Clean, Aggregate and Analyze Cricket dataset using Pandas and Pivot Tables

If you perform a quick value_counts() method on the Result column, you will realize that the column contains incoherent values. Make corrections as follows:

• Transform win to Win.
• Change ' Win D/L' to Win.
• Change ' Lose D/L' to Lose.

Note: You should notice that there is space in ' Win D/L' and ' Lose D/L'.

After cleaning the Win and Lose entries, you will realize that there are a few rows with No Result in the Result column. Remove them.

• Use the Team value if the Result is Win.
• Use the Opponent if the Result is Lose.

​Your solution should look like the image given below. ​

Calculate and rank the number of victories each team achieves annually. Group the cricket data by Year and Victorious_Team, then sort the results in descending order of both Victories and Year. Subsequently, index the sorted results by both Year and Victorious_Team and store them in a variable named victories_count_per_year.

Your solution should look like the image given below.

Determine the team that has the most victories each year from the victories_count_per_year DataFrame, which we generated in the 4th activity. Arrange the data in descending order and store these in the most_victories_per_year variable. Be sure to reset the index.

Your solution should look like the image given below.

Calculate the total runs scored by each team in the dataset by grouping the data by the Team column and summing the Run Scored for each group. Store it in the variable total_runs_by_team.

Your solution should be like this image which is given below.

Identify the team with the highest average batting strike rate. To do this, First remove rows with missing data in Avg Bat Strike Rate column.Then, group the data by the Team column, calculate the mean of the Avg Bat Strike Rate column for each team, and enter your answer in the input box below as a string.

Find out how many times each team has won the toss. Filter the dataset to include only rows where the Toss column indicates a successful win, then group the data by team to determine the count of toss wins for each team. Store this information in the variable toss_wins_by_team.

Your solution should be like this image which is given below.

Analyze the dominance of each team over its opponents using the historical match data. Create a variable named team_wins_sorted to store and display the count of wins for each team against every opponent. Group the dataset by Team and Opponent columns, then reset the index by the Wins column and sort the dataframe by Wins in descending order and Team in ascending order, Your solution should look like the image given below.

Find the player who has received the most Player of the Match awards at the Mirpur ground, along with the total number of awards they have won there.

Write the answer in this way: player_name, number of awards. For example, Rohit Sharma, 6.

Identify the players who have won the most Player of the Match awards in both the ODI and T20i game formats. Your answer should be written in this format: Player of the Match in ODI, Player of the Match in T20i. For example, if Rohit Sharma has won the most awards in ODI format and Virat Kohli has won the most awards in T20i format, your answer would be: Rohit Sharma, Virat Kohli.

Identify the players who have won the fewest Player of the Match awards in both the ODI and T20i game formats Your answer should be written in this format: Player of the Match in ODI, Player of the Match in T20i. For example, if Karn Sharma has won the fewest awards in ODI format and Virat Kohli has won the fewest awards in T20i format, your answer would be: Karn Sharma, Virat Kohli.

Determine which team has won the most matches at each cricket ground. Store it in the variable most_wins_per_ground.

Your solution should look like the image given below.

Find the player who has won the most Player of the Match awards in matches where their team lost. Write the Player's name in input box.