Assignment 3 - Jupyter Notebook
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Western University *
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Course
1000A
Subject
Statistics
Date
Jan 9, 2024
Type
Pages
7
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Assignment 3 - Jupyter Notebook
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Question 4
Labels for entire sample: 0.00001 - 0.14987
Labels in each subgroup
Community Pharmacy: 0.00001 - 0.11323 (ex. Community Pharmacist #587 has label 0.00587)
Hospital or Other Health Care Facility: 0.11324 - 0.13987 (ex. the 1504th individual from this
subgroup has label 0.12827)
Academia or Governement: 0.13988 - 0.14320 (ex. the 28th individual from this subgroup has label
0.14015)
Industry: 0.14321 - 0.14820 (ex. the 462nd individual from this subgroup has label 0.14782)
Corporate, professional practice or clinic: 0.14822 - 0.14987 (ex. the 18th individual from this
subgroup has label 0.14800)
In [1]:
In [2]:
Out[2]:
ID
Practice
0
1
Community Pharmacy
1
2
Community Pharmacy
2
3
Community Pharmacy
3
4
Community Pharmacy
4
5
Community Pharmacy
...
...
...
14982
14983
Corporate, professional practice or clinic
14983
14984
Corporate, professional practice or clinic
14984
14985
Corporate, professional practice or clinic
14985
14986
Corporate, professional practice or clinic
14986
14987
Corporate, professional practice or clinic
14987 rows × 2 columns
import
numpy as
np
import
pandas as
pd
import
random
df =
pd.DataFrame({
'ID'
: np.arange(
1
,
14988
).tolist(),
'Practice'
: [
'Community Pharmacy'
]
*
11323 +
[
'Hospital or +
[
'Academia or Governement'
]
*
333 +
[
'Industry'
]
*
500 +
[
'Corporate, professional practice or clinic'
]
*
167
})
df
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Assignment 3 - Jupyter Notebook
localhost:8888/notebooks/Documents/data science/Assignment 3.ipynb
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In [3]:
Selected individuals 0.02470, 0.00794, 0.11061, 0.08303, 0.09996, 0.02463, 0.02456, 0.02390,
0.08724, 0.08562
In [5]:
Selected Individuals 0.13137, 0.11745, 0.13922, 0.13778, 0.11919, 0.12236, 0.12093, 0.12128,
0,11833, 0.13877
ID Practice 86 87 Community Pharmacy 10133 10134 Community Pharmacy 5450 5451 Community Pharmacy 10462 10463 Community Pharmacy 6031 6032 Community Pharmacy 9611 9612 Community Pharmacy 5735 5736 Community Pharmacy 2405 2406 Community Pharmacy 2002 2003 Community Pharmacy 7521 7522 Community Pharmacy ID Practice 13049 13050 Hospital or Other Health Care Facility 12648 12649 Hospital or Other Health Care Facility 13069 13070 Hospital or Other Health Care Facility 11947 11948 Hospital or Other Health Care Facility 12507 12508 Hospital or Other Health Care Facility 11545 11546 Hospital or Other Health Care Facility 11361 11362 Hospital or Other Health Care Facility 11967 11968 Hospital or Other Health Care Facility 11657 11658 Hospital or Other Health Care Facility 12038 12039 Hospital or Other Health Care Facility df_Community_Pharmacy
=
df[df[
'Practice'
] ==
'Community Pharmacy'
]
df_Community_Pharmacy
random_rows =
random.sample(
range
(
11323
), 10
)
print
(df_Community_Pharmacy.iloc[random_rows])
df_Hospital_other =
df[df[
'Practice'
] ==
'Hospital or Other Health Care Fac
df_Hospital_other
random_rows =
random.sample(
range
(
2664
), 10
)
print
(df_Hospital_other.iloc[random_rows])
11/18/22, 10:04 PM
Assignment 3 - Jupyter Notebook
localhost:8888/notebooks/Documents/data science/Assignment 3.ipynb
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In [7]:
Selected Individuals: 0.14277, 0.14258. 0.14259, 0.14244, 0.14006, 0.14314, 0.14138, 0.14107,
0.14215, 0.14165
In [8]:
Selected individuals: 0.14350, 0.14551, 0.14472, 0.14465, 0.14369, 0.14424, 0.14432, 0.14401,
0.14715, 0.14779
ID Practice 14248 14249 Academia or Governement 14109 14110 Academia or Governement 14137 14138 Academia or Governement 14233 14234 Academia or Governement 14093 14094 Academia or Governement 14261 14262 Academia or Governement 14294 14295 Academia or Governement 14178 14179 Academia or Governement 14277 14278 Academia or Governement 14025 14026 Academia or Governement ID Practice 14626 14627 Industry 14392 14393 Industry 14552 14553 Industry 14413 14414 Industry 14420 14421 Industry 14775 14776 Industry 14598 14599 Industry 14540 14541 Industry 14725 14726 Industry 14365 14366 Industry df_Academia_governement =
df[df[
'Practice'
] ==
'Academia or Governement'
]
df_Academia_governement
random_rows =
random.sample(
range
(
333
), 10
)
print
(df_Academia_governement.iloc[random_rows])
df_Industry =
df[df[
'Practice'
] ==
'Industry'
]
df_Industry
random_rows =
random.sample(
range
(
500
), 10
)
print
(df_Industry.iloc[random_rows])
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In [10]:
Selected individuals: 0.14861, 0.14907, 0.14935, 0.14831, 0.14936, 0.14962, 0.14924, 0.14872,
0.14837, 0.14847
All Selected individuals Community Pharmacy: 0.02470, 0.00794, 0.11061, 0.08303, 0.09996,
0.02463, 0.02456, 0.02390, 0.08724, 0.08562
Hospital and other Health care facilities: 0.13137, 0.11745, 0.13922, 0.13778, 0.11919, 0.12236,
0.12093, 0.12128, 0,11833, 0.13877
Academia or Governement:0.14277, 0.14258. 0.14259, 0.14244, 0.14006, 0.14314, 0.14138,
0.14107, 0.14215, 0.14165
Industry: 0.14350, 0.14551, 0.14472, 0.14465, 0.14369, 0.14424, 0.14432, 0.14401, 0.14715,
0.14779
Corporate, professional practice or clinic: 0.14861, 0.14907, 0.14935, 0.14831, 0.14936, 0.14962,
0.14924, 0.14872, 0.14837, 0.14847
Question 5
b)
ID Practice 14872 14873 Corporate, professional practice or clinic 14954 14955 Corporate, professional practice or clinic 14837 14838 Corporate, professional practice or clinic 14875 14876 Corporate, professional practice or clinic 14820 14821 Corporate, professional practice or clinic 14905 14906 Corporate, professional practice or clinic 14925 14926 Corporate, professional practice or clinic 14927 14928 Corporate, professional practice or clinic 14897 14898 Corporate, professional practice or clinic 14953 14954 Corporate, professional practice or clinic df_CPPC =
df[df[
'Practice'
] ==
'Corporate, professional practice or clinic'
df_CPPC
random_rows =
random.sample(
range
(
167
), 10
)
print
(df_CPPC.iloc[random_rows])
11/18/22, 10:04 PM
Assignment 3 - Jupyter Notebook
localhost:8888/notebooks/Documents/data science/Assignment 3.ipynb
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In [11]:
In [12]:
In [13]:
Out[11]:
ID
0
1
1
2
2
3
3
4
4
5
...
...
1405
1406
1406
1407
1407
1408
1408
1409
1409
1410
1410 rows × 1 columns
26 df_Trees =
pd.DataFrame({
'ID'
: np.arange(
1
,
1411
).tolist()})
df_Trees
def
systematic_sampling
(df, starting_index, step):
indices =
np.arange(starting_index, len
(df), step =
step)
systematic_sample
=
df.iloc[indices]
return
systematic_sample
random.seed(
90
)
random_start =
random.randint(
0
,
78
)
print
(random_start)
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localhost:8888/notebooks/Documents/data science/Assignment 3.ipynb
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In [14]:
Out[14]:
ID
26
27
53
54
80
81
107
108
134
135
161
162
188
189
215
216
242
243
269
270
296
297
323
324
350
351
377
378
404
405
431
432
458
459
485
486
512
513
539
540
566
567
593
594
620
621
647
648
674
675
701
702
728
729
755
756
782
783
809
810
836
837
863
864
systematic_sample
=
systematic_sampling(df
=
df_Trees, starting_index
=
random_s
systematic_sample
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In [ ]:
ID
890
891
917
918
944
945
971
972
998
999
1025
1026
1052
1053
1079
1080
1106
1107
1133
1134
1160
1161
1187
1188
1214
1215
1241
1242
1268
1269
1295
1296
1322
1323
1349
1350
1376
1377
1403
1404
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