Creating Tables in Python using Matplotlib: A Beginner’s Guide
Tables are an essential aspect of data visualization, and Matplotlib, an open-source data visualization library for Python, provides an excellent platform to create them. There are various ways to create tables using Matplotlib, but in this article, we’ll focus on two: creating tables from pandas DataFrames and creating tables from custom values.
Method 1: Creating Tables from pandas DataFrames
Pandas is an open-source data manipulation library for Python that is widely used in data science. DataFrame is one of the fundamental data structures in Pandas, and it is essentially a two-dimensional table with rows and columns.
By using Matplotlib’s table function, we can easily create tables that are based on pandas DataFrames. Here’s how we can do it:
Step 1: Import the Required Libraries
The first step is to import the necessary libraries.
We’ll need Matplotlib, NumPy, and Pandas.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
Step 2: Create a Pandas DataFrame
Let’s create a simple pandas DataFrame to use as an example.
data = [[10, 20], [30, 40], [50, 60]]
df = pd.DataFrame(data, columns=["Column 1", "Column 2"])
Step 3: Create the Table
Now we can use the table function of Matplotlib to create the table.
fig, ax = plt.subplots(1, 1)
ax.axis("off")
table_data = []
for i in range(len(df)):
table_data.append(list(df.iloc[i]))
table = ax.table(cellText=table_data, colLabels=df.columns, loc='center')
ax.set_title("Example Table")
plt.show()
In the above code, we first create a figure and axis object using the subplots function. We pass in parameters to remove the axis as we do not need them in the table.
We then create an empty list, table_data, that we’ll use to store the rows of the DataFrame. We then loop through the DataFrame using its index and append each row in the table_data list.
Finally, we use the table function of Matplotlib to plot the table by passing in our data, column labels, and the location.
Method 2: Creating Tables from Custom Values
Sometimes, we may need to create a table from custom values instead of a DataFrame.
In such a situation, we can use NumPy to create a two-dimensional array of values and then use Matplotlib’s table function to create the table. Here’s how we can do it:
Step 1: Import the Required Libraries
As in the previous method, we’ll need Matplotlib and NumPy.
import matplotlib.pyplot as plt
import numpy as np
Step 2: Create Custom Data
Let’s create a NumPy array to use as an example.
data = np.array([[1, 2], [3, 4], [5, 6]])
Step 3: Create the Table
We can now use the table function of Matplotlib to create the table.
fig, ax = plt.subplots(1, 1)
ax.axis("off")
table = ax.table(cellText=data, colLabels=["Column 1", "Column 2"], loc='center')
ax.set_title("Example Table")
plt.show()
In the above code, we first create a figure and axis object using the subplots function. We pass in parameters to remove the axis as we do not need them in the table.
We then use the table function of Matplotlib to plot the table by passing in the data and column labels.
Conclusion
In this article, we explored two methods of creating tables in Matplotlib: creating tables from pandas DataFrames and creating tables from custom values using NumPy. We also showed examples of how to use these methods to plot tables. Understanding how to create tables using Matplotlib is essential in data visualization, and these methods provide a simple yet powerful way of doing so.
With this knowledge, you can now explore other features of Matplotlib to create beautiful and informative visualizations.