In this article, we will discuss how to create a table with Matplotlib in Python.
Method 1: Create a Table using matplotlib.plyplot.table() function
In this example, we create a database of average scores of subjects for 5 consecutive years. We import packages and plotline plots for each consecutive year. A table can be added to Axes using matplotlib.pyplot.table(). We can plot the table by taking columns on the x-axis and the y-axis for values.
Syntax
matplotlib.pyplot.table(cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None, edges='closed', **kwargs)
# importing packages and modules
import numpy as np
import matplotlib.pyplot as plt
# average marks data for 5 consecutive years
data = [[98, 95, 93, 96, 97],
[97, 92, 95, 94, 96],
[98, 95, 93, 95, 94],
[96, 94, 94, 92, 95],
[95, 90, 91, 94, 98]]
columns = ('English', 'Maths', 'Physics',
'Chemistry', 'Biology')
rows = ['%d academic year' % x for x in (2015, 2016, 2017, 2018, 2019)]
# Get some pastel shades for the colors
colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))
n_rows = len(data)
index = np.arange(len(columns)) + 0.3
bar_width = 0.4
# Initialize the vertical-offset for
# the line plots.
y_offset = np.zeros(len(columns))
# Plot line plots and create text labels
# for the table
cell_text = []
for row in range(n_rows):
plt.plot(index, data[row], color=colors[row])
y_offset = data[row]
cell_text.append([x for x in y_offset])
# Reverse colors and text labels to display
# the last value at the top.
colors = colors[::-1]
cell_text.reverse()
# Add a table at the bottom of the axes
the_table = plt.table(cellText=cell_text,
rowLabels=rows,
rowColours=colors,
colLabels=columns,
loc='bottom')
# Adjust layout to make room for the table:
plt.subplots_adjust(left=0.2, bottom=0.2)
plt.ylabel("marks".format(value_increment))
plt.xticks([])
plt.title('average marks in each consecutive year')
plt.show()
Output:
Method 2: Create a Table using pandas.plotting.table() method
The code starts with importing packages, we load the iris dataset from sklearn.datasets, next step is grouping data to form a 2-d dataset. after that, we plot bar plots for each species and create a table using the pandas.plotting.table() method.
# import packages and modules
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from pandas.plotting import table
# loading the iris dataset
iris = load_iris()
# creating a 2 dimensional dataframe out of the given data
iris_df = pd.DataFrame(data=np.c_[iris['data'],
iris['target']],
columns=iris['feature_names'] + ['target'])
# grouping data and calculating average
grouped_dataframe = iris_df.groupby('target').mean().round(1)
grouped_dataframe['species_name'] = ['setosa', 'versicolor', 'virginica']
# plotting data
ax = plt.subplot(211)
plt.title("Iris Dataset Average by Plant Type")
plt.ylabel('Centimeters (cm)')
ticks = [4, 8, 12, 16]
a = [x - 1 for x in ticks]
b = [x + 1 for x in ticks]
plt.xticks([])
plt.bar(a, grouped_dataframe.loc[0].values.tolist()[
:-1], width=1, label='setosa')
plt.bar(ticks, grouped_dataframe.loc[1].values.tolist()[
:-1], width=1, label='versicolor')
plt.bar(b, grouped_dataframe.loc[2].values.tolist()[
:-1], width=1, label='virginica')
plt.legend()
plt.figure(figsize=(12, 8))
table(ax, grouped_dataframe.drop(['species_name'], axis=1), loc='bottom')
Output:
The grouped dataframe looks as:
The plot looks as:
