Basic Python

Question 1

Write a lambda function that adds a column to the dataframe ‘shoes’ that returns Not Vegan if the shoe is made out of leather and Vegan if it is not.

Answer 1

df[‘vegan’] = df.shoe_type.apply(lambda x: ‘Not_Vegan’ if x == ‘Leather’ else ‘Vegan’)

Question 2

Use the columns ‘gender’ and ‘last_name’ from ‘df’ to make a new column called ‘salutation’ that returns ‘Dear Mr.’ and the last name if gender is male or ‘Dear Ms.’ and last name if female.

Answer 2

df[‘salutations’] = df.apply(lambda row: “Dear Mr. “ + row.last_name if row.gender == ‘male’ else “Dear Ms. “, axis = 1)

Question 3

Get the last name only from a column that has the format “Skye Long” called “name”

Answer 3

get_last_name = lambda x: x.split()[-1]

df[‘last_name’] = df.name.apply(get_last_name)

Question 4

Rename individual, compared to all, columns in a df

Answer 4

df.rename(columns = {‘old’:’new, ‘old2’:’new2’}, inplace = True)

Question 5

Rename the following columns in order:

name, last name, user

Answer 5

df.columns = [‘first_name’, ‘last_name’, ‘id’]

Only works if you have the same # of columns as in the original. You can also accidentally overwrite something. Better to use the df.rename(columns={old:new}, inplace = True).

Question 6

What would this produce:

[1,2] + [3,4]

Answer 6

[1,2,3,4]

Question 7

What is the simple syntax to apply lambda to a row in a dataframe?

Answer 7

df.apply(lambda row: “what will be returned” row[‘name of row to act on’] , axis = 1)

Example:

total_earned = lambda row: (row.hourly_wage * 40) + ((row.hourly_wage * 1.5) * (row.hours_worked - 40)) if row.hours_worked > 40 else row.hourly_wage * row.hours_worked

df[‘total_earned’] = df.apply(total_earned, axis = 1)

Here the lambda is written seperatly, but it could be combined

Question 8

What is the syntax for the .apply attribute?

Answer 8

df.col.apply(func)

EXample:

get_last_name = lambda x: x.split()[-1]

df[‘last_name’] = df.name.apply(get_last_name)

df.apply(row func, axis = 1)

Question 9

Nested List Comprehensions

List 1 = [0,1,2,3,4,5]

List2 = [‘a’,’b’,’c’,’d’,’e’,’f’]

I want the value of list 2 for every value of list 1 that is less than 3

Answer 9

new_list = [list2[i] for i in range (0, len(list2)) if list1[i] < 3]

Question 10

Create a new list named double_nums by multiplying each number in nums by two

nums = [4, 8, 15, 16, 23, 42]

Answer 10

double_nums = [i * 2 for i in nums]

Question 11

create a list from 0 to 5

Answer 11

new_list = range(6)

Question 12

Write this for loop as a list comp:

for x in l:

if x>=45: x+1

else: x+5

Answer 12

[x+1 if x >=45 else x + 5 for x in l]

Question 13

Write this as an if statement

[x+1 for x in l if x >= 45]

Answer 13

if x >= 45:

x+1

Question 14

What will this code produce?

nums = [4, 8, 15, 16, 23, 42]

parity = [0 if i%2 == 0 else 1 for i in nums]

print(parity)

Answer 14

[0, 0, 1, 0, 1, 0]

Question 15

Write this as a list comp:

nums2 = [4, 8, 15, 16, 23, 42]

parity2 = []

for i in nums2:

if i%2 == 0:

parity2.append(0)

else:

parity2.append(1)

Answer 15

parity = [0 if i%2 == 0 else 1 for i in nums]

Question 16

If numbers are above 45 then add 1, if num <10 subtract 1, else add 5

l = [22, 13, 45, 50, 98, 69, 43, 44, 1]

Answer 16

for i in l:

if i >45:

l_2.append(i+1)

elif i

l_2.append(i-1)

else:

l_2.append(i+5)

print(l_2)

l_3 = [i+1 if i > 45 else (i-1 if i<10 else i+5) for i in l]

Question 17

Create a new list named first_character that contains the first character from every name in the list names

names = [“Elaine”, “George”, “Jerry”, “Cosmo”]

Answer 17

names = [“Elaine”, “George”, “Jerry”, “Cosmo”]

first_character = [i[0] for i in names]

print(first_character)

Question 18

Create a new list called greater_than_two, in which an entry at position i is True if the entry in nums at position i is greater than 2.

Answer 18

nums = [5, -10, 40, 20, 0]

greater_than_two = [True if i >2 else False for i in nums]

print(greater_than_two)

Question 19

Create a new list named product that contains the product of each sub-list of nested_lists

Answer 19

product = [x1 * x2 for (x1, x2) in nested_lists]

Question 20

Create a new list named greater_than that contains True if the first number in the sub-list is greater than the second number in the sub-list, and False otherwise.

Answer 20

nested_lists = [[4, 8], [16, 15], [23, 42]]

greater_than = [True if x1 > x2 else False for (x1, x2) in nested_lists]

https://colab.research.google.com/github/skyelong/Code-Academy/blob/master/Python/List_Comprehension_Code_Challenge.ipynb#scrollTo=f9aK6u58ROeq

Question 21

Create a new list named first_only that contains the first element in each sub-list of nested_lists.

Answer 21

nested_lists = [[4, 8], [16, 15], [23, 42]]

first_only = [x1 for (x1, x2) in nested_lists]

https://colab.research.google.com/github/skyelong/Code-Academy/blob/master/Python/List_Comprehension_Code_Challenge.ipynb#scrollTo=f2y5829cSLPQ

Question 22

Use list comprehension and the zip function to create a new list named sums that sums corresponding items in lists a and b. For example, the first item in the new list should be 5 from adding 1 and 4 together.

a = [1.0, 2.0, 3.0]

b = [4.0, 5.0, 6.0]

Answer 22

sums = [x1 + x2 for (x1, x2) in zip(a,b)]

https://colab.research.google.com/github/skyelong/Code-Academy/blob/master/Python/List_Comprehension_Code_Challenge.ipynb#scrollTo=zzd_TyqLS24I&line=4&uniqifier=1

Question 23

You’ve been given two lists: a list of capitals and a list of countries. Create a new list named locations that contains the string “capital, country” for each item in the original lists. For example, if the 5th item in the capitals list is “Lima” and the 5th item in the countries list is “Peru”, then the 5th item in the new list should be “Lima, Peru”

Answer 23

capitals = [“Santiago”, “Paris”, “Copenhagen”]

countries = [“Chile”, “France”, “Denmark”]

locations = [x1 + “, “ + x2 for (x1,x2) in zip(capitals, countries)]

https://colab.research.google.com/github/skyelong/Code-Academy/blob/master/Python/List_Comprehension_Code_Challenge.ipynb#scrollTo=jOeCN_NHUIy7

Question 24

You’ve been given two lists: a list of names and a list of ages. Create a new list named users that contains the string “Name: name, Age: age” for each pair of elements in the original lists. For example, if the 5th item in the names list is “John”and the 5th item in ages is 42, then the 5th item in the new list should be”Name: John, Age: 42”.

As you did in the previous exercise, concatenate your strings together using +. Make sure to add proper capitalization and spaces.

Answer 24

names = [“Jon”, “Arya”, “Ned”]

ages = [14, 9, 35]

users = [“Name: “ + x1 + “, Age: “ + str(x2) for (x1,x2) in zip(names, ages)]

print(users)

https://colab.research.google.com/github/skyelong/Code-Academy/blob/master/Python/List_Comprehension_Code_Challenge.ipynb#scrollTo=6Ol4V5VjUva5

Question 25

Create a new list named greater_than that contains True or False depending on whether the corresponding item in list a is greater than the one in list b. For example, if the 2nd item in list a is 3, and the 2nd item in list b is 5, the 2nd item in the new list should be False.

Answer 25

a = [30, 42, 10]

b = [15, 16, 17]

greater_than2= [True if x1 > x2 else False for (x1, x2) in zip(a,b)]

print(greater_than2)

https://colab.research.google.com/github/skyelong/Code-Academy/blob/master/Python/List_Comprehension_Code_Challenge.ipynb#scrollTo=qefmBpfVVkUh

Question 26

Create a lambda function named contains_a that takes an input word and returns True if the input contains the letter ‘a’. Otherwise, return False.

Answer 26

contains_a = lambda n: “a” in n

https://colab.research.google.com/github/skyelong/Code-Academy/blob/master/Python/Lambda.ipynb#scrollTo=bnAxko3shzmc

Question 27

Create a lambda function named long_string that takes an input str and returns True if the string has over 12 characters in it. Otherwise, return False.

Answer 27

long_string = lambda x: True if len(x) > 12 else False

Question 28

Create a lambda function named ends_in_a that takes an input str and returns True if the last character in the string is an a. Otherwise, return False.

Answer 28

ends_in_a = lambda x: True if x[-1] == “a” else False

Question 29

Create a lambda function named add_random that takes an input named num. The function should return num plus a random integer number between 1 and 10 (inclusive).

Answer 29

add_random = lambda num: num + random.randint(1,10)

Question 30

You run an online clothing store called Panda’s Wardrobe. You need a DataFrame containing information about your products.

Create a DataFrame with the following data that your inventory manager sent you:

Product ID Product Name Color

1 t-shirt blue

2 t-shirt green

3 skirt red

4 skirt black

Answer 30

df1 = pd.DataFrame({

‘Product ID’: [1, 2, 3, 4],

‘Product Name’: [‘t-shirt’, ‘t-shirt’, ‘skirt’, ‘skirt’],

‘Color’: [‘blue’, ‘green’, ‘red’, ‘black’]})

Question 31

from this dataframe select all row for clinic north and clinic south

Answer 31

clinic_north_south = df[[‘clinic_north’, ‘clinic_south’]]

Question 32

Use iloc to return the third row from df

Answer 32

march = df.iloc[2]

Question 33

select rows 1-5 with .iloc from ‘df’

Answer 33

df_1 = df.iloc[1:6]

Question 34

select rows from ‘df’ where the month is equal to ‘january’ and store it into a new series

Answer 34

january = df[df.month == ‘January’]

Question 35

select all rows for both ‘march’ and ‘april’ from df and store them in a new df called march_april

Answer 35

march_april = df[(df.month == ‘March’) | (df.month == ‘April’)]

Question 36

Use .isin to find rows containing ‘january’ and ‘march’ in the column month

Answer 36

january_february_march = df[df.month.isin([‘January’, ‘February’, ‘March’])

Question 37

reset the index for a dataframe that you have subsettted. remove the old index

Answer 37

df2.reset_index(drop=True, inplace = True)

Question 38

Create a new column that changes the names to lower case using the str.lower and .apply

df = pd.DataFrame([

[‘JOHN SMITH’, ‘john.smith@gmail.com’],

[‘Jane Doe’, ‘jdoe@yahoo.com’],

[‘joe schmo’, ‘joeschmo@hotmail.com’]

],

columns=[‘Name’, ‘Email’])

Answer 38

df[‘Lowercase Name’] = df.Name.apply(str.lower)

Question 39

subject = [“physics”, “calculus”, “poetry”, “history”]

append ‘computer science’ to this list

Answer 39

subject.append(“computer science”)

Question 40

subject = [“physics”, “calculus”, “poetry”, “history”]

grades = [98, 97, 85, 88]

zip these two together

and add ‘visual arts’ and the grade ‘93’

Answer 40

gradebook = list(zip(subject, grades))

gradebook.append((“visual arts”, 93))

Question 41

inventory = [‘twin bed’, ‘twin bed’, ‘headboard’, ‘queen bed’, ‘king bed’, ‘dresser’, ‘dresser’, ‘table’, ‘table’, ‘nightstand’, ‘nightstand’, ‘king bed’, ‘king bed’, ‘twin bed’, ‘twin bed’, ‘sheets’, ‘sheets’, ‘pillow’, ‘pillow’]

find the len of this inventory

Answer 41

inventory_len = len(inventory)

Question 42

inventory = [‘twin bed’, ‘twin bed’, ‘headboard’, ‘queen bed’, ‘king bed’, ‘dresser’, ‘dresser’, ‘table’, ‘table’, ‘nightstand’, ‘nightstand’, ‘king bed’, ‘king bed’, ‘twin bed’, ‘twin bed’, ‘sheets’, ‘sheets’, ‘pillow’, ‘pillow’]

return the third object

return the last object

return objects 1-4

Answer 42

third = inventory[2]

last = inventory[-1]

inventory1_4 = inventory[1:5]

Question 43

inventory = [‘twin bed’, ‘twin bed’, ‘headboard’, ‘queen bed’, ‘king bed’, ‘dresser’, ‘dresser’, ‘table’, ‘table’, ‘nightstand’, ‘nightstand’, ‘king bed’, ‘king bed’, ‘twin bed’, ‘twin bed’, ‘sheets’, ‘sheets’, ‘pillow’, ‘pillow’]

return the number of twin beds in the inventory

Answer 43

twin_beds = inventory.count(‘twin bed’)

Question 44

Write a function named append_sum that has one parameter — a list named named lst.

The function should add the last two elements of lst together and append the result to lst. It should do this process three times and then return lst.

For example, if lst started as [1, 1, 2], the final result should be [1, 1, 2, 3, 5, 8].

Answer 44

def append_sum(lst):

for x in range(3):

lst.append(lst[-1] + lst[-2])

return lst

Question 45

Write a function named larger_list that has two parameters named lst1 and lst2.

The function should return the last element of the list that contains more elements. If both lists are the same size, then return the last element of lst1.

Answer 45

def larger_list(lst1, lst2):

list1_len = len(lst1)

list2_len = len(lst2)

if list1_len > list2_len:

return lst1[-1]

elif list1_len < list2_len:

return lst2[-1]

elif list1_len == list2_len:

return lst1[-1]

Question 46

Create a function named more_than_n that has three parameters named lst, item, and n.

The function should return True if item appears in the list more than n times. The function should return False otherwise.

Answer 46

def more_than_n(lst,item,n):

if lst.count(item) > n :

return True

else:

return False

Question 47

Create a function called append_size that has one parameter named lst.

The function should append the size of lst (inclusive) to the end of lst. The function should then return this new list.

For example, if lst was [23, 42, 108], the function should return [23, 42, 108, 3] because the size of lst was originally 3.

Answer 47

def append_size(lst):

x = len(lst)

lst.append(x)

return lst

lst = [23,42,108]

append_size(lst)

Question 48

Create a function called every_three_nums that has one parameter named start.

The function should return a list of every third number between start and 100 (inclusive). For example, every_three_nums(91) should return the list [91, 94, 97, 100]. If start is greater than 100, the function should return an empty list.

Answer 48

def every_three_nums(start):

if start <= 100:

lst = list(range(start, 101, 3))

return lst

else:

lst = []

return lst

Question 49

Create a function named remove_middle which has three parameters named lst, start, and end.

The function should return a list where all elements in lst with an index between start and end (inclusive) have been removed.

For example, the following code should return [4, 23, 42] because elements at indices 1, 2, and 3 have been removed: remove_middle([4, 8 , 15, 16, 23, 42], 1, 3)

Answer 49

def remove_middle(lst,start,end):

new_list = lst[0:start] + lst[end+1:]

return new_list

Question 50

Create a function named more_frequent_item that has three parameters named lst, item1, and item2.

Return either item1 or item2 depending on which item appears more often in lst.

If the two items appear the same number of times, return item1.

Answer 50

def more_frequent_item (lst, item1, item2):

cnt_item1 = lst.count(item1)

cnt_item2 = lst.count(item2)

if cnt_item1 > cnt_item2:

return item1

elif cnt_item1 < cnt_item2:

return item2

elif cnt_item1 == cnt_item2:

return item1

print(more_frequent_item([2, 3], 2, 3))

Question 51

Create a function named double_index that has two parameters: a list named lst and a single number named index.

The function should return a new list where all elements are the same as in lst except for the element at index. The element at index should be double the value of the element at index of the original lst.

If index is not a valid index, the function should return the original list.

For example, the following code should return [1,2,6,4] because the element at index 2 has been doubled:

double_index([1, 2, 3, 4], 2)

Answer 51

def double_index(lst, index):

before = lst[:index]

after = lst[index+1:]

new = [lst[index] * 2]

new_list = before + new + after

return new_list

Question 52

Given a dataframe df, add a new column square which contains the square of each value in the points column for each row.

Answer 52

df[‘square’] = df.points.apply(lambda x: x**2)

Question 53

Select the rows from the column location that contain the information for Staten Island from the dataframe inventory and save them to staten_island.

Answer 53

staten_island = inventory[inventory.location == ‘Staten Island’]

Question 54

A customer just emailed you asking what products are sold at your Staten Island location. Select the column product_description from staten_island and save it to the variable product_request.

Answer 54

product_request = staten_island.product_description

Question 55

Another customer emails to ask what types of seeds are sold at the Brooklyn location.

Select all rows where location is equal to Brooklyn and product_type is equal to seeds and save them to the variable seed_request.

Answer 55

seed_request = inventory[(inventory.location == ‘Brooklyn’) | (inventory.product_type == ‘seeds’)]

Question 56

Add a column to inventory called in_stock which is True if quantity is greater than 0 and False if quantity equals 0.

Answer 56

inventory[‘in_stock’] = inventory.quantity.apply(lambda x: True if x > 0 else False)

Question 57

Petal Power wants to know how valuable their current inventory is.

Create a column called total_value that is equal to price multiplied by quantity.

Answer 57

total = lambda row: row.price * row.quantity

inventory[‘total_value’] = inventory.apply(total, axis = 1)

Question 58

The DataFrame customers contains the names and ages of all of your customers. You want to find the median age:

Answer 58

median_price = orders.price.median()

print(median_price)

Question 59

how many unique types of shoes from the df orders were bought? The name of the column is shoe_type

Answer 59

unique_type = orders.shoe_type.nunique()

print(unique_type)

Question 60

Print out all the unique types of shoes that are in ‘shoe_type’ in the dataframe orders

Answer 60

Print out all the unique types of shoes that are in ‘shoe_type’ in the dataframe orders

Question 61

Our finance department wants to know the price of the most expensive pair of shoes purchased. Save your answer to the variable most_expensive.

Answer 61

most_expensive = orders.price.max()

Question 62

Our fashion department wants to know how many different colors of shoes we are selling. Save your answer to the variable num_colors.

Answer 62

num_colors = orders.shoe_color.nunique()

num_colors

Question 63

Suppose we have a grade book with columns student, assignment_name, and grade. We want to get an average grade for each student across all assignments. We could do some sort of loop, but Pandas gives us a much easier option: the method .groupby. Use .groupby to get the average grade

Answer 63

grades = df.groupby(‘student’).grade.mean()

Question 64

This is the general syntax of .groupby

Answer 64

df.groupby(‘column1’).column2.measurement()

Question 65

In the previous exercise, our finance department wanted to know the most expensive shoe that we sold.

Now, they want to know the most expensive shoe for each shoe_type (i.e., the most expensive boot, the most expensive ballet flat, etc.).

Save your answer to the variable pricey_shoes.

Answer 65

pricey_shoes = orders.groupby(‘shoe_type’).price.max()

Question 66

Usually, we’d prefer that those indices were actually a column. In order to get that, we can use reset_index(). This will transform our Series into a DataFrame and move the indices into their own column.

Generally, you’ll always see a groupby statement followed by reset_index:

Answer 66

df.groupby(‘column1’).column2.measurement().reset_index()

Question 67

For example, suppose we have a DataFrame teas containing data on types of tea:

id tea category caffeine price

0 earl grey black 38 3 1

english breakfast black 41 3 2

irish breakfast black 37 2.5 3

jasmine green 23 4.5 4

matcha green 48 5 5

camomile herbal 0 3 …
We want to find the number of each category of tea we sell.

Answer 67

teas_counts = teas.groupby(‘category’).id.count().reset_index()

Question 68

use rename to rename the columns ‘id’ to ‘counts’

Answer 68

df = df.rename(columns = {‘id’ : ‘counts’})

Question 69

Modify the code that finds the most expensive shoe from each shoe type so that it ends with reset_index, which will change pricey_shoes into a DataFrame.

Answer 69

pricey_shoes = orders.groupby(‘shoe_type’).price.max().reset_index()

pricey_shoes

Question 70

we have a DataFrame of employee information called df that has the following columns:

id: the employee’s id number name: the employee’s name wage: the employee’s hourly wage category: the type of work that the employee does Our data might look something like this:

id name wage category

10131 Sarah Carney 39 product

14189 Heather Carey 17 design

15004 Gary Mercado 33 marketing

11204 Cora Copaz 27 design …

If we want to calculate the 75th percentile (i.e., the point at which 75% of employees have a lower wage and 25% have a higher wage) for each category, we can use the following combination of apply and a lambda function

Answer 70

high_earners = df.groupby(‘category’).wage.apply(lambda x: np.percentile(x,75)).reset_index()

Question 71

Let’s calculate the 25th percentile for shoe price for each shoe_color to help Marketing decide if we have enough cheap shoes on sale. Save the data to the variable cheap_shoes.

Answer 71

cheap_shoes = orders.groupby(‘shoe_color’).price.apply(lambda x: np.percentile(x, 25)).reset_index()

Question 72

Pivot Tables general syntax

Answer 72

df.pivot(columns=’ColumnToPivot’, index=’ColumnToBeRows’, values=’ColumnToBeValues’)

Question 73

pivot this table to make it easier to read, save as shoe_counts_pivot. We want tp lmpw tje number of orders for each color for each shoe type

shoe\_type shoe\_color id

0 ballet flats black 2

1 ballet flats brown 5

2 ballet flats red 3

Answer 73

shoe_counts.pivot(columns= ‘shoe_color’, index= ‘shoe_type’, values= ‘id’).reset_index()

Question 74

The column utm_source contains information about how users got to ShoeFly’s homepage. For instance, if utm_source = Facebook, then the user came to ShoeFly by clicking on an ad on Facebook.com.

Use a groupby statement to calculate how many visits came from each of the different sources. Save your answer to the variable click_source.

Remember to use reset_index()!

Answer 74

click_source = user_visits.groupby(‘utm_source’).id.count().reset_index()

Question 75

Our Marketing department thinks that the traffic to our site has been changing over the past few months. Use groupby to calculate the number of visits to our site from each utm_source for each month. Save your answer to the variable click_source_by_month.

Answer 75

click_source_by_month = user_visits.groupby([‘utm_source’, ‘month’]).id.count().reset_index()

Question 76

The head of Marketing is complaining that this table is hard to read. Use pivot to create a pivot table where the rows are utm_source and the columns are month. Save your results to the variable click_source_by_month_pivot.

Answer 76

click_source_by_month_pivot = click_source_by_month.pivot(index=’utm_source’, columns=’month’, values=’id’).reset_index()

Question 77

A movie review website employs several different critics. They store these critics’ movie ratings in a DataFrame called movie_ratings, which has three columns: critic, movie, and rating.

Write a command to find the average rating for each movie

Answer 77

movie_ratings.groupby(‘movie’).rating.mean().reset_index()

Question 78

The City Library has several branches throughout the area. They collect all of their book checkout data in a DataFrame called checkouts. The DataFrame contains the columns ‘location’, ‘date’, and ‘book_title’. If we want to compare the total number of books checked out at each branch, what code could we use?

Answer 78

checkouts.groupby(‘location’).book_title.count().reset_index()

Question 79

ad_clicks[‘is_click’] = ~ad_clicks\ .ad_click_timestamp.isnull()

What does ~ do in this operation?

Answer 79

The ~ is a NOT operator, and isnull() tests whether or not the value of ad_click_timestamp is null.

Question 80

Create a new column called is_click, which is True if ad_click_timestamp is not null and False otherwise.

Answer 80

ad_clicks[‘is_click’] = ~ad_clicks\ .ad_click_timestamp.isnull()

Question 81

What is the difference in outcome between these two codes:

crushing_it = sales_vs_targets[sales_vs_targets.revenue > sales_vs_targets.target]

crushing_it = sales_vs_targets.revenue > sales_vs_targets.target

Answer 81

The top will provide you with a df that contains only the rows where revenue is greater than targets

the bottom will provide you with a series of True/False for the conditions revenue>target

Question 82

What is a left merge?

Answer 82

. A Left Merge includes all rows from the first (left) table, but only rows from the second (right) table that match the first table.

Question 83

What is an inner merge

Answer 83

this will result in a table that has only the rows with matching values. Non matching values will be dropped

Question 84

What is an outer merge

Answer 84

this will result in a table with ‘NaN’ values for rows that do not match

Question 85

What is a Right Merge

Answer 85

Here, the merged table will include all rows from the second (right) table, but only rows from the first (left) table that match the second table.

Question 86

What does pd.concat([df1,df2]) do?

Answer 86

It stacks the two dfs together. This is most useful when the two dataframes are chuncks of the same original df

Question 87

A veterinarian’s office stores all of their data on pets and their owners in two dataframes: pets and owners. The owners dataframe has the columns ‘id’, ‘first_name’, ‘last_name’ and ‘address’. The ‘pets’ dataframe has the columns id, name, owner_id, and type. If the office wanted to combine the two dataframes into one dataframe called pets_owners, what code could work?

Answer 87

pets_owners = pd.merge(pets, customers.rename(columns = {‘id’:’owner_id’}))

Question 88

A veterinarians office is run by two vets, Greg and Susan, and stores each of their appointment data in separate DataFrames, called greg_appointments and susan_appointments respectively. These DataFrames have the same columns. If the vet office wanted to combine the two DataFrames into a single DataFrame called appointments_all which of the following commands would they use?

Answer 88

appointments_all = pd.concat([greg_appointments, susan_appointments])

Question 89

What is the correct syntax for performing an outer merge on two Dataframes: df_one and df_two?

Answer 89

merged_df = pd.merge(df1, df2, how=’outer’)

Question 90

How would I select all the null values from a column of a dataframe?

Answer 90

null_df = df[df.column1.isnull()]

Question 91

basic syntax of matplot lib

Answer 91

x_values = [0, 1, 2, 3, 4] y_values = [0, 1, 4, 9, 16] plt.plot(x_values, y_values) plt.show()

Question 92

Specify a different color for a line in matplot lob

Answer 92

plt.plot(days, money_spent, color=’green’) plt.plot(days, money_spent_2, color=’#AAAAAA’)

Question 93

Which line of code will get the axes object of a plot and store it in a variable ax?

Answer 93

ax = plt.subplot()

Question 94

Which line of code will create a figure with a height of 7 inches and a width of 6 inches?

Answer 94

plt.figure(figsize=(6,7))

Question 95

What is the command to set a plot to display from x=-5 to x=5 and from y=0 to y=10?

Answer 95

plt.axis([-5,5,0,10])

Question 96

What is the command to label the x-axis with the label ‘Time’?

Answer 96

plt.xlabel(‘Time’)

Question 97

Which line of code will set the y-axis ticks to be at 0, 1, 2, 4, and 9?

Answer 97

ax.set_yticks([0,1,2,4,9])

Question 98

Which line of code will set the x-axis labels to be [“Monday”, “Tuesday”, “Wednesday”]?

Answer 98

ax.set_xticklabels([‘Monday’,’Tuesday’,’Wednesday’])

Question 99

What is the command to set the color of a line to be ‘green’?

Answer 99

plt.plot = (x,y,color=’green’)

Question 100

What is the command to set the linestyle of a line to be dashed?

Answer 100

plt.plot(x,y,linestyle=’–’

Question 101

What is the command to add a legend to a plot with the labels [‘Cats’, ‘Dogs’]?

Answer 101

plt.legend([‘cats’,’Dogs’])

Question 102

What is the command to create a figure with 3 rows and 2 columns, and a subplot in the second row and the first column?

Answer 102

plt.subplot(3,2,3)

Question 103

What is the command to set the horizontal spacing of subplots within a figure to 0.35?

Answer 103

plt.subplots_adjust(wspace=0.35)

Question 104

What is the result of adding autopct=’%d%%’ to a plt.pie function call?

Answer 104

pie chart will show the percentages of each slice to the nearest int

Question 105

What does it mean to normalize a histogram?

Answer 105

dividing the height of each column by a constant so the area under the curve sums to 1. maintains the relationship of the data, but allows you to compare data that has different distributions

Question 106

What is the command to stack a set of bars representing y2 on top of the set of bars representing y1?

Answer 106

plt.bar(range(len(y2)), y2, bottom=y1)

Question 107

In the following function call, what does the list [0, 2, 4, 6, 8] represent?

plt.fill_between(range(5), [0, 2, 4, 6, 8], [4, 6, 8, 10, 12], alpha=0.2)

Answer 107

lower bound y values

Question 108

What is the command to set x-axis ticks to be “Carbohydrates”, “Lipids”, “Protein”?

Answer 108

ax.set_xticklabels([’])

Question 109

What is a KDE plot

Question 110

extract a column from pandas df as a list

Answer 110

newlist = df.col.values

counts = cuisine_counts.name.values