Data Visualization - A Useful tool to Explore Data (2023)

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Data visualization and its importance

Let’s see what does technical definition says:

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. (Source:What Is Data Visualization? Definition & Examples | Tableau)

In simple words, it is very difficult to gain knowledge from a large amount of data and this is where data visualization comes into the picture. Be it numerical or categorical or mixed type of data, visualization techniques help see the trends, outliers, or any kind of patterns in the data. All this information helps data scientists or anyone in any field of work to make better decisions to achievetheirobjective.

There is a well-known fact that we can grasp and retain knowledge from pictures and visuals way better than any numbers or text. With the evolution of big data in recent years visualization techniques becomes far more important than ever before because it is almost impossible for anyone to extract any information from terabytes or petabytes of data without using visualization techniques.

Most of the time data scientists deal with structured data in most of their data analysis journey and familiar with the concepts of the structured query language(SQL) tables. SQL tables represent data in terms of rows and columns and make it convenient to explore and apply transformations.Data visualization techniques are also helpful for the feature engineering part in machine learning. In this article, we will be discussing various visualization techniques that we can use in python.

Once you finish the article you will have a pretty good understanding of the following data visualization techniques:

1. Drawing plots

2. Bar chart

3. Histogram

4. Density plot or Distribution

5. Box Plot

6. Scatter Plot

7. Pair Plot

8. Correlation and HeatMap

Now we will explore each of the methods in detail.

1. Drawing Plot

Matplotlib is a python 2D plotting library and the most widely used library for data visualization. It provides an extensive set of plotting APIs to create various plots such as scatter, bar, box, and distribution plots with custom styling and annotation. Detailed documentation for matplotlib can be found at

Seaborn is also a python data visualization library based on matplotlib. It provides a high-level interface for drawing innovative and informative statical charts(

Matplotlib is a library for creating 2D plots of arrays in python.

Matplotlib is written in python and makes use of Numpy arrays. Seaborn which is built on top of matplotlib, is a library for making elegant charts in python and well-integrated with pandas dataframe.

To create graphs and plots, we need to import ‘matplotlib. pyplot’ and ‘seaborn’ modules.

To display the plots on the jupyter Notebook, we need to provide a directive ‘%matplotlib inline’. Only if the directive is provided, the plots will be displayed in the notebook.

import matplotlib.pyplot as pltimport seaborn as sn%matplotlib inline

2. Bar Chart

The bar chart is a frequency chart for a qualitative variable. A bar chart can be used to access the most-occurring and least-occurring categories within a dataset.

To draw a bar chart, call ‘barplot()’ of the seaborn library. The Data frame should be passed in the parameter data here.


To display the average fare by each category, pass fare as y parameter and who as x parameter.

# importing the required libraryimport seaborn as snsimport matplotlib.pyplot as plt # read a titanic.csv file# from seaborn librarydf = sns.load_dataset('titanic') # who v/s fare barplotsns.barplot(x = 'who', y = 'fare', data = df) # Show the

The below figure show, a bar plot created to show the average fare for each category.

Data Visualization - A Useful tool to Explore Data (1)

3. Histogram

A histogram is a plot that shows the frequency distribution of a set of continuous variables.

The histogram gives an insight into the underlying distribution of the variable, outliers, skewness, etc. To draw a histogram, invoke the ‘hist()’ method of the matplotlib library.


The following is an example of how to draw a histogram for fare and understand its distribution.

Data Visualization - A Useful tool to Explore Data (2)

figure:- histogram for fare

4. Distribution or Density plot

A distribution or density plot depicts the distribution of data over a continuous interval. A density plot is like a smoothed histogram and visualizes the distribution of data over a continuous interval. So a density plot also gives into what might be the distribution of the population.


To draw the distribution plot, we can use the distplot() of the seaborn library.

Data Visualization - A Useful tool to Explore Data (3)

fig. distribution plot for fare

5. Box Plot

Box plot is a graphical representation of numerical data that can be used to understand the variability of the data and the existence of outliers. Box plot is designed by identifying the following descriptive statistics.

1. Lower quartile, median and upper quartile

2. Lowest and highest values

3. Interquartile range(IQR)

Box plot is constructed using IQR, minimum and maximum values. IQR is the distance between the 3rd quartile and 1st quartile. The length of the box is equivalent to IQR.

To draw the box plot, call boxplot() of the seaborn library.

box = sns.boxplot(df['fare'])

The box plot for the fare is shown in the figure and indicates that there are few outliers in the data.Data Visualization - A Useful tool to Explore Data (4)

To obtained min, max, 25 percentile(1st quantile), and 75 percentile(3rd quantile) values in the boxplot, the ‘boxplot()’ method of matplotlib library can be used.

box = plt.boxplot(df['fare'])

Let us return the value of boxplot() to a variable box.

Data Visualization - A Useful tool to Explore Data (5)Data Visualization - A Useful tool to Explore Data (6)

6. Scatter Plot

In a scatter plot, the values of two variables are plotted along two axes and the resulting pattern can reveal correlation present between the variables if any.

A scatter plot is also useful for assessing the strength of the relationship and to find if there are any outliers in the data.

import numpyimport matplotlib.pyplot as plt
x = numpy.random.normal(5.0, 1.0, 1000)y = numpy.random.normal(10.0, 2.0, 1000)
plt.scatter(x, y)

The ‘scatter()’ method of matplotlib can be used to draw the scatter plot which takes both the variables.

Data Visualization - A Useful tool to Explore Data (7)

7. Pair Plot

If there are many variables, it is not convenient to draw scatter plots for each pair of variables to understand the relationship. So that we have to use a pair plot to depict the relationship in a single diagram which can be plotted using the ‘pairplot()’ method.

import seabornimport matplotlib.pyplot as pltdf = seaborn.load_dataset('tips')seaborn.pairplot(df, hue ='sex')
Data Visualization - A Useful tool to Explore Data (8)

8. Correlation and HeatMap

Correlation is used for measuring the strength and direction of the linear relationship between two continuous random variables x and y. A positive correlation means the variables increase or decrease together. A negative correlation means if one variable increases then the other decrease.

Correlation values can be computed using the ‘corr()’ method of the DataFrame and rendered using heatmap.

df.corr()# plotting correlation heatmapdataplot = sns.heatmap(df.corr(), cmap="YlGnBu", annot=True) # displaying
Data Visualization - A Useful tool to Explore Data (9)

The color map scale is shown along with the heatmap. Setting the ‘annot’ attribute to ‘True’ prints the correlation values in each box of the heatmap and improves the readability of the heatmap.


  • The objective of descriptive analytics is simple comprehension of data using summarization basic statistical measures and visualization.
  • Matplotlib and seaborn are the two most widely used libraries for creating a visualization.
  • Plots like histograms, distribution plots, box plots, scatter plots, pair plots, heatmap, can be created to find insights during exploratory analysis.

About the Author

Hi, I am Kajal Kumari. I have completed my Master’s from IIT(ISM) Dhanbad in Computer Science & Engineering. As of now, I am working as Machine Learning Engineer in Hyderabad. You can also check out few other blogs that I have written here.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.



Data Visualization - A Useful tool to Explore Data? ›

Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets.

What is the best tool to explore data? ›

Some of the best data visualization tools include Google Charts, Tableau, Grafana, Chartist, FusionCharts, Datawrapper, Infogram, and ChartBlocks etc. These tools support a variety of visual styles, be simple and easy to use, and be capable of handling a large volume of data.

What is data visualization in data exploration? ›

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

What are the tools for exploring data? ›

List of data exploration and visualization tools
  • Matplotlib.
  • Scikit Learn.
  • Plotly.
  • Seaborn.
  • Pandas.
  • D3. js.
  • Bokeh.
  • Altair.

What are data Visualisation tools used for? ›

Data visualization tools are software applications that render information in a visual format such as a graph, chart, or heat map for data analysis purposes. Such tools make it easier to understand and work with massive amounts of data.

What is the difference between data exploration and data visualization? ›

Data exploration refers to the initial step in data analysis. Data analysts use data visualization and statistical techniques to describe dataset characterizations, such as size, quantity, and accuracy, to understand the nature of the data better.

How can we explore the data? ›

Data exploration is typically conducted using a combination of automated and manual activities. Automated activities can include data profiling or data visualization or tabular reports to give the analyst an initial view into the data and an understanding of key characteristics.

What is data visualization and its benefits? ›

Data visualization is a powerful way for people, especially data professionals, to display data so that it can be interpreted easily. It helps tell a story with data, by turning spreadsheets of numbers into stunning graphs and charts.

Why is data visualization important in exploratory data analysis? ›

Data visualization is an important component of Exploratory Data Analysis (EDA) because it allows a data analyst to “look at” their data and get to know the variables and relationships between them.

What makes a good data visualization? ›

Accurate: The visualization should accurately represent the data and its trends. Clear: Your visualization should be easy to understand. Empowering: The reader should know what action to take after viewing your visualization. Succinct: Your message shouldn't take long to resonate.

Which chart type tends to work best for exploring data? ›

Bar charts are good for comparisons, while line charts work better for trends. Scatter plot charts are good for relationships and distributions, but pie charts should be used only for simple compositions — never for comparisons or distributions. There is a chart selection diagram created by Dr.

How do you explore and clean data? ›

How to clean data
  1. Step 1: Remove duplicate or irrelevant observations. Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. ...
  2. Step 2: Fix structural errors. ...
  3. Step 3: Filter unwanted outliers. ...
  4. Step 4: Handle missing data. ...
  5. Step 5: Validate and QA.

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