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From version 2.0 we hava our clenaed data with no rows and columns with total values we have pure data assoctated wth each crime bow lets sepearte all crimes individually

Note file name:

>>crimescleaned.csv



In [1]:

    
#Include libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd



In [22]:

    
crimes = pd.read_csv('crimescleaned.csv', index_col=0)
crimes.head() #seems and unwanted column is added









    Out[22]:






  
    
      
      STATE/UT
      CRIME HEAD
      2001
      2002
      2003
      2004
      2005
      2006
      2007
      2008
      2009
      2010
      2011
      2012
    
  
  
    
      0
          ANDHRA PRADESH
       RAPE
       871
       1002
        946
       1016
        935
       1049
       1070
       1257
       1188
       1362
       1442
       1341
    
    
      1
       ARUNACHAL PRADESH
       RAPE
        33
         38
         31
         42
         35
         37
         48
         42
         59
         47
         42
         46
    
    
      2
                   ASSAM
       RAPE
       817
        970
       1095
       1171
       1238
       1244
       1437
       1438
       1631
       1721
       1700
       1716
    
    
      3
                   BIHAR
       RAPE
       888
       1040
        985
       1390
       1147
       1232
       1555
       1302
        929
        795
        934
        927
    
    
      4
            CHHATTISGARH
       RAPE
       959
        992
        898
        969
        990
        995
        982
        978
        976
       1012
       1053
       1034



In [31]:

    
pd.unique(crimes['CRIME HEAD'])









    Out[31]:





array(['RAPE', 'KIDNAPPING and ABDUCTION', 'DOWRY DEATHS',
       'ASSAULT ON WOMEN WITH INTENT TO OUTRAGE HER MODESTY',
       'INSULT TO THE MODESTY OF WOMEN',
       'CRUELTY BY HUSBAND OR HIS RELATIVES (IPC SECTION 498A)',
       'IMPORTATION OF GIRLS FROM FOREIGN COUNTRY',
       'IMMORAL TRAFFIC (P) ACT', 'DOWRY PROHIBITION ACT',
       'INDECENT REPRESENTATION OF WOMEN (P) ACT',
       'COMMISSION OF SATI PREVENTION ACT'], dtype=object)

Now lets seprate each crime from the data and make sperate datframe and then csv files



In [43]:

    
crimelist = pd.unique(crimes['CRIME HEAD'])
crimelist









    Out[43]:





array(['RAPE', 'KIDNAPPING and ABDUCTION', 'DOWRY DEATHS',
       'ASSAULT ON WOMEN WITH INTENT TO OUTRAGE HER MODESTY',
       'INSULT TO THE MODESTY OF WOMEN',
       'CRUELTY BY HUSBAND OR HIS RELATIVES (IPC SECTION 498A)',
       'IMPORTATION OF GIRLS FROM FOREIGN COUNTRY',
       'IMMORAL TRAFFIC (P) ACT', 'DOWRY PROHIBITION ACT',
       'INDECENT REPRESENTATION OF WOMEN (P) ACT',
       'COMMISSION OF SATI PREVENTION ACT'], dtype=object)



In [61]:

    
#rape,kidnapping  = crimes[crimes['CRIME HEAD']=='RAPE'], crimes[crimes['CRIME HEAD']=='KIDNAPPING and ABDUCTION']
for crime in crimelist:
    print crime









    



RAPE
KIDNAPPING and ABDUCTION
DOWRY DEATHS
ASSAULT ON WOMEN WITH INTENT TO OUTRAGE HER MODESTY
INSULT TO THE MODESTY OF WOMEN
CRUELTY BY HUSBAND OR HIS RELATIVES (IPC SECTION 498A)
IMPORTATION OF GIRLS FROM FOREIGN COUNTRY
IMMORAL TRAFFIC (P) ACT
DOWRY PROHIBITION ACT
INDECENT REPRESENTATION OF WOMEN (P) ACT
COMMISSION OF SATI PREVENTION ACT



In [65]:

    
for crime in crimelist:
    df = crimes[crimes['CRIME HEAD']== crime]
    df.to_csv(crime+'.csv')

Now we have a seperate csv files with different crimes which we can load sperately whenever we want



In [74]:

    
import os
DIR_PATH = os.getcwd() + '//crimes'



In [75]:

    
DIR_PATH









    Out[75]:





'F:\\Educational Stuff\\Hebi\\Machine Learning and Data Science\\Data Science\\My_Work\\crime_against_women//crimes'



In [86]:

    
for filenames in (os.walk(DIR_PATH)):
    for filename in filenames:
        print filename









    



F:\Educational Stuff\Hebi\Machine Learning and Data Science\Data Science\My_Work\crime_against_women//crimes
[]
['ASSAULT ON WOMEN WITH INTENT TO OUTRAGE HER MODESTY.csv', 'COMMISSION OF SATI PREVENTION ACT.csv', 'CRUELTY BY HUSBAND OR HIS RELATIVES (IPC SECTION 498A).csv', 'DOWRY DEATHS.csv', 'DOWRY PROHIBITION ACT.csv', 'IMMORAL TRAFFIC (P) ACT.csv', 'IMPORTATION OF GIRLS FROM FOREIGN COUNTRY.csv', 'INDECENT REPRESENTATION OF WOMEN (P) ACT.csv', 'INSULT TO THE MODESTY OF WOMEN.csv', 'KIDNAPPING and ABDUCTION.csv', 'RAPE.csv']



In [88]:

    
#This sounds Cool
for filename in os.listdir(DIR_PATH):
    print  filename









    



ASSAULT ON WOMEN WITH INTENT TO OUTRAGE HER MODESTY.csv
COMMISSION OF SATI PREVENTION ACT.csv
CRUELTY BY HUSBAND OR HIS RELATIVES (IPC SECTION 498A).csv
DOWRY DEATHS.csv
DOWRY PROHIBITION ACT.csv
IMMORAL TRAFFIC (P) ACT.csv
IMPORTATION OF GIRLS FROM FOREIGN COUNTRY.csv
INDECENT REPRESENTATION OF WOMEN (P) ACT.csv
INSULT TO THE MODESTY OF WOMEN.csv
KIDNAPPING and ABDUCTION.csv
RAPE.csv



In [ ]:

	STATE/UT	CRIME HEAD	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012
0	ANDHRA PRADESH	RAPE	871	1002	946	1016	935	1049	1070	1257	1188	1362	1442	1341
1	ARUNACHAL PRADESH	RAPE	33	38	31	42	35	37	48	42	59	47	42	46
2	ASSAM	RAPE	817	970	1095	1171	1238	1244	1437	1438	1631	1721	1700	1716
3	BIHAR	RAPE	888	1040	985	1390	1147	1232	1555	1302	929	795	934	927
4	CHHATTISGARH	RAPE	959	992	898	969	990	995	982	978	976	1012	1053	1034