

In [1]:

    
import matplotlib.pyplot as plt
import json
import pandas as pd

China - CN: CN-01

This notebook contains few data analysis through the China's CN-01 data from Cultural Crowds dataset¹

¹ _{FAVARETTO, R.; DIHL, L. ; BARRETO, R. ; MUSSE, S. R. Using Group Behaviors To Detect Hofstede Cultural Dimensions. IEEE International Conference on Image Processing (ICIP), 2016.}



In [2]:

    
d = None
with open('..\..\group_analysis.json') as f:
    d = json.load(f)

Number of groups for frame

Collected data about the number of groups through time. The average of the number of groups was 15.7777, minimum of 14 and maximum of 19.



In [3]:

    
df_num_groups = pd.DataFrame(data={'Min. Num. of Groups': d['min_num_groups'], 'Avg. Num. of Groups': d['avg_num_groups'], 'Max. Num. of Groups': d['max_num_groups']})
df_num_groups









    Out[3]:







  
    
      
      Avg. Num. of Groups
      Max. Num. of Groups
      Min. Num. of Groups
    
  
  
    
      0
      18.000000
      18
      18
    
    
      1
      18.500000
      19
      18
    
    
      2
      18.666667
      19
      18
    
    
      3
      18.500000
      19
      18
    
    
      4
      18.600000
      19
      18
    
    
      5
      18.500000
      19
      18
    
    
      6
      18.428571
      19
      18
    
    
      7
      18.250000
      19
      17
    
    
      8
      18.111111
      19
      17
    
    
      9
      17.900000
      19
      16
    
    
      10
      17.727273
      19
      16
    
    
      11
      17.583333
      19
      16
    
    
      12
      17.384615
      19
      15
    
    
      13
      17.214286
      19
      15
    
    
      14
      17.000000
      19
      14
    
    
      15
      16.812500
      19
      14
    
    
      16
      16.647059
      19
      14
    
    
      17
      16.500000
      19
      14
    
    
      18
      16.473684
      19
      14
    
    
      19
      16.400000
      19
      14
    
    
      20
      16.333333
      19
      14
    
    
      21
      16.227273
      19
      14
    
    
      22
      16.173913
      19
      14
    
    
      23
      16.125000
      19
      14
    
    
      24
      16.080000
      19
      14
    
    
      25
      16.038462
      19
      14
    
    
      26
      16.000000
      19
      14
    
    
      27
      15.964286
      19
      14
    
    
      28
      15.931034
      19
      14
    
    
      29
      15.866667
      19
      14
    
    
      ...
      ...
      ...
      ...
    
    
      69
      15.971429
      19
      14
    
    
      70
      15.985915
      19
      14
    
    
      71
      16.000000
      19
      14
    
    
      72
      16.013699
      19
      14
    
    
      73
      16.027027
      19
      14
    
    
      74
      16.026667
      19
      14
    
    
      75
      16.026316
      19
      14
    
    
      76
      16.012987
      19
      14
    
    
      77
      16.000000
      19
      14
    
    
      78
      15.987342
      19
      14
    
    
      79
      15.975000
      19
      14
    
    
      80
      15.962963
      19
      14
    
    
      81
      15.939024
      19
      14
    
    
      82
      15.915663
      19
      14
    
    
      83
      15.892857
      19
      14
    
    
      84
      15.894118
      19
      14
    
    
      85
      15.895349
      19
      14
    
    
      86
      15.885057
      19
      14
    
    
      87
      15.875000
      19
      14
    
    
      88
      15.865169
      19
      14
    
    
      89
      15.866667
      19
      14
    
    
      90
      15.857143
      19
      14
    
    
      91
      15.858696
      19
      14
    
    
      92
      15.849462
      19
      14
    
    
      93
      15.851064
      19
      14
    
    
      94
      15.842105
      19
      14
    
    
      95
      15.833333
      19
      14
    
    
      96
      15.814433
      19
      14
    
    
      97
      15.795918
      19
      14
    
    
      98
      15.777778
      19
      14
    
  

99 rows × 3 columns



In [4]:

    
plt.figure()
ax = df_num_groups.plot(title='Number of Groups Analysis')
plt.xlabel('Frame nº')
plt.ylabel('Number of Groups')
plt.show()









    





<matplotlib.figure.Figure at 0x207bda6c588>

Number of elements on each group for frame

Collected data about the number persons for each group through time. The average of the number of people for group was 2.528361, minimum of 1 and maximum of 7.



In [5]:

    
df_group_elements = pd.DataFrame(data={'Min. Group Elements': d['min_group_elements'], 'Avg. Group Elements': d['avg_group_elements'], 'Max. Group Elements': d['max_group_elements']})
df_group_elements









    Out[5]:







  
    
      
      Avg. Group Elements
      Max. Group Elements
      Min. Group Elements
    
  
  
    
      0
      2.222222
      5
      1
    
    
      1
      2.163743
      5
      1
    
    
      2
      2.144250
      5
      1
    
    
      3
      2.163743
      5
      1
    
    
      4
      2.152047
      5
      1
    
    
      5
      2.163743
      5
      1
    
    
      6
      2.187970
      5
      1
    
    
      7
      2.223297
      5
      1
    
    
      8
      2.250774
      5
      1
    
    
      9
      2.288197
      5
      1
    
    
      10
      2.318815
      5
      1
    
    
      11
      2.344330
      5
      1
    
    
      12
      2.379382
      6
      1
    
    
      13
      2.409426
      6
      1
    
    
      14
      2.453560
      6
      1
    
    
      15
      2.492176
      6
      1
    
    
      16
      2.526250
      6
      1
    
    
      17
      2.556538
      6
      1
    
    
      18
      2.563431
      7
      1
    
    
      19
      2.578592
      7
      1
    
    
      20
      2.589136
      7
      1
    
    
      21
      2.611058
      7
      1
    
    
      22
      2.622171
      7
      1
    
    
      23
      2.632359
      7
      1
    
    
      24
      2.641731
      7
      1
    
    
      25
      2.650382
      7
      1
    
    
      26
      2.658393
      7
      1
    
    
      27
      2.665831
      7
      1
    
    
      28
      2.665860
      7
      1
    
    
      29
      2.672236
      7
      1
    
    
      ...
      ...
      ...
      ...
    
    
      69
      2.581099
      7
      1
    
    
      70
      2.578714
      7
      1
    
    
      71
      2.576396
      7
      1
    
    
      72
      2.572529
      7
      1
    
    
      73
      2.568766
      7
      1
    
    
      74
      2.565349
      7
      1
    
    
      75
      2.562023
      7
      1
    
    
      76
      2.559918
      7
      1
    
    
      77
      2.557868
      7
      1
    
    
      78
      2.555870
      7
      1
    
    
      79
      2.553921
      7
      1
    
    
      80
      2.552021
      7
      1
    
    
      81
      2.552258
      7
      1
    
    
      82
      2.552489
      7
      1
    
    
      83
      2.552714
      7
      1
    
    
      84
      2.549153
      7
      1
    
    
      85
      2.545674
      7
      1
    
    
      86
      2.544000
      7
      1
    
    
      87
      2.542364
      7
      1
    
    
      88
      2.540764
      7
      1
    
    
      89
      2.537533
      7
      1
    
    
      90
      2.536022
      7
      1
    
    
      91
      2.532913
      7
      1
    
    
      92
      2.531484
      7
      1
    
    
      93
      2.529819
      7
      1
    
    
      94
      2.529154
      7
      1
    
    
      95
      2.528503
      7
      1
    
    
      96
      2.528946
      7
      1
    
    
      97
      2.529379
      7
      1
    
    
      98
      2.528361
      7
      1
    
  

99 rows × 3 columns



In [6]:

    
plt.figure()
ax = df_group_elements.plot(title='Number of Group Elements')
plt.xlabel('Frame nº')
plt.ylabel('Number of Group Elements')
plt.show()









    





<matplotlib.figure.Figure at 0x207c06fae80>



In [ ]:

	Avg. Num. of Groups	Max. Num. of Groups	Min. Num. of Groups
0	18.000000	18	18
1	18.500000	19	18
2	18.666667	19	18
3	18.500000	19	18
4	18.600000	19	18
5	18.500000	19	18
6	18.428571	19	18
7	18.250000	19	17
8	18.111111	19	17
9	17.900000	19	16
10	17.727273	19	16
11	17.583333	19	16
12	17.384615	19	15
13	17.214286	19	15
14	17.000000	19	14
15	16.812500	19	14
16	16.647059	19	14
17	16.500000	19	14
18	16.473684	19	14
19	16.400000	19	14
20	16.333333	19	14
21	16.227273	19	14
22	16.173913	19	14
23	16.125000	19	14
24	16.080000	19	14
25	16.038462	19	14
26	16.000000	19	14
27	15.964286	19	14
28	15.931034	19	14
29	15.866667	19	14
...	...	...	...
69	15.971429	19	14
70	15.985915	19	14
71	16.000000	19	14
72	16.013699	19	14
73	16.027027	19	14
74	16.026667	19	14
75	16.026316	19	14
76	16.012987	19	14
77	16.000000	19	14
78	15.987342	19	14
79	15.975000	19	14
80	15.962963	19	14
81	15.939024	19	14
82	15.915663	19	14
83	15.892857	19	14
84	15.894118	19	14
85	15.895349	19	14
86	15.885057	19	14
87	15.875000	19	14
88	15.865169	19	14
89	15.866667	19	14
90	15.857143	19	14
91	15.858696	19	14
92	15.849462	19	14
93	15.851064	19	14
94	15.842105	19	14
95	15.833333	19	14
96	15.814433	19	14
97	15.795918	19	14
98	15.777778	19	14

	Avg. Group Elements	Max. Group Elements	Min. Group Elements
0	2.222222	5	1
1	2.163743	5	1
2	2.144250	5	1
3	2.163743	5	1
4	2.152047	5	1
5	2.163743	5	1
6	2.187970	5	1
7	2.223297	5	1
8	2.250774	5	1
9	2.288197	5	1
10	2.318815	5	1
11	2.344330	5	1
12	2.379382	6	1
13	2.409426	6	1
14	2.453560	6	1
15	2.492176	6	1
16	2.526250	6	1
17	2.556538	6	1
18	2.563431	7	1
19	2.578592	7	1
20	2.589136	7	1
21	2.611058	7	1
22	2.622171	7	1
23	2.632359	7	1
24	2.641731	7	1
25	2.650382	7	1
26	2.658393	7	1
27	2.665831	7	1
28	2.665860	7	1
29	2.672236	7	1
...	...	...	...
69	2.581099	7	1
70	2.578714	7	1
71	2.576396	7	1
72	2.572529	7	1
73	2.568766	7	1
74	2.565349	7	1
75	2.562023	7	1
76	2.559918	7	1
77	2.557868	7	1
78	2.555870	7	1
79	2.553921	7	1
80	2.552021	7	1
81	2.552258	7	1
82	2.552489	7	1
83	2.552714	7	1
84	2.549153	7	1
85	2.545674	7	1
86	2.544000	7	1
87	2.542364	7	1
88	2.540764	7	1
89	2.537533	7	1
90	2.536022	7	1
91	2.532913	7	1
92	2.531484	7	1
93	2.529819	7	1
94	2.529154	7	1
95	2.528503	7	1
96	2.528946	7	1
97	2.529379	7	1
98	2.528361	7	1