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import pandas as pd
import matplotlib.pyplot as plt #需要声明才能在notebook中画图
%matplotlib inline #下载的罗曼的警务数据,这里以ri代表罗德曼岛警务数据
ri=pd.read_csv('police.csv') ri.head()
Out[2]:
  stop_date stop_time county_name driver_gender driver_age_raw driver_age driver_race violation_raw violation search_conducted search_type stop_outcome is_arrested stop_duration drugs_related_stop
0 2005-01-02 01:55 NaN M 1985.0 20.0 White Speeding Speeding False NaN Citation False 0-15 Min False
1 2005-01-18 08:15 NaN M 1965.0 40.0 White Speeding Speeding False NaN Citation False 0-15 Min False
2 2005-01-23 23:15 NaN M 1972.0 33.0 White Speeding Speeding False NaN Citation False 0-15 Min False
3 2005-02-20 17:15 NaN M 1986.0 19.0 White Call for Service Other False NaN Arrest Driver True 16-30 Min False
4 2005-03-14 10:00 NaN F 1984.0 21.0 White Speeding Speeding False NaN Citation False 0-15 Min False
In [3]:
ri.shape
Out[3]:
(91741, 15)
In [4]:
ri.isnull().sum()
Out[4]:
stop_date                 0
stop_time 0
county_name 91741
driver_gender 5335
driver_age_raw 5327
driver_age 5621
driver_race 5333
violation_raw 5333
violation 5333
search_conducted 0
search_type 88545
stop_outcome 5333
is_arrested 5333
stop_duration 5333
drugs_related_stop 0
dtype: int64
 

移除某列

In [5]:
ri.head()
Out[5]:
  stop_date stop_time county_name driver_gender driver_age_raw driver_age driver_race violation_raw violation search_conducted search_type stop_outcome is_arrested stop_duration drugs_related_stop
0 2005-01-02 01:55 NaN M 1985.0 20.0 White Speeding Speeding False NaN Citation False 0-15 Min False
1 2005-01-18 08:15 NaN M 1965.0 40.0 White Speeding Speeding False NaN Citation False 0-15 Min False
2 2005-01-23 23:15 NaN M 1972.0 33.0 White Speeding Speeding False NaN Citation False 0-15 Min False
3 2005-02-20 17:15 NaN M 1986.0 19.0 White Call for Service Other False NaN Arrest Driver True 16-30 Min False
4 2005-03-14 10:00 NaN F 1984.0 21.0 White Speeding Speeding False NaN Citation False 0-15 Min False
In [6]:
#写法等同于ri.drop('county_name', axis=1 , inplace=True)
#删除空值的
ri.drop('county_name', axis='columns', inplace=True)
In [7]:
ri.shape
Out[7]:
(91741, 14)
In [8]:
ri.columns
Out[8]:
Index(['stop_date', 'stop_time', 'driver_gender', 'driver_age_raw',
'driver_age', 'driver_race', 'violation_raw', 'violation',
'search_conducted', 'search_type', 'stop_outcome', 'is_arrested',
'stop_duration', 'drugs_related_stop'],
dtype='object')
In [9]:
#删除有空值的行
ri.dropna(axis='columns',how='all').shape
Out[9]:
(91741, 14)
 

pandas过滤功能

保留布尔值为真的数据,这里我们保留violaton值为真的数据

In [10]:
ri[ri.violation=='Speeding'].head()
Out[10]:
  stop_date stop_time driver_gender driver_age_raw driver_age driver_race violation_raw violation search_conducted search_type stop_outcome is_arrested stop_duration drugs_related_stop
0 2005-01-02 01:55 M 1985.0 20.0 White Speeding Speeding False NaN Citation False 0-15 Min False
1 2005-01-18 08:15 M 1965.0 40.0 White Speeding Speeding False NaN Citation False 0-15 Min False
2 2005-01-23 23:15 M 1972.0 33.0 White Speeding Speeding False NaN Citation False 0-15 Min False
4 2005-03-14 10:00 F 1984.0 21.0 White Speeding Speeding False NaN Citation False 0-15 Min False
6 2005-04-01 17:30 M 1969.0 36.0 White Speeding Speeding False NaN Citation False 0-15 Min False
 

values_counts

In [11]:
## 超速违规的驾驶员男女各多少人
print(ri[ri.violation=='Speeding'].driver_gender.value_counts()
)
 
M    32979
F 15482
Name: driver_gender, dtype: int64
In [12]:
# 超速男女各占多少比例 normalize归一化处理
print(ri[ri.violation=='Speeding'].driver_gender.value_counts(normalize=True))
 
M    0.680527
F 0.319473
Name: driver_gender, dtype: float64
In [13]:
ri.loc[ri.violation=='Speeding','driver_gender'].value_counts(normalize=True)
Out[13]:
M    0.680527
F 0.319473
Name: driver_gender, dtype: float64
In [14]:
#男性驾驶员中,各种交通违规的比例
ri[ri.driver_gender == 'M'].violation.value_counts(normalize=True)
Out[14]:
Speeding               0.524350
Moving violation 0.207012
Equipment 0.135671
Other 0.057668
Registration/plates 0.038461
Seat belt 0.036839
Name: violation, dtype: float64
In [15]:
#女性驾驶员中各种交通违规的比例
ri[ri.driver_gender=='F'].violation.value_counts(normalize=True)
Out[15]:
Speeding               0.658500
Moving violation 0.136277
Equipment 0.105780
Registration/plates 0.043086
Other 0.029348
Seat belt 0.027009
Name: violation, dtype: float64
 

groupby方法

查看不同driver_gender,violation的各种值的占比

In [16]:
#对比以上两种数据
ri.groupby('driver_gender').violation.value_counts(normalize=True)
Out[16]:
driver_gender  violation
F Speeding 0.658500
Moving violation 0.136277
Equipment 0.105780
Registration/plates 0.043086
Other 0.029348
Seat belt 0.027009
M Speeding 0.524350
Moving violation 0.207012
Equipment 0.135671
Other 0.057668
Registration/plates 0.038461
Seat belt 0.036839
Name: violation, dtype: float64
 

mean方法

mean可以默认计算占比

In [17]:
#True为执行搜查,False为未执行搜查
print(ri.search_conducted.value_counts(normalize=True))
 
False    0.965163
True 0.034837
Name: search_conducted, dtype: float64
In [18]:
#这例men可以计算出True的咋还占比
print(ri.search_conducted.mean())
 
0.03483720473942948
 

男女分组看他们的搜索值

In [19]:
ri.groupby('driver_gender').search_conducted.mean()
Out[19]:
driver_gender
F 0.020033
M 0.043326
Name: search_conducted, dtype: float64
 

男的搜查比例比女的高

 

再看一下如果是多重分组,男女搜查的比例

In [20]:
ri.groupby(['violation','driver_gender']).search_conducted.mean()
Out[20]:
violation            driver_gender
Equipment F 0.042622
M 0.070081
Moving violation F 0.036205
M 0.059831
Other F 0.056522
M 0.047146
Registration/plates F 0.066140
M 0.110376
Seat belt F 0.012598
M 0.037980
Speeding F 0.008720
M 0.024925
Name: search_conducted, dtype: float64
In [21]:
ri.isnull().sum()
Out[21]:
stop_date                 0
stop_time 0
driver_gender 5335
driver_age_raw 5327
driver_age 5621
driver_race 5333
violation_raw 5333
violation 5333
search_conducted 0
search_type 88545
stop_outcome 5333
is_arrested 5333
stop_duration 5333
drugs_related_stop 0
dtype: int64
In [22]:
#是否search_conducted为false的时候,search_type都丢失了
ri.search_conducted.value_counts()
Out[22]:
False    88545
True 3196
Name: search_conducted, dtype: int64
 
是不是数值和上面的search_type丢失的值相同啊
再次验证一下
In [23]:
ri[ri.search_conducted==False].search_type.value_counts()
Out[23]:
Series([], Name: search_type, dtype: int64)
In [24]:
#value_counts()这个方法时候默认忽略丢失值(空值)
ri[ri.search_conducted==False].search_type.value_counts(dropna=False)
Out[24]:
NaN    88545
Name: search_type, dtype: int64
In [25]:
#当searcch_conducted的值为True,search_type从来不丢失
ri[ri.search_conducted==True].search_type.value_counts(dropna=False)
Out[25]:
Incident to Arrest                                          1219
Probable Cause 891
Inventory 220
Reasonable Suspicion 197
Protective Frisk 161
Incident to Arrest,Inventory 129
Incident to Arrest,Probable Cause 106
Probable Cause,Reasonable Suspicion 75
Incident to Arrest,Inventory,Probable Cause 34
Incident to Arrest,Protective Frisk 33
Probable Cause,Protective Frisk 33
Inventory,Probable Cause 22
Incident to Arrest,Reasonable Suspicion 13
Incident to Arrest,Inventory,Protective Frisk 11
Protective Frisk,Reasonable Suspicion 11
Inventory,Protective Frisk 11
Incident to Arrest,Probable Cause,Protective Frisk 10
Incident to Arrest,Probable Cause,Reasonable Suspicion 6
Incident to Arrest,Inventory,Reasonable Suspicion 4
Inventory,Reasonable Suspicion 4
Inventory,Probable Cause,Protective Frisk 2
Inventory,Probable Cause,Reasonable Suspicion 2
Incident to Arrest,Protective Frisk,Reasonable Suspicion 1
Probable Cause,Protective Frisk,Reasonable Suspicion 1
Name: search_type, dtype: int64
In [26]:
ri[ri.search_conducted==True].search_type.isnull().sum()
Out[26]:
0
 

查看搜索类型

In [27]:
ri.search_type.value_counts(dropna=False)
Out[27]:
NaN                                                         88545
Incident to Arrest 1219
Probable Cause 891
Inventory 220
Reasonable Suspicion 197
Protective Frisk 161
Incident to Arrest,Inventory 129
Incident to Arrest,Probable Cause 106
Probable Cause,Reasonable Suspicion 75
Incident to Arrest,Inventory,Probable Cause 34
Incident to Arrest,Protective Frisk 33
Probable Cause,Protective Frisk 33
Inventory,Probable Cause 22
Incident to Arrest,Reasonable Suspicion 13
Inventory,Protective Frisk 11
Incident to Arrest,Inventory,Protective Frisk 11
Protective Frisk,Reasonable Suspicion 11
Incident to Arrest,Probable Cause,Protective Frisk 10
Incident to Arrest,Probable Cause,Reasonable Suspicion 6
Incident to Arrest,Inventory,Reasonable Suspicion 4
Inventory,Reasonable Suspicion 4
Inventory,Probable Cause,Reasonable Suspicion 2
Inventory,Probable Cause,Protective Frisk 2
Incident to Arrest,Protective Frisk,Reasonable Suspicion 1
Probable Cause,Protective Frisk,Reasonable Suspicion 1
Name: search_type, dtype: int64
In [28]:
ri['frisk']=ri.search_type=='Protective Frisk'
In [29]:
ri.frisk.dtype
Out[29]:
dtype('bool')
In [30]:
ri.frisk.sum()
Out[30]:
161
In [31]:
ri.frisk.mean()
Out[31]:
0.0017549405391264537
In [32]:
ri.frisk.value_counts()
Out[32]:
False    91580
True 161
Name: frisk, dtype: int64
In [33]:
161/(91580+161)
Out[33]:
0.0017549405391264537
 

字符操作

In [35]:
#上面的操作是把ri.search_type=='Protective Frisk'的值付给日['firsk']这一列
#现在是字符串的包含操作
ri['frisk']=ri.search_type.str.contains('Protective Frisk')
In [36]:
ri.frisk.sum()
Out[36]:
274
In [37]:
ri.frisk.mean()
Out[37]:
0.08573216520650813
In [38]:
#用mean()计算符合条件和不符合条件的占比
ri.frisk.value_counts()
Out[38]:
False    2922
True 274
Name: frisk, dtype: int64
In [41]:
#再看一下他们的计算是否和men()的结构一样
274/(2922+274)
Out[41]:
0.08573216520650813
 

上面的这一部分是计算字符串匹配操作

用正确的关键字去计算比例

pandas计算式忽略缺失值的

In [42]:
#那一年的数据最少
ri.stop_date.str.slice(0,4).value_counts()
Out[42]:
2012    10970
2006 10639
2007 9476
2014 9228
2008 8752
2015 8599
2011 8126
2013 7924
2009 7908
2010 7561
2005 2558
Name: stop_date, dtype: int64
In [43]:
#将ri.stop_date转化为datetime的格式的dataframe,存到stop_datetime新列中
ri['stop_datetime'] = pd.to_datetime(ri.stop_date) #注意这里有dt方法,类似于上面的str方法
#dt后可以使用year、month等方法
ri.stop_datetime.dt.year.value_counts()
Out[43]:
2012    10970
2006 10639
2007 9476
2014 9228
2008 8752
2015 8599
2011 8126
2013 7924
2009 7908
2010 7561
2005 2558
Name: stop_datetime, dtype: int64
In [44]:
ri.stop_datetime.dt.month.value_counts()
Out[44]:
1     8479
5 7935
11 7877
10 7745
3 7742
6 7630
8 7615
7 7568
4 7529
9 7427
12 7152
2 7042
Name: stop_datetime, dtype: int64
In [46]:
#关于毒驾
ri.drugs_related_stop.dtype
Out[46]:
dtype('bool')
In [48]:
#基础比例
ri.drugs_related_stop.mean()
Out[48]:
0.008883705213590434
In [55]:
#不能使用小时分组,除非你创建了小时这一列
#取出小时列,转换成时间格式,再转化才成小时分组
ri['stop_time_datetime']=pd.to_datetime(ri.stop_time)
ri.groupby(ri.stop_time_datetime.dt.hour).drugs_related_stop.mean()
Out[55]:
stop_time_datetime
0 0.019728
1 0.013507
2 0.015462
3 0.017065
4 0.011811
5 0.004762
6 0.003040
7 0.003281
8 0.002687
9 0.006288
10 0.005714
11 0.006976
12 0.004467
13 0.010326
14 0.007810
15 0.006416
16 0.005723
17 0.005517
18 0.010148
19 0.011596
20 0.008084
21 0.013342
22 0.013533
23 0.016344
Name: drugs_related_stop, dtype: float64
In [58]:
#按小时的时毒驾频率分布图
ri.groupby(ri.stop_time_datetime.dt.hour).drugs_related_stop.mean().plot()
Out[58]:
<matplotlib.axes._subplots.AxesSubplot at 0x9d72d30>
 
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" alt="" />
In [63]:
#按小时的,毒驾数量分布图
ri.stop_time_datetime.dt.hour.value_counts().plot()
Out[63]:
<matplotlib.axes._subplots.AxesSubplot at 0x5460710>
 
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9wjao+565nDfAld7VfU9V1k/dRTLGoCPk5bdY0Tps1LaVcVTlwbDAZCmPsNcyZXsGiRmevIb73sKSp2p64ZYzH8ewBXKSqBz3zNwGPqeotInKTO/8F4HKg1f05F7gVONcNjJuBNpwRgE0i8qCqHp6Ez2FKgIgws7aCmbUVXOC5dz5AfzjCzoN9KeEw1l7D4qZqZ8+h2QkH22swpehEhoBWAyvd6XXAEzgBsBq4S50B6KdFpE5EZrp116tqN4CIrAcuA+49gTYYA0BlKJDxwe3xvYbtnU4gxAPi2Z3d/OL5NxL1vHsN8T2Gxe7xBttrMMUq2wBQ4NciosC/q+rtQIuq7gdQ1f0i0uzWnQ3s9Szb7paNVp5CRNYCawHmzZt3HB/FmJG8ew1va82817C9q48dXanhMDCc3GuoiR9rcANhUZPtNZjikG0AXKCq+9xOfr2IvD5G3UybSjpGeWqBEy63A7S1teXgeT2mVIy21xCLJY817DjYy/bOXnYc7OOZHYcy7jU4w0nOAWhnD8L2GkxhyCoAVHWf+9opIr8AzgE6RGSmu/U/E+h0q7cDcz2LzwH2ueUr08qfOKHWG3MS+HzCrLoKZtVl3mtwgsHZa4jvPTyzI/Neg3c4aVFTNfMbKm2vweSNcQNARKoAn6r2uNOXAl8BHgTWALe4rw+4izwIfFJE7sM5CHzUDYlHga+LyHS33qXAFyf10xhzklWGAiyfXcvy2aPvNTgHoZ1weHrHIf7Ts9fgE5gzvTIlHBY1VrOwsYqKoB+fz7la3CfOjzM9+gPGjTkR2ewBtAC/cP8AA8A9qvqIiGwA7heR64A9wBVu/YdwTgHdhnMa6LUAqtotIl8FNrj1vhI/IGxMoct2ryE+nLS9c+Rew1hEwC+Czw2E5LR4AiMZHvHg8PnEqevW9/vgA2+ew0fftvBkfA2mwIwbAKq6A1iRofwQcEmGcgVuHGVddwB3HH8zjSlcY+017D82yI6uXnYd7ONgb5jD/WG6++KvwxzuC9PdHyYciRFRZSKPDKsuC1BbEWRaRZCasiA15Xb9p3HYX4Ixk0TVeZ7vkf5hjg44P0f6hzk2MMyRgXBKWXw6Pt8zODxm3x4K+KirCFJbEaSu0nmtrQi5r56yxHtB6txOP+j3Td2XYAqKBYAxacKRmNs5h0d02CM773jHHuHoQHjMW337fZLonGsrgkyvDLGwsSqlzOnMR3bsduDYnAwWAKYoRWNKz2Ba5x3vuPvH3hofb1y+pjyQ0jnPqC1PbI3XZdgCj5dVlwXsYK7JKxYAJm+pKn3haOqWdsYO3VsW5mj/MD1DkTFv3V0R9Ce3uiuDzK2v5Iy0re5pnq3x+PBLTXmAgA2pmCJhAWBOusHhqDsOnuywM26NZ+jQx3rOcNAvyY66IkhjdYglzdUpZelb5fEx8rKADakYYwFgMgpHYvSHI/SHo/SHI/QNRRPT3rKB4Sh9Q05Z71AkcdAzsTU+MMzg8OhPWxOBaeWpHfWsuorMnXfaMEtlyG9DKsacAAuAAqaqhKMx+oei9A9H6Xc74r5wZERZfzhCX9h5lnHfUCTxXqIsHEm+F46OueWdLuATKkN+53TDyhC1FQEWNFZSV1GXclaKt/OuczvzmvKAPX/YmByxAJgCqspQJJboaJ0OOa3D9pZl6JwTW9/ulni8wz6ejjroFypDASpDfipDfqrKnOcNN9WUMS9USVXIn3g//l5Vmd+zTPy91LLE4zSNMQXFAsAj3lH3u1vC3uEN7/BHprLEkIh3a9qzJR49jo465PdRWeY8KL6yLNlht9SUU9HgpyrkPFTe2xFnKkt/zzpqY4yXBYDHh/79KTbsyv75NH6fUFMeoMqzVV0ZCjBjWjmVZQGqQn6nU453zqFkh54sCzidvafDtgt3jDFTwQLA49oLFnL6rFq6+5zL8Q/2DiWmMw21RGNKNKoE/EJ1eYCGqjIaqkLUV4doqArRUB2i3i1zpkN29okxJm9YAHisOmMmq86YOaJcVTk2EOFQ35AbDGE3GIYS04f6hmg/3M+L7UdGDQxwbhMcD4j0cGioDtFQVZaYtsAwxpxMFgBZEBHnbJbKIIuaxq/vDYxDfWEOxUOi15k/kcBodIOhvipEY3XZiGkb5zfGZMsC4CSYjMA41DdEd2/YmXf3NtoP9/On9iMcHicwGhIhkTkwvHsaFhjGlC4LgDww0cA46A5JnVBglAfcvYsQDdVlI6YTw1MWGMYUHQuAAuQNjMVZBEYsphwbHE4OP8WHotICY293Py/sdYakRjttNR4YDfG9iVEOdltgGJP/LABKgM8n1FWGqKsMTVpgHOrNPjBSj1WMPjw1vdICw5ipZAFgRjiRwHAOeKcd/J5gYKTvUaRPW2AYc2KyDgAR8QMbgTdU9T0ishC4D6gHngOuVtWwiJQBdwFnAYeAK1V1l7uOLwLXAVHg06r66GR+GJMbkxEYyVNrk9df7Onu57k9RzjcP3pgTCsPZBiOSoaE97RaCwxjUh3PHsBngNeAae78N4Bvq+p9InIbTsd+q/t6WFWXiMhVbr0rReQ04CrgdGAW8BsRWaqq2T0V2xSNiQZGpusvJhoYyYPdqSERn26sDjG9KmRXZZuillUAiMgc4M+AfwQ+J849eC8GPuxWWQd8GScAVrvTAD8DvufWXw3cp6pDwE4R2QacAzw1KZ/EFC1vYGQjFlOODmQ4hpE2vftQdoHhPYYxWnhYYJhClO0ewHeAvwdq3PkG4IiqRtz5dmC2Oz0b2AugqhEROerWnw087VmndxljJo3PJ0yvcjrkbGQTGId6k4HR3Tc06gPcayuCKQGR6eyoBvfCPgsMk2vjBoCIvAfoVNVNIrIyXpyhqo7z3ljLeH/fWmAtwLx588ZrnjEn7EQC45A7/JTpAPiug/1s2n38gZHx4j0LDHMSZLMHcAHwXhFZBZTjHAP4DlAnIgF3L2AOsM+t3w7MBdpFJADUAt2e8jjvMgmqejtwO0BbW1v291A2Zop4A2NJc/W49TMFxkH3tNruvqHEtBMYh+nuC48bGJmu9k4fnqqvDNnzi82Yxg0AVf0i8EUAdw/gb1X1r0Tkp8AHcc4EWgM84C7yoDv/lPv+b1VVReRB4B4R+RbOQeBW4NnJ/TjG5J+JB8ZQ4lTaEwqMxL2kxr7a2wKj9JzIdQBfAO4Tka8BzwM/cMt/ANztHuTtxjnzB1V9RUTuB14FIsCNdgaQMSOlBsb49WMx5cjAsDP85L26271FSHx658G+cQOjrjKYPKW2qixxI8IVc+p452ktk/tBTc6Jav6OsrS1tenGjRtz3Qxjiko8MA72DvHqvmM8v+cwz+05wmv7j416z6iFjVU8/rcrp7ahZsJEZJOqto1Xz64ENqaIDUWi7O0eYE93H7sP9bP7UD97uvvZfaiPvYcHCEdiiboBn7CgoZJ5DVXMr69kfkMl8+ormd9QxfyGyhx+CnOyWAAYU+CODQ6zx+3cd3f3Jab3dPez7+gA3p38ypCfefWVLGmu5pJTW5jfUMn8eqeDn1lbbscASowFgDF5TlXp6hlid7fbyR9yt+a7+9lzqI/D/cMp9RurQ8yrr+SchfXuFnx8a76KxuoQznWZxlgAGJMXhqMx3jg8kOjUkx28syU/MJw8X8InMKuugvkNlVy2fKa7FV/JvAZnuKa6zP5Zm+zYX4oxU6Q/HPGMw/d5xuP7eePIQMrtKMoCvsT4+9taG1PG42fXVdhN7cyksAAwZhKpKi+9cZQdXX2JMfl4p3+wdyilbl1lkPn1layYW8d7V8xytuDdTr65pgyfz4ZqzMllAWDMJPrdli6uuXNDVnWHhmPsPNhH++EBNvh9BANC0O8j5PcR9PsI+L3zznQw4CPoS0573wv4fYTi9RLvCwFfcjrxnnedfh+hQHI64JdEG+L17LhBcbIAMGYSvW1JI3deczZ94QjD0RjDESUcjTnT0RjDUU2ZDkec6YhbHs5Qb2A4yrHBWKJu8j3vupz5kyUYDxK/EAr4RgkLN7ACPgKjhNTI4PEl130cIRVMqxdyAzTeRgus7FgAGDOJAn4fF52SxeW7J4GqMhxVIrFMwZMaGOGIjhJKMcJRZTgSc9bjCamMARaNMRwPsZhTt28okqgX9oSb83uT6xjtorPJkB4QyXnxBI8TVMlAS+5xBTKEVMZQSgme9JBy6laVBVjcNP4tQHLBAsCYIiEihAJCCB9kd2PTnIoHVjJ4nHCIZAipEUHihtRwNMZwzDMdf88NpkgsNaTioRXxTPcOJffWhmOx5LRnjywS1RMKrB+saeOSU/PvVhoWAMaYnEgEVgGc0XSkP8yOg31s7+xlm/uztbOXvYf7Sb+bTkNVyL32wrnAblFTNe9YmsWj73LAAsAYU/K8F9vtOtjHnu5+dnkuujs6kHqx3czacubVV3L+ogbmNVSywO3s5zdUUlMezNGnOH4WAMaUGFVFFWKqxNzX+Lzivsbi7zt11FM3Xl91/Drp741Vx5lXYjGS7UipH1/eqZNoBxnqxEZ+tphbV9W5IV7PUCTRwe/p7qc/nLzYzu8TZrsX2/35ipluB1+VuB6jPOjP2f+/yWQBYEboG4pwdGD4uP+hZ/oHP9o/dDL9wx/rHzrp6xz9H3qy8yDt/ZHLK+PXGbvdo383I7+XTN/NyA45/XemLO/eu23U9cUydKqMbJeBkN+XuPbirYsbWdDodO4LGqqYPb2iJJ6+ZgFQwo4NDjvjmR29bOnoYas7tvnGkYFcN+2kEAGfCD4BQVLmfeLO+yRZx1M3Me+LL5NpeUEgUUc873nX5/P5EsuPWifD75BRfufI5eP1xq7jcz+veD7jiM/ly7C897sb87Mmv4+U35nyO8b6bCPrpH8349aJt4+Rn83vs9NFLQBKwNH+YbZ2Oh38lo4e5wBWRy8Hjg0m6pQFfCxprubsBdP5cMs86qtC+CW7f+jxf1zZ/UPPrjNI/8c83j90PB1e5s6Zkv/Hbkw6C4Ai0t0XZmtHD1s6e9nmbtFv7eylqyd5C4KKoJ/WlmreuqSB1uYaWpuraW2pZs70Svx26wFjSooFQIFRVQ72htna6WzJb+noYWuHM3RzqC+cqFcV8rOkpYaVS5tobammtbmGJc3VzK6rsHvMGGMAC4C8pap09gyxtaOXrZ09bOnoZZs7jHPEc//3mvIArc3VvOu0FpY0V9Pa4mzVz6wttyEPY8yYxg0AESkHngTK3Po/U9WbRWQhcB9QDzwHXK2qYREpA+4CzgIOAVeq6i53XV8ErgOiwKdV9dHJ/0iFRVXZf3TQGa5xt+bj4/U9g5FEvdqKIEtbqll1xkxn2Ka5htaWappryqyjN8ZMSDZ7AEPAxaraKyJB4A8i8jDwOeDbqnqfiNyG07Hf6r4eVtUlInIV8A3gShE5DbgKOB2YBfxGRJaqajTTLy02sZjyxpGB5LCNOz6/raOHPs/5xw1VIZY0V7P6zFksbXGGbVqba+xJTsaYSTduAKiqAr3ubND9UeBi4MNu+TrgyzgBsNqdBvgZ8D1xeq7VwH2qOgTsFJFtwDnAU5PxQfJFNKa0H+5na0cvWzp72NbRmzi90vtUp6aaMlqbq/ngWXNY0lLD0uZqljRX01BdlsPWG2NKSVbHAETED2wClgD/BmwHjqhqfIyiHZjtTs8G9gKoakREjgINbvnTntV6lylYuw/18asX9ycOxm7v6mUoEkupIwILGqo4ZUYNy2bUsKylhulVIfyec879PmH/0UE6jg3h9wl+95RKv4h7vrJTx++eipmY9pFaJzFtewvGmLFlFQDuMM2ZIlIH/AI4NVM19zVTz6NjlKcQkbXAWoB58+Zl07ycuu13O7j32T1j1lGFnQf72Hmwj4dfPjBFLfMGRoZpNyTigRE/z98v4l4M5c7HQ8on+BPXBMTrpdURJ7hS6rgXE/nFsx7PxVR+3yh1vMuO2fYsgjJTHU9QZlp/5s+f1lZfMrzTP48FsCkEx3UWkKoeEZEngPOAOhEJuHsBc4B9brV2YC7QLiIBoBbo9pTHeZfx/o7bgdsB2tra8v6i9a+uPp0bL1pMLAbRxOX66ky7tzGIxjRx6X40Rmod93YFGesklsVTXz31k+uPupf4R933VJ2yaMw50DyijnvbA6c+nvqj1FHPetzMU7OWAAAR3ElEQVSfcHTk74q5nyPquU3DmJ8tXidR35kvdPGQSYREYtobmJ7w8KXVyRgwGcJmxPqTdbIOZl9anTGDmbTPMXpQlgf9tC2YXlA3Rys12ZwF1AQMu51/BfBOnAO7jwMfxDkTaA3wgLvIg+78U+77v1VVFZEHgXtE5Fs4B4FbgWcn+fNMuYDfx5zplbluRlHx3rcm6gmFZGCMFrDJsEmUpQdzLNO6JzOYU+ucrGCOxGIMRUYGs3rCNNmu9BDO1Pbk9zSZ9woK+X28Y2kjly+fyTtPbaG20sIgn2SzBzATWOceB/AB96vqr0TkVeA+Efka8DzwA7f+D4C73YO83Thn/qCqr4jI/cCrQAS4sVTOADLHxxmuAT9Ckdx0saCojhKUmfZyxwjKw/1hHnutk4df2s9vXusk6BfeuriRVWfM4F2nzaC+qgCeWlPkRPP41oBtbW26cePGXDfDGHMCVJU/tR/l4Zf289DL+9nbPYDfJ5y/qIHLz5jBpafNoKnGzn6bTCKySVXbxq1nAWCMmSqqyiv7jvHwy/t56KUD7DzYh0/g7AX1rDpjJpctn0HLtPJcN7PgWQAYY/KaqrK5o4eHXjrAIy/vZ0uHc7lR2/zpXLZ8BpefMZPZdRU5bmVhsgAwxhSUbZ09PPzSAR56+QCv7T8GwIq5daxaPoPLl89kXoOdbJEtCwBjTMHa5V4z8/DL+3mx/SgAp8+axqozZnL58hksaqrOcQvzmwWAMaYo7O3u55GXD/DQy/t5fs8RAE6ZUcPly2ey6owZtLbU5LiF+ccCwBhTdPYfHeCRlw/w8EsH2LC7G1VY3FTFP6w6lUtObcl18/JGtgFQ/E89NsYUjZm1FVx7wULu//j5/M8XLmZ+QyXbu/p4dd+xXDetIFkAGGMKTu9QhC/+50vsPtTPjRct5pMXL8l1kwqSPRHMGFNQOo8Ncu0PN/D6gR6+/v4z+PC5+X/TyHxlAWCMKRhbO3q45s4NHO4P8/2PtHHRKc25blJBswAwxhSEp7YfYu3dGykP+rn/+vNZPrs2100qeBYAxpi898ALb/B3P32ReQ2V3HnN2cytt4vCJoMFgDEmb6kqt/5uO//0yGbOXVjP7Ve32S2lJ5EFgDEmL0WiMb78X6/wo6f38OcrZvHNK95EWcDuDz6ZLACMMXmnPxzhU/c8z2Ovd/LxCxfz9+9ehs9nj9mcbBYAxpi80tUzxHXrNvDyG0f56vuWc/V583PdpKJlAWCMyRvbOnu55s5nOdQb5j8+0ma3dzjJ7EpgY0zeuG7dBtoPD3D9hYu4aJmd43+yWQAYY/LGZ9/Zyuy6Cr7zm61c9t0n+fmmdoajsVw3q2iNGwAiMldEHheR10TkFRH5jFteLyLrRWSr+zrdLRcR+VcR2SYiL4rIWzzrWuPW3yoia07exzLGFKL3v3kOT/zdSr5z5Zn4RPj8T//Eyn9+gjv+sJP+cCTXzSs6494OWkRmAjNV9TkRqQE2Ae8DrgG6VfUWEbkJmK6qXxCRVcCngFXAucB3VfVcEakHNgJtgLrrOUtVD4/2u+120MaULlXlic1d3PrEdp7d1U1dZZA15y9gzVsXUF8VynXz8tqk3Q5aVfer6nPudA/wGjAbWA2sc6utwwkF3PK71PE0UOeGyLuB9ara7Xb664HLjvNzGWNKhIhw0SnN3P/x8/n5DefTNr+e7z62lQtu+S1ffvAV2g/357qJBe+4zgISkQXAm4FngBZV3Q9OSIhI/IjNbGCvZ7F2t2y08vTfsRZYCzBvnt3lzxgDZ82v5/tr6tna0cO/P7mDHz29m7uf3s3qFbO4/sLFLJthTwWbiKwPAotINfBz4LOqOtbTFzJdraFjlKcWqN6uqm2q2tbU1JRt84wxJaC1pYZvXrGCJ//+Iq556wIeeeUA7/7Ok1z3ww1s2NWd6+YVnKwCQESCOJ3/j1X1P93iDndoJ36coNMtbwfmehafA+wbo9wYY47LrLoK/vd7TuOPN13M5961lOf3HuGK257iL279I795tYNYLH8fdZtPsjkLSIAfAK+p6rc8bz0IxM/kWQM84Cn/iHs20HnAUXeo6FHgUhGZ7p4xdKlbZowxE1JXGeLTl7TyP1+4mP/vvadz4OggH7trI+/+jp1Cmo1szgJ6G/B74CUg/m3+A85xgPuBecAe4ApV7XYD43s4B3j7gWtVdaO7ro+6ywL8o6reOdbvtrOAjDHHYzga479f3M9tv9vO6wd6mFVbzsfevoirzplLZah0bnyQ7VlA4wZALlkAGGMmQlV5Yot7CulO5xTSj5y/gGtK5BRSCwBjjAE27T7Mbb/bzvpXOygP+rjq7Hl87O0LmTO9eB8qYwFgjDEe8VNIf/n8Gyjw3hWzuP7CRZwyY1qumzbpLACMMSaDfUcG+MEfdnLvs3voD0e5+JRmbli5mLMX1Oe6aZPGAsAYY8ZwpD/M3U/t5s4/7qK7L8xZ86fz8QsXc8kpzQX/8BkLAGOMycJAOMr9G/fyH7/fQfvhAVqbq7n+wsW8d8UsQoHCvGGyBYAxxhyHSDTGf7+0n1ufSJ5Cet3bF3HV2XOpKiusU0gtAIwxZgLip5De9sR2ninQU0gtAIwxZhSqylAkxuBwlIHhKIPDMQbC8eloovyP2w/x0417GY4q5UEff33ufL646lT8eX6MINsAKKz9GmNMUYvFlMFI1NMZJzvpgXDU02G7nbanPFHP7cyHPOsZGI4y5K0fiXK8276DwzF++cIb3HjREqYXyJ7AeCwAjDHjikRjiY50MBxL66Q9nW84ltJJxzvbEeXue0ORWEonHY5M7N49ZQEf5UE/FUE/FSE/5UE/5UEfFUE/tRVBykN+ygN+KkJOWbn7k6yfLE+8hpzpMs97QX9hHhQejQWAMQVKVQlHYwyGPZ1zopNOdrzJLeDULeLE1nWGjjy+dT3ovheZwN01RUjrVH1OZxvwU10WoLG6LFke9FMe8qfU93a+FZ73ylPmfZQH/AV/2mauWAAYM8liMWd8eSB9S3iUzjU5dOF23MOpy6YOgcRS6k/kEJ7fJ1S6HW6883U6Wz91lSFmeraKvZ1xeUonnVwuuXWd2nGXBXw494Y0+coCwJSMSDTGoDvkkN6xZhxvjiQ76fSOOzkEEhuxrqEJDmOEAp5ONd75up1rc03QswXsSxvqiA9l+BIdubfTdjrp4h3GMBNnAWBySlUZjmqGMeO0g32jjTeP6KTdTjlt63pwOMpwdGJnvHmHIMo8W76VoQD1VfH3fCPGj8sDvkQnnT6unLJ1HfJTFvDn/ZklpvhYAJiM4qfJDaR1roPDyXHlEQf7hmMZhy1SzsBIbCUn35vIw5v8PvF0qr7EEER50E9dRZDyaWUpnat3eMK7dZ1+QDA+Th0vt2EMU8wsAApMNKYjTosbHKNz9XbK6afRec9/zlQ+ESG/b0QnGu9cm2rKUrZ8RztrY7Rx5fKgLzEGbcMYxpw4C4Ac6OoZ4meb2ukbimTYuh45Hu3dwg5P8BF36WdbeDvX6ZWhlHHl1HFkz1j0iI7bl7b1bMMYxhQSC4AcePz1Tr7xyOv4BCpDgYxbxdMqgrRMK8swrpz5YN+IszY8nbwNYxhjMhk3AETkDuA9QKeqLnfL6oGfAAuAXcCHVPWw+zzg7wKrcJ4HfI2qPucuswb4krvar6nqusn9KIXj8jNm8P8//BrLZ9dy93Xn5ro5xpgSlc1A6g9xHvDudRPwmKq2Ao+58wCXA63uz1rgVkgExs3AucA5wM0iMv1EG1+oasqDfGLlEn6/9SBP7ziU6+YYY0rUuAGgqk8C3WnFq4H4Fvw64H2e8rvU8TRQJyIzgXcD61W1W1UPA+sZGSol5erz59MyrYxvPrqZfL4hnzGmeE30VIoWVd0P4L42u+Wzgb2eeu1u2WjlJas86OeTF7eycfdhntjSlevmGGNK0GSfS5fpSKOOUT5yBSJrRWSjiGzs6irujvHKtrnMra/gm49uJjaRk+GNMeYETDQAOtyhHdzXTre8HZjrqTcH2DdG+QiqeruqtqlqW1NT0wSbVxhCAR+fvWQpr+w7xiOvHMh1c4wxJWaiAfAgsMadXgM84Cn/iDjOA466Q0SPApeKyHT34O+lblnJe9+bZ7OkuZpvrd9C1PYCjDFTaNwAEJF7gaeAZSLSLiLXAbcA7xKRrcC73HmAh4AdwDbgP4BPAKhqN/BVYIP78xW3rOT5fcLn3rWUbZ29/PL5N3LdHGNMCbFHQuaBWEx577/9gaMDwzz2uZWEAnabA2PMxGX7SEjrafKAzyd8/tJl7O0e4Ccb946/gDHGTAILgDyxcmkTbfOn873fbmVwOJrr5hhjSoAFQJ4QEf7u3cvoODbE3U/tznVzjDElwAIgj5y7qIG3tzbyf57YRs/gcK6bY4wpchYAeeZvL13G4f5h7vjDrlw3xRhT5CwA8syKuXW8+/QWvv/7HRzpD+e6OcaYImYBkIc+f+kyesMRbvvdjlw3xRhTxCwA8tDSlhpWr5jFD/+4k85jg7lujjGmSFkA5KnPvnMpkajyb49vy3VTjDFFygIgTy1orOKKtrnc8+we2g/357o5xpgiZAGQxz59yRJEhO/+Zmuum2KMKUIWAHlsZm0FV583n58/1872rt5cN8cYU2QsAPLcDSsXUx708+31W3LdFGNMkbEAyHON1WV89IKF/OrF/by671ium2OMKSIWAAXgb96xiGnlAf7l1/YAeWPM5AnkugFmfLUVQa6/cDH//OhmTvnfj9BUU0ZjdRlNNWWp09WhlPnKkP3vNcaMznqIAvE3b19EbUWQPd39dPUM0dUzxN7ufp7bfZju/jCZdgyqQn4aa8r4xMrFXHn2vKlvtDEmr1kAFIhQwMdfnzc/43uRaIzuvjBdvU4wHOwNu6/OfENV2RS31hhTCCwAikDA76N5WjnN08pz3RRjTAGZ8oPAInKZiGwWkW0ictNU/35jjDGOKQ0AEfED/wZcDpwG/KWInDaVbTDGGOOY6j2Ac4BtqrpDVcPAfcDqKW6DMcYYpj4AZgN7PfPtbpkxxpgpNtUBIBnKUk5gFJG1IrJRRDZ2dXVNUbOMMab0THUAtANzPfNzgH3eCqp6u6q2qWpbU1PTlDbOGGNKyVQHwAagVUQWikgIuAp4cIrbYIwxhim+DkBVIyLySeBRwA/coaqvTGUbjDHGOCSfby4mIl3A7hNYRSNwcJKaU8jse3DY9+Cw78FRzN/DfFUddww9rwPgRInIRlVty3U7cs2+B4d9Dw77Hhz2PdjtoI0xpmRZABhjTIkq9gC4PdcNyBP2PTjse3DY9+Ao+e+hqI8BGGOMGV2x7wEYY4wZRVEGgN1y2iEiu0TkJRF5QUQ25ro9U0lE7hCRThF52VNWLyLrRWSr+zo9l22cCqN8D18WkTfcv4sXRGRVLts4FURkrog8LiKvicgrIvIZt7zk/ia8ii4A7JbTI1ykqmeW4OluPwQuSyu7CXhMVVuBx9z5YvdDRn4PAN92/y7OVNWHprhNuRABPq+qpwLnATe6/UIp/k0kFF0AYLecNoCqPgl0pxWvBta50+uA901po3JglO+h5KjqflV9zp3uAV7DuRNxyf1NeBVjANgtp5MU+LWIbBKRtbluTB5oUdX94HQIQHOO25NLnxSRF90hopIa9hCRBcCbgWco8b+JYgyAcW85XUIuUNW34AyH3Sgi78h1g0xeuBVYDJwJ7Af+JbfNmToiUg38HPisqh7LdXtyrRgDYNxbTpcKVd3nvnYCv8AZHitlHSIyE8B97cxxe3JCVTtUNaqqMeA/KJG/CxEJ4nT+P1bV/3SLS/pvohgDwG45DYhIlYjUxKeBS4GXx16q6D0IrHGn1wAP5LAtORPv8FzvpwT+LkREgB8Ar6nqtzxvlfTfRFFeCOae1vYdkrec/sccN2nKicginK1+cG77fU8pfQ8ici+wEueOjx3AzcAvgfuBecAe4ApVLeoDpKN8Dytxhn8U2AVcHx8HL1Yi8jbg98BLQMwt/gec4wAl9TfhVZQBYIwxZnzFOARkjDEmCxYAxhhToiwAjDGmRFkAGGNMibIAMMaYEmUBYIwxJcoCwBhjSpQFgDHGlKj/C4c8WezvdWG/AAAAAElFTkSuQmCC" alt="" />
In [65]:
#按小时分组,毒驾数量排序分布图
ri.stop_time_datetime.dt.hour.value_counts().sort_index().plot()
Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x5420860>
 
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" alt="" />
In [66]:
ri.groupby(ri.stop_time_datetime.dt.hour).stop_date.count().plot()
Out[66]:
<matplotlib.axes._subplots.AxesSubplot at 0x557c2e8>
 
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" alt="" />
In [68]:
#把无用的数据标记为丢失值
ri.stop_duration.value_counts()
Out[68]:
0-15 Min     69543
16-30 Min 13635
30+ Min 3228
1 1
2 1
Name: stop_duration, dtype: int64
In [73]:
ri[(ri.stop_duration=='1')|(ri.stop_duration=='2')].stop_duration='NaN'
 
C:\Anaconda3\lib\site-packages\pandas\core\generic.py:4401: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self[name] = value
In [74]:
ri.stop_duration.value_counts()
Out[74]:
0-15 Min     69543
16-30 Min 13635
30+ Min 3228
1 1
2 1
Name: stop_duration, dtype: int64
In [75]:
ri.loc[(ri.stop_duration=='1')|(ri.stop_duration=='2'),'stop_duration']='NaN'
In [76]:
ri.stop_duration.value_counts(dropna=False)
Out[76]:
0-15 Min     69543
16-30 Min 13635
NaN 5333
30+ Min 3228
NaN 2
Name: stop_duration, dtype: int64
In [77]:
#用执行的nan类型替换NaN
import numpy as np
ri.loc[ri.stop_duration == 'NaN', 'stop_duration'] = np.nan
In [79]:
ri.stop_duration.value_counts(dropna=False)
Out[79]:
0-15 Min     69543
16-30 Min 13635
NaN 5335
30+ Min 3228
Name: stop_duration, dtype: int64
In [80]:
ri.stop_duration.replace(['1', '2'], value=np.nan, inplace=True)
In [118]:
# stop_duration中的各种比例
#Series的map方法可以接受一个函数或含有映射关系的字典型对象。
#对某一个列进行批操作,本文中是批量替换
mapping={'0-15 Min':8,'16-30 Min':23,'30+ Min':45} #记得这不是原地操作原始数据,需要新建一列存储map后的结果
ri['stop_minutes'] = ri.stop_duration.map(mapping)
In [119]:
#为各种粘皮匹配值
ri.stop_minutes.value_counts()
Out[119]:
8.0     69543
23.0 13635
45.0 3228
Name: stop_minutes, dtype: int64
In [120]:
ri.groupby('violation_raw').stop_minutes.mean()
Out[120]:
violation_raw
APB 20.987342
Call for Service 22.034669
Equipment/Inspection Violation 11.460345
Motorist Assist/Courtesy 16.916256
Other Traffic Violation 13.900265
Registration Violation 13.745629
Seatbelt Violation 9.741531
Special Detail/Directed Patrol 15.061100
Speeding 10.577690
Suspicious Person 18.750000
Violation of City/Town Ordinance 13.388626
Warrant 21.400000
Name: stop_minutes, dtype: float64
In [143]:
# 使用某种方法如mean、count对某类数据进行操作。

# 过去agg只能groupby之后的数据进行操作,现在还可以对dataframe类、series类进行操作。
ri.groupby('violation_raw').stop_minutes.agg(['mean','count'])
Out[143]:
  mean count
violation_raw    
APB 20.987342 79
Call for Service 22.034669 1298
Equipment/Inspection Violation 11.460345 11020
Motorist Assist/Courtesy 16.916256 203
Other Traffic Violation 13.900265 16223
Registration Violation 13.745629 3432
Seatbelt Violation 9.741531 2952
Special Detail/Directed Patrol 15.061100 2455
Speeding 10.577690 48462
Suspicious Person 18.750000 56
Violation of City/Town Ordinance 13.388626 211
Warrant 21.400000 15
 

plot 默认是折线方法

In [165]:
ri.groupby('violation_raw').stop_minutes.mean().plot()
Out[165]:
<matplotlib.axes._subplots.AxesSubplot at 0x10873ef0>
 
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" alt="" />
In [167]:
#换成bartu
ri.groupby('violation_raw').stop_minutes.mean().plot(kind='bar')
Out[167]:
<matplotlib.axes._subplots.AxesSubplot at 0x1092eb38>
 
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iSwhrEyoHvA4cAr7X9wkrxtgW+SEweCtgQ2Mf2TyrEWpJYJdEbYz4V+ETlVTytjWlL2hE4ADiF+LfcFPig7SNrxeyLvSSRI/5VMUbbvwtnAi+2fVd5vARwvO0NK8Vrd9J+MiV0AEmvBp4FXGr7+JZiXtib8R7rWIPxzgA+3FthImkzYmy71g/VBbbXlvQx4C+2D6k50y7pYuKX5pby+KnACbbXqBDrGGJI6bBy6E3AGpXG6weNae8InG+78Z66ipm7ZYhhjnWIhH627b81HWtE3McB2/LopZmfqBCr7d+Fi2yvOd6xBuOdbbvaYo6RJtUYuqSvA88FzgA+KWld259sIfQCkp5o+/ZyHk+i7r/NYv3LBW3/TlLN4aS7JH2QSHablJn9hSrGW6CXzItbqbcreSXb2/Y93r8sTatltDHtxhO6bUv6qe0X0G5p6p8RK0/Op2+ispK2fxfukbRGGeNG0ppAleGWotVJ+0mV0InbyTVsPyRpUeKWvY2EfiBwhqQfl8evBz5VMd61kj5K3GoCvBG4rmK87YE3ALva/puk5YCaq4ZOkPRr5l72Vutu615JG9v+AzwyZnlvpVg9TyD2SkCs6KnpLEnr1NqDMYplbL+0pVht/y7sA/xEUm/4djniLquWViftJ9WQy8hhgJrDAgNir0b8Iws42fYVFWM9Edgf2LjEOw3Yr3eHUCnm8sR255PKm+WM3jhihVgCtgM2Ys7392NX+GErV1iHEYlVRKJ9S+8KrEK8Vse0JV0BrAJcD9zNnEm1mpOiBwMH2b60Voy+WCN/F04F9q/8u/A44Dkl3uWOJvedMNkSem+VC8y90kXAw02PwUqaaftOjVI3wx2plyHp7cRSrSfZXkmxI/cbtrcY8qk1RtJMANsjl7vWiLU0LY1pj7ZQoPIKpSuIeazriGGC6m8iJe4MYgim6v+hWq4zJGlrYih5kb54jc9HwOQbcnnOgGO9iaEPVYh3OPAK5tTN6I/ZeL0MSV+yvbekX/DordXYfmWT8frsQdnuXOJcLekpTQeRdKrtF0q6nQH/nrbHLDj1GGO90fYPJL17xHGg+Y0pkp5t+ypJvTvGm8qfT5f09Iobmf7H9ptGnMv3ifmQWl5W8bXnIulwYh/IQ8Tv4ZKSvmC7ypCgWqwzVOJ9A1gU2JyoF/U64Jwxv2g+TKqE3n/VUW6l30Dcul8HNF4rw/Yryp8rNv3ao+iNE36+pXg993nu7c4LMuANpQGblz+XqvDaI/UmzpYY8FyN7+3dxF3OgaPEq7WR6bn9D8pV7AtqBOrdsRK7GduyWrlL3onofvYBIrHXmuNZn5bqDBUb2n6+ol7M/pIOJCZIq5hUCV3SKsQE2o7AP4AfEcNCm4/5hfMf92fAkcDPPHg3ZSNs97YAr2n7yyPOYS9i/LCGUyV9CHi8pK2AdwK/aDpI3y/JIbbf0v9cuTJ6Cw2x3dvdd5Lt00fE2mjAl8xvvN3KX182chNK2azSqLIqqfd/1huCEHA/9dqYjbxj7a9xUqvC40KSFgJeDXzV9gOSao4Dt1lnCOasoLlH0tOJvFbvAtL2pPkgtvefCjyr79i1LcR9IVFC83rgaOK2aJGK8S4YcOzCivEWIEq+Hg38GHh75X/PC0Y8nkFMPrX1b/moY1M43mdq/l8N+wPYE/gLcXUuYkfl7yvGO4lIqr8irpSPBY6tGO+jxKqobYG/ATcTG9+qxJtUV+jEN70DsXbzOOKqufFKaCPZPpW4ip1B3Dq/ndj63Gh9jrJC4g3AipL61xUvQfyQ1bKn447gW33nspdH3CXML0kfINZjLyGpN6Hcm484pOFYGxA7UGeNGEefSbyBNErS04haP4+XtBZzfi5nEmOkVdj+YFkJsjJzT6qd1nSsvvmB0c6l0XkCxbb7W2w/o+/YDcwZuquhtTpD5fs72bHT9hhJvyQuFOvtYi7vIpNK2VjwamLo5UXEsrSf2D6hYszHExUJtydqnfzS9p4Nx1ieuN36DHNvRLkLuMR2lbKag5Z/qsJO2LJccQYjvj/bjZcilvRCYDNiQu0bfU/dBfzC9tUNx9uZGDKaTVTR64/3XdtVxkUlvY0oIrUMMZG3PnCm7cbH7CX1NvgsQnyfFxNvXM8nVvNsXCHmabY3Hf8zpyZJZ9reoLV4kzGh9ytLCl8PbF/jh7jE+BGxAeA44CiiPkdbkybV9N0RbEKsBe9ZAnjIdpUuLSV2ryNM/1XlGRXiLO+KS/gGxNvWLTazUDSbWAc4y/aakp5NrNPevmLMI4FPuaxDV9Rbea9HzIs0FOujxEawHxHr7IF6S4YVhccOIlbUPY54w7rPlaplStofuIQY1qmebCd9Qq+t3BZ9iBirrNnUoj/m+sz5oVqYuKq9u+kfqiHeEewKvIcYouhPSJtViDWLKIk6cp1vrVUnra4rlnSu7XUU5QzWs32fKtYeKTFbq3ciadCuULteC8Fzid2oRxJLed8CLGv7Y5Xi3UWsyHqQmCDtLeGt8gYy2cbQW2f7YUlb2/6fFsN+lZgrOJq4tX0zsZGjUbavl3QT8WZRawXNIPsQ39eZtjeR9FzgI5Vi/ZC4unsFMfyyM1E7poq21xUDN0l6AvBTotHL7UTzhJqulPRt4AfE/McbqdTwxe0tGe5ZwPYfJS3oqFP+LUWBsMYTehmCfK4rNrweqVbBpKnmBEnbSqo+Adtj+xpi+/1Dtg+l0kRQueu4pwyBtOU/tu8FkLSwoxTqsyvFerLtQ4AHbJ/qKHm8fqVYEOuK3wzcbnt/YANg2VrBbL/G9r9s70esmDiEmF+qaRdied9eRBvIK8qxxklaVNJHFOUGkLSypFfUiFXcrehKdrGkTyuaVC8+3hdNRBliabxk9Fim/RV68W7itughSfdS+baISLALAxdJ+iyxlKlmhbn/EG3ETmTuccp3VYp3c7mq/AVwfFnxcss4XzNRvW4wN5ehkL8SE4i19Ap/VV1XXNa2704pJU2s7W/lLsv2f8qdyK9t12hR2O9QYt17r1zuTcSda60a828hLmT/mxgWXJm4y6ql1eJq034MfRjK2PbfiRK2+xCFpb5ertprxNt50HHbhw063nDsLYjv71e2Gy/FWq7mfk9cJR9ELCPc33aVcrNlEu8gYAvga8SQxLdtf7ThOD8i3qx+T2zFv972Xk3GGCP2K4mdmgvbXrHs2v6EK5SmkHSe7dn9q64kXewKtfOHQS0XV8uEziNjXTsBK9r+pKRlgaU9p5lymgcqBbJG4xYKZ7VJUbWvyrpiSZfafl75+4LAOSOXntYi6XxiufDv+pLsJTWSUBm/3gI43dGEZSXgCNs1et32FiR8nDkt4QCwvUqleK0WV8shl/B1Ypfqi4j66/8mrr7WaTJIWYI26jtotXftWEkwqBhY0ysJLufRW8YfCUfUnm6EpIMY+9+y0eEkSaN2QJJEhXXojzQWtv1gi9M7AA/avqOlmPsRy4WXlfRDouTyWyrGO5RYFTVXS7haeolbUQyv8RIRI2VCD+uVq4MLAWzfXsa4m1Zzsmcs/cX1FyHW9TdW+bDHdrXJwQHOG/9TGrXNGM+Z5gsuraG5a7j0arrUnt8BuEzSG4AZilLL7yK6iDXO9gnljmB94nvby3Ubit9pu/E6RqMpw1cHAk8nhlmXJ1YMPXesr5twvBxyAUlnE5My55bEPovogVmlp2iJ+VTm3AGcY7utYkG9+H+osfOv7/VfztyNlI+rFavEa6Vr/HSgaIDyYeDF5dDxRBnfxlq1lSvWDzFn0vczbQzJSept/R/ZEu6SSvEuJu78T7K9lqTNgR09p9hbs/EyoYOidGdvy/9hxKz3R2wfXSnedsSk0++Iq5JNgPfZ/vFYXzcf8frHXhcgrtj/q9bEk6RPEbfO/S3ozrDd+Fp0zd01XsQa9Jpd45ckxmB7b1anEhOG1epzDIukxWzfPf5nTui1jyOGPU4j7lyXqLETdUDc3w84bFcqP9A36XsxsFbZ93JOtTmCTOihbKnudfD5re0qGylKrIuBrXpX5eWO4KSKCfaUvocPAn8GPl9rSZqkS4gf3ofK4wWJioS1JtXa7Bp/DHAZ8cYP0WhiDdujjrFPNZI2JDZNLW57OUlrAO+w/c4GY8y181Qttptsk6STiH0DBwBPJoZd1qn18zmtx9DLreUDth9wdKMx0dX9OVTaGVcsMGKI5R9U3OTlyvXkRzET6PWFHNSEoiltd41fyfa2fY/3L9vyu+SLwEuAnwPYvlhS01ewUlSR7M28zuh/7IZruUja0fYRkgZOltv+SsPx9gZOJ5L5PcQGrZ2IJbxVykTANE/oxOz6W4GrJT0LOJPYSv6Kshngg7XiSjoeOKI83p6oB13FEIYJPgtcIOlk4hd0MypsrS7a7hp/r6SNbf8B6DXTuHecr5lybN84YpVL0ytCliSGXPqD9Mrz1mim8cTy56yGX3c0ywBfJnZIX0JMKp9OVAKt1qt4Wg+5jFjr+0miifIeZYXL+b3nKsXelhhnFnCa7WpbhIcxTCDpGUQFSxGFuf5SKc7IrvGnAfu5Utf4ssnmMCIhCfgn8BbbFzcc5y7GXpZZbZWLpB8DXyBqDq1PrHKZbXuHWjG7quSS2cSiiw3Kx79sr1Yl3jRP6I9slpB0OvA52z8tj7u0W62V6nmSVnY0oB44Vl5rJcEw9DZR1V6ZIekTRKeb7xNvIDsRE4ifrRhzKeLqcktiKPB4YjlhzSYsVUkas2m47XeP9fx8xF2SSOIblT+fAFxqu0ptnOk+5HKJpM8TLbCeBZwAoKhD0rjeUsEBV1+11xa3NUzwQWBXYlPWSGbOkM98k/Ql23tL+gWDN001uk1d0htt/0Bzd0eiNyxhe8yEMR9eYnu9vsf/V5bZVkvoZR34TrVef0h6q57WB1Yn+h5ArGhrvM6KotjYc4lS1WcTQy5fqHXn2DPdE/rbiYpyKwAv9pwG0asBn286WG/dt+2ak4SD7A58T3MqLt5Ond14bwewvUmF1x6pN2be+P/TKHoTrW3/3z1UltUeSbxx7UjlHY6Snklcoa9fYp4J7GP72ppxa3JU5OwtUd7UUToXSV8j5tKathzRQONq4oLxJuBfFeLMZVoPubRN0VPwCKKd3j3jfX6F+FWHCSTdQmzYONz2oPW+TcZ6L/Aj2zfWjNMXb7bttnenImkFIrluRCTX04G9bf+5YsyziLus3qT9DkRf2vVG/6oJx1oJuMnRuGMzot3d9xx9OBsn6Y/EzvB/lcdPINrrrVohloir9A3Lx+rEnMuZtj/edDzIhN4qSa8ifjm2BH5L/ML82vb9leK9G7ijd3XSd3xPohb7lxqO9xSirMAOxBbno4hCS+c3GafE+iJxu3wd8e94dM0t46UsxOIl1pG2r6gVa9gknT0yeUs6y3bjdebLks/ZxF3y8cRSyVVtv7zpWCXe24hmKyeVQy8idsF+p0a8EnMZ4g15Q2IT1ZNt1xnWzYTePkVD6lcSiW8DYsniEbZPbDjOZcDaI98wFFUCz62x0acvxrLAdsT3uCTx/TV6VVKugDYtMV5FNDXu3QHd1WSsEm/VEmt74H7mJPdqPU0lrQL8H/BU26uXCedXumKHLUkHEMMDvWGe7Ynhg69Bs2vEexuKJL2PaIxykCo0MC+xBDyNmOjtvTlVWYFV1rtvSCTyB4g7qzPLn5e6Us/iaZ/QJc0ADrD9viHFfz6xDO75tmc0/NqPLMt8LM81GH9RYmPF+4i+jUtVjDWDuPM5gLjCW7RWrBJvDSK5bwf8zfZGleKcSvz7fdNzStleZnv1GvHK6/fW8feSQ/9acbvBKp1lgvdLRO2YbWxfV/P7k3S+7RfUeO0Rcb5AWXtu++ba8Xqm+6Qoth+S9AJJckvvborCXL2r16WJDi21Wnw91fYtI4/ViFVee2Fga2Ly7oXEre3HqDPx1Iv5POZcOf+DKPpUjaKx+FOApxKTpdV6mAKL2j5nxCafWs291wFudOnzqWiMsi1RKmK/ShtidiEm7T9VkvmKRC/TWs6RtLbtC8b/1ImrtQxyPNP+Ch1A0oFEK6qjmbtFW6MlUSW9nUh0qxKTh0faPr3JGCPivZnYFPIe5uzCewGx5O1rbrhjkaTvAS8lrkyOBH5ea/JXUdZ1B+as+jiSGNapthJD0iaPnmMzAAAgAElEQVQl3quJjVpHAsdU3HGLpN8Q7dKOLkMTrwPeavtlFWJdAGxp+5+Krf5HAnsCawLPsV2zVVsrFD0JVgOuYe4OQp2oI5MJHZB06IDDdjQcbjrOEUQhripjaANivgzYl5hhN7Ee9wDbv6kQ661Egqu/PEu6ljlj2Je2EO9G4AYiyR018q6nYtxnAgcT47G3E5PAO9UYt+/fTFeW893qaE5dZSNaed22mq/04q006Ljt/1cjXtsyoXeYpB2Juu5TdoffZCFp+ZqTn2PEXbEMRSxGFHW7q3esQqzLgDUdHZKuAnazfVrvuRrj2pKe3PfwkeYrtqvU/il3Wc8l3kSuqL28tm2Z0HlkWdFBzFnr+wdiq/NNQz2x+SRpX6JJwULAycBviGYa+Z8+RWhAWdlaE3uSPkxUG72N2Biztm0rCtcdVmvid8B5NN58RdLSwDHE73evKNjaxIqX17Y5cVlTJnRA0olEM4b+in072d5qeGfVHEU3ny2J8e11idLAxwHHtzV0kB4bRX3+5xLzHf0rsGYSzVDqtDCLJspLE3d2d5djqxC10RufSFRLzVckHUvs+fj2iOO7EstAX91kvGHJhM7g8cFaY4Z9rz+DWCXR33n8hlrxRsReDXgZUe7gJRVef3dibLu3G++JwOttH9x0rLZJ2mjkRPagYw3EeRUx+fpKSl3y4i7i37ZKj8+2qaXmK5L+ONpuUEl/sr1Kk/GGJRM6oOgq8l3mbHXeEdjF9hajftH8xduTqE9+C9CbHHWtjT6STh75vQw61mC8QW+QtTaLbER0jl+eeHPsrVqoNak2aAikWrcdSRvYPrPGa08nkq6x/awBxwVcPei5qWjar0MvdiVqP3+RGGM7oxyrZS9i80vVyUpJiwCLAktp7u4wM4ku5LXMtUGqrNteqFKsQ4B9iHHRakWrJG1ArDSZpbkrLs5kxPfbsN0lXTnibufApldgDYvaa77ya0nfIIqM3VtiL0oUd6vawLxN0zqhS/pf2x8givU0Wm51HDcCbTQVfgfR+urpzN0d5k4Gl7htyomSjgC+QbxB/hdzamc07Y4aSzAHWJio5bIgc1dcvJOoKVPL8/uXgdq+XVLjdzpD9B1iTf925fGbgEOBppuvvJeYj7ihLHntdUU6HPhAw7GGZloPuZRNBmsT1dZa21gg6RBic9GvgPt6x12pprakPW0fVOO1R4k3A3gn0XRbRJ35b9pufIdjqTsyg9io1f9vWWUnYNvLFxUNxTdzqaMt6UnAqbXLNrSl7fkrSYsTmwgF/Mn2v2vEGZZpfYVO3GrdBiwm6U7K+CtUbzhxQ/lYuHzU9jdJS5Q1zB8h3sT+p1bSs/0QsQy0jTeRXlXA2f2nQFTRa4z6GmmM2IYfAevd4R0InKFoC2fiSvZTlWINQ6s9WksCv7DW6w/btL5C75H0M9uvGkLcJYg3jqpXCSqt9iRtDHyGGDf8kBuuby3pCNs7KkrNDtr9N2W3V0t64VjP2z61YuzViDcoASe7Q6V71VKP1ukiE/oQSFqdWPP+pHLoNuDNti8f/avmK96FtteS9BmidOfhNVadSFrO9g1tbq9ucVKtP+bjgeWaXlo3RryNgZVtHyppFrEmvPGdosOklnq0dl0m9CGQdAbwYdunlMebAZ+2vWGleL8k2mBtSRTnupfYMdr05o3zbb9A0ndtv6XJ1x4j5jHEpFqv0NibgDVsNz2p1ou3DXGHs7DtFcsV5idqDblI+jgxnLSq7VUkPZ0o1NXKrs1aNEqP1p5a80kl9tOInbD9e0A6sa5/uo+hD8tivWQOYPt3pVZHLdsRu0Q/b/tfZRt0jfrvj1P0bNxE0qMSnO2fD/ia+bWS7W37Hu+v6IJTy37EbtvfAdi+SNEmrpbXAGtRqmXa/msZqpvqhtKjVdKniZ3gVzFnmauJkgdTXib04bhW0keZu9RAjWJLM8st7CKUBFRWSdwH1OiPuQfxvTyBKLLUz8y947EprU6qAQ/avmPQxGgl95d6Kr0J2Zpv/K2x/c3y5/4th94WWMX2f1qO24ppndDLssVBY069VS61WrTtCuxPLLUTcBp1GlwcTvQwPJ85q3d6eutwm/Rk22+XdF7vF7YF/wUcVsbSH5lUqxjvMklvAGYoarK/i9iIVstRkr4JPEFRT39X4FsV47VK0mFEIby2Nk5dR9SM6aRpPYYuafmxnm9zvXEXaE5/yGpb4ceI3cqkWtld+GGiiqWIxsafrHnFJ2mr/nhuuPfsMA2anK9VJqK89tHA84mNbv37FobSYahp0zqht03Sl2zv3b+muV/FibWNgIts3y3pjcQ69C81XQxM0slEbZrZwCkjn29yonKYk2ol/swI03wz6r4YM4gEvmWtGMPW9sYpRROWR7F9SI14bZvuQy53MfaQS9Mbi3pj5p9v+HXH83/AGorGxu8n6p98n+j52aStiWR+KHVLC8DYk2rVrlIUfTe/04sr6Q5gV9vnNx3L0e/2HklL1lyGOWT9G6cg5l5qbpy6mtgZft+4nzkF5RX6EEjay/aXxzvWYLzeUMjHgL/YPqTmsIikpd1SwwC1VM6277UvAfZw6XRT1oh/vdZ8i6SjgPWBE5m73+27asQbhjY3Tkk6nFil9Ddi7ur3wOldWf+eCb2PpKcQK0KAevXJByXTyuOGpxJlDnYhNuDcSgzB1LqtXYoohrQac/97vrhCrLbL2Z4+cg34oGMNxtt50HE33OC7bb0VWGWI5VFs/7Ny/OWJevPvAZa2XasaaKum9ZBLT1kzfSBRlfDvRG3tK4mOMU3G2RF4A7CipP4lfEsANUvpbl/ivtX23yQtB3yuYrwfAD8hfmH2AHYmrogao5bL2WpOZ51zyqqTI4ihne0pS0JrmOqJewxtr8ACQNIOwCbAmkTF028QV+mdkFfoPDIx8yLgpLJFfnNgR9u7NRxneWBFop7Kvn1P3QVc4grVCEvcxYD/lDHZVYBnA7+x/UCleL0do70aMgJOsb1ZgzFeCGwG7E78UvbcBfzC9tVNxSrxHjXJ28e2my4GdpTt7UZbWltxSW2nSbod+BPR/+AUT/G+wSNlQgfKuunZJbGvZfthSefYXrdSvGcCf+0tdSu1QZ5q+8+V4p1PXJU8ETiL2FR0j+2dKsU7y/b6kk4g7nz+CvzU9sAaL/MZa/k2l5dKeqbta8c71kCcpW3fPNrS2i4tqZX0WmBj4o3r97Z/WjGWiGWLm5aYywNX2q6xD6R1OeQS/qWok3wa8ENJfyf6G9ZyFDFc0PMQcDSwTqV4sn1PWbJ1kO3PVt4e/+my0ee9xGqXmdQpNQBwj6TPEcNj/eP1jV4x9/kxseyz39FEjZzG9E0q30HU74ao392p1S6Svg48izntH3eXtJXtPSqFXBR4CtHP92nAk2mnhHUrMqGHVxHbxfcBdiJKeX6iYrwFbd/fe2D7fkk1f6hUxpx3AnrrcKu0TStrp1codVsuIe4Mavoh8CNiPHZ3Yrz+1qaDSHo28aaxZLmi7JlJ3xtJg/EWBg4m5iGuI8aYl5f0E2D3/p+fKe6FwOouQwVl5+ilFeOdA5wO/AE4xB2rWtnZLbDzQtKzyhK3u20/bPvBMgl1EVGPpJZb1Ve8StHh/baK8fYCPgj8xPblZchnrDHhCXM0t6hS6XAUTy6bQh6wfWrZMr5+hTirEm8aTwC26ftYG3h7hXgfIfqwLmt7LUcHn16FwI9WiDcsfyS+r55liQuBKmw/t8yNHU3DE/WTwbQeQ1eUlf2Q7UtGHJ8NfNz2NpXirkRcWT6DGDe8iaiHfk2NeAPiLwJsY/voSq//P8TKnSOZe+1047+ofeP1xwNfIcbrf1xjvL7E28D2mTVee0Scy4B1bd8z4vjiwFm2V699Dm0oS2rXIa6cKX8/E7gHmt89Xda8HwYsTVzQ3gjsUnPte5um+5DLCoOSjO3zVLEkqqPRw/rll1M1t4/3lKGQFwM7Ai8hlmpVSejM2YHaP9Zs5jShaNL/lPH69xAt72YSQ2e1XChpDx49Zt90MamHRybzEuffKpUXO+JjLcc7mLiIOxFA0pbl2MYtn0cV0z2hjzX2+fhaQSU9Ffg08HTbLytXDRvUqCchaVNiDfrWxFXQRsCKg5JFA7Fea/tY27XHzXvxZhCdfH5JTB5u3kLY7xO1tF9CzLPsROxZaJoVlQcH1el9uEK8YTkPuLesLKu+pBZYwn3FzWyfJOnASrFaN63H0IFzFSVJ51JWgzRem6PPd4kqfU8vj/8E7N10EEk3AQcQk0CrORpB3FsjmRcfqfS6A5Xx+lrNmUfzLNsfBe4u8y1bAzV23C5JNLU4f8BHFxpc9JwGLCLpGcDJxG7m71aM92dJH5S0TPnYF+jMEtDpfoW+N/ATRZedXgKfTSxjek3FuEvZPkrSBwFsPyjpofG+aAKOIVZJbA88JOlnVCxcNSRnSPoqsdKlf7z+gkrxeleO/1L0hv0bsEKFOM+qtdFskml7Se2uwCeBX5fHtXoRDMW0Tui2bwE2LDtDe5NMv7L928qh75b0ZEpylbQ+MWTQKNt7SdqbGIrYkdjuP1PSdsCvbf+74ZDPVhSvGqlmw5Deev7+ZaYmdv7WcHAZCvko0YFpceqMA59V7rCOA46rtelsEmhlSa2kT9v+kO1/AO9s+vUni2m9ymVYSl2Qg4g3kcuAWcDraqwCGRF3IaK36I7Ai20v1fDrX84YvRlr7G5sa+fmMJRdoi8j/s+eQayd/g1RL7wT5V9LCYf3EBUP/7csqd3bDVeT1BCargxDJvQhkbQgsbZZwB8rTgKNFv/xthvtvamKFSPHiDmo2uL5thvdudn32q1NaI+IuxCxSeulRA2bW21vXTNml5SyHpsxeJK5enXHtkzrIZdhKevA30lf/QpJ33CLjWubTuZFlRrkg7S9c7PPd4kGHh8uj/9EjN9XTejlDf+35YMyiTjllaJng4qPNT1k9mxinmxQQq9W3bFtmdCH43tEVcCDyuMdieVwrx/aGTXA9n+3GG7kzs2eu6izc7OnlQnt0aos9nSo2uJ7+/6+CLAtdeooXdH23eMwZEIfjlVtr9H3+JRyS1iFpNeP3BU66NhUYvtnwM/a2rnZp5UJbeLNqvP86NZ9p5fdo2kCpvs69GG5sCQCACStR93hig/O47H5JmkBSRuO/5mNeY2kmZIWknSypNsUjbBreTexumUlSacTd1t7Nh3E9vVjfTQdb1gkPanvYylJLyGqIDbtaEmdv0LPSdEhkHQlMWTQa3G3HLHb8GEaXN4n6WXEqpPtiHHenpnERqNa9d7PtL1BjdceEOsi22tKeg2x5n4fonHBGuN86fzErD6hLekPtjfWoxuZ12pgPhSSrmNOx6IHicqSn7D9h4bj7EBMKK8BXEysFjrB9u1Nxhm2HHIZjpe2FOevxNbqVzL3zte7qFvv5ARJ2wLHuv4VQ68X5MuBI2z/Uxq4kGG+SFoHuNH238q4+QuI8d7rJe3X9CoJ2xuXP7u0K/RRbK/YUpwjiWJxlCv1lwLHlvIRJxFr/c8Z4yWmhLxCH4JSbfEm2/dJ2ozooPI92/+qFG+h3lVk2RSzbM017+WqcjGicce9VLyqlHQAcWV+L9HN/QnAL22v13CcC4AtyxvGpkRy2JPoTfkc269rMt6A+K00MG9L/xtkefxmyhsk0Pgb5BjnMRPYCniJG245OQyZ0IegbG2eTWwZP54Yk13V9qibcuYz3u+Iq/QFiVrvtxKbU9491tdNFeVN6k5Hz9RFgZm9RNFgjIt7wziSvkasA9+vPL7IUa+8cRqlgbntRhuYt21Yb5CSziOWnR7eteEWyEnRYXm41Ol4LfAl2/sQ9ZlrWdL2nSXeoWXTzZa1gim8UdJHy+NlJTU6Xi/pReXP1xKlDV5V/v5S5m7v15QZZewcYAvKevCi5tDlJ4mGHX8qwxNb0OJ6/4pm9F2Fbw8cbPuYUvjsWRXj7kC8OZ4r6UhJL1GNMbohyYQ+HA9I2hF4M/DLcmyhMT5/fi0oaWlicvSX431yA74ObECU7QX4N9FbtEm9muvbDPioseTvCODUUuDsXqKePJKeRZ1liz0PlPojC0hawPYpxFXsVDeUN0jb19j+MLAKcDjwHeAGSftLelKtuG3JSdHh2IXof/kp29dJWhH4QcV4nyCGdv5g+9xSL+PqivHWs722pAsBbN+uhnum2v54+bOVSnm2PyXpZOJO6oS+yd4FqLBssU/bDczb0nuDvI123yCR9Hzid/DlREXSHxK7tn/LFH+zzDH01DhJZxPDHueWxD6LSIKNrwOWNGge4A7gfNs1y7C2QtJiRMJbgDkNzH/QhdojZS9G7w3y7nJsFWBxVyp/LOl84F9EqYZj+oucSTrWdpv9cBuXCX0IJG0E7EdMcC3InFUgjdaTkPR+R33pgxhcL6PRinZ9cXcixkXXJvo3vg74SI2dqZIOJyaYf1EObQ2cS9TuONr2Z5uO2SZJ/2v7A+MdS/NGg6tzrmj7umGdU5MyoQ+BpKuIdeDnE0v7AChjpU3G2cb2LyTtPOh5R8edKkrxrC2IN6uTbddo04aiOfS2LrXdy/DEj4kGJefbXq1G3LZocDXJSzpUy6VVo/x7VqvO2bYcQx+OO2z/pnaQksxnAKvbfl/teCNcDdxJ+RmTtFyltdPLAff3PX4AWN72vZKmbM1wSf9FVOR8puZuGrIE3Vjl0ioNrzpnqzKhD8cpkj4HHAs8knRqjBuWtdmtXn1I2hP4OHALcQciYsinxlXl4UR3n5+Vx9sAR5Sx5ysqxGvL4cT29M8A+/Ydv6sL4+dDMKzqnK3KIZchUNSAHsluvgZ0L96BwMrA0czdd/PYSvGuIVa6NDqENEa8FxCrFESs5DmvjbhtkbQxsLLtQyUtRXSu78SYb9vUfnXOVmVCnwYkHTrgsG3vWineKcBWbqnJ8YiEN4tYJdGJhCfp48Sk76q2V5H0dGKyd6Mhn9qUMqwFAm3LIZcWjbLE7hG2v1Ajbltrtfu+v2uB30n6FXMPKTX+/fUnPGJL90LEmv6uJLzXAGsBFwDY/qukThfsqqQ3Kd+pu7eRMqG3ayi/iJKWIbojbURcnfwB2Mv2TQ2H6n1/N5SPhcsHjNF9Zz51PeHdb9uSeg01Fhv2CU1RJ0qaNXJlVyl6dueQzqlxmdDbdTVwfFtjy30OJSbZei3u3liObdVkENv7w+gdkpqM1afrCe8oSd8EniDp7cCuwLeGfE5T0VeA44iFCP22IuZf/qv1M6ogx9BbJGlf4MXEsMDJxCqGc1z5P2FQNcDKFQIHrfV91LGGYr2XmPDdilgRsitRF/0rTccaFklbET83Ii4IThzyKU05kq4YbU+CpMunevXKnkzoQ1CGBLYkKgOuS4zvHUf8st5SId5JRLf6I8qhHYFdbG/RcJxhdUjqbMKT9ATiDQui4mLVOiddJelK2895rM9NNZnQJwFJqwEvA15s+yUVXn854KtEBUQDZwDvanqjj6Q1iPHs/YGP9T11F9EWrnr96bKRagfbP6wdq6ZSzOxgonnHtUQtl+WBnwC7275/jC9PIygaT7/PI7oSKRptHGh70+GcWbMyoQ+BpJNHXh0POtZgvI1snz7esQbjLU407zDw/2z/p0KMmcAewDOIBiEnlsfvAy6y/aqmY7ZJ0ieAlYjkfVc5tgRRhvj6Ujc8zSNFPf6jiDvVXjvG2UQJ6x1snz2kU2tUJvQWSVoEWBQ4BdiMGCKAGJL4Ta3bvrbGtEt9608TpUlvIK4qlyEmYD/sBpspl52htwNnEjVjnkisqNmrI1UWLwPWtX3PiOOLA2fZXn04ZzZ1lRUtewC9f7vLga/a/vvwzqpZucqlXe8A9iY6ppzPnIR+J803gEDSBkQZ21kj1sDPBGY0HQ/4HLF08Zl9V5Uzgc+Xj70ajPVM288rMb4N3AYs14vbAQ+PTOYAtv/dW9GTHpuSuD8+7POoKRN6i2x/GfiypD1tH9RCyIWBxYn/5/612XcSJW2b9gpglf5VO7bvLIWmrqLZhP7I1X6pV3Ndh5I5gBW9Uge1R3u47ZNJU0MOuQyJpA2JceZH3lRtf69SrOVtX1/GYN0rNVshzp9sr/JYn5tgrIeYU5dGwOOBe5hTW35mU7GGQdKficQ9KKHbDdfOT92QV+hDIOn7xITXRcyph26gSkIHllC0g3tSiX8bsLPtyxqOc4WkN498Y5L0RuIKvTG2awwZTRq2Vxj2OXSJpO/bfpOkvcqdciflFfoQSLqSWJfdyj++pDOISclTyuPNgE/b3rDhOM8gduLdS8wRGFiHuHp+je2/NBkvpXkl6QpiafDPmXtBAgBdKUmcV+jDcRnwNODmluIt1kvmALZ/V2OLfEnY60l6EdFMQMTqnZObjpXSY/QNYvPeM5l7QQLEhUcnhrDyCn0ISnnZNYFzmLsa4SsrxfsJUbzq++XQG4HZtl9dI15Kk5Wk/7Pdibotg2RCHwJJLxx03PapleI9kdi92WsCcRqwXxs7N9PESHrSWM93ZYhgGMqO5k3Kw9NsXzLW508lmdCHRNLyRFOGkyQtCszo2LK7NB8kXUcMBeQqlwZJehewG3OqLr4GOLilZcTVZUIfglIGdTfgSbZXkrQy8I0KxbJ+PtbztYZ4UpqsSsPtDWzfXR4vBpxpu0a/29blpOhw7EFUWTwbwPbVZVty0zYAbiSqLJ7N4Ku9NMmVIbOV6etOb/u04Z3RlCbmLBWGOU3MOyET+nDcZ/t+KX6OSg2UGrdKTyPqhO8IvAH4FVEr/PIKsVIFkt5G7LBdhti3sD5Rv6ZKQ/Fp4FDg7LJQAKKa5SFDPJ9GLTDsE5imTpX0IeDxpZb30cAvmg5i+yHbx9nemUgE1xC9PvdsOlaqZi9iLf/1tjcnyhPfOtxTmrocfW13Af5JFHfbxfaXhntWzckx9CGQtADwVvqaMgDfrrHRSNLjgK2Jq/QViI0V38lNPlODpHNtryPpImA92/fV7DaVprZM6B0m6TCiVOhvgCMrbPVPlZWhgV2IKp0vIq4qF7L98qGeWJqUMqEPgaRXAJ8kOtAsSKWCUpIeZk4Bq/7/6E4UsJpuyv6FJYHjsmNRGiQT+hBIugZ4LXBpW/Vc0tQiaWYpPTxwg1FuLEqD5CqX4bgRuCyTeRrD4UR9+V6RM434MzcWTYCk1wL/CzyF+Lfs1N1qXqEPQWlM+0ngVOau5fKFoZ1UStNAuTvexvaVwz6XGnLZ4nB8imjGsAjRSaj3kdJcJL1G0pJ9j58gKYuqTdwtXU3mkFfoQyHpPNuzh30eafIbtERR0oW21xrWOU1lkr5MbLj7KXPfHR876hdNITmGPhwnSXqx7ROGfSJp0ht0F52/txM3k7g7fnHfMTOnWNeUllfoQyDpLmAx4H7mNDvuzMRMao6k7wD/Ar5GJJ49gSfafsswzytNTjmGPgS2l7C9gO1Fyt+XyGSeRrEn8cb/I+Aoor3fHkM9oylM0jKSfiLp75JukXSMpGWGfV5NySv0ISnLpzYmrrp+b/unQz6lNIlJWtz2v4d9HlOdpBOJJaH93bt2sr3V8M6qOXmFPgSSvg7sDlxK9BfdXdLXhntWaTKStGFpcHxFebxG+flJEzPL9qG2Hywf3wVmDfukmpKTK8PxQmD13saiUnPl0uGeUpqkvgi8hCiqhu2LJW063FOa0m6T9EaiRwBE0bp/DPF8GpVX6MPxR2C5vsfLAp3pa5iaZfvGEYceGviJaV7sCmwH/A24GXhdOdYJeYU+HE8GrpR0Tnm8DnBmr2VctoZLfW6UtCFgSQsD7wI6uzGmNts3AJ39/cpJ0SEoVfNGZfvUts4lTW6SlgK+DGxJ3FEfD+xluzPDBG2Q9H7bn5V0EAO6g9l+1xBOq3F5hT4EmbDTvLJ9G7DTsM+jA3p3NecN9Swqy4TeIkl/sL1x2ViU9cnTuCQ9k7hCX5/4mTkT2Mf2tUM9sSnGdq/F4z22j+5/TtLrh3BKVeSQS0qTmKSziF2ivVUZOwB72l5veGc1dUm6wPba4x2bqvIKfQgkLTfoeJmwSamfbH+/7/EPJP330M5mipL0MuDlwDMkfaXvqZnAg8M5q+ZlQh+OX/X9fRFgRWIp43OHczppEjtF0r7AkcSQy/bAr3qdjLJz0Tz7KzF+/kqiaUjPXcA+QzmjCnLIZRKQtDbwDtvvGPa5pMlF0nVjPG3b2bnoMZC0kO0Hxv/MqSkT+iTRpXG8lCYrSSsDnwFWI+6OAejKG2MOuQyBpHf3PVwAWBu4dUinkyah0qbwRtt/K4/fDGwLXA/sl0MtE3Yo8HGipMLmwC7EKrNOyK3/w9Hfdu5xxJj6q4Z6Rmmy+SZRNpdSu+UA4HvAHcDBQzyvqe7xtk8mRieut70f8KIhn1Nj8gp9CGzvP+xzSJPejL6r8O2Bg20fAxwj6aIhntdU9x9JCwBXl9VCfwGeMuRzakwm9CHo1WwZTdZyScAMSQvafhDYAtit77n8vZ24vYFFiZo4nySuznce6hk1KH8whuM6olHtD8rjHYE/E3U6UoLYSHSqpNuILkW/B5D0LGLYJU2A7XPLX/9NjJ93Sq5yGQJJp9nedLxjaXqTtD6wNHCC7bvLsVWAxW1fMNSTm2Ik/YIBRbl6unJXnFfowzFL0jN79TgkrUiHuqakZtg+a8CxPw3jXDrg88M+gTZkQh+OfYDfSeoVWFoByE1FKVXSX+G01JVfpTz8Y5c2GuWQy5BIehzw7PLwKtv3DfN8UpoOJG0GHEbMWYnoFraz7dOGeFqNyXXoLZL0/r6Hr7R9cfm4T9Knh3ZiKU0fBwIvtv3CMmf1EmKTUSdkQm/XDn1//+CI517a5omkNE0tZPuPvQdlTmKhIZ5Po3IMvV0a5e+DHqeUmneepEOAXkninZi7+uKUlgm9XR7l74Mep5Sa91/AHsTGIgGnAV8f6hk1KCdFWyTpIeBu4mjSN/QAAAOySURBVAfp8cA9vaeARWx35tYvpdS+TOgppc6TdJTt7SRdyoC7YdvPH8JpNS4Tekqp8yQtbftmScsPet729W2fUw2Z0FNKnSfpq8Dhts8Y9rnUlMsWU0rTwdXAgZL+LOl/Ja057BOqIa/QU0rTRhly2aF8LEJUtTyyKzVyMqGnlKYlSWsB3wGeb3vGsM+nCTnkklKaNiQtJGkbST8EfgP8iejV2gl5hZ5S6jxJWxGNZLYGzgGOBH7aqzPfFZnQU0qdJ+kU4HDgmL5erZ2TCT2llDoix9BTSqkjMqGnlFJHZEJPKaWOyISeJi1JT5f043E+ZzNJvxznc9aU9PK+x6+UtG9T55nSZJEJPU1atv9q+3UNvNSawCMJ3fbPbR/QwOs+iqRObFBJU1Mm9DQplPoa7+x7vJ+k90i6rDxeRNKhki6VdKGkzQe8xrqSzijPnyFp1dLh/RPA9pIukrS9pLeUYk1IWl7SyZIuKX8uV45/V9JXyutcK2nUN5Zyl3CKpMOBS8uxn0o6X9LlknYrx7aT9IXy970kXVv+vpKkPzT0T5mmsUzoabI4Eti+7/F2wLl9j/cAsP08YoPIYZIWGfEaVwGb2l4L+Bjwadv3l7//yPaatn804mu+Cnyv1MP+IfCVvueWBjYGXgGMd0W/LvBh26uVx7vafgEwG3iXpCcT3XE2Kc9vAvxD0jNKjN+P8/opjStb0KVJwfaFkp4i6enALOB24Ia+T9kYOKh87lWSrgdWGfEySxKJfmWiicG8dIDaAHht+fv3gc/2PfdT2w8DV0h66jivc47t6/oev0vSa8rflwVWtn2WpMUlLVGOHQ5sSiT3Y+fhXFMaU16hp8nkx8DriCv1I0c8Ny9NtD8JnGJ7dWAbopreY9W/0+6+xxD/kS3kkjYDtgQ2sL0GcGHfuZwJ7AL8kbgq34R4Uzl9Auea0lwyoafJ5EiirOnriOTe7zSiQzuSVgGWI5JivyWBv5S/v6Xv+F3AEqPEPKPEpLx+E2PZSwK3275H0rOB9fueOw14b/nzQmBz4D7bdzQQN01zmdDTpGH7ciLx/sX2zSOe/jowo/SE/BHwFtv3jficzwKfkXQ60L/a5BRgtd6k6IiveRewi6RLgDcBezXwrRwHLFhe85PAWX3P/Z4YbjnN9kPAjTTzJpJS1nJJKaWuyCv0lFLqiFzlktI8kvQ8YiVMv/tsrzeM80lppBxySSmljsghl5RS6ohM6Cml1BGZ0FNKqSMyoaeUUkdkQk8ppY74/41XB3urFbZ+AAAAAElFTkSuQmCC" alt="" />
In [168]:
ri.groupby('violation_raw').stop_minutes.mean().plot(kind='barh')
Out[168]:
<matplotlib.axes._subplots.AxesSubplot at 0x10a4a5f8>
 
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" alt="" />
In [147]:
ri.groupby('violation').driver_age.describe()
Out[147]:
  count mean std min 25% 50% 75% max
violation                
Equipment 11007.0 31.781503 11.400900 16.0 23.0 28.0 38.0 89.0
Moving violation 16164.0 36.120020 13.185805 15.0 25.0 33.0 46.0 99.0
Other 4204.0 39.536870 13.034639 16.0 28.0 39.0 49.0 87.0
Registration/plates 3427.0 32.803035 11.033675 16.0 24.0 30.0 40.0 74.0
Seat belt 2952.0 32.206301 11.213122 17.0 24.0 29.0 38.0 77.0
Speeding 48361.0 33.530097 12.821847 15.0 23.0 30.0 42.0 90.0
In [148]:
ri.driver_age.plot(kind='hist')
Out[148]:
<matplotlib.axes._subplots.AxesSubplot at 0x1003a518>
 
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" alt="" />
In [149]:
ri.driver_age.value_counts().sort_index().plot()
Out[149]:
<matplotlib.axes._subplots.AxesSubplot at 0x10088080>
 
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" alt="" />
In [150]:
ri.hist('driver_age', by='violation')
Out[150]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000100D8438>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010111208>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001013B898>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010163F28>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000101945F8>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010194630>]],
dtype=object)
 
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" alt="" />
In [151]:
ri.hist('driver_age',by='violation',sharex=True)
Out[151]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000102C6C50>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000103243C8>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010346908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010370E80>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000103A1438>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000103A1470>]],
dtype=object)
 
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" alt="" />
In [152]:
ri.hist('driver_age',by='violation',sharex=True,sharey=True)
Out[152]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000104C4F98>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000001059D358>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000105C0748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x00000000105E9B38>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010613F28>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000010613F60>]],
dtype=object)
 
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152gRgNulWAMrzbFfFNuORSP2NBJ6PiDUNpi3O0ztzWUQ8GRGvAtPI3yiBzwMzImJGRPwpIm4FZpE6TLvLI+KxiFgTEa/3w/uw1vFzSatJN4pbCpwpScDxwN9FxPKIWA18m3TFbUgj48vyNvMKafTcmdeB7YAdI42sfxP5k7ELZ0XEy3lbJiKmRMTqiPgj6UvUhyUN7+H7OxI4OyKWRsSyHOtfd4jv7BzbDOAl4L0AkjYBdgXuKMx/U0TcmWP5Z2BPSTt0bDQibo+IR3Kfe5j0BfBTXcR5IilJP5H7/7eB8Xk08jopib6PlICfiIjFPXz//cpJpP6eB0Z2si94uzy9M88Vnr8CtB8g3RE4JO9OWClpJfCJXF+74p0mbWg5OCI2J31zfh/pi8oo0ghjdmGb+WUuB9ietbeZrraffyV9w75F0jOSTu9BTG/WJ2l9SZMlPS3pRdIoA7r+QlW0PTCv8HpeLmv3QocvbcW+szdpNPBao9gi4iVgeYf62uPeXdKvJS2TtAr4Ujcx7whcWFjfy0mjjtER8Svg+8APgCWSLsm7vpvOSaT+7gH+CHy2WChpU2AScBsN7gLZjQXAlRExovDYNCImF+bxlTmHuIi4g7S75jukLyuvAjsVtpnhkQ7AQxoVjyksvs438UK9qyPi1Ih4J/AZ4O8l7d0+ubPFCs8/BxwE7EM60aQtl6vBvI0sYu279o3NZT2xP9DxXrlvvlelM9m26qS+q0m7A3eIiOGkXXpdxbwAOLFDPx0WEXcDRMRFEfFR0i7E95BOvGk6J5Gai4hVpOH2v0uaKGlDSW3AdaR70V9J2rfa1ouzqK4CPiNpQv5W97Z8QHFMt0vaUHMB6ZjZh4BLge9J2gZA0mhJE/J804BjJb0/7/L5RmcVSjpQ0rvzLrIXgTfyA9K23N2JHJuTvli9QBodfbvD9O7quAb4mqRRSiebfIPUJ3piEuvesmJ/SZ9QOlPyHOC+iGg0EtscWB4Rr0najZQM2y0D/tQh7ouBMwonDQyXdEh+vmse2WwIvAy8xlvrsKmcRFpARPwL6Qys75A63X2kbyl75/2w1+VZX5D0QA/qW0D6JvdV0sa7gPQtxtuDrSUfM7gC+DrwT6TdUPfm3Uj/TT5WEBE3AxcBv87z3JOr+GODasflZV/K8/2/eOs3HOeRPuBXSvqHTsK6grQL6lngcdJB8qIfAx/Idfy8wfLnko4BPkw6ceCBXNalfPbTSxHR8V7kVwNnknY3fZR0zKWRvwHOzsebvkFKvADk40jfAu7Kce8REdeTTu2/Nq/vR0lJDGALUlJfQVoXL5A+H5rO9xMxs34n6f2kD72NOzkppOVIOg0YGRGnFcouBxZGxNcqC6xi/uZpZv1C0l9K2kjSlqRv0DcOlgSSzQUuqzqIunESMbP+ciJp9+jTpP3zJ1UbTv+KiGkR8UTVcdSNd2eZmVlpHomYmVlpTiJmZlZa5VfELGvkyJHR1tZWdRg2yMyePfv5iBjV/Zz14b5gA6GnfaFlk0hbWxuzZs2qOgwbZCTN636uenFfsIHQ077g3VlmZlaak4iZmZXmJGJmZqU5iZiZWWlOImZmVpqTiJmZleYkYmZmpTmJmJlZad0mEUlTJC2V9GihbCtJt0p6Kv/dMpdL0kWS5kh6WNIuhWWOzvM/JenoQvlHJT2Sl7ko3+3MzMxaQE9GIpcDEzuUnQ7cFhHjSPf4Pj2XTyLdtWwccALwQ0hJh3Tnr92B3YAz2xNPnueEwnId2zIzs5rqNolExJ2k2z4WHQRMzc+nAgcXyq+I5F5ghKTtgAnArRGxPCJWALcCE/O0LSLinkjXpL+iUJeZmdVc2WMi20bEYoD8d5tcPpp0v+52C3NZV+ULG5SbmVkL6O8D642OZ0SJ8saVSyfkYyrL5s+fXzJEs9bnvmB1UTaJLMm7osh/l+byhcAOhfnGAIu6KR/ToLyhiLgkIsZFxKixY8eWDN2s9bkvWF2UTSLTgfYzrI4GbiiUH5XP0toDWJV3d80E9pO0ZT6gvh8wM09bLWmPfFbWUYW6zMys5rq9n4ika4C9gJGSFpLOspoMTJP0RWA+cEiefQawPzAHeAU4FiAilks6B7g/z3d2RLQfrD+JdAbYMODm/DAzsxbQbRKJiCM6mbR3g3kDOLmTeqYAUxqUzwJ27i4OMzOrH/9i3czMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxKcxIxM7PSnETMzKw0JxEzMyvNScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxKcxIxM7PSnETMzKw0JxEzMyvNScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxKcxIxM7PSnETMzKy0DaoOoJ2kicCFwPrAjyJicsUhmQ0pbaff1O08cycf0IRIrJXUIolIWh/4AbAvsBC4X9L0iHi8WTH0pAPVkTu1NVNP+4m3y6GjFkkE2A2YExHPAEi6FjgIKJ1EWjUp9FZv3qc79tBURV/wqGboqEsSGQ0sKLxeCOxeUSyDVm8/TNzJbSA1O7l5ex4YdUkialAW68wknQD8IzACeE3SYyXbGwvML7lsf6i6/R7FoPOrbb8JGsWwYxWB9Jb7Qu91sT0PmXXQyxh61BcUsc5nddNJ2hM4KyIm5NdnAETEeQPU3rKIGDUQdbdC+3WIoer26xJD1apeB1W3X4cYqm6/rzHU5RTf+4Fxkt4haSPgcGD6ALa3cgDrboX2ofoYqm4f6hFD1apeB1W3D9XHUHX70IcYarE7KyLWSPoyMJN0iu+UiCg7PO+JVQNYdyu0D9XHUHX7UI8Yqlb1Oqi6fag+hqrbhz7EUIskAhARM4AZTWrukia1U9f2ofoYqm4f6hFD1apeB1W3D9XHUHX70IcYanFMxMzMWlNdjomYmVkLchIxM7PSnETMzKw0JxEzMyvNScTMzEpzEhmiJM2VtE+zlzWrK0m3SzouPz9S0i1Vx9QKnEQqJOkTku6WtErSckl3Sdq1H+o9S9JV/RFjndqywWug+kJZEfGfEbFfVe23ktr82HCokbQF8AvgJGAasBHwSeCPVcZl1mzuC63NI5HqvAcgIq6JiDci4tWIuCUiHm6fQdIXJD0haYWkmZJ2LEy7UNICSS9Kmi3pk7l8IvBV4DBJL0l6qIsYdpX0eK7/MklvK9R/oKQHJa3M3xA/1HHhXrZl1plO+4KkY/Ko5N/zKOX3kvZuX1DScEk/lrRY0rOSzs03uWuf3lUf2jfXt0rS9ylcTTy3+9vC65D0JUlP5bp+IEl52vqSvivpeUl/kPTlPP+Q+JLuJFKdJ4E3JE2VNEnSlsWJkg4mfUB/FhgF/Aa4pjDL/cB4YCvgauA6SW+LiF8C3wZ+EhGbRcSHu4jhSGAC8C5SR/5abnsXYApwIrA18B/AdEkbFxfuZVtmnemyL5DuLfQMMBI4E/gvSVvlaVOBNcC7gY8A+wHtxzU67UOSRgI/I23zI4GngT/rJs4DgV2BDwOHkvoOwPHAJFJ/3AU4uHdvv7U5iVQkIl4EPkG6b8qlwDJJ0yVtm2c5ETgvIp6IiDWkD+vx7d+kIuKqiHghItZExHeBjYH39jKM70fEgohYDnwLOCKXHw/8R0Tcl78ZTiXtWtijD2/ZrKEe9IWlwAUR8XpE/AT4X+CAPH0S8H8j4uWIWAp8j3QVcOi6D+0PPB4RP42I14ELgOe6CXVyRKyMiPnAr0lJA1JCuTAiFkbECmByX9dJK3ESqVDeuI+JiDHAzsD2pI0Z0g1hLsy7k1YCy0nD7dEAkk7Nw/RVefpw0jeq3ijeTXJebr+97VPb287171CYbtavuukLz8baF/lr31Z3BDYEFhe20/8AtsnzddWHtqew/ef6i/2hkWKSeQXYLD9fq64e1DOoOInURET8Hric1IEgbYgnRsSIwmNYRNydj3/8E+kb0JYRMYJ0Kef2fbo9varmDoXnY4FFhba/1aHtTSLimnWr6HFbZj3SoC+Mbj/+kLVvqwtII+SRhe10i4jYKc/XaR8CFlPY/nP9xf7QG4uBMYXXZetpSU4iFZH0vjyaGJNf70DanXRvnuVi4AxJO+XpwyUdkqdtTtoPvAzYQNI3gC0K1S8B2iR19/89WdKYvH/5q8BPcvmlwJck7a5kU0kHSNq8QR09bcusoR70hW2AUyRtmPvA+4EZEbEYuAX4rqQtJK0n6V2SPpWX66oP3QTsJOmz+QD4KcDbS76FacBXJI2WNIL0BW/IcMevzmrSAcP7JL1M6jCPAqcCRMT1wPnAtZJezNMm5WVnAjeTDkjOA15j7SH0dfnvC5Ie6CKGq0md8Jn8ODe3PYt0XOT7wApgDnBMJ3X0tC2zznTZF4D7gHHA86Rjd38VES/kaUeRTgl+nLSt/hTYDrruQxHxPHAI6fjFC7n+u0rGfympHz0M/I50X6Q1wBsl62spvp+ImdWWpGOA4yLiE1XH0lOSJgEXR8SO3c48CHgkYmbWB5KGSdpf0gaSRpNOQ76+6riaxUnEzKxvBHyTtDvtd8ATwDcqjaiJvDvLzMxK80jEzMxKcxIxM7PSWvYCYSNHjoy2traqw7BBZvbs2c9HxKiq4+gN9wUbCD3tCy2bRNra2pg1a1bVYdggI2le1TH0lvuCDYSe9oWWTSJm1v/aTr+pV/PPnXzAAEVircLHRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxK6/Z3IpKmAAcCSyNi51y2FekueG3AXODQiFiRbzF5IbA/6R7Ex0TEA3mZo4Gv5WrPjYipufyjpFthDiPdzOUr4atCmvWL3v7uw6y3ejISuRyY2KHsdOC2iBgH3JZfQ7pr2Lj8OAH4IbyZdM4k3b1sN+BMSVvmZX6Y521frmNbZmZWU90mkYi4E1jeofggYGp+PhU4uFB+RST3AiMkbQdMAG6NiOURsQK4FZiYp20REffk0ccVhbrMzKzmyh4T2TYiFgPkv9vk8tGsfa/vhbmsq/KFDcrNzKwF9PeBdTUoixLljSuXTpD0lKRl8+fPLxmiWetzX7C6KJtEluRdUeS/S3P5QmCHwnxjgEXdlI9pUN5QRFwSEeMiYtTYsWNLhm7W+twXrC7KJpHpwNH5+dHADYXyo5TsAazKu7tmAvtJ2jIfUN8PmJmnrZa0Rz6z66hCXWZmVnM9OcX3GmAvYKSkhaSzrCYD0yR9EZgPHJJnn0E6vXcO6RTfYwEiYrmkc4D783xnR0T7wfqTeOsU35vzw8zMWkC3SSQijuhk0t4N5g3g5E7qmQJMaVA+C9i5uzjMzKx+/It1MzMrzXc2NLPSfCdE80jEzMxKcxIxM7PSnETMzKw0JxEzMyvNScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0nzZEzNrmt5eJgV8qZS680jEzMxK80jEzGrNF3msN49EzMysNCcRMzMrzUnEzMxKcxIxM7PSnETMzKw0JxEzMyvNScTMzEpzEjEzs9L8Y0MzG1T848Tm8kjEzMxKcxIxM7PSnETMzKw0JxEzMyvNScTMzErz2VlmNqT5bK6+8UjEzMxKq81IRNJE4EJgfeBHETG5WW339JuIv4GYma2tFiMRSesDPwAmAR8AjpD0gWqjMjOz7tRlJLIbMCcingGQdC1wEPB4Xyvu7f7O/qjLIxazwcvHUNZWlyQyGlhQeL0Q2L2iWPqsPxNXfxvsG7RZ3ZT5PGilflqXJKIGZbHOTNIJwD8CI4DXJD1Wsr2xwPySy/aHytrX+dXHUJP2O4thxyoC6S33hUEVwzrtF/ppZTHQw76giHU+q5tO0p7AWRExIb8+AyAizhug9pZFxKiBqLsV2q9DDFW3X5cYqlb1Oqi6/TrEUHX7fY2hFgfWgfuBcZLeIWkj4HBg+gC2t3IA626F9qH6GKpuH+oRQ9WqXgdVtw/Vx1B1+9CHGGqxOysi1kj6MjCTdIrvlIgoOzzviVUDWHcrtA/Vx1B1+1CPGKpW9Tqoun2oPoaq24c+xFCLJAIQETOAGU1q7pImtVPX9qH6GKpuH+oRQ9WqXgdVtw/Vx1B1+9CHGGpxTMTMzFpTXY7ocvipAAAEbElEQVSJmJlZC3ISMTOz0pxEzMysNCcRMzMrrTZnZw0EScOBM4CDgfYf0iwFbgAmR8SAnp8taQPgi8BfAtuTfoW/KLf/44h4fSDbzzFUug5yDJWvh0Is25IusxPAoohY0qy2q1b1tlCH7cDrYK1Y+qUvDOqzsyTNBH4FTI2I53LZ24GjgX0iYt8Bbv8a0o94ppKuBwYwJre/VUQcNpDt5xgqXQe5vTqsh/HAxcBw4NlCDCuBv4mIBwY6hqpVvS3UZDvwOujvvhARg/YB/G+ZaU1q/8mhsA5qtB4eBHZvUL4H8FAzYqj6UfW2UJPtwOugn/vCYD8mMk/SaXnYBqQhnKR/Yu2rBg+UFZIOkfTmepa0nqTDgBVNaB+qXwdQj/WwaUTc17EwIu4FNm1SDFWreluow3bgddDPfWGwJ5HDgK2BOyStkLQcuB3YCji0Ce0fDvwVsETSk5KeAp4DPpunNUPV6wDeWg/P5fXwJM1fDzdLuknSYZI+nh+HSboJ+GWTYqha1duC+8Mg7AuD+pgIgKT3kfb33RsRLxXKJ0ZE0z48JG1NuuT9BRHx+Sa2uzvw+4hYJWkT4HRgF+Ax4NsRMeDX7ckX1TyCdADxAdIdLD+eY7gkmnQwUdIk0s3ORpP+FwuB6ZEuuTMkuD9U2x8GY18Y1ElE0inAycATwHjgKxFxQ572QETsMsDtN7oS8adJB/aIiP8zkO3nGB4DPhzpIpeXAC8DPwP2zuWfbUIM/0k6E3AY6UJvmwLX5xgUEUcPdAzm/pBjqLQ/DMa+MKhP8QWOBz4aES9JagN+KqktIi6k8Y2w+tsY0i1+f0Q6jU7ArsB3m9B2u/UiYk1+/rHCB8VvJT3YpBg+GBEfyqc3PgtsHxFvSLoKeKgZARRO7TwI2CYXN/VU5xpwf6i+Pwy6vjDYj4ms3z5kj4i5wF7AJEn/RnM6zceA2cA/A6si4nbg1Yi4IyLuaEL7AI9KOjY/f0jSxwAkvQdo1jnp6+Vh/ObAJqRTCwE2BjZsUgzTSAcu/yIito6IrYG/IJ3WeF2TYqia+0P1/WHw9YVmnFJW1YM0TB7foWwD4ArgjSbGMSb/c74PzG/yOhgOXA48DdxH6ijPAHeQhu/NiOHvcpvzgFOA24BLgUeAM5sUQ+WnOlf9cH+ovj8Mxr4w2I+JjAHWRP5RUYdpfxYRdzU5ngOAP4uIrzaz3dz25sA7SR8aC6PJv9SWtD1ARCySNALYh/QB8j9Nav8W4L9JPzJbksu2BY4B9o2IfZoRR5XcH9Zqu7L+MNj6wqBOImbtJG1JOhOnuB94Cek2zJMjolnn6JtVqr/7gpOIDXmSjo2Iy6qOw6xqZfqCk4gNeZLmR8TYquMwq1qZvjDYT/E1A0DSw51NArbtZJrZoNPffcFJxIaKbYEJrHt9IgF3Nz8cs8r0a19wErGh4hfAZhGxzg/KJN3e/HDMKtOvfcHHRMzMrLTB/ot1MzMbQE4iZmZWmpOImZmV5iRiZmalOYmYmVlp/x9phdDwXDnr2QAAAABJRU5ErkJggg==" alt="" />
In [153]:
ri.head()
Out[153]:
  stop_date stop_time driver_gender driver_age_raw driver_age driver_race violation_raw violation search_conducted search_type stop_outcome is_arrested stop_duration drugs_related_stop frisk stop_datetime stop_time_datetime stop_minutes new_age
0 2005-01-02 01:55 M 1985.0 20.0 White Speeding Speeding False NaN Citation False 0-15 Min False NaN 2005-01-02 2019-04-05 01:55:00 8.0 20.0
1 2005-01-18 08:15 M 1965.0 40.0 White Speeding Speeding False NaN Citation False 0-15 Min False NaN 2005-01-18 2019-04-05 08:15:00 8.0 40.0
2 2005-01-23 23:15 M 1972.0 33.0 White Speeding Speeding False NaN Citation False 0-15 Min False NaN 2005-01-23 2019-04-05 23:15:00 8.0 33.0
3 2005-02-20 17:15 M 1986.0 19.0 White Call for Service Other False NaN Arrest Driver True 16-30 Min False NaN 2005-02-20 2019-04-05 17:15:00 23.0 19.0
4 2005-03-14 10:00 F 1984.0 21.0 White Speeding Speeding False NaN Citation False 0-15 Min False NaN 2005-03-14 2019-04-05 10:00:00 8.0 21.0
In [154]:
ri.tail()
Out[154]:
  stop_date stop_time driver_gender driver_age_raw driver_age driver_race violation_raw violation search_conducted search_type stop_outcome is_arrested stop_duration drugs_related_stop frisk stop_datetime stop_time_datetime stop_minutes new_age
91736 2015-12-31 20:27 M 1986.0 29.0 White Speeding Speeding False NaN Warning False 0-15 Min False NaN 2015-12-31 2019-04-05 20:27:00 8.0 29.0
91737 2015-12-31 20:35 F 1982.0 33.0 White Equipment/Inspection Violation Equipment False NaN Warning False 0-15 Min False NaN 2015-12-31 2019-04-05 20:35:00 8.0 33.0
91738 2015-12-31 20:45 M 1992.0 23.0 White Other Traffic Violation Moving violation False NaN Warning False 0-15 Min False NaN 2015-12-31 2019-04-05 20:45:00 8.0 23.0
91739 2015-12-31 21:42 M 1993.0 22.0 White Speeding Speeding False NaN Citation False 0-15 Min False NaN 2015-12-31 2019-04-05 21:42:00 8.0 22.0
91740 2015-12-31 22:46 M 1959.0 56.0 Hispanic Speeding Speeding False NaN Citation False 0-15 Min False NaN 2015-12-31 2019-04-05 22:46:00 8.0 56.0
In [155]:
ri['new_age']=ri.stop_datetime.dt.year-ri.driver_age_raw
In [156]:
ri[['driver_age','new_age']].hist()
Out[156]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000107FE7F0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000001083C2E8>]],
dtype=object)
 
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" alt="" />
In [157]:
ri[['driver_age','new_age']].describe()
Out[157]:
  driver_age new_age
count 86120.000000 86414.000000
mean 34.011333 39.784294
std 12.738564 110.822145
min 15.000000 -6794.000000
25% 23.000000 24.000000
50% 31.000000 31.000000
75% 43.000000 43.000000
max 99.000000 2015.000000
In [158]:
ri[(ri.new_age<15)|(ri.new_age>99)].shape
Out[158]:
(294, 19)
In [159]:
ri.driver_age_raw.isnull().sum()
Out[159]:
5327
In [160]:
ri.driver_age.isnull().sum()
Out[160]:
5621
In [161]:
5621-5327
Out[161]:
294
In [162]:
ri[(ri.driver_age_raw.notnull())&(ri.driver_age.isnull())].head()
Out[162]:
  stop_date stop_time driver_gender driver_age_raw driver_age driver_race violation_raw violation search_conducted search_type stop_outcome is_arrested stop_duration drugs_related_stop frisk stop_datetime stop_time_datetime stop_minutes new_age
146 2005-10-05 08:50 M 0.0 NaN White Other Traffic Violation Moving violation False NaN Citation False 0-15 Min False NaN 2005-10-05 2019-04-05 08:50:00 8.0 2005.0
281 2005-10-10 12:05 F 0.0 NaN White Other Traffic Violation Moving violation False NaN Warning False 0-15 Min False NaN 2005-10-10 2019-04-05 12:05:00 8.0 2005.0
331 2005-10-12 07:50 M 0.0 NaN White Motorist Assist/Courtesy Other False NaN No Action False 0-15 Min False NaN 2005-10-12 2019-04-05 07:50:00 8.0 2005.0
414 2005-10-17 08:32 M 2005.0 NaN White Other Traffic Violation Moving violation False NaN Citation False 0-15 Min False NaN 2005-10-17 2019-04-05 08:32:00 8.0 0.0
455 2005-10-18 18:30 F 0.0 NaN White Speeding Speeding False NaN Warning False 0-15 Min False NaN 2005-10-18 2019-04-05 18:30:00 8.0 2005.0
In [163]:
ri.loc[(ri.new_age<15)|(ri.new_age>99),'new_age']=np.nan
In [164]:
ri.new_age.equals(ri.driver_age)
Out[164]:
True

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