In [1]:

    

import pandas


    

In [2]:

    

df = pandas.read_csv("./Cluster-Crime-Janeiro.csv")


    

In [3]:

    

novo = df[df['CLUSTER'] == 1] #Fazer For loop para percorrer todas regiões


    

In [4]:

    

crime_hours = novo[['HORA']]


    

In [5]:

    

crime_hours_total = crime_hours.groupby('HORA').size()


    

In [6]:

    

crime_hours_counts = novo[['HORA']].groupby('HORA').sum()


    

In [7]:

    

crime_hours_counts['TOTAL'] = crime_hours_total


    

In [8]:

    

all_hours_types = crime_hours_counts.sort_values(by='TOTAL', ascending=False)


    

In [9]:

    

all_hours_types


    

    
Out[9]:







  

    

      

      
TOTAL
    
    

      
HORA
      

    
  
  

    

      
17
      
4
    
    

      
13
      
3
    
    

      
11
      
2
    
    

      
14
      
2
    
    

      
18
      
2
    
    

      
02
      
1
    
    

      
05
      
1
    
    

      
06
      
1
    
    

      
08
      
1
    
    

      
09
      
1
    
    

      
10
      
1
    
    

      
15
      
1
    
    

      
19
      
1
    
    

      
20
      
1
    
  

In [10]:

    

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as ticker


    

In [15]:

    

all_hours_types.plot(kind='barh', figsize=(10,8), color='#cc0000')
plt.title('Crimes by Hour (Jan 2017)')
plt.xlabel('Número de ocorrências')
plt.ylabel('Hora do dia')
plt.tight_layout()
ax = plt.gca()
ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))
plt.show()


    

In [18]:

    

horas_mes = df.HORA.value_counts()


    

In [19]:

    

horas_mes.plot(kind='barh', figsize=(10,8), color='#cc0000')
plt.title('Crimes by Hour (Jan 2017)')
plt.xlabel('Número de ocorrências')
plt.ylabel('Hora do dia')
plt.tight_layout()
ax = plt.gca()
ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))
plt.show()


    

In [ ]: