Python Pandas

Question 1

Create a dictionary called my_dict with the following three key value pairs:
key ‘country’ and value names.
key ‘drives_right’ and value dr.
key ‘cars_per_cap’ and value cpc.

Answer 1

my_dict={‘country’: names, ‘drives_right’: dr, ‘cars_per_cap’: cpc}

Question 2

Use pd.read_csv() to import cars.csv data as a DataFrame. Store this DataFrame as cars.

Answer 2

cars = pd.read_csv(‘cars.csv’)

Question 3

Specify the index_col argument inside pd.read_csv(): set it to 0, so that the first column is used as row labels.

Answer 3

cars = pd.read_csv(‘cars.csv’, index_col=0)

Question 4

Use single square brackets to print out the ‘country’ column of cars as a Pandas Series.

Answer 4

print(cars[‘country’])

Question 5

Use double square brackets to print out the ‘country’ column of cars as a Pandas DataFrame.

Answer 5

print(cars[[‘country’]])

Question 6

Use double square brackets to print out a DataFrame with both the ‘country’ and ‘drives_right’ columns of cars, in this order.

Answer 6

print(cars[[‘country’, ‘drives_right’]])

Question 7

Select the first 3 observations from cars and print them out.

Answer 7

print(cars[0:3])

Question 8

Select the fourth, fifth and sixth observation, corresponding to row indexes 3, 4 and 5, and print them out.

Answer 8

print(cars[3:6])

Question 9

3 ways to inspect a dataframe

Answer 9

Inspect the first few rows (including index labels)
print(df.head())

Inspect the last few rows
print(df.tail())

Inspect random sample rows
print(df.sample(5))

Question 10

Use loc or iloc to select the observation corresponding to Japan as a Series. The label of this row is JPN, the index is 2. Make sure to print the resulting Series.

Answer 10

print(cars.loc[‘JPN’])

print(cars.iloc[2])

Question 11

Print out the ‘drives_right’ value of the row corresponding to Morocco (its row label is MOR)

Answer 11

print(cars.loc[‘MOR’, ‘drives_right’])

Question 12

Print out a sub-DataFrame, containing the observations for Russia and Morocco and the columns ‘country’ and ‘drives_right’.

Answer 12

print(cars.loc[[‘RU’, ‘MOR’], [‘country’, ‘drives_right’]])

Question 13

Print out from the df cars the drives_right column as a Series using loc

Answer 13

print(cars.loc[:,’drives_right’])

Question 14

Print out the drives_right column as a DataFrame using loc

Answer 14

print(cars.loc[:, [‘drives_right’]])

Question 15

Print out both the cars_per_cap and drives_right column as a DataFrame using loc

Answer 15

print(cars.loc[:, [‘cars_per_cap’, ‘drives_right’]])

Question 16

Which areas in my_house are greater than 18.5 or smaller than 10?

Answer 16

print(np.logical_or(my_house > 18.5, my_house < 10))

Question 17

Which areas are smaller than 11 in both my_house and your_house? Make sure to wrap both commands in print() statement, so that you can inspect the output.

Answer 17

print(np.logical_and(my_house < 11, your_house < 11))

Question 18

make an if statement that prints out “looking around in the kitchen.” if room equals “kit”.

Answer 18

if room == “kit” :
print(“looking around in the kitchen.”)

Question 19

Write another if statement that prints out “big place!” if area is greater than 15.

Answer 19

if area >15:
print(“big place!”)

Question 20

Extract the drives_right column as a Pandas Series and store it as dr.

Answer 20

dr = cars[‘drives_right’]

Question 21

Use dr, a boolean Series, to subset the ‘cars’ DataFrame. Store the resulting selection in ‘sel’.

Answer 21

sel = cars[dr]

Question 22

Select the cars_per_cap column as a Pandas Series and store it as cpc

Answer 22

cpc = cars[‘cars_per_cap’]

Question 23

Create car_maniac: observations that have a cars_per_cap over 500

Answer 23

cpc = cars[‘cars_per_cap’]
many_cars = cpc > 500 # This creates a boolean Series
print(many_cars)

Question 24

Create medium: observations with cars_per_cap between 100 and 500

Answer 24

cpc = cars[‘cars_per_cap’]
between = np.logical_and(cpc > 100, cpc < 500)
medium = cars[between]

Question 25

Write a for loop that iterates over all elements of the areas list (area in ‘areas’df)

Answer 25

for area in areas:
print(area)

Question 26

write a for loop using enumerate(). Print() so that on each run, a line of the form “room x: y” should be printed, where x is the index of the list element and y is the actual list element, i.e. the area.

Answer 26

for index, area in enumerate(areas) :
print(‘room’+ str(index)+ ‘: ‘ + str(area))

Question 27

adapt the following so that the first printout becomes “room 1: 11.25”, the second one “room 2: 18.0” and so on:

for index, area in enumerate(areas) :
print(“room” + str(index) + “: “ + str(area))

Answer 27

for index, area in enumerate(areas) :
print(“room” + str(index + 1) + “: “ + str(area))

Question 28

Write a for loop that goes through each sublist of house and prints out the x is y sqm, where x is the name of the room and y is the area of the room.

Answer 28

for x in house:
print(“the “ + x[0] + “ is “ + str(x[1]) + “ sqm”)

Question 29

Write a for loop that goes through each key:value pair of europe. On each iteration, “the capital of x is y” should be printed out, where x is the key and y is the value of the pair.

Answer 29

for key, value in europe.items():
print(“the capital of “ + str(key) + “ is “ + str(value))

Question 30

Write a for loop that iterates over all elements in np_height and prints out “x inches” for each element, where x is the value in the array.

Answer 30

for x in np_height:
print(str(x) + “ inches”)

Question 31

Write a for loop that visits every element of the np_baseball array and prints it out.

Answer 31

for x in np.nditer(np_baseball):
print(x)

Question 32

Write a for loop that iterates over the rows of cars and on each iteration perform two print() calls: one to print out the row label and one to print out all of the rows contents.

Answer 32

for lab. row in cars,iterrows():
print(lab)
print(row)

Question 33

add the length of the country names of the brics DataFrame in a new column

Answer 33

for lab, row in brics.iterrows() :
brics.loc[lab, “name_length”] = len(row[“country”])

Question 34

Use a for loop to add a new column, named COUNTRY, that contains a uppercase version of the country names in the “country” column. You can use the string method upper() for this

Answer 34

for lab, row in cars.iterrows():
cars.loc[lab, “COUNTRY”] = row[“country”].upper()

everything in the ‘for loop’ is indented though