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MachineLearning - Assignment1 - Solved
Problem 1
Import the numpy package under the name np
Create a vector or 1D array with 10 zeros and print it
Find the memory size of this array
[ ]: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
[ ]: print("The Size of the array is", arr.itemsize*arr.size, "Bytes")
The Size of the array is 80 Bytes
2 Problem 2:
Create another vector or 1D array with values ranging from 10 to 20
Reverse the created vector (first element becomes last) -- Is there any NumPy method that you can use?
[ ]: array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
[ ]: array([20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10])
3 Problem 3:
Create a 3x4 array with random values (standard normal distribution) and find the minimum and maximum values
[ ]: array([[8672, 9272, 6342],
[3784, 5232, 7887],
[5001, 4270, 3926],
[6843, 5154, 7836]])
[ ]: arr3.max()
[ ]: 9272
[ ]: arr3.min()
[ ]: 3784
4 Problem 4:
Given the following 1D array, negate all elements which are between 3 and 8, in place. (include both 3 and 8 in conditional statements)
[ ]: Z
[ ]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
[ ]: array([ 0, 1, 2, -3, -4, -5, -6, -7, -8, 9, 10])
Given the 1D array Z, find the closest value to the given scalar v?
[ ]: Z
[ ]: array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
Closet Value is %d 33
Subtract the mean of each row of the following matrix
␣
,→---------------------------------------------------------------------------
NameError Traceback (most recent call␣
,→last)
<ipython-input-1-3b30b7c6267b> in <module>() ----> 1 np.random.seed(2)
2 X = np.random.rand(3, 4)
3 print(X)
NameError: name 'np' is not defined
[ ]: array([[0.3617265 , 0.3617265 , 0.3617265 , 0.3617265 ],
[0.39365556, 0.39365556, 0.39365556, 0.39365556],
[0.42918947, 0.42918947, 0.42918947, 0.42918947]])
[[ 0.0742684 -0.33580027 0.18793598 0.07359589] [ 0.02671225 -0.06332073 -0.18900692 0.22561541]
[-0.1295348 -0.16236219 0.19194436 0.09995263]]
Timing comparison for multiplication of 4 arrays. Find the fastest way to compute the multiplication ABCD. Make sure you report the elapsed time. (hint: you can find relevant information at https://youtu.be/SeBRHg9ZrSs) Complete the following:
5 Problem 5
Import and print the file ’parks.csv’ (Park Code should be the index column)
<IPython.core.display.HTML object>
[ ]: Park Name ... Longitude
Park Code ...
ACAD
Acadia National Park ...
-68.21
ARCH
Arches National Park ...
-109.57
BADL
Badlands National Park ...
-102.50
BIBE
Big Bend National Park ...
-103.25
BISC
Biscayne National Park ...
-80.08
BLCA
Black Canyon of the Gunnison National Park ...
-107.72
BRCA
Bryce Canyon National Park ...
-112.18
CANY
Canyonlands National Park ...
-109.93
CARE
Capitol Reef National Park ...
-111.17
CAVE
Carlsbad Caverns National Park ...
-104.44
CHIS
Channel Islands National Park ...
-119.42
CONG
Congaree National Park ...
-80.78
CRLA
Crater Lake National Park ...
-122.10
CUVA
Cuyahoga Valley National Park ...
-81.55
DENA
Denali National Park and Preserve
...
-150.50
DEVA
Death Valley National Park
...
-116.82
DRTO
Dry Tortugas National Park
...
-82.87
EVER
Everglades National Park
...
-80.93
GAAR
Gates Of The Arctic National Park and Preserve
...
-153.30
GLAC
Glacier National Park
...
-114.00
GLBA
Glacier Bay National Park and Preserve
...
-137.00
GRBA
Great Basin National Park
...
-114.30
GRCA
Grand Canyon National Park
...
-112.14
GRSA
Great Sand Dunes National Park and Preserve
...
-105.51
GRSM
Great Smoky Mountains National Park
...
-83.53
GRTE
Grand Teton National Park
...
-110.80
GUMO
Guadalupe Mountains National Park
...
-104.87
HALE
Haleakala National Park
...
-156.17
HAVO
Hawaii Volcanoes National Park
...
-155.20
HOSP
Hot Springs National Park
...
-93.05
ISRO
Isle Royale National Park
...
-88.55
JOTR
Joshua Tree National Park
...
-115.90
KATM
Katmai National Park and Preserve
...
-155.00
KEFJ
Kenai Fjords National Park
...
-149.65
KOVA
Kobuk Valley National Park
...
-159.28
LACL
Lake Clark National Park and Preserve
...
-153.42
LAVO
Lassen Volcanic National Park
...
-121.51
MACA
Mammoth Cave National Park
...
-86.10
MEVE
Mesa Verde National Park
...
-108.49
MORA
Mount Rainier National Park
...
-121.75
NOCA
North Cascades National Park
...
-121.20
OLYM
Olympic National Park
...
-123.50
PEFO
Petrified Forest National Park
...
-109.78
PINN
Pinnacles National Park
...
-121.16
REDW
Redwood National Park
...
-124.00
ROMO
Rocky Mountain National Park
...
-105.58
SAGU
Saguaro National Park
...
-110.50
SEKI
Sequoia and Kings Canyon National Parks
...
-118.68
SHEN
Shenandoah National Park
...
-78.35
THRO
Theodore Roosevelt National Park
...
-103.45
VOYA
Voyageurs National Park
...
-92.88
WICA
Wind Cave National Park
...
-103.48
WRST
Wrangell - St Elias National Park and Preserve
...
-142.00
YELL
Yellowstone National Park
...
-110.50
YOSE
Yosemite National Park ...
-119.50
ZION
Zion National Park ...
-113.05
[56 rows x 5 columns]
Print all column names
[ ]: list(parks.columns) ['Park Name ', 'State ', 'Acres ', 'Latitude ', 'Longitude '] Make sure tha all letters are lower case and replace space with _
[ ]: parks = parks.astype(str).apply(lambda x: x.str.lower())
[ ]: parks = parks.astype(str).apply(lambda x: x.str.rstrip())
[ ]: parks = parks.astype(str).apply(lambda x: x.str.replace(' ','_'))
[ ]: parks["State "] = parks["State "].apply(lambda state: state.replace('_','')) Which state has the smallest national park?
[ ]: 5550
[ ]: Park Name State Acres Latitude Longitude
Park Code
HOSP hot_springs_national_park ar 5550 34.51 -93.05
State is Arkansas
Produce a histogram plot that shows the distribution of ’acres’.
[ ]:
