traffic-citations-server
Science Score: 31.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Unable to calculate vocabulary similarity
Last synced: 10 months ago
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JSON representation
·
Repository
Basic Info
- Host: GitHub
- Owner: akbaragha0712
- Language: Jupyter Notebook
- Default Branch: main
- Size: 62.5 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Created almost 5 years ago
· Last pushed over 4 years ago
Metadata Files
Readme
Citation
README.md
Traffic-Citations-Server
Owner
- Name: AKBAR
- Login: akbaragha0712
- Kind: user
- Repositories: 1
- Profile: https://github.com/akbaragha0712
Citation (Citations_EDA.ipynb)
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import dependencies"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from scipy.stats import kde\n",
"import pylab as pl"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"file_dtypes = {'Ticket number': str, 'Issue time': np.float64, 'Meter Id': str, 'Marked Time': str,\n",
" 'RP State Plate': str, 'Plate Expiry Date': str, 'VIN': str, 'Make': str, 'Body Style': str,\n",
" 'Color': str, 'Location': str, 'Route': str, 'Agency': np.float64, 'Violation code': str,\n",
" 'Violation Description': str, 'Fine amount': np.float32, 'Latitude': np.float32, 'Longitude': np.float32}\n",
"\n",
"df_tc = pd.read_csv('data/parking_citations.corrupted.csv',\n",
" dtype=file_dtypes,\n",
" parse_dates=['Issue Date'],\n",
" index_col=['Ticket number'])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Ticket number</th>\n",
" <th>Issue Date</th>\n",
" <th>Issue time</th>\n",
" <th>Meter Id</th>\n",
" <th>Marked Time</th>\n",
" <th>RP State Plate</th>\n",
" <th>Plate Expiry Date</th>\n",
" <th>VIN</th>\n",
" <th>Make</th>\n",
" <th>Body Style</th>\n",
" <th>Color</th>\n",
" <th>Location</th>\n",
" <th>Route</th>\n",
" <th>Agency</th>\n",
" <th>Violation code</th>\n",
" <th>Violation Description</th>\n",
" <th>Fine amount</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1103341116</td>\n",
" <td>2015-12-21</td>\n",
" <td>1251.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>200304.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PA</td>\n",
" <td>GY</td>\n",
" <td>13147 WELBY WAY</td>\n",
" <td>01521</td>\n",
" <td>1.0</td>\n",
" <td>4000A1</td>\n",
" <td>NO EVIDENCE OF REG</td>\n",
" <td>50.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1103700150</td>\n",
" <td>2015-12-21</td>\n",
" <td>1435.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201512.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>VN</td>\n",
" <td>WH</td>\n",
" <td>525 S MAIN ST</td>\n",
" <td>1C51</td>\n",
" <td>1.0</td>\n",
" <td>4000A1</td>\n",
" <td>NO EVIDENCE OF REG</td>\n",
" <td>50.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>1104803000</td>\n",
" <td>2015-12-21</td>\n",
" <td>2055.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201503.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PA</td>\n",
" <td>BK</td>\n",
" <td>200 WORLD WAY</td>\n",
" <td>2R2</td>\n",
" <td>2.0</td>\n",
" <td>8939</td>\n",
" <td>WHITE CURB</td>\n",
" <td>58.0</td>\n",
" <td>6439998.0</td>\n",
" <td>1802686.375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>1104820732</td>\n",
" <td>2015-12-26</td>\n",
" <td>1515.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PA</td>\n",
" <td>WH</td>\n",
" <td>100 WORLD WAY</td>\n",
" <td>2F11</td>\n",
" <td>2.0</td>\n",
" <td>000</td>\n",
" <td>17104h</td>\n",
" <td>NaN</td>\n",
" <td>6440041.0</td>\n",
" <td>1802686.250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>1105461453</td>\n",
" <td>2015-09-15</td>\n",
" <td>115.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>200316.0</td>\n",
" <td>NaN</td>\n",
" <td>CHEV</td>\n",
" <td>PA</td>\n",
" <td>BK</td>\n",
" <td>GEORGIA ST/OLYMPIC</td>\n",
" <td>1FB70</td>\n",
" <td>1.0</td>\n",
" <td>8069A</td>\n",
" <td>NO STOPPING/STANDING</td>\n",
" <td>93.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>1106226590</td>\n",
" <td>2015-09-15</td>\n",
" <td>19.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201507.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>VN</td>\n",
" <td>GY</td>\n",
" <td>SAN PEDRO S/O BOYD</td>\n",
" <td>1A35W</td>\n",
" <td>1.0</td>\n",
" <td>4000A1</td>\n",
" <td>NO EVIDENCE OF REG</td>\n",
" <td>50.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>1106500452</td>\n",
" <td>2015-12-17</td>\n",
" <td>1710.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201605.0</td>\n",
" <td>NaN</td>\n",
" <td>MAZD</td>\n",
" <td>PA</td>\n",
" <td>BL</td>\n",
" <td>SUNSET/ALVARADO</td>\n",
" <td>00217</td>\n",
" <td>1.0</td>\n",
" <td>8070</td>\n",
" <td>PARK IN GRID LOCK ZN</td>\n",
" <td>163.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>1106500463</td>\n",
" <td>2015-12-17</td>\n",
" <td>1710.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201602.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PA</td>\n",
" <td>BK</td>\n",
" <td>SUNSET/ALVARADO</td>\n",
" <td>00217</td>\n",
" <td>1.0</td>\n",
" <td>8070</td>\n",
" <td>PARK IN GRID LOCK ZN</td>\n",
" <td>163.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>1106506402</td>\n",
" <td>2015-12-22</td>\n",
" <td>945.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201605.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PA</td>\n",
" <td>BR</td>\n",
" <td>721 S WESTLAKE</td>\n",
" <td>2A75</td>\n",
" <td>1.0</td>\n",
" <td>8069AA</td>\n",
" <td>NO STOP/STAND AM</td>\n",
" <td>93.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>1106506413</td>\n",
" <td>2015-12-22</td>\n",
" <td>1100.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201701.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PA</td>\n",
" <td>SI</td>\n",
" <td>1159 HUNTLEY DR</td>\n",
" <td>2A75</td>\n",
" <td>1.0</td>\n",
" <td>8069AA</td>\n",
" <td>NO STOP/STAND AM</td>\n",
" <td>93.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>1106506424</td>\n",
" <td>2015-12-22</td>\n",
" <td>1100.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201511.0</td>\n",
" <td>NaN</td>\n",
" <td>FORD</td>\n",
" <td>TR</td>\n",
" <td>WH</td>\n",
" <td>1159 HUNTLEY DR</td>\n",
" <td>2A75</td>\n",
" <td>1.0</td>\n",
" <td>8069AA</td>\n",
" <td>NO STOP/STAND AM</td>\n",
" <td>93.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11</td>\n",
" <td>1106506435</td>\n",
" <td>2015-12-22</td>\n",
" <td>1105.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201701.0</td>\n",
" <td>NaN</td>\n",
" <td>CHRY</td>\n",
" <td>PA</td>\n",
" <td>GO</td>\n",
" <td>1159 HUNTLEY DR</td>\n",
" <td>2A75</td>\n",
" <td>1.0</td>\n",
" <td>8069AA</td>\n",
" <td>NO STOP/STAND AM</td>\n",
" <td>93.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>1106506446</td>\n",
" <td>2015-12-22</td>\n",
" <td>1110.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201511.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PA</td>\n",
" <td>BK</td>\n",
" <td>1200 W MIRAMAR</td>\n",
" <td>2A75</td>\n",
" <td>1.0</td>\n",
" <td>4000A1</td>\n",
" <td>NO EVIDENCE OF REG</td>\n",
" <td>50.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>1106549754</td>\n",
" <td>2015-12-15</td>\n",
" <td>825.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201607.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>TR</td>\n",
" <td>BK</td>\n",
" <td>4TH/STATE</td>\n",
" <td>CM96</td>\n",
" <td>1.0</td>\n",
" <td>8069A</td>\n",
" <td>NO STOPPING/STANDING</td>\n",
" <td>93.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14</td>\n",
" <td>1107179581</td>\n",
" <td>2015-12-27</td>\n",
" <td>1055.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>201605.0</td>\n",
" <td>NaN</td>\n",
" <td>TOYO</td>\n",
" <td>PA</td>\n",
" <td>BK</td>\n",
" <td>3100 N HOLLYRIDGE DR</td>\n",
" <td>NaN</td>\n",
" <td>54.0</td>\n",
" <td>8058L</td>\n",
" <td>PREF PARKING</td>\n",
" <td>68.0</td>\n",
" <td>99999.0</td>\n",
" <td>99999.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Ticket number Issue Date Issue time Meter Id Marked Time \\\n",
"0 0 1103341116 2015-12-21 1251.0 NaN NaN \n",
"1 1 1103700150 2015-12-21 1435.0 NaN NaN \n",
"2 2 1104803000 2015-12-21 2055.0 NaN NaN \n",
"3 3 1104820732 2015-12-26 1515.0 NaN NaN \n",
"4 4 1105461453 2015-09-15 115.0 NaN NaN \n",
"5 5 1106226590 2015-09-15 19.0 NaN NaN \n",
"6 6 1106500452 2015-12-17 1710.0 NaN NaN \n",
"7 7 1106500463 2015-12-17 1710.0 NaN NaN \n",
"8 8 1106506402 2015-12-22 945.0 NaN NaN \n",
"9 9 1106506413 2015-12-22 1100.0 NaN NaN \n",
"10 10 1106506424 2015-12-22 1100.0 NaN NaN \n",
"11 11 1106506435 2015-12-22 1105.0 NaN NaN \n",
"12 12 1106506446 2015-12-22 1110.0 NaN NaN \n",
"13 13 1106549754 2015-12-15 825.0 NaN NaN \n",
"14 14 1107179581 2015-12-27 1055.0 NaN NaN \n",
"\n",
" RP State Plate Plate Expiry Date VIN Make Body Style Color \\\n",
"0 CA 200304.0 NaN NaN PA GY \n",
"1 CA 201512.0 NaN NaN VN WH \n",
"2 CA 201503.0 NaN NaN PA BK \n",
"3 CA NaN NaN NaN PA WH \n",
"4 CA 200316.0 NaN CHEV PA BK \n",
"5 CA 201507.0 NaN NaN VN GY \n",
"6 CA 201605.0 NaN MAZD PA BL \n",
"7 CA 201602.0 NaN NaN PA BK \n",
"8 CA 201605.0 NaN NaN PA BR \n",
"9 CA 201701.0 NaN NaN PA SI \n",
"10 CA 201511.0 NaN FORD TR WH \n",
"11 CA 201701.0 NaN CHRY PA GO \n",
"12 CA 201511.0 NaN NaN PA BK \n",
"13 CA 201607.0 NaN NaN TR BK \n",
"14 CA 201605.0 NaN TOYO PA BK \n",
"\n",
" Location Route Agency Violation code Violation Description \\\n",
"0 13147 WELBY WAY 01521 1.0 4000A1 NO EVIDENCE OF REG \n",
"1 525 S MAIN ST 1C51 1.0 4000A1 NO EVIDENCE OF REG \n",
"2 200 WORLD WAY 2R2 2.0 8939 WHITE CURB \n",
"3 100 WORLD WAY 2F11 2.0 000 17104h \n",
"4 GEORGIA ST/OLYMPIC 1FB70 1.0 8069A NO STOPPING/STANDING \n",
"5 SAN PEDRO S/O BOYD 1A35W 1.0 4000A1 NO EVIDENCE OF REG \n",
"6 SUNSET/ALVARADO 00217 1.0 8070 PARK IN GRID LOCK ZN \n",
"7 SUNSET/ALVARADO 00217 1.0 8070 PARK IN GRID LOCK ZN \n",
"8 721 S WESTLAKE 2A75 1.0 8069AA NO STOP/STAND AM \n",
"9 1159 HUNTLEY DR 2A75 1.0 8069AA NO STOP/STAND AM \n",
"10 1159 HUNTLEY DR 2A75 1.0 8069AA NO STOP/STAND AM \n",
"11 1159 HUNTLEY DR 2A75 1.0 8069AA NO STOP/STAND AM \n",
"12 1200 W MIRAMAR 2A75 1.0 4000A1 NO EVIDENCE OF REG \n",
"13 4TH/STATE CM96 1.0 8069A NO STOPPING/STANDING \n",
"14 3100 N HOLLYRIDGE DR NaN 54.0 8058L PREF PARKING \n",
"\n",
" Fine amount Latitude Longitude \n",
"0 50.0 99999.0 99999.000 \n",
"1 50.0 99999.0 99999.000 \n",
"2 58.0 6439998.0 1802686.375 \n",
"3 NaN 6440041.0 1802686.250 \n",
"4 93.0 99999.0 99999.000 \n",
"5 50.0 99999.0 99999.000 \n",
"6 163.0 99999.0 99999.000 \n",
"7 163.0 99999.0 99999.000 \n",
"8 93.0 99999.0 99999.000 \n",
"9 93.0 99999.0 99999.000 \n",
"10 93.0 99999.0 99999.000 \n",
"11 93.0 99999.0 99999.000 \n",
"12 50.0 99999.0 99999.000 \n",
"13 93.0 99999.0 99999.000 \n",
"14 68.0 99999.0 99999.000 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# print(df_tc)\n",
"df_tc.head(15)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8726014, 20)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8726014 entries, 0 to 8726013\n",
"Data columns (total 20 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 Unnamed: 0 int64 \n",
" 1 Ticket number object \n",
" 2 Issue Date object \n",
" 3 Issue time float64\n",
" 4 Meter Id object \n",
" 5 Marked Time float64\n",
" 6 RP State Plate object \n",
" 7 Plate Expiry Date float64\n",
" 8 VIN object \n",
" 9 Make object \n",
" 10 Body Style object \n",
" 11 Color object \n",
" 12 Location object \n",
" 13 Route object \n",
" 14 Agency float64\n",
" 15 Violation code object \n",
" 16 Violation Description object \n",
" 17 Fine amount float64\n",
" 18 Latitude float64\n",
" 19 Longitude float64\n",
"dtypes: float64(7), int64(1), object(12)\n",
"memory usage: 1.3+ GB\n"
]
}
],
"source": [
"df_tc.info()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TOYT 721411\n",
"HOND 491961\n",
"FORD 382695\n",
"NISS 311324\n",
"CHEV 297076\n",
" ... \n",
"CMDR 1\n",
"SATO 1\n",
"FRKT 1\n",
"DOOH 1\n",
"HOU 1\n",
"Name: Make, Length: 1477, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc.Make.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CA 8127986\n",
"AZ 77649\n",
"TX 55337\n",
"NV 51944\n",
"FL 37924\n",
" ... \n",
"AS 16\n",
"NF 13\n",
"YU 8\n",
"| 2\n",
"ML 1\n",
"Name: RP State Plate, Length: 78, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc['RP State Plate'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-01-19T00:00:00 9830\n",
"2014-12-31T00:00:00 9605\n",
"2016-05-05T00:00:00 9366\n",
"2016-02-23T00:00:00 9339\n",
"2015-06-09T00:00:00 9275\n",
" ... \n",
"2012-08-10T00:00:00 1\n",
"2014-04-20T00:00:00 1\n",
"2012-02-21T00:00:00 1\n",
"2014-06-08T00:00:00 1\n",
"2014-11-14T00:00:00 1\n",
"Name: Issue Date, Length: 1843, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc['Issue Date'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PA 7673976\n",
"PU 298897\n",
"VN 233505\n",
"TK 213271\n",
"CM 140263\n",
" ... \n",
"RH 1\n",
"DU 1\n",
"US 1\n",
"NH 1\n",
"JA 1\n",
"Name: Body Style, Length: 191, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc['Body Style'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NO PARK/STREET CLEAN 2530294\n",
"METER EXP. 1524589\n",
"RED ZONE 646198\n",
"PREFERENTIAL PARKING 599200\n",
"DISPLAY OF TABS 535798\n",
" ... \n",
"134200 1\n",
"8813C 1\n",
"5202C 1\n",
"6444K 1\n",
"52011 1\n",
"Name: Violation Description, Length: 795, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc['Violation Description'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Frequency'>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_tc['Fine amount'].plot(kind=\"hist\", bins=200, xlim=(0,100), figsize=(20,10))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fine_arr = np.array(df_tc['Fine amount'].dropna())\n",
"fine_arr = np.sort(fine_arr)\n",
"# print(fine_arr)\n",
"\n",
"# density = kde.gaussian_kde(fine_arr)\n",
"# y=density(fine_arr)\n",
"\n",
"# plt.plot(fine_arr, y)\n",
"\n",
"# sns.set(rc = {'figure.figsize':(15,8)})\n",
"# sns.displot(fine_arr,kde=True)\n",
"plt.figure(figsize=(20, 10))\n",
"sns.kdeplot(data=fine_arr)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"73.0 2921569\n",
"63.0 1672550\n",
"68.0 1245160\n",
"93.0 1027851\n",
"25.0 818683\n",
"58.0 723588\n",
"163.0 99990\n",
"50.0 89410\n",
"363.0 59883\n",
"78.0 32156\n",
"293.0 9589\n",
"133.0 8734\n",
"103.0 7001\n",
"105.0 725\n",
"53.0 383\n",
"60.0 372\n",
"143.0 357\n",
"98.0 291\n",
"128.0 274\n",
"40.0 210\n",
"10.0 172\n",
"88.0 154\n",
"35.0 136\n",
"55.0 63\n",
"45.0 59\n",
"345.0 39\n",
"255.0 30\n",
"80.0 24\n",
"353.0 15\n",
"30.0 7\n",
"505.0 6\n",
"70.0 6\n",
"85.0 5\n",
"65.0 5\n",
"75.0 4\n",
"123.0 3\n",
"113.0 1\n",
"330.0 1\n",
"155.0 1\n",
"Name: Fine amount, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc['Fine amount'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_tc['Fine amount'].plot(kind=\"box\", figsize=(20,10))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4042322160.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc['Latitude'].value_counts()\n",
"df_tc['Latitude'].max()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_tc['Latitude'].plot(kind=\"box\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"corr = df_tc.corr()\n",
"sns.heatmap(corr, linewidth=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 False\n",
"Ticket number False\n",
"Issue Date True\n",
"Issue time True\n",
"Meter Id True\n",
"Marked Time True\n",
"RP State Plate True\n",
"Plate Expiry Date True\n",
"VIN True\n",
"Make True\n",
"Body Style True\n",
"Color True\n",
"Location True\n",
"Route True\n",
"Agency True\n",
"Violation code False\n",
"Violation Description True\n",
"Fine amount True\n",
"Latitude True\n",
"Longitude True\n",
"dtype: bool"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc.isna().any()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 0\n",
"Issue Date 536\n",
"Issue time 2583\n",
"RP State Plate 765\n",
"Plate Expiry Date 794827\n",
"Make 4368470\n",
"Body Style 8890\n",
"Color 4115\n",
"Location 854\n",
"Route 65354\n",
"Agency 545\n",
"Violation code 0\n",
"Violation Description 872\n",
"Fine amount 6507\n",
"Latitude 3\n",
"Longitude 3\n",
"dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df_tc = df_tc.drop(columns=['Meter Id', 'Marked Time', 'VIN'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df_tc_make_exists = df_tc.dropna(subset=['Make'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df_tc_make_exists = df_tc_make_exists.loc[df_tc_make_exists['Latitude']!=99999]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Issue Date</th>\n",
" <th>Issue time</th>\n",
" <th>RP State Plate</th>\n",
" <th>Plate Expiry Date</th>\n",
" <th>Make</th>\n",
" <th>Body Style</th>\n",
" <th>Color</th>\n",
" <th>Location</th>\n",
" <th>Route</th>\n",
" <th>Agency</th>\n",
" <th>Violation code</th>\n",
" <th>Violation Description</th>\n",
" <th>Fine amount</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Ticket number</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1109455266</th>\n",
" <td>43</td>\n",
" <td>2015-12-26</td>\n",
" <td>1815.0</td>\n",
" <td>NY</td>\n",
" <td>NaN</td>\n",
" <td>TOYO</td>\n",
" <td>PA</td>\n",
" <td>WH</td>\n",
" <td>3200 CANYON DR</td>\n",
" <td>NaN</td>\n",
" <td>36.0</td>\n",
" <td>8603</td>\n",
" <td>PK IN PROH AREA</td>\n",
" <td>68.0</td>\n",
" <td>6467477.0</td>\n",
" <td>1868721.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1109811360</th>\n",
" <td>56</td>\n",
" <td>2015-12-26</td>\n",
" <td>2231.0</td>\n",
" <td>CA</td>\n",
" <td>201506.0</td>\n",
" <td>SCIO</td>\n",
" <td>SU</td>\n",
" <td>BK</td>\n",
" <td>1141 EAGLE VISTA DR</td>\n",
" <td>NaN</td>\n",
" <td>36.0</td>\n",
" <td>8603</td>\n",
" <td>PK IN PROH AREA</td>\n",
" <td>68.0</td>\n",
" <td>6504463.5</td>\n",
" <td>1874621.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1109818625</th>\n",
" <td>58</td>\n",
" <td>2015-12-27</td>\n",
" <td>2235.0</td>\n",
" <td>CA</td>\n",
" <td>201601.0</td>\n",
" <td>CADI</td>\n",
" <td>PA</td>\n",
" <td>BK</td>\n",
" <td>2551 MOTOR AVE</td>\n",
" <td>NaN</td>\n",
" <td>34.0</td>\n",
" <td>6344K2</td>\n",
" <td>NO PK BET 1-3AM</td>\n",
" <td>68.0</td>\n",
" <td>6438048.0</td>\n",
" <td>1839690.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1111259715</th>\n",
" <td>81</td>\n",
" <td>2015-12-26</td>\n",
" <td>1920.0</td>\n",
" <td>CA</td>\n",
" <td>201702.0</td>\n",
" <td>OLDS</td>\n",
" <td>NaN</td>\n",
" <td>BU</td>\n",
" <td>501 S VENICE BLVD</td>\n",
" <td>NaN</td>\n",
" <td>36.0</td>\n",
" <td>80714</td>\n",
" <td>PRIVATE PROPERTY</td>\n",
" <td>68.0</td>\n",
" <td>6420102.0</td>\n",
" <td>1817944.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1111881573</th>\n",
" <td>82</td>\n",
" <td>2015-12-23</td>\n",
" <td>2150.0</td>\n",
" <td>CA</td>\n",
" <td>201606.0</td>\n",
" <td>BMW</td>\n",
" <td>SU</td>\n",
" <td>WH</td>\n",
" <td>380 WORLD WAY U/L</td>\n",
" <td>2F91</td>\n",
" <td>2.0</td>\n",
" <td>8939</td>\n",
" <td>WHITE CURB</td>\n",
" <td>58.0</td>\n",
" <td>6439920.0</td>\n",
" <td>1802686.75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Issue Date Issue time RP State Plate \\\n",
"Ticket number \n",
"1109455266 43 2015-12-26 1815.0 NY \n",
"1109811360 56 2015-12-26 2231.0 CA \n",
"1109818625 58 2015-12-27 2235.0 CA \n",
"1111259715 81 2015-12-26 1920.0 CA \n",
"1111881573 82 2015-12-23 2150.0 CA \n",
"\n",
" Plate Expiry Date Make Body Style Color Location \\\n",
"Ticket number \n",
"1109455266 NaN TOYO PA WH 3200 CANYON DR \n",
"1109811360 201506.0 SCIO SU BK 1141 EAGLE VISTA DR \n",
"1109818625 201601.0 CADI PA BK 2551 MOTOR AVE \n",
"1111259715 201702.0 OLDS NaN BU 501 S VENICE BLVD \n",
"1111881573 201606.0 BMW SU WH 380 WORLD WAY U/L \n",
"\n",
" Route Agency Violation code Violation Description Fine amount \\\n",
"Ticket number \n",
"1109455266 NaN 36.0 8603 PK IN PROH AREA 68.0 \n",
"1109811360 NaN 36.0 8603 PK IN PROH AREA 68.0 \n",
"1109818625 NaN 34.0 6344K2 NO PK BET 1-3AM 68.0 \n",
"1111259715 NaN 36.0 80714 PRIVATE PROPERTY 68.0 \n",
"1111881573 2F91 2.0 8939 WHITE CURB 58.0 \n",
"\n",
" Latitude Longitude \n",
"Ticket number \n",
"1109455266 6467477.0 1868721.00 \n",
"1109811360 6504463.5 1874621.50 \n",
"1109818625 6438048.0 1839690.75 \n",
"1111259715 6420102.0 1817944.75 \n",
"1111881573 6439920.0 1802686.75 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc_make_exists.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 0\n",
"Issue Date 0\n",
"Issue time 0\n",
"RP State Plate 0\n",
"Plate Expiry Date 0\n",
"Make 0\n",
"Body Style 0\n",
"Color 0\n",
"Location 0\n",
"Route 0\n",
"Agency 0\n",
"Violation code 0\n",
"Violation Description 0\n",
"Fine amount 0\n",
"Latitude 0\n",
"Longitude 0\n",
"dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc_make_exists.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"df_tc_make_exists = df_tc_make_exists.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3343449, 16)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc_make_exists.shape"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TOYT 588404\n",
"HOND 388221\n",
"FORD 288480\n",
"NISS 240737\n",
"CHEV 222612\n",
"BMW 149805\n",
"MERZ 134826\n",
"VOLK 116310\n",
"HYUN 104970\n",
"LEXS 98686\n",
"DODG 95197\n",
"JEEP 78248\n",
"KIA 78023\n",
"AUDI 64059\n",
"MAZD 63261\n",
"OTHR 51054\n",
"GMC 43583\n",
"CHRY 43190\n",
"INFI 43117\n",
"ACUR 41684\n",
"SUBA 37367\n",
"VOLV 32149\n",
"MITS 29334\n",
"CADI 25106\n",
"MNNI 24852\n",
"Name: Make, dtype: int64\n"
]
}
],
"source": [
"df_top25_makes = df_tc_make_exists.Make.value_counts().head(25)\n",
"print(df_top25_makes)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3083275"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_top25_makes.sum()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['TOYT' 'HOND' 'FORD' 'NISS' 'CHEV' 'BMW' 'MERZ' 'VOLK' 'HYUN' 'LEXS'\n",
" 'DODG' 'JEEP' 'KIA' 'AUDI' 'MAZD' 'OTHR' 'GMC' 'CHRY' 'INFI' 'ACUR'\n",
" 'SUBA' 'VOLV' 'MITS' 'CADI' 'MNNI']\n"
]
}
],
"source": [
"top25_arr = df_top25_makes.index.to_numpy()\n",
"print(top25_arr)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TOP 25 3083275\n",
"THE REST 260174\n",
"Name: Make_mod, dtype: int64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_tc_make_exists['Make_mod'] = np.where(df_tc_make_exists['Make'].isin(top25_arr), \"TOP 25\", \"THE REST\")\n",
"# df_tc_make_exists.loc[df_tc_make_exists['Make'].isin(top25_arr),'Make_mod'] = \"TOP 25\"\n",
"# df_tc_make_exists.loc[~df_tc_make_exists['Make'].isin(top25_arr),'Make_mod'] = \"THE REST\"\n",
"df_tc_make_exists.Make_mod.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# df_tc['Time'] = pd.to_datetime(df_tc['Issue time'],\n",
"# format=\"%H%M\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}