Player Stats: Wrangling with Football Data

Enter the name of the club with the highest number of players whose preferred foot is Either. If multiple clubs have the same number, choose the one that comes first alphabetically.

Calculate the average age of players for each club. Store the results in a dataframe named avg_age_per_club.

Compute the total value of players within each division. Store the result in a dataframe named total_value_per_division.

Find the maximum wage of players from each nation. Store the result in a dataframe named max_wage_per_nation.

Enter the country's three-letter country code (e.g., FRA for France).

Provide the answer in the following format: Nation with Lowest Height, Nation with Highest Weight (e.g., ALB, ZIM).

Enter name of the club which has players with most stamina. If the answer is Vélez enter Velez.

Enter the value rounded off to two decimal points.

Store the result in the variable avg_market_value

Store the result in player_counts_nation_pf

Store the result in a dataframe named club_aggregations

Create a custom function called age_range that computes the difference between the maximum and minimum ages. Apply this custom function using the agg() function to calculate the age range for each nation. Save the results in a dataframe named age_range_per_nation.

Find out the answers for the above questions and Select the correct answer from the options given below.

Create a custom function called variance() that computes the variance of a series. Then, calculate the mean value and the variance of current ability for players within each club. Store the result in a dataframe named club_statistics.

Define a function player_type that classifies players as Star if their current ability exceeds 180 and their potential ability exceeds 190; otherwise, classify them as Regular and create a new column Player Type to store these classifications.

Create a function called categorize_by_value that categorizes players based on their market value into three categories: - High for values greater than 50,000,000 - Medium for values between 20,000,001 and 50,000,000 - Low for values of 20,000,000 or below Then, create a new column named Value Type to store these categories.

Create a function called categorize_by_age that classifies players into three age groups:

• Young for ages below 25
• Mid-age for ages between 25 and 29
• Senior for ages 30 and above.

Then, create a new column named Age Group to store these classifications.

Provide your answer in the format: Player1, Player2 (e.g., Lionel Messi, Cristiano Ronaldo).

Define a function calculate_bmi that computes the Body Mass Index (BMI) of a player using their height and weight. First, convert the player's height from centimeters to meters. Then, apply the BMI formula: weight (kg) divided by height (m) squared. Create a new column BMI to store the calculated BMI values.

Use the groupby method to group players by their nation and then apply the transform method with a ranking function to assign a rank to each player's market value within their nation. The ranking is done in descending order, so the player with the highest value gets rank 1. Create a new column Value Rank to store these ranks.

Create a function named standardize that standardizes a series by subtracting the mean and dividing by the standard deviation. Apply this function to standardize the vision ratings within each column. Finally, add a new column named Standardized Vision to store the result.

Define a function named calculate_percentile to compute the percentile rank of each value in a series. Utilize this function to calculate the age percentile values within each club. Then, add a new column named Age Percentile to store these percentile ranks.

Create a function named deviation_from_mean to compute the deviation of each value from the mean. Utilize this function to calculate the mean pace deviation within each club. Finally, add a new column titled Pace Deviation to store the result.

Create a function called rank_wage that sorts player wages in descending order. Apply this function to calculate the ranked wages within each club. Introduce a new column named Wage Rank to store these rankings.