3.2 Modify or Create new columns of Data

Question 1

How do you create a column?

Answer 1

pg[‘yellow_cards’] = 1

where the datafile is loaded in as “pg”, and “yellow_cards” is the column that we are creating and 1 is the assigned value.

Question 2

What are the three main column types?

Answer 2

Number, String and Boolean

Question 3

How would you calculate a shot-goal percentage from a datafile?

Answer 3

pg[‘shot_pct’] = 100*pg[‘goal’]/pg[‘shot’]

–> This creates a new column called “shot_pct”, which is calculated by dividing the column “goal” by “shot”, and multiplying this by 100 (for a percentage)

Question 4

Which python library is more “raw” and “math” oriented?

Answer 4

Numpy (usually imported as “np”)

Question 5

How do you output a number of random rows from your DataFrame?

Answer 5

use sample()

Question 6

How do you manipulate a column into becoming a string?

Answer 6

call .str on a column

Question 7

How can you concatenate string columns together?

Answer 7

Use ‘+’!

(pg[‘name’] + “, “ + pg[‘pos’]).sample(5)

–> prints a string of “name, pos”

Question 8

How could you use booleans to check if a player is a defender?

Answer 8

pg[‘is_defender’] = (pg[‘pos’] == ‘DEF’)

–> returns TRUE when he is a defender, FALSE when the player is NOT a defender

Question 9

What should you add to a string if it has backslashes (as that may cause an error…)?

Answer 9

r”windows path here”

an r preceding a string converts it to a raw string and ignores any of the backlashes within the string, useful for when using windows paths

Question 10

What is “ | “ used for in python?

Answer 10

Short way of calling the __ or __ method

Question 11

How do you reverse True and False? (eg. you want true values to be false?)

Answer 11

add a ~ (twisty line) in front, so:

pg[‘is_a_mid_or_fwd’] = ~((pg[‘pos’] == ‘MID’) |

Question 12

How do you have your column data go through a function?

Answer 12

create a function (eg. “is_south_america”)

pg[‘is_SA’] = pg[‘team’].apply(is_south_america)

So use “.apply(function)”

Question 13

How can you rename a column?

Answer 13

pg.rename(columns={‘min’: ‘minutes’}, inplace=True)

call the rename method and pass it through a dictionary, for example renaming ‘min’ to ‘minutes’

Question 14

What are missing values represented by in Pandas?

Answer 14

np.nan

(because numpy is the library that pandas is built upon, and nan stands for “not a number”)

Question 15

What functions can be used to determine whether values are missing?

Answer 15

isnull and notnull

–> These return a column of booleans indicating whether the column is or is not missing values

Question 16

What method can be used to replace all missing values with a value of choice?

Answer 16

.fillna(any_value_here)

Question 17

If a date is expressed as a string [‘20180618’], so 18 june 2018, how would you print exclusively the month?

Answer 17

pg[‘month’] = pg[‘date’].astype(str).str[4:6]

–> This creates a new column called ‘month’, which looks at the date string and selects only values 4:6.

Question 18

What can .astype() be used for?

Answer 18

The astype() method returns a new DataFrame where the data types has been changed to the specified type.

Question 19

How can you drop a column?

Answer 19

pg.drop(‘name’, axis = 1, inplace=True)

This drops a column from a dataframe, the “axis = 1” is necessary because the default behaviour of drop is to operate on rows. Its much easier to pass axis=1 so that it’ll drop the name of the column you provide instead.