Plotnine Chart

1 Introduction to Plotnine

Plotnine is a Python implementation of the Grammar of Graphics, inspired by R’s ggplot2. It allows you to create complex and beautiful plots by adding layers of data, aesthetics, and geoms. This document provides a guide to creating various plots with Plotnine.

Code

import plotnine
print(plotnine.__version__)

0.14.5

Code

from plotnine import *
import seaborn as sns
import pandas as pd

# Load an example dataset
tips = sns.load_dataset("tips")
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

2 Scatter Plot

A scatter plot is used to visualize the relationship between two continuous variables.

Code

p = (
    ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point()
)
p

2.1 Color by Group

You can color the points based on a categorical variable to see how the relationship varies across different groups.

Code

p = (
    ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point(aes(color="sex"))
)
p

2.2 Size by Group

The size of the points can be mapped to a variable to add another dimension to the plot.

Code

p = (
    ggplot(data=tips) + aes(x="tip", y="total_bill", size="size") + geom_point()
)
p

3 Line Plot

A line plot is suitable for showing the trend of a variable over time.

Code

dowjones = sns.load_dataset("dowjones")
dowjones.head()

	Date	Price
0	1914-12-01	55.00
1	1915-01-01	56.55
2	1915-02-01	56.00
3	1915-03-01	58.30
4	1915-04-01	66.45

Code

p = (
    ggplot(data=dowjones) + aes(x="Date", y="Price") + geom_line()
)
p

3.1 Line Plot with Dots

You can also add markers to the line plot to highlight the data points.

Code

p = (
    ggplot(data=dowjones) + aes(x="Date", y="Price") + geom_line() + geom_point()
)
p

Code

import random

# Create datasets for comparison
dowjones2 = dowjones.copy()
dowjones2['type'] = 'old'

dowjones3 = dowjones.copy()
dowjones3['Price'] = dowjones3['Price'] + random.random() * 200
dowjones3['type'] = 'new'

dowjones4 = pd.concat([dowjones2, dowjones3], ignore_index=True)
dowjones4 = dowjones4.sort_values('Date').reset_index(drop=True)

Code

dowjones4.head()

	Date	Price	type
0	1914-12-01	55.000000	old
1	1914-12-01	57.601244	new
2	1915-01-01	59.151244	new
3	1915-01-01	56.550000	old
4	1915-02-01	56.000000	old

3.2 Color by Group

You can plot multiple lines on the same plot to compare different groups.

Code

p = (
    ggplot(data=dowjones4) + aes(x="Date", y="Price") + geom_line(aes(color="type"))
)
p

4 Histogram

A histogram visualizes the distribution of a single continuous variable.

Code

p = (
    ggplot(data=tips) + aes(x="tip") + geom_histogram()
)
p

4.1 Color by Group

You can create separate histograms for different groups to compare their distributions.

Code

p = (
    ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge')
)
p

5 Bar Chart

A bar chart is used to display categorical data with bars of lengths proportional to the values they represent.

Code

p = (
    ggplot(data=tips) + aes(x='sex', y='tip', fill="sex") + geom_col()
)
p

6 Box Plot

A box plot shows the distribution of a dataset, including the median, quartiles, and potential outliers.

Code

p = (
    ggplot(data=tips) + aes(x='day', y='tip', fill="day") + geom_boxplot()
)
p

6.1 Color by Group

You can group box plots by a categorical variable to compare distributions.

Code

p = (
    ggplot(data=tips) + aes(x='day', y='tip', fill="sex") + geom_boxplot()
)
p

7 Strip Plot

A strip plot is a scatter plot for a categorical variable, showing the distribution of data points.

Code

p = (
    ggplot(data=tips) + aes(x='day', y='tip') + geom_jitter(width=0.1)
)
p

7.1 Color by Group

You can color the points in a strip plot to distinguish between different groups.

Code

p = (
    ggplot(data=tips) + aes(x='day', y='tip', fill="sex") + geom_jitter(position=position_jitterdodge())
)
p

8 Facet Plot

Facet plots create subplots for different subsets of the data, allowing for easy comparison.

Code

p = (
    ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point(aes(color="sex"))
    + facet_wrap("day")
)
p

8.1 Three Plots per Row

You can control the layout of the facet grid by specifying the number of columns.

Code

p = (
    ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point(aes(color="sex"))
    + facet_wrap("day", ncol=3)
)
p

9 Customizing Title, Size, and Axis Names

9.1 Add Title

You can add a title to your plot to provide context.

Code

p = (
    ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + ggtitle("Tip by Sex")
)
p

9.2 Adjust Size

The size of the plot can be customized to fit your needs.

Code

p = (
    ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + ggtitle("Tip by Sex") + theme(figure_size=(4, 3))
)
p

9.3 Change Axis Names

You can change the labels of the x and y axes for clarity.

Code

p = (
    ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point() + scale_x_continuous(name="New X Name") + scale_y_continuous(name="New Y Name")
)
p

10 Applying Themes

Plotnine offers several built-in themes to change the appearance of your plots. You can find all themes here.

This theme gives your plots a hand-drawn, xkcd-style look.

Code

p = (
    ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + theme_xkcd()
)
p

This theme mimics the style of the FiveThirtyEight website.

Code

p = (
    ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + theme_538()
)
p

This theme uses a dark background for a modern look.

Code

p = (
    ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + theme_dark()
)
p

11 Saving a Plot

You can save your plot to a file in various formats.

Code

p = (
    ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + theme_dark()
)
p.save(filename='test3.png')

12 Animated Plot

Plotnine can also be used to create animated plots.

Code

from plotnine.animation import PlotnineAnimation

Code

new_data = dowjones4.sample(50, random_state=42)
new_data = new_data.sort_values(by=['Date'], ascending=True)

def plot(x):
    df2 = dowjones4.query('Date <= @x')
    p = (
        ggplot(df2, aes(x='Date', y='Price'))
        + geom_line(aes(color="type"))
        + theme(subplots_adjust={'right': 0.85})
    )
    return p

plots = (plot(i) for i in new_data["Date"])

Code

from matplotlib import rc

rc("animation", html="html5")

animation = PlotnineAnimation(plots, interval=300, repeat_delay=500)
animation

13 Reference

Plotnine Official Documentation

--- title: "Plotnine Chart" execute: warning: false error: false format: html: toc: true toc-location: right code-fold: show code-tools: true number-sections: true code-block-bg: true code-block-border-left: "#31BAE9" --- ![](images/logo-540.png){width="300"} # Introduction to Plotnine Plotnine is a Python implementation of the Grammar of Graphics, inspired by R's `ggplot2`. It allows you to create complex and beautiful plots by adding layers of data, aesthetics, and geoms. This document provides a guide to creating various plots with Plotnine. ```{python} import plotnine print(plotnine.__version__) ``` ```{python} from plotnine import * import seaborn as sns import pandas as pd # Load an example dataset tips = sns.load_dataset("tips") tips.head() ``` # Scatter Plot A scatter plot is used to visualize the relationship between two continuous variables. ```{python} p = ( ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point() ) p ``` ## Color by Group You can color the points based on a categorical variable to see how the relationship varies across different groups. ```{python} p = ( ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point(aes(color="sex")) ) p ``` ## Size by Group The size of the points can be mapped to a variable to add another dimension to the plot. ```{python} p = ( ggplot(data=tips) + aes(x="tip", y="total_bill", size="size") + geom_point() ) p ``` # Line Plot A line plot is suitable for showing the trend of a variable over time. ```{python} dowjones = sns.load_dataset("dowjones") dowjones.head() ``` ```{python} p = ( ggplot(data=dowjones) + aes(x="Date", y="Price") + geom_line() ) p ``` ## Line Plot with Dots You can also add markers to the line plot to highlight the data points. ```{python} p = ( ggplot(data=dowjones) + aes(x="Date", y="Price") + geom_line() + geom_point() ) p ``` ```{python} #| code-fold: true import random # Create datasets for comparison dowjones2 = dowjones.copy() dowjones2['type'] = 'old' dowjones3 = dowjones.copy() dowjones3['Price'] = dowjones3['Price'] + random.random() * 200 dowjones3['type'] = 'new' dowjones4 = pd.concat([dowjones2, dowjones3], ignore_index=True) dowjones4 = dowjones4.sort_values('Date').reset_index(drop=True) ``` ```{python} dowjones4.head() ``` ## Color by Group You can plot multiple lines on the same plot to compare different groups. ```{python} p = ( ggplot(data=dowjones4) + aes(x="Date", y="Price") + geom_line(aes(color="type")) ) p ``` # Histogram A histogram visualizes the distribution of a single continuous variable. ```{python} p = ( ggplot(data=tips) + aes(x="tip") + geom_histogram() ) p ``` ## Color by Group You can create separate histograms for different groups to compare their distributions. ```{python} p = ( ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') ) p ``` # Bar Chart A bar chart is used to display categorical data with bars of lengths proportional to the values they represent. ```{python} p = ( ggplot(data=tips) + aes(x='sex', y='tip', fill="sex") + geom_col() ) p ``` # Box Plot A box plot shows the distribution of a dataset, including the median, quartiles, and potential outliers. ```{python} p = ( ggplot(data=tips) + aes(x='day', y='tip', fill="day") + geom_boxplot() ) p ``` ## Color by Group You can group box plots by a categorical variable to compare distributions. ```{python} p = ( ggplot(data=tips) + aes(x='day', y='tip', fill="sex") + geom_boxplot() ) p ``` # Strip Plot A strip plot is a scatter plot for a categorical variable, showing the distribution of data points. ```{python} p = ( ggplot(data=tips) + aes(x='day', y='tip') + geom_jitter(width=0.1) ) p ``` ## Color by Group You can color the points in a strip plot to distinguish between different groups. ```{python} p = ( ggplot(data=tips) + aes(x='day', y='tip', fill="sex") + geom_jitter(position=position_jitterdodge()) ) p ``` # Facet Plot Facet plots create subplots for different subsets of the data, allowing for easy comparison. ```{python} p = ( ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point(aes(color="sex")) + facet_wrap("day") ) p ``` ## Three Plots per Row You can control the layout of the facet grid by specifying the number of columns. ```{python} p = ( ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point(aes(color="sex")) + facet_wrap("day", ncol=3) ) p ``` # Customizing Title, Size, and Axis Names ## Add Title You can add a title to your plot to provide context. ```{python} p = ( ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + ggtitle("Tip by Sex") ) p ``` ## Adjust Size The size of the plot can be customized to fit your needs. ```{python} p = ( ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + ggtitle("Tip by Sex") + theme(figure_size=(4, 3)) ) p ``` ## Change Axis Names You can change the labels of the x and y axes for clarity. ```{python} p = ( ggplot(data=tips) + aes(x="tip", y="total_bill") + geom_point() + scale_x_continuous(name="New X Name") + scale_y_continuous(name="New Y Name") ) p ``` # Applying Themes Plotnine offers several built-in themes to change the appearance of your plots. You can find all themes [here](https://github.com/has2k1/plotnine/tree/main/plotnine/themes). ::: {.panel-tabset .nav-pills} ## xkcd Theme This theme gives your plots a hand-drawn, xkcd-style look. ```{python} p = ( ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + theme_xkcd() ) p ``` ## theme_538 This theme mimics the style of the FiveThirtyEight website. ```{python} p = ( ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + theme_538() ) p ``` ## theme_dark This theme uses a dark background for a modern look. ```{python} p = ( ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + theme_dark() ) p ``` ::: # Saving a Plot You can save your plot to a file in various formats. ```{python} p = ( ggplot(data=tips) + aes(x="tip", fill='sex') + geom_histogram(position='dodge') + theme_dark() ) p.save(filename='test3.png') ``` # Animated Plot Plotnine can also be used to create animated plots. ```{python} from plotnine.animation import PlotnineAnimation ``` ```{python} #| output: false new_data = dowjones4.sample(50, random_state=42) new_data = new_data.sort_values(by=['Date'], ascending=True) def plot(x): df2 = dowjones4.query('Date <= @x') p = ( ggplot(df2, aes(x='Date', y='Price')) + geom_line(aes(color="type")) + theme(subplots_adjust={'right': 0.85}) ) return p plots = (plot(i) for i in new_data["Date"]) ``` ```{python} from matplotlib import rc rc("animation", html="html5") animation = PlotnineAnimation(plots, interval=300, repeat_delay=500) animation ``` # Reference - [Plotnine Official Documentation](https://plotnine.org/)