성별로 위반 유형이 다를까?

pandas로 경찰 활동 분석하기

Kevin Markham

Founder, Data School

고유값 세기 (1)

.value_counts(): Series의 고유값 개수를 셉니다
범주형 데이터에 적합합니다

ri.stop_outcome.value_counts()

Citation            77091
Warning              5136
Arrest Driver        2735
No Action             624
N/D                   607
Arrest Passenger      343
Name: stop_outcome, dtype: int64

고유값 세기 (2)

ri.stop_outcome.value_counts().sum()

ri.shape

(86536, 13)

빈도를 비율로 표현하기

ri.stop_outcome.value_counts()

77091/86536

0.8908546731995932

Citation            77091
Warning              5136
Arrest Driver        2735
No Action             624
N/D                   607
Arrest Passenger      343

ri.stop_outcome.value_counts(
  normalize=True)

Citation            0.890855
Warning             0.059351
Arrest Driver       0.031605
No Action           0.007211
N/D                 0.007014
Arrest Passenger    0.003964

DataFrame 행 필터링

ri.driver_race.value_counts()

White       61870
Black       12285
Hispanic     9727
Asian        2389
Other         265

white = ri[ri.driver_race == 'White']

white.shape

(61870, 13)

두 그룹의 정차 결과 비교

white.stop_outcome.value_counts(
  normalize=True)

Citation            0.902263
Warning             0.057508
Arrest Driver       0.024018
No Action           0.007031
N/D                 0.006433
Arrest Passenger    0.002748

asian = ri[ri.driver_race ==
           'Asian']

asian.stop_outcome.value_counts(
  normalize=True)

Citation            0.922980
Warning             0.045207
Arrest Driver       0.017581
No Action           0.008372
N/D                 0.004186
Arrest Passenger    0.001674

연습해 봅시다!

pandas로 경찰 활동 분석하기