python

Question 1

Find the last char in str

Answer 1

str[-1]

Question 2

Find the second to last char in str

Answer 2

str[-2]

Question 3

Find the whole string apart for the last two chars

Answer 3

str[ :-2]

Question 4

Find the whole string apart for the first two chars

Answer 4

str[ 2: ]

Question 5

iterate over str

Answer 5

for i in range(len(str)):

Question 6

Find the end of [ ] of numbers then check ==9

Answer 6

end = len(nums)
for i in range (end):
if nums [ i ] == 9:
return True

Question 7

Look for one number in an array of numbers

Answer 7

for number in nums:

Question 8

a function with if and then else if and else to find your fashion level.

Answer 8

def date_fashion(you, date):
    if you < 3 or date < 3:
        return 0
    elif you >= 8 or date >=8 :
        return 2
    else:
        return 1

Question 9

Find the position of w

Answer 9

astring = “Hello world!”

print(astring.index(‘w’))

Question 10

Put a string in reverse

Answer 10

print(astring[::-1])

Question 11

String to upper then lower

Answer 11

print(astring.upper())

print(astring.lower())

Question 12

The environment

Answer 12

consists of Python standard libraries and pre-installed packages

Question 13

What does a package always have

Answer 13

__init__.py

Question 14

What error do you get if the package is missing in your system?

Answer 14

ModuleNotFoundError

Question 15

List

Answer 15

[1,2,3,4]

Question 16

List properties

Answer 16

1. They are ordered.
2. They can contain any objects.
3. Their size can be varied.
4. They are nestable that is
   they can contain other lists
   as elements.
5. Their elements can be accessed
   by index.
6. They are mutable that is
   you can perform various functions
   on it.

Question 17

The keys of the dictionary are

Answer 17

immutable , unordered and Unique

Question 18

Tuples

Answer 18

immutable list.
heterogeneous sequence of elements,
impossible to append, edit or remove any individual elements within a tuple.

Question 19

while Panel data

Answer 19

are observations over time, of the same characteristic for multiple entities

Question 20

Using the map function

Answer 20

anw = list(map(lambda x,y: x+y , list_1, list_3 ))

Question 21

An array ?

Answer 21

Array is it a list of related data types, [ [] [] [] ], used to multidimensional array

Question 22

Numpy arrays advantage ?

Answer 22

Numpy arrays are helpful due to vectorisation and the ability to broadcast one row against another row, they can only work Arrays.

Question 23

one dimensional arrays’ are know as ?

Answer 23

‘vectors’. ‘Scalars

Question 24

NumPy array

Answer 24

np.array( [ [] [] [] ] )

Question 25

print (A2[2:4:1,2:7:2]) what will happen

Answer 25

Brings row 2 & every digit | and brings columb 2, 6 every other digit

Question 26

Make a few lists of the same type in np

Answer 26

V2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

Question 27

to make up a 50 item array and then reshape it to 10 rows by 5 columns ?

Answer 27

np.linspace(1,50).reshape(10,5)

Question 28

Example of a tuple ?

Answer 28

Tuples have no append or extend method. Elements cannot be removed from a tuple. (2,3,4,5,6)

Question 29

Change a row to a column

Answer 29

B = np.array([1, 2, 3]) output = B[ : , np.newaxis ] print (output)

Question 30

Used to delaet column

Answer 30

DataFrame.drop()

Question 31

Which of the following functions is used for indexing within a dataframe?

Answer 31

DataFrame.iloc()

Question 32

What does the series constructor look like

Answer 32

pd.Series([10, 20, 30, 40, 50, 60])

Question 33

how to times Series data with tan

Answer 33

My_Series = (S3+S4).apply(np.tan)

Question 34

DataFrame drop index rows

Answer 34

shop = shop.drop(shop.index[0 : 247]) | shop

Question 35

out of dataframe slice first 3 lines and first 3 columns

Answer 35

df.iloc[:2,:3]

Question 36

group items ?

Answer 36

df.groupby( ['Market', 'Sector'] ).groups

Question 37

Loop

Answer 37

for i in range(len(shop)): if shop.iloc[i]["Close"] > 1420 : print( shop.iloc[ i ] [ "Close" ] )

Question 38

Work out percent change on row and column

Answer 38

shop.iloc[ 1 : 4 , 3 : 4 ].pct_change()

Question 39

To create event driven trading

Answer 39

schedule strategy stream data place orders

Question 40

How do you change from the ‘base’ environment to an environment named ‘quantra_py’?

Answer 40

. conda activate quantra_py

Question 41

VWAP

Answer 41

Volume weighted average price

Question 42

FInd help in Pythong

Answer 42

Help( ) or ? after item

Question 43

print statement

Answer 43

print ( " {0:1s} " has {1 : .1 f} outstanding " . format( name, number ) )

Question 44

magic commands

Answer 44

%lmagic , %magic , %who

Question 45

add list item

Answer 45

list . append ("jonathon") | list . insert (0,"jon" )

Question 46

ballian check in list

Answer 46

print ( "jon" in family_list) | True

Question 47

Index using np

Answer 47

np.argmax(list)

Question 48

Looking for items in a list > 200

Answer 48

np.where( (list >200) & (list <300) )

Question 49

What is a set

Answer 49

set = {'a','a','u'}

Question 50

a = a + 1`

Answer 50

a += 1

Question 51

how to get the current working dir

Answer 51

import os | os.getcwd()

Question 52

FInd if about data frame

Answer 52

df.info() df.describe()

Question 53

read in csv set index

Answer 53

data3 = pd.read_csv( " shop.csv", index_col="Date", parse_dates=True, dayfirst=True)

Question 54

make a data frame

Answer 54

close_open = df [ [ 'Close', 'Open'] ]

Question 55

to delete column

Answer 55

del df['New']

Question 56

sum a whole column

Answer 56

df['Volume'].sum()

Question 57

df open <1250

Answer 57

df [ ( df['Open'] < 1250 ) ]

Question 58

selecting rows

Answer 58

df.iloc[ [ 4, 8, 20] ]

Question 59

Selecting two rows and two columns

Answer 59

df.iloc[[4, 5], [0,3]]

Question 60

closing change for day before

Answer 60

df['Close_to_Close'] = 100 * df['Close'] . pct_change()

Question 61

Creating a new column called 'Previous_Close' by using the shift operator on Close column.

Answer 61

df['Previous_Close'] = df[ 'Close' ].shift(1) | df.head()

Question 62

Creating a new column called 'MA' containing 5 day Moving Average of Close prices.

Answer 62

df['MA_5'] = df[ 'Close' ] . rolling (window=n).mean()

Question 63

finding missing values

Answer 63

df.isnull().sum()

Question 64

drop nana rows

Answer 64

df.dropna(axis=0).head()

Question 65

fill forward

Answer 65

df.fillna( method='ffill' ).head()

Question 66

function to find range then populate

Answer 66

``` def daily_range(x): return x['Close']-x['Open'] df['daily_range'] = df.apply ( daily_range, axis=1 ) ```

Question 67

supervised learning example

Answer 67

classification or regression learning

Question 68

unsupervised learning

Answer 68

clustering

Question 69

a set order doesn't matter

Answer 69

{ , , ,}

Question 70

what is the difference in continue and break

Answer 70

continue is more lie skip | break

Question 71

Mak 1 col all 0

Answer 71

df.iloc { 10:50 , 6 } = 0

Question 72

find out directories in package

Answer 72

dir( read_csv )

Question 73

show all variables in memory

Answer 73

%%whos

Question 74

work out average over 5 days

Answer 74

df{new_col} = df[ 'close" ].rolling(5).mean()

Question 75

Sum of all null values

Answer 75

df . isnull() . sum()

Question 76

Change to datatime

Answer 76

df = pd.to_datetime( df.index )

Question 77

logical condition

Answer 77

con = (df1_msft["p"] > 0) | df1_msft.loc[ con ]

Question 78

Make the first col an index

Answer 78

index_col= [ 0 ] , parse_dates=True

Question 79

Help on function

Answer 79

yf . download?

Question 80

today's date

Answer 80

today = datetime.date.today()

Question 81

to get all instance attributes

Answer 81

__dict__

Question 82

family tree

Answer 82

__mro__

Question 83

pass attributes and methods to child class

Answer 83

cls pass

Question 84

Inherit methods from parents

Answer 84

Use supper | parent1.__init__(self,age) / parent2.__init__(self,age)

Question 85

Find to days date

Answer 85

pd.datetime.now()

Question 86

Date in the past

Answer 86

pd.Timedelta(days=30)

Question 87

make a df

Answer 87

list1 =[ 1,2,3,4,5,6,7] | test_df = pd.DataFrame( { "name" : list1 } )

Question 88

add to list

Answer 88

stock_list.extend( )

Question 89

How to plot scatter

Answer 89

DataFrame.plot.scatter(test_df.plot.scatter( x="name",y="name",c="red", figsize=(12,5) ) )

Question 90

work out log returns for stock

Answer 90

stock_df[stock] = np.log( ['Close'] / stock_df['Open'] )