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.
Python3
# 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:
How to Create a Table with Matplotlib?
In this article, we will discuss how to create a table with Matplotlib in Python.