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'] #Outcome: 2

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' } #Outcome: True

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'] ``` #Outcome: 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']) #Outcome: 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

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

df.iloc[1,] #Outcome: 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

print(df.loc[['rank_1']]) #Outcome: 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

``` #Answer: print(my_house>11) ``` #There are many possible answer

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]) #Outcome: [False, True, False]

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") ``` #Outcome: 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

``` #71 Filtering in pandas #Complete code ``` name age 0 nick 15 1 jon 18 filter_= ... selection= df[filter_] print(selection) #Outcome: 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[] #Outcome: 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... #Outcome: 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] #Outcome: 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]] #Outcome: 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

``` #78 Loop over dictionary #Complete code ``` world = { "afghanistan":30.55, "albania":2.77, "algeria":39.21 } for ...in world ...() : ...(key + " -- " + str(value)) #Outcome: 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 #Outcome: 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

``` #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 #Outcome: 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)] #Outcome: 9

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

``` #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 ``` #random_walk =[0,1,2,3,2,3,4,5,6] 0=starting position #Outcome: steps_walked: 8 meters_forward: 3

Answer 91

``` #Get the amount the meters advance in this random_walk. #Get the number of steps given #Use matplotlib line plot to display the walk ``` #random_walk =[0,1,2,3,2,3,4,5,6] 0=starting position #Answer: 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()