Introduction to plottable

Plottable is a python library for plotting nice table outputs . You can custom style, colors, add images and even more with a light python syntax!
In this post, we'll go over the most basic features of this library. For more advanced customizations, see How to customize colors in plottable and How to add an image in plottable .
Since there is 3 ways to custom your table with plottable , we'll briefly go over each one.

Libraries

First, you need to install plottable with the following command: pip install plottable . Plottable is almost the single python library made especially for creating nice output tables. According to the main author, it's inspired from the R packages gt and reactable .

We'll also need the following libraries:

pandas for creating a dataframe with our data

matplotlib for customizing the table and display it

import matplotlib.pyplot as plt
import pandas as pd
from plottable import Table

Dataset

Tables are often built from a pandas dataframe . Let's create one using the DataFrame() function.

This table is our starting point of the tutorial. Let's make it look good!

data = {'Score': [8, 6, 10, 3, 9],
        'Value': [50.42, 17.01, 42.90, 6.83, 92.06],
        'Comment': ['Nice', 'Cool', 'Fun', 'Bad', 'Okay']}
df = pd.DataFrame(data)

Most simple plottable output

Now let's see how to create the default plottable output . The syntax is quite similar to matplotlib , since we need to initiate a figure, create a Table() and display the result with show() .

# Init a figure 
fig, ax = plt.subplots(figsize=(5, 5))
# Create the Table() object
tab = Table(df)
# Display the output
plt.show()

First method: column definition

This method consists in changing the properties of interest for a given column . By specifying the name of the column to be changed, we can specify a number of parameters.

We'll also need to import another Class from plottable: ColumnDefinition

In this case, we'll:

change the title of the


               Score

column

change position and weight of the texts

from plottable import ColumnDefinition
# Init a figure 
fig, ax = plt.subplots(figsize=(5, 5))
# Define the things to change
column_definitions = [ColumnDefinition(name='Score', # name of the column to change
                                       title='Score custom', # new title for the column
                                       textprops={"ha": "left", "weight": "bold"}, # properties to apply
# Create the table object with the column definitions parameter
tab = Table(df, column_definitions=column_definitions)
# Display the output
plt.show()

Second method: keywords

If you want to apply exactly the same changes to each column, the keyword method may be just what you're looking for.

In a similar way to the previous method, we give which properties we want to change, but this time we put them directly in the Table() argument, and they will automatically apply to all columns.

In this case, we'll:

change some text properties

add a footer line

remove lines between rows

# Init a figure 
fig, ax = plt.subplots(figsize=(5, 5))
# Create the table object with the column definitions parameter
tab = Table(df, textprops={"ha": "left", "weight": "bold"}, # text properties
            row_dividers=False, # remove lines between rows
            footer_divider=True, # add a reference line at the end
# Display the output
plt.show()

Third method: accessing rows and cols

In this method, we first define the Table() , then apply any modifications we wish to the rows and columns.

# Init a figure 
fig, ax = plt.subplots(figsize=(5, 5))
# Create the table object with the column definitions parameter
tab = Table(df)
# Apply modifications to the Score col and the second (idx=1) row
tab.columns["Score"].set_facecolor('lightblue')
tab.rows[1].set_fontcolor('green')
# Display the output
plt.show()

Going further

This post explains the basic features in the plottable library.