Data Visualization packages in Python

In the previous article, we saw how dplyr and tidyr packages in R can be used to visualize and plot data to gain a better understanding of the underlying properties. Now let’s explore the data visualization techniques in Python and see how similar tasks can be performed.

Pandas:

Pandas is a Python package aimed toward creating a platform to carry out practical data analysis and manipulation tasks. It has data structures that are equivalent to dataframe in R, and it can handle multiple types of data like SQL or Excel data, information present in the form of matrices, time series data, and labeled/unlabeled data. Here’s a preview of the tasks that can be carried out using Pandas:

groupby(..) function that is used to aggregate data through split-apply-combine operations
Merging, joining, slicing, reshaping and subsetting of data
Flexibility to change the dimension of data: addition and removal of columns
Handling of missing data similar to libraries in R

You can import Pandas just like you would import any other library in Python.

@ import pandas as pd

First step of dealing with Pandas involves reading data from a csv file.

@ data = pd.read_csv(file_path, header)

File_path: the location of the csv file to be read

Header: Can be None if you want the column heading to be Null. If column names are needed, then pass them as a list to the header argument.

After reading the data, placing it into a dataframe gives us the flexibility to perform various operations on it. To convert data into dataframe format use:

@ data_df = pd.DataFrame(data, column_list)

We are going to use the following dataframe in our later examples:

>>> marks_df

      names   marks

0     Ross      90
1     Joey      72
2     Monica    81
3     Phoebe    80
4     Chandler  45
5     Rachel    78

It is always important to have an estimate about the extreme values of the data. It is also convenient to have the data in a sorted format. To accomplish this, data can be sorted based on a column value in the dataframe using the sorting function:

@ df_sort = dataframe.sort_values(column, ascending)

column : column object of the dataframe

ascending: default value is True. If set to False, the data is sorted in descending order.

>>> list_sort = marks_df.sort_values(['marks'])
>>> list_sort

      names    marks
4    Chandler   45
1    Joey       72
5    Rachel     78
3    Phoebe     80
2    Monica     81
0    Ross       90

To get the entity with the maximum value (which is the last value in the sorted dataframe), tail(n) function can be used. n is the number of values from the last elements that need to be taken into consideration:

@ df_sort.tail(1)
>>> list_sort.tail(1)

   names  marks
0  Ross     90

Similarly, head() collects values from the top:

>> list_sort.head(2)

      names  marks
4   Chandler  45
1   Joey      72

Both head and tail, by default, will display 5 values from the top and bottom, respectively.

To get the information about the dataframe, use info():

@ marks_df.info()

>>> marks_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5

Data columns (total 2 columns):
names    6 non-null object
marks    6 non-null int64

dtypes: int64(1), object(1)
memory usage: 168.0+ bytes

In the examples that follow, we are going to use the following dataframe that contains the complete exam results of all the 6 students (there are 6 subjects):

>>> allmarks_df

When there are multiple entries for each object, the aggregate option comes into play. We use the groupby() function to accomplish it. To get the total marks for each student, we need to aggregate all the name objects using the sum function:

@ agg_object = dataframe.groupby(column_name, as_index)

column_name: takes the list of columns based on which grouping needs to be done.

as_index: default value is True and means that the columns mentioned in list will be considered as indices for the new dataframe formed. When set to False, numerical numbering starting from 0 is given as the index.

>>> marks_agg = allmarks_df.groupby('Name')
>>> total_df = marks_agg.sum()
>>> total_df

Name      Marks      
Ross       495
Chandler   404
Rachel     422
Monica     443
Joey       475
Phoebe     395


>>> total_df = allmarks_df.groupby('Name', as_index=False).sum()
>>> total_df

      Name     Marks
0     Ross      495
1     Chandler  404
2     Rachel    422
3     Monica    443
4     Joey      475
5     Phoebe    395

Data can also be plotted using Pandas, but it requires pyplot from matplotlib:

>>> import matplotlib.pyplot as plt
>>> total_df['Marks'].plot(title="Total marks of all students")
<matplotlib.axes.AxesSubplot object at 0x10cde0d10>
>>> plt.show()

Data Visualization - Pandas

>>> total_df['Marks'].plot.bar()
<matplotlib.axes.AxesSubplot object at 0x10c2d1e90>
>>> plt.show()

Data Visualization in Python - Pandas

To get the frequencies of the values in a particular column, use value_counts():

@ dataframe[column_name].value_counts()
>>> allmarks_df['Name'].value_counts()

Chandler   6
Ross       6
Rachel     6
Phoebe     6
Monica     6
Joey       6

Name: Name, dtype: int64

To get the unique values in a column:

@ dataframe[column_name].unique()
>>> allmarks_df['Name'].unique()
array(['Ross', 'Joey', 'Monica', 'Phoebe ', 'Chandler', 'Rachel'], dtype=object)

Dataframes can be accessed using the index too. ix() function is used to extract data using the index in numerical values:

@ dataframe.ix(index_range, columns_range)
>>> allmarks_df.ix[0:6,:]

      Name   Marks
0     Ross     77
1     Joey     73
2     Monica   80
3     Phoebe   58
4     Chandler 54
5     Rachel   51
6     Ross     98

>>> allmarks_df.ix[0:6,0]

0    Ross
1    Joey
2    Monica
3    Phoebe
4    Chandler
5    Rachel
6    Ross

Name: Name, dtype: object

>>> allmarks_df.ix[0:6,0:1]

       Name
0      Ross
1      Joey
2      Monica
3      Phoebe
4      Chandler
5      Rachel
6      Ross

Adding a column to data is quite easy in case of dataframe in Pandas:

@ dataframe[new_column] = value
>>> total_df['Pass'] = [total_df['Marks'][i] >= 420 for i in range(6)]
>>> total_df

Name      Marks   Pass             
Ross       495    True
Chandler   404    False
Rachel     422    True
Monica     443    True
Joey       475    True
Phoebe     395    False

loc() can be used to extract subset of a dataframe:

@ dataframe.loc[index / index_range]
>>> total_df.loc['Monica']

Marks     443
Pass     True

Name: Monica, dtype: object

>>> total_df.loc['Monica':'Phoebe ']

Name        Marks   Pass
Monica      443     True
Joey        475     True
Phoebe      395     False

iloc() is similar to loc() but here the index can be represented as numerals rather than as actual object names:

Subset of the dataframe can also be extracted by imposing a condition over the column values using logical operators:

>>> total_pass = total_df[total_df['Pass'] == True]
>>> total_pass

In the above example, all the rows with ‘Pass’ column value as True are separated out using the logical equality condition.

You can use the del function to delete a column.

@ del dataframe[column_name]
>>> del total_df['Pass']
>>> total_df

Data can be changed into different storage formats. stack() and unstack() functions are used for this. stack() is used to bring down the column names into index values and unstack() is used to revert the stacking action. Give it a try and see the output.

@ dataframe.stack()
>>> total_df.stack()
>>> total_df.unstack()

The rows and columns interchange positions after unstacking. We can revert this using the transpose function.

>>> total_df = total_df.T 
>>> total_df

Name     Ross  Chandler Rachel Monica Joey  Phoebe
Marks    495   404      422    443    475   395
Pass     True  False    True   True   True  False

>>> total_df = total_df.T
>>> total_df

Name     Marks   Pass
Ross      495    True
Chandler  404    False
Rachel    422    True
Monica    443    True
Joey      475    True
Phoebe    395    False

Subset of the dataframe can also be extracted by imposing a condition over the column values using logical operators:

>>> total_pass = total_df[total_df['Pass'] == True]
>>> total_pass

In the above example, all the rows with ‘Pass’ column value as True are separated out using the logical equality condition.

You can use the del function to delete a column.

@ del dataframe[column_name]
>>> del total_df['Pass']
>>> total_df

@ dataframe.stack()
>>> total_df.stack()
>>> total_df.unstack()

The rows and columns interchange positions after unstacking. We can revert this using the transpose function.

>>> total_df = total_df.T 
>>> total_df

Name     Ross  Chandler Rachel Monica Joey  Phoebe
Marks    495   404      422    443    475   395
Pass     True  False    True   True   True  False

>>> total_df = total_df.T
>>> total_df

Name     Marks   Pass
Ross      495    True
Chandler  404    False
Rachel    422    True
Monica    443    True
Joey      475    True
Phoebe    395    False

Subset of the dataframe can also be extracted by imposing a condition over the column values using logical operators:

>>> total_pass = total_df[total_df['Pass'] == True]
>>> total_pass

In the above example, all the rows with ‘Pass’ column value as True are separated out using the logical equality condition.

You can use the del function to delete a column.

@ del dataframe[column_name]
>>> del total_df['Pass']
>>> total_df

@ dataframe.stack()
>>> total_df.stack()
>>> total_df.unstack()

The rows and columns interchange positions after unstacking. We can revert this using the transpose function.

>>> total_df = total_df.T 
>>> total_df

Name     Ross  Chandler Rachel Monica Joey  Phoebe
Marks    495   404      422    443    475   395
Pass     True  False    True   True   True  False

>>> total_df = total_df.T
>>> total_df

Name     Marks   Pass
Ross      495    True
Chandler  404    False
Rachel    422    True
Monica    443    True
Joey      475    True
Phoebe    395    False

Mean and standard deviation for a particular value of the data can be calculated using standard functions. Mean: mean() and standard deviation: std()

@ dataframe[column_name].mean()
@ dataframe[column_name].std()

>>> total_df['Marks'].mean()
439.0

>>> total_df['Marks'].std()
39.744181964156716

>>> total_df['dis-Mean'] = total_df['Marks'] - total_df['Marks'].mean()
>>> total_df

      Name    Marks  dis-Mean
0     Ross      495      56.0
1     Chandler  404     -35.0
2     Rachel    422     -17.0
3     Monica    443       4.0
4     Joey      475      36.0
5     Phoebe    395     -44.0

The above example adds a column to the dataframe containing the deviation from the mean value of Marks.

Generating a time series data:

>>> time = pd.date_range('1/1/2012', periods=48, freq='MS')
>>> time

DatetimeIndex(['2012-01-01', '2012-02-01', '2012-03-01', '2012-04-01',
               '2012-05-01', '2012-06-01', '2012-07-01', '2012-08-01',
               '2012-09-01', '2012-10-01', '2012-11-01', '2012-12-01',
               '2013-01-01', '2013-02-01', '2013-03-01', '2013-04-01',
               '2013-05-01', '2013-06-01', '2013-07-01', '2013-08-01',
               '2013-09-01', '2013-10-01', '2013-11-01', '2013-12-01',
               '2014-01-01', '2014-02-01', '2014-03-01', '2014-04-01',
               '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
               '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01',
               '2015-01-01', '2015-02-01', '2015-03-01', '2015-04-01',
               '2015-05-01', '2015-06-01', '2015-07-01', '2015-08-01',
               '2015-09-01', '2015-10-01', '2015-11-01', '2015-12-01'],
              dtype='datetime64[ns]', freq='MS')

>>> stock = pd.DataFrame([np.random.randint(low=0, high=50) for i in range(48)], index=time, columns=['Value'])
>>> stock['dev'] = stock['Value'] - stock['Value'].mean()
>>> stock

             Value       dev
2012-01-01     37   10.104167
2012-02-01     48   21.104167
2012-03-01     41   14.104167
2012-04-01      5  -21.895833
2012-05-01     13  -13.895833
2012-06-01      7  -19.895833
2012-07-01     37   10.104167
2012-08-01     31    4.104167
2012-09-01     32    5.104167
2012-10-01     46   19.104167
2012-11-01     40   13.104167
2012-12-01     18   -8.895833
2013-01-01     38   11.104167
2013-02-01     23   -3.895833
2013-03-01     17   -9.895833
2013-04-01     21   -5.895833
2013-05-01     12  -14.895833
2013-06-01     40   13.104167
2013-07-01      9  -17.895833
2013-08-01     47   20.104167
2013-09-01     42   15.104167
2013-10-01      3  -23.895833
2013-11-01     24   -2.895833
2013-12-01     38   11.104167
2014-01-01     33    6.104167
2014-02-01     41   14.104167
2014-03-01     25   -1.895833
2014-04-01     11  -15.895833
2014-05-01     44   17.104167
2014-06-01     47   20.104167
2014-07-01      6  -20.895833
2014-08-01     49   22.104167
2014-09-01     11  -15.895833
2014-10-01     14  -12.895833
2014-11-01     23   -3.895833
2014-12-01     35    8.104167
2015-01-01     23   -3.895833
2015-02-01      1  -25.895833
2015-03-01     46   19.104167
2015-04-01     49   22.104167
2015-05-01     16  -10.895833
2015-06-01     25   -1.895833
2015-07-01     22   -4.895833
2015-08-01     36    9.104167
2015-09-01     30    3.104167
2015-10-01      3  -23.895833
2015-11-01     12  -14.895833
2015-12-01     20   -6.895833

Plotting the value of stock over 4 years using pyplot:

>>> stock['Value'].plot()
<matplotlib.axes.AxesSubplot object at 0x10a29bb10>
>>> plt.show()

Data visualization in Python Pandas

>>> stock['dev'].plot.bar() 
<matplotlib.axes.AxesSubplot object at 0x10c3e09d0>
>>> plt.show()

Plot.bar() Data Visualization in Python

There are more plotting tools like the seaborn library that can create more sophisticated plots. With these data visualization packages in R and Python, we are ready to advance to the core concepts of Machine Learning.

We have our Machine Learning practice section coming soon. Stay tuned.

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

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Assess the Skills That Truly Matter

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Streamlining Your Assessment Workflow

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A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

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What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

Develop an understanding of basics of system design and architecture
Familiarize yourself with commonly asked systems design interview questions
Read about system design case studies for popular applications
Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

Plan the extent of the topics and scope of discussion in advance
Clearly define the evaluation criteria and communicate expectations
Quantify constraints, inputs, boundaries and assumptions
Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

Ask follow-up questions to challenge a solution
Probe candidates to gauge real-time logical reasoning skills
Make it a conversation and take notes of important pointers and outcomes
Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

Encourage candidates to explore and consider alternative solutions
Work with the candidate to drill the problem into smaller tasks
Provide context and supporting details to help candidates stay on track
Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

Data Visualization packages in Python - Pandas

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Discover more articles

The Mobile Dev Hiring Landscape Just Changed

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

Introducing a New Era in Mobile Assessment

Assess the Skills That Truly Matter

Streamlining Your Assessment Workflow

Quantifiable Impact on Hiring Success

A Better Experience for Everyone

Unlock a New Era of Mobile Talent Assessment

Vibe Coding: Shaping the Future of Software

A New Era of Code

From Machine Language to Natural Language

The Promise and the Pitfalls

The Economic Impact

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?

What is a System Design Interview?

What are some common topics for a System Design Interview

The Difference between a System Design Interview and a Coding Interview

How to Conduct an Effective System Design Interview

Step 1: Understand the subject at hand

Step 2: Prepare for the interview

Step 3: Stay actively involved

Step 4: Be a collaborator

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

Explore HackerEarth’s top products for Hiring & Innovation

Data Visualization packages in Python - Pandas

Subscribe to The HackerEarth Blog

Thank you for subscribing!

Hire top tech talent with our recruitment platform

Discover more articles

The Mobile Dev Hiring Landscape Just Changed

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

Introducing a New Era in Mobile Assessment

Assess the Skills That Truly Matter

Streamlining Your Assessment Workflow

Quantifiable Impact on Hiring Success

A Better Experience for Everyone

Unlock a New Era of Mobile Talent Assessment

Vibe Coding: Shaping the Future of Software

A New Era of Code

From Machine Language to Natural Language

The Promise and the Pitfalls

The Economic Impact

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?

What is a System Design Interview?

What are some common topics for a System Design Interview

The Difference between a System Design Interview and a Coding Interview

How to Conduct an Effective System Design Interview

Step 1: Understand the subject at hand

Step 2: Prepare for the interview

Step 3: Stay actively involved

Step 4: Be a collaborator

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

Explore HackerEarth’s top products for Hiring & Innovation