groupby() operation.
We can use any of the summary functions we’ve used before with this data. For example, to get the cheapest wine in each point value category, we can do the following:
reviews.groupby('points').price.min()
Out[3]:
points
80 5.0
81 5.0
...
99 44.0
100 80.0
Name: price, Length: 21, dtype: float64
You can think of each group we generate as being a slice of our DataFrame containing only data with values that match. This DataFrame is accessible to us directly using the apply() method, and we can then manipulate the data in any way we see fit. For example, here’s one way of selecting the name of the first wine reviewed from each winery in the dataset:
reviews.groupby('winery').apply(lambda df: df.title.iloc[0])
Out[4]:
winery
1+1=3 1+1=3 NV Rosé Sparkling (Cava)
10 Knots 10 Knots 2010 Viognier (Paso Robles)
...
àMaurice àMaurice 2013 Fred Estate Syrah (Walla Walla V...
Štoka Štoka 2009 Izbrani Teran (Kras)
Length: 16757, dtype: object
For even more fine-grained control, you can also group by more than one column. For an example, here’s how we would pick out the best wine by country and province:In [5]:
reviews.groupby(['country', 'province']).apply(lambda df: df.loc[df.points.idxmax()])
425 rows × 13 columns
Another groupby() method worth mentioning is agg(), which lets you run a bunch of different functions on your DataFrame simultaneously. For example, we can generate a simple statistical summary of the dataset as follows:
reviews.groupby(['country']).price.agg([len, min, max])
| len | min | max | |
|---|---|---|---|
| country | |||
| Argentina | 3800 | 4.0 | 230.0 |
| Armenia | 2 | 14.0 | 15.0 |
| … | … | … | … |
| Ukraine | 14 | 6.0 | 13.0 |
| Uruguay | 109 | 10.0 | 130.0 |
43 rows × 3 columns
Effective use of groupby() will allow you to do lots of really powerful things with your dataset.
Multi-indexes
In all of the examples we’ve seen thus far we’ve been working with DataFrame or Series objects with a single-label index. groupby() is slightly different in the fact that, depending on the operation we run, it will sometimes result in what is called a multi-index.
A multi-index differs from a regular index in that it has multiple levels. For example:In [7]:
countries_reviewed = reviews.groupby(['country', 'province']).description.agg([len])
countries_reviewed
Out[7]:
| len | ||
|---|---|---|
| country | province | |
| Argentina | Mendoza Province | 3264 |
| Other | 536 | |
| … | … | … |
| Uruguay | San Jose | 3 |
| Uruguay | 24 |
425 rows × 1 columnsIn [8]:
mi = countries_reviewed.index
type(mi)
Out[8]:
pandas.core.indexes.multi.MultiIndex
Multi-indices have several methods for dealing with their tiered structure which are absent for single-level indices. They also require two levels of labels to retrieve a value. Dealing with multi-index output is a common “gotcha” for users new to pandas.
The use cases for a multi-index are detailed alongside instructions on using them in the MultiIndex / Advanced Selection section of the pandas documentation.
However, in general the multi-index method you will use most often is the one for converting back to a regular index, the reset_index() method:In [9]:
countries_reviewed.reset_index()
Out[9]:
| country | province | len | |
|---|---|---|---|
| 0 | Argentina | Mendoza Province | 3264 |
| 1 | Argentina | Other | 536 |
| … | … | … | … |
| 423 | Uruguay | San Jose | 3 |
| 424 | Uruguay | Uruguay | 24 |
425 rows × 3 columns
Sorting
Looking again at countries_reviewed we can see that grouping returns data in index order, not in value order. That is to say, when outputting the result of a groupby, the order of the rows is dependent on the values in the index, not in the data.
To get data in the order want it in we can sort it ourselves. The sort_values() method is handy for this.In [10]:
countries_reviewed = countries_reviewed.reset_index()
countries_reviewed.sort_values(by='len')
Out[10]:
| country | province | len | |
|---|---|---|---|
| 179 | Greece | Muscat of Kefallonian | 1 |
| 192 | Greece | Sterea Ellada | 1 |
| … | … | … | … |
| 415 | US | Washington | 8639 |
| 392 | US | California | 36247 |
425 rows × 3 columns
sort_values() defaults to an ascending sort, where the lowest values go first. However, most of the time we want a descending sort, where the higher numbers go first. That goes thusly:In [11]:
countries_reviewed.sort_values(by='len', ascending=False)
Out[11]:
| country | province | len | |
|---|---|---|---|
| 392 | US | California | 36247 |
| 415 | US | Washington | 8639 |
| … | … | … | … |
| 63 | Chile | Coelemu | 1 |
| 149 | Greece | Beotia | 1 |
425 rows × 3 columns
To sort by index values, use the companion method sort_index(). This method has the same arguments and default order:In [12]:
countries_reviewed.sort_index()
Out[12]:
| country | province | len | |
|---|---|---|---|
| 0 | Argentina | Mendoza Province | 3264 |
| 1 | Argentina | Other | 536 |
| … | … | … | … |
| 423 | Uruguay | San Jose | 3 |
| 424 | Uruguay | Uruguay | 24 |
425 rows × 3 columns
Finally, know that you can sort by more than one column at a time:In [13]:
countries_reviewed.sort_values(by=['country', 'len'])
Out[13]:
| country | province | len | |
|---|---|---|---|
| 1 | Argentina | Other | 536 |
| 0 | Argentina | Mendoza Province | 3264 |
| … | … | … | … |
| 424 | Uruguay | Uruguay | 24 |
| 419 | Uruguay | Canelones | 43 |
425 rows × 3 columns