2 Introduction to Python (II)

Question 1

1 What is Matplotlib?

Answer 1

A plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI

Question 2

2 Complete code:

import matplot… as …

Answer 2

import matplotlib.pyplot as plt

Question 3

3 Make a line plot (year x-axis, pop y-axis)

year=[‘1975’,’1976’,’1977’]
pop=[2340,2405,2890]

Answer 3

import matplotlib.pyplot as plt

plt. plot(year,pop)
plt. show()

Question 4

4 How to display a matplotlib plot?

Answer 4

plt.show()

Question 5

5 Print the last item of the list year:

year=[‘1975’,’1976’,’1977’]

Answer 5

print(year[-1])

print(year[2])

Question 6

6 What is a scatter plot?

Answer 6

A type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data

Question 7

7 Complete code (scatter plot):

x = [1,3,5]
y= [2,6,7]

’'’import mat….
…
plt.show()’’’

Answer 7

import matplotlib.pyplot as plt

plt. scatter(x,y)
plt. show()

Question 8

8 Change the line plot below to a scatter plot

year=[‘1975’,’1976’,’1977’]
pop=[2340,2405,2890]

import matplotlib.pyplot as plt

plt. plot(year,pop)
plt. show()

Answer 8

plt. scatter(year,pop)

plt. show()

Question 9

9 Put the x-axis on a logarithmic scale

day=[‘1’,’2’,’3’]
virus=[18,55,320]

import matplotlib.pyplot as plt

plt. scatter(day,virus)
plt. show()

Answer 9

plt. scatter(day,virus)
plt. xscale(‘log’)
plt. show()

Question 10

10 What is a correlation coefficient?

Answer 10

A value that indicates the strength of the relationship between variables. The coefficient can take any values from -1 to 1.

Question 11

11 What is a histogram?

Answer 11

An approximate representation of the distribution of numerical or categorical data

Question 12

12 Create histogram

years = [1975,1976,1978,1975]

Answer 12

import matplotlib.pyplot as plt

plt. hist(years)
plt. show()

Question 13

13 Create histogram with 5 bins using data (list)

data = [random.randint(1, 5) for _ in range(100)]

Answer 13

plt.hist(data,bins=5)

Question 14

14 What is the use of plt.clf() ?

Answer 14

Cleans a plot up again so you can start afresh

Question 15

15 You want to visually assess if the grades on your exam follow a particular distribution. Which plot do you use?

Answer 15

Histogram

Question 16

16 You want to visually assess if longer answers on exam questions lead to higher grades. Which plot do you use?

Answer 16

Scatter plot

Question 17

17 Add labels

year =list(range(1975,2000))
scores = list(range(1,26))

plt.scatter(year,scores)
…

Answer 17

plt. xlabel(‘year’)
plt. ylabel(‘scores’)
plt. show()

Question 18

18 Add ‘scores’ as a title

data = [int(random.randint(1, 5)) for _ in range(100)]
plt.hist(data,bins=5)
…
plt.plot()

Answer 18

plt.title(‘years’)

Question 19

19 Add log scale

year =list(range(1975,2000))
scores= [2**n for n in range(25)]

plt.scatter(year,scores)
…

Answer 19

plt. yscale(‘log’)

plt. show()

Question 20

20 What are ticks in matplotlib?

Answer 20

Ticks are the values used to show specific points on the coordinate axis. It can be a number or a string.

Question 21

21 What is a legend in matplotlib?

Answer 21

The legend of a graph reflects the data displayed in the graph’s Y-axis

Question 22

22 Change the ticks in the x-axis to strings

x=[1, 3, 5]
y=[1, 5, 9]

import matplotlib.pyplot as plt
plt.scatter(x,y)

Answer 22

plt. xticks(x, [“one”,”three”,”five”])

plt. show()

Question 23

23 Write a scatter plot with gdp as independent variable and population size as the size argument

gdp=[100, 200, 300]
life_exp=[50, 70, 82]
pop_size=[30,20,40]

Answer 23

import matplotlib.pyplot as plt

plt. scatter(gdp, life_exp, s =pop_size)
plt. show()

Question 24

24 What is a dependent variable?

Answer 24

A variable (often denoted by y ) whose value depends on that of another.

Question 25

25 What is an independent variable?

Answer 25

A variable (often denoted by x ) whose variation does not depend on that of another.

Question 26

26 Code: Scatter plot with text ‘A’ pointing at the second element

gdp=[100, 200, 300]
life_exp=[50, 70, 82]

Answer 26

import matplotlib.pyplot as plt

plt. scatter(gdp, life_exp)
plt. text(195,65,’A’)
plt. show()

Question 27

27 Add a grid to a matplot figure

Answer 27

plt.grid(True)

Question 28

28 Get the position of germany

countries = [‘spain’, ‘france’, ‘germany’, ‘norway’]

Answer 28

countries.index(‘germany’)

Question 29

29 What is the difference between list and dictionary in Python?

Answer 29

A list is an ordered sequence of objects, whereas dictionaries are unordered sets. But the main difference is that items in dictionaries are accessed via keys and not via their position.

Question 30

30 Get the keys

europe = {‘spain’:’madrid’, ‘france’:’paris’, ‘germany’:’berlin’, ‘norway’:’oslo’ }

Outcome:
dict_keys([‘spain’, ‘france’, ‘germany’, ‘norway’])

Answer 30

print(europe.keys())

Question 31

31 Get the capital of norway

europe = {‘spain’:’madrid’, ‘france’:’paris’, ‘germany’:’berlin’, ‘norway’:’oslo’ }

Outcome: oslo

Answer 31

print(europe[‘norway’])

Question 32

32 Add italy and rome to the dictionary

europe = {‘spain’:’madrid’, ‘france’:’paris’,
‘germany’:’berlin’ }

Answer 32

europe[‘italy’]=’rome’

Question 33

33 Check whether the dictionary has spain

europe = {‘spain’:’madrid’, ‘france’:’paris’,
‘germany’:’berlin’ }

Answer 33

print(‘spain’ in europe)

Question 34

34 Outcome of:

europe = {‘spain’:’madrid’, ‘france’:’paris’, ‘germany’:’berlin’, ‘norway’:’oslo’ }

print(‘madrid’ in europe)

Answer 34

FALSE

Question 35

35 Delete spain

europe = {‘spain’:’madrid’, ‘france’:’paris’,
‘norway’:’oslo’}

Answer 35

del(europe[‘spain’])

Question 36

36 Update the capital of spain with madrid

europe = {‘spain’:’Barcelona’, ‘france’:’paris’,
‘norway’:’oslo’}

Answer 36

europe[‘spain’]=’madrid’

Question 37

37 Get the capital of france

europe = { ‘spain’:
{ ‘capital’:’madrid’, ‘population’:46.77 },
‘france’: { ‘capital’:’paris’, ‘population’:66.03 }}

Answer 37

print(europe[‘france’][‘capital’])

Question 38

38 Complete Code

dr =[False, False, True]
names = ['Spain','France','UK']
...
...
#Outcome:
 country drives_right
0 Spain False
1 France False
2 UK True

Answer 38

import pandas as pd

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

print(pd.DataFrame(my_dict))

Question 39

39 Use row_labels as index of the dataframe

ages = [i for i in range(3)]
df_ages = pd.DataFrame(ages, columns = ['Ages'])
names = ['Jon','Jorge','Ana']

Ages
Jon 0
Jorge 1
Ana 2

Answer 39

df_ages.index = names

print(df_ages)

Question 40

40 Transform the csv to a dataframe called cars

cars.csv

Answer 40

import pandas as pd

cars = pd.read_csv(‘cars.csv’)

Question 41

41 Set the first column as row labels

import pandas as pd
cars = pd.read_csv(‘cars.csv’,..(code)..)

Answer 41

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

Question 42

42 What is a panda series?

Answer 42

A one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). Pandas Series is nothing but a column in an excel sheet.

Question 43

43 Print the column country of df as Panda Series

countries = [‘Spain’,’France’,’UK’]
df =pd.DataFrame(countries, columns = [‘country’])

0 Spain
1 France
2 UK
Name: country, dtype: object

Answer 43

print(df[[‘country’]])

Question 44

44 Print the column country of df as dataframe

countries = [‘Spain’,’France’,’UK’]
df =pd.DataFrame(countries, columns = [‘country’])

#Outcome:
 country
0 Spain
1 France
2 UK

Answer 44

print(df[[‘country’]])

Question 45

45 Print out columns a, b from df

Answer 45

print(df[[‘a’,’b’]])

Question 46

46 Print out first 2 observations (2 methods)

import pandas as pd
n = [i for i in range(3)]
df =pd.DataFrame(n, columns = [‘number’])

Answer 46

Outcome

print(df[:2])
print(df.head(2))

number
0 0
1 1

Question 47

47 Print out the fourth, fifth and sixth observation

import pandas as pd
n = [i for i in range(0,20,2)]
df =pd.DataFrame(n, columns = [‘number’])

Answer 47

print(df.iloc[3:6])

Question 48

48 What is loc in python?

Answer 48

A method that takes only index labels and returns row or dataframe if the index label exists in the caller data frame

Question 49

49 What is a DataFrame in Python?

Answer 49

is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns)

Question 50

50 Use iloc to get jon’s row as dataframe

name age
0 nick 15
1 jon 18

Answer 50

Outcome:

df.iloc[1,]

name jon
age 18
Name: 1, dtype: object

Question 51

51 Use iloc to get nick value

name age
0 nick 15
1 jon 18

#Outcome:
nick

Answer 51

print(df.iloc[0,0])

Question 52

52 Use loc to get nick’s row as dataframe

name age
rank_1 nick 15
rank_2 jon 18

Answer 52

Outcome:

print(df.loc[[‘rank_1’]])

name age
rank_1 nick 15

Question 53

53 Output of:

dict ={'name': ['nick','jon'],
 'age':[15,18]}
index_rows = ['rank_1','rank_2']
df = pd.DataFrame(dict)
df.index = index_rows

df.loc[‘rank_2’]

Answer 53

name jon
age 18
Name: rank_2, dtype: object

Question 54

54 Use loc to get jon’s age:

name age
rank_1 nick 15
rank_2 jon 18

Answer 54

df.loc[‘rank_2’,’age’]

Question 55

55 Use iloc to get age column as a dataframe

name age
rank_1 nick 15
rank_2 jon 18

Answer 55

df.iloc[:,[1]]

Question 56

56 Outcome of:

print(True == False)

Answer 56

FALSE

Question 57

57 Outcome of:

print(- 1!= 75)

Answer 57

TRUE

Question 58

58 Outcome of:

print(True == 1)

Answer 58

TRUE

Question 59

59 Outcome of:

print(True == 0)

Answer 59

FALSE

Question 60

60 Outcome of:

x = -3 * 6
print(x>=-10)

Answer 60

FALSE

Question 61

61 Complete code:

import numpy as np
my_house = np.array([18.0, 20.0, 10.75])
…

#Outcome: 
[ True True False]

Answer 61

There are many possible answer

#Answer:
print(my_house>11)

Question 62

62 List out and name comparison operators

Answer 62

Equal: 2 == 2 True
Not equal: 2 != 2 False
Greater than: 2 > 3 False
Less than: 2 < 3 True
Greater than or equal to: 2 >= 3 True
Less than or equal to: 2 <= 3 True

Question 63

63 Outcome of:

a,b =[2,3]
a > b and a < b

Answer 63

FALSE

Question 64

64 Outcome of:

a,b =[2,3]
a > b or a < b

Answer 64

TRUE

Question 65

65 Outcome of:

a,b =[2,3]
not(a < 3)

Answer 65

FALSE

Question 66

66 List out the three Numpy Boolean operators

Answer 66

np. logical_and()
np. logical_or()
np. logical_not()

Question 67

67 Use a numpy boolean

my_house = np.array([18.0, 20.0, 10.75])

Answer 67

print(np.logical_and(my_house>18, my_house<21))

Question 68

68 What is flow control statement in python

Answer 68

Order in which the program’s code executes. The control flow of a Python program is regulated by conditional statements, loops, and function calls.

Question 69

69 Outcome of:

for i in range(4):
 if(i <2) :
 print("small")
 elif(i ==2 ) :
 print("medium")
 else :
 print("large")

Answer 69

small
small
medium
large

Question 70

70 Complete code:

house=[2,4,6]
...house:
 ...(i <4) :
 print("small")
 ...(i ==4 ) :
 print("medium")
 else :
 print("large")

small
medium
large

Answer 70

house=[2,4,6]
for i in house:
 if(i <4) :
 print("small")
 elif(i ==4 ) :
 print("medium")
 else :
 print("large")

Question 71

Outcome:

#71 Filtering in pandas
#Complete code

name age
0 nick 15
1 jon 18

filter_= …
selection= df[filter_]
print(selection)

name age
0 nick 15

Answer 71

filter_ = df[‘name’] == ‘nick’
selection =df[filter_]
print(selection)

Question 72

#Filtering in pandas
#Complete code

Name Country
rank1 Tom Spain
rank2 Jack USA

…[df……]

#Outcome: 
 Name Country
rank1 Tom Spain

Answer 72

df[df[‘Country’]==’Spain’]

Question 73

72 Complete code using np boolean and

data = [['tom', 10], ['nick', 15], ['juli', 14]]
df = pd.DataFrame(data, columns = ['Name', 'Age'])

age = …
between = np…(…>10,..<15)
df[]

Name Age
2 juli 14

Answer 73

age = df[‘Age’]
between = np.logical_and(age>10,age<15)
df[between]

Question 74

73 Complete code

x = 1
…x < 4 :
print(x)
x = x…

1
2
3

Answer 74

x = 1
while x < 4 :
print(x)
x = x + 1

Question 75

74 Outcome of:

offset=4

while offset !=0:
offset=offset-1
print(‘correcting…’)
print(offset)

Answer 75

correcting...
3
correcting...
2
correcting...
1
correcting...
0

Question 76

75 Loop over areas and print each element

areas = [11.25, 18.0, 20.0, 10.75, 9.50]

Answer 76

for area in areas :

print(area)

Question 77

76 Loop and enumerate

areas = [11.25, 18.0, 20.0]

1-11.25
2-18.0
3-20.0

Answer 77

for index, area in enumerate(areas,1) :

print( str(index)+ “-“ + str(area))

Question 78

77 Loop and use enumerate

house = [[“hallway”, 11.25],
[“kitchen”, 18.0],
[“living room”, 20.0]]

hallway-11.25
kitchen-18.0
living room-20.0

Answer 78

for x in house :

print( str(x[0]) + “-“ + str(x[1]) )

Question 79

Outcome:

#78 Loop over dictionary
#Complete code

world = { “afghanistan”:30.55,
“albania”:2.77,
“algeria”:39.21 }

for …in world …() :
…(key + “ – “ + str(value))

afghanistan – 30.55
albania – 2.77
algeria – 39.21

Answer 79

for key, value in world.items() :

print(key + “ – “ + str(value))

Question 80

79 Outcome of:

import numpy as np
x = [i for i in range(1,8,2)]
np_x=np.array(x)
for i in np_x:
print(i**2)

Answer 80

1
9
25
49

Question 81

#80 Loop over DataFrame
(two ways)

name age
rank_1 nick 15
rank_2 jon 18

#Output: 
rank_1
15
rank_2
18

Answer 81

for ind,col in df.iterrows():
print(ind)
print(col[1])

Question 82

81 Build this dataframe:

Name Country
rank1 Tom Spain
rank2 Jack USA

Answer 82

import pandas as pd

data = {'Name':['Tom', 'Jack'],'Country':['Spain','USA']}
df = pd.DataFrame(data, index =['rank1', 'rank2'])

Question 83

82 Loop over the dataframe and create a column with the length of them names

Name Country
0 Tom Spain
1 Jack USA

Answer 83

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

Outcome:
Name Country name_length
0 Tom Spain 3.0
1 Jack USA 4.0

Question 84

83 How does work .seed() method?

Answer 84

Seeding a pseudo-random number generator gives it its first “previous” value. Each seed value will correspond to a sequence of generated values for a given random number generator.

Question 85

84 Generate the same random number twice

Answer 85

import numpy as np
np.random.seed(123) #any number
print(np.random.rand())

np.random.seed(123)
print(np.random.rand())

Question 86

85 Use randint() to simulate the throw of a dice

Answer 86

print(np.random.randint(1,7))

Question 87

86 Use control flow and random numbers to simulate a simple walk with a dice:

Instructions:

np.random.seed(124)

1 or 2 is a step back
3 or 4 no step
5 or 6 step forward

dice: 5
step: 1

Answer 87

import numpy as np
np.random.seed(124)
step = 0
dice=np.random.randint(1,7)
if dice <= 2 :
 step = step - 1
elif dice>4 :
 step=step+1
else:
 step = step

print(‘dice:’,dice)
print(‘step:’,step)

Question 88

Outcome:

#87 Simulate a random walk with a dice:
#How many meters did the ‘person’ advance:

Instructions:
np.random.seed(124)

1 or 2 is a step back
3 or 4 no step
5 or 6 step forward

steps_walked: 10
meters_forward: 3

Answer 88

np.random.seed(124)

random_walk=[0]
step = 0
for i in range(10):
dice=np.random.randint(1,7)
if dice <= 2 :
 step = step - 1
elif dice>4 :
 step=step+1
else:
 step = step
random_walk.append(random_walk[-1]+step)

meters_forward = random_walk[-1]
steps_walked = len(random_walk)-1 #First step is 0

print(‘steps_walked:’, steps_walked)
print(‘meters_forward:’, meters_forward)

Question 89

88 Get the maximum value of this list comprehension

[i for i in range(10)]

Answer 89

max_value=max([i for i in range(10)])

Question 90

89 What are list comprehensions used for?

Answer 90

They are used for creating new lists from other iterables.

Question 91

random_walk =[0,1,2,3,2,3,4,5,6] 0=starting position

#90 Get the amount the meters advance in this random_walk.
#Get the number of steps given
#Use matplotlib line plot to display the walk

steps_walked: 8
meters_forward: 3

Answer 91

random_walk =[0,1,2,3,2,3,4,5,6] 0=starting position

#Get the amount the meters advance in this random_walk.
#Get the number of steps given
#Use matplotlib line plot to display the walk

import matplotlib.pyplot as plt

random_walk =[0,1,1,0,-1,0,1,2,3]
steps_walked = len(random_walk) -1
meters_forward = random_walk [-1]
print('steps_walked:',steps_walked)
print('meters_forward:',meters_forward)

plt. plot(random_walk)
plt. show()