Module 4

Question 1

List all the built in data types in python

Answer 1

Integers - int
Floating-point numbers - float
Strings - str
Booleans -bool
Lists - list
Tuples - tuple
Sets - set
Dictionaries - dict

Question 2

what data type is this ?

age = 6
type(age)

Answer 2

int

Question 3

age = 6.0
type(age)

what data type ?

Answer 3

float

Question 4

which value cannot be converted to any other type other than a float ?

Answer 4

NaN

Question 5

What is NoneType ?

Answer 5

NoneType is its own type, with only one possible value, None.

Question 6

which type has two values: True and False.

Answer 6

Boolean

Question 7

name_of_bed_monster = ‘Mike Wazowski’
name_of_bed_monster

Which DT is this ?

Answer 7

string

Question 8

If the string contains quotation marks or apostrophes, how do we declare those strings in python ?

Answer 8

we can use double quotes or triple single quotes, or triple-double quotes to define the string.

Question 9

len()

Answer 9

returns the length of the string

Question 10

.upper()

Answer 10

converts into upper case

Question 11

.lower()

Answer 11

converts into lower case

Question 12

count()

Answer 12

We can also count the number of times a substring or character is present in a string with .count().

Question 13

float(‘five’)

output ?

Answer 13

ValueError: could not convert string to float: ‘five’

Detailed traceback:
File “<string>", line 1, in <module></module></string>

Question 14

float(None)

output ?

Answer 14

TypeError: float() argument must be a string or a number, not ‘NoneType’

Detailed traceback:
File “<string>", line 1, in <module></module></string>

Question 15

sentence = “I always lose at least one sock when I do laundry.”
words = sentence.split()
words

output ?

Answer 15

[‘I’, ‘always’, ‘lose’, ‘at’, ‘least’, ‘one’, ‘sock’, ‘when’, ‘I’, ‘do’, ‘laundry.’]

Question 16

sentence = “I always lose at least one sock when I do laundry.” sentence.split(“e”)

output ?

Answer 16

This argument uses the character “e” to separate the string and discards the separator.

[‘I always los’, ‘ at l’, ‘ast on’, ‘ sock wh’, ‘n I do laundry.’]

Question 17

What type of elements are present in a List ?

Answer 17

The elements in a list can be any objects, and they don’t all need to have the same type.

Question 18

lists_of_lists = [[1,2], [‘buckle’, ‘My’, ‘Shoe’], 3, 4]
lists_of_lists

output ?

Answer 18

4

Question 19

Lists vs strings

Answer 19

Lists are mutable whereas strings are not

Question 20

append() ?

Answer 20

adds new item to end of list

Question 21

List down some verbs in regards to lists in python

Answer 21

max, sum, append

Question 22

What is difference between lists and tuples ?

Answer 22

They are represented with parentheses instead of square brackets, and
They are immutable

Question 23

Difference between sets and lists and tuple ?

Answer 23

They are unordered, meaning there is no element 0 and element 1, and
The values contained are unique - meaning there are no duplicate entries.

Question 24

Which verb can be used to add elements into a Set ?

Answer 24

add

Question 25

What is a dictionary ?

Answer 25

A dictionary is a map between key-value pairs.

Question 26

Can we change dictionary values ? If so, why ?

Answer 26

And since dictionaries are mutable, we can change the values.

Question 27

how can we add values to a dictionary ?

Answer 27

using a new key name.

Question 28

What kind of data types can the dictionary keys represent ?

Answer 28

Keys are not limited to strings and can be many different data types, including numerical values and tuples (but they cannot be dictionaries or lists).

Question 29

How can you access all the key-value pairs in a dictionary ?

Answer 29

We can access all of the key-value pairs in a dictionary with the verb .items().

Question 30

What is a panda series ?

Answer 30

A pandas Series is a one-dimensional array of values with an axis label, sort of like a list with a name attached to it.

Question 31

how can you find the dtype of a df column ?

Answer 31

We can use the noun .dtypes to find the dtype of a column.

Question 32

what are dtypes ?

Answer 32

columns in a Pandas dataframe have types called dtypes.

Question 33

dtype(‘O’)

What does O stand for ?

Answer 33

Object

Question 34

‘The monster under my bed’ * 3

Output ?

Answer 34

‘The monster under my bedThe monster under my bedThe monster under my bed’

Question 35

‘The monster under my bed’ + 1200

output ?

Answer 35

TypeError: can only concatenate str (not “int”) to str

Detailed traceback:
File “<string>", line 1, in <module></module></string>

Question 36

‘The monster under my bed’ + str(1200)

Output ?

Answer 36

‘The monster under my bed1200’

Question 37

What happens if we add lists ?

Answer 37

If we add lists, similarly to strings, the lists concatenate together to create a single list containing the elements of both lists.

Question 38

How can a list support subtraction and multiplication ?

Answer 38

subtraction and multiplication, are not supported when working with lists.

Question 39

If we have these columns in a df, which of these columns will show up when we call .describe () ?

name object
mfr_type object
calories object
protein int64
fiber float64
fat int64
carbo float64
rating float64
hot bool
dtype: object

Answer 39

only numerical columns, not objects

Question 40

which method needs to be used to convert the string column “calories” into an int column ?

Answer 40

cereal[‘calories’].astype(‘int’)

Question 41

cereal = cereal.assign(calories=cereal[‘calories’].astype(‘int’))

Whaat is going on here ?

Answer 41

We are using the assign verb to cast and assign the calories column

Question 42

How can you calculate the mean for the “hot” column of the “cereal” DF ?

Answer 42

cereal[‘hot’].mean()

Question 43

cereal.loc[:, ‘protein’: ‘carbo’].sum(axis=1)

What is going on here ?

Answer 43

calculating the sum of each row from protien till carbo

Question 44

axis=1 meaning ?

Answer 44

refers to the calculation being done for each row, across multiple columns,

Question 45

axis = 0 meaning ?

Answer 45

(which is the default for aggregation verbs) refers to the calculation for each column across multiple rows.

Question 46

cereal = cereal.assign(total_pffc=cereal.loc[:, ‘protein’: ‘carbo’].sum(axis=1))

Answer 46

calculating the sum and also assigning the sum value into a separate column

Question 47

new = cereal_amended[‘mfr_type’].str.split(‘-‘, expand=True)

Answer 47

splitting a column

Question 48

new = cereal_amended[‘mfr_type’].str.split(‘-‘, expand=False)

Answer 48

Our output is now a Pandas Series data type with a list containing both column values as the Series values.