notebook.community

Edit and run



In [2]:

    
import pandas as pd
import math, time, datetime
from dateutil.parser import parse
import dateutil
import locale
locale.setlocale(locale.LC_ALL, 'en_US')
import re
import matplotlib.pyplot as plt
%matplotlib inline



In [3]:

    
!head -n 10000 violations.csv > small-violations.csv



In [4]:

    
df = pd.read_csv("small-violations.csv")



In [5]:

    
df.head()









    Out[5]:






  
    
      
      Summons Number
      Plate ID
      Registration State
      Plate Type
      Issue Date
      Violation Code
      Vehicle Body Type
      Vehicle Make
      Issuing Agency
      Street Code1
      ...
      Vehicle Color
      Unregistered Vehicle?
      Vehicle Year
      Meter Number
      Feet From Curb
      Violation Post Code
      Violation Description
      No Standing or Stopping Violation
      Hydrant Violation
      Double Parking Violation
    
  
  
    
      0
      1283294138
      GBB9093
      NY
      PAS
      08/04/2013
      46
      SUBN
      AUDI
      P
      37250
      ...
      GY
      0
      2013
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      1
      1283294151
      62416MB
      NY
      COM
      08/04/2013
      46
      VAN
      FORD
      P
      37290
      ...
      WH
      0
      2012
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      2
      1283294163
      78755JZ
      NY
      COM
      08/05/2013
      46
      P-U
      CHEVR
      P
      37030
      ...
      NaN
      0
      0
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      3
      1283294175
      63009MA
      NY
      COM
      08/05/2013
      46
      VAN
      FORD
      P
      37270
      ...
      WH
      0
      2010
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      4
      1283294187
      91648MC
      NY
      COM
      08/08/2013
      41
      TRLR
      GMC
      P
      37240
      ...
      BR
      0
      2012
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
  

5 rows × 43 columns



In [6]:

    
df['Date First Observed']









    Out[6]:





0              0
1              0
2              0
3              0
4              0
5              0
6              0
7              0
8              0
9              0
10             0
11             0
12             0
13             0
14             0
15             0
16             0
17             0
18             0
19             0
20             0
21             0
22             0
23             0
24             0
25             0
26             0
27             0
28             0
29             0
          ...   
9969    20130723
9970           0
9971           0
9972           0
9973           0
9974           0
9975           0
9976           0
9977           0
9978           0
9979           0
9980           0
9981           0
9982           0
9983           0
9984           0
9985           0
9986           0
9987           0
9988           0
9989           0
9990           0
9991           0
9992           0
9993           0
9994           0
9995           0
9996           0
9997           0
9998           0
Name: Date First Observed, dtype: int64



In [7]:

    
df.columns









    Out[7]:





Index(['Summons Number', 'Plate ID', 'Registration State', 'Plate Type',
       'Issue Date', 'Violation Code', 'Vehicle Body Type', 'Vehicle Make',
       'Issuing Agency', 'Street Code1', 'Street Code2', 'Street Code3',
       'Vehicle Expiration Date', 'Violation Location', 'Violation Precinct',
       'Issuer Precinct', 'Issuer Code', 'Issuer Command', 'Issuer Squad',
       'Violation Time', 'Time First Observed', 'Violation County',
       'Violation In Front Of Or Opposite', 'House Number', 'Street Name',
       'Intersecting Street', 'Date First Observed', 'Law Section',
       'Sub Division', 'Violation Legal Code', 'Days Parking In Effect    ',
       'From Hours In Effect', 'To Hours In Effect', 'Vehicle Color',
       'Unregistered Vehicle?', 'Vehicle Year', 'Meter Number',
       'Feet From Curb', 'Violation Post Code', 'Violation Description',
       'No Standing or Stopping Violation', 'Hydrant Violation',
       'Double Parking Violation'],
      dtype='object')

1. I want to make sure my Plate ID is a string. Can't lose the leading zeroes!



In [8]:

    
df.dtypes #dtype: Data type for data or columns









    Out[8]:





Summons Number                         int64
Plate ID                              object
Registration State                    object
Plate Type                            object
Issue Date                            object
Violation Code                         int64
Vehicle Body Type                     object
Vehicle Make                          object
Issuing Agency                        object
Street Code1                           int64
Street Code2                           int64
Street Code3                           int64
Vehicle Expiration Date                int64
Violation Location                   float64
Violation Precinct                     int64
Issuer Precinct                        int64
Issuer Code                            int64
Issuer Command                        object
Issuer Squad                           int64
Violation Time                        object
Time First Observed                   object
Violation County                      object
Violation In Front Of Or Opposite     object
House Number                          object
Street Name                           object
Intersecting Street                   object
Date First Observed                    int64
Law Section                            int64
Sub Division                          object
Violation Legal Code                 float64
Days Parking In Effect                object
From Hours In Effect                  object
To Hours In Effect                    object
Vehicle Color                         object
Unregistered Vehicle?                  int64
Vehicle Year                           int64
Meter Number                          object
Feet From Curb                         int64
Violation Post Code                  float64
Violation Description                float64
No Standing or Stopping Violation    float64
Hydrant Violation                    float64
Double Parking Violation             float64
dtype: object



In [9]:

    
print("The data type is",(type(df['Plate ID'][0])))









    



The data type is <class 'str'>

2. I don't think anyone's car was built in 0AD. Discard the '0's as NaN.



In [10]:

    
df['Vehicle Year'] = df['Vehicle Year'].replace("0","NaN") #str.replace(old, new[, max])
df.head()









    Out[10]:






  
    
      
      Summons Number
      Plate ID
      Registration State
      Plate Type
      Issue Date
      Violation Code
      Vehicle Body Type
      Vehicle Make
      Issuing Agency
      Street Code1
      ...
      Vehicle Color
      Unregistered Vehicle?
      Vehicle Year
      Meter Number
      Feet From Curb
      Violation Post Code
      Violation Description
      No Standing or Stopping Violation
      Hydrant Violation
      Double Parking Violation
    
  
  
    
      0
      1283294138
      GBB9093
      NY
      PAS
      08/04/2013
      46
      SUBN
      AUDI
      P
      37250
      ...
      GY
      0
      2013
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      1
      1283294151
      62416MB
      NY
      COM
      08/04/2013
      46
      VAN
      FORD
      P
      37290
      ...
      WH
      0
      2012
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      2
      1283294163
      78755JZ
      NY
      COM
      08/05/2013
      46
      P-U
      CHEVR
      P
      37030
      ...
      NaN
      0
      NaN
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      3
      1283294175
      63009MA
      NY
      COM
      08/05/2013
      46
      VAN
      FORD
      P
      37270
      ...
      WH
      0
      2010
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
    
      4
      1283294187
      91648MC
      NY
      COM
      08/08/2013
      41
      TRLR
      GMC
      P
      37240
      ...
      BR
      0
      2012
      -
      0
      NaN
      NaN
      NaN
      NaN
      NaN
    
  

5 rows × 43 columns

3. I want the dates to be dates! Read the read_csv documentation to find out how to make pandas automatically parse dates.



In [67]:

    
# Function to use for converting a sequence of string columns to an array of datetime instances: dateutil.parser.parser



In [11]:

    
type(df['Issue Date'][0])









    Out[11]:





str



In [12]:

    
def to_dates(date):
    yourdate = dateutil.parser.parse(date)
    return yourdate
df['Issue Date Converted'] = df['Issue Date'].apply(to_dates) #DataFrame.apply(func):apply function to apply to each column/row
df['Issue Date Converted'].head()









    Out[12]:





0   2013-08-04
1   2013-08-04
2   2013-08-05
3   2013-08-05
4   2013-08-08
Name: Issue Date Converted, dtype: datetime64[ns]

4. "Date first observed" is a pretty weird column, but it seems like it has a date hiding inside. Using a function with .apply, transform the string (e.g. "20140324") into a Python date. Make the 0's show up as NaN.



In [13]:

    
df['Date First Observed'].tail()









    Out[13]:





9994    0
9995    0
9996    0
9997    0
9998    0
Name: Date First Observed, dtype: int64



In [14]:

    
import numpy as np #numpy object
def pydate(num):
    num = str(num) #to work with dateutil.parser.parse():it has to be a string
    print(num)
    if num == "0":
        print("replacing 0")
        return np.NaN #if number==0,replace 0 with NaN
    else:
        print("parsing date")
        yourdate = dateutil.parser.parse(num)#recognize the string as a time object
        strf = yourdate.strftime("%Y-%B-%d")#strftime turns a time object into a date and time format
        print(strf)
        return strf



In [15]:

    
df['Date First Observed Converted'] = df['Date First Observed'].apply(pydate)









    



0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
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0
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0
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0
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replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130719
parsing date
2013-July-19
20130719
parsing date
2013-July-19
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130715
parsing date
2013-July-15
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130804
parsing date
2013-August-04
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
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replacing 0
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replacing 0
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replacing 0
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replacing 0
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replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130717
parsing date
2013-July-17
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
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replacing 0
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replacing 0
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replacing 0
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replacing 0
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replacing 0
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replacing 0
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replacing 0
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replacing 0
0
replacing 0
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replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130715
parsing date
2013-July-15
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
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0
replacing 0
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replacing 0
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0
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0
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0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130726
parsing date
2013-July-26
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
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0
replacing 0
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replacing 0
0
replacing 0
0
replacing 0
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replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130721
parsing date
2013-July-21
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
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0
replacing 0
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replacing 0
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0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
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0
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replacing 0
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replacing 0
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0
replacing 0
0
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replacing 0
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0
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0
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0
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0
replacing 0
0
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0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130713
parsing date
2013-July-13
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
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0
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0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130809
parsing date
2013-August-09
0
replacing 0
0
replacing 0
0
replacing 0
20130809
parsing date
2013-August-09
20130809
parsing date
2013-August-09
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130810
parsing date
2013-August-10
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130811
parsing date
2013-August-11
20130811
parsing date
2013-August-11
0
replacing 0
0
replacing 0
20130727
parsing date
2013-July-27
20130801
parsing date
2013-August-01
20130717
parsing date
2013-July-17
20130801
parsing date
2013-August-01
20130801
parsing date
2013-August-01
20130801
parsing date
2013-August-01
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130806
parsing date
2013-August-06
0
replacing 0
20130812
parsing date
2013-August-12
20130813
parsing date
2013-August-13
0
replacing 0
20130722
parsing date
2013-July-22
20130722
parsing date
2013-July-22
0
replacing 0
20130806
parsing date
2013-August-06
20130801
parsing date
2013-August-01
20130801
parsing date
2013-August-01
20130729
parsing date
2013-July-29
0
replacing 0
20130729
parsing date
2013-July-29
20130805
parsing date
2013-August-05
20130731
parsing date
2013-July-31
0
replacing 0
0
replacing 0
0
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0
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0
replacing 0
20130805
parsing date
2013-August-05
20130802
parsing date
2013-August-02
0
replacing 0
20130813
parsing date
2013-August-13
20130815
parsing date
2013-August-15
0
replacing 0
0
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0
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0
replacing 0
20130725
parsing date
2013-July-25
20130802
parsing date
2013-August-02
0
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0
replacing 0
20130721
parsing date
2013-July-21
0
replacing 0
20130731
parsing date
2013-July-31
20130807
parsing date
2013-August-07
20130811
parsing date
2013-August-11
0
replacing 0
0
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0
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0
replacing 0
20130718
parsing date
2013-July-18
0
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0
replacing 0
0
replacing 0
20130810
parsing date
2013-August-10
20130811
parsing date
2013-August-11
20130718
parsing date
2013-July-18
20130720
parsing date
2013-July-20
20130812
parsing date
2013-August-12
20130721
parsing date
2013-July-21
0
replacing 0
0
replacing 0
20130720
parsing date
2013-July-20
20130814
parsing date
2013-August-14
0
replacing 0
0
replacing 0
20130807
parsing date
2013-August-07
0
replacing 0
20130721
parsing date
2013-July-21
0
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0
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0
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0
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20130717
parsing date
2013-July-17
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20130809
parsing date
2013-August-09
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20130724
parsing date
2013-July-24
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parsing date
2013-July-18
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20130801
parsing date
2013-August-01
20310801
parsing date
2031-August-01
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20130822
parsing date
2013-August-22
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20130709
parsing date
2013-July-09
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20130710
parsing date
2013-July-10
20130712
parsing date
2013-July-12
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20130727
parsing date
2013-July-27
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parsing date
2013-August-17
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2013-August-17
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parsing date
2013-July-23
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20130801
parsing date
2013-August-01
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parsing date
2013-August-06
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2013-August-02
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20130721
parsing date
2013-July-21
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20130719
parsing date
2013-July-19
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parsing date
2013-June-22
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20130624
parsing date
2013-June-24
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20130718
parsing date
2013-July-18
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20130717
parsing date
2013-July-17
20130717
parsing date
2013-July-17
20130717
parsing date
2013-July-17
20130814
parsing date
2013-August-14
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2013-August-08
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2013-August-06
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parsing date
2013-July-31
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parsing date
2013-August-02
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2013-August-08
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20130730
parsing date
2013-July-30
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20130723
parsing date
2013-July-23
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20130812
parsing date
2013-August-12
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parsing date
2013-July-20
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2013-July-22
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2013-August-03
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2013-August-03
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parsing date
2013-August-14
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2013-August-13
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2013-August-06
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parsing date
2013-August-08
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parsing date
2013-August-12
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2013-July-21
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parsing date
2013-August-02
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parsing date
2013-August-09
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parsing date
2013-August-21
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parsing date
2013-August-13
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2013-July-12
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20130822
parsing date
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parsing date
2013-July-27
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20130813
parsing date
2013-August-13
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20130802
parsing date
2013-August-02
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2013-August-02
20130726
parsing date
2013-July-26
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20130821
parsing date
2013-August-21
20130821
parsing date
2013-August-21
20130821
parsing date
2013-August-21
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20130725
parsing date
2013-July-25
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20130724
parsing date
2013-July-24
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parsing date
2013-July-30
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20130813
parsing date
2013-August-13
20130813
parsing date
2013-August-13
20130814
parsing date
2013-August-14
20130814
parsing date
2013-August-14
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parsing date
2013-July-24
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parsing date
2013-August-10
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parsing date
2013-July-24
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parsing date
2013-August-10
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parsing date
2013-August-17
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parsing date
2013-July-17
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20130817
parsing date
2013-August-17
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20130717
parsing date
2013-July-17
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20130711
parsing date
2013-July-11
20130711
parsing date
2013-July-11
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20130724
parsing date
2013-July-24
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parsing date
2013-July-22
20130811
parsing date
2013-August-11
20130802
parsing date
2013-August-02
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parsing date
2013-August-10
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20130722
parsing date
2013-July-22
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20130731
parsing date
2013-July-31
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20130724
parsing date
2013-July-24
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20130728
parsing date
2013-July-28
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2013-August-03
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parsing date
2013-August-05
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2013-August-12
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parsing date
2013-August-12
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parsing date
2013-August-12
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20130807
parsing date
2013-August-07
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2013-August-14
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parsing date
2013-July-08
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2013-July-31
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2013-July-31
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parsing date
2013-August-15
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parsing date
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parsing date
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parsing date
2013-July-23
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2013-July-25
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2013-July-23
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2013-August-14
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2013-July-18
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parsing date
2013-July-31
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2013-July-22
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2013-July-22
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2013-July-22
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parsing date
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0
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0
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0
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0
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0
replacing 0
0
replacing 0
20130718
parsing date
2013-July-18
0
replacing 0
20130809
parsing date
2013-August-09
20130809
parsing date
2013-August-09
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
0
replacing 0
20130731
parsing date
2013-July-31
20130731
parsing date
2013-July-31
20130731
parsing date
2013-July-31
0
replacing 0
0
replacing 0
0
replacing 0
20130804
parsing date
2013-August-04
20130805
parsing date
2013-August-05
20130811
parsing date
2013-August-11
20130811
parsing date
2013-August-11
20130813
parsing date
2013-August-13
20130814
parsing date
2013-August-14
20130730
parsing date
2013-July-30
0
replacing 0
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20130719
parsing date
2013-July-19
0
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0
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0
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0
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0
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0
replacing 0
20130812
parsing date
2013-August-12
0
replacing 0
0
replacing 0
20130812
parsing date
2013-August-12
0
replacing 0
0
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0
replacing 0
20130813
parsing date
2013-August-13
0
replacing 0
20130723
parsing date
2013-July-23
20130716
parsing date
2013-July-16
0
replacing 0
0
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20130716
parsing date
2013-July-16
0
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20130802
parsing date
2013-August-02
0
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0
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20130722
parsing date
2013-July-22
0
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20130803
parsing date
2013-August-03
20130722
parsing date
2013-July-22
0
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20130720
parsing date
2013-July-20
0
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0
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0
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0
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20130718
parsing date
2013-July-18
20130718
parsing date
2013-July-18
20130718
parsing date
2013-July-18
0
replacing 0
0
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20130716
parsing date
2013-July-16
0
replacing 0
20130731
parsing date
2013-July-31
0
replacing 0
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20130723
parsing date
2013-July-23
0
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5. "Violation time" is... not a time. Make it a time.



In [16]:

    
df['Violation Time'].head()









    Out[16]:





0    0752A
1    1240P
2    1243P
3    0232P
4    1239P
Name: Violation Time, dtype: object



In [17]:

    
type(df['Violation Time'][0])









    Out[17]:





str



In [18]:

    
def str_to_time(time_str):
    s = str(time_str).replace("P"," PM").replace("A"," AM") #str(time_str) because str.replace()
    x = s[:2] + ":" + s[2:] 
    return x
str_to_time("1239P")









    Out[18]:





'12:39 PM'



In [19]:

    
df['Violation Time Converted'] = df['Violation Time'].apply(str_to_time)



In [20]:

    
df['Violation Time Converted']









    Out[20]:





0       07:52 AM
1       12:40 PM
2       12:43 PM
3       02:32 PM
4       12:39 PM
5       06:17 PM
6       07:41 PM
7       04:25 AM
8       04:37 AM
9       08:39 AM
10      08:45 AM
11      09:07 AM
12      05:14 PM
13      06:56 PM
14      11:45 PM
15      05:46 PM
16      11:42 AM
17      07:24 AM
18      07:58 AM
19      07:36 AM
20      08:47 AM
21      11:20 AM
22      10:20 AM
23      03:24 PM
24      10:18 AM
25      07:43 AM
26      08:50 AM
27      09:15 AM
28      10:23 AM
29      11:20 AM
          ...   
9969    12:51 PM
9970    06:45 PM
9971    02:30 AM
9972    03:15 AM
9973    02:30 AM
9974    12:13 PM
9975    08:30 AM
9976    03:05 AM
9977    08:55 AM
9978    10:21 AM
9979    03:30 AM
9980    05:24 PM
9981    05:05 PM
9982    10:18 PM
9983    10:18 PM
9984    02:15 AM
9985    01:30 AM
9986    10:20 PM
9987    10:00 PM
9988    10:00 PM
9989    10:30 PM
9990    11:15 AM
9991    08:42 AM
9992    11:50 AM
9993    11:41 AM
9994    11:40 AM
9995    03:15 PM
9996    03:10 PM
9997    10:26 AM
9998    01:48 PM
Name: Violation Time Converted, dtype: object

6. There sure are a lot of colors of cars, too bad so many of them are the same. Make "BLK" and "BLACK", "WT" and "WHITE", and any other combinations that you notice.



In [21]:

    
df['Vehicle Color'].value_counts()









    Out[21]:





WHITE    1753
BLACK     896
GY        637
BLUE      607
GRAY      553
RED       525
WH        513
BLK       487
BK        450
SILVE     392
GREY      363
BROWN     305
WHT       299
GREEN     268
BL        192
TAN       173
GRY       167
BR        116
GOLD      116
YELLO     114
RD        104
SILVR      83
WT         62
GR         57
GRN        51
TN         45
ORANG      41
BRN        34
SILV       34
SIL        29
         ... 
TH          1
YW/BL       1
LT.GR       1
SLIVE       1
BU          1
BLCK        1
NAVY/       1
BUR         1
GY/BE       1
B;L         1
G/Y         1
BEGE        1
BG          1
SROWN       1
BWN         1
GD          1
B LUE       1
GREW        1
CHAMP       1
WWHIT       1
METBL       1
LIGHT       1
TEAL        1
WYH         1
B           1
BLE         1
VLACK       1
BIRG        1
MAR         1
BT          1
Name: Vehicle Color, dtype: int64



In [22]:

    
def to_color(color):
    if color=='BLK'or color=='BK'or color=='BLCK':
        return "BLACK"
    if color=='WH' or color=='WHT'or color=='WT':
        return "WHITE"
    if color=='GY'or color=='GREY'or color=='GRY':
        return "GRAY"
    if color=='BL'or color=='BLE' or color=='B LUE' or color=='BU':
        return "BLUE"
    if color=='BR' or color == 'BRN' or color == 'BRWN':
        return "BROWN"
    if color== 'SILV' or color ==  'SIL' or color == 'SILVR' or color == 'SILVE' or color == 'SL':
        return "SILVER"
    else:
        return color



In [23]:

    
df['Vehicle Color'] = df['Vehicle Color'].apply(to_color)

7. Join the data with the Parking Violations Code dataset from the NYC Open Data site.



In [25]:

    
df2=pd.read_csv("DOF_Parking_Violation_Codes.csv")



In [26]:

    
df2.head()









    Out[26]:






  
    
      
      CODE
      DEFINITION
      Manhattan  96th St. & below
      All Other Areas
    
  
  
    
      0
      10
      Stopping, standing or parking where a sign, st...
      $115
      $115
    
    
      1
      11
      Hotel Loading/Unloading: Standing or parking w...
      $115
      $115
    
    
      2
      12
      Snow Emergency: Standing or parking where stan...
      $95
      $95
    
    
      3
      13
      Taxi Stand: Standing or parking where standing...
      $115
      $115
    
    
      4
      14
      General No Standing: Standing or parking where...
      $115
      $115



In [32]:

    
df2.iloc[[38]]#how to select a row from dataframe









    Out[32]:






  
    
      
      CODE
      DEFINITION
      Manhattan  96th St. & below
      All Other Areas
    
  
  
    
      38
      53
      Standing or parking in a safety zone, between ...
      $115
      $115

8. How much money did NYC make off of parking violations?



In [ ]:

9. What's the most lucrative kind of parking violation? The most frequent?



In [ ]:

10. New Jersey has bad drivers, but does it have bad parkers, too? How much money does NYC make off of all non-New York vehicles?



In [ ]:

11. Make a chart of the top few.



In [ ]:



In [ ]:

12. What time of day do people usually get their tickets? You can break the day up into several blocks - for example 12am-6am, 6am-12pm, 12pm-6pm, 6pm-12am.



In [ ]:

13. What's the average ticket cost in NYC?



In [ ]:

14. Make a graph of the number of tickets per day.



In [ ]:

15. Make a graph of the amount of revenue collected per day.



In [ ]:

16. Manually construct a dataframe out of https://dmv.ny.gov/statistic/2015licinforce-web.pdf (only NYC boroughts - bronx, queens, manhattan, staten island, brooklyn), having columns for borough name, abbreviation, and number of licensed drivers.



In [ ]:

17. What's the parking-ticket-$-per-licensed-driver in each borough of NYC? Do this with pandas and the dataframe you just made, not with your head!



In [ ]:

	Summons Number	Plate ID	Registration State	Plate Type	Issue Date	Violation Code	Vehicle Body Type	Vehicle Make	Issuing Agency	Street Code1	...	Vehicle Color	Vehicle Year	Meter Number	Violation Post Code	Violation Description	No Standing or Stopping Violation	Hydrant Violation	Double Parking Violation
0	1283294138	GBB9093	NY	PAS	08/04/2013	46	SUBN	AUDI	P	37250	...	GY	2013	-	NaN	NaN	NaN	NaN	NaN
1	1283294151	62416MB	NY	COM	08/04/2013	46	VAN	FORD	P	37290	...	WH	2012	-	NaN	NaN	NaN	NaN	NaN
2	1283294163	78755JZ	NY	COM	08/05/2013	46	P-U	CHEVR	P	37030	...	NaN	0	-	NaN	NaN	NaN	NaN	NaN
3	1283294175	63009MA	NY	COM	08/05/2013	46	VAN	FORD	P	37270	...	WH	2010	-	NaN	NaN	NaN	NaN	NaN
4	1283294187	91648MC	NY	COM	08/08/2013	41	TRLR	GMC	P	37240	...	BR	2012	-	NaN	NaN	NaN	NaN	NaN

	CODE	DEFINITION	Manhattan 96th St. & below	All Other Areas
0	10	Stopping, standing or parking where a sign, st...	$115	$115
1	11	Hotel Loading/Unloading: Standing or parking w...	$115	$115
2	12	Snow Emergency: Standing or parking where stan...	$95	$95
3	13	Taxi Stand: Standing or parking where standing...	$115	$115
4	14	General No Standing: Standing or parking where...	$115	$115