Python data visualization gives you a wealth of benefits. It enables you to quickly understand complex relationships between data. As a result, you can easily identify patterns and trends. You can convey the information to others through different charts and graphs. Also, Python data visualization tools help you to grasp complex data. More importantly, they enable you to effectively make strategic business decisions. That’s why you need to learn the way of visualizing the data with Python.
Python makes it very easy to represent complex information. It supports a variety of libraries to help you get the job done with just a few lines of code. In this post, you will find all the details.
Table of Contents
What is data visualization in Python?
Data visualization in Python refers to the graphical representation of complex information through visual aids, like charts, plots, and infographics. It allows you to effectively visualize data relationships. Hence, you can easily uncover key insights. You can use the information to make data-driven decisions and grow your business.
How can you perform Python data visualization?
Python offers several graphing libraries to help you effortlessly visualize data. The most popular ones are:
- Matplotlib: A comprehensive library for creating interactive visualizations in Python
- Pandas: A Python library used for analyzing data.
- Seaborn: A data visualization library built on top of matplotlib
- Plotnine: An implementation of a grammar of graphics in Python, based on ggplot2.
- Plotly: An open-source plotting library that supports over 40 unique chart types
In this tutorial, we will use Matplotlib, Pandas, and Seaborn to create basic data visualizations in Python. Let’s get started.
Read: Why Is Python Best for Programming Data Visualization?
What are the prerequisites for Python data visualizations?
- Python: You need to have it installed on your PC.
- Jupyter Notebook: Install the latest version on your PC.
Which datasets do I need for Python data visualization?
The very first step is to download the required datasets. In this tutorial, we will be using two different datasets: Iris [1] and Wine Reviews [2]. They are available for free download.
Once you are done with the download, put the two files in the root folder of your project. The file structure will look like this:
Then you have to create a new file, called “Introduction-to-Data-Visualization-with-Python-Ehsanul,” in the same folder.
Now, open the file with a double-click.
How can I import the required libraries for Python data visualization?
The second step is to import the required libraries. We will use Matplotlib, Pandas, Pylab, and Numpy. You can import them by using these lines:
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import matplotlib.pyplot as plt import pandas as pd import pylab as pl import numpy as np %matplotlib inline |
How can I read the datasets for Python data visualization?
Next, let’s read the datasets that we have downloaded in the first step. Let’s get started with the iris.csv file. Use this line to read the first five rows of sepal_length, sepal_width, petal_length, petal_width, and class columns.
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iris = pd.read_csv('iris.csv', names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']) print(iris.head()) |
The output will look like this:
Next, let’s read the winemag-data-130k-v2.csv file. Use this line to read the first five rows.
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wine_reviews = pd.read_csv('winemag-data-130k-v2.csv', index_col=0) wine_reviews.head() |
The output will look like this:
Now, you are completely ready for creating data visualization with Python.
Read: 10 Resources That’ll Make You Better At Python Programming Software
How can I create Python data visualization with Matplotlib?
You can use Maplotlib to build beautiful data visualization. Let’s create a scatter plot, a line chart, and a bar chart.
How can I create the scatter plot?
1. First, you have to create a figure and axis.
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fig, ax = plt.subplots() |
2. Then you can scatter the sepal_length against the sepal_width with this line.
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ax.scatter(iris['sepal_length'], iris['sepal_width']) |
3. Next, you have to set the title and labels with this code:
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ax.set_title('Iris Dataset') ax.set_xlabel('sepal_length') ax.set_ylabel('sepal_width') |
You will see this output:
4. Finally, let’s make the graph meaningful by adding three different colors to the three classes. You will use red for Iris-setosa, green for Iris-versicolor, and blue for Iris-virginica. Add this code:
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# create a color dictionary colors = {'Iris-setosa':'r', 'Iris-versicolor':'g', 'Iris-virginica':'b'} # create a figure and axis fig, ax = plt.subplots() # plot each data-point for i in range(len(iris['sepal_length'])): ax.scatter(iris['sepal_length'][i], iris['sepal_width'][i],color=colors[iris['class'][i]]) # set a title and labels ax.set_title('Iris Dataset') ax.set_xlabel('sepal_length') ax.set_ylabel('sepal_width') |
You will see this output:
How can I create a line chart?
Creating a line chart is very simple. You need to utilize the plt() method. Here are the steps:
1. First, you need to get columns to plot.
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columns = iris.columns.drop(['class']) |
2. Then you have to create x_data.
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x_data = range(0, iris.shape[0]) |
3. Next, you need to create the figure and axis.
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fig, ax = plt.subplots() |
4. Then you can plot each column with this code:
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for column in columns: ax.plot(x_data, iris[column]) |
5. Finally, you need to set the title and legend with this line:
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ax.set_title('Iris Dataset') ax.legend() |
The output will look like this:
How can I create a bar chart?
You can create a bar chart by utilizing the bar method. Here are the steps:
1. First, you have to create a figure and axis.
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fig, ax = plt.subplots() |
2. Then you have to count the occurrence of each class with this line:
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data = wine_reviews['points'].value_counts() |
3. Next, you have to get x and y data
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points = data.index frequency = data.values |
4. Then you can create the bar chart by passing points and frequency into ax.bar() method.
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ax.bar(points, frequency) |
5. Finally, you have to set the title and labels.
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ax.set_title('Wine Review Scores') ax.set_xlabel('Points') ax.set_ylabel('Frequency') |
You will see this output:
How can I create Python data visualization with Seaborn?
Seaborn is another popular visualization library. It is built on top of Matplotlib. Let’s use it to create a histogram and a line chart.
How can I import Seaborn?
You can import Seaborn with this line:
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import seaborn as sns |
How can I create a histogram with Seaborn?
You just need to use this code to create the histogram:
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sns.distplot(wine_reviews['points'], bins=10, kde=False) |
You will get this output:
How can I create a line chart with Seaborn?
Simply use this line:
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sns.lineplot(data=iris.drop(['class'], axis=1)) |
You will get this output:
Read: How To Use Data Visualization Tools In Python
Should I use Python for data visualization?
Python is one of the most popular languages on the planet. It is open-source. Hence, you don’t have to spend any money to use its features and libraries. Also, you can easily connect Python to any database system. Therefore, you get flexibility. On top of that, Python is highly scalable. It can be very useful for handling and representing large amounts of data. That’s why you should strongly consider using Python for data visualization.
Does PyScripter support Jupyter Notebook?
PyScripter supports Jupyter Notebook. However, you need to install “jupiter” using “pip” on your PC. Once the installation is done, you can start using notebooks on PyScripter.
FAQ for Python Data Visualization
Which data visualization tool is best for python?
The best Python data visualization tool is Matplotlib. It is a comprehensive library for conveniently creating static, dynamic, and interactive visualizations. Matplotlib was released in 2003. Right now, it is the most popular Python plotting library on the planet.
What is meant by data visualization in Python?
Data visualization in Python refers to the practice of representing complex data in visual formats, like charts, graphs, maps, etc. It enables you to quickly comprehend key insights and effectively make important business decisions. Python offers several amazing data visualization libraries. The most popular ones are Matplotlib, Seaborn, and Pandas.
How do you visualize big data in python?
You can visualize big data in Python using different libraries, including Panda and Matlotlib. They are very easy to use. They require you to write a few lines of code to visualize complex data. As a result, you can boost your efficiency and workflow.
Is Python good for data visualization?
Python is the best programming language for visualizing data. It offers powerful graphing packages, like Matplotlib, Panda, Seaborn, etc. They enable you to effortlessly visualize complex data with just a few lines of code.
Which Python library is used for data visualization?
You can use a variety of libraries for visualizing data in Python, including Matplotlib, Seaborn, Pandas, Plotly, and Plotnine.
[1] https://archive.ics.uci.edu/ml/datasets/iris
[2] https://www.kaggle.com/datasets/zynicide/wine-reviews
