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Mastering Table Creation in Python’s Matplotlib: A Beginner’s Guide

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. Method 2: Creating Tables from Custom Values

Creating tables in Matplotlib is a useful skill, especially when working with data in Python.

In the previous section, we have seen how to create tables from pandas DataFrames, which provide a convenient way to store and manipulate data. However, in some cases, we may need to create tables from custom values that are not in a DataFrame format.

In this section, we will show you how to create tables in Matplotlib using custom values. Step 1: Import the Required Libraries

We begin by importing the required libraries, Matplotlib and NumPy. Matplotlib is an open-source plotting library that allows us to create a wide variety of graphs and charts, including tables.

NumPy is a popular numerical computing library in Python that provides a convenient interface for handling arrays. import matplotlib.pyplot as plt

import numpy as np

Step 2: Create Custom Data

We will demonstrate how to create a table from custom values using NumPy. In this example, we will create a new two-dimensional array called data that contains our custom values. data = np.array([[10, 20, 30],

[40, 50, 60],

[70, 80, 90]])

The array data contains three rows and three columns.

We can use this data to create our table. Step 3: Create the Table

Now that we have our data, we can proceed to create the table using Matplotlib.

fig, ax = plt.subplots()

# Hide axes

ax.axis(‘off’)

# Create a table

table = ax.table(cellText=data, loc=’center’)

# Adjust font size

table.auto_set_font_size(False)

table.set_fontsize(14)

# Set column width

table.auto_set_column_width(col=list(range(len(data[0]))))

# Set title

ax.set_title(“Custom Table Example”)

# Display the table

plt.show()

In the above code, we first create a figure and axis object using the subplots function. We then set the axis to be invisible as we want our table to be the only visible element in the plot.

We create a table object by calling the table function of Matplotlib and passing in our data. We then adjust the font size of the table and set the column width to be uniform.

Finally, we set the title and display the table using the show function.

Additional Resources

In addition to the methods discussed in this article, there are many other ways to create tables in Matplotlib. For example, we can customize the appearance of tables by manipulating various properties such as font size, style, and color.

Furthermore, we can include cell colors and borders to enhance the readability of our tables. To learn more about creating tables in Matplotlib, we recommend the following resources:

1.

The Matplotlib documentation provides detailed information on creating tables. It contains many examples and code snippets that can help you get started quickly.

2. The DataCamp website provides a comprehensive tutorial on creating tables in Matplotlib using pandas DataFrames.

The tutorial covers various topics such as formatting tables, adding colors and borders, and creating complex tables. 3.

The Python Graph Gallery is another excellent resource for learning how to create tables in Matplotlib. The website provides many examples of table visualizations, along with the code and data required to reproduce them.

By exploring these resources and experimenting with the code, you can develop a deeper understanding of how to create tables in Matplotlib and use them effectively in your data visualizations. 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 discussed the importance of tables in data visualization and provided examples and code snippets to help readers get started.

Additionally, we provided additional resources that can help readers further develop their skills in creating tables in Matplotlib. Creating tables in Matplotlib is a powerful way to visualize data and share insights effectively, and by mastering these techniques, readers will be able to create professional-looking and informative visualizations.

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