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Análisis

Crímenes en Chicago

  • Para el siguiente ejercicio utilizaremos la base de datos de crímenes para la zona de Chicago link

  • Se tienen 4 archivos:

    • Chicago_Crimes_2001_to_2004.csv
    • Chicago_Crimes_2005_to_2007.csv
    • Chicago_Crimes_2008_to_2011.csv
    • Chicago_Crimes_2012_to_2017.csv

  • Resolver las siguientes preguntas.

    • Número total de delitos para el rango 2001-2017
    • Gráfica de barras del total de delitos claificados por años
    • Los 5 delitos con mayor número de incidencia




In [1]:



    


# Librerias
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
%matplotlib inline



    











In [2]:



    


# leer archivos
data1 = pd.read_csv('../../data/crimes-in-chicago/Chicago_Crimes_2001_to_2004.csv', low_memory=False)
data2 = pd.read_csv('../../data/crimes-in-chicago/Chicago_Crimes_2005_to_2007.csv', low_memory=False)
data3 = pd.read_csv('../../data/crimes-in-chicago/Chicago_Crimes_2008_to_2011.csv', low_memory=False)
data4 = pd.read_csv('../../data/crimes-in-chicago/Chicago_Crimes_2012_to_2017.csv', low_memory=False)



    











In [3]:



    


# crear un solo dataframe
datap = pd.concat([data1, data2, data3, data4], ignore_index=True)



    











In [4]:



    


# checar integridad del dataframe
datap.head()



    


















    
Out[4]:











  
    

      
      Unnamed: 0
      ID
      Case Number
      Date
      Block
      IUCR
      Primary Type
      Description
      Location Description
      Arrest
      ...
      Ward
      Community Area
      FBI Code
      X Coordinate
      Y Coordinate
      Year
      Updated On
      Latitude
      Longitude
      Location
    

  
  
    

      0
      879
      4786321
      HM399414
      01/01/2004 12:01:00 AM
      082XX S COLES AVE
      0840
      THEFT
      FINANCIAL ID THEFT: OVER $300
      RESIDENCE
      False
      ...
      7.0
      46.0
      06
      NaN
      NaN
      2004.0
      08/17/2015 03:03:40 PM
      NaN
      NaN
      NaN
    

    

      1
      2544
      4676906
      HM278933
      03/01/2003 12:00:00 AM
      004XX W 42ND PL
      2825
      OTHER OFFENSE
      HARASSMENT BY TELEPHONE
      RESIDENCE
      False
      ...
      11.0
      61.0
      26
      1173974.0
      1876757.0
      2003.0
      04/15/2016 08:55:02 AM
      41.817229156
      -87.637328
      (41.817229156, -87.637328162)
    

    

      2
      2919
      4789749
      HM402220
      06/20/2004 11:00:00 AM
      025XX N KIMBALL AVE
      1752
      OFFENSE INVOLVING CHILDREN
      AGG CRIM SEX ABUSE FAM MEMBER
      RESIDENCE
      False
      ...
      35.0
      22.0
      20
      NaN
      NaN
      2004.0
      08/17/2015 03:03:40 PM
      NaN
      NaN
      NaN
    

    

      3
      2927
      4789765
      HM402058
      12/30/2004 08:00:00 PM
      045XX W MONTANA ST
      0840
      THEFT
      FINANCIAL ID THEFT: OVER $300
      OTHER
      False
      ...
      31.0
      20.0
      06
      NaN
      NaN
      2004.0
      08/17/2015 03:03:40 PM
      NaN
      NaN
      NaN
    

    

      4
      3302
      4677901
      HM275615
      05/01/2003 01:00:00 AM
      111XX S NORMAL AVE
      0841
      THEFT
      FINANCIAL ID THEFT:$300 &UNDER
      RESIDENCE
      False
      ...
      34.0
      49.0
      06
      1174948.0
      1831051.0
      2003.0
      04/15/2016 08:55:02 AM
      41.691784636
      -87.635116
      (41.691784636, -87.635115968)
    

  



5 rows × 23 columns



















In [5]:



    


# información del dataframe
datap.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7941283 entries, 0 to 7941282
Data columns (total 23 columns):
Unnamed: 0              int64
ID                      int64
Case Number             object
Date                    object
Block                   object
IUCR                    object
Primary Type            object
Description             object
Location Description    object
Arrest                  bool
Domestic                bool
Beat                    int64
District                float64
Ward                    float64
Community Area          float64
FBI Code                object
X Coordinate            float64
Y Coordinate            object
Year                    float64
Updated On              object
Latitude                object
Longitude               float64
Location                object
dtypes: bool(2), float64(6), int64(3), object(12)
memory usage: 1.3+ GB

















In [6]:



    


# elimnar las columnas que no tienen información relevante
data = datap[['Primary Type', 'Description', 'Location Description', 'Arrest','Domestic', 'Year', 'Date']]



    











In [7]:



    


# checar la integridad del dataFrame
data.head()



    


















    
Out[7]:











  
    

      
      Primary Type
      Description
      Location Description
      Arrest
      Domestic
      Year
      Date
    

  
  
    

      0
      THEFT
      FINANCIAL ID THEFT: OVER $300
      RESIDENCE
      False
      False
      2004.0
      01/01/2004 12:01:00 AM
    

    

      1
      OTHER OFFENSE
      HARASSMENT BY TELEPHONE
      RESIDENCE
      False
      True
      2003.0
      03/01/2003 12:00:00 AM
    

    

      2
      OFFENSE INVOLVING CHILDREN
      AGG CRIM SEX ABUSE FAM MEMBER
      RESIDENCE
      False
      False
      2004.0
      06/20/2004 11:00:00 AM
    

    

      3
      THEFT
      FINANCIAL ID THEFT: OVER $300
      OTHER
      False
      False
      2004.0
      12/30/2004 08:00:00 PM
    

    

      4
      THEFT
      FINANCIAL ID THEFT:$300 &UNDER
      RESIDENCE
      False
      False
      2003.0
      05/01/2003 01:00:00 AM
    

  




















In [8]:



    


# información del dataframe a trabajar
data.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7941283 entries, 0 to 7941282
Data columns (total 7 columns):
Primary Type            object
Description             object
Location Description    object
Arrest                  bool
Domestic                bool
Year                    float64
Date                    object
dtypes: bool(2), float64(1), object(4)
memory usage: 318.1+ MB

















In [9]:



    


# desplegar gráfica de delitos por año
data.groupby('Year').count()['Arrest'].plot.bar()



    


















    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0x1b9e746d8>










    
























In [10]:



    


# aplicar un estilo a la gráfica
plt.style.use('ggplot')



    











In [11]:



    


# clasificación por año
data.groupby('Year').count()['Arrest'].plot.bar()



    


















    
Out[11]:








<matplotlib.axes._subplots.AxesSubplot at 0x10fcef860>










    
























In [12]:



    


# descripción del dataframe
data.describe()



    


















    
Out[12]:











  
    

      
      Year
    

  
  
    

      count
      7.941283e+06
    

    

      mean
      2.007672e+03
    

    

      std
      4.123451e+00
    

    

      min
      4.178983e+01
    

    

      25%
      2.005000e+03
    

    

      50%
      2.008000e+03
    

    

      75%
      2.010000e+03
    

    

      max
      2.017000e+03
    

  




















In [13]:



    


# eliminar datos atípicos
data = data.loc[data['Year'] >= 2001]



    











In [14]:



    


# clasificación por año
data.groupby('Year').count()['Arrest'].plot.bar()



    


















    
Out[14]:








<matplotlib.axes._subplots.AxesSubplot at 0x10fccdc18>










    
























In [15]:



    


# cambiar el tipo de dato de la columna 'Year'
data['Year'] = data['Year'].astype(int)



    











In [16]:



    


# clasificación por año
data.groupby('Year').count()['Arrest'].plot.bar()



    


















    
Out[16]:








<matplotlib.axes._subplots.AxesSubplot at 0x16e421dd8>










    
























In [18]:



    


# generar columnas
for i in np.unique(data['Primary Type']):
    data[i] = [1 if x==i else 0 for x in data['Primary Type']]



    











In [20]:



    


# checar integridad data frame
data.head()



    


















    
Out[20]:











  
    

      
      Primary Type
      Description
      Location Description
      Arrest
      Domestic
      Year
      Date
      ARSON
      ASSAULT
      BATTERY
      ...
      OTHER OFFENSE
      PROSTITUTION
      PUBLIC INDECENCY
      PUBLIC PEACE VIOLATION
      RITUALISM
      ROBBERY
      SEX OFFENSE
      STALKING
      THEFT
      WEAPONS VIOLATION
    

  
  
    

      0
      THEFT
      FINANCIAL ID THEFT: OVER $300
      RESIDENCE
      False
      False
      2004
      01/01/2004 12:01:00 AM
      0
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
    

    

      1
      OTHER OFFENSE
      HARASSMENT BY TELEPHONE
      RESIDENCE
      False
      True
      2003
      03/01/2003 12:00:00 AM
      0
      0
      0
      ...
      1
      0
      0
      0
      0
      0
      0
      0
      0
      0
    

    

      2
      OFFENSE INVOLVING CHILDREN
      AGG CRIM SEX ABUSE FAM MEMBER
      RESIDENCE
      False
      False
      2004
      06/20/2004 11:00:00 AM
      0
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
    

    

      3
      THEFT
      FINANCIAL ID THEFT: OVER $300
      OTHER
      False
      False
      2004
      12/30/2004 08:00:00 PM
      0
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
    

    

      4
      THEFT
      FINANCIAL ID THEFT:$300 &UNDER
      RESIDENCE
      False
      False
      2003
      05/01/2003 01:00:00 AM
      0
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
    

  



5 rows × 42 columns



















In [22]:



    


# checar columnas
data.columns



    


















    
Out[22]:








Index(['Primary Type', 'Description', 'Location Description', 'Arrest',
       'Domestic', 'Year', 'Date', 'ARSON', 'ASSAULT', 'BATTERY', 'BURGLARY',
       'CONCEALED CARRY LICENSE VIOLATION', 'CRIM SEXUAL ASSAULT',
       'CRIMINAL DAMAGE', 'CRIMINAL TRESPASS', 'DECEPTIVE PRACTICE',
       'DOMESTIC VIOLENCE', 'GAMBLING', 'HOMICIDE', 'HUMAN TRAFFICKING',
       'INTERFERENCE WITH PUBLIC OFFICER', 'INTIMIDATION', 'KIDNAPPING',
       'LIQUOR LAW VIOLATION', 'MOTOR VEHICLE THEFT', 'NARCOTICS',
       'NON - CRIMINAL', 'NON-CRIMINAL', 'NON-CRIMINAL (SUBJECT SPECIFIED)',
       'OBSCENITY', 'OFFENSE INVOLVING CHILDREN', 'OTHER NARCOTIC VIOLATION',
       'OTHER OFFENSE', 'PROSTITUTION', 'PUBLIC INDECENCY',
       'PUBLIC PEACE VIOLATION', 'RITUALISM', 'ROBBERY', 'SEX OFFENSE',
       'STALKING', 'THEFT', 'WEAPONS VIOLATION'],
      dtype='object')

















In [24]:



    


# clasificación por año
data.groupby('Year').sum()[['LIQUOR LAW VIOLATION','SEX OFFENSE']].plot.bar()



    


















    
Out[24]:








<matplotlib.axes._subplots.AxesSubplot at 0x16841dc18>










    
























In [26]:



    


# scatter plot
data.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 7941282 entries, 0 to 7941282
Data columns (total 42 columns):
Primary Type                         object
Description                          object
Location Description                 object
Arrest                               bool
Domestic                             bool
Year                                 int64
Date                                 object
ARSON                                int64
ASSAULT                              int64
BATTERY                              int64
BURGLARY                             int64
CONCEALED CARRY LICENSE VIOLATION    int64
CRIM SEXUAL ASSAULT                  int64
CRIMINAL DAMAGE                      int64
CRIMINAL TRESPASS                    int64
DECEPTIVE PRACTICE                   int64
DOMESTIC VIOLENCE                    int64
GAMBLING                             int64
HOMICIDE                             int64
HUMAN TRAFFICKING                    int64
INTERFERENCE WITH PUBLIC OFFICER     int64
INTIMIDATION                         int64
KIDNAPPING                           int64
LIQUOR LAW VIOLATION                 int64
MOTOR VEHICLE THEFT                  int64
NARCOTICS                            int64
NON - CRIMINAL                       int64
NON-CRIMINAL                         int64
NON-CRIMINAL (SUBJECT SPECIFIED)     int64
OBSCENITY                            int64
OFFENSE INVOLVING CHILDREN           int64
OTHER NARCOTIC VIOLATION             int64
OTHER OFFENSE                        int64
PROSTITUTION                         int64
PUBLIC INDECENCY                     int64
PUBLIC PEACE VIOLATION               int64
RITUALISM                            int64
ROBBERY                              int64
SEX OFFENSE                          int64
STALKING                             int64
THEFT                                int64
WEAPONS VIOLATION                    int64
dtypes: bool(2), int64(36), object(4)
memory usage: 2.4+ GB

















In [ ]:



    


data.sum()



    











In [ ]:

Content source: jorgemauricio/INIFAP_Course

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