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Bar Plots in Plotly
A bar plot or a bar chart is a type of data visualisation that represents data in the form of rectangular bars, with lengths or heights proportional to the values and data which they represent. The bar plots can be plotted both vertically and horizontally.
It is one of the most widely used type of data visualisation as it is easy to interpret and is pleasing to the eyes.
Plotly is a very powerful library for creating modern visualizations and it provides a very easy and intuitive method to create highly customized bar plots.
Prerequisites
Before creating bar plots in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system.
Introduction
There are various ways to create bar plots in plotly. One of the prominent and easiest one is using plotly.express. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. On the other hand you can also use plotly.graph_objects to create various plots.
Here, we'll be using plotly.express to create the bar plots. Also we'll be converting our datasets into pandas DataFrames which makes it extremely convenient to create plots.
Also, note that when you execute the codes in a simple python file, the output plot will be shown in your browser, rather than a pop-up window like in matplotlib. If you do not want that, it is recommended to create the plots in a notebook (like jupyter). For this, install an additional library nbformat. This way you can see the output on the notebook itself, and can also render its format to png, jpg, etc.
Creating a simple bar plot using plotly.express.bar
With plotly.express.bar, each row of the DataFrame is represented as a rectangular mark.
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Years', y='Number of Cars sold')
# Showing plot
fig.show()
Here, we are first creating the dataset and converting it into Pandas DataFrames using dictionaries, with its keys being DataFrame columns. Next, we are plotting the bar chart by using px.bar. In the x and y parameters, we have to specify a column name in the DataFrame.
Note: When you generate the image using above code, it will show you an interactive plot, if you want image, you can download it from their itself.
Customizing Bar Plots
Adding title to the graph
Let us create an imaginary graph of number of cars sold in a various years. Simply pass the title of your graph as a parameter in px.bar.
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Years', y='Number of Cars sold',
title='Number of cars sold in various years')
# Showing plot
fig.show()
Adding bar colors and legends
To add different colors to different bars, simply pass the column name of the x-axis or a custom column which groups different bars in color parameter.
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Years', y='Number of Cars sold',
title='Number of cars sold in various years',
color='Years')
# Showing plot
fig.show()
Now, let us consider our previous example of number of cars sold in various years and suppose that we want to add different colors to the bars from different centuries and respective legends for better interpretation.
The easiest way to achieve this is to add a new column to the dataframe and then pass it to the color parameter.
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
# Creating the relevant colors dataset
colors = ['1900s','1900s','2000s','2000s','2000s']
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Years', y='Number of Cars sold',
title='Number of cars sold in various years',
color='Century')
# Showing plot
fig.show()
Adding labels to bars
We may want to add labels to bars representing their absolute (or truncated) values for instant and accurate reading. This can be achieved by setting text_auto parameter to True. If you want custom text then you can pass a column name to the text parameter.
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
colors = ['1900s','1900s','2000s','2000s','2000s']
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Years', y='Number of Cars sold',
title='Number of cars sold in various years',
color='Century',
text_auto=True)
# Showing plot
fig.show()
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
colors = ['1900s','1900s','2000s','2000s','2000s']
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Years', y='Number of Cars sold',
title='Number of cars sold in various years',
color='Century',
text='Century')
# Showing plot
fig.show()
You can also change the features of text (or any other element of your plot) using fig.update_traces.
Here, we are changing the position of text to position it outside the bars.
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
colors = ['1900s','1900s','2000s','2000s','2000s']
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Years', y='Number of Cars sold',
title='Number of cars sold in various years',
color='Century',
text_auto=True)
# Updating bar text properties
fig.update_traces(textposition="outside", cliponaxis=False)
# Showing plot
fig.show()
Rounded Bars
You can create rounded by specifying the radius value to barcornerradius in fig.update_layout.
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
colors = ['1900s','1900s','2000s','2000s','2000s']
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Years', y='Number of Cars sold',
title='Number of cars sold in various years',
color='Century',
text_auto=True)
# Updating bar text properties
fig.update_traces(textposition="outside", cliponaxis=False)
# Updating figure layout
fig.update_layout({
'plot_bgcolor': 'rgba(255, 255, 255, 1)',
'paper_bgcolor': 'rgba(255, 255, 255, 1)',
'barcornerradius': 15
})
# Showing plot
fig.show()
Horizontal Bar Plot
To create a horizontal bar plot, you just have to interchange your x and y DataFrame columns. Plotly takes care of the rest!
import plotly.express as px
import pandas as pd
# Creating dataset
years = ['1998', '1999', '2000', '2001', '2002']
num_of_cars_sold = [200, 300, 500, 700, 1000]
colors = ['1900s','1900s','2000s','2000s','2000s']
# Converting dataset to pandas DataFrame
dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
df = pd.DataFrame(dataset)
# Creating bar plot
fig = px.bar(df, x='Number of Cars sold', y='Years',
title='Number of cars sold in various years',
color='Century',
text_auto=True)
# Updating bar text properties
fig.update_traces(textposition="outside", cliponaxis=False)
# Updating figure layout
fig.update_layout({
'barcornerradius': 30
})
# Showing plot
fig.show()
Plotting Long Format and Wide Format Data
Long-form data has one row per observation, and one column per variable. This is suitable for storing and displaying multivariate data i.e. with dimension greater than 2.
# Plotting long format data
import plotly.express as px
# Long format dataset
long_df = px.data.medals_long()
# Creating Bar Plot
fig = px.bar(long_df, x="nation", y="count", color="medal", title="Long-Form Input")
# Showing Plot
fig.show()
print(long_df)
# Output
nation medal count
0 South Korea gold 24
1 China gold 10
2 Canada gold 9
3 South Korea silver 13
4 China silver 15
5 Canada silver 12
6 South Korea bronze 11
7 China bronze 8
8 Canada bronze 12
Wide-form data has one row per value of one of the first variable, and one column per value of the second variable. This is suitable for storing and displaying 2-dimensional data.
# Plotting wide format data
import plotly.express as px
# Wide format dataset
wide_df = px.data.medals_wide()
# Creating Bar Plot
fig = px.bar(wide_df, x="nation", y=["gold", "silver", "bronze"], title="Wide-Form Input")
# Showing Plot
fig.show()
print(wide_df)
# Output
nation gold silver bronze
0 South Korea 24 13 11
1 China 10 15 8
2 Canada 9 12 12