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1. Data Science (Python) > 2 Introduction to Python (II) > Flashcards

2 Introduction to Python (II) Flashcards

Matplotlib, dictionaries, dataframes... https://colab.research.google.com/drive/1fKMFrRbIJQE8Tpa06us0qQPnamBn957z?usp=sharing

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1
Q

1 What is Matplotlib?

A

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

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2
Q

2 Complete code:

import matplot… as …

A

import matplotlib.pyplot as plt

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3
Q

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

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

A

import matplotlib.pyplot as plt

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

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4
Q

4 How to display a matplotlib plot?

A

plt.show()

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5
Q

5 Print the last item of the list year:

year=[‘1975’,’1976’,’1977’]

A

print(year[-1])

print(year[2])

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6
Q

6 What is a scatter plot?

A

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

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7
Q

7 Complete code (scatter plot):

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

’'’import mat….
…
plt.show()’’’

A

import matplotlib.pyplot as plt

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

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8
Q

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()

A

plt. scatter(year,pop)

plt. show()

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9
Q

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()

A

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

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10
Q

10 What is a correlation coefficient?

A

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

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11
Q

11 What is a histogram?

A

An approximate representation of the distribution of numerical or categorical data

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12
Q

12 Create histogram

years = [1975,1976,1978,1975]

A

import matplotlib.pyplot as plt

plt. hist(years)
plt. show()

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13
Q

13 Create histogram with 5 bins using data (list)

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

A

plt.hist(data,bins=5)

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14
Q

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

A

Cleans a plot up again so you can start afresh

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15
Q

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

A

Histogram

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16
Q

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

A

Scatter plot

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17
Q

17 Add labels

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

plt.scatter(year,scores)
…

A

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

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18
Q

18 Add ‘scores’ as a title

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

A

plt.title(‘years’)

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19
Q

19 Add log scale

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

plt.scatter(year,scores)
…

A

plt. yscale(‘log’)

plt. show()

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20
Q

20 What are ticks in matplotlib?

A

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

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21
Q

21 What is a legend in matplotlib?

A

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

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22
Q

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)

A

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

plt. show()

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23
Q

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]

A

import matplotlib.pyplot as plt

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

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24
Q

24 What is a dependent variable?

A

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

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25
Q

25 What is an independent variable?

A

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

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26
Q

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

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

A

import matplotlib.pyplot as plt

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

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27
Q

27 Add a grid to a matplot figure

A

plt.grid(True)

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28
Q

28 Get the position of germany

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

A

countries.index(‘germany’)

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29
Q

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

A

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.

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30
Q

30 Get the keys

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

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

A

print(europe.keys())

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31
Q

31 Get the capital of norway

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

Outcome: oslo

A

print(europe[‘norway’])

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32
Q

32 Add italy and rome to the dictionary

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

A

europe[‘italy’]=’rome’

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33
Q

33 Check whether the dictionary has spain

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

A

print(‘spain’ in europe)

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34
Q

34 Outcome of:

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

print(‘madrid’ in europe)

A

FALSE

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35
Q

35 Delete spain

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

A

del(europe[‘spain’])

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36
Q

36 Update the capital of spain with madrid

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

A

europe[‘spain’]=’madrid’

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37
Q

37 Get the capital of france

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

A

print(europe[‘france’][‘capital’])

38
Q

38 Complete Code

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

import pandas as pd

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

print(pd.DataFrame(my_dict))

39
Q

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

A

df_ages.index = names

print(df_ages)

40
Q

40 Transform the csv to a dataframe called cars

cars.csv

A

import pandas as pd

cars = pd.read_csv(‘cars.csv’)

41
Q

41 Set the first column as row labels

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

A

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

42
Q

42 What is a panda series?

A

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.

43
Q

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

A

print(df[[‘country’]])

44
Q

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
A

print(df[[‘country’]])

45
Q

45 Print out columns a, b from df

A

print(df[[‘a’,’b’]])

46
Q

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’])

A

Outcome

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

number
0 0
1 1

47
Q

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’])

A

print(df.iloc[3:6])

48
Q

48 What is loc in python?

A

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

49
Q

49 What is a DataFrame in Python?

A

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

50
Q

50 Use iloc to get jon’s row as dataframe

name age
0 nick 15
1 jon 18

A

Outcome:

df.iloc[1,]

name jon
age 18
Name: 1, dtype: object

51
Q

51 Use iloc to get nick value

name age
0 nick 15
1 jon 18

#Outcome:
nick
A

print(df.iloc[0,0])

52
Q

52 Use loc to get nick’s row as dataframe

name age
rank_1 nick 15
rank_2 jon 18

A

Outcome:

print(df.loc[[‘rank_1’]])

name age
rank_1 nick 15

53
Q

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’]

A

name jon
age 18
Name: rank_2, dtype: object

54
Q

54 Use loc to get jon’s age:

name age
rank_1 nick 15
rank_2 jon 18

A

df.loc[‘rank_2’,’age’]

55
Q

55 Use iloc to get age column as a dataframe

name age
rank_1 nick 15
rank_2 jon 18

A

df.iloc[:,[1]]

56
Q

56 Outcome of:

print(True == False)

A

FALSE

57
Q

57 Outcome of:

print(- 1!= 75)

A

TRUE

58
Q

58 Outcome of:

print(True == 1)

A

TRUE

59
Q

59 Outcome of:

print(True == 0)

A

FALSE

60
Q

60 Outcome of:

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

A

FALSE

61
Q

61 Complete code:

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

#Outcome: 
[ True True False]
A

There are many possible answer

#Answer:
print(my_house>11)
62
Q

62 List out and name comparison operators

A 
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
63
Q

63 Outcome of:

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

A

FALSE

64
Q

64 Outcome of:

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

A

TRUE

65
Q

65 Outcome of:

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

A

FALSE

66
Q

66 List out the three Numpy Boolean operators

A

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

67
Q

67 Use a numpy boolean

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

A

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

68
Q

68 What is flow control statement in python

A

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

69
Q

69 Outcome of:

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

small
small
medium
large

70
Q

70 Complete code:

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

small
medium
large

A 
house=[2,4,6]
for i in house:
 if(i <4) :
 print("small")
 elif(i ==4 ) :
 print("medium")
 else :
 print("large")
71
Q

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

A

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

72
Q 
#Filtering in pandas
#Complete code

Name Country
rank1 Tom Spain
rank2 Jack USA

…[df……]

#Outcome: 
 Name Country
rank1 Tom Spain
A

df[df[‘Country’]==’Spain’]

73
Q

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

A

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

74
Q

73 Complete code

x = 1
…x < 4 :
print(x)
x = x…

1
2
3

A

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

75
Q

74 Outcome of:

offset=4

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

A 
correcting...
3
correcting...
2
correcting...
1
correcting...
0
76
Q

75 Loop over areas and print each element

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

A

for area in areas :

print(area)

77
Q

76 Loop and enumerate

areas = [11.25, 18.0, 20.0]

1-11.25
2-18.0
3-20.0

A

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

print( str(index)+ “-“ + str(area))

78
Q

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

A

for x in house :

print( str(x[0]) + “-“ + str(x[1]) )

79
Q

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

A

for key, value in world.items() :

print(key + “ – “ + str(value))

80
Q

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)
A

1
9
25
49

81
Q 
#80 Loop over DataFrame
(two ways)

name age
rank_1 nick 15
rank_2 jon 18

#Output: 
rank_1
15
rank_2
18
A

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

82
Q

81 Build this dataframe:

Name Country
rank1 Tom Spain
rank2 Jack USA

A

import pandas as pd

data = {'Name':['Tom', 'Jack'],'Country':['Spain','USA']}
df = pd.DataFrame(data, index =['rank1', 'rank2'])
83
Q

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

Name Country
0 Tom Spain
1 Jack USA

A

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

84
Q

83 How does work .seed() method?

A

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.

85
Q

84 Generate the same random number twice

A

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

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

86
Q

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

A

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

87
Q

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

A 
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)

88
Q

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

A

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)

89
Q

88 Get the maximum value of this list comprehension

[i for i in range(10)]

A

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

90
Q

89 What are list comprehensions used for?

A

They are used for creating new lists from other iterables.

91
Q

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

A

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()

1. Data Science (Python) (9 decks)

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