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What is Data Visualization in Python?

In this blog, we’ll explore the basics of data visualization in Python, its benefits, and how it can help you make better decisions based on data-driven insights. So, buckle up and get ready to unleash the power of Python for data visualization!

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Introduction to Data Visualization in Python

Data visualization is an essential part of data analysis since it helps display complex data clearly and clearly. Python is a popular programming language with many data visualization packages to construct visually attractive data visualizations.

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Python Data Visualization Libraries

Python is well known for its applications in data analysis and visualization. Here are some data visualization libraries in python you can use to gain insights for given data. 


Matplotlib is a Python library for creating static, animated, and interactive visualizations. It is one of the most popular visualization libraries in Python, and it is used by data scientists, engineers, and researchers around the world. Matplotlib can be used to create a wide variety of visualizations, including line charts, bar charts, pie charts, and scatter plots. It can also be used to create more complex visualizations, such as heatmaps and 3D plots.

pip install matplotlib

Pandas Visualization

Pandas Visualization is a collection of plotting functions that are built on top of Matplotlib. It provides a high-level interface for creating visualizations from Pandas DataFrames. Pandas Visualization makes it easy to create and customize visualizations, and it also provides a number of features that are not available in Matplotlib, such as the ability to drill down into dataframes and create interactive visualizations.

pip install pandas-visualization


Seaborn is a Python visualization library that is built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. Seaborn makes it easy to create a wide variety of visualizations, including line plots, bar charts, heatmaps, and distribution plots. It also provides a number of features that are not available in Matplotlib, such as the ability to color code data and add annotations to plots.

pip install seaborn


Plotnine is a Python visualization library that is based on the grammar of graphics. The grammar of graphics is a declarative language for describing statistical graphics. plotnine provides a parser that can translate grammar of graphics descriptions into Matplotlib code. This makes it possible to create complex visualizations with a concise and easy-to-read syntax.

pip install plotnine


Plotly is a Python visualization library that can be used to create interactive visualizations. Plotly visualizations can be embedded in web pages or shared on social media. Plotly provides a wide variety of visualization types, including line charts, bar charts, pie charts, and scatter plots. It also provides a number of features that are not available in other visualization libraries, such as the ability to create animations and 3D plots.

pip install plotly

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Data Visualization in Python using Matplotlib

Matplotlib is a popular Python data visualization package that allows you to generate a wide range of charts and plots. Here are the fundamental steps for creating a simple visualization with Matplotlib.

1. Import the necessary libraries and modules:

import matplotlib.pyplot as plt
import numpy as np

2. Create some data to plot:

x = np.linspace(0, 10, 100)
y = np.sin(x)

3. Create a figure and an axis object:

fig, ax = plt.subplots()

4. Plot the data:

ax.plot(x, y)

5. Customize the plot with titles, labels, and other features:

ax.set_title("Sine Wave")

6. Show the plot:

This code facilitates the generation of a straightforward sine wave plot.  Additionally, Matplotlib enables the creation of various plot types, including bar charts, scatter plots, and histograms. Moreover, it provides options for modifying plot colors, fonts, and styles. Due to the comprehensive documentation and strong community backing of Matplotlib, one can find numerous online examples and tools that aid in the creation of desired visualizations.

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Python Data Visualization Libraries

Python Data Visualization Libraries

In addition to Matplotlib, there are many other Python libraries available for data visualization. Here are some key points to consider regarding Python libraries for data visualization:

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How to Visualize Data in Python

How to Visualize Data in Python

Python has many tools and packages for data visualization. Following are some general procedures to take while visualizing data in Python:

Selecting the right plot type for the data you’re displaying is critical. Line plots, for example, are suitable for displaying trends across time, whereas scatter plots help show connections between variables.

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Visualization of CSV Data by Python

To visualize CSV data using Python, you can utilize popular data visualization libraries such as Matplotlib or Seaborn. Here’s a general outline of the steps involved:

import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('your_file.csv')
# View the first few rows of the DataFrame
# Get basic statistics of the data
# Check the column names
# Example: Create a line plot of a numeric column
plt.title('Line Plot')
plt.xlabel('X-axis label')
plt.ylabel('Y-axis label')
# Save the plot as an image file
# Display the plot

Remember to customize the plot according to your specific data and visualization requirements. 

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Visualization of TSV Data by Python

Below is an example of data visualization of TSV data by Python in the form of Line Graph:

We took a dataset of Open food facts from Kaggle. Open Food Facts is a free, open, collaborative database of food products from around the world, with ingredients, allergens, nutrition facts, and other information 

You can download the dataset from Kaggle and import it to Google Colab using the following steps:

from google.colab import drive
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_tsv('/drive/MyDrive/openfoodfacts/', sep='t')

For example, to plot the line graph of nutrition values of the products, you can run this code:

import as px
import numpy as np

data_sorted = data.sort_values(by='nutrition-score-fr_100g')

bins = np.linspace(data_sorted['nutrition-score-fr_100g'].min(), data_sorted['nutrition-score-fr_100g'].max(), 51)

data_grouped = data_sorted.groupby(pd.cut(data_sorted['nutrition-score-fr_100g'], bins)).size()

plt.plot((bins[:-1] + bins[1:]) / 2, data_grouped)
plt.xlabel('Nutri-Score (kcal/100g)')
plt.title('Line graph of Nutrition values')
Line graph of Nutrition Values

Data Visualization Projects in Python

Here are some examples of data visualization projects that with Python:

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Best Practices for Data Visualization in Python

Creating compelling data visualizations in Python requires more than selecting a library and creating a plot. Some best practices can ensure that the visualizations are clear, informative, and accurate. Here are some key points to consider when creating data visualizations in Python:

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In conclusion, Python empowers data scientists and analysts with a powerful set of tools and packages for creating stunning and informative data visualizations. From basic charts to interactive plots, Python’s versatile libraries enable users to transform raw data into captivating visual narratives. By adhering to best practices in data visualization, practitioners can unlock the true potential of their data and effectively communicate insights to diverse audiences. With Python’s rich ecosystem of visualization options and a commitment to best practices, the world of data becomes more accessible and impactful. So, let us embrace the power of Python and unleash the art of storytelling through compelling visualizations, bridging the gap between data and understanding.

We tried our best to clarify the topic, but if you are still left with any doubts, reach out to our  python community page!

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