

In [5]:

    
%load_ext autoreload
%matplotlib nbagg
%autoreload 2
import copy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl









    



The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

MECA653: Traitement de donnée - Analyse de la base de donnée de la sécurité routière

L'objectif ici est d'analyser les données fournies par le ministère de l'intérieure sur les accidents de la route resencés en 2016.

Le Module Panda sera largement utilisé.

Sources

Lien vers data.gouv.fr : https://www.data.gouv.fr/fr/datasets/base-de-donnees-accidents-corporels-de-la-circulation/#_

Documentation de la base de donnée : DATA/Description_des_bases_de_donneesONISR-Annees_2005_a_2016.pdf

1 - Charger les bases de donnée



In [66]:

    
dfc = pd.read_csv('./DATA/caracteristiques_2016.csv') 
dfu = pd.read_csv('./DATA/usagers_2016.csv')
dfl = pd.read_csv('./DATA/lieux_2016.csv')
df = pd.concat([dfu, dfc, dfl], axis=1)









    



C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:2698: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)



In [71]:

    
dfc.tail()









    Out[71]:







  
    
      
      Num_Acc
      an
      mois
      jour
      hrmn
      lum
      agg
      int
      atm
      col
      com
      adr
      gps
      lat
      long
      dep
    
  
  
    
      59427
      201600059428
      16
      12
      1
      1201
      1
      2
      1
      1
      6.0
      611
      passamanty-Vahib‚ ( rout
      NaN
      NaN
      NaN
      976
    
    
      59428
      201600059429
      16
      12
      3
      2215
      1
      2
      1
      1
      7.0
      611
      mamoudzou centre-Adrian
      NaN
      NaN
      NaN
      976
    
    
      59429
      201600059430
      16
      12
      5
      1710
      1
      2
      1
      1
      3.0
      611
      kaweni-Kaweni sud  (Z I)
      NaN
      NaN
      NaN
      976
    
    
      59430
      201600059431
      16
      12
      6
      1005
      1
      2
      3
      1
      3.0
      611
      kaweni-Kaweni nord ( Z I
      NaN
      NaN
      NaN
      976
    
    
      59431
      201600059432
      16
      12
      24
      635
      1
      2
      6
      1
      3.0
      611
      kaweni-Kaweni sud  (Z I)
      NaN
      NaN
      NaN
      976



In [65]:

    
dfu.head()









    Out[65]:







  
    
      
      Num_Acc
      place
      catu
      grav
      sexe
      trajet
      secu
      locp
      actp
      etatp
      an_nais
      num_veh
    
  
  
    
      0
      201600000001
      1.0
      1
      1
      2
      0.0
      11.0
      0.0
      0.0
      0.0
      1983.0
      B02
    
    
      1
      201600000001
      1.0
      1
      3
      1
      9.0
      21.0
      0.0
      0.0
      0.0
      2001.0
      A01
    
    
      2
      201600000002
      1.0
      1
      3
      1
      5.0
      11.0
      0.0
      0.0
      0.0
      1960.0
      A01
    
    
      3
      201600000002
      2.0
      2
      3
      1
      0.0
      11.0
      0.0
      0.0
      0.0
      2000.0
      A01
    
    
      4
      201600000002
      3.0
      2
      3
      2
      0.0
      11.0
      0.0
      0.0
      0.0
      1962.0
      A01



In [70]:

    
dfl.tail()









    Out[70]:







  
    
      
      Num_Acc
      catr
      voie
      v1
      v2
      circ
      nbv
      pr
      pr1
      vosp
      prof
      plan
      lartpc
      larrout
      surf
      infra
      situ
      env1
    
  
  
    
      59427
      201600059428
      3
      3
      2.0
      NaN
      2.0
      0.0
      0.0
      842.0
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      99.0
    
    
      59428
      201600059429
      2
      2
      2.0
      NaN
      2.0
      0.0
      0.0
      50.0
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      99.0
    
    
      59429
      201600059430
      2
      1
      2.0
      NaN
      2.0
      0.0
      1.0
      50.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      99.0
    
    
      59430
      201600059431
      2
      1
      2.0
      NaN
      2.0
      0.0
      3.0
      438.0
      0.0
      1.0
      4.0
      0.0
      0.0
      1.0
      0.0
      1.0
      99.0
    
    
      59431
      201600059432
      2
      1
      2.0
      NaN
      2.0
      0.0
      1.0
      157.0
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      99.0



In [68]:

    
df.head()









    Out[68]:







  
    
      
      Num_Acc
      place
      catu
      grav
      sexe
      trajet
      secu
      locp
      actp
      etatp
      ...
      pr1
      vosp
      prof
      plan
      lartpc
      larrout
      surf
      infra
      situ
      env1
    
  
  
    
      0
      201600000001
      1.0
      1
      1
      2
      0.0
      11.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      1.0
      3.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
    
    
      1
      201600000001
      1.0
      1
      3
      1
      9.0
      21.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      1.0
      2.0
      0.0
      58.0
      1.0
      0.0
      1.0
      0.0
    
    
      2
      201600000002
      1.0
      1
      3
      1
      5.0
      11.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      1.0
      3.0
      0.0
      68.0
      2.0
      0.0
      3.0
      99.0
    
    
      3
      201600000002
      2.0
      2
      3
      1
      0.0
      11.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      99.0
    
    
      4
      201600000002
      3.0
      2
      3
      2
      0.0
      11.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      3.0
    
  

5 rows × 46 columns



In [63]:

    
df = pd.concat([df, dfl], axis=1)
df.head()









    Out[63]:







  
    
      
      Num_Acc
      place
      catu
      grav
      sexe
      trajet
      secu
      locp
      actp
      etatp
      ...
      pr1
      vosp
      prof
      plan
      lartpc
      larrout
      surf
      infra
      situ
      env1
    
  
  
    
      0
      201600000001
      1.0
      1
      1
      2
      0.0
      11.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      1.0
      3.0
      0.0
      0.0
      1.0
      0.0
      1.0
      0.0
    
    
      1
      201600000001
      1.0
      1
      3
      1
      9.0
      21.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      1.0
      2.0
      0.0
      58.0
      1.0
      0.0
      1.0
      0.0
    
    
      2
      201600000002
      1.0
      1
      3
      1
      5.0
      11.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      1.0
      3.0
      0.0
      68.0
      2.0
      0.0
      3.0
      99.0
    
    
      3
      201600000002
      2.0
      2
      3
      1
      0.0
      11.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      99.0
    
    
      4
      201600000002
      3.0
      2
      3
      2
      0.0
      11.0
      0.0
      0.0
      0.0
      ...
      NaN
      0.0
      0.0
      1.0
      0.0
      0.0
      1.0
      0.0
      1.0
      3.0
    
  

5 rows × 64 columns

2 - Quelle est la poportion Homme/Femme impliquée dans les accidents ? Représenter le résultat sous forme graphique.



In [33]:

    
# methode pas propre
(h,c)=df[df.sexe==1].shape
(f,c)=df[df.sexe==2].shape

(t,c)=df.shape

print('h/t=', h/t)
print('f/t=', f/t)









    



h/t= 0.6999145568197149
f/t= 0.3000854431802851



In [34]:

    
# methode panda
df["sexe"].value_counts(normalize=True)









    Out[34]:





1    0.699915
2    0.300085
Name: sexe, dtype: float64



In [55]:

    
fig = plt.figure()
df[df.grav==2].sexe.value_counts(normalize=True).plot.pie(labels=['Homme', 'Femme'], colors= ['r', 'g'], autopct='%.2f')









    














    











    Out[55]:





<matplotlib.axes._subplots.AxesSubplot at 0xe859748>

2 - Quelle est la poportion des accidents ayant eu lieu le jour, la nuit ou a l'aube/crépuscule? Représenter le résultat sous forme graphique.



In [46]:

    
dlum = df["lum"].value_counts(normalize=True)
dlum = dlum.sort_index()



In [47]:

    
dlum









    Out[47]:





1.0    0.682763
2.0    0.060035
3.0    0.085627
4.0    0.008548
5.0    0.163027
Name: lum, dtype: float64



In [57]:

    
dlum[3] = dlum[3:5].sum()
fig = plt.figure()
dlum[1:3].plot.pie(labels=['Jour','Aube/crépuscule', 'Nuit'], colors= ['y', 'g' , 'b'], autopct='%.2f')









    














    











    Out[57]:





<matplotlib.axes._subplots.AxesSubplot at 0xe918978>

3- Position géographique



In [109]:

    
df.lat=df.lat/100000
df.long=df.long/100000
dfp = df[df.gps=='M']
dfp = dfp[['lat','long']]
dfp = dfp[(dfp.long!=0.0) & (dfp.lat!=0.0)]
dfp.head()









    Out[109]:







  
    
      
      lat
      long
    
  
  
    
      21
      5.084579e-09
      2.264070e-07
    
    
      50
      5.068000e-09
      2.793000e-07
    
    
      51
      5.064100e-09
      2.712000e-07
    
    
      55
      5.070800e-09
      2.765000e-07
    
    
      57
      5.070266e-09
      2.620940e-07



In [110]:

    
#fig = plt.figure()
dfp.plot.scatter(x='long', y='lat',s=1);



In [111]:

    
df[(df.long!=0.0) & (df.lat!=0.0) & (df.gps=='M')].plot.scatter(x='long', y='lat',s=.5);



In [ ]:

	Num_Acc	an	mois	jour	hrmn	lum	agg	int	atm	col	com	adr	gps	lat	long	dep
59427	201600059428	16	12	1	1201	1	2	1	1	6.0	611	passamanty-Vahib‚ ( rout	NaN	NaN	NaN	976
59428	201600059429	16	12	3	2215	1	2	1	1	7.0	611	mamoudzou centre-Adrian	NaN	NaN	NaN	976
59429	201600059430	16	12	5	1710	1	2	1	1	3.0	611	kaweni-Kaweni sud (Z I)	NaN	NaN	NaN	976
59430	201600059431	16	12	6	1005	1	2	3	1	3.0	611	kaweni-Kaweni nord ( Z I	NaN	NaN	NaN	976
59431	201600059432	16	12	24	635	1	2	6	1	3.0	611	kaweni-Kaweni sud (Z I)	NaN	NaN	NaN	976

	Num_Acc	place	catu	grav	sexe	trajet	secu	an_nais	num_veh
0	201600000001	1.0	1	1	2	0.0	11.0	1983.0	B02
1	201600000001	1.0	1	3	1	9.0	21.0	2001.0	A01
2	201600000002	1.0	1	3	1	5.0	11.0	1960.0	A01
3	201600000002	2.0	2	3	1	0.0	11.0	2000.0	A01
4	201600000002	3.0	2	3	2	0.0	11.0	1962.0	A01

	Num_Acc	catr	voie	v1	v2	circ	pr	pr1	prof	plan	surf	situ	env1
59427	201600059428	3	3	2.0	NaN	2.0	0.0	842.0	1.0	1.0	1.0	1.0	99.0
59428	201600059429	2	2	2.0	NaN	2.0	0.0	50.0	1.0	1.0	1.0	1.0	99.0
59429	201600059430	2	1	2.0	NaN	2.0	1.0	50.0	1.0	1.0	0.0	1.0	99.0
59430	201600059431	2	1	2.0	NaN	2.0	3.0	438.0	1.0	4.0	1.0	1.0	99.0
59431	201600059432	2	1	2.0	NaN	2.0	1.0	157.0	1.0	1.0	1.0	1.0	99.0

	lat	long
21	5.084579e-09	2.264070e-07
50	5.068000e-09	2.793000e-07
51	5.064100e-09	2.712000e-07
55	5.070800e-09	2.765000e-07
57	5.070266e-09	2.620940e-07