Spring 2022 Final Exam
pdf
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School
University of California, San Diego *
*We aren’t endorsed by this school
Course
10
Subject
Mathematics
Date
Feb 20, 2024
Type
Pages
22
Uploaded by BaronJackal4036
24. 2. 12. 오후
9:13
Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
1/22
ikea
ikea
'product'
str
'category'
str
'price'
int
'assembly_cost'
int
'packages'
int
ikea
ikea
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Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
2/22
offers
offers
'offer'
str
'category'
str
'percent_off'
int
offers
app_data
app_data
'product'
str
'category'
str
'assembly_time'
str
'x hr, y min'
x
y
app_data
app_data
24. 2. 12. 오후
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Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
3/22
import babypandas as bpd
import numpy as np
'category'
'price'
(ikea.assign(assembly_per_package =
___(a)___)
.sort_values(by
=
'assembly_per_package'
).___(b)___)
ikea.___(a)___.get(___(b)___)
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24. 2. 12. 오후
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Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
4/22
ikea
'product'
ikea
extract_product_line
extract_product_line
(ikea.assign(product_line = ikea.get(
'product'
)
.apply(extract_product_line)))
def
extract_product_line(x):
return
_________
24. 2. 12. 오후
9:13
Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
5/22
offers
with_offers
with_offers
with_offers
'percent_off'
'product'
with_offers
with_offers
(with_offers = ikea.take(np.arange(
6
))
.merge(offers, left_on
=
'category'
, right_on
=
'eligible_category'
))
with_offers.groupby(
'product'
).max().get(
'offer'
)
24. 2. 12. 오후
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Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
6/22
with_offers.get('price') - with_offers.get('percent_off')/100
with_offers.get('price')*(100 - with_offers.get('percent_off'))/100
with_offers.get('price') - with_offers.get('price')*with_offers.get('percent_off')/100
with_offers.get('price')*(100 - with_offers.get('percent_off')/100)
app_data
app_data
app_data
'assembly_time'
with_offers = with_offers.assign(after_price = _________)
with_offers
(app_data.take(np.arange(
4
))
.sort_values(by
=
'assembly_time'
)
.get(
'assembly_time'
)
.iloc[
0
])
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24. 2. 12. 오후
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Spring 2022 Final Exam
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7/22
to_minutes
'x hr, y min'
x
y
int
to_minutes('3 hr, 5 min')
app_data
'minutes'
to_minutes('assembly_time')
to_minutes(app_data.get('assembly_time'))
app_data.get('assembly_time').apply(to_minutes)
app_data.get('assembly_time').apply(to_minutes(time))
app_data
def
to_minutes(time):
first_split = time.split(
' hr, '
)
second_split = first_split[
1
].split(
' min'
)
return
_________
app_data = app_data.assign(minutes = _________)
app_data
24. 2. 12. 오후
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Spring 2022 Final Exam
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8/22
'minutes'
'product'
'minutes'
'minutes'
'product'
'minutes'
app_data
app_data
'bed'
'outdoor'
24. 2. 12. 오후
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Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
9/22
app_data
obs_diff
outdoor = (app_data.get(
'category'
)
==
'outdoor'
) bed = (app_data.get(
'category'
)
==
'bed'
)
obs_diff = ( ___(a)___ - ___(b)___ ) / ___(c)___
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10/22
multi = np.random.multinomial(
2500
, [
0.5
,
0.5
]) (multi[
0
] - multi[
1
])
/
2500
outdoor = np.random.multinomial(
2500
, [
0.5
,
0.5
])[
0
]
/
2500 bed = np.random.multinomial(
2500
, [
0.5
,
0.5
])[
1
]
/
2500 outdoor - bed choice = np.random.choice([
0
, 1
], 2500
, replace
=
True
) choice_sum = choice.sum( ) (choice_sum - (
2500 - choice_sum))
/
2500
choice = np.random.choice([
'bed'
, 'outdoor'
], 2500
, replace
=
True
) bed = np.count_nonzero(choice
==
'bed'
)
outdoor = np.count_nonzero(choice
==
'outdoor'
)
outdoor
/
2500 - bed
/
2500
outdoor = (app_data.get(
'category'
)
==
'outdoor'
) bed = (app_data.get(
'category'
)
==
'bed'
)
samp = app_data[outdoor
|
bed].sample(
2500
, replace
=
True
) samp[samp.get(
'category'
)
==
'outdoor'
].shape[
0
]
/
2500 - samp[samp.get(
'category'
)
==
'bed'
].shape[
0
]
/
2500
)
outdoor = (app_data.get(
'category'
)
==
'outdoor'
) bed = (app_data.get(
'category'
)
==
'bed'
)
samp = (app_data[outdoor
|
bed].groupby(
'category'
).count( ).reset_index( ).sample(
2500
, replace
=
True
)) samp[samp.get(
'category'
)
==
'outdoor'
].shape[
0
]
/
2500 - samp[samp.get(
'category'
)
==
'bed'
].shape[
0
]
/
2500
24. 2. 12. 오후
9:13
Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
11/22
simulated_diffs
<
<=
>
>=
app_data
app_data
app_data
'minutes'
app_data
'minutes'
np.count_nonzero(simulated_diffs _________ obs_diff)
/
10000
24. 2. 12. 오후
9:13
Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
12/22
np.random.permutation
billy_lommarp[lommarp].get('minutes').mean()
np.random.choice(billy_lommarp.get('minutes'), lommarp.sum()).mean()
billy_lommarp.get('minutes').mean() - billy_mean
(billy_lommarp.get('minutes').sum() - billy_mean * billy.sum())/lommarp.sum()
billy = (app_data.get(
'product'
) == 'BILLY Bookcase, white, 31 1/2x11x79 1/2'
)
lommarp = (app_data.get(
'product'
) == 'LOMMARP Bookcase, dark blue-green, 25 5/8x78 3/8'
)
billy_lommarp = app_data[billy
|
lommarp]
billy_mean = np.random.choice(billy_lommarp.get(
'minutes'
), billy.sum()).mean()
lommarp_mean = _________
billy_mean - lommarp_mean
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24. 2. 12. 오후
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Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
13/22
calculate_meatball_plates
def
calculate_meatball_plates(prices): meatball_plates = 0
for
price in
prices:
if
price >= 100 and
price <= 199
:
meatball_plates = 1
elif
price >= 200
:
meatball_plates = 2
return
meatball_plates
def
calculate_meatball_plates(prices): meatball_plates = 0
for
price in
prices:
if
price >= 200
:
meatball_plates = meatball_plates + 1
if
price >= 100
:
meatball_plates = meatball_plates + 1
return
meatball_plates
def
calculate_meatball_plates(prices): return
np.count_nonzero(prices >= 100
) + np.count_nonzero(prices >= 200
)
def
calculate_meatball_plates(prices): return
(prices >= 200
).sum()
*
2 + ((
100 <= prices) & (prices <= 199
)).sum()
*
1
24. 2. 12. 오후
9:13
Spring 2022 Final Exam
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24. 2. 12. 오후
9:13
Spring 2022 Final Exam
https://practice.dsc10.com/sp22-final/index.html
15/22
area_left_of(z)
z
-4 <= z <= 12
area_between(x, y)
x
y
-4 <= x <= y <= 12
z
= −4
z
= 4
z
= 0
z
= 8
z
= −4
z
= 4
import scipy
def
area_left_of(z):
'''Returns the area under the armchair curve to the left of z.
Assume -4 <= z <= 12'''
if
___(a)___: return
___(b)___ return
scipy.stats.norm.cdf(z)
def
area_between(x, y):
'''Returns the area under the armchair curve between x and y. Assume -4 <= x <= y <= 12.'''
return
___(c)___
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area_between(x, y)
x
y
-4 <= x <= y <= 12
area_between(-2, 10)
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area_between(0.37, 8.37)
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