Player Picks: A Dive into Series Filtering

Let's start off by creating a pandas series using the PlayerName column from the dataset to analyze player names.

Store the series in a variable named player_names.

Your result would look something like this :

Using the previously defined player_names series, find out the unique player names from it!

Use the unique() method for it.

Store your results in a variable named unique_player_names.

Your result would look something like this :

Filter the match_stats DataFrame using the IsGoalkeeper column to identify goalkeepers.

From the filtered result, select only the PlayerName column. The resulting Series will contain the names of all players who are goalkeepers.

Choose the correct name of the player who is a goalkeeper from the following options:

Create a series of player names, where the PlayerSurname is Alioski.

Store the result in a variable named player_surname.

Your result would look something like this :

Create a series of PlayerID, of players who have played for less than 30 minutes of PlayedTime.

Store the result in a variable named player_time.

Your result would look something like this :

Create a series of player names, who have played for the HomeTeamName - Wales.

Store the result in a variable named home_team_wales.

Your result would look something like this :

Create a series of PlayerID, of players where PlayerName is missing (null).

Store the result in a variable named missing_playername_id.

Your result would look something like this :

Using isin method create a series of player names who belong to either North Macedonia or Germany as their home team.

Store the result in a variable named multiple_team_name.

Your result would look something like this :

Using loc method, retrieve the PlayerID for the player named Stefan.