How to Visualize a Kaggle Dataset with Pandas, Matplotlib, and Seaborn

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The Indian Premier League or IPL is a T20 cricket event organized yearly by the Board of Control for Cricket In India (BCCI). Eight city-based franchises compete with one another over 6 weeks to discover the winner.

In this text, I’m going to analyze information from the IPL’s previous seasons to see which groups have gained essentially the most video games, how groups behave when profitable a toss, who has the best legacy, and so on.

I’ve accomplished this evaluation from a historic viewpoint, giving an summary of what has occurred within the IPL over time. I’ve used instruments corresponding to Pandas, Matplotlib and Seaborn alongside with Python to give a visible in addition to numeric illustration of the info in entrance of us.

Pandas stands for Python Data Analysis library. It is often used for working with tabular information (related to the info saved in a spreadsheet). Pandas gives helper features to learn information from numerous file codecs like CSV, Excel spreadsheets, HTML tables, JSON, SQL and carry out operations on them.

Matplotlib and Seaborn are two Python libraries which are used to produce plots. Matplotlib is mostly used for plotting traces, pie charts, and bar graphs.

Seaborn gives some extra superior visualization options with much less syntax and extra customizations. I change back-and-forth between them in the course of the evaluation.

Table of Contents

  1. Getting the Dataset
  2. Data Preparation and Cleaning
  3. Exploratory Analysis and Visualization
  4. Asking and Answering Questions
  5. Inferences From the Analysis
  6. Conclusion

1. Getting the Dataset

I downloaded the dataset from Kaggle. You will see there are two CSV (Comma Separated Value) recordsdata, matches.csv and deliveries.csv. I selected to do my evaluation on matches.csv.

To discover extra attention-grabbing datasets, you’ll be able to have a look at this web page.

2. Data Preparation and Cleaning

A dataset comprises many columns and rows. It is at all times attainable that sure rows have lacking values or NaN for a number of columns.

It can also be attainable that there is likely to be sure columns or rows that you really want to discard out of your evaluation. You can even mix two or extra datasets for an in-depth evaluation.

Cleaning the info entails making corrections to that information, leaving out pointless columns or rows, merging datasets, and so on.

Before taking these steps, I wanted to set up and import the instruments (libraries) to be used in the course of the evaluation. I imported the libraries with completely different aliases corresponding to pd, plt and sns.  I then set some fundamental types for the plots.

Notice the particular command %matplotlib inline. It makes certain that plots are proven and embedded throughout the Jupyter pocket book itself. Without this command, generally plots might present up in pop-up home windows.

Using the read_csv() technique from the Pandas library, I loaded the matches.csv file.

Data from the file is learn and saved in a DataBody object – one of many core information buildings in Pandas for storing and working with tabular information. I used the _df suffix within the variable names for information frames.

I used the title matches_raw_df for the info body. This signifies that that is unprocessed information that I’ll clear, filter, and modify to put together a information body that is prepared for evaluation.


Using the form property of a Dataframe object, I discovered that the dataset comprises 756 rows and 18 columns. To discover the names of these columns I used the columns property. It returned a record of the columns in a information body.


To get a abstract of what the info body comprises, I used data(). This provides details about columns, variety of non-null values in every column, their information sort, and reminiscence utilization.

Almost all columns besides umpire3 don’t have any or only a few null values. The presence of null values might consequence from a lack of awareness or an incorrect information entry.

An attention-grabbing factor to observe is that, though there aren’t any null values for the consequence column, there are some for winner and player_of_match columns. Let’s discover out why.

I first accessed the consequence column utilizing dot notation (matches_raw_df.consequence). Then I used vaule_counts() technique on the consequence column.

value_counts() returns a collection which comprises counts of distinctive values. Here, it tells us concerning the completely different values current in consequence and the entire quantity for every of them.

So, out of 756 matches (rows), Four matches ended as no consequence.

Cricket is an outside sport and not like, say, soccer, play is not attainable when it is raining. It is quite common to have matches deserted due to incessant raining. Therefore, we’ve got no winners or participant of the match for these Four matches.

For this evaluation, the umpire3 column is not wanted. So I eliminated the column utilizing the drop() technique by passing the column title and axis worth. If you need to take away a number of columns, the column names are to be given in a record.

I assigned this cleaned information body to matches_df. I used this information body for additional evaluation.


3. Exploratory Analysis and Visualization

Exploratory evaluation entails performing operations on the dataset to perceive the info and discover patterns. It helps us make sense of the info we’ve got.

Visualization is the graphic illustration of knowledge. It entails producing charts that talk these patterns among the many represented information to viewers.

Now, let’s take a have a look at the info I analyzed and what I discovered within the course of.

Number of matches and groups

I attempted to discover the variety of matches performed in every season within the IPL from its inception to 2019.

Since I wanted matches performed every season, it made sense to group our information in accordance to completely different seasons. Pandas has a groupby() technique to obtain this, whereby I handed season as an argument.

Since an id is exclusive for every match (row), counting the variety of ids for every season leads to what we would like. I used the rely() technique on the id column to discover the variety of matches held every season. This collection is assigned to the variable matches_per_season.

I then used the barplot() technique from the Seaborn library to plot the collection. The index of the collection, that’s the seasons, got because the x-value whereas the values of these indices got as y-values.

I used numerous matpllotlib.pyplot strategies corresponding to determine(), xticks() and title() to set the scale of the plot, title of the plot, and so on.

determine takes a parameter, figsize, which I set to (12,6). Notice that the scale was given as a tuple. To xticks(), I gave the rotation parameter a worth of 75 to make it simpler to learn.


Each season, virtually 60 matches have been performed. However, we see a spike within the variety of matches from 2011 to 2013. This is as a result of two new franchises, the Pune Warriors and Kochi Tuskers Kerala, have been launched, rising the variety of groups to 10.

However, Kochi was eliminated within the very subsequent season, whereas the Pune Warriors have been eliminated in 2013, bringing the quantity down to eight from 2014 onwards.

Before the beginning of the 2016 season, two groups, the Chennai Super Kings and Rajasthan Royals have been banned for 2 seasons. To make up for his or her absence, two new groups (the Rising Pune Supergiants and Gujarat Lions) entered the competitors.

When the Chennai Super Kings and Rajasthan Royals returned, these two groups have been faraway from the competitors.

Analyzing the Toss outcomes

One of essentially the most important occasions in any cricket match is the toss, which occurs on the very begin of a match. The toss winner can select whether or not they need to bat first or second (fielding first).

Let’s see what the development has been amongst the groups throughout completely different seasons.

Again I grouped the rows by season and then counted the completely different values of the toss_decision column through the use of value_counts().

Since a share provides a clearer image, I divided the above consequence with matches_per_season and multiplied it by 100. This collection was assigned to toss_decision_percentage.


Here, toss_decision_percentage is a collection with multi-index. If we print the index of the collection utilizing the index property, we see it’s of the shape (2008, 'bat'), (2008, 'subject') and so on.

The collection used each season and toss_decision as an index. But I solely needed the seasons to be an index. I used unstack() to obtain this.

By utilizing the unstack() technique on the collection, it transformed the values of toss_decision (that’s, bat and subject) into separate columns.

Next I used the plot() technique from Matplotlib to symbolize these values as bar charts. plot() has a parameter sort which decides what sort of plot to draw. The worth was set to bar.

For 2008-2013, groups appeared to favour each batting first and second. For this era, groups selected to bat first extra in 2009, 2010 and 2013. On the opposite hand, they selected fielding first extra in 2008 and 2011. Things have been even-steven in 2012.

This could possibly be as a result of IPL and T20 cricket normally was in its budding phases. So, groups have been most likely studying and making an attempt to work out which possibility could be extra useful.

However, since 2014, groups have overwhelmingly chosen to bat second. Especially since 2016, groups have chosen to subject first greater than 80% of the time.

Batting first requires that the workforce gauge the situations and the pitch and then set a goal accordingly. Chasing is simpler, as there’s a mounted goal to obtain.

Conditions have additionally turn into extra batsman-friendly and the talents of the batsmen have elevated tremendously (learn extra right here).

Number of Wins

We noticed how groups within the current previous have chosen to bat second greater than Four out of 5 occasions. Did this determination remodel the outcomes? Let’s see.

For wins_batting_first, the values of win_by_wickets has to be 0. Also, the consequence column ought to have a worth of regular since tied matches even have win margins as 0. This situation was saved as filter1.

Similarly, for wins_fielding_first, the the worth of win_by_runs has to be 0 and the consequence column ought to have a worth of regular. This situation was saved as filter1.

In each the collection, I used rely() technique on winner column to discover the gained matches within the filtered situations. I divided the outcomes with matches_per_season calculated earlier to give a higher understanding.




To plot these two collection collectively, I mixed them utilizing Pandas’ concat() technique. I handed the 2 collection names as a record and set the worth of axis as 1. This provides us a new information body which was saved as combined_wins_df.

Next I plotted combined_wins_df as a bar chart utilizing plot().

We noticed earlier that for 2008-2013, groups confronted a conundrum whether or not to bat first or subject first. This is partially seen within the outcomes as effectively.

The wins from batting first are very shut to that from fielding first. However, there is only one season the place groups batting first gained extra, with issues being equal in 2013.

Again, since 2014, issues have been in favour of groups chasing besides 2015. Leaving out 2015, issues have been overwhelmingly in favour of groups fielding first.

So, groups selecting to subject extra have been justified of their choices.

Teams with “History”

In leagues throughout completely different sports activities, there’s at all times speak about groups with “history” – groups which have performed essentially the most within the league and proceed to achieve this. Let’s discover these groups within the IPL.

Now, between two groups A and B, it may be “A vs B” or “B vs A”, relying on how the info entry has been accomplished. So I made a decision to rely the entire variety of completely different values for each the team1 and team2 columns utilizing value_counts(). Then I added them collectively.

I sorted the ends in descending order utilizing the sort_values() technique from Pandas. The ascending parameter was set to False.

Here, I used sns.barplot() to plot the graph.

The Mumbai Indians have performed essentially the most matches. They are adopted by the Royal Challengers Bangalore, Kolkata Knight Riders, Kings XI Punjab and Chennai Super Kings.

The Chennai Super Kings and Rajasthan Royals might have been larger had they not been banned.

You will see there are two groups from Delhi, the Delhi Daredevils and Delhi Capitals. This resulted from a change in possession and then workforce title in 2018.

It’s a related story for the Deccan Chargers and Sunrisers Hyderabad, because the Deccan Chargers have been faraway from the IPL in 2013 and the Sunrisers got here of their place.

Also, there are two groups with virtually identical title: the Rising Pune Supergiants and Rising Pune Supergiant. They are identical workforce, and there was no change in possession – it has extra to do with superstitions.

In the 2016 season, the Rising Pune Supergiants completed seventh. The homeowners modified the captain for 2017 and additionally dropped the ‘s’ from Supergiants. Well, it paid off as they completed as runner-up that season!

Teams with “Legacy”

Now, groups might have a lot of historical past nevertheless it’s their “legacy” – how usually they win – that makes them fashionable and attracts new and impartial followers.

To discover such groups, I merely used value_counts() on the winner column. This provides us the variety of matches that every workforce has gained.


So Mumbai has essentially the most wins. But a higher metric to choose could be the win share. To discover the win share, I divided most_wins by total_matches_played to discover the win_percentage for every workforce.


The Rising Pune Supergiant and Delhi Capitals have the very best win share. This is essentially as a result of they’ve performed fewer matches in contrast to most groups. Especially Rising Pune Supergiant, which technically grew to become a new workforce after dropping the ‘s’.

The Chennai Super Kings, regardless of enjoying two fewer seasons than the Mumbai Indians, had solely 9 fewer victories. They, alongside with the Mumbai Indians, are the one two groups within the high 5 that have been additionally a part of the IPL in 2008.

Chennai and Mumbai are the groups with essentially the most legacy.

4. Asking and Answering Questions from the Data

We’ve already gained some insights concerning the IPL by exploring numerous columns of our dataset.

Let’s ask some particular questions, and strive to reply them utilizing information body operations and attention-grabbing visualizations.

Q. Who has gained the IPL event?

  • Group the rows in accordance to seasons utilizing groupby().
  • Find the final match of every season, that’s, the ultimate utilizing tail(). It returns the final n rows from a Dataframe object or collection primarily based on place.
  • Sort the values per season utilizing sort_values().
  • Count the completely different winners and the occasions they gained utilizing value_counts() on winner.


Then I plotted the collection ipl_winners utilizing sns.barplot().

Mumbai and Chennai, our legacy groups, have gained the IPL at the least Three occasions. The Sunrisers Hyderabad are the one workforce that joined the league later and gained the trophy.

Q. Which are essentially the most and least constant groups throughout all seasons?

  • Created a information body between completely different values of winner and season utilizing pd.crosstab().
  • Plotted the info body as a heatmap.


pd.crosstab() provides a easy cross-tabulation of the winner and season columns. For every completely different worth of winner, pd.crosstab() finds its frequency for every completely different worth in season.

Then I plotted  matches_won_each_season utilizing sns.heatmap(). I handed the info body matches_won_each_season, with annot as True to have the values proven as effectively. Here, the darker colour signifies extra matches gained.

The Chennai Super Kings have been essentially the most constant workforce, profitable at the least eight matches in every of the seasons they’ve performed. This is backed up by the truth that they’re the solely workforce to attain the playoffs stage each season.

At the opposite finish of the spectrum are Three groups, the Delhi Daredevils, Kings XI Punjab and Rajasthan Royals. All three of them have had two seasons the place they carried out very well. However, they’ve been fairly common in the course of the different seasons.

Q. What has been the largest margin of victory by way of runs within the IPL?

  • Filter the info body utilizing the required situation.
  • Sort the values in descending order utilizing sort_values().
  • Find the largest 10 victories within the record utilizing the head() technique. It works reverse to tail(), returning the primary n rows.

I plotted the filtered information body highest_wins_by_runs_df utilizing sns.scatterplot(). For the x parameter I used season, and I used win_by_runs because the y parameter. I made the scale of the factors larger for the highest 10 victories utilizing the s parameter.

To put emphasis on the highest 10 victories, I used a completely different colour in addition to annotated these information factors utilizing plt.annotate(). The first parameter is the textual content of the annotation. The place of the purpose to be annotated is given as a tuple.

The largest margin of victory by runs is 146 runs. In 2017, the Mumbai Indians defeated the Delhi Daredevils by this margin. The Royal Challengers Bangalore have Three victories amongst the highest 5.

Q. Mumbai and Chennai are the 2 most profitable groups thus far. Which workforce leads within the head-to-head file?

  • Filter the info body utilizing the required situation to discover the matches performed between the 2 groups.
  • Use the value_counts() on the winner column to discover what number of occasions every of the groups have gained.


I plotted the collection mivcsk as a bar chart for a higher visualization.

MI have dominated CSK and are main the head-to-head file 17-11. We can see their dominance particularly within the 2019 season, the place the MI defeated the CSK Four out of Four occasions they met, together with the playoff and the ultimate.

5. Inferences from the Analysis

We have drawn some attention-grabbing inferences and now know extra concerning the IPL than once we began. Here’s a abstract of what we discovered by means of our evaluation:

  • Almost 60 matches are performed in each IPL season amongst eight groups.
  • There has been an try to broaden the IPL to 10 groups however the eight groups thought was introduced again and has been continued since.
  • For the primary six seasons (2008-2013), groups have been determining whether or not batting first or chasing could be higher after profitable the toss. This could possibly be down to the truth that the IPL and T20 cricket have been each of their early phases so groups have been making an attempt completely different methods.
  • But, since 2014, groups have most popular chasing, particularly up to now Four seasons (2016-2019) the place groups have chosen to subject greater than Four occasions out of 5. This is probably going as a result of having a set whole to chase makes issues easier. This might additionally consequence from groups preferring to chase in ODIs as effectively.
  • Though groups have overwhelmingly chosen to subject first, the win share after selecting to bat or subject isn’t that one-sided. However, their distinction is on the rise.
  • Mumbai Indians have performed essentially the most matches within the IPL. Due to the temporary growth, change of householders, and removing and banning of groups, there have been 15 groups who’ve performed within the IPL.
  • Chennai and Mumbai are the 2 groups with the very best win share. The incontrovertible fact that they’re the one two groups that have been a part of the primary season as effectively, within the high 5, exhibits their dominance.
  • Mumbai Indians have the gained the IPL Four occasions, essentially the most. They are adopted by Chennai at 3 and Kolkata Knight Riders at 2. Sunrisers Hyderabad, Deccan Chargers and Rajasthan Royals full the IPL Champions record, all profitable as soon as every.
  • 146 runs is the biggest margin of victory by runs. Mumbai Indians defeated Delhi Daredevils by this margin in 2017. The largest margin for victory by wickets is 10, which has been achieved many occasions.
  • The two heavyweights, Mumbai and Chennai, have a head-to-head file in favour of Mumbai at 17-11. Mumbai have had the higher hand within the 2019 season each time they met, together with the ultimate.

6. Conclusion

In this text, we did a bunch of research and noticed some attention-grabbing visualizations. However, this was simply scratching the floor.

You can carry out extra attention-grabbing evaluation on matches.csv as a standalone information set. But combining deliveries.csv with this dataset could lead on to extra in-depth evaluation.

I did this information evaluation and visualization as a challenge for the 6-week course Data Analysis with Python: Zero to Pandas. This course was performed by Jovian.ml in partnership with freeCodeCamp.org. Check out the challenge right here.

Also, the IPL is on proper now. Go watch it and take pleasure in!

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