Plotly Chart

1 Introduction to Plotly

Plotly’s Python graphing library makes it easy to create interactive, publication-quality graphs. This document demonstrates how to create various plots, including line plots, scatter plots, bar charts, box plots, histograms, and more.

Code

library(reticulate)
py_require(c('plotly','pandas','seaborn','psutil','requests'))

Code

import plotly
print(plotly.__version__)

6.2.0

Code

import seaborn as sns
import plotly.express as px
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

fig = px.scatter(tips, x="tip", y="total_bill")
fig.show()

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

fig = px.scatter(tips, x="tip", y="total_bill", color="sex")
fig.show()

2.2 Size by Group

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

Code

fig = px.scatter(tips, x="tip", y="total_bill", size="size")
fig.show()

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

import plotly.express as px
fig = px.line(dowjones, x="Date", y="Price")
fig.show()

3.1 Line Plot with Dots

You can also add markers to the line plot to highlight the data points. ::: {.cell}

Code

import plotly.express as px
fig = px.line(dowjones, x="Date", y="Price", markers=True)
fig.show()

:::

3.2 Color by Group

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

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  202.637115  new
2 1915-01-01  204.187115  new
3 1915-01-01   56.550000  old
4 1915-02-01   56.000000  old

Code

import plotly.express as px
fig = px.line(dowjones4, x="Date", y="Price", color='type')
fig.show()

4 Histogram

A histogram visualizes the distribution of a single continuous variable.

Code

fig = px.histogram(tips, x="total_bill")
fig.show()

4.1 Color by Group

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

Code

fig = px.histogram(tips, x="total_bill", color='sex', barmode='group')
fig.show()

5 Bar Chart

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

Code

fig = px.bar(tips, x='sex', y='total_bill', color='sex')
fig.show()

6 Box Plot

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

Code

fig = px.box(tips, y="total_bill")
fig.show()

6.1 Color by Group

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

Code

fig = px.box(tips, y="total_bill", x='sex', color='sex')
fig.show()

7 Strip Plot

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

Code

fig = px.strip(tips, x="day", y="total_bill")
fig.show()

7.1 Color by Group

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

Code

fig = px.strip(tips, x="day", y="total_bill", color='sex')
fig.show()

8 Facet Plot

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

Code

fig = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="day")
fig.show()

8.1 Three Plots per Row

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

Code

fig = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="day", facet_col_wrap=3)
fig.show()

9 Customizing Title, Size, and Axis Names

9.1 Add Title

You can add a title to your plot and center it.

Code

fig = px.scatter(tips, x="tip", y="total_bill", title="Total Bill Title").update_layout(title_x=0.5)
fig.show()

9.2 Adjust Size

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

Code

fig = px.scatter(tips, x="tip", y="total_bill")
fig.update_layout(
    autosize=False,
    width=200,
    height=200
)

Code

fig.show()

9.3 Change Axis Names

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

Code

fig = px.scatter(tips, x="tip", y="total_bill",
                 labels={
                     "tip": "New X Label",
                     "total_bill": "New Y Label"
                 })
fig.show()

10 Applying Themes

Plotly offers several built-in themes to change the appearance of your plots.

This theme mimics the style of the popular ggplot2 library in R.

Code

fig = px.scatter(tips, x="tip", y="total_bill", template="ggplot2")
fig.show()

This theme applies a style similar to the Seaborn library.

Code

fig = px.scatter(tips, x="tip", y="total_bill", template="seaborn")
fig.show()

This theme uses a dark background for a modern look.

Code

fig = px.scatter(tips, x="tip", y="total_bill", template="plotly_dark")
fig.show()

11 Saving a Plot

To save a plot, you may need to install the kaleido package: pip install -U kaleido.

Code

df = px.data.gapminder().query("continent=='Oceania'")
fig = px.line(df, x="year", y="lifeExp", title='Life expectancy in Canada')

12 Animated Plot

Plotly supports animations for many chart types, though smooth transitions are best supported for scatter and bar plots. More information can be found here.

Code

import plotly.express as px
df = px.data.gapminder()
px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country",
           size="pop", color="continent", hover_name="country",
           log_x=True, size_max=55, range_x=[100, 100000], range_y=[25, 90])

13 Reference

Plotly Python Library

--- title: "Plotly 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/Plotly-logo.png){width="650"} # Introduction to Plotly Plotly's Python graphing library makes it easy to create interactive, publication-quality graphs. This document demonstrates how to create various plots, including line plots, scatter plots, bar charts, box plots, histograms, and more. ```{r} library(reticulate) py_require(c('plotly','pandas','seaborn','psutil','requests')) ``` ```{python} import plotly print(plotly.__version__) ``` ```{python} import seaborn as sns import plotly.express as px 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} fig = px.scatter(tips, x="tip", y="total_bill") fig.show() ``` ## Color by Group You can color the points based on a categorical variable to see how the relationship varies across different groups. ```{python} fig = px.scatter(tips, x="tip", y="total_bill", color="sex") fig.show() ``` ## Size by Group The size of the points can be mapped to a variable to add another dimension to the plot. ```{python} fig = px.scatter(tips, x="tip", y="total_bill", size="size") fig.show() ``` # 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} import plotly.express as px fig = px.line(dowjones, x="Date", y="Price") fig.show() ``` ## Line Plot with Dots You can also add markers to the line plot to highlight the data points. ```{python} import plotly.express as px fig = px.line(dowjones, x="Date", y="Price", markers=True) fig.show() ``` ## Color by Group You can plot multiple lines on the same plot to compare different groups. ```{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() ``` ```{python} import plotly.express as px fig = px.line(dowjones4, x="Date", y="Price", color='type') fig.show() ``` # Histogram A histogram visualizes the distribution of a single continuous variable. ```{python} fig = px.histogram(tips, x="total_bill") fig.show() ``` ## Color by Group You can create separate histograms for different groups to compare their distributions. ```{python} fig = px.histogram(tips, x="total_bill", color='sex', barmode='group') fig.show() ``` # Bar Chart A bar chart is used to display categorical data with bars of lengths proportional to the values they represent. ```{python} fig = px.bar(tips, x='sex', y='total_bill', color='sex') fig.show() ``` # Box Plot A box plot shows the distribution of a dataset, including the median, quartiles, and potential outliers. ```{python} fig = px.box(tips, y="total_bill") fig.show() ``` ## Color by Group You can group box plots by a categorical variable to compare distributions. ```{python} fig = px.box(tips, y="total_bill", x='sex', color='sex') fig.show() ``` # Strip Plot A strip plot is a scatter plot for a categorical variable, showing the distribution of data points. ```{python} fig = px.strip(tips, x="day", y="total_bill") fig.show() ``` ## Color by Group You can color the points in a strip plot to distinguish between different groups. ```{python} fig = px.strip(tips, x="day", y="total_bill", color='sex') fig.show() ``` # Facet Plot Facet plots create subplots for different subsets of the data, allowing for easy comparison. ```{python} fig = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="day") fig.show() ``` ## Three Plots per Row You can control the layout of the facet grid by specifying the number of columns to wrap. ```{python} fig = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="day", facet_col_wrap=3) fig.show() ``` # Customizing Title, Size, and Axis Names ## Add Title You can add a title to your plot and center it. ```{python} fig = px.scatter(tips, x="tip", y="total_bill", title="Total Bill Title").update_layout(title_x=0.5) fig.show() ``` ## Adjust Size The size of the plot can be customized to fit your needs. ```{python} fig = px.scatter(tips, x="tip", y="total_bill") fig.update_layout( autosize=False, width=200, height=200 ) fig.show() ``` ## Change Axis Names You can change the labels of the x and y axes for clarity. ```{python} fig = px.scatter(tips, x="tip", y="total_bill", labels={ "tip": "New X Label", "total_bill": "New Y Label" }) fig.show() ``` # Applying Themes Plotly offers several built-in themes to change the appearance of your plots. ::: {.panel-tabset .nav-pills} ## ggplot2 Theme This theme mimics the style of the popular `ggplot2` library in R. ```{python} fig = px.scatter(tips, x="tip", y="total_bill", template="ggplot2") fig.show() ``` ## Seaborn Theme This theme applies a style similar to the Seaborn library. ```{python} fig = px.scatter(tips, x="tip", y="total_bill", template="seaborn") fig.show() ``` ## Plotly Dark Theme This theme uses a dark background for a modern look. ```{python} fig = px.scatter(tips, x="tip", y="total_bill", template="plotly_dark") fig.show() ``` ::: # Saving a Plot To save a plot, you may need to install the `kaleido` package: `pip install -U kaleido`. ```{python} df = px.data.gapminder().query("continent=='Oceania'") fig = px.line(df, x="year", y="lifeExp", title='Life expectancy in Canada') ``` # Animated Plot Plotly supports animations for many chart types, though smooth transitions are best supported for scatter and bar plots. More information can be found [here](https://plotly.com/python/animations/). ```{python} import plotly.express as px df = px.data.gapminder() px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country", size="pop", color="continent", hover_name="country", log_x=True, size_max=55, range_x=[100, 100000], range_y=[25, 90]) ``` # Reference - [Plotly Python Library](https://plotly.com/python/)