Best Data Visualization Tools to Buy in October 2025

1

Storytelling with Data: A Data Visualization Guide for Business Professionals

• MASTER STORYTELLING TECHNIQUES TO ENHANCE DATA ENGAGEMENT.
• TRANSFORM COMPLEX DATA INTO COMPELLING VISUAL NARRATIVES.
• BOOST DECISION-MAKING WITH IMPACTFUL, ACTIONABLE VISUAL INSIGHTS.

BUY & SAVE

$23.05 $41.95

Save 45%

2

Hands-On Data Visualization: Interactive Storytelling From Spreadsheets to Code

BUY & SAVE

$36.49 $65.99

Save 45%

3

Data Visualization with Microsoft Power BI: How to Design Savvy Dashboards

BUY & SAVE

$41.33 $59.99

Save 31%

4

Python Data Science Handbook: Essential Tools for Working with Data

BUY & SAVE

$44.18 $79.99

Save 45%

5

Advanced Analytics with Power BI and Excel: Learn powerful visualization and data analysis techniques using Microsoft BI tools along with Python and R (English Edition)

BUY & SAVE

$37.95

6

Good Charts Workbook: Tips, Tools, and Exercises for Making Better Data Visualizations

BUY & SAVE

$17.58 $35.00

Save 50%

7

Data Visualization with Excel Dashboards and Reports

BUY & SAVE

$23.39 $42.00

Save 44%

8

Become a Great Data Storyteller: Learn How You Can Drive Change with Data

BUY & SAVE

$24.31 $40.00

Save 39%

9

Beginning Data Science with Python and Jupyter: Use powerful tools to unlock actionable insights from data

BUY & SAVE

$14.64 $16.99

Save 14%

10

Good Charts, Updated and Expanded: The HBR Guide to Making Smarter, More Persuasive Data Visualizations

BUY & SAVE

$24.87 $35.00

Save 29%

+

ONE MORE?

To plot a histogram in matplotlib in Python, you can use the hist function from the matplotlib.pyplot module. The hist function takes in an array of data as input and bins the data into intervals to create a histogram. You can specify the number of bins, the range of values to include in the histogram, and other parameters to customize the appearance of the histogram. Once you have specified the parameters, you can call the hist function with your data array as input to display the histogram. This will generate a plot showing the distribution of your data in the form of a histogram.

How to create a cumulative histogram in matplotlib?

To create a cumulative histogram in Matplotlib, you can use the hist function with the parameter cumulative=True. Here is an example code snippet to create a cumulative histogram:

import matplotlib.pyplot as plt import numpy as np

Generate some random data

data = np.random.randn(1000)

Create a cumulative histogram

plt.hist(data, bins=30, cumulative=True, color='skyblue', edgecolor='black')

Add labels and title

plt.xlabel('Value') plt.ylabel('Frequency') plt.title('Cumulative Histogram')

Display the plot

plt.show()

This code will create a cumulative histogram of the random data using 30 bins. You can adjust the number of bins and customize the plot according to your preference.

How to create a horizontal histogram in matplotlib?

To create a horizontal histogram in matplotlib, you can follow these steps:

1. Import the necessary libraries:

import matplotlib.pyplot as plt import numpy as np

1. Create some sample data for the histogram:

data = np.random.randn(1000)

1. Create the histogram using the hist function with the orientation parameter set to 'horizontal':

plt.hist(data, orientation='horizontal')

1. Customize the histogram as needed:

plt.xlabel('Frequency') plt.ylabel('Value') plt.title('Horizontal Histogram') plt.grid(True) plt.show()

This code will generate a horizontal histogram using the sample data with frequency on the x-axis and value on the y-axis. You can customize the plot further by adjusting the labels, title, grid, and other properties as needed.

How to plot a matrix histogram in matplotlib?

To plot a matrix histogram in matplotlib, you can use the imshow function along with hist function. Here is a step-by-step guide on how to do this:

1. Import the necessary libraries:

import matplotlib.pyplot as plt import numpy as np

1. Create a random matrix:

matrix = np.random.rand(10, 10)

1. Plot the matrix as an image using imshow:

plt.imshow(matrix, cmap='viridis', aspect='auto') plt.colorbar()

1. Calculate the histogram of the matrix values:

hist, bins = np.histogram(matrix.flatten(), bins=20)

1. Plot the histogram using plt.bar:

plt.figure() plt.bar(bins[:-1], hist, width=np.diff(bins)) plt.xlabel('Value') plt.ylabel('Frequency') plt.title('Matrix Histogram') plt.show()

Running this code will display the matrix as an image with a colorbar, as well as the histogram of values in the matrix. You can customize the colormap, number of bins, and other parameters according to your needs.

What is the significance of the weights parameter in the hist() function in matplotlib?

The weights parameter in the hist() function in matplotlib allows for the weighted histogram calculation and plotting. This means that you can assign different weights to each data point, which will affect the height of the bars in the histogram. This can be useful when you have data points with different significances and want to give them different weights in the histogram representation. This can help in emphasizing certain data points over others, depending on their importance.