In [1]:
import pandas as pd
import numpy as np
In [2]:
commune_metropole = pd.read_csv('data/commune_metropole.csv', encoding='utf-8')
In [3]:
commune_metropole.shape
Out[3]:
(36742, 6)
In [4]:
commune_metropole.head()
Out[4]:
COM
status
mean_altitude
superficie
is_metropole
metropole_name
0
39007
Commune simple
587.0
582.0
0
NaN
1
88086
Commune simple
454.0
396.0
0
NaN
2
62627
Commune simple
49.0
426.0
0
NaN
3
14020
Commune simple
33.0
987.0
0
NaN
4
11091
Chef-lieu canton
455.0
1605.0
0
NaN
In [5]:
insee = pd.read_csv('data/insee.csv',
sep=";", # séparateur du fichier
dtype={'COM' : np.dtype(str)}, # On force la colonne COM est être en string
encoding='utf-8') # encoding
/Users/babou/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (0,5,6,7,9,336,337,339,340) have mixed types. Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
In [6]:
insee.shape
Out[6]:
(51989, 1031)
In [7]:
insee.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 51989 entries, 0 to 51988
Columns: 1031 entries, CODGEO to C12_ACTOCC15P_TCOM
dtypes: float64(1016), int64(2), object(13)
memory usage: 408.9+ MB
In [9]:
insee.head()
Out[9]:
CODGEO
LIBGEO
COM
LIBCOM
REG
DEP
ARR
CV
ZE2010
UU2010
...
P12_ACTOCC15P_ILT3
P12_ACTOCC15P_ILT4
P12_ACTOCC15P_ILT5
P12_ACTOCC15P_ILT45D
C12_ACTOCC15P
C12_ACTOCC15P_PAS
C12_ACTOCC15P_MAR
C12_ACTOCC15P_DROU
C12_ACTOCC15P_VOIT
C12_ACTOCC15P_TCOM
0
010040101
Les Perouses-Triangle D'Activite
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
112.198631
4.828213
0.000000
NaN
715.635129
75.289455
119.660483
21.756694
407.353725
91.574773
1
010040102
Longeray-Gare
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
251.078104
1.056829
0.000000
NaN
1478.540370
57.685313
168.621858
26.249674
1003.722497
222.261029
2
010040201
Centre-St Germain-Vareilles
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
303.454850
12.282740
1.049194
NaN
1528.972551
26.832875
139.834053
47.053809
1123.438076
191.813737
3
010040202
Tiret-Les Allymes
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
385.888110
37.225126
6.920884
NaN
2069.196949
47.754605
61.886062
46.099580
1575.388689
338.068012
4
01004ZZZZ
Non Localisé À L'Iris
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
5 rows × 1031 columns
In [11]:
pd.set_option('display.max_columns', 30) # Changer le nombre de colonnnes afficher dans le notebook
In [16]:
insee.head()
Out[16]:
CODGEO
LIBGEO
COM
LIBCOM
REG
DEP
ARR
CV
ZE2010
UU2010
NB_B101
NB_B102
NB_B103
NB_B201
NB_B202
...
P12_NSAL15P_EMPLOY
P12_NSAL15P_AIDFAM
P12_ACTOCC15P_ILT1
P12_ACTOCC15P_ILT2P
P12_ACTOCC15P_ILT2
P12_ACTOCC15P_ILT3
P12_ACTOCC15P_ILT4
P12_ACTOCC15P_ILT5
P12_ACTOCC15P_ILT45D
C12_ACTOCC15P
C12_ACTOCC15P_PAS
C12_ACTOCC15P_MAR
C12_ACTOCC15P_DROU
C12_ACTOCC15P_VOIT
C12_ACTOCC15P_TCOM
0
010040101
Les Perouses-Triangle D'Activite
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
0.0
2.0
3.0
1.0
1.0
...
14.839853
0.000000
379.827854
342.532076
225.505232
112.198631
4.828213
0.000000
NaN
715.635129
75.289455
119.660483
21.756694
407.353725
91.574773
1
010040102
Longeray-Gare
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
0.0
2.0
0.0
0.0
2.0
...
35.102346
5.079221
650.063733
828.476637
576.341703
251.078104
1.056829
0.000000
NaN
1478.540370
57.685313
168.621858
26.249674
1003.722497
222.261029
2
010040201
Centre-St Germain-Vareilles
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
0.0
0.0
0.0
0.0
1.0
...
37.081294
1.049194
588.866660
951.155325
634.368542
303.454850
12.282740
1.049194
NaN
1528.972551
26.832875
139.834053
47.053809
1123.438076
191.813737
3
010040202
Tiret-Les Allymes
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
0.0
1.0
0.0
0.0
2.0
...
121.758030
0.000000
769.804668
1285.933176
855.899057
385.888110
37.225126
6.920884
NaN
2069.196949
47.754605
61.886062
46.099580
1575.388689
338.068012
4
01004ZZZZ
Non Localisé À L'Iris
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
0.0
0.0
0.0
0.0
0.0
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
5 rows × 1031 columns
In [10]:
data = insee.merge(commune_metropole, on='COM', how='left')
In [13]:
data.shape
Out[13]:
(51989, 1036)
In [14]:
data.head()
Out[14]:
CODGEO
LIBGEO
COM
LIBCOM
REG
DEP
ARR
CV
ZE2010
UU2010
...
C12_ACTOCC15P_PAS
C12_ACTOCC15P_MAR
C12_ACTOCC15P_DROU
C12_ACTOCC15P_VOIT
C12_ACTOCC15P_TCOM
status
mean_altitude
superficie
is_metropole
metropole_name
0
010040101
Les Perouses-Triangle D'Activite
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
75.289455
119.660483
21.756694
407.353725
91.574773
Chef-lieu canton
379.0
2448.0
0.0
NaN
1
010040102
Longeray-Gare
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
57.685313
168.621858
26.249674
1003.722497
222.261029
Chef-lieu canton
379.0
2448.0
0.0
NaN
2
010040201
Centre-St Germain-Vareilles
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
26.832875
139.834053
47.053809
1123.438076
191.813737
Chef-lieu canton
379.0
2448.0
0.0
NaN
3
010040202
Tiret-Les Allymes
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
47.754605
61.886062
46.099580
1575.388689
338.068012
Chef-lieu canton
379.0
2448.0
0.0
NaN
4
01004ZZZZ
Non Localisé À L'Iris
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
NaN
NaN
NaN
NaN
NaN
Chef-lieu canton
379.0
2448.0
0.0
NaN
5 rows × 1036 columns
Il y a bien les colonnes "status", "mean_altitude", "superficie", "is_metropole" et "metropole_name"
In [15]:
# Clefs pour regrouper par ville
key = ['CODGEO',
'LIBGEO',
'COM',
'LIBCOM',
'REG',
'DEP',
'ARR',
'CV',
'ZE2010',
'UU2010',
'TRIRIS',
'REG2016',
'status_rank']
# 'is_metropole']
In [18]:
# Autres valeurs
features = [col for col in data.columns if col not in key]
# Nom des colonnes qui ne sont pas dans les colonnes de key
In [ ]:
In [24]:
# On cherche à regrouper nos données sur le nom de la métropole :
# On somme tous nos indicateurs
metropole_sum = data[features][data.is_metropole == 1].groupby('metropole_name').sum().reset_index()
metropole_sum.shape
Out[24]:
(11, 1019)
In [25]:
metropole_sum
Out[25]:
metropole_name
NB_B101
NB_B102
NB_B103
NB_B201
NB_B202
NB_B203
NB_B204
NB_B205
NB_B206
...
P12_ACTOCC15P_ILT45D
C12_ACTOCC15P
C12_ACTOCC15P_PAS
C12_ACTOCC15P_MAR
C12_ACTOCC15P_DROU
C12_ACTOCC15P_VOIT
C12_ACTOCC15P_TCOM
mean_altitude
superficie
is_metropole
0
Bordeaux
18.0
89.0
42.0
44.0
217.0
385.0
169.0
17.0
26.0
...
NaN
315398.253489
9154.443892
20158.046550
24129.383739
208231.699409
53724.679898
6815.0
969169.0
294.0
1
Brest
8.0
29.0
6.0
8.0
29.0
97.0
44.0
8.0
16.0
...
NaN
82987.131510
2507.613904
6320.578554
3367.858679
61279.056005
9512.024368
5792.0
412218.0
98.0
2
Grenoble
6.0
33.0
12.0
37.0
106.0
192.0
91.0
6.0
8.0
...
NaN
185680.285698
5479.800653
15946.430502
15593.498465
112178.102149
36482.453930
73562.0
272420.0
210.0
3
Lille
23.0
148.0
33.0
63.0
376.0
493.0
264.0
25.0
14.0
...
NaN
440833.385317
13747.865175
31634.209792
16058.100643
302423.675325
76969.534382
15476.0
832162.0
533.0
4
Montpellier
7.0
39.0
9.0
26.0
179.0
215.0
118.0
8.0
21.0
...
NaN
169548.999584
5459.065915
13383.140507
12424.520266
113863.952781
24418.320115
7873.0
624365.0
165.0
5
Nantes
15.0
61.0
31.0
18.0
96.0
280.0
109.0
15.0
26.0
...
NaN
259150.983693
8295.338284
15193.962942
15399.276632
168483.240321
51779.165514
5923.0
1026079.0
245.0
6
Nice
9.0
60.0
33.0
61.0
347.0
394.0
204.0
25.0
38.0
...
NaN
213206.375345
8674.074706
23632.334244
18373.040516
124067.371558
38459.554321
64199.0
1310251.0
242.0
7
Rennes
8.0
32.0
16.0
24.0
57.0
164.0
55.0
10.0
6.0
...
NaN
181441.426846
6178.023403
13400.290113
9872.994887
118397.871923
33592.246521
8232.0
656169.0
186.0
8
Rouen
7.0
50.0
10.0
20.0
131.0
218.0
118.0
9.0
12.0
...
NaN
191608.362343
5993.245483
16698.491789
5982.375395
133840.099577
29094.150099
15951.0
317920.0
258.0
9
Strasbourg
9.0
53.0
4.0
21.0
156.0
242.0
65.0
11.0
2.0
...
NaN
194571.481827
6138.438943
16222.621017
19879.830213
109387.756750
42942.834903
27742.0
989336.0
197.0
10
Toulouse
12.0
84.0
30.0
79.0
196.0
339.0
211.0
19.0
37.0
...
NaN
322210.059178
8568.720406
20934.029996
21972.689673
209807.637776
60926.981328
40037.0
1958347.0
262.0
11 rows × 1019 columns
In [26]:
voiture_colonnes = ['metropole_name' ,'C12_ACTOCC15P_VOIT', 'C11_ACTOCC15P_VOIT','P12_ACTOCC15P', 'P11_ACTOCC15P']
In [28]:
voiture = metropole_sum[voiture_colonnes].copy()
voiture
Out[28]:
metropole_name
C12_ACTOCC15P_VOIT
C11_ACTOCC15P_VOIT
P12_ACTOCC15P
P11_ACTOCC15P
0
Bordeaux
208231.699409
207506.371166
315256.899143
312371.688110
1
Brest
61279.056005
61762.076746
83156.388709
83282.064006
2
Grenoble
112178.102149
112880.782653
185738.532132
186029.971637
3
Lille
302423.675325
302677.703395
440485.598808
440017.331865
4
Montpellier
113863.952781
112637.282501
169943.126723
167496.348235
5
Nantes
168483.240321
167552.014965
259371.500676
256694.008786
6
Nice
124067.371558
125035.820113
213022.822499
213710.043704
7
Rennes
118397.871923
118196.572269
181838.467426
180302.002739
8
Rouen
133840.099577
136860.989128
191125.767932
194911.253285
9
Strasbourg
109387.756750
110145.294190
194552.345854
194813.085280
10
Toulouse
209807.637776
208920.689602
322379.008362
318497.417281
Il va falloir re-travailler la données pour pouvoir donner le pourcentage de personnes qui prenne la voiture en 2011 / 2012 ainsi qu'avoir la progression
In [29]:
voiture['pourcentage_car_11'] = (voiture["C11_ACTOCC15P_VOIT"] / voiture["P11_ACTOCC15P"])*100
In [30]:
voiture
Out[30]:
metropole_name
C12_ACTOCC15P_VOIT
C11_ACTOCC15P_VOIT
P12_ACTOCC15P
P11_ACTOCC15P
pourcentage_car_11
0
Bordeaux
208231.699409
207506.371166
315256.899143
312371.688110
66.429315
1
Brest
61279.056005
61762.076746
83156.388709
83282.064006
74.160118
2
Grenoble
112178.102149
112880.782653
185738.532132
186029.971637
60.678815
3
Lille
302423.675325
302677.703395
440485.598808
440017.331865
68.787678
4
Montpellier
113863.952781
112637.282501
169943.126723
167496.348235
67.247605
5
Nantes
168483.240321
167552.014965
259371.500676
256694.008786
65.273052
6
Nice
124067.371558
125035.820113
213022.822499
213710.043704
58.507227
7
Rennes
118397.871923
118196.572269
181838.467426
180302.002739
65.554775
8
Rouen
133840.099577
136860.989128
191125.767932
194911.253285
70.217079
9
Strasbourg
109387.756750
110145.294190
194552.345854
194813.085280
56.538961
10
Toulouse
209807.637776
208920.689602
322379.008362
318497.417281
65.595725
In [31]:
voiture['pourcentage_car_12'] = (voiture["C12_ACTOCC15P_VOIT"] / voiture["P12_ACTOCC15P"])*100
voiture
Out[31]:
metropole_name
C12_ACTOCC15P_VOIT
C11_ACTOCC15P_VOIT
P12_ACTOCC15P
P11_ACTOCC15P
pourcentage_car_11
pourcentage_car_12
0
Bordeaux
208231.699409
207506.371166
315256.899143
312371.688110
66.429315
66.051433
1
Brest
61279.056005
61762.076746
83156.388709
83282.064006
74.160118
73.691339
2
Grenoble
112178.102149
112880.782653
185738.532132
186029.971637
60.678815
60.395708
3
Lille
302423.675325
302677.703395
440485.598808
440017.331865
68.787678
68.656881
4
Montpellier
113863.952781
112637.282501
169943.126723
167496.348235
67.247605
67.001211
5
Nantes
168483.240321
167552.014965
259371.500676
256694.008786
65.273052
64.958270
6
Nice
124067.371558
125035.820113
213022.822499
213710.043704
58.507227
58.241352
7
Rennes
118397.871923
118196.572269
181838.467426
180302.002739
65.554775
65.111565
8
Rouen
133840.099577
136860.989128
191125.767932
194911.253285
70.217079
70.027240
9
Strasbourg
109387.756750
110145.294190
194552.345854
194813.085280
56.538961
56.225360
10
Toulouse
209807.637776
208920.689602
322379.008362
318497.417281
65.595725
65.081048
In [34]:
def augmentation(depart, arrive):
"""
Calcul de l'augmentation entre 2 valeurs :
# ( ( valeur d'arrivée - valeur de départ ) / valeur de départ ) x 100
"""
return ((arrive - depart) / depart) * 100
In [35]:
voiture['augmentation'] = augmentation(voiture['pourcentage_car_11'], voiture['pourcentage_car_12'])
In [36]:
# Les métropole qui utilise le moins la voiture pour aller travailler :
voiture.sort_values('augmentation')
Out[36]:
metropole_name
C12_ACTOCC15P_VOIT
C11_ACTOCC15P_VOIT
P12_ACTOCC15P
P11_ACTOCC15P
pourcentage_car_11
pourcentage_car_12
augmentation
10
Toulouse
209807.637776
208920.689602
322379.008362
318497.417281
65.595725
65.081048
-0.784619
7
Rennes
118397.871923
118196.572269
181838.467426
180302.002739
65.554775
65.111565
-0.676091
1
Brest
61279.056005
61762.076746
83156.388709
83282.064006
74.160118
73.691339
-0.632118
0
Bordeaux
208231.699409
207506.371166
315256.899143
312371.688110
66.429315
66.051433
-0.568848
9
Strasbourg
109387.756750
110145.294190
194552.345854
194813.085280
56.538961
56.225360
-0.554664
5
Nantes
168483.240321
167552.014965
259371.500676
256694.008786
65.273052
64.958270
-0.482254
2
Grenoble
112178.102149
112880.782653
185738.532132
186029.971637
60.678815
60.395708
-0.466566
6
Nice
124067.371558
125035.820113
213022.822499
213710.043704
58.507227
58.241352
-0.454431
4
Montpellier
113863.952781
112637.282501
169943.126723
167496.348235
67.247605
67.001211
-0.366398
8
Rouen
133840.099577
136860.989128
191125.767932
194911.253285
70.217079
70.027240
-0.270361
3
Lille
302423.675325
302677.703395
440485.598808
440017.331865
68.787678
68.656881
-0.190145
In [37]:
transp_com_colonnes = ['metropole_name' , 'C11_ACTOCC15P_TCOM', 'C12_ACTOCC15P_TCOM','P12_ACTOCC15P', 'P11_ACTOCC15P']
In [38]:
transp_com = metropole_sum[transp_com_colonnes].copy()
transp_com
Out[38]:
metropole_name
C11_ACTOCC15P_TCOM
C12_ACTOCC15P_TCOM
P12_ACTOCC15P
P11_ACTOCC15P
0
Bordeaux
52335.460630
53724.679898
315256.899143
312371.688110
1
Brest
9274.863519
9512.024368
83156.388709
83282.064006
2
Grenoble
36544.785029
36482.453930
185738.532132
186029.971637
3
Lille
75632.953278
76969.534382
440485.598808
440017.331865
4
Montpellier
23380.301333
24418.320115
169943.126723
167496.348235
5
Nantes
51097.255541
51779.165514
259371.500676
256694.008786
6
Nice
37629.560450
38459.554321
213022.822499
213710.043704
7
Rennes
32958.353317
33592.246521
181838.467426
180302.002739
8
Rouen
29055.207775
29094.150099
191125.767932
194911.253285
9
Strasbourg
42245.427933
42942.834903
194552.345854
194813.085280
10
Toulouse
58721.165631
60926.981328
322379.008362
318497.417281
In [39]:
transp_com['pourcentage_trans_com_12'] = (transp_com["C12_ACTOCC15P_TCOM"] / transp_com["P12_ACTOCC15P"])*100
transp_com
Out[39]:
metropole_name
C11_ACTOCC15P_TCOM
C12_ACTOCC15P_TCOM
P12_ACTOCC15P
P11_ACTOCC15P
pourcentage_trans_com_12
0
Bordeaux
52335.460630
53724.679898
315256.899143
312371.688110
17.041556
1
Brest
9274.863519
9512.024368
83156.388709
83282.064006
11.438717
2
Grenoble
36544.785029
36482.453930
185738.532132
186029.971637
19.641834
3
Lille
75632.953278
76969.534382
440485.598808
440017.331865
17.473791
4
Montpellier
23380.301333
24418.320115
169943.126723
167496.348235
14.368525
5
Nantes
51097.255541
51779.165514
259371.500676
256694.008786
19.963321
6
Nice
37629.560450
38459.554321
213022.822499
213710.043704
18.054194
7
Rennes
32958.353317
33592.246521
181838.467426
180302.002739
18.473674
8
Rouen
29055.207775
29094.150099
191125.767932
194911.253285
15.222516
9
Strasbourg
42245.427933
42942.834903
194552.345854
194813.085280
22.072638
10
Toulouse
58721.165631
60926.981328
322379.008362
318497.417281
18.899178
In [40]:
transp_com['pourcentage_trans_com_11'] = (transp_com["C11_ACTOCC15P_TCOM"] / transp_com["P11_ACTOCC15P"])*100
transp_com
Out[40]:
metropole_name
C11_ACTOCC15P_TCOM
C12_ACTOCC15P_TCOM
P12_ACTOCC15P
P11_ACTOCC15P
pourcentage_trans_com_12
pourcentage_trans_com_11
0
Bordeaux
52335.460630
53724.679898
315256.899143
312371.688110
17.041556
16.754227
1
Brest
9274.863519
9512.024368
83156.388709
83282.064006
11.438717
11.136688
2
Grenoble
36544.785029
36482.453930
185738.532132
186029.971637
19.641834
19.644568
3
Lille
75632.953278
76969.534382
440485.598808
440017.331865
17.473791
17.188630
4
Montpellier
23380.301333
24418.320115
169943.126723
167496.348235
14.368525
13.958693
5
Nantes
51097.255541
51779.165514
259371.500676
256694.008786
19.963321
19.905901
6
Nice
37629.560450
38459.554321
213022.822499
213710.043704
18.054194
17.607764
7
Rennes
32958.353317
33592.246521
181838.467426
180302.002739
18.473674
18.279527
8
Rouen
29055.207775
29094.150099
191125.767932
194911.253285
15.222516
14.906891
9
Strasbourg
42245.427933
42942.834903
194552.345854
194813.085280
22.072638
21.685108
10
Toulouse
58721.165631
60926.981328
322379.008362
318497.417281
18.899178
18.436936
In [41]:
transp_com['augmentation'] = augmentation(transp_com['pourcentage_trans_com_11'], transp_com['pourcentage_trans_com_12'])
In [43]:
transp_com.sort_values('augmentation')
Out[43]:
metropole_name
C11_ACTOCC15P_TCOM
C12_ACTOCC15P_TCOM
P12_ACTOCC15P
P11_ACTOCC15P
pourcentage_trans_com_12
pourcentage_trans_com_11
augmentation
2
Grenoble
36544.785029
36482.453930
185738.532132
186029.971637
19.641834
19.644568
-0.013920
5
Nantes
51097.255541
51779.165514
259371.500676
256694.008786
19.963321
19.905901
0.288457
7
Rennes
32958.353317
33592.246521
181838.467426
180302.002739
18.473674
18.279527
1.062103
3
Lille
75632.953278
76969.534382
440485.598808
440017.331865
17.473791
17.188630
1.659008
0
Bordeaux
52335.460630
53724.679898
315256.899143
312371.688110
17.041556
16.754227
1.714964
9
Strasbourg
42245.427933
42942.834903
194552.345854
194813.085280
22.072638
21.685108
1.787079
8
Rouen
29055.207775
29094.150099
191125.767932
194911.253285
15.222516
14.906891
2.117309
10
Toulouse
58721.165631
60926.981328
322379.008362
318497.417281
18.899178
18.436936
2.507149
6
Nice
37629.560450
38459.554321
213022.822499
213710.043704
18.054194
17.607764
2.535417
1
Brest
9274.863519
9512.024368
83156.388709
83282.064006
11.438717
11.136688
2.712024
4
Montpellier
23380.301333
24418.320115
169943.126723
167496.348235
14.368525
13.958693
2.936031
In [49]:
transp_velo_colonnes = ['metropole_name' , 'C11_ACTOCC15P_DROU', 'C12_ACTOCC15P_DROU','P12_ACTOCC15P', 'P11_ACTOCC15P']
In [50]:
transp_velo = metropole_sum[transp_com_colonnes].copy()
transp_velo
Out[50]:
metropole_name
C11_ACTOCC15P_TCOM
C12_ACTOCC15P_TCOM
P12_ACTOCC15P
P11_ACTOCC15P
0
Bordeaux
52335.460630
53724.679898
315256.899143
312371.688110
1
Brest
9274.863519
9512.024368
83156.388709
83282.064006
2
Grenoble
36544.785029
36482.453930
185738.532132
186029.971637
3
Lille
75632.953278
76969.534382
440485.598808
440017.331865
4
Montpellier
23380.301333
24418.320115
169943.126723
167496.348235
5
Nantes
51097.255541
51779.165514
259371.500676
256694.008786
6
Nice
37629.560450
38459.554321
213022.822499
213710.043704
7
Rennes
32958.353317
33592.246521
181838.467426
180302.002739
8
Rouen
29055.207775
29094.150099
191125.767932
194911.253285
9
Strasbourg
42245.427933
42942.834903
194552.345854
194813.085280
10
Toulouse
58721.165631
60926.981328
322379.008362
318497.417281
In [53]:
transp_velo['pourcentage_trans_com_12'] = (transp_velo["C12_ACTOCC15P_TCOM"] / transp_velo["P12_ACTOCC15P"])*100
transp_velo
Out[53]:
metropole_name
C11_ACTOCC15P_TCOM
C12_ACTOCC15P_TCOM
P12_ACTOCC15P
P11_ACTOCC15P
pourcentage_trans_com_12
0
Bordeaux
52335.460630
53724.679898
315256.899143
312371.688110
17.041556
1
Brest
9274.863519
9512.024368
83156.388709
83282.064006
11.438717
2
Grenoble
36544.785029
36482.453930
185738.532132
186029.971637
19.641834
3
Lille
75632.953278
76969.534382
440485.598808
440017.331865
17.473791
4
Montpellier
23380.301333
24418.320115
169943.126723
167496.348235
14.368525
5
Nantes
51097.255541
51779.165514
259371.500676
256694.008786
19.963321
6
Nice
37629.560450
38459.554321
213022.822499
213710.043704
18.054194
7
Rennes
32958.353317
33592.246521
181838.467426
180302.002739
18.473674
8
Rouen
29055.207775
29094.150099
191125.767932
194911.253285
15.222516
9
Strasbourg
42245.427933
42942.834903
194552.345854
194813.085280
22.072638
10
Toulouse
58721.165631
60926.981328
322379.008362
318497.417281
18.899178
In [54]:
transp_velo['pourcentage_trans_com_11'] = (transp_velo["C11_ACTOCC15P_TCOM"] / transp_velo["P11_ACTOCC15P"])*100
transp_velo
Out[54]:
metropole_name
C11_ACTOCC15P_TCOM
C12_ACTOCC15P_TCOM
P12_ACTOCC15P
P11_ACTOCC15P
pourcentage_trans_com_12
pourcentage_trans_com_11
0
Bordeaux
52335.460630
53724.679898
315256.899143
312371.688110
17.041556
16.754227
1
Brest
9274.863519
9512.024368
83156.388709
83282.064006
11.438717
11.136688
2
Grenoble
36544.785029
36482.453930
185738.532132
186029.971637
19.641834
19.644568
3
Lille
75632.953278
76969.534382
440485.598808
440017.331865
17.473791
17.188630
4
Montpellier
23380.301333
24418.320115
169943.126723
167496.348235
14.368525
13.958693
5
Nantes
51097.255541
51779.165514
259371.500676
256694.008786
19.963321
19.905901
6
Nice
37629.560450
38459.554321
213022.822499
213710.043704
18.054194
17.607764
7
Rennes
32958.353317
33592.246521
181838.467426
180302.002739
18.473674
18.279527
8
Rouen
29055.207775
29094.150099
191125.767932
194911.253285
15.222516
14.906891
9
Strasbourg
42245.427933
42942.834903
194552.345854
194813.085280
22.072638
21.685108
10
Toulouse
58721.165631
60926.981328
322379.008362
318497.417281
18.899178
18.436936
In [44]:
data.head()
Out[44]:
CODGEO
LIBGEO
COM
LIBCOM
REG
DEP
ARR
CV
ZE2010
UU2010
...
C12_ACTOCC15P_PAS
C12_ACTOCC15P_MAR
C12_ACTOCC15P_DROU
C12_ACTOCC15P_VOIT
C12_ACTOCC15P_TCOM
status
mean_altitude
superficie
is_metropole
metropole_name
0
010040101
Les Perouses-Triangle D'Activite
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
75.289455
119.660483
21.756694
407.353725
91.574773
Chef-lieu canton
379.0
2448.0
0.0
NaN
1
010040102
Longeray-Gare
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
57.685313
168.621858
26.249674
1003.722497
222.261029
Chef-lieu canton
379.0
2448.0
0.0
NaN
2
010040201
Centre-St Germain-Vareilles
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
26.832875
139.834053
47.053809
1123.438076
191.813737
Chef-lieu canton
379.0
2448.0
0.0
NaN
3
010040202
Tiret-Les Allymes
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
47.754605
61.886062
46.099580
1575.388689
338.068012
Chef-lieu canton
379.0
2448.0
0.0
NaN
4
01004ZZZZ
Non Localisé À L'Iris
01004
Ambérieu-en-Bugey
82
1
11
101
8201.0
1302
...
NaN
NaN
NaN
NaN
NaN
Chef-lieu canton
379.0
2448.0
0.0
NaN
5 rows × 1036 columns
In [45]:
bdx = data[data.LIBCOM == "Bordeaux"]
In [87]:
bdx
Out[87]:
CODGEO
LIBGEO
COM
LIBCOM
REG
DEP
ARR
CV
ZE2010
UU2010
NB_B101
NB_B102
NB_B103
NB_B201
NB_B202
...
P12_ACTOCC15P_ILT3
P12_ACTOCC15P_ILT4
P12_ACTOCC15P_ILT5
P12_ACTOCC15P_ILT45D
C12_ACTOCC15P
C12_ACTOCC15P_PAS
C12_ACTOCC15P_MAR
C12_ACTOCC15P_DROU
C12_ACTOCC15P_VOIT
C12_ACTOCC15P_TCOM
status
mean_altitude
superficie
is_metropole
metropole_name
2987
330630101
Le Lac 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
1.0
1.0
1.0
0.0
1.0
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2988
330630103
Le Lac 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
1.0
1.0
...
0.984268
16.826016
0.000000
NaN
1075.782731
32.635032
84.254284
56.517603
482.873435
419.502376
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2989
330630201
Bacalan 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
1.0
0.0
3.0
...
0.000000
16.485367
0.000000
NaN
527.428927
20.733607
61.799049
23.556735
297.530070
123.809466
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2990
330630202
Bacalan 2
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
0.000000
11.039434
0.000000
NaN
1068.936042
58.280446
38.807450
88.446137
633.401373
250.000636
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2991
330630203
Bacalan 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
1.0
...
0.000000
5.466127
3.211711
NaN
910.818388
24.660676
66.252502
53.412298
554.086179
212.406733
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2992
330630204
Bacalan 4
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
0.000000
4.751087
0.000000
NaN
575.439318
16.314981
17.252895
38.478811
307.787403
195.605227
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2993
330630301
Chartrons-Grand-Parc 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
2.0
...
7.065795
51.641779
3.041529
NaN
2168.974828
57.568895
223.342810
203.938225
971.625756
712.499142
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2994
330630302
Chartrons-Grand-Parc 2
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
1.0
1.0
...
12.915476
52.107202
0.000000
NaN
1745.051678
24.866154
222.667423
246.271102
735.280705
515.966295
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2995
330630303
Chartrons-Grand-Parc 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
2.0
0.0
0.0
0.0
...
1.101075
22.283622
1.101075
NaN
1464.620127
55.014838
160.289309
112.744223
684.108292
452.463465
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2996
330630305
Chartrons-Grand-Parc 5
33063
Bordeaux
72
33
332
3399
7204.0
33701
1.0
0.0
0.0
0.0
1.0
...
7.969752
5.902744
2.948031
NaN
1200.363306
37.333653
164.216779
65.196631
539.849989
393.766254
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2997
330630306
Chartrons-Grand-Parc 6
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
0.0
...
0.000000
2.899867
0.000000
NaN
913.484031
35.647605
127.023188
88.056634
500.258063
162.498541
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2998
330630307
Chartrons-Grand-Parc 7
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
1.0
...
9.755280
19.818507
6.503520
NaN
1183.044495
18.248191
121.125200
106.735672
613.280478
323.654954
Préfecture de région
9.0
4970.0
1.0
Bordeaux
2999
330630308
Chartrons-Grand-Parc 8
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
9.035349
15.942717
0.000000
NaN
685.198690
23.723276
72.298559
81.896087
359.265702
148.015065
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3000
330630309
Chartrons-Grand-Parc 9
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
2.0
...
12.739243
22.247174
3.215893
NaN
1156.226743
34.894862
212.571336
190.018457
445.325919
273.416169
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3001
330630310
Chartrons-Grand-Parc 10
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
1.0
1.0
...
6.232375
37.957532
0.000000
NaN
1211.365059
42.107124
126.216622
173.854253
575.584565
293.602494
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3002
330630311
Chartrons-Grand-Parc 11
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
4.0
2.0
...
12.491915
21.793786
0.000000
NaN
1166.244612
65.821396
161.629323
180.632615
396.752553
361.408724
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3003
330630312
Chartrons-Grand Parc 12
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
0.0
...
14.645985
31.062753
3.343597
NaN
1306.995133
27.800255
107.555695
193.645295
642.742251
335.251636
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3004
330630313
Chartrons-Grand Parc 13
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
22.101178
54.088972
0.000000
NaN
1853.608843
43.710530
116.984719
191.520634
1009.922135
491.470826
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3005
330630401
La Bastide 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
0.0
...
18.533762
68.277834
3.657013
NaN
1911.360637
73.016892
106.249643
230.115104
872.431956
629.547042
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3006
330630402
La Bastide 2
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
1.0
...
10.256919
8.975522
3.104032
NaN
1103.280123
26.937928
60.484248
124.671476
573.319275
317.867195
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3007
330630403
La Bastide 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
2.0
0.0
0.0
2.0
...
18.303813
39.590150
0.000000
NaN
1588.394445
34.553206
28.019609
165.844142
825.408778
534.568710
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3008
330630404
La Bastide 4
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
0.0
...
0.000000
9.840782
0.000000
NaN
795.757196
10.107983
68.623730
72.958614
334.055838
310.011031
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3009
330630405
La Bastide 5
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
3.0
...
5.843029
32.602542
0.000000
NaN
1335.130087
53.025190
88.921292
153.568120
636.099689
403.515796
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3010
330630501
Hotel De Ville-Quinconces 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
4.0
...
6.117833
38.975941
0.000000
NaN
1280.959314
62.693700
310.775977
152.256072
256.582653
498.650911
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3011
330630502
Hotel De Ville-Quinconces 2
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
1.0
1.0
13.0
...
5.289589
59.410405
0.000000
NaN
1834.708522
35.152362
408.815038
190.114666
428.385623
772.240833
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3012
330630503
Hotel De Ville-Quinconces 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
4.0
...
4.159456
48.128560
3.307361
NaN
970.486906
71.836300
222.905547
75.650671
222.855616
377.238771
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3013
330630504
Hotel De Ville-Quinconces 4
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
1.0
3.0
...
9.080909
12.276097
0.000000
NaN
954.897656
45.660619
288.201101
108.705186
203.817217
308.513532
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3014
330630505
Hotel De Ville-Quinconces 5
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
3.0
...
6.515722
21.936912
0.000000
NaN
1215.499062
57.465649
344.467969
186.407474
213.604120
413.553850
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3015
330630506
Hotel De Ville-Quinconces 6
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
5.0
...
6.126279
37.188614
6.066931
NaN
1339.513193
45.508785
338.377043
134.888601
489.925847
330.812917
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3016
330630507
Hotel De Ville-Quinconces 7
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
2.0
0.0
0.0
0.0
...
11.498874
30.365472
0.000000
NaN
1142.765392
121.388226
230.262378
57.513383
444.870716
288.730689
Préfecture de région
9.0
4970.0
1.0
Bordeaux
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
3046
330630905
Saint-Augustin 5
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
9.834463
31.825869
0.000000
NaN
1333.808940
34.517602
167.004370
164.450440
759.723742
208.112785
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3047
330631001
Saint-Bruno-Saint-Victor 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
1.0
...
8.774589
38.351014
0.000000
NaN
1621.589036
62.149812
317.399223
235.992480
670.675434
335.372087
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3048
330631002
Saint-Bruno-Saint-Victor 2
33063
Bordeaux
72
33
332
3399
7204.0
33701
1.0
0.0
0.0
0.0
0.0
...
7.614239
11.947477
3.185863
NaN
574.301653
21.736033
182.901757
61.244664
165.558510
142.860690
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3049
330631003
Saint-Bruno-Saint-Victor 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
2.0
2.0
...
10.851299
59.466062
0.000000
NaN
1677.827491
32.964916
292.204256
224.304457
652.943680
475.410182
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3050
330631004
Saint-Bruno-Saint-Victor 4
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
1.0
2.0
...
26.580402
70.176332
9.428806
NaN
2093.742706
56.546818
372.184315
304.188181
813.155456
547.667936
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3051
330631005
Saint-Bruno-Saint-Victor 5
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
1.0
...
19.979224
37.214197
4.305358
NaN
1234.006170
68.276754
278.208296
123.719938
408.049540
355.751643
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3052
330631006
Saint-Bruno-Saint-Victor 6
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
24.103348
24.093710
0.000000
NaN
1132.835895
33.746832
159.212106
206.283033
519.595931
213.997993
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3053
330631101
Capucins-Victoire 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
5.0
...
13.055004
30.444882
0.000000
NaN
1238.274859
19.316257
182.871849
191.707413
307.973693
536.405647
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3054
330631102
Capucins-Victoire 2
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
1.0
2.0
...
14.085393
10.795935
0.000000
NaN
1021.121984
45.695199
270.408203
104.823927
243.149956
357.044698
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3055
330631103
Capucins-Victoire 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
1.0
1.0
0.0
0.0
5.0
...
22.900442
25.025038
3.184751
NaN
912.913989
46.267560
240.260854
80.295632
188.108030
357.981913
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3056
330631104
Capucins-Victoire 4
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
1.0
14.0
...
10.434807
56.895497
6.326180
NaN
1300.421109
23.035512
246.156865
160.796539
349.799836
520.632357
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3057
330631105
Capucins-Victoire 5
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
6.0
...
15.122340
19.761601
3.431785
NaN
950.699696
40.266946
125.484965
165.133497
263.687905
356.126383
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3058
330631106
Capucins-Victoire 6
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
1.0
0.0
9.0
...
10.376491
58.028106
0.982262
NaN
1135.081237
48.374059
204.165659
141.333822
333.701359
407.506338
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3059
330631107
Capucins-Victoire 7
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
1.0
3.0
...
2.916737
24.344799
0.000000
NaN
859.822128
17.485069
134.320231
145.396212
193.411566
369.209050
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3060
330631108
Capucins-Victoire 8
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
1.0
15.0
...
3.029802
21.810140
0.000000
NaN
835.614894
26.023990
129.642297
69.941673
263.886355
346.120578
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3061
330631201
Nansouty 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
3.0
...
17.334859
32.298526
2.814470
NaN
1278.117824
48.750480
197.873220
177.289815
518.552063
335.652246
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3062
330631202
Nansouty 2
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
11.014459
41.804828
3.641776
NaN
1156.802074
68.546489
138.538846
199.036354
502.971180
247.709206
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3063
330631203
Nansouty 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
1.0
2.0
...
3.302762
49.098562
0.000000
NaN
1061.321942
13.913825
61.780501
131.931862
520.837991
332.857763
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3064
330631204
Nansouty 4
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
1.0
...
1.080848
32.181240
4.536238
NaN
1221.672788
16.152455
112.219006
179.018555
539.553256
374.729516
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3065
330631205
Nansouty 5
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
1.0
...
7.090952
29.348485
0.000000
NaN
1053.417020
32.702018
75.612038
123.975689
460.324685
360.802590
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3066
330631206
Nansouty 6
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
0.000000
7.317065
3.278899
NaN
931.665994
56.881280
65.107630
172.913335
436.265106
200.498643
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3067
330631207
Nansouty 7
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
2.0
...
7.537769
25.779011
0.000000
NaN
962.411552
54.559630
117.239476
167.025878
419.697545
203.889023
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3068
330631208
Nansouty 8
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
1.0
...
15.520073
24.583560
0.000000
NaN
1143.434128
34.462169
58.201376
198.697628
639.646106
212.426849
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3069
330631209
Nansouty 9
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
0.0
1.0
...
15.512962
17.387046
0.000000
NaN
1062.396650
44.126184
122.409764
185.150063
436.303078
274.407561
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3070
330631301
Gare Saint-Jean 1
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
6.141192
19.890974
0.000000
NaN
1064.553847
25.653647
69.355939
149.815893
475.876393
343.851975
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3071
330631302
Gare Saint-Jean 2
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
16.957075
19.723478
0.000000
NaN
1093.132181
12.764667
109.843409
205.568621
564.924254
200.031230
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3072
330631303
Gare Saint-Jean 3
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
0.0
...
4.711913
13.212117
0.000000
NaN
880.244959
29.092326
38.630424
70.925674
421.378980
320.217554
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3073
330631304
Gare Saint-Jean 4
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
3.0
0.0
2.0
4.0
...
26.595657
27.750920
0.000000
NaN
1324.463090
45.930591
183.394021
113.663298
462.336551
519.138631
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3074
330631305
Gare Saint-Jean 5
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
0.0
0.0
0.0
1.0
...
9.347643
14.578850
3.098782
NaN
1010.770736
42.806486
101.040981
179.421500
435.121648
252.380121
Préfecture de région
9.0
4970.0
1.0
Bordeaux
3075
33063ZZZZ
Non Localisé À L'Iris
33063
Bordeaux
72
33
332
3399
7204.0
33701
0.0
1.0
0.0
1.0
1.0
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Préfecture de région
9.0
4970.0
1.0
Bordeaux
89 rows × 1036 columns
In [88]:
bdx.LIBGEO.unique()
Out[88]:
array([u'Le Lac 1', u'Le Lac 3', u'Bacalan 1', u'Bacalan 2', u'Bacalan 3',
u'Bacalan 4', u'Chartrons-Grand-Parc 1', u'Chartrons-Grand-Parc 2',
u'Chartrons-Grand-Parc 3', u'Chartrons-Grand-Parc 5',
u'Chartrons-Grand-Parc 6', u'Chartrons-Grand-Parc 7',
u'Chartrons-Grand-Parc 8', u'Chartrons-Grand-Parc 9',
u'Chartrons-Grand-Parc 10', u'Chartrons-Grand-Parc 11',
u'Chartrons-Grand Parc 12', u'Chartrons-Grand Parc 13',
u'La Bastide 1', u'La Bastide 2', u'La Bastide 3', u'La Bastide 4',
u'La Bastide 5', u'Hotel De Ville-Quinconces 1',
u'Hotel De Ville-Quinconces 2', u'Hotel De Ville-Quinconces 3',
u'Hotel De Ville-Quinconces 4', u'Hotel De Ville-Quinconces 5',
u'Hotel De Ville-Quinconces 6', u'Hotel De Ville-Quinconces 7',
u'Hotel De Ville-Quinconces 8', u'Saint-Seurin-Fondaudege 1',
u'Saint-Seurin-Fondaudege 2', u'Saint-Seurin-Fondaudege 3',
u'Saint-Seurin-Fondaudege 4', u'Saint-Seurin-Fondaudege 5',
u'Saint-Seurin-Fondaudege 6', u'Saint-Seurin-Fondaudege 7',
u'Saint-Seurin-Fondaudege 8', u'Saint-Seurin-Fondaudege 9',
u'Villa Primerose Parc Bor.-Cauderan 1',
u'Villa Primerose Parc Bor.-Cauderan 2',
u'Villa Primerose Parc Bor.-Cauderan 3',
u'Villa Primerose Parc Bor.-Cauderan 4',
u'Villa Primerose Parc Bor.-Cauderan 5',
u'Villa Primerose Parc Bor.-Cauderan 6', u'Lestonat-Monsejour 1',
u'Lestonat-Monsejour 2', u'Lestonat-Monsejour 3',
u'Lestonat-Monsejour 4', u'Lestonat-Monsejour 5',
u'Lestonat-Monsejour 6', u'Lestonat-Monsejour 7',
u'Lestonat-Monsejour 8', u'Lestonat-Monsejour 9',
u'Saint-Augustin 1', u'Saint-Augustin 2', u'Saint-Augustin 3',
u'Saint-Augustin 4', u'Saint-Augustin 5',
u'Saint-Bruno-Saint-Victor 1', u'Saint-Bruno-Saint-Victor 2',
u'Saint-Bruno-Saint-Victor 3', u'Saint-Bruno-Saint-Victor 4',
u'Saint-Bruno-Saint-Victor 5', u'Saint-Bruno-Saint-Victor 6',
u'Capucins-Victoire 1', u'Capucins-Victoire 2',
u'Capucins-Victoire 3', u'Capucins-Victoire 4',
u'Capucins-Victoire 5', u'Capucins-Victoire 6',
u'Capucins-Victoire 7', u'Capucins-Victoire 8', u'Nansouty 1',
u'Nansouty 2', u'Nansouty 3', u'Nansouty 4', u'Nansouty 5',
u'Nansouty 6', u'Nansouty 7', u'Nansouty 8', u'Nansouty 9',
u'Gare Saint-Jean 1', u'Gare Saint-Jean 2', u'Gare Saint-Jean 3',
u'Gare Saint-Jean 4', u'Gare Saint-Jean 5',
u"Non Localis\xe9 \xc0 L'Iris"], dtype=object)
In [48]:
bdx[bdx.LIBGEO.str.contains("Cauderan")][['P11_POP15P_CELIB', 'P12_POP15P_CELIB', 'P12_F1529', 'P12_H1529']].sum()
Out[48]:
P11_POP15P_CELIB 5886.463042
P12_POP15P_CELIB 5868.660327
P12_F1529 1521.839555
P12_H1529 1497.865838
dtype: float64
In [47]:
bdx[bdx.LIBGEO.str.contains("Chartron")][['P11_POP15P_CELIB', 'P12_POP15P_CELIB', 'P12_F1529', 'P12_H1529']].sum()
Out[47]:
P11_POP15P_CELIB 16376.767589
P12_POP15P_CELIB 16648.790570
P12_F1529 4944.671688
P12_H1529 4530.768996
dtype: float64
In [93]:
commune_metropole.head()
Out[93]:
COM
status
mean_altitude
superficie
is_metropole
metropole_name
0
39007
Commune simple
587.0
582.0
0
NaN
1
88086
Commune simple
454.0
396.0
0
NaN
2
62627
Commune simple
49.0
426.0
0
NaN
3
14020
Commune simple
33.0
987.0
0
NaN
4
11091
Chef-lieu canton
455.0
1605.0
0
NaN
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Content source: armgilles/presentation
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