Pitch Perfect Analysis: Cricket Stats with Pandas¶

Setting the Stage¶

In [1]:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
df = pd.read_csv("preprocessed_data.csv")

In [2]:

# Take a peek at the data
df.head()

Out[2]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled
0	2024	Ruturaj Gaikwad	2	0	61	46.0	30.50	51	119.61	0	...	2
1	2023	Ruturaj Gaikwad	16	1	590	92.0	42.14	400	147.50	0	...	16
2	2022	Ruturaj Gaikwad	14	0	368	99.0	26.29	291	126.46	0	...	14
3	2021	Ruturaj Gaikwad	16	2	635	NaN	45.35	466	136.26	1	...	16
4	2020	Ruturaj Gaikwad	6	2	204	72.0	51.00	169	120.71	0	...	6

5 rows × 25 columns

In [3]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1130 entries, 0 to 1129
Data columns (total 25 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Year                 1130 non-null   int64  
 1   Player_Name          1130 non-null   object 
 2   Matches_Batted       1130 non-null   int64  
 3   Not_Outs             1130 non-null   int64  
 4   Runs_Scored          1130 non-null   int64  
 5   Highest_Score        700 non-null    float64
 6   Batting_Average      1130 non-null   float64
 7   Balls_Faced          1130 non-null   int64  
 8   Batting_Strike_Rate  1130 non-null   float64
 9   Centuries            1130 non-null   int64  
 10  Half_Centuries       1130 non-null   int64  
 11  Fours                1130 non-null   int64  
 12  Sixes                1130 non-null   int64  
 13  Catches_Taken        1130 non-null   int64  
 14  Stumpings            1130 non-null   int64  
 15  Matches_Bowled       1130 non-null   int64  
 16  Balls_Bowled         1130 non-null   int64  
 17  Runs_Conceded        1130 non-null   int64  
 18  Wickets_Taken        1130 non-null   int64  
 19  Best_Bowling_Match   419 non-null    float64
 20  Bowling_Average      1130 non-null   float64
 21  Economy_Rate         1130 non-null   float64
 22  Bowling_Strike_Rate  1130 non-null   float64
 23  Four_Wicket_Hauls    1130 non-null   int64  
 24  Five_Wicket_Hauls    1130 non-null   int64  
dtypes: float64(7), int64(17), object(1)
memory usage: 220.8+ KB

In [4]:

df.describe()

Out[4]:

	Year	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	Half_Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
count	1130.000000	1130.000000	1130.000000	1130.000000	700.000000	1130.000000	1130.000000	1130.000000	1130.000000	1130.000000	...	1130.000000	1130.000000	1130.000000	1130.000000	419.0	1130.000000	1130.000000	1130.000000	1130.000000	1130.000000
mean	2019.233628	9.016814	1.487611	132.253097	31.438571	17.442761	98.638053	102.314991	0.042478	0.735398	...	9.049558	92.946903	125.013274	4.459292	0.0	18.572442	5.471097	13.050177	0.079646	0.017699
std	3.992500	5.527504	1.697267	171.061138	30.996985	16.530296	122.684820	59.184873	0.269437	1.383147	...	5.516159	118.389409	153.815733	6.363274	0.0	23.806204	4.568269	15.710709	0.310478	0.131914
min	2008.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000
25%	2017.000000	3.000000	0.000000	5.000000	2.000000	1.000000	6.000000	66.165000	0.000000	0.000000	...	3.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000
50%	2020.000000	10.000000	1.000000	42.000000	22.000000	14.575000	38.000000	118.950000	0.000000	0.000000	...	10.000000	27.000000	44.500000	1.000000	0.0	15.340000	7.250000	12.000000	0.000000	0.000000
75%	2022.750000	14.000000	2.000000	227.000000	54.000000	28.287500	167.000000	139.820000	0.000000	1.000000	...	14.000000	168.750000	236.000000	8.000000	0.0	29.382500	8.800000	21.967500	0.000000	0.000000
max	2024.000000	19.000000	10.000000	973.000000	129.000000	101.000000	640.000000	400.000000	4.000000	9.000000	...	19.000000	408.000000	564.000000	32.000000	0.0	189.000000	36.000000	120.000000	3.000000	1.000000

8 rows × 24 columns

Warm Up Activities¶

In [6]:

# Run the cell below and find out which players we'll be expecting to see 🙌🏻

In [9]:

df['Player_Name'].unique().shape

Out[9]:

(206,)

1. Know the players!¶

In [10]:

# Write your code here
df['Runs_Scored'].max()

Out[10]:

2. Find the Maximum Runs Scored¶

In [ ]:

# Write your code here

3. How old is the dataset?¶

In [12]:

# Write your code here
df['Year'].unique().shape

Out[12]:

(17,)

4. What is the maximum Batting Strike Rate value?¶

In [14]:

df.columns

Out[14]:

Index(['Year', 'Player_Name', 'Matches_Batted', 'Not_Outs', 'Runs_Scored',
       'Highest_Score', 'Batting_Average', 'Balls_Faced',
       'Batting_Strike_Rate', 'Centuries', 'Half_Centuries', 'Fours', 'Sixes',
       'Catches_Taken', 'Stumpings', 'Matches_Bowled', 'Balls_Bowled',
       'Runs_Conceded', 'Wickets_Taken', 'Best_Bowling_Match',
       'Bowling_Average', 'Economy_Rate', 'Bowling_Strike_Rate',
       'Four_Wicket_Hauls', 'Five_Wicket_Hauls'],
      dtype='object')

In [15]:

# Write your code here
df['Batting_Strike_Rate'].max()

Out[15]:

400.0

Basic Filtering with `loc[]` and Sorting with `sort_values()`¶

5. Let's filter out players who played in the 2023!¶

In [16]:

players_2023 = df.loc[df['Year'] == 2023]
players_2023.head()

Out[16]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate
1	2023	Ruturaj Gaikwad	16	1	590	92.0	42.14	400	147.50	...	16	0	0	0	0.0	0.00	0.00	0.0
5	2023	Devon Conway	16	2	672	NaN	51.69	481	139.71	...	16	0	0	0	0.0	0.00	0.00	0.0
8	2023	MS Dhoni	16	8	104	NaN	26.00	57	182.46	...	16	0	0	0	0.0	0.00	0.00	0.0
25	2023	Ajinkya Rahane	14	1	326	NaN	32.60	189	172.49	...	14	0	0	0	0.0	0.00	0.00	0.0
42	2023	Ravindra Jadeja	16	4	190	NaN	23.75	133	142.86	...	16	342	431	20	NaN	21.55	7.56	17.1

5 rows × 25 columns

6. Top 5 Players with Max Not Outs¶

In [24]:

top_not_outs = df.sort_values(by='Not_Outs', ascending=False).head(5)
top_not_outs.head()

Out[24]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate
925	2022	Dinesh Karthik	16	10	330	NaN	55.00	180	183.33	...	16	0	0	0	0.0	0.0	0.00	0.0
17	2014	MS Dhoni	16	10	371	NaN	74.20	250	148.40	...	16	0	0	0	0.0	0.0	0.00	0.0
13	2018	MS Dhoni	16	9	455	NaN	75.83	302	150.66	...	16	0	0	0	0.0	0.0	0.00	0.0
222	2022	David Miller	16	9	481	NaN	68.71	337	142.73	...	16	0	0	0	0.0	0.0	0.00	0.0
616	2017	Hardik Pandya	17	9	250	NaN	35.71	160	156.25	...	17	156	213	6	NaN	35.5	8.19	26.0

5 rows × 25 columns

Just for exploration!¶

Analyzing High Scorers of the Season¶

Before we dive into the details of players scoring the most runs in 2023, let’s take a broader look at the run distribution over the years.
A histogram will help us understand in which years players scored more freely, potentially reflecting changes in playing conditions or rules.

In [ ]:

# Run the below cell to see the high run scorers of the season!

In [26]:

# Histogram of runs scored by year

plt.figure(figsize=(10, 6))
df.groupby('Year')['Runs_Scored'].sum().plot(kind='bar', color='lightcoral')
plt.title('Total Runs Scored Per Year')
plt.xlabel('Year')
plt.ylabel('Total Runs')
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

7. Top 5 Run Scorers of 2023¶

In [28]:

top_scorers_2023 = df.loc[df['Year'] == 2023].sort_values(by='Runs_Scored', ascending=False).head(5)
top_scorers_2023.head()

Out[28]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled
214	2023	Shubman Gill	17	2	890	129.0	59.33	564	157.80	3	...	17
888	2023	Faf du Plessis	14	1	730	84.0	56.15	475	153.68	0	...	14
5	2023	Devon Conway	16	2	672	NaN	51.69	481	139.71	0	...	16
903	2023	Virat Kohli	14	2	639	NaN	53.25	457	139.82	2	...	14
800	2023	Yashasvi Jaiswal	14	1	625	124.0	48.08	382	163.61	1	...	14

5 rows × 25 columns

8. Selecting Players with High Batting Average¶

In [30]:

df.columns

Out[30]:

Index(['Year', 'Player_Name', 'Matches_Batted', 'Not_Outs', 'Runs_Scored',
       'Highest_Score', 'Batting_Average', 'Balls_Faced',
       'Batting_Strike_Rate', 'Centuries', 'Half_Centuries', 'Fours', 'Sixes',
       'Catches_Taken', 'Stumpings', 'Matches_Bowled', 'Balls_Bowled',
       'Runs_Conceded', 'Wickets_Taken', 'Best_Bowling_Match',
       'Bowling_Average', 'Economy_Rate', 'Bowling_Strike_Rate',
       'Four_Wicket_Hauls', 'Five_Wicket_Hauls'],
      dtype='object')

In [33]:

high_average_players = df.loc[df['Batting_Average'] > 50.0]
high_average_players.head()

Out[33]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	...	Matches_Bowled
4	2020	Ruturaj Gaikwad	6	2	204	72.0	51.00	169	120.71	...	6
5	2023	Devon Conway	16	2	672	NaN	51.69	481	139.71	...	16
12	2019	MS Dhoni	15	7	416	NaN	83.20	309	134.62	...	15
13	2018	MS Dhoni	16	9	455	NaN	75.83	302	150.66	...	16
17	2014	MS Dhoni	16	10	371	NaN	74.20	250	148.40	...	16

5 rows × 25 columns

Just for Exploration¶

The year 2023 was a real treat for cricket fans – it saw the highest number of centuries! Was it the pitches, the bats, or just plain skill?
Let’s check out a histogram of centuries made over the years and see how 2023 stood out

In [ ]:

# Run the below cell to find out the maximum number of centuries hit each season!

In [35]:

# Histogram of centuries by year
import matplotlib.pyplot as plt

centuries_per_year = df.groupby('Year')['Centuries'].sum()

plt.figure(figsize=(10, 6))
centuries_per_year.plot(kind='bar', color='orange')
plt.title('Centuries Recorded Per Year')
plt.xlabel('Year')
plt.ylabel('Total Centuries')
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

Multi Condition Filtering¶

9. Compound Filtering for Centuries and Wickets¶

In [36]:

century_and_wickets = df.loc[(df['Centuries'] >= 1) & (df['Wickets_Taken'] >= 3)]
century_and_wickets.head()

Out[36]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
973	2023	Cameron Green	16	7	452	NaN	50.22	282	160.28	1	...	16	228	361	6	NaN	60.17	9.5	38.0	0	0

1 rows × 25 columns

10. Recent High Scores¶

In [38]:

high_scores_2021 = df.loc[(df['Year'] == 2021) & (df['Runs_Scored'] > 50)]
high_scores_2021.head()

Out[38]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate
3	2021	Ruturaj Gaikwad	16	2	635	NaN	45.35	466	136.26	1	...	16	0	0	0	0.0	0.00	0.00	0.00
10	2021	MS Dhoni	16	4	114	NaN	16.28	107	106.54	0	...	16	0	0	0	0.0	0.00	0.00	0.00
44	2021	Ravindra Jadeja	16	9	227	NaN	75.66	156	145.51	0	...	16	294	346	13	NaN	26.61	7.06	22.61
63	2021	Moeen Ali	15	1	357	58.0	25.50	260	137.30	0	...	15	152	161	6	NaN	26.83	6.35	25.33
70	2021	Shivam Dube	9	1	230	NaN	28.75	193	119.17	0	...	9	30	49	0	NaN	0.00	9.80	0.00

5 rows × 25 columns

11. Filter out the Efficient Strikers from the dataset¶

The distribution of batting strike rates is heavily concentrated around the median of approximately 125. However, there are exceptional instances where the batting strike rates far exceed the average, reaching as high as 350 and beyond. These outliers represent the top performers, indicating a highly aggressive batting style.

Here is the distribution of batting strike rates, with a reference line at the value 250, highlighting these extraordinary performances

In [ ]:

# Run the below cell for distribution

In [40]:

# Boxplot of batting strike rates
plt.figure(figsize=(10, 6))
sns.boxplot(x=df['Batting_Strike_Rate'])
plt.title('Boxplot of Batting Strike Rates')
plt.axvline(x=250, color='red', linestyle='--', label='Reference Line')
plt.xlabel('Batting Strike Rate')
plt.show()

In [41]:

efficient_strikers = df.loc[(df['Batting_Strike_Rate'] > 150) & (df['Runs_Scored'] > 200)]
efficient_strikers.head()

Out[41]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate
13	2018	MS Dhoni	16	9	455	NaN	75.83	302	150.66	...	16	0	0	0	0.0	0.0	0.00	0.00
18	2013	MS Dhoni	18	5	461	NaN	41.90	283	162.89	...	18	0	0	0	0.0	0.0	0.00	0.00
20	2011	MS Dhoni	16	4	392	NaN	43.55	247	158.70	...	16	0	0	0	0.0	0.0	0.00	0.00
25	2023	Ajinkya Rahane	14	1	326	NaN	32.60	189	172.49	...	14	0	0	0	0.0	0.0	0.00	0.00
45	2020	Ravindra Jadeja	14	6	232	50.0	46.40	135	171.85	...	14	218	318	6	NaN	53.0	8.75	36.33

5 rows × 25 columns

12. Filtering with Multiple Conditions¶

In [46]:

specific_players = df.loc[(df['Runs_Scored'] > 300) & (df['Wickets_Taken'] == 0)]

In [47]:

specific_players.head()

Out[47]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled
1	2023	Ruturaj Gaikwad	16	1	590	92.0	42.14	400	147.50	0	...	16
2	2022	Ruturaj Gaikwad	14	0	368	99.0	26.29	291	126.46	0	...	14
3	2021	Ruturaj Gaikwad	16	2	635	NaN	45.35	466	136.26	1	...	16
5	2023	Devon Conway	16	2	672	NaN	51.69	481	139.71	0	...	16
12	2019	MS Dhoni	15	7	416	NaN	83.20	309	134.62	0	...	15

5 rows × 25 columns

13. Who was the First Player to Achieve a Five Wicket Haul?!¶

In [50]:

df.columns

Out[50]:

Index(['Year', 'Player_Name', 'Matches_Batted', 'Not_Outs', 'Runs_Scored',
       'Highest_Score', 'Batting_Average', 'Balls_Faced',
       'Batting_Strike_Rate', 'Centuries', 'Half_Centuries', 'Fours', 'Sixes',
       'Catches_Taken', 'Stumpings', 'Matches_Bowled', 'Balls_Bowled',
       'Runs_Conceded', 'Wickets_Taken', 'Best_Bowling_Match',
       'Bowling_Average', 'Economy_Rate', 'Bowling_Strike_Rate',
       'Four_Wicket_Hauls', 'Five_Wicket_Hauls'],
      dtype='object')

In [54]:

first_five_wicket_haul = df.loc[df['Five_Wicket_Hauls'] > 0].nsmallest(1, 'Year')

In [55]:

first_five_wicket_haul.head()

Out[55]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
556	2008	Amit Mishra	6	0	37	31.0	12.33	42	88.09	0	...	6	119	142	11	NaN	12.9	7.15	10.81	0	1

1 rows × 25 columns

In [57]:

df[df['Five_Wicket_Hauls'] > 0].sort_values(by='Year').nsmallest(1, 'Five_Wicket_Hauls')

Out[57]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
556	2008	Amit Mishra	6	0	37	31.0	12.33	42	88.09	0	...	6	119	142	11	NaN	12.9	7.15	10.81	0	1

1 rows × 25 columns

Query Activities¶

14. Query for Five Wicket Hauls¶

In [60]:

five_wicket_hauls = df.query("Five_Wicket_Hauls >= 1")
five_wicket_hauls.head()

Out[60]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
53	2012	Ravindra Jadeja	19	2	191	48.0	15.91	151	126.49	...	19	210	273	12	NaN	22.75	7.80	17.50	0	1
199	2011	Ishant Sharma	12	5	4	NaN	0.00	5	80.00	...	12	264	314	11	NaN	28.54	7.13	24.00	0	1
338	2023	Mohit Sharma	14	1	0	NaN	0.00	1	0.00	...	14	265	361	27	NaN	13.37	8.17	9.81	2	1
402	2021	Andre Russell	10	2	183	54.0	26.14	120	152.50	...	10	114	188	11	NaN	17.09	9.89	10.36	0	1
437	2012	Sunil Narine	15	1	9	NaN	9.00	7	128.57	...	15	355	324	24	NaN	13.50	5.47	14.79	1	1

5 rows × 25 columns

By the way , comment down below which is your fav IPL team this season!¶

In [ ]:

15. Filtering with Query¶

In [62]:

active_high_scorers = df.query("Matches_Batted > 15 & Runs_Scored > 500")
active_high_scorers.head()

Out[62]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Best_Bowling_Match	Economy_Rate
1	2023	Ruturaj Gaikwad	16	1	590	92.0	42.14	400	147.50	0	...	16	0	0	0.0	0.0
3	2021	Ruturaj Gaikwad	16	2	635	NaN	45.35	466	136.26	1	...	16	0	0	0.0	0.0
5	2023	Devon Conway	16	2	672	NaN	51.69	481	139.71	0	...	16	0	0	0.0	0.0
36	2012	Ajinkya Rahane	16	2	560	NaN	40.00	433	129.33	1	...	16	0	0	0.0	0.0
124	2020	David Warner	16	2	548	NaN	39.14	407	134.64	0	...	16	1	2	NaN	12.0

5 rows × 25 columns

Multi Condition Sorting¶

16. Who is the Titan ?¶

In [64]:

sorted_by_runs_and_score = df.sort_values(by=['Runs_Scored', 'Highest_Score'], ascending = False)
sorted_by_runs_and_score.head()

Out[64]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Best_Bowling_Match	Economy_Rate
910	2016	Virat Kohli	16	4	973	113.0	81.08	640	152.03	4	...	16	6	13	NaN	13.0
214	2023	Shubman Gill	17	2	890	129.0	59.33	564	157.80	3	...	17	0	0	0.0	0.0
786	2022	Jos Buttler	17	2	863	116.0	57.53	579	149.05	4	...	17	0	0	0.0	0.0
127	2016	David Warner	17	3	848	NaN	60.57	560	151.42	0	...	17	0	0	0.0	0.0
257	2018	Kane Williamson	17	3	735	84.0	52.50	516	142.44	0	...	17	0	0	0.0	0.0

5 rows × 25 columns

17. How well do you understand the impact of economy rate changes over the years?¶

In [ ]:

Understanding Economy Rate¶

The economy rate is a key bowling metric that calculates the average runs conceded per over bowled—the lower the economy rate, the better the bowling!

From the below boxplot, it's clear that most bowlers consistently maintained a moderate economy rate over the years, with recent seasons like 2023 and 2024 showing a few instances of unusually high rates, hinting at some challenging games for the bowlers.

In [ ]:

# Run the below cell for understanding the 'Economy rate' over the seasons

In [66]:

df_clean = df.dropna(subset=['Economy_Rate'])

# Create a boxplot of economy rates across years
plt.figure(figsize=(14, 8))
sns.boxplot(x='Year', y='Economy_Rate', data=df_clean, palette='coolwarm')
plt.title('Boxplot of Economy Rates by Year')
plt.xlabel('Year')
plt.ylabel('Economy Rate')
plt.xticks(rotation=45)
plt.grid(True, linestyle='--', alpha=0.5)
plt.tight_layout()
plt.show()

In [ ]:

18. Sorting by Economy Rate for 2022¶

In [69]:

sorted_economy_2022 = df.loc[df['Year'] == 2022].sort_values(by='Economy_Rate', ascending=True)
sorted_economy_2022.head()

Out[69]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	...	Matches_Bowled
2	2022	Ruturaj Gaikwad	14	0	368	99.0	26.29	291	126.46	...	14
465	2022	Devdutt Padikkal	17	0	376	54.0	22.12	306	122.88	...	17
233	2022	Matthew Wade	10	0	157	35.0	15.70	138	113.77	...	10
237	2022	Wriddhiman Saha	11	1	317	68.0	31.70	259	122.39	...	11
253	2022	Kane Williamson	13	2	216	57.0	19.64	231	93.51	...	13

5 rows × 25 columns

Filtering using : `isin()`, `nsmallest()`, and `nlargest()`¶

19. Filtering Players by Specific Match Conditions¶

In [71]:

years = [2021, 2022]
players_recent_years = df.loc[df['Year'].isin(years)] 
players_recent_years.head()

Out[71]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled
2	2022	Ruturaj Gaikwad	14	0	368	99.0	26.29	291	126.46	0	...	14
3	2021	Ruturaj Gaikwad	16	2	635	NaN	45.35	466	136.26	1	...	16
6	2022	Devon Conway	7	1	252	87.0	42.00	173	145.66	0	...	7
9	2022	MS Dhoni	14	6	232	NaN	33.14	188	123.40	0	...	14
10	2021	MS Dhoni	16	4	114	NaN	16.28	107	106.54	0	...	16

5 rows × 25 columns

In [ ]:

Just for exploration¶

Let's plot the averages before we focus on players with extraordinary averages.
This line graph will help us see trends and anomalies in batting performances.

In [ ]:

# Run the below cell for understanding the  'Batting Averages' over the years

In [73]:

# Line plot of batting averages over the years
average_by_year = df.groupby('Year')['Batting_Average'].mean()

plt.figure(figsize=(10, 6))
average_by_year.plot(kind='line', marker='o', linestyle='-', color='navy')
plt.title('Average Batting Averages Over the Years')
plt.xlabel('Year')
plt.ylabel('Batting Average')
plt.grid(True)
plt.show()

20. Analyze Rohit Sharma's Matches Across Different Years¶

In [76]:

rohit_matches = df.loc[df['Player_Name'] == 'Rohit Sharma', ['Year', 'Matches_Batted']].sort_values(by='Year', ascending=True)
rohit_matches

Out[76]:

	Year	Matches_Batted
577	2008	13
576	2009	16
575	2010	16
574	2011	16
573	2012	17
572	2013	19
571	2014	15
570	2015	16
569	2016	14
568	2017	17
567	2018	14
566	2019	15
565	2020	12
564	2021	13
563	2022	14
562	2023	16
561	2024	1

Just for Exploration¶

Let’s use a violin plot to visualize the distribution of wickets taken across all matches, emphasizing performances in games where bowlers took more than five wickets.
The widest part of the plot indicates that most wickets taken fall around the 5 to 10 range, suggesting this is the most common performance for bowlers in significant matches.
The plot's long, thin tails extending towards the higher wicket counts, such as 20 and above, indicate these are rarer, exceptional performances.

In [ ]:

# Run the below cell

In [78]:

# Filter for high-performance games
high_wicket_games = df[df['Wickets_Taken'] >= 5]

# Violin plot for distribution of wickets in high-performance games
plt.figure(figsize=(12, 6))
sns.violinplot(x='Wickets_Taken', data=high_wicket_games)
plt.title('Distribution of Wickets Taken in High-Performance Games')
plt.xlabel('Wickets Taken')
plt.grid(True)
plt.show()

21. Who is the Purple Cap Winner of 2022?!¶

In [79]:

df.columns

Out[79]:

Index(['Year', 'Player_Name', 'Matches_Batted', 'Not_Outs', 'Runs_Scored',
       'Highest_Score', 'Batting_Average', 'Balls_Faced',
       'Batting_Strike_Rate', 'Centuries', 'Half_Centuries', 'Fours', 'Sixes',
       'Catches_Taken', 'Stumpings', 'Matches_Bowled', 'Balls_Bowled',
       'Runs_Conceded', 'Wickets_Taken', 'Best_Bowling_Match',
       'Bowling_Average', 'Economy_Rate', 'Bowling_Strike_Rate',
       'Four_Wicket_Hauls', 'Five_Wicket_Hauls'],
      dtype='object')

In [81]:

max_wickets_2022 = df.loc[df['Year'] == 2022].nlargest(1,'Wickets_Taken')
max_wickets_2022

Out[81]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
873	2022	Yuzvendra Chahal	17	1	5	5.0	5.0	8	62.5	0	...	17	408	527	27	NaN	19.52	7.75	15.11	1	1

1 rows × 25 columns

22. Who is the player with the Lowest Economy Rate?¶

In [84]:

lowest_economy_rate = df.loc[df['Wickets_Taken'] >= 1].nsmallest(1, 'Economy_Rate')
lowest_economy_rate

Out[84]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
828	2009	Ravichandran Ashwin	2	0	8	8.0	8.0	15	53.33	0	...	2	24	13	2	NaN	6.5	3.25	12.0	0	0

1 rows × 25 columns

23. Best Bowling Performance of that Player¶

In [87]:

best_bowling_performance = df.loc[df['Player_Name'] == 'Ravichandran Ashwin'].nlargest(1,'Best_Bowling_Match')
best_bowling_performance

Out[87]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
814	2024	Ravichandran Ashwin	1	0	0	0.0	0.0	0	0.0	0	...	1	24	35	1	NaN	35.0	8.75	24.0	0	0

1 rows × 25 columns

24. Identify Consistent Performers¶

In [99]:

year_run_df = df[(df['Year'].isin([2020, 2021, 2022, 2023])) & (df['Runs_Scored'] > 300)]
year_run_df

Out[99]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Five_Wicket_Hauls
1	2023	Ruturaj Gaikwad	16	1	590	92.0	42.14	400	147.50	0	...	16	0	0	0	0.0	0.0	0.0	0.0	0	0
2	2022	Ruturaj Gaikwad	14	0	368	99.0	26.29	291	126.46	0	...	14	0	0	0	0.0	0.0	0.0	0.0	0	0
3	2021	Ruturaj Gaikwad	16	2	635	NaN	45.35	466	136.26	1	...	16	0	0	0	0.0	0.0	0.0	0.0	0	0
5	2023	Devon Conway	16	2	672	NaN	51.69	481	139.71	0	...	16	0	0	0	0.0	0.0	0.0	0.0	0	0
25	2023	Ajinkya Rahane	14	1	326	NaN	32.60	189	172.49	0	...	14	0	0	0	0.0	0.0	0.0	0.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1018	2021	Rahul Tripathi	17	2	397	NaN	28.35	283	140.28	0	...	17	0	0	0	0.0	0.0	0.0	0.0	0	0
1028	2021	Mayank Agarwal	12	1	441	NaN	40.09	314	140.44	0	...	12	0	0	0	0.0	0.0	0.0	0.0	0	0
1029	2020	Mayank Agarwal	11	0	424	106.0	38.54	271	156.45	1	...	11	0	0	0	0.0	0.0	0.0	0.0	0	0
1043	2023	Heinrich Klaasen	12	2	448	104.0	49.78	253	177.08	1	...	12	0	0	0	0.0	0.0	0.0	0.0	0	0
1051	2022	Abhishek Sharma	14	0	426	75.0	30.43	320	133.13	0	...	14	24	38	0	NaN	0.0	9.5	0.0	0	0

102 rows × 25 columns

In [97]:

# Filter the dataframe for players who scored more than 300 runs in the years 2020, 2021, 2022, and 2023
year_run_df = [2020, 2021, 2022, 2023]
consistent_performers = df.loc[(df['Year'].isin(year_run_df)) & (df['Runs_Scored'] > 300)]
consistent_performers.head()

Out[97]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled
1	2023	Ruturaj Gaikwad	16	1	590	92.0	42.14	400	147.50	0	...	16
2	2022	Ruturaj Gaikwad	14	0	368	99.0	26.29	291	126.46	0	...	14
3	2021	Ruturaj Gaikwad	16	2	635	NaN	45.35	466	136.26	1	...	16
5	2023	Devon Conway	16	2	672	NaN	51.69	481	139.71	0	...	16
25	2023	Ajinkya Rahane	14	1	326	NaN	32.60	189	172.49	0	...	14

5 rows × 25 columns

In [104]:

year_run_df['Player_Name'].value_counts()[year_run_df['Player_Name'].value_counts() == 4]

Out[104]:

Player_Name
Sanju Samson        4
Shikhar Dhawan      4
Suryakumar Yadav    4
Nitish Rana         4
Virat Kohli         4
Shubman Gill        4
Faf du Plessis      4
Name: count, dtype: int64

In [101]:

# Check if a player is present in all four years using apply method and filter the dataframe accordingly
# Run the following code, run see if your output matches the expected output 

consistent_performers = year_run_df[year_run_df['Player_Name'].isin(year_run_df['Player_Name'].value_counts()[year_run_df['Player_Name'].value_counts() == 4].index)]
consistent_performers

Out[101]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	Centuries	...	Matches_Bowled	Balls_Bowled	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate
214	2023	Shubman Gill	17	2	890	129.0	59.33	564	157.80	3	...	17	0	0	0	0.0	0.0	0.00	0.00
215	2022	Shubman Gill	16	2	483	96.0	34.50	365	132.33	0	...	16	0	0	0	0.0	0.0	0.00	0.00
216	2021	Shubman Gill	17	0	478	57.0	28.11	402	118.90	0	...	17	0	0	0	0.0	0.0	0.00	0.00
217	2020	Shubman Gill	14	1	440	NaN	33.84	373	117.96	0	...	14	0	0	0	0.0	0.0	0.00	0.00
364	2023	Nitish Rana	14	1	413	75.0	31.77	293	140.96	0	...	14	53	75	3	NaN	25.0	8.49	17.67
365	2022	Nitish Rana	14	1	361	57.0	27.77	251	143.82	0	...	14	12	23	0	NaN	0.0	11.50	0.00
366	2021	Nitish Rana	17	3	383	80.0	29.46	314	121.97	0	...	17	12	12	0	NaN	0.0	6.00	0.00
367	2020	Nitish Rana	14	0	352	87.0	25.14	254	138.58	0	...	14	12	23	0	NaN	0.0	11.50	0.00
580	2023	Suryakumar Yadav	16	2	605	NaN	43.21	334	181.14	1	...	16	0	0	0	0.0	0.0	0.00	0.00
581	2022	Suryakumar Yadav	8	1	303	NaN	43.29	208	145.67	0	...	8	0	0	0	0.0	0.0	0.00	0.00
582	2021	Suryakumar Yadav	14	0	317	82.0	22.64	221	143.43	0	...	14	0	0	0	0.0	0.0	0.00	0.00
583	2020	Suryakumar Yadav	16	3	480	NaN	40.00	331	145.01	0	...	16	0	0	0	0.0	0.0	0.00	0.00
671	2023	Shikhar Dhawan	11	2	373	NaN	41.44	261	142.91	0	...	11	0	0	0	0.0	0.0	0.00	0.00
672	2022	Shikhar Dhawan	14	2	460	NaN	38.33	375	122.67	0	...	14	0	0	0	0.0	0.0	0.00	0.00
673	2021	Shikhar Dhawan	16	1	587	92.0	39.13	471	124.62	0	...	16	0	0	0	0.0	0.0	0.00	0.00
674	2020	Shikhar Dhawan	17	3	618	NaN	44.14	427	144.73	2	...	17	0	0	0	0.0	0.0	0.00	0.00
773	2023	Sanju Samson	14	2	362	NaN	30.17	236	153.39	0	...	14	0	0	0	0.0	0.0	0.00	0.00
774	2022	Sanju Samson	17	1	458	55.0	28.63	312	146.79	0	...	17	0	0	0	0.0	0.0	0.00	0.00
775	2021	Sanju Samson	14	2	484	119.0	40.33	354	136.72	1	...	14	0	0	0	0.0	0.0	0.00	0.00
776	2020	Sanju Samson	14	1	375	85.0	28.84	236	158.89	0	...	14	0	0	0	0.0	0.0	0.00	0.00
888	2023	Faf du Plessis	14	1	730	84.0	56.15	475	153.68	0	...	14	0	0	0	0.0	0.0	0.00	0.00
889	2022	Faf du Plessis	16	1	468	96.0	31.20	367	127.52	0	...	16	0	0	0	0.0	0.0	0.00	0.00
890	2021	Faf du Plessis	16	2	633	NaN	45.21	458	138.20	0	...	16	0	0	0	0.0	0.0	0.00	0.00
891	2020	Faf du Plessis	13	2	449	NaN	40.81	319	140.75	0	...	13	0	0	0	0.0	0.0	0.00	0.00
903	2023	Virat Kohli	14	2	639	NaN	53.25	457	139.82	2	...	14	0	0	0	0.0	0.0	0.00	0.00
904	2022	Virat Kohli	16	1	341	73.0	22.73	294	115.99	0	...	16	0	0	0	0.0	0.0	0.00	0.00
905	2021	Virat Kohli	15	1	405	NaN	28.92	339	119.46	0	...	15	0	0	0	0.0	0.0	0.00	0.00
906	2020	Virat Kohli	15	4	466	NaN	42.36	384	121.35	0	...	15	0	0	0	0.0	0.0	0.00	0.00

28 rows × 25 columns

25. Rank Players Based on Their Scores¶

In [108]:

df['Runs_Rank'] = df['Runs_Scored'].rank(method='max', ascending=False) 

In [109]:

print(df[['Player_Name', 'Year', 'Runs_Scored', 'Runs_Rank']])

          Player_Name  Year  Runs_Scored  Runs_Rank
0     Ruturaj Gaikwad  2024           61      515.0
1     Ruturaj Gaikwad  2023          590       23.0
2     Ruturaj Gaikwad  2022          368      143.0
3     Ruturaj Gaikwad  2021          635       14.0
4     Ruturaj Gaikwad  2020          204      307.0
...               ...   ...          ...        ...
1125  Mayank Markande  2023           21      674.0
1126  Mayank Markande  2022            0     1130.0
1127  Mayank Markande  2021            0     1130.0
1128  Mayank Markande  2019            6      838.0
1129  Mayank Markande  2018           21      674.0

[1130 rows x 4 columns]

Just for Exploration !¶

Every year we see a great number of new talents!
Let's plot a pie chart to understand the percentage of number of new debutants every year!

In [ ]:

# Run the below cell for understanding the % of debutants every season!

In [114]:

df.sort_values('Year').drop_duplicates('Player_Name')['Year']

Out[114]:

23     2008
577    2008
918    2008
556    2008
686    2008
       ... 
277    2024
310    2024
886    2024
142    2024
173    2024
Name: Year, Length: 206, dtype: int64

In [111]:

df['Debut_Year'] = df.sort_values('Year').drop_duplicates('Player_Name')['Year']
debutants_count = df[df['Debut_Year'].notna()]['Debut_Year'].value_counts().sort_index()

plt.figure(figsize=(10, 8))
plt.pie(debutants_count, labels=debutants_count.index, autopct='%1.1f%%', startangle=140, colors=plt.cm.Paired(range(len(debutants_count))))
plt.title('Percentage of Debutants by Year')
plt.axis('equal')  
plt.show()

26. Career Starters¶

In [116]:

debutants_2022 = df.loc[df['Debut_Year'] == 2022]
debutants_2022

Out[116]:

	Year	Player_Name	Matches_Batted	Not_Outs	Runs_Scored	Highest_Score	Batting_Average	Balls_Faced	Batting_Strike_Rate	...	Runs_Conceded	Wickets_Taken	Best_Bowling_Match	Bowling_Average	Economy_Rate	Bowling_Strike_Rate	Four_Wicket_Hauls	Runs_Rank	Debut_Year
6	2022	Devon Conway	7	1	252	87.0	42.00	173	145.66	...	0	0	0.0	0.00	0.00	0.00	0	251.0	2022.0
83	2022	Daryl Mitchell	2	0	33	17.0	16.50	44	75.00	...	27	0	NaN	0.00	13.50	0.00	0	614.0	2022.0
95	2022	Maheesh Theekshana	9	1	7	NaN	7.00	7	100.00	...	261	12	NaN	21.75	7.46	17.50	1	823.0	2022.0
96	2022	Mukesh Choudhary	13	1	6	4.0	6.00	6	100.00	...	424	16	NaN	26.50	9.32	17.06	1	838.0	2022.0
104	2022	Prashant Solanki	2	0	0	0.0	0.00	0	0.00	...	38	2	NaN	19.00	6.33	18.00	0	1130.0	2022.0
105	2022	Simarjeet Singh	6	2	7	NaN	7.00	8	87.50	...	138	4	NaN	34.50	7.67	27.00	0	823.0	2022.0
111	2022	Matheesha Pathirana	2	0	0	0.0	0.00	0	0.00	...	52	2	NaN	26.00	7.61	20.50	0	1130.0	2022.0
150	2022	Tristan Stubbs	2	0	2	2.0	1.00	4	50.00	...	0	0	0.0	0.00	0.00	0.00	0	911.0	2022.0
262	2022	Abhinav Manohar	8	1	108	43.0	18.00	75	144.00	...	0	0	0.0	0.00	0.00	0.00	0	426.0	2022.0
265	2022	B. Sai Sudharsan	5	1	145	NaN	36.25	114	127.19	...	0	0	0.0	0.00	0.00	0.00	0	373.0	2022.0
267	2022	Darshan Nalkande	2	0	0	0.0	0.00	0	0.00	...	59	2	NaN	29.50	11.42	15.50	0	1130.0	2022.0
313	2022	Sai Kishore	5	0	0	0.0	0.00	0	0.00	...	121	6	NaN	20.17	7.56	16.00	0	1130.0	2022.0
398	2022	Ramandeep Singh	5	2	45	NaN	22.50	40	112.50	...	54	6	NaN	9.00	9.00	6.00	0	556.0	2022.0
421	2022	Dushmantha Chameera	12	4	43	17.0	10.75	28	153.57	...	384	9	NaN	42.67	8.73	29.33	0	564.0	2022.0
424	2022	Harshit Rana	2	1	2	2.0	2.00	2	100.00	...	51	1	NaN	51.00	10.20	30.00	0	911.0	2022.0
439	2022	Vaibhav Arora	5	2	5	2.0	2.50	13	38.46	...	164	3	NaN	54.67	9.20	35.67	0	851.0	2022.0
488	2022	Ayush Badoni	13	3	161	54.0	20.13	130	123.85	...	11	2	NaN	5.50	5.50	6.00	0	350.0	2022.0
525	2022	Prerak Mankad	1	1	4	NaN	0.00	1	400.00	...	0	0	0.0	0.00	0.00	0.00	0	866.0	2022.0
536	2022	Mohsin Khan	9	2	23	NaN	23.00	16	143.75	...	197	14	NaN	14.07	5.97	14.14	1	656.0	2022.0
579	2022	Dewald Brevis	7	0	161	49.0	23.00	113	142.48	...	8	1	NaN	8.00	16.00	3.00	0	350.0	2022.0
602	2022	N. Tilak Varma	14	3	397	61.0	36.09	303	131.02	...	13	0	NaN	0.00	6.50	0.00	0	118.0	2022.0
626	2022	Romario Shepherd	3	1	58	NaN	29.00	41	141.46	...	98	3	NaN	32.67	10.89	18.00	0	526.0	2022.0
643	2022	Kumar Kartikeya Singh	4	0	3	3.0	3.00	5	60.00	...	102	5	NaN	20.40	7.85	15.60	0	882.0	2022.0
689	2022	Jitesh Sharma	12	2	234	44.0	29.25	143	163.64	...	0	0	0.0	0.00	0.00	0.00	0	274.0	2022.0
714	2022	Shashank Singh	10	1	69	NaN	17.25	47	146.81	...	20	0	NaN	0.00	10.00	0.00	0	490.0	2022.0
813	2022	Rovman Powell	14	2	250	NaN	25.00	167	149.70	...	17	0	NaN	0.00	17.00	0.00	0	254.0	2022.0
838	2022	Kuldeep Sen	7	1	0	NaN	0.00	3	0.00	...	237	8	NaN	29.63	9.42	18.88	1	1130.0	2022.0
941	2022	Suyash S Prabhudessai	5	0	67	34.0	13.40	59	113.56	...	0	0	0.0	0.00	0.00	0.00	0	498.0	2022.0
978	2022	Akash Deep	5	0	0	0.0	0.00	2	0.00	...	205	5	NaN	41.00	10.88	22.60	0	1130.0	2022.0
999	2022	Yash Dayal	9	0	0	0.0	0.00	1	0.00	...	296	11	NaN	26.91	9.25	17.45	0	1130.0	2022.0
1100	2022	Fazalhaq Farooqi	3	1	2	NaN	0.00	8	25.00	...	110	2	NaN	55.00	9.17	36.00	0	911.0	2022.0

31 rows × 27 columns

Statement of Completion#4467ddd9

Intro to Pandas for Data Analysis

Explore, Filter, Sort and Analyse Cricket Sports Data Using Pandas

Pitch Perfect Analysis: Cricket Stats with Pandas¶

Setting the Stage¶

Warm Up Activities¶

1. Know the players!¶

2. Find the Maximum Runs Scored¶

3. How old is the dataset?¶

4. What is the maximum Batting Strike Rate value?¶

Basic Filtering with loc[] and Sorting with sort_values()¶

5. Let's filter out players who played in the 2023!¶

6. Top 5 Players with Max Not Outs¶

Just for exploration!¶

Analyzing High Scorers of the Season¶

7. Top 5 Run Scorers of 2023¶

8. Selecting Players with High Batting Average¶

Just for Exploration¶

Multi Condition Filtering¶

9. Compound Filtering for Centuries and Wickets¶

10. Recent High Scores¶

11. Filter out the Efficient Strikers from the dataset¶

12. Filtering with Multiple Conditions¶

13. Who was the First Player to Achieve a Five Wicket Haul?!¶

Query Activities¶

14. Query for Five Wicket Hauls¶

By the way , comment down below which is your fav IPL team this season!¶

15. Filtering with Query¶

Multi Condition Sorting¶

16. Who is the Titan ?¶

17. How well do you understand the impact of economy rate changes over the years?¶

Understanding Economy Rate¶

18. Sorting by Economy Rate for 2022¶

Filtering using : isin(), nsmallest(), and nlargest()¶

19. Filtering Players by Specific Match Conditions¶

Just for exploration¶

20. Analyze Rohit Sharma's Matches Across Different Years¶

Just for Exploration¶

21. Who is the Purple Cap Winner of 2022?!¶

22. Who is the player with the Lowest Economy Rate?¶

23. Best Bowling Performance of that Player¶

24. Identify Consistent Performers¶

25. Rank Players Based on Their Scores¶

Just for Exploration !¶

26. Career Starters¶

GAME-OVER 😔¶

Basic Filtering with `loc[]` and Sorting with `sort_values()`¶

Filtering using : `isin()`, `nsmallest()`, and `nlargest()`¶