Pandas

Question 1

Select multiple columns of pandas dataframe?

Answer 1

df[[‘column 1’, ‘column 2’]]

Question 2

What are the three ways to index data in pandas?

Answer 2

df[ ]

df. loc[ ]
df. iloc[ ]

Question 3

How do you select one column from a dataframe as a series?

Answer 3

df[‘food’]

Question 4

How do you selection one column from a dataframe as a dataframe?

Answer 4

df[[‘food’]]

Question 5

How do you select multiple columns from a dataframe?

Answer 5

df[[‘color’, ‘food’, ‘score’]]

Question 6

Can you change the column order when selecting columns?

Answer 6

Yes

Question 7

When selecting a column from a dataframe as a series what happens to the column label?

Answer 7

Becomes the name of the series

Question 8

How do you select rows and columns using .loc?

Answer 8

df.loc[row_selection, column_selection]

Question 9

How do you select multiple rows and columns using .loc?

Answer 9

df.loc[[‘Dean’, ‘Cornelia’], [‘age’, ‘state’, ‘score’]]

Question 10

Do you need apotosphes when using list names for selecting rows / columns?

Answer 10

no

Question 11

Does .loc include the last item?

Answer 11

Yes

Question 12

What is iloc index on?

Answer 12

Integer index location

Question 13

What does df.iloc[3] find?

Answer 13

The 4th row

Question 14

To select multiple rows using integers .iloc what do you have to use?

Answer 14

A list df.iloc[[5, 2, 4]]

Question 15

How do you slice rows using .iloc?

Answer 15

df.iloc[3:5] (no double bracket required)

Question 16

Selecting rows and columns using iloc and integreers?

Answer 16

f.iloc[[2,3], [0, 4]]

Question 17

How do you select rows using a slice and columns using integers using iloc?

Answer 17

df.iloc[3:6, [1, 4]]

Question 18

What should you use the indexing operator for?

Answer 18

Columns
1) A string - returns a series
2) A list of strings - returns a dataframe
Rows
3) A slice
4) Booleans

Question 19

Can you use the indexing operator to select both rows and columns?

Answer 19

No

Question 20

Can you use the indexing operator to select rows?

Answer 20

Yes but don’t

Question 21

How do you set the index after reading in the csv

Answer 21

df.set_index()

Question 22

What can you use dot notation for?

Answer 22

Selecting a single column

Question 23

What 2 methods can you use for boolean selection?

Answer 23

[] and .loc

Question 24

What method can you use to test multiple conditions in the same column?

Answer 24

isin

Question 25

What method can find all missing values in a column?

Answer 25

isnull

Question 26

What operators are used in pandas?

Answer 26

And (&), or (|), and not (~)

Question 27

When should you use [] and .loc when using boolean queries

Answer 27

[] for just rows, .loc when both rows and columns

Question 28

When slicing using .loc do you need to put the slice in []?

Answer 28

No

Question 29

What is the difference between df[2,:] and df[[2],:]

Answer 29

The second returns a dataframe

Question 30

When grouping data, how can you sort by value?

Answer 30

df.sort_values()

Question 31

When grouping data, how can you sort by index?

Answer 31

df.sort_index()

Question 32

When grouping data and sorting by value, how do you sort by decreasing value?

Answer 32

df.sort_values(by = 'col name', ascending =False)

Question 33

How do you sort by more than one column at a time?

Answer 33

df.sort_values(by=['col1' , 'col2' ])

Question 34

How do you use more than one calculation on a column?

Answer 34

df.groupby('name').col.agg([min,max])

Question 35

How do you sort by more than one column?

Answer 35

df.sort_values(by = ['col1','col2'])

Question 36

What does size() do?

Answer 36

For Series, returns the number of rows

Question 37

What is the difference between size and count?

Answer 37

size includes NAN, count does not

Question 38

What is the format for dtype?

Answer 38

No brackets at the end

Question 39

How do you change the type of data?

Answer 39

astype()

Question 40

How can you add up how many rows of a boolean query are true?

Answer 40

.sum()

Question 41

How can you count by category?

Answer 41

.value_counts()

Question 42

What method can you change a column name?

Answer 42

df.rename()

Question 43

How do you rename column names?

Answer 43

df.rename(columns = {'original': 'new', 'original','new'}

Question 44

What do you use when you want to add more rows to data when it has the same column names?

Answer 44

pd.concat([df1,df2])

Question 45

When using concat do you pass it the df names or a list?

Answer 45

a list

Question 46

What is shape?

Answer 46

An attribute

Question 47

shape() or shape

Answer 47

shape

Question 48

What is dtype

Answer 48

An attribute

Question 49

dtype() or dtype

Answer 49

dtype

Question 50

How do you tell the data type of one column?

Answer 50

df['col'].dtype

Question 51

How do you get the count of the number of columns?

Answer 51

df.shape[1]

Question 52

How do you get the data types of a dataframe?

Answer 52

df.dtypes

Question 53

How do you work out how many unique instances there are?

Answer 53

df['col'].nunique()

Question 54

How do you get summary statistics for a dataframe?

Answer 54

df.describe()

Question 55

With the default options, what does describe give you?

Answer 55

Just the numerical columns

Question 56

What do you need to include in describe to give you a summary of all the columns?

Answer 56

include = all

Question 57

What is the code to import a CSV?

Answer 57

pd.read_csv(file, sep = 'x')

Question 58

How do you import a tab separated file?

Answer 58

sep = '\t'

Question 59

How do you divide elementwise in a data frame?

Answer 59

.div

Question 60

What does passing {} to agg do?

Answer 60

Selects a column and does that action to that column

Question 61

What does unstack do?

Answer 61

Pivot second level index to columns

Question 62

How do you pivot second level index?

Answer 62

.unstack()

Question 63

How do you convert data to a time / date?

Answer 63

pd.to_datetime

Question 64

How do you remove a column of data?

Answer 64

drop

Question 65

How does drop work?

Answer 65

You have to put the column in brackets rather than selection it before the method

Question 66

How do you remove blank columns?

Answer 66

dropna but make sure axis = 1

Question 67

How do you join 2 dataframes together?

Answer 67

pd.concat([frames])

Question 68

What can you do when joining 2 dataframes together to work out which came from each?

Answer 68

keys = [x,y]

Question 69

How do you generate random numbers?

Answer 69

np.random.randint

Question 70

What is an alternative to pd.concat?

Answer 70

pd.append()

Question 71

How do you identify where the data came from when concatenating?

Answer 71

pd.concat([frames], keys = ['x','y']

Question 72

What is an alternative to keys when merging 2 dataframes?

Answer 72

Instead of pd.concat([frames]), pass a dictionary pd.concat({'x' : data1, 'y': data2})

Question 73

By default what is the way that pd.concat joins the data?

Answer 73

Adds it to the bottom?

Question 74

How do you make pd.concat add data as columns?

Answer 74

pd.concat([frames], axis = 1)

Question 75

What is the default behaviour of merge?

Answer 75

It selects only the rows with the IDs that match between 2 DFs

Question 76

How do you get full outer join?

Answer 76

pd.merge(df1,df2, on = 'x', how = 'outer')

Question 77

When you merge different dataframes how to you tell which came from each column?

Answer 77

By default pandas adds _x / _y but you can change this using suffixes argumnet

Question 78

What does a right join do?

Answer 78

takes all the entries from the 'right' table and returns matching entries from the 'left'

Question 79

What are the different options for merging data?

Answer 79

concat, merge, join

Question 80

What are the defaults for concat, merge and join?

Answer 80

``` concat = row wise, outer merge = column wise, inner join = column wise, left ```

Question 81

How do concat and merge take DF?

Answer 81

concat([frames]) | merge(df1,df2)

Question 82

Why choose merge over concat?

Answer 82

concat has to match the elements along the axis, merge you specify how to match the data

Question 83

What datatypes can pd.concat take?

Answer 83

SEries or DF, i.e. not numpy arrays

Question 84

How do you read space separated data?

Answer 84

sep =' \s+'

Question 85

How do you read a string as a datetime?

Answer 85

from dateutil.parser import parse | parse('January 31, 2010')

Question 86

What are the different classes under datetime?

Answer 86

Datetime, date, time, timedelta

Question 87

In datetime format what is the difference between %Y and %y

Answer 87

% Y is 2020 and %y is 20

Question 88

How do you convert a date to a string?

Answer 88

strftime

Question 89

How do you get the weekend

Answer 89

.weekday()

Question 90

When you pass a dictionary to pd.DataFrame does it retain the original order of columns?

Answer 90

No - alphabetical as dictionaries are inherently unordered

Question 91

What is the syntax to create a dictionary

Answer 91

{'col': ['x','y','z'], | 'col2': [1,2,3]}

Question 92

How do you order the columns in pd.DataFrame?

Answer 92

pd.DataFrame(data, columns = [])

Question 93

pd.DataFrame or pd.Dataframe?

Answer 93

pd.DataFrame

Question 94

How do you set an index when using pd.DataFrame?

Answer 94

pd.DataFrame(data, columns = [], index = []) You cannot use a column name as an index - it must be the same length. But you can follow this with set_index()

Question 95

How do you establish if there are any duplicate values in a series?

Answer 95

is_unique (without parentheses)

Question 96

Can you use is_unique on a df?

Answer 96

No

Question 97

is_unique or is_unique()

Answer 97

is_unique

Question 98

Does slicing .loc with label names include the last item or not?

Answer 98

It includes the last item