Visualize data from CSV file in Python

In today’s world, visualizing data is an important part of any domain. Visualized data is easy to understand that is why it is preferred over excel sheets. Python came to our rescue with its libraries like pandas and matplotlib so that we can represent our data in a graphical form.
In this tutorial, we will be learning how to visualize the data in the CSV file using Python.

Visualize a Data from CSV file in Python

First of all, we need to read data from the CSV file in Python.

Now since you know how to read a CSV file, let’s see the code.

import pandas as pd
import matplotlib.pyplot as plt
csv_file='data.csv'
data = pd.read_csv(csv_file)

We have imported matplotlib. It will be used for data visualization.

Let’s see our data.

We will now extract Genre and TotalVotes from this dataset.

Votes = data["TotalVotes"]
Genre = data["Genre"]

Now, we will store these data into two different lists. We need to create two empty lists first.

x=[]
y=[]

We will use a method list() which converts a dataset into Python list.

x=list(Genre)
y=list(Votes)

If we print x and y, we get

x=['Biography', 'Action', 'Romance', 'Comedy', 'Horror']
y=[65, 75, 80, 90, 60] 

matplotlib lets us draw different types of graphs like,

• Bar charts and Histograms
• Scatter plot
• Stem plots
• Line plots
• Spectrograms
• Pie charts
• Contour plots
• Quiver plots

Today, We will see a scatter plot, bar chart, and pie chart.

Scatter Plot from CSV data in Python

To draw a scatter plot, we write

plt.scatter(x,y)
plt.xlabel('Genre->')
plt.ylabel('Total Votes->')
plt.title('Data')
plt.show()

xlabel and ylable denote the type of data along the x-axis and y-axis respectively.
plt.title allows us to mention a title for our graph.
To show the graph, we use a function show().

This is our scatter plot.

Bar Plot from CSV data in Python

Similarly, for a bar chart:

plt.bar(x,y)

We get,

Pie Chart from CSV Data in Python

And for the pie chart, we write:

plt.pie(x,labels=y,autopct='%.2f%%')

Here, label is used to provide a name inside the respective portion in the chart.
autopct shows the percentage for each portion.

So, this is how we can visualize our data using Python. If you have any doubts, don’t forget to mention them in the comment section below.

 

Also, learn:

• Plotting sine and cosine graph using matloplib in python
• Print frequency of each character in a string in Python