

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 [6]:

    
dfc = pd.read_csv('./DATA/caracteristiques_2016.csv') 
dfu = pd.read_csv('./DATA/usagers_2016.csv')
dfl = pd.read_csv('./DATA/lieux_2016.csv')









    



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 [7]:

    
dfc.head()









    Out[7]:







  
    
      
      Num_Acc
      an
      mois
      jour
      hrmn
      lum
      agg
      int
      atm
      col
      com
      adr
      gps
      lat
      long
      dep
    
  
  
    
      0
      201600000001
      16
      2
      1
      1445
      1
      2
      1
      8
      3.0
      5
      46, rue Sonneville
      M
      0.0
      0.0
      590
    
    
      1
      201600000002
      16
      3
      16
      1800
      1
      2
      6
      1
      6.0
      5
      1a rue du cimetière
      M
      0.0
      0.0
      590
    
    
      2
      201600000003
      16
      7
      13
      1900
      1
      1
      1
      1
      6.0
      11
      NaN
      M
      0.0
      0.0
      590
    
    
      3
      201600000004
      16
      8
      15
      1930
      2
      2
      1
      7
      3.0
      477
      52 rue victor hugo
      M
      0.0
      0.0
      590
    
    
      4
      201600000005
      16
      12
      23
      1100
      1
      2
      3
      1
      3.0
      11
      rue Joliot curie
      M
      0.0
      0.0
      590



In [8]:

    
dfl.tail()









    Out[8]:







  
    
      
      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 [9]:

    
dfu.tail()









    Out[9]:







  
    
      
      Num_Acc
      place
      catu
      grav
      sexe
      trajet
      secu
      locp
      actp
      etatp
      an_nais
      num_veh
    
  
  
    
      133417
      201600059431
      1.0
      1
      4
      1
      0.0
      21.0
      0.0
      0.0
      0.0
      1994.0
      A01
    
    
      133418
      201600059431
      1.0
      1
      4
      1
      0.0
      21.0
      0.0
      0.0
      0.0
      1990.0
      B01
    
    
      133419
      201600059432
      1.0
      1
      4
      1
      3.0
      21.0
      0.0
      0.0
      0.0
      1967.0
      A01
    
    
      133420
      201600059432
      2.0
      2
      4
      1
      0.0
      21.0
      0.0
      0.0
      0.0
      1967.0
      A01
    
    
      133421
      201600059432
      1.0
      1
      1
      1
      0.0
      11.0
      0.0
      0.0
      0.0
      1981.0
      B01



In [10]:

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









    Out[10]:







  
    
      
      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

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



In [11]:

    
# 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 [12]:

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









    Out[12]:





1    0.699915
2    0.300085
Name: sexe, dtype: float64



In [13]:

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









    














    











    Out[13]:





<matplotlib.axes._subplots.AxesSubplot at 0xa4b0d68>

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 [14]:

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



In [15]:

    
dlum









    Out[15]:





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 [16]:

    
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[16]:





<matplotlib.axes._subplots.AxesSubplot at 0xada1588>

3- Repésenter graphiquement la position géographique des accidents ayant eu lieu en metropole



In [17]:

    
# methode pas a pas
dfp = df[df.gps=='M']
dfp = dfp[['lat','long']]
dfp = dfp[(dfp.long!=0.0) & (dfp.lat!=0.0)]
dfp.head()



In [18]:

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



In [ ]:

    
# methode direct
plt.figure()
df[(df.long!=0.0) & (df.lat!=0.0) & (df.gps=='M')].plot.scatter(x='long', y='lat',s=.5)

4 - Age des personnes impliquées



In [32]:

    
df=df.assign(age=2016.-dfu.an_nais)
nb_bin=df.age.max().astype(np.int)



In [34]:

    
plt.figure()
df['age'].hist(bins=nb_bin)
plt.show()



In [35]:

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









    














    











    














    











    Out[35]:





<matplotlib.axes._subplots.AxesSubplot at 0xb571f60>



In [ ]:



In [ ]:

	lat	long
21	5084579.0	226407.0
50	5068000.0	279300.0
51	5064100.0	271200.0
55	5070800.0	276500.0
57	5070266.0	262094.0

	Num_Acc	an	mois	jour	hrmn	lum	agg	int	atm	col	com	adr	gps	dep
0	201600000001	16	2	1	1445	1	2	1	8	3.0	5	46, rue Sonneville	M	590
1	201600000002	16	3	16	1800	1	2	6	1	6.0	5	1a rue du cimetière	M	590
2	201600000003	16	7	13	1900	1	1	1	1	6.0	11	NaN	M	590
3	201600000004	16	8	15	1930	2	2	1	7	3.0	477	52 rue victor hugo	M	590
4	201600000005	16	12	23	1100	1	2	3	1	3.0	11	rue Joliot curie	M	590

	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

	Num_Acc	place	catu	grav	sexe	trajet	secu	an_nais	num_veh
133417	201600059431	1.0	1	4	1	0.0	21.0	1994.0	A01
133418	201600059431	1.0	1	4	1	0.0	21.0	1990.0	B01
133419	201600059432	1.0	1	4	1	3.0	21.0	1967.0	A01
133420	201600059432	2.0	2	4	1	0.0	21.0	1967.0	A01
133421	201600059432	1.0	1	1	1	0.0	11.0	1981.0	B01