# Meet Pandas: Grouping and Boxplot

June 14, 2020 | 4 min read | 40 views

🐼Welcome to the “Meet Pandas” series (a.k.a. my memorandum of understanding Pandas)!🐼

Last time, I discussed differences between Pandas `loc`

, `iloc`

, `at`

, and `iat`

methods.

Today, I summarize how to group data by some variable and draw boxplots on it using Pandas and Seaborn. Let’s begin!

## Load Example Data

In this post, I use the “tips” dataset provided by seaborn. This is a data of food servers’ tips in restaurants with six factors that might influence tips.

The snippets in this post are supposed to be executed on Jupyter Notebook, Colaboratory, and stuff.

```
import pandas as pd
import seaborn as sns
sns.set()
df = sns.load_dataset('tips')
df
```

The dataframe should look something like this:

## Group by Categorical or Discrete Variable

First, let’s group by the categorical variable `time`

and create a boxplot for `tip`

. This is done just by two pandas methods: `groupby`

and `boxplot`

.

`df.groupby("time").boxplot(column="tip");`

* You can also group by discrete variables in the same way.

It’s not bad, but maybe too simple. If you want to make it prettier, use seaborn’s `boxplot()`

.

`sns.boxplot(x="time", y="tip", data=df);`

Or, `catplot()`

should produce the same output.

`sns.catplot(x="time", y="tip", kind="box", data=df);`

I’m not sure why it produced a figure of a little different size…

## Other Distribution Plots

For larger datasets, `boxenplot()`

gives more information about the shape of the distribution.

`sns.boxenplot(x="time", y="tip", data=df);`

`violinplot()`

combines a boxplot with the **kernel density estimation**.

`sns.violinplot(x="time", y="tip", data=df);`

## Group by Continuous Variable

Next, let’s group by the continuous numerical variable `total_bill`

and create boxplot for `tip`

. What happens if I use seaborn’s `boxplot()`

function in the same way as above?

`sns.boxplot(x="total_bill", y="tip", data=df);`

It divides the data into too many groups! This doesn’t really make sense. Well, I should have first bin the data by pandas `cut()`

function.

```
df["bin"] = pd.cut(df["total_bill"], 3)
sns.boxplot(x="bin", y="tip", data=df);
```

Or, use `qcut()`

(quantile-based cut) if you want equal-sized bins.

```
df["qbin"] = pd.qcut(df["total_bill"], 3)
sns.boxplot(x="qbin", y="tip", data=df);
```

## References

[1] pandas.core.groupby.DataFrameGroupBy.boxplot — pandas 1.0.4 documentation

[2] seaborn.boxplot — seaborn 0.10.1 documentation

[3] Plotting with categorical data — seaborn 0.10.1 documentation

Written by **Shion Honda**. If you like this, please share!