To generate a statistical summary for each of our numerical attributes, use the Pandas DataFrame describe() function on our forestfire_df.   # TODO 1 ff_describe = display(ff_describe) todo_check([ (ff_describe.shape == (8,11),'ff_describe shape did not match (8, 11)'), (np.all(np.isclose(ff_describe.values[:4,2:4].flatten(), np.array([517. ,517. ,90.64468085,110.87234043,5.52011085,64.04648225,18.7,1.1]),rtol=.01)),'The values of ff_describe were wrong!'), ])

Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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TODO 1: To generate a statistical summary for each of our numerical attributes, use the Pandas DataFrame describe() function on our forestfire_df.

 

# TODO 1
ff_describe =
display(ff_describe)

todo_check([
(ff_describe.shape == (8,11),'ff_describe shape did not match (8, 11)'),
(np.all(np.isclose(ff_describe.values[:4,2:4].flatten(), np.array([517. ,517. ,90.64468085,110.87234043,5.52011085,64.04648225,18.7,1.1]),rtol=.01)),'The values of ff_describe were wrong!'),
])
 
 
75%
X
max
Y
FFMC
DMC
DC
ISI
RH
temp
count 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000
9.021663 18.889168 44.288201 4.017602 0.021663 12.847292
mean 4.669246 4.299807 90.644681 110.872340 547.940039
std 2.313778 1.229900 5.520111 64.046482 248.066192
63.655818
4.559477 5.806625 16.317469 1.791653 0.295959
0.000000 2.200000 15.000000 0.400000
min 1.000000 2.000000 18.700000 1.100000 7.900000
0.000000
0.000000
0.000000
25%
50%
42.000000 4.000000 0.000000
0.520000
3.000000 4.000000 90.200000 68.600000 437.700000
4.000000 4.000000 91.600000 108.300000 664.200000 8.400000 19.300000
7.000000 5.000000 92.900000 142.400000 713.900000 10.800000 22.800000 53.000000 4.900000 0.000000
9.000000 9.000000 96.200000 291.300000 860.600000 56.100000 33.300000 100.000000 9.400000 6.400000 1090.840000
6.570000
wind
rain
6.500000 15.500000 33.000000 2.700000
area
0.000000
Transcribed Image Text:75% X max Y FFMC DMC DC ISI RH temp count 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 9.021663 18.889168 44.288201 4.017602 0.021663 12.847292 mean 4.669246 4.299807 90.644681 110.872340 547.940039 std 2.313778 1.229900 5.520111 64.046482 248.066192 63.655818 4.559477 5.806625 16.317469 1.791653 0.295959 0.000000 2.200000 15.000000 0.400000 min 1.000000 2.000000 18.700000 1.100000 7.900000 0.000000 0.000000 0.000000 25% 50% 42.000000 4.000000 0.000000 0.520000 3.000000 4.000000 90.200000 68.600000 437.700000 4.000000 4.000000 91.600000 108.300000 664.200000 8.400000 19.300000 7.000000 5.000000 92.900000 142.400000 713.900000 10.800000 22.800000 53.000000 4.900000 0.000000 9.000000 9.000000 96.200000 291.300000 860.600000 56.100000 33.300000 100.000000 9.400000 6.400000 1090.840000 6.570000 wind rain 6.500000 15.500000 33.000000 2.700000 area 0.000000
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