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
Today, I summarize how to group data by some variable and draw boxplots on it using Pandas and Seaborn. Let’s begin!
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:
First, let’s group by the categorical variable
time and create a boxplot for
tip. This is done just by two pandas methods:
* 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
sns.boxplot(x="time", y="tip", data=df);
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…
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);
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
df["bin"] = pd.cut(df["total_bill"], 3) sns.boxplot(x="bin", y="tip", data=df);
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);
Written by Shion Honda. If you like this, please share!