Pandas_JAS

Question 1

How to refer to a single column in a DF

Answer 1

DFname[‘columnname’].head() ##head() by default gives first 5 rows

Question 2

Technically, a single column is a ______ not a ______

Answer 2

Series; DataFrame

A Series is part of a DataFrame

Question 3

After typing code into the ______, highlight the code of interest and hit ____ to sent it to the ________

Answer 3

Editor
F9
REPL (console)

Question 4

Normally you to install third party libraries with a tool like _____, but if you’re using ________ it comes with Pandas installed

Answer 4

pip

Anaconda Python Bundle

Question 5

When importing Pandas the convention is to name it ______

Answer 5

pd.

Question 6

To load a csv file:

DFname = pd._______(_______,(DATA_DIR, ‘filename.csv’))

Answer 6

read_csv
path.join

DATA_DIR is a variable where you have given the path to your file, e.g., DATA_DIR = ‘/Users/UserName/PythonDirectory’

Question 7

What result does this give:

type(DFname)

Answer 7

pandas.core.frame.DataFrame

Question 8

what method is called to give (by default) the first 5 rows of a DataFrame?

Answer 8

head()

DFname.head()

Question 9

head() is a method because you can pass it the number of rows to print, ________ are used without passing any data in parenthesis

Answer 9

attributes

Question 10

the attribute _______ returns the names of each column in the DF

Answer 10

columns

Question 11

the attribute ______ returns the number of rows and columns in the DF

Answer 11

shape

Question 12

______ will turn any Series into a one-column DF

Answer 12

to_frame()

DFname.[‘columnname’].to_frame().head()

Question 13

to refer to multiple columns in a DF, you pass it a ______, and the result is _______

Answer 13

list
a DataFrame

DFname[[‘col1’, ‘col2’, ‘col3’]].head()
when working with multiple columns you must use double brackets

Question 14

An index is a built in column of ________. If you don’t designate a column as a specific index, the default is a ________.

Answer 14

row IDs

series of numbers starting at zero

Question 15

Indexes can be _______

Answer 15

Any type of data (strings, dates, etc)

Question 16

How to assign an index to a DF?

Answer 16

DFname.set_index(‘columnname’)

This creates a copy of the DF with this index.

Question 17

What must be passed to set_index() to create the index on the original DF?

Answer 17

Inplace = True

DFname.set_index(‘columnname’, inplace=True)

Or overwrite DFname
DFname = DFname.set_index(‘columnname’)

Question 18

Most DataFrame methods return copies unless ________ is explicity included

Answer 18

Inplace = True

Question 19

The opposite of set_index() is ______

Answer 19

Reset_index()

Question 20

How to sort a DF?

Answer 20

DFname.sort_value(‘columnname’, ascending = False, inplace = True)

Question 21

How to add a column from another DF?

Answer 21

NewDF [‘columnname’] = DFname[‘columnname’]

If the indexes are the same (pg 56 FantasyFootball), similar to cutting a excel column from one wookbook to another - the data has to match the row to which it belongs

Question 22

How to write to csv

Answer 22

to_csv()

DFname.to_csv(path.join(DATA_DIR, ‘filename.csv’), sep = ‘|’, index = false)

Sep = is separator, in this case a pipe |

Use index = True to include index in output

Question 23

Three primary column types

Answer 23

Number
String
Boolean

Question 24

Add a column with a value or math

Answer 24

DFname[‘newcolumn’] = 4
Or
DFname = DFname[‘column2’] * 8

DFname[[’newcolumn’,’column2’]]

Question 25

______ is a library for math

Answer 25

Numpy | Import numpy as np

Question 26

_____ method to return random rows

Answer 26

sample() DFname.sample(5) Returns a random 5 rows from DF

Question 27

How to concatenate strings?

Answer 27

+

Question 28

How to call string methods

Answer 28

.str str. upper() str. replace() DFname[‘columnname’].str.replace(‘.’, ‘’).str.lower()

Question 29

How to negate (change True to False and vis versa)?

Answer 29

~ | DFname[‘is_not’] = ~(DFname[‘column’] == ‘RB’)

Question 30

How to check multiple columns for True/False at once

Answer 30

(DF[[‘col1’,’col2’]] > 100) Returns new columns for each column with True or False for each row

Question 31

When you think ‘flagging rows’ you should be thinking

Answer 31

To make a column of booleans (true/false)

Question 32

Which method takes a function and _____ it to every row in a column?

Answer 32

apply() applies ``` Def is_skill(pos): Return pos in [‘rb’, ‘wr’] ``` DF[‘is_skill’] = DF[‘column’].apply(is_skill) Apply defined function is_skill to every row in the column Alternative: DF[‘is_skill’] = DF[‘pos’].apply( lambda x: x in [‘rb’,’wr’])

Question 33

Method to drop a column

Answer 33

DF.drop(‘col’, axis = 1, inplace = True) Default is to drop rows, axis = 1 changes to drop column

Question 34

Method to rename a column

Answer 34

DF.rename(columns={‘col1’ : ‘newname’}, inplace= True)

Question 35

How does numpy represent a missing value?

Answer 35

nan | Not a Number

Question 36

Methods to detect nan or null value?

Answer 36

isnull() notnull() DF.[‘col’].isnull Returns true/false in new column for each row

Question 37

Method to place custom value in place of nan?

Answer 37

fillna() | DF.[‘col’].fillna(-99)

Question 38

How to parse day, month, and year from string in non-pandas Python?

Answer 38

gameid = ‘2021090700’ ``` year = gameid[0:4] month = gameid[4:6] day = gameid[6:8] ```

Question 39

How to parse day, month, and year in Pandas, including changing the data type?

Answer 39

gameid = ‘2021090700’ DF[‘month’] = DF[‘gameid’].astype(str).str[4:6]

Question 40

How to change a datatype?

Answer 40

astype(str) | astype(int)

Question 41

Which attribute shows data type of each column in DF?

Answer 41

dtype

Question 42

Pandas calls a string (str) a ______

Answer 42

Object

Question 43

Name six summary statistic functions.

Answer 43

``` Mean() Std() Count() Sum() Min() Max() Note that min and max also work with strings, and goes by alpha ```

Question 44

What is the axis default for summary statistic functions, and how can it be changed?

Answer 44

Default is columns (axis = 0), change to summarize by rows by axis = 1

Question 45

In Summary Stats, what values will be returned for True & False

Answer 45

True = 1; False = 0

Question 46

.any() evaluates what?

Answer 46

If any value in a column is True

Question 47

.all() evaluates what?

Answer 47

If all values in a column are True

Question 48

Code to determine how often a certain criteria is met?

Answer 48

(pg[[‘rush_yards, ‘rec_yards’]] > 100).any(axis =1).sum()

Question 49

What does value_counts() do?

Answer 49

Summarizes each element in a column: DF[‘position’].valuecounts() WR 10 RB 20 QB 10 To summarize by frequency: DF[‘position’].valuecounts(normalize = True) WR 25% RB 50% QB 10%

Question 50

What does crosstab do, and what is the syntax?

Answer 50

Similar to valuecounts() but returns for two columns pd.crosstab(adp[‘team’], adp[‘position’]) Crosstab also takes a normalize argument

Question 51

How to see all Panda methods that operate on single columns? For DataFrames?

Answer 51

pd. series. and tab completing | pd. DataFrame. and tab completing