Open In App

Data Visualization in jupyter notebook

In this article, we will learn how to visualize data in Jupyter Notebook there are different libraries available in Python for data visualization like Matplotlib, seaborn, Plotly, GGPlot, Bokeh, etc. But in this article, we will use different libraries like Matplotlib, searborn, and Plotly which are widely used for data visualization. We will generate different graphs and plots in Jupyter Notebook using these libraries such as bar graphs, pie charts, line charts, scatter graphs, histograms, and box plots. We will also discuss how to install these libraries and use examples to understand each graph.

Jupyter Notebook

The Jupyter Notebook is the original web application for creating and sharing computational documents that contain live code, equations, visualizations, and narrative text. It offers a simple, streamlined, document-centric experience. Jupyter has support for over 40 different programming languages and Python is one of them.



Prerequisites

In this article, we will use different libraries to create graphs and plots and you have to install the library to function the below example you can use the following code snippetes to install the dependencies.

Install matplotlib



pip install matplotlib

Install Seaborn

pip install seaborn

Install Plotly

pip install plotly

Data Visualization

Data visualization is the graphical representation of information and data in a pictorial or graphical format like line chart, bar graph, pie chart etc. Data visualization helps to gain insights from the data to understand the underlying trends in the data helps the organization to make data-driven decisions. Reasons why data visulization is important:

Types of Data Visualizations

Data Prepration: It is also known as data preprocessing it is a method to convert the raw data into meaninful data it is a multi-level process it includes data collection, data cleaning, data transformation.

Data Visualization using Matplotlib

Bar graph in Jupyter Notebook

Bar Graph represents data using rectangular bars of variable length and the length of bar corresponds the value it represents. It is effective for comparing categories or discrete data points.

Follow the below steps to use bar graph in you Jupyter Notebook:

Example:




import matplotlib.pyplot as plt
x = [10, 20, 30, 40, 50, 60]
y = [13, 45, 23, 34, 96, 76]
plt.title('Bar Graph')
plt.bar(x, y, color='dodgerblue', width=5)
plt.show()

Output:

Bar Graph

Pie Chart in Jupyter Notebook

A pie chart displays data as circular graph divided into slices, and each slice represents a proportion or percentage of the whole.

Follow the below steps to use pie chart in you Jupyter Notebook:

Example:




import matplotlib.pyplot as plt
x = [35, 20, 30, 40, 50, 30]
y = ['Apple', 'Bananna', 'Grapes', 'Orange', 'PineApple', 'Dragon Fruit']
plt.title('Pie Chart')
plt.pie(x, labels=y)
plt.show()

Output:

Data Visualization using Seaborn

Line Plot in Jupyter Notebook

A line plot shows data points connected by lines, it helps visualize changes, patterns, and fluctuations in data, line plot is useful for tracing patterns in data. We will use seaborn library to plot the line chart or line plot.

Follow the below steps to use line chart in you Jupyter Notebook:

Example:




# importing packages
import seaborn as sns
# loading dataset
data = sns.load_dataset("iris")
# draw lineplot
sns.lineplot(x="sepal_length", y="sepal_width", data=data)

Output:

Scatter Graph in Jupyter Notebook

A scatter graph represents data points as individual dots on a 2D plane. It’s used to show the relationship or correlation between two variables. We will use seaborn library to plot scatter graph.

Follow the below steps to use scatter graph in you Jupyter Notebook:

Example:




import seaborn
data = seaborn.load_dataset("iris")
seaborn.scatterplot(data=data)

Output:

Data Visualization using Plotly

Box Plot in Jupyter Notebook

Box plot is a graphical represntation of dataset and is usally used to find the outliers in the dataset. Box are much beneficial for comparing the groups of data. To plot a box plot we will use plotly library.

Follow the below steps to use scatter graph in you Jupyter Notebook:

Example:




import plotly.express as px
df = px.data.iris()
fig = px.box(df, x="sepal_width", y="sepal_length")
fig.show()

Output:

Histogram in Jupyter Notebook

Histogram is used to graphically represent the data and typically used in statistics to compare the historical data. To plot a histogram we will use Plotly library.

Follow the below steps to use scatter graph in you Jupyter Notebook:

Example:




import plotly.express as px
df = px.data.tips()
fig = px.histogram(df.total_bill)
fig.show()

Output:

Conclusion

In the article we discussed the widely used graphs and charts in the data visualization there are other graphs also available which you can checkout here.


Article Tags :