-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathanalysis.py
More file actions
157 lines (109 loc) · 4.66 KB
/
analysis.py
File metadata and controls
157 lines (109 loc) · 4.66 KB
1
2
3
4
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
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import warnings
import seaborn as sns
import calendar as cd
sns.set(style="darkgrid", font="SimHei",font_scale=1.5, rc={"axes.unicode_minus": False})
warnings.filterwarnings("ignore")
# In[13]:
data=pd.read_csv("data/Crime_Data_from_2010_to_2019.csv")
print(data.shape)
data.info()
#data.isnull().sum(axis=0)
data.columns
# In[3]:
data["DAYTIME"].value_counts().sort_values().plot(kind='barh')
# In[4]:
data["WEEK"].value_counts().sort_values().plot(kind='barh')
# In[5]:
data["Crm Cd Desc"].value_counts().iloc[:10].sort_values().plot(kind='barh')
# In[7]:
result=pd.DataFrame()
# In[8]:
year=["2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021"]
result["Year"]=year
# In[9]:
num_cases=[]
severe_rate=[]
severe_perday=[]
severe_perweekdays=[]
severe_perweekends=[]
gun_rate=[]
gun_perday=[]
gun_perweekdays=[]
gun_perweekends=[]
weekdays_perday=[]
weekends_perday=[]
dawn=[]
morning=[]
noon=[]
afternoon=[]
dusk=[]
evening=[]
midnight=[]
for i in year:
year_cases=data[data['DATE'].str.contains(str(i))]
year_severe=year_cases[(year_cases['Crm Cd']==110) | (year_cases['Crm Cd']==113) | (year_cases['Crm Cd']==121) | (year_cases['Crm Cd']==122) | (year_cases['Crm Cd']==815) | (year_cases['Crm Cd']==820) | (year_cases['Crm Cd']==821) | (year_cases['Crm Cd']==210) | (year_cases['Crm Cd']==220) | (year_cases['Crm Cd']==230) | (year_cases['Crm Cd']==231) | (year_cases['Crm Cd']==235) | (year_cases['Crm Cd']==236) | (year_cases['Crm Cd']==250) | (year_cases['Crm Cd']==251)]
weekdays_severe=year_severe[year_severe["WEEK"].str.contains("Mon|Tue|Wed|Thu|Fri")]
weekends_severe=year_severe[year_severe["WEEK"].str.contains("Sat|Sun")]
year_gun=year_cases[year_cases['Weapon Desc'].str.contains("GUN|PISTOLFIREARM|REVOLVER|RIFLE|SHOTGUN|SEMIAUTOMATIC",na=False)]
weekdays_gun=year_gun[year_gun["WEEK"].str.contains("Mon|Tue|Wed|Thu|Fri")]
weekends_gun=year_gun[year_gun["WEEK"].str.contains("Sat|Sun")]
year_weekdays_cases=year_cases[year_cases['WEEK'].str.contains("Mon|Tue|Wed|Thu|Fri")]
year_weekends_cases=year_cases[year_cases['WEEK'].str.contains("Sat|Sun")]
year_dawn=year_cases[year_cases['DAYTIME'].str.contains("Dawn")]
year_morning=year_cases[year_cases['DAYTIME'].str.contains("Morning")]
year_noon=year_cases[year_cases['DAYTIME'].str.contains("Noon")]
year_afternoon=year_cases[year_cases['DAYTIME'].str.contains("Afternoon")]
year_dusk=year_cases[year_cases['DAYTIME'].str.contains("Dusk")]
year_evening=year_cases[year_cases['DAYTIME'].str.contains("Evening")]
year_midnight=year_cases[year_cases['DAYTIME'].str.contains("Midnight")]
num_cases.append(len(year_cases))
severe_rate.append(len(year_severe)/len(year_cases))
severe_perday.append(len(year_severe)/(365+1*cd.isleap(int(i))))
severe_perweekdays.append(len(weekdays_severe)/(261+cd.isleap(int(i))))
severe_perweekends.append(len(weekends_severe)/104)
gun_rate.append(len(year_gun)/len(year_cases))
gun_perday.append(len(year_gun)/(365+1*cd.isleap(int(i))))
gun_perweekdays.append(len(weekdays_gun)/(261+cd.isleap(int(i))))
gun_perweekends.append(len(weekends_gun)/104)
weekdays_perday.append(len(year_weekdays_cases)/(261+cd.isleap(int(i))))
weekends_perday.append(len(year_weekends_cases)/104)
dawn.append(len(year_dawn)/len(year_cases))
morning.append(len(year_morning)/len(year_cases))
noon.append(len(year_noon)/len(year_cases))
afternoon.append(len(year_afternoon)/len(year_cases))
dusk.append(len(year_dusk)/len(year_cases))
evening.append(len(year_evening)/len(year_cases))
midnight.append(len(year_midnight)/len(year_cases))
result["Num_Cases"]=num_cases
result["Severe_Rate"]=severe_rate
result["Severe_Perday"]=severe_perday
result["Severe_PerWeekdays"]=severe_perweekdays
result["Severe_PerWeekends"]=severe_perweekends
result["Gun_Rate"]=gun_rate
result["Gun_Perday"]=gun_perday
result["Gun_PerWeekdays"]=gun_perweekdays
result["Gun_PerWeekends"]=gun_perweekends
result["Weekdays_Perday"]=weekdays_perday
result["Weekends_Perday"]=weekends_perday
result["Dawn_Rate"]=dawn
result["Morning_Rate"]=morning
result["Noon_Rate"]=noon
result["Afternoon_Rate"]=afternoon
result["Dusk_Rate"]=dusk
result["Evening_Rate"]=evening
result["Midnight_Rate"]=midnight
# In[10]:
print(result)
# In[11]:
result.to_csv("data/result.csv",index=0)
# In[17]:
gun_crime=pd.DataFrame()
gun=data[data['Weapon Desc'].str.contains("GUN|PISTOLFIREARM|REVOLVER|RIFLE|SHOTGUN|SEMIAUTOMATIC",na=False)]
gun_crime=gun
gun_crime.to_csv("data/gun_crime.csv")