Practicing Filtering and Selection with FIFA data
Practicing Filtering and Selection with FIFA data Data Science Project
Intro to Pandas for Data Analysis

Practicing Filtering and Selection with FIFA data

In this project you'll have to perform multiple queries to analyze and answer questions about the FIFA (the game) data. You'll need to create different type of queries using the `.loc` or `.query` methods, boolean and comparison operators. This is the foundation of Data Analysis!
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Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

codevalidated

Select all the rows and the first 5 columns from the dataframe and store them in the variable `df_first_five_cols`

The first five columns with all their rows should be displayed to check the result.

codevalidated

Select all rows and the given Columns number 3,6,4,1 from the dataframe in the given order and store your selection in the variable `df_order_cols`

You can use .iloc or .loc. This question is extension of the previous question. Play with how to using column indices.

codevalidated

Filter out 50 rows from the Colum number 3,6,4,1 into the variable `fifty_rows`

This question is just extention of the above. Play with the row slicing.

codevalidated

Select the all rows and the Columns ['short_name','age','player_positions', 'overall', 'value_eur'] from the dataframe store your selection in the variable `named_cols`

Whenever the names of column are given always use df.loc[]. (Location indexer/Label Indexer) Whenever the indice are given for rows or columns always go for df.iloc[]. (Integer locator. And be carefull indices start from 0)

codevalidated

Filter our the first 13 rows and the Columns ['short_name','age','player_positions', 'overall', 'value_eur'] from the dataframe and store the result in the variable `named_cols_rows`

This question is just the extention of the previous question. Play with the row indices.

input

Find out how many players of overall rating greater than 90 exits in the dataset?

multiplechoice

Find out how many players of value_eur of less than or equal to 20000 in the dataset

codevalidated

Select the long_name and age of the players having the club_name='Manchester City' and store your selection in the variable name `long_name_age`

Here you have to incorporate the condition with filtering techniques learned in the previous page.

codevalidated

Filter out the players who are players of Liverpool club and who are from Brazil and store them in the variable `liverpool_brazil`

Note: Use club_name & nationality_name

multiplechoice

How many players are there who have either mentality_aggression > 91 or power_stamina < 80

codevalidated

Select all players from France who have either mentality_aggression > 91 or power_stamina < 80 and store the filtered data in the variable `france_player`

codevalidated

how many players have Left foot as their preferred_foot, also print their `short_name`,`age` and `preferred_foot`

Practicing Filtering and Selection with FIFA dataPracticing Filtering and Selection with FIFA data
Author

Jawad haider

This project is part of

Intro to Pandas for Data Analysis

Explore Skill Track
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