Numpy

Question 1

Import numpy and assign to the alias np.

Answer 1

import numpy as np

Question 2

Create a NumPy ndarray from the list [10, 20, 30]. Assign the result to the variable data_ndarray.

Answer 2

data_ndarray = np.array([10,20,30])

Question 3

What is the difference in using Numpy vs a List of lists

Answer 3

Our computer would take eight processor cycles to process the eight rows of our data.

The NumPy library takes advantage of a processor feature called Single Instruction Multiple Data (SIMD) to process data faster. SIMD allows a processor to perform the same operation, on multiple data points, in a single processor cycle

Question 4

Convert values from a list of lists to a numpy array

Answer 4

converted_taxi_list = []
for row in taxi_list:
converted_row = []
for item in row:
converted_row.append(float(item))
converted_taxi_list.append(converted_row)

Question 5

Convert a variable called coverted_taxi_list to a numpy array

Answer 5

taxi = np.array(converted_taxi_list)

Question 6

What is the difference between tuples and lists

Answer 6

Tuples are very similar to Python lists, but can’t be modified.

Question 7

If data.shape returns the following tuple (2, 3) How do you interpret it?

Answer 7

The first number tells us that there are 2 rows in data_ndarray.
The second number tells us that there are 3 columns in data_ndarray.

Question 8

How do you get the shape of a numpy array called taxi?

Answer 8

taxi_shape=(taxi.shape)

Question 9

What is the sintax of any 2D array, for selecting data?

Answer 9

ndarray[row_index,column_index]

Question 10

Slicing to select the items at index 1, 2, and 3

Answer 10

[1:4]

Question 11

lect the item at row index 21 and column index 5. Assign it to row_21_column_5

Answer 11

row_21_column_5= taxi[21,5]

Question 12

Select every column for the rows at indexes 391 to 500 inclusive. Assign them to rows_391_to_500.

Answer 12

rows_391_to_500=taxi[391:501]

Question 13

Select the row at index 0. Assign it to row_0.

Answer 13

row_0=taxi[0]

Question 14

Select every row for the columns at indexes 1, 4, and 7. Assign them to columns_1_4_7.

Answer 14

columns = [1,4,7]

Question 15

Select the columns at indexes 5 to 8 inclusive for the row at index 99. Assign them to row_99_columns_5_to_8.

Answer 15

row_99_columns_5_to_8= taxi[99,5:9]

Question 16

Select the rows at indexes 100 to 200 inclusive for the column at index 14. Assign them to rows_100_to_200_column_14.

Answer 16

rows_100_to_200_column_14= taxi[100:201,14]

Question 17

Use vector addition to add fare_amount and fees_amount. Assign the result to fare_and_fees.

Answer 17

fare_amount = taxi[:,9]
fees_amount = taxi[:,10]

fare_and_fees=fare_amount+fees_amount

Question 18

Select all of the rows from colums 0 and 1

Answer 18

col1 = my_numbers[:,0]
col2 = my_numbers[:,1]

Question 19

Use vector division to divide trip_distance_miles by trip_length_hours. Assign the result to trip_mph.

trip_distance_miles = taxi[:,7]
trip_length_seconds = taxi[:,8]

Answer 19

trip_length_hours = trip_length_seconds / 3600 
# 3600 seconds is one hour
trip_mph=trip_distance_miles/trip_length_hours

Question 20

calculate the maximum value of trip_mph. Assign the result to mph_max.

Answer 20

mph_max=trip_mph.max()

Question 21

calculate the average value of trip_mph. Assign the result to mph_mean.

Answer 21

mph_mean=trip_mph.mean()

Question 22

Calculate the min value of trip_mph

Answer 22

mph_min = trip_mph.min()

Question 23

Calculate the median average value of trip_mph

Answer 23

np.median(trip_mph)

Question 24

Function representations and method representations

Answer 24

Calculation Function Representation Method Representation
Calculate the minimum value of trip_mph np.min(trip_mph) trip_mph.min()
Calculate the maximum value of trip_mph np.max(trip_mph) trip_mph.max()
Calculate the mean average value of trip_mph np.mean(trip_mph) trip_mph.mean()
Calculate the median average value of trip_mph np.median(trip_mph) There is no ndarray median method

Question 25

Use the ndarray.sum() method to calculate the sum of each row in fare_components. Assign the result to fare_sums.

Answer 25

fare_sums = fare_components.sum(axis=1)

Question 26

Extract the 14th column in taxi_first_five. Assign to fare_totals.

Answer 26

fare_totals= taxi_first_five[:,13]

Question 27

Use the numpy.genfromtxt() function to read the nyc_taxis.csv file into NumPy. Assign the result to taxi.

Answer 27

taxi=np.genfromtxt(“nyc_taxis.csv”, delimiter=”,”)

Question 28

Evaluate whether the elements in array a are less than 3. Assign the result to a_bool.
a = np.array([1, 2, 3, 4, 5])
b = np.array([“blue”, “blue”, “red”, “blue”])
c = np.array([80.0, 103.4, 96.9, 200.3])

Answer 28

a_bool = a < 3

Question 29

Evaluate whether the elements in array b are equal to “blue”. Assign the result to b_bool.
a = np.array([1, 2, 3, 4, 5])
b = np.array([“blue”, “blue”, “red”, “blue”])
c = np.array([80.0, 103.4, 96.9, 200.3])

Answer 29

b_bool =b == “blue”

Question 30

valuate whether the elements in array c are greater than 100. Assign the result to c_bool.

Answer 30

c_bool = c > 100

Question 31

Create a boolean array, february_bool, that evaluates whether the items in pickup_month are equal to 2.

Answer 31

february_bool = pickup_month== 2

Question 32

Use the february_bool boolean array to index pickup_month. Assign the result to february.

Answer 32

february = pickup_month[february_bool]

Question 33

Use the ndarray.shape attribute to find the number of items in february. Assign the result to february_rides.

Answer 33

february_rides = february.shape[0]

Question 34

Create a boolean array, tip_bool, that determines which rows have values for the tip_amount column of more than 50.

tip_amount = taxi[:,12]

Answer 34

tip_bool= tip_amount > 50

Question 35

Use the tip_bool array to select all rows from taxi with values tip amounts of more than 50, and the columns from indexes 5 to 13 inclusive. Assign the resulting array to top_tips.

Answer 35

top_tips= taxi[tip_bool,5:14]

Question 36

a = np.array([‘red’,’blue’,’black’,’blue’,’purple’])
a[0] = ‘orange’
print(a)

Answer 36

[‘orange’, ‘blue’, ‘black’, ‘blue’, ‘purple’]

Question 37

a = np.array([‘red’,’blue’,’black’,’blue’,’purple’])
a[3:] = ‘pink’
print(a)

Answer 37

[‘orange’, ‘blue’, ‘black’, ‘pink’, ‘pink’]

Question 38

ones = np.array([[1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1]])
ones[1,2] = 99
print(ones)

Answer 38

[[ 1, 1, 1, 1, 1],
[ 1, 1, 99, 1, 1],
[ 1, 1, 1, 1, 1]]

Question 39

ones = np.array([[1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1]])
ones[0] = 42
print(ones)

Answer 39

[[42, 42, 42, 42, 42],
[ 1, 1, 99, 1, 1],
[ 1, 1, 1, 1, 1]]

Question 40

ones = np.array([[1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1],
                 [1, 1, 1, 1, 1]])
ones[:,2] = 0
print(ones)

Answer 40

[[42, 42, 0, 42, 42],
[ 1, 1, 0, 1, 1],
[ 1, 1, 0, 1, 1]]

Question 41

The value at column index 5 (pickup_location) of row index 28214 is incorrect. Use assignment to change this value to 1 in the taxi_modified ndarray.

Answer 41

taxi_modified[28214,5]= 1

Question 42

The first column (index 0) contains year values as four digit numbers in the format YYYY (2016, since all trips in our data set are from 2016). Use assignment to change these values to the YY format (16) in the taxi_modified ndarray.

Answer 42

taxi_modified[:,0]=16

Question 43

The values at column index 7 (trip_distance) of rows index 1800 and 1801 are incorrect. Use assignment to change these values in the taxi_modified ndarray to the mean value for that column.

Answer 43

taxi_modified=[1800:1802,7]= taxi_modified[:,7].mean()

Question 44

a2 = np.array([1, 2, 3, 4, 5])

a2_bool = a2 > 2

a2[a2_bool] = 99

print(a2)

Answer 44

[ 1 2 99 99 99]

Question 45

Select the fourteenth column (index 13) in taxi_copy. Assign it to a variable named total_amount.

Answer 45

total_amount= taxi_copy[:,13]

Question 46

For rows where the value of total_amount is less than 0, use assignment to change the value to 0.

Answer 46

total_amount[total_amount <0] =0

Question 47

taxi_modified with an additional column containing the value 0 for every row.

Answer 47

zeros = np.zeros([taxi.shape[0], 1])
taxi_modified = np.concatenate([taxi, zeros], axis=1)

Question 48

For rows where the value for the column index 5 is equal to 2 (JFK Airport), assign the value 1 to column index 15.

Answer 48

taxi_modified[taxi_modified[:,5]==2,15]=1

Question 49

For rows where the value for the column index 5 is equal to 3 (LaGuardia Airport), assign the value 1 to column index 15.

Answer 49

taxi_modified[taxi_modified[:,5]==3,15]=1

Question 50

For rows where the value for the column index 5 is equal to 5 (Newark Airport), assign the value 1 to column index 15.

Answer 50

taxi_modified[taxi_modified[:,5]==5,15]=1

Question 51

Use boolean indexing to select only the rows where the dropoff_location_code column (column index 6) has a value that corresponds to JFK (number 2). Assign the result to jfk.

Answer 51

jfk= taxi[taxi[:,6]==2]

Question 52

Calculate how many rows are in the new jfk array and assign the result to jfk_count.

jfk= taxi[taxi[:,6]==2]

Answer 52

jfk_count= jfk.shape[0]

Question 53

Calculate how many trips from taxi had Laguardia Airport as their destination:
Use boolean indexing to select only the rows where the dropoff_location_code column (column index 6) has a value that corresponds to Laguardia (number 3). Assign the result to laguardia.

Answer 53

laguardia=taxi[taxi[:,6]==3]

Question 54

Calculate how many rows are in the new laguardia array. Assign the result to laguardia_count.

Answer 54

laguardia_count=laguardia.shape[0]

Question 55

reate a new ndarray, cleaned_taxi, containing only rows for which the values of trip_mph are less than 100.

trip_mph = taxi[:,7] / (taxi[:,8] / 3600)

Answer 55

cleaned_taxi= taxi[trip_mph<100]

Question 56

Calculate the mean of the trip_distance column of cleaned_taxi. Assign the result to mean_distance.

Answer 56

mean_distance=cleaned_taxi[:,7].mean()

Question 57

Calculate the mean of the trip_length column of cleaned_taxi. Assign the result to mean_length.

Answer 57

mean_length=cleaned_taxi[:,8].mean()

Question 58

Calculate the mean of the total_amount column of cleaned_taxi. Assign the result to mean_total_amount.

Answer 58

mean_total_amount=cleaned_taxi[:,13].mean()