First

Question 1

Type of variable

Answer 1

type(x)

Question 2

Python lists

Answer 2

y = [‘a’,’b’,’c’]

Question 3

List of lists

Answer 3

y = [[‘a’,’b’],[‘c,’d’],[‘e’,’f’]]

Question 4

Subsetting lists

Answer 4

y[6]

Question 5

Subset last variable in list

Answer 5

y[-1]

Question 6

List slicing

Answer 6

y[#:#]

[inclusive:exclusive]

Question 7

Remove from a list

Answer 7

del(y[#])

del()

Question 8

List copy

Answer 8

When you copy a list, you create a reference not a new list.

To create a new list, you have to slice"
x = ['a', 'b', 'c']
y = list(x)
or
y = x[:]

Question 9

Find maximum

Answer 9

max()

Question 10

Round

Answer 10

round(df, #)

Question 11

Length of a list or string

Answer 11

len()

Question 12

List in ascending order

Answer 12

sorted()

Question 13

Find where something is indexed

Answer 13

index()

> y = [‘a’, 1, ‘b’, 2, ‘de’]
y.index(‘b’)
2

Question 14

Change/add to your list

Answer 14

append()

> y = [‘a’, 1, ‘b’, 2, ‘de’]
y.append(44)
[‘a’, 1, ‘b’, 2, ‘de’, 44]

Question 15

Make all upper case

Answer 15

string.upper()

Question 16

Count occurrences of x in a string

Answer 16

string.count(‘x’)

Question 17

Remove first x of a list to a matched input

Answer 17

list.remove()

> y = [‘a’, 1, ‘b’, 2, ‘de’]
y.remove(1)
[‘a’, ‘b’, 2, ‘de’, 44]

Question 18

Reverse the order of elements in the list

Answer 18

list.reverse()

Question 19

Create numpy array

Answer 19

y = np.array(list)

Question 20

Numpy subsetting

Answer 20

> y = array([1, 3, 5])

> y[1]
3

> y > 3
array[(False, False, True)]

> y[y > 3]
array[(5)]

Question 21

Numpy dimensions of an 2-D array

Answer 21

df.shape

> y = array([1, 3, 5],
[4, 5, 6])
y.shape
(2, 3) # 2 rows, 3 cols

Question 22

Numpy Subsetting 2-D array

Answer 22

> y = array([1, 3, 5],
[4, 5, 6])

> y[0][2]
5

> y[0,2]
5

> y[: , 1:2]
array([3, 5],
[5, 6])

> y[1, :]
array([4, 5, 6])

Question 23

Numpy mean

Answer 23

np.mean()

also subset with
np.mean(df[:, 0])

Question 24

Numpy median

Answer 24

np.median()

also subset with
np.median(df[:, 0])

Question 25

Numpy coefficient

Answer 25

Are two things related np.corrcoef(x, y) also subset with np.corrcoef(df[:, 0], df[:,1])

Question 26

Numpy std

Answer 26

np.std(x) also subset with np.std(df[:, 0])

Question 27

Numpy sum

Answer 27

np.sum(x) also subset with np.sum(df[:, 0])

Question 28

Numpy join two different lists into a single array

Answer 28

np.columnstack((df_x, df_y))

Question 29

Matplotlib Line Chart

Answer 29

plt. plot(x, y) | plt. show()

Question 30

Matplotlib Scatter Plot

Answer 30

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

Question 31

Matplotlib Histogram

Answer 31

plt. hist(x, bins = #) | plt. show()

Question 32

Matplotlib Customize (x axis, y axis, title, ticks)

Answer 32

plt. xlabel('x') plt. ylabel('y') plt. title('title') plt. yticks([0,1,2,3,4]) plt. xticks([0,1,2,3], ['0', '1B', '2B', '3B']) # Reassign numbers on y -axis and change the name of y-axis ticks)

Question 33

Dictionary

Answer 33

dict = {'k':v, 'k1':v1, 'k2',v2....} world = {'Nepal': 30.5, 'India': 1000, 'Bhutan' : 0.5}

Question 34

Dictionary find all keys

Answer 34

dict.keys() > world = {'Nepal': 30.5, 'India': 1000, 'Bhutan' : 0.5} > print(world.keys()) Nepal, India, Bhutan

Question 35

Dictionary add Key

Answer 35

dict['k'] = v > world = {'Nepal': 30.5, 'India': 1000, 'Bhutan' : 0.5} > world['China'] = 1050 > print(world) {'Nepal': 30.5, 'India': 1000, 'Bhutan' : 0.5, 'China' : 1050}

Question 36

Dictionary delete key

Answer 36

del(dict['k']) > world = {'Nepal': 30.5, 'India': 1000, 'Bhutan' : 0.5} > del(world['Bhutan']) > world world = {'Nepal': 30.5, 'India': 1000}

Question 37

Pandas dataframe

Answer 37

pd.DataFrame(dict) > world = {'Nepal': 30.5, 'India': 1000, 'Bhutan' : 0.5} > df = pd.DataFrame(world)

Question 38

Pandas CSV

Answer 38

pd.read_csv('path/to/dataframe.csv', index_col = 0) index_col = 0 means that the pd will not index the df

Question 39

Pandas select columns

Answer 39

df['colname']

Question 40

Pandas select columns but keep in df

Answer 40

df[['colname']]

Question 41

Pandas select two columns

Answer 41

df[['col1', 'col2']]

Question 42

Pandas select rows

Answer 42

df[#:#]

Question 43

Pandas Label Based Discovery

Answer 43

df.loc[['k']] >df.loc[['RU']] Country Capital Area RU Russsia Moscow 17.1

Question 44

Pandas Label Discovery Multiple Rows

Answer 44

df.loc[['k1', 'k2', 'k3']] >df.loc[['RU', 'IN']] Country Capital Area RU Russsia Moscow 17.1 IN India Delhi 3.2

Question 45

Pandas Label Discovery Multiple Rows and columns

Answer 45

df.loc[['k1', 'k2', 'k3'], ['col1', 'col2'] >df.loc[['RU', 'IN'], ['Country', 'Capital']] Country Capital RU Russsia Moscow IN India Delhi

Question 46

Pandas Index Discovery

Answer 46

df.iloc[[#]] >df.iloc[[1]] Country Capital Area RU Russsia Moscow 17.1

Question 47

Pandas Index Discovery Multiple Rows

Answer 47

df.iloc[[#, #, #]] >df.loc[[1, 2]] Country Capital Area RU Russsia Moscow 17.1 IN India Delhi 3.2

Question 48

Pandas Index Discovery Multiple Rows and Columns

Answer 48

df.iloc[[#, #, #], [#, #] >df.loc[[1, 2], [0, 1]] Country Capital RU Russsia Moscow IN India Delhi

Question 49

Pandas [ ] vs [[ ]]

Answer 49

[ ] is a pd. series where as [[ ]] is a pd. dataframe

Question 50

and

Answer 50

both booleans need to be true > False and False True > x = 12 > x > 7 and x < 15 True > False and True False

Question 51

or

Answer 51

at least one boolean needs to be true >True or False True > x = 5 > x < 7 or x > 13

Question 52

Numpy array equivalent of: and, or, not

Answer 52

logical_and() logical_or() logical_not() > y = [[5, 7, 9]] > np.logical_and(y > 5, y <9) [[False, True, False]]

Question 53

Filtering (subset) pd dataframe

Answer 53

Filter > df2 = df[‘col’] > # or Subset > df2 = df[df[‘col’] > #]

Question 54

Subset using NP

Answer 54

Filter > np.logical_and(df['col'] > #, df['col'] < #) or subset > df[np.logical_and(df['col'] > #, df['col'] < #)]

Question 55

Enumerate FOR loop

Answer 55

> fam = [1.5, 1.6, 1.7] ``` > for index, height in enumerate(fam): > print(str(index) + ' : ' + str(height)) 1 : 1.5 2 : 1.6 3 : 1.7 ```

Question 56

FOR loop over a dictionary

Answer 56

First always key and then value > world = {'Nepal': 30.5, 'India': 1000, 'Bhutan' : 0.5} > for k, v in world.items(): > print(k + ' : ' + str(v)) Nepal : 30.5 India : 1000 Bhutan : 0.5

Question 57

FOR loop over rows

Answer 57

iterrows() not very efficient because on every iteration you are creating a new pandas series > for lab, row in brics.iterrows(): > print(lab + ' : ' + row['captial'] BR : Brasilia RU : Moscow

Question 58

Calculate new column (Non math)

Answer 58

apply() > brics['name_length'] = brics['country'].apply(len) name_length BR Brazil Brasilia 6

Question 59

Random number generator bw 1 - 0

Answer 59

np.random.rand()

Question 60

Set random number manually

Answer 60

np.random.seed(#) sets the random seed, so that your results are reproducible between simulations. As an argument, it takes an integer of your choosing. If you call the function, no output will be generated. np.random.rand() if you don't specify any arguments, it generates a random float between zero and one. > np.random.seed(123) > coin np.random.rand(0, 2) #Randomly generate 1 or 0

Question 61

Transpose and array

Answer 61

np.transpose(df)