In [1]:
import pandas as pd
In [2]:
# Read the file
raw_data = pd.ExcelFile('London_Sundays_2000.xlsx')
data = raw_data.parse('Sheet1')
In [3]:
data.head()
Out[3]:
Accident_Index
Location_Easting_OSGR
Location_Northing_OSGR
Longitude
Latitude
Police_Force
Accident_Severity
Number_of_Vehicles
Number_of_Casualties
Date
...
Pedestrian_Crossing-Human_Control
Pedestrian_Crossing-Physical_Facilities
Light_Conditions
Weather_Conditions
Road_Surface_Conditions
Special_Conditions_at_Site
Carriageway_Hazards
Urban_or_Rural_Area
Did_Police_Officer_Attend_Scene_of_Accident
LSOA_of_Accident_Location
5118301
20000141T3256
534060
200000
-0.062236
51.682716
1
3
8
5
29/10/2000
...
0
0
4
5
5
0
0
2
1
E01001412
5118310
20000141T3324
529670
200520
-0.125507
51.688419
1
3
2
1
17/12/2000
...
0
0
4
1
2
0
0
2
1
E01001412
5118318
20000142G0092
556810
192270
0.263122
51.607380
1
3
3
1
06/02/2000
...
0
0
4
1
2
0
0
2
1
E01002296
5118326
20000142G0220
557210
191300
0.268459
51.598554
1
3
2
4
02/04/2000
...
0
0
1
1
1
0
0
2
1
E01002296
5118331
20000142G0373
558450
188310
0.284999
51.571343
1
3
2
1
14/05/2000
...
0
0
1
1
1
0
0
2
1
E01002252
5 rows × 32 columns
In [10]:
# Raining + high winds = 5
# darkness + unlit roads = 5
# When it was raining + dark
dark_and_stormy = data[(data.Light_Conditions == 5) & (data.Weather_Conditions == 2) ]
len(dark_and_stormy)
Out[10]:
1
In [11]:
dark_and_stormy
Out[11]:
Accident_Index
Location_Easting_OSGR
Location_Northing_OSGR
Longitude
Latitude
Police_Force
Accident_Severity
Number_of_Vehicles
Number_of_Casualties
Date
...
Pedestrian_Crossing-Human_Control
Pedestrian_Crossing-Physical_Facilities
Light_Conditions
Weather_Conditions
Road_Surface_Conditions
Special_Conditions_at_Site
Carriageway_Hazards
Urban_or_Rural_Area
Did_Police_Officer_Attend_Scene_of_Accident
LSOA_of_Accident_Location
5154805
200001ZD00633
532170
165110
-0.10264
51.36962
1
3
1
1
28/05/2000
...
0
0
5
2
2
0
0
1
1
E01001042
1 rows × 32 columns
In [14]:
# snow
data[(data.Road_Surface_Conditions == 3)]
Out[14]:
Accident_Index
Location_Easting_OSGR
Location_Northing_OSGR
Longitude
Latitude
Police_Force
Accident_Severity
Number_of_Vehicles
Number_of_Casualties
Date
...
Pedestrian_Crossing-Human_Control
Pedestrian_Crossing-Physical_Facilities
Light_Conditions
Weather_Conditions
Road_Surface_Conditions
Special_Conditions_at_Site
Carriageway_Hazards
Urban_or_Rural_Area
Did_Police_Officer_Attend_Scene_of_Accident
LSOA_of_Accident_Location
5138674
200001PY01070
544130
165840
0.069358
51.373273
1
3
1
1
05/11/2000
...
0
0
4
2
3
0
0
1
1
E01000753
1 rows × 32 columns
In [17]:
# icy and frosty
data_frosty = data[(data.Road_Surface_Conditions == 4)]
data_frosty
Out[17]:
Accident_Index
Location_Easting_OSGR
Location_Northing_OSGR
Longitude
Latitude
Police_Force
Accident_Severity
Number_of_Vehicles
Number_of_Casualties
Date
...
Pedestrian_Crossing-Human_Control
Pedestrian_Crossing-Physical_Facilities
Light_Conditions
Weather_Conditions
Road_Surface_Conditions
Special_Conditions_at_Site
Carriageway_Hazards
Urban_or_Rural_Area
Did_Police_Officer_Attend_Scene_of_Accident
LSOA_of_Accident_Location
5130517
200001KG00240
546940
182930
0.116793
51.526117
1
3
2
1
09/04/2000
...
0
0
4
1
4
0
0
1
1
E01000094
5136556
200001PL00032
538190
171850
-0.013588
51.428757
1
3
2
1
16/01/2000
...
0
0
4
1
4
0
0
1
1
E01003352
5140110
200001QK00006
523730
182970
-0.217584
51.532035
1
2
2
2
09/01/2000
...
0
5
1
1
4
0
0
1
1
E01000586
5142616
200001RY09024
548990
175130
0.143033
51.455494
1
3
2
1
17/12/2000
...
0
0
4
1
4
0
0
1
2
E01000362
5142674
200001SA00059
518950
200420
-0.280547
51.689892
1
3
1
1
13/02/2000
...
0
0
6
1
4
0
0
2
1
E01023582
5146308
200001TX01414
512480
178040
-0.381274
51.490076
1
3
2
1
17/12/2000
...
0
0
4
8
4
0
0
1
1
E01002631
5149135
200001XH00041
507010
184580
-0.458047
51.549920
1
2
1
2
16/01/2000
...
0
0
4
1
4
0
0
1
1
E01002513
5149137
200001XH00043
511080
184420
-0.399422
51.547696
1
3
1
1
16/01/2000
...
0
0
1
1
4
0
0
1
1
E01002499
5151532
200001YE00012
533270
192330
-0.076577
51.613979
1
2
1
1
09/01/2000
...
0
1
4
1
4
0
0
1
1
E01001562
9 rows × 32 columns
In [18]:
data_frosty.to_csv("London_frosty.csv")
In [ ]:
Content source: shantnu/Pandas-London-Accidents
Similar notebooks: