Overview

Brought to you by YData

Dataset statistics

Number of variables48
Number of observations136
Missing cells2001
Missing cells (%)30.7%
Total size in memory50.4 KiB
Average record size in memory379.5 B

Variable types

Numeric13
Text34
Categorical1

Alerts

Brouillon has constant value "0" Constant
Adresse IP has constant value "0.0.0.0" Constant
UID has constant value "0" Constant
Nom d'utilisateur has constant value "Anonymous" Constant
Autre diplôme has constant value "Université de foresterie - Sofia Bulgarie" Constant
activités d’enseignement has constant value "X" Constant
activités associatives has constant value "X" Constant
activités de paysagiste conseils de l’état has constant value "X" Constant
responsabilités universitaire has constant value "X" Constant
activité non salariée, en tant que micro-entrepreneurs has constant value "X" Constant
activité non salariée, en tant qu’entrepreneurs individuels classiques has constant value "X" Constant
activité non salariée, en tant gérants majoritaires de sociétés has constant value "X" Constant
activité non déclarée has constant value "X" Constant
Revenu salarial annuel net imposable : somme de tous les salaires perçus par un individu au cours d'une année donnée, nets de toutes cotisations sociales, y compris contribution sociale généralisée (CSG) et contribution au remboursement de la dette sociale (CRDS). has constant value "X" Constant
Revenu d’activité annuel : rémunération issue de l’activité non salariée, déduction faite des cotisations sociales payées dans l’année mais pas des contributions sociales (CSG non déductible, CRDS). Il est calculé à partir du revenu professionnel imposable auquel sont réintégrés certains allègements fiscaux et cotisations sociales facultatives : chiffre d’affaires des micro-entrepreneurs après abattement pour frais professionnels, bénéfice net des entrepreneurs individuels classiques, rémunération des gérants majoritaires (incluant une partie des dividendes). has constant value "X" Constant
Autre diplôme has 135 (99.3%) missing values Missing
Autre localisation has 125 (91.9%) missing values Missing
Précision concernant le type de structure has 83 (61.0%) missing values Missing
activités d’enseignement has 130 (95.6%) missing values Missing
activités associatives has 134 (98.5%) missing values Missing
activités de paysagiste conseils de l’état has 134 (98.5%) missing values Missing
responsabilités universitaire has 135 (99.3%) missing values Missing
activité non salariée, en tant que micro-entrepreneurs has 125 (91.9%) missing values Missing
activité non salariée, en tant qu’entrepreneurs individuels classiques has 135 (99.3%) missing values Missing
activité non salariée, en tant gérants majoritaires de sociétés has 134 (98.5%) missing values Missing
activité non déclarée has 135 (99.3%) missing values Missing
Si vous êtes chef-fe d'entreprise, êtes-vous optimiste pour votre activité dans les deux années à venir ? has 93 (68.4%) missing values Missing
Autre type de poste has 128 (94.1%) missing values Missing
Quel est votre revenu ANNUEL NET imposable en 2022 ? has 31 (22.8%) missing values Missing
Revenu salarial annuel net imposable : somme de tous les salaires perçus par un individu au cours d'une année donnée, nets de toutes cotisations sociales, y compris contribution sociale généralisée (CSG) et contribution au remboursement de la dette sociale (CRDS). has 16 (11.8%) missing values Missing
Revenu d’activité annuel : rémunération issue de l’activité non salariée, déduction faite des cotisations sociales payées dans l’année mais pas des contributions sociales (CSG non déductible, CRDS). Il est calculé à partir du revenu professionnel imposable auquel sont réintégrés certains allègements fiscaux et cotisations sociales facultatives : chiffre d’affaires des micro-entrepreneurs après abattement pour frais professionnels, bénéfice net des entrepreneurs individuels classiques, rémunération des gérants majoritaires (incluant une partie des dividendes). has 118 (86.8%) missing values Missing
Quel est votre salaire ANNUEL NET imposable en 2022 ? has 31 (22.8%) missing values Missing
Quel est votre revenu d’activité ANNUEL en 2022 ? has 119 (87.5%) missing values Missing
Quels sont vos avantages ? has 41 (30.1%) missing values Missing
Pensez-vous que la loi MOP permet une juste rémunération des paysagistes ? has 16 (11.8%) missing values Missing
Experience has 3 (2.2%) missing values Missing
Séquentiel has unique values Unique
SID has unique values Unique
Brouillon has 136 (100.0%) zeros Zeros
UID has 136 (100.0%) zeros Zeros
Combien d'enfants à charge avez-vous ? has 92 (67.6%) zeros Zeros
Années d'ancienneté has 7 (5.1%) zeros Zeros
Nombre d'années depuis diplôme has 3 (2.2%) zeros Zeros

Reproduction

Analysis started2025-03-21 06:31:27.396718
Analysis finished2025-03-21 06:31:27.588276
Duration0.19 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Séquentiel
Real number (ℝ)

Unique 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.5
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:27.643240image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.75
Q134.75
median68.5
Q3102.25
95-th percentile129.25
Maximum136
Range135
Interquartile range (IQR)67.5

Descriptive statistics

Standard deviation39.40389152
Coefficient of variation (CV)0.5752392922
Kurtosis-1.2
Mean68.5
Median Absolute Deviation (MAD)34
Skewness0
Sum9316
Variance1552.666667
MonotonicityStrictly increasing
2025-03-21T07:31:27.716996image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
94 1
 
0.7%
88 1
 
0.7%
89 1
 
0.7%
90 1
 
0.7%
91 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
95 1
 
0.7%
2 1
 
0.7%
Other values (126) 126
92.6%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
ValueCountFrequency (%)
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%

SID
Real number (ℝ)

Unique 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14754833.73
Minimum14514435
Maximum17516710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:27.782053image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum14514435
5-th percentile14514740.5
Q114518761.75
median14575395
Q314697090
95-th percentile15949641.75
Maximum17516710
Range3002275
Interquartile range (IQR)178328.25

Descriptive statistics

Standard deviation533077.3157
Coefficient of variation (CV)0.03612899511
Kurtosis15.53013346
Mean14754833.73
Median Absolute Deviation (MAD)60297
Skewness3.820362564
Sum2006657387
Variance2.841714245 × 1011
MonotonicityStrictly increasing
2025-03-21T07:31:27.852417image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14514435 1
 
0.7%
14679373 1
 
0.7%
14677365 1
 
0.7%
14677465 1
 
0.7%
14678518 1
 
0.7%
14678522 1
 
0.7%
14678760 1
 
0.7%
14678959 1
 
0.7%
14680025 1
 
0.7%
14514470 1
 
0.7%
Other values (126) 126
92.6%
ValueCountFrequency (%)
14514435 1
0.7%
14514470 1
0.7%
14514608 1
0.7%
14514637 1
0.7%
14514655 1
0.7%
ValueCountFrequency (%)
17516710 1
0.7%
17508465 1
0.7%
17505117 1
0.7%
16350034 1
0.7%
16261289 1
0.7%

Heure de soumission
Text

Distinct134
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:27.970699image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters2448
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)97.1%

Sample

1st row03/01/2023 - 12:18
2nd row03/01/2023 - 12:20
3rd row03/01/2023 - 12:28
4th row03/01/2023 - 12:29
5th row03/01/2023 - 12:30
ValueCountFrequency (%)
136
33.3%
03/01/2023 45
 
11.0%
04/01/2023 11
 
2.7%
13/01/2023 11
 
2.7%
05/01/2023 8
 
2.0%
16/01/2023 7
 
1.7%
06/01/2023 4
 
1.0%
11/01/2023 4
 
1.0%
29/01/2023 4
 
1.0%
17/01/2023 4
 
1.0%
Other values (155) 174
42.6%
2025-03-21T07:31:28.103518image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Heure de complétion
Text

Distinct134
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:28.213610image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters2448
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)97.1%

Sample

1st row03/01/2023 - 12:18
2nd row03/01/2023 - 12:20
3rd row03/01/2023 - 12:28
4th row03/01/2023 - 12:29
5th row03/01/2023 - 12:30
ValueCountFrequency (%)
136
33.3%
03/01/2023 45
 
11.0%
04/01/2023 11
 
2.7%
13/01/2023 11
 
2.7%
05/01/2023 8
 
2.0%
16/01/2023 7
 
1.7%
06/01/2023 4
 
1.0%
11/01/2023 4
 
1.0%
29/01/2023 4
 
1.0%
17/01/2023 4
 
1.0%
Other values (155) 174
42.6%
2025-03-21T07:31:28.346066image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Heure de modification
Text

Distinct134
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:28.451917image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters2448
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)97.1%

Sample

1st row03/01/2023 - 12:18
2nd row03/01/2023 - 12:20
3rd row03/01/2023 - 12:28
4th row03/01/2023 - 12:29
5th row03/01/2023 - 12:30
ValueCountFrequency (%)
136
33.3%
03/01/2023 45
 
11.0%
04/01/2023 11
 
2.7%
13/01/2023 11
 
2.7%
05/01/2023 8
 
2.0%
16/01/2023 7
 
1.7%
06/01/2023 4
 
1.0%
11/01/2023 4
 
1.0%
29/01/2023 4
 
1.0%
17/01/2023 4
 
1.0%
Other values (155) 174
42.6%
2025-03-21T07:31:28.605434image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 415
17.0%
2 379
15.5%
1 332
13.6%
/ 272
11.1%
272
11.1%
3 258
10.5%
- 136
 
5.6%
: 136
 
5.6%
4 62
 
2.5%
5 59
 
2.4%
Other values (4) 127
 
5.2%

Brouillon
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros136
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:28.631379image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2025-03-21T07:31:28.650015image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 136
100.0%
ValueCountFrequency (%)
0 136
100.0%
ValueCountFrequency (%)
0 136
100.0%

Adresse IP
Text

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:28.677344image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters952
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0.0.0
2nd row0.0.0.0
3rd row0.0.0.0
4th row0.0.0.0
5th row0.0.0.0
ValueCountFrequency (%)
0.0.0.0 136
100.0%
2025-03-21T07:31:28.729018image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 544
57.1%
. 408
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 544
57.1%
. 408
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 544
57.1%
. 408
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 544
57.1%
. 408
42.9%

UID
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros136
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:28.747469image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2025-03-21T07:31:28.765887image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 136
100.0%
ValueCountFrequency (%)
0 136
100.0%
ValueCountFrequency (%)
0 136
100.0%

Nom d'utilisateur
Text

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:28.797272image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1224
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnonymous
2nd rowAnonymous
3rd rowAnonymous
4th rowAnonymous
5th rowAnonymous
ValueCountFrequency (%)
anonymous 136
100.0%
2025-03-21T07:31:28.855533image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 272
22.2%
o 272
22.2%
A 136
11.1%
y 136
11.1%
m 136
11.1%
u 136
11.1%
s 136
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 272
22.2%
o 272
22.2%
A 136
11.1%
y 136
11.1%
m 136
11.1%
u 136
11.1%
s 136
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 272
22.2%
o 272
22.2%
A 136
11.1%
y 136
11.1%
m 136
11.1%
u 136
11.1%
s 136
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 272
22.2%
o 272
22.2%
A 136
11.1%
y 136
11.1%
m 136
11.1%
u 136
11.1%
s 136
11.1%

De quelle école êtes-vous diplômé-e?
Text

Distinct12
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:28.907656image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length37
Median length24
Mean length16.38235294
Min length4

Characters and Unicode

Total characters2228
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st rowAgrocampus Ouest (INH)
2nd rowAgrocampus Ouest (INH)
3rd rowHEPIA de Genève
4th rowENSP Versailles
5th rowHEPIA de Genève
ValueCountFrequency (%)
agrocampus 28
 
8.9%
inh 28
 
8.9%
insa-cvl-enp 28
 
8.9%
ex 28
 
8.9%
ensnp 28
 
8.9%
ensp 28
 
8.9%
ouest 28
 
8.9%
versailles 18
 
5.7%
ensapl 16
 
5.1%
ensapbx 11
 
3.5%
Other values (18) 73
23.2%
2025-03-21T07:31:29.041000image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 195
 
8.8%
178
 
8.0%
e 153
 
6.9%
E 128
 
5.7%
P 120
 
5.4%
S 118
 
5.3%
s 105
 
4.7%
A 102
 
4.6%
l 71
 
3.2%
I 69
 
3.1%
Other values (34) 989
44.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 195
 
8.8%
178
 
8.0%
e 153
 
6.9%
E 128
 
5.7%
P 120
 
5.4%
S 118
 
5.3%
s 105
 
4.7%
A 102
 
4.6%
l 71
 
3.2%
I 69
 
3.1%
Other values (34) 989
44.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 195
 
8.8%
178
 
8.0%
e 153
 
6.9%
E 128
 
5.7%
P 120
 
5.4%
S 118
 
5.3%
s 105
 
4.7%
A 102
 
4.6%
l 71
 
3.2%
I 69
 
3.1%
Other values (34) 989
44.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 195
 
8.8%
178
 
8.0%
e 153
 
6.9%
E 128
 
5.7%
P 120
 
5.4%
S 118
 
5.3%
s 105
 
4.7%
A 102
 
4.6%
l 71
 
3.2%
I 69
 
3.1%
Other values (34) 989
44.4%

Autre diplôme
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing135
Missing (%)99.3%
Memory size1.2 KiB
2025-03-21T07:31:29.082515image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length42
Median length42
Mean length42
Min length42

Characters and Unicode

Total characters42
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowUniversité de foresterie - Sofia Bulgarie
ValueCountFrequency (%)
université 1
16.7%
de 1
16.7%
foresterie 1
16.7%
1
16.7%
sofia 1
16.7%
bulgarie 1
16.7%
2025-03-21T07:31:29.150423image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6
14.3%
6
14.3%
i 5
11.9%
r 4
 
9.5%
s 2
 
4.8%
t 2
 
4.8%
f 2
 
4.8%
o 2
 
4.8%
a 2
 
4.8%
U 1
 
2.4%
Other values (10) 10
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6
14.3%
6
14.3%
i 5
11.9%
r 4
 
9.5%
s 2
 
4.8%
t 2
 
4.8%
f 2
 
4.8%
o 2
 
4.8%
a 2
 
4.8%
U 1
 
2.4%
Other values (10) 10
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6
14.3%
6
14.3%
i 5
11.9%
r 4
 
9.5%
s 2
 
4.8%
t 2
 
4.8%
f 2
 
4.8%
o 2
 
4.8%
a 2
 
4.8%
U 1
 
2.4%
Other values (10) 10
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6
14.3%
6
14.3%
i 5
11.9%
r 4
 
9.5%
s 2
 
4.8%
t 2
 
4.8%
f 2
 
4.8%
o 2
 
4.8%
a 2
 
4.8%
U 1
 
2.4%
Other values (10) 10
23.8%

En quelle année avez-vous été diplômé-e ?
Real number (ℝ)

Distinct26
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.977941
Minimum1989
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:29.174715image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile2002
Q12010
median2016
Q32019
95-th percentile2021
Maximum2022
Range33
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.59906493
Coefficient of variation (CV)0.003276632179
Kurtosis2.782191208
Mean2013.977941
Median Absolute Deviation (MAD)4
Skewness-1.501322036
Sum273901
Variance43.54765795
MonotonicityNot monotonic
2025-03-21T07:31:29.202253image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2018 16
11.8%
2020 14
10.3%
2016 13
9.6%
2017 11
 
8.1%
2010 11
 
8.1%
2021 10
 
7.4%
2019 9
 
6.6%
2011 9
 
6.6%
2012 7
 
5.1%
2008 5
 
3.7%
Other values (16) 31
22.8%
ValueCountFrequency (%)
1989 1
0.7%
1990 1
0.7%
1992 1
0.7%
1993 1
0.7%
1997 1
0.7%
ValueCountFrequency (%)
2022 3
 
2.2%
2021 10
7.4%
2020 14
10.3%
2019 9
6.6%
2018 16
11.8%

Combien d'années d'expérience avez-vous ?
Real number (ℝ)

Distinct27
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.198529412
Minimum0
Maximum34
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:29.229611image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q312
95-th percentile20
Maximum34
Range34
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.653119405
Coefficient of variation (CV)0.8115015598
Kurtosis2.724394295
Mean8.198529412
Median Absolute Deviation (MAD)4
Skewness1.500176994
Sum1115
Variance44.26399782
MonotonicityNot monotonic
2025-03-21T07:31:29.257173image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5 16
11.8%
2 15
11.0%
4 12
 
8.8%
12 12
 
8.8%
3 12
 
8.8%
1 9
 
6.6%
6 8
 
5.9%
10 7
 
5.1%
11 6
 
4.4%
8 6
 
4.4%
Other values (17) 33
24.3%
ValueCountFrequency (%)
0 1
 
0.7%
1 9
6.6%
2 15
11.0%
3 12
8.8%
4 12
8.8%
ValueCountFrequency (%)
34 1
0.7%
33 1
0.7%
29 1
0.7%
28 1
0.7%
25 1
0.7%

Quelle est votre région actuelle ?
Text

Distinct16
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:29.309702image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length26
Median length18
Mean length13.88235294
Min length5

Characters and Unicode

Total characters1888
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st rowParis
2nd rowAuvergne-Rhône-Alpes
3rd rowGrand Est
4th rowAuvergne-Rhône-Alpes
5th rowGrand Est
ValueCountFrequency (%)
la 25
11.3%
pays 21
9.5%
de 21
9.5%
loire 21
9.5%
île-de-france 19
 
8.6%
auvergne-rhône-alpes 18
 
8.1%
paris 11
 
5.0%
autre 11
 
5.0%
provence-alpes-côte 11
 
5.0%
d'azur 11
 
5.0%
Other values (11) 52
23.5%
2025-03-21T07:31:29.386052image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 287
 
15.2%
r 129
 
6.8%
a 123
 
6.5%
- 121
 
6.4%
n 117
 
6.2%
85
 
4.5%
l 84
 
4.4%
A 76
 
4.0%
s 74
 
3.9%
i 74
 
3.9%
Other values (29) 718
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 287
 
15.2%
r 129
 
6.8%
a 123
 
6.5%
- 121
 
6.4%
n 117
 
6.2%
85
 
4.5%
l 84
 
4.4%
A 76
 
4.0%
s 74
 
3.9%
i 74
 
3.9%
Other values (29) 718
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 287
 
15.2%
r 129
 
6.8%
a 123
 
6.5%
- 121
 
6.4%
n 117
 
6.2%
85
 
4.5%
l 84
 
4.4%
A 76
 
4.0%
s 74
 
3.9%
i 74
 
3.9%
Other values (29) 718
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 287
 
15.2%
r 129
 
6.8%
a 123
 
6.5%
- 121
 
6.4%
n 117
 
6.2%
85
 
4.5%
l 84
 
4.4%
A 76
 
4.0%
s 74
 
3.9%
i 74
 
3.9%
Other values (29) 718
38.0%

Autre localisation
Text

Missing 

Distinct11
Distinct (%)100.0%
Missing125
Missing (%)91.9%
Memory size1.2 KiB
2025-03-21T07:31:29.429896image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length20
Median length14
Mean length11.72727273
Min length6

Characters and Unicode

Total characters129
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st rowSuisse
2nd rowDanemark
3rd rowBruxelles
4th rowLuxembourg
5th rowBelgique, Bruxelles
ValueCountFrequency (%)
belgique 3
18.8%
suisse 2
12.5%
bruxelles 2
12.5%
danemark 1
 
6.2%
luxembourg 1
 
6.2%
charleroi 1
 
6.2%
europe 1
 
6.2%
canton 1
 
6.2%
chine 1
 
6.2%
united 1
 
6.2%
Other values (2) 2
12.5%
2025-03-21T07:31:29.502450image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 18
 
14.0%
i 10
 
7.8%
u 10
 
7.8%
l 8
 
6.2%
r 8
 
6.2%
7
 
5.4%
s 6
 
4.7%
n 6
 
4.7%
o 5
 
3.9%
g 5
 
3.9%
Other values (24) 46
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18
 
14.0%
i 10
 
7.8%
u 10
 
7.8%
l 8
 
6.2%
r 8
 
6.2%
7
 
5.4%
s 6
 
4.7%
n 6
 
4.7%
o 5
 
3.9%
g 5
 
3.9%
Other values (24) 46
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18
 
14.0%
i 10
 
7.8%
u 10
 
7.8%
l 8
 
6.2%
r 8
 
6.2%
7
 
5.4%
s 6
 
4.7%
n 6
 
4.7%
o 5
 
3.9%
g 5
 
3.9%
Other values (24) 46
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18
 
14.0%
i 10
 
7.8%
u 10
 
7.8%
l 8
 
6.2%
r 8
 
6.2%
7
 
5.4%
s 6
 
4.7%
n 6
 
4.7%
o 5
 
3.9%
g 5
 
3.9%
Other values (24) 46
35.7%

Quel est votre sexe ?
Text

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:29.537663image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length27
Median length5
Mean length5.485294118
Min length5

Characters and Unicode

Total characters746
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemme
2nd rowHomme
3rd rowHomme
4th rowFemme
5th rowHomme
ValueCountFrequency (%)
femme 78
52.7%
homme 55
37.2%
je 3
 
2.0%
ne 3
 
2.0%
souhaite 3
 
2.0%
pas 3
 
2.0%
répondre 3
 
2.0%
2025-03-21T07:31:29.596578image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 266
35.7%
e 223
29.9%
F 78
 
10.5%
o 61
 
8.2%
H 55
 
7.4%
12
 
1.6%
n 6
 
0.8%
s 6
 
0.8%
r 6
 
0.8%
a 6
 
0.8%
Other values (8) 27
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 266
35.7%
e 223
29.9%
F 78
 
10.5%
o 61
 
8.2%
H 55
 
7.4%
12
 
1.6%
n 6
 
0.8%
s 6
 
0.8%
r 6
 
0.8%
a 6
 
0.8%
Other values (8) 27
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 266
35.7%
e 223
29.9%
F 78
 
10.5%
o 61
 
8.2%
H 55
 
7.4%
12
 
1.6%
n 6
 
0.8%
s 6
 
0.8%
r 6
 
0.8%
a 6
 
0.8%
Other values (8) 27
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 266
35.7%
e 223
29.9%
F 78
 
10.5%
o 61
 
8.2%
H 55
 
7.4%
12
 
1.6%
n 6
 
0.8%
s 6
 
0.8%
r 6
 
0.8%
a 6
 
0.8%
Other values (8) 27
 
3.6%

Combien d'enfants à charge avez-vous ?
Real number (ℝ)

Zeros 

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5294117647
Minimum0
Maximum4
Zeros92
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:29.618303image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8687255584
Coefficient of variation (CV)1.640926055
Kurtosis1.745049606
Mean0.5294117647
Median Absolute Deviation (MAD)0
Skewness1.559601168
Sum72
Variance0.7546840959
MonotonicityNot monotonic
2025-03-21T07:31:29.640353image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 92
67.6%
1 21
 
15.4%
2 19
 
14.0%
3 3
 
2.2%
4 1
 
0.7%
ValueCountFrequency (%)
0 92
67.6%
1 21
 
15.4%
2 19
 
14.0%
3 3
 
2.2%
4 1
 
0.7%
ValueCountFrequency (%)
4 1
 
0.7%
3 3
 
2.2%
2 19
 
14.0%
1 21
 
15.4%
0 92
67.6%

Dans quel type de structure êtes-vous ?
Text

Distinct12
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:29.689565image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length36
Median length17
Mean length17.69117647
Min length6

Characters and Unicode

Total characters2406
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st rowAgence de paysage
2nd rowEntreprise de réalisation
3rd rowBureau d'études
4th rowFreelance / Indépendant
5th rowBureau d'études
ValueCountFrequency (%)
agence 66
19.2%
de 61
17.7%
paysage 55
16.0%
fonction 22
 
6.4%
publique 22
 
6.4%
bureau 20
 
5.8%
d'études 20
 
5.8%
freelance 9
 
2.6%
9
 
2.6%
indépendant 9
 
2.6%
Other values (15) 51
14.8%
2025-03-21T07:31:29.764912image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 395
16.4%
208
 
8.6%
a 181
 
7.5%
n 162
 
6.7%
d 138
 
5.7%
c 122
 
5.1%
g 121
 
5.0%
u 120
 
5.0%
p 101
 
4.2%
t 95
 
3.9%
Other values (25) 763
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 395
16.4%
208
 
8.6%
a 181
 
7.5%
n 162
 
6.7%
d 138
 
5.7%
c 122
 
5.1%
g 121
 
5.0%
u 120
 
5.0%
p 101
 
4.2%
t 95
 
3.9%
Other values (25) 763
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 395
16.4%
208
 
8.6%
a 181
 
7.5%
n 162
 
6.7%
d 138
 
5.7%
c 122
 
5.1%
g 121
 
5.0%
u 120
 
5.0%
p 101
 
4.2%
t 95
 
3.9%
Other values (25) 763
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 395
16.4%
208
 
8.6%
a 181
 
7.5%
n 162
 
6.7%
d 138
 
5.7%
c 122
 
5.1%
g 121
 
5.0%
u 120
 
5.0%
p 101
 
4.2%
t 95
 
3.9%
Other values (25) 763
31.7%

Précision concernant le type de structure
Text

Missing 

Distinct49
Distinct (%)92.5%
Missing83
Missing (%)61.0%
Memory size1.2 KiB
2025-03-21T07:31:29.863034image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length108
Median length50
Mean length32.47169811
Min length3

Characters and Unicode

Total characters1721
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)84.9%

Sample

1st rowIdverde
2nd rowStatut d'entrepreneur salariée en SCOP - seule paysagiste concepteur dans la structure
3rd rowCréation de jardins entreprises et particuliers
4th rowCPIE
5th rowSARL
ValueCountFrequency (%)
de 15
 
6.2%
en 11
 
4.6%
9
 
3.8%
et 8
 
3.3%
agence 7
 
2.9%
paysage 7
 
2.9%
scop 6
 
2.5%
environnement 5
 
2.1%
entreprise 4
 
1.7%
commune 4
 
1.7%
Other values (129) 164
68.3%
2025-03-21T07:31:29.994748image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 226
13.1%
194
 
11.3%
n 119
 
6.9%
a 107
 
6.2%
t 106
 
6.2%
i 106
 
6.2%
r 106
 
6.2%
s 74
 
4.3%
u 65
 
3.8%
c 58
 
3.4%
Other values (56) 560
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 226
13.1%
194
 
11.3%
n 119
 
6.9%
a 107
 
6.2%
t 106
 
6.2%
i 106
 
6.2%
r 106
 
6.2%
s 74
 
4.3%
u 65
 
3.8%
c 58
 
3.4%
Other values (56) 560
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 226
13.1%
194
 
11.3%
n 119
 
6.9%
a 107
 
6.2%
t 106
 
6.2%
i 106
 
6.2%
r 106
 
6.2%
s 74
 
4.3%
u 65
 
3.8%
c 58
 
3.4%
Other values (56) 560
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 226
13.1%
194
 
11.3%
n 119
 
6.9%
a 107
 
6.2%
t 106
 
6.2%
i 106
 
6.2%
r 106
 
6.2%
s 74
 
4.3%
u 65
 
3.8%
c 58
 
3.4%
Other values (56) 560
32.5%

Quelle est la taille de cette structure ?
Text

Distinct9
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:30.034635image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length8
Median length6
Mean length4.801470588
Min length1

Characters and Unicode

Total characters653
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row51-200
2nd row1001-...
3rd row2-5
4th row51-200
5th row2-5
ValueCountFrequency (%)
2-5 31
22.8%
6-10 21
15.4%
11-25 20
14.7%
1001 16
11.8%
26-50 16
11.8%
51-200 12
 
8.8%
201-500 8
 
5.9%
1 8
 
5.9%
501-1000 4
 
2.9%
2025-03-21T07:31:30.099992image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 133
20.4%
1 129
19.8%
- 128
19.6%
5 91
13.9%
2 87
13.3%
. 48
 
7.4%
6 37
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 653
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 133
20.4%
1 129
19.8%
- 128
19.6%
5 91
13.9%
2 87
13.3%
. 48
 
7.4%
6 37
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 653
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 133
20.4%
1 129
19.8%
- 128
19.6%
5 91
13.9%
2 87
13.3%
. 48
 
7.4%
6 37
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 653
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 133
20.4%
1 129
19.8%
- 128
19.6%
5 91
13.9%
2 87
13.3%
. 48
 
7.4%
6 37
 
5.7%

De quelle convention collective dépend cette structure ?
Text

Distinct11
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:30.156983image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length140
Median length69
Mean length60.30147059
Min length14

Characters and Unicode

Total characters8201
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st rowConvention collective nationale des entreprises d'architecture
2nd rowConvention collective nationale des entreprises du paysage
3rd rowConvention collective nationale des entreprises d'architecture
4th rowJe ne sais pas
5th rowConvention collective nationale des entreprises d'architecture
ValueCountFrequency (%)
des 135
 
14.0%
convention 96
 
9.9%
collective 86
 
8.9%
nationale 82
 
8.5%
entreprises 54
 
5.6%
d'architecture 40
 
4.1%
de 28
 
2.9%
cabinets 26
 
2.7%
et 26
 
2.7%
syntec 25
 
2.6%
Other values (30) 369
38.2%
2025-03-21T07:31:30.245656image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1134
13.8%
831
10.1%
n 757
 
9.2%
i 645
 
7.9%
t 624
 
7.6%
s 586
 
7.1%
o 513
 
6.3%
c 435
 
5.3%
a 376
 
4.6%
l 354
 
4.3%
Other values (31) 1946
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1134
13.8%
831
10.1%
n 757
 
9.2%
i 645
 
7.9%
t 624
 
7.6%
s 586
 
7.1%
o 513
 
6.3%
c 435
 
5.3%
a 376
 
4.6%
l 354
 
4.3%
Other values (31) 1946
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1134
13.8%
831
10.1%
n 757
 
9.2%
i 645
 
7.9%
t 624
 
7.6%
s 586
 
7.1%
o 513
 
6.3%
c 435
 
5.3%
a 376
 
4.6%
l 354
 
4.3%
Other values (31) 1946
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1134
13.8%
831
10.1%
n 757
 
9.2%
i 645
 
7.9%
t 624
 
7.6%
s 586
 
7.1%
o 513
 
6.3%
c 435
 
5.3%
a 376
 
4.6%
l 354
 
4.3%
Other values (31) 1946
23.7%

activités d’enseignement
Text

Constant  Missing 

Distinct1
Distinct (%)16.7%
Missing130
Missing (%)95.6%
Memory size1.2 KiB
2025-03-21T07:31:30.263679image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX
ValueCountFrequency (%)
x 6
100.0%
2025-03-21T07:31:30.308034image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 6
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 6
100.0%

activités associatives
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing134
Missing (%)98.5%
Memory size1.2 KiB
2025-03-21T07:31:30.398002image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
ValueCountFrequency (%)
x 2
100.0%
2025-03-21T07:31:30.436977image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 2
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 2
100.0%

activités de paysagiste conseils de l’état
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing134
Missing (%)98.5%
Memory size1.2 KiB
2025-03-21T07:31:30.451628image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
ValueCountFrequency (%)
x 2
100.0%
2025-03-21T07:31:30.490655image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 2
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 2
100.0%

responsabilités universitaire
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing135
Missing (%)99.3%
Memory size1.2 KiB
2025-03-21T07:31:30.506032image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowX
ValueCountFrequency (%)
x 1
100.0%
2025-03-21T07:31:30.544431image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 1
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 1
100.0%

activité non salariée, en tant que micro-entrepreneurs
Text

Constant  Missing 

Distinct1
Distinct (%)9.1%
Missing125
Missing (%)91.9%
Memory size1.2 KiB
2025-03-21T07:31:30.558923image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX
ValueCountFrequency (%)
x 11
100.0%
2025-03-21T07:31:30.596871image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 11
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 11
100.0%

activité non salariée, en tant qu’entrepreneurs individuels classiques
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing135
Missing (%)99.3%
Memory size1.2 KiB
2025-03-21T07:31:30.611572image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowX
ValueCountFrequency (%)
x 1
100.0%
2025-03-21T07:31:30.649135image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 1
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 1
100.0%

activité non salariée, en tant gérants majoritaires de sociétés
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing134
Missing (%)98.5%
Memory size1.2 KiB
2025-03-21T07:31:30.663576image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
ValueCountFrequency (%)
x 2
100.0%
2025-03-21T07:31:30.699754image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 2
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 2
100.0%

activité non déclarée
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing135
Missing (%)99.3%
Memory size1.2 KiB
2025-03-21T07:31:30.713623image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowX
ValueCountFrequency (%)
x 1
100.0%
2025-03-21T07:31:30.753402image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 1
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 1
100.0%

Si vous êtes chef-fe d'entreprise, êtes-vous optimiste pour votre activité dans les deux années à venir ?
Text

Missing 

Distinct4
Distinct (%)9.3%
Missing93
Missing (%)68.4%
Memory size1.2 KiB
2025-03-21T07:31:30.797834image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length54
Median length46
Mean length37.79069767
Min length18

Characters and Unicode

Total characters1625
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2 - Je ne sais pas
2nd row3 - Plutôt oui, il y a foisonnement de projets
3rd row3 - Plutôt oui, il y a foisonnement de projets
4th row3 - Plutôt oui, il y a foisonnement de projets
5th row3 - Plutôt oui, il y a foisonnement de projets
ValueCountFrequency (%)
43
 
11.3%
a 27
 
7.1%
de 27
 
7.1%
plutôt 26
 
6.8%
3 23
 
6.1%
oui 23
 
6.1%
il 23
 
6.1%
y 23
 
6.1%
foisonnement 23
 
6.1%
projets 23
 
6.1%
Other values (19) 119
31.3%
2025-03-21T07:31:30.871388image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
337
20.7%
e 154
 
9.5%
t 109
 
6.7%
n 106
 
6.5%
o 106
 
6.5%
s 96
 
5.9%
i 86
 
5.3%
l 60
 
3.7%
a 60
 
3.7%
u 59
 
3.6%
Other values (20) 452
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
337
20.7%
e 154
 
9.5%
t 109
 
6.7%
n 106
 
6.5%
o 106
 
6.5%
s 96
 
5.9%
i 86
 
5.3%
l 60
 
3.7%
a 60
 
3.7%
u 59
 
3.6%
Other values (20) 452
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
337
20.7%
e 154
 
9.5%
t 109
 
6.7%
n 106
 
6.5%
o 106
 
6.5%
s 96
 
5.9%
i 86
 
5.3%
l 60
 
3.7%
a 60
 
3.7%
u 59
 
3.6%
Other values (20) 452
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
337
20.7%
e 154
 
9.5%
t 109
 
6.7%
n 106
 
6.5%
o 106
 
6.5%
s 96
 
5.9%
i 86
 
5.3%
l 60
 
3.7%
a 60
 
3.7%
u 59
 
3.6%
Other values (20) 452
27.8%

Quel est votre statut ?
Text

Distinct7
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:30.913073image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length43
Median length5
Mean length11.29411765
Min length5

Characters and Unicode

Total characters1536
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC.D.D
2nd rowC.D.I
3rd rowDirigeant de SARL
4th rowC.D.I
5th rowDirigeant de SARL
ValueCountFrequency (%)
c.d.i 80
36.0%
dirigeant 18
 
8.1%
c.d.d 17
 
7.7%
de 14
 
6.3%
sarl 14
 
6.3%
10
 
4.5%
auto-entrepreneur 10
 
4.5%
me 10
 
4.5%
micro-entrepreneur 10
 
4.5%
fonctionnaire 6
 
2.7%
Other values (7) 33
14.9%
2025-03-21T07:31:30.985765image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 194
12.6%
e 140
 
9.1%
D 132
 
8.6%
r 111
 
7.2%
C 102
 
6.6%
n 100
 
6.5%
86
 
5.6%
I 80
 
5.2%
i 76
 
4.9%
t 73
 
4.8%
Other values (25) 442
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 194
12.6%
e 140
 
9.1%
D 132
 
8.6%
r 111
 
7.2%
C 102
 
6.6%
n 100
 
6.5%
86
 
5.6%
I 80
 
5.2%
i 76
 
4.9%
t 73
 
4.8%
Other values (25) 442
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 194
12.6%
e 140
 
9.1%
D 132
 
8.6%
r 111
 
7.2%
C 102
 
6.6%
n 100
 
6.5%
86
 
5.6%
I 80
 
5.2%
i 76
 
4.9%
t 73
 
4.8%
Other values (25) 442
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 194
12.6%
e 140
 
9.1%
D 132
 
8.6%
r 111
 
7.2%
C 102
 
6.6%
n 100
 
6.5%
86
 
5.6%
I 80
 
5.2%
i 76
 
4.9%
t 73
 
4.8%
Other values (25) 442
28.8%

Quel est votre temps de travail hebdomadaire effectif annualisé ?
Real number (ℝ)

Distinct24
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.57352941
Minimum15
Maximum3835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:31.012296image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile30
Q135
median39
Q340
95-th percentile50
Maximum3835
Range3820
Interquartile range (IQR)5

Descriptive statistics

Standard deviation325.5879896
Coefficient of variation (CV)4.890652374
Kurtosis135.9069987
Mean66.57352941
Median Absolute Deviation (MAD)1.75
Skewness11.6559682
Sum9054
Variance106007.539
MonotonicityNot monotonic
2025-03-21T07:31:31.043315image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
39 49
36.0%
35 25
18.4%
40 11
 
8.1%
38 7
 
5.1%
42 6
 
4.4%
45 5
 
3.7%
50 4
 
2.9%
37 4
 
2.9%
30 3
 
2.2%
36 3
 
2.2%
Other values (14) 19
 
14.0%
ValueCountFrequency (%)
15 1
0.7%
22 1
0.7%
24 1
0.7%
25 1
0.7%
28 2
1.5%
ValueCountFrequency (%)
3835 1
 
0.7%
60 2
1.5%
55 3
2.2%
50 4
2.9%
46 1
 
0.7%

Etes-vous satisfait-e de votre charge horaire ?
Text

Distinct6
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:31.082548image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length25
Median length1
Mean length5.382352941
Min length1

Characters and Unicode

Total characters732
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row4
4th row4
5th row3
ValueCountFrequency (%)
4 50
20.4%
33
13.5%
satisfait 33
13.5%
5 28
11.4%
très 28
11.4%
3 25
10.2%
2 20
 
8.2%
1 8
 
3.3%
0 5
 
2.0%
pas 5
 
2.0%
Other values (2) 10
 
4.1%
2025-03-21T07:31:31.156143image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
14.9%
s 99
13.5%
t 76
10.4%
a 71
9.7%
i 66
9.0%
4 50
 
6.8%
- 33
 
4.5%
f 33
 
4.5%
è 28
 
3.8%
r 28
 
3.8%
Other values (10) 139
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
109
14.9%
s 99
13.5%
t 76
10.4%
a 71
9.7%
i 66
9.0%
4 50
 
6.8%
- 33
 
4.5%
f 33
 
4.5%
è 28
 
3.8%
r 28
 
3.8%
Other values (10) 139
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
109
14.9%
s 99
13.5%
t 76
10.4%
a 71
9.7%
i 66
9.0%
4 50
 
6.8%
- 33
 
4.5%
f 33
 
4.5%
è 28
 
3.8%
r 28
 
3.8%
Other values (10) 139
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
109
14.9%
s 99
13.5%
t 76
10.4%
a 71
9.7%
i 66
9.0%
4 50
 
6.8%
- 33
 
4.5%
f 33
 
4.5%
è 28
 
3.8%
r 28
 
3.8%
Other values (10) 139
19.0%

Etes-vous favorable au télétravail ?
Text

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:31.208045image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length50
Median length16
Mean length20.13970588
Min length16

Characters and Unicode

Total characters2739
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st rowOui, si flexbile
2nd rowOui, si flexbile
3rd rowNon, pas du tout
4th rowOui, si flexbile
5th rowNon, pas du tout
ValueCountFrequency (%)
oui 119
24.3%
si 105
21.5%
flexbile 105
21.5%
non 17
 
3.5%
y 14
 
2.9%
compris 14
 
2.9%
à 14
 
2.9%
temps 14
 
2.9%
complet 14
 
2.9%
pour 11
 
2.2%
Other values (11) 62
12.7%
2025-03-21T07:31:31.286773image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 366
13.4%
353
12.9%
e 335
12.2%
l 255
9.3%
s 159
 
5.8%
u 154
 
5.6%
, 136
 
5.0%
x 125
 
4.6%
O 119
 
4.3%
f 116
 
4.2%
Other values (14) 621
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 366
13.4%
353
12.9%
e 335
12.2%
l 255
9.3%
s 159
 
5.8%
u 154
 
5.6%
, 136
 
5.0%
x 125
 
4.6%
O 119
 
4.3%
f 116
 
4.2%
Other values (14) 621
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 366
13.4%
353
12.9%
e 335
12.2%
l 255
9.3%
s 159
 
5.8%
u 154
 
5.6%
, 136
 
5.0%
x 125
 
4.6%
O 119
 
4.3%
f 116
 
4.2%
Other values (14) 621
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 366
13.4%
353
12.9%
e 335
12.2%
l 255
9.3%
s 159
 
5.8%
u 154
 
5.6%
, 136
 
5.0%
x 125
 
4.6%
O 119
 
4.3%
f 116
 
4.2%
Other values (14) 621
22.7%

Quel type de poste occupez-vous ?
Text

Distinct15
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:31.339883image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length48
Median length28
Mean length24.63970588
Min length5

Characters and Unicode

Total characters3351
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChargé-e d'étude débutant-e
2nd rowChargé-e d'affaires / d'opérations / de missions
3rd rowDirecteur-trice/Dirigeant-e de société
4th rowChef-fe de projet
5th rowDirecteur-trice/Dirigeant-e de société
ValueCountFrequency (%)
de 73
17.8%
projet 45
10.9%
chargé-e 41
10.0%
chef-fe 39
9.5%
d'étude 35
8.5%
23
 
5.6%
directeur-trice/dirigeant-e 20
 
4.9%
société 20
 
4.9%
confirmé-e 16
 
3.9%
responsable 13
 
3.2%
Other values (13) 86
20.9%
2025-03-21T07:31:31.440951image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 507
15.1%
275
 
8.2%
t 231
 
6.9%
r 229
 
6.8%
d 184
 
5.5%
i 176
 
5.3%
- 164
 
4.9%
é 158
 
4.7%
a 142
 
4.2%
s 131
 
3.9%
Other values (22) 1154
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3351
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 507
15.1%
275
 
8.2%
t 231
 
6.9%
r 229
 
6.8%
d 184
 
5.5%
i 176
 
5.3%
- 164
 
4.9%
é 158
 
4.7%
a 142
 
4.2%
s 131
 
3.9%
Other values (22) 1154
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3351
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 507
15.1%
275
 
8.2%
t 231
 
6.9%
r 229
 
6.8%
d 184
 
5.5%
i 176
 
5.3%
- 164
 
4.9%
é 158
 
4.7%
a 142
 
4.2%
s 131
 
3.9%
Other values (22) 1154
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3351
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 507
15.1%
275
 
8.2%
t 231
 
6.9%
r 229
 
6.8%
d 184
 
5.5%
i 176
 
5.3%
- 164
 
4.9%
é 158
 
4.7%
a 142
 
4.2%
s 131
 
3.9%
Other values (22) 1154
34.4%

Autre type de poste
Text

Missing 

Distinct7
Distinct (%)87.5%
Missing128
Missing (%)94.1%
Memory size1.2 KiB
2025-03-21T07:31:31.518502image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length86
Median length28
Mean length29.375
Min length4

Characters and Unicode

Total characters235
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)75.0%

Sample

1st rowInspecteur des sites
2nd rowFormateur et enseignant
3rd rowAssistante de projets et d'études : assistance à maitrise d'ouvrage, maitrise d'oeuvre
4th rowCharge exploitation
5th rowChef de service
ValueCountFrequency (%)
des 3
 
9.4%
directeur 2
 
6.2%
services 2
 
6.2%
techniques 2
 
6.2%
et 2
 
6.2%
de 2
 
6.2%
maitrise 2
 
6.2%
à 1
 
3.1%
service 1
 
3.1%
chef 1
 
3.1%
Other values (14) 14
43.8%
2025-03-21T07:31:31.622124image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 39
16.6%
26
11.1%
s 25
10.6%
t 20
 
8.5%
i 17
 
7.2%
r 16
 
6.8%
a 10
 
4.3%
u 10
 
4.3%
n 9
 
3.8%
d 9
 
3.8%
Other values (22) 54
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 39
16.6%
26
11.1%
s 25
10.6%
t 20
 
8.5%
i 17
 
7.2%
r 16
 
6.8%
a 10
 
4.3%
u 10
 
4.3%
n 9
 
3.8%
d 9
 
3.8%
Other values (22) 54
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 39
16.6%
26
11.1%
s 25
10.6%
t 20
 
8.5%
i 17
 
7.2%
r 16
 
6.8%
a 10
 
4.3%
u 10
 
4.3%
n 9
 
3.8%
d 9
 
3.8%
Other values (22) 54
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 39
16.6%
26
11.1%
s 25
10.6%
t 20
 
8.5%
i 17
 
7.2%
r 16
 
6.8%
a 10
 
4.3%
u 10
 
4.3%
n 9
 
3.8%
d 9
 
3.8%
Other values (22) 54
23.0%

Années d'ancienneté
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.911764706
Minimum0
Maximum34
Zeros7
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:31.658696image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q11
median3
Q36
95-th percentile13
Maximum34
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.266906588
Coefficient of variation (CV)1.072304335
Kurtosis8.866317989
Mean4.911764706
Median Absolute Deviation (MAD)2
Skewness2.492483323
Sum668
Variance27.74030501
MonotonicityNot monotonic
2025-03-21T07:31:31.693945image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 28
20.6%
2 27
19.9%
6 13
9.6%
3 12
8.8%
5 11
 
8.1%
0 7
 
5.1%
4 6
 
4.4%
10 6
 
4.4%
12 5
 
3.7%
7 4
 
2.9%
Other values (9) 17
12.5%
ValueCountFrequency (%)
0 7
 
5.1%
1 28
20.6%
2 27
19.9%
3 12
8.8%
4 6
 
4.4%
ValueCountFrequency (%)
34 1
0.7%
28 1
0.7%
20 2
1.5%
18 1
0.7%
14 1
0.7%

Etes-vous satisfait-e de votre emploi ?
Text

Distinct6
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:31.728390image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length25
Median length1
Mean length5.176470588
Min length1

Characters and Unicode

Total characters704
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row3
2nd row4
3rd row5 - Très satisfait
4th row4
5th row5 - Très satisfait
ValueCountFrequency (%)
4 60
25.3%
33
13.9%
satisfait 33
13.9%
3 32
13.5%
5 32
13.5%
très 32
13.5%
2 7
 
3.0%
1 4
 
1.7%
0 1
 
0.4%
pas 1
 
0.4%
Other values (2) 2
 
0.8%
2025-03-21T07:31:31.801957image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
101
14.3%
s 99
14.1%
t 68
9.7%
a 67
9.5%
i 66
9.4%
4 60
8.5%
- 33
 
4.7%
f 33
 
4.7%
r 32
 
4.5%
è 32
 
4.5%
Other values (10) 113
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
101
14.3%
s 99
14.1%
t 68
9.7%
a 67
9.5%
i 66
9.4%
4 60
8.5%
- 33
 
4.7%
f 33
 
4.7%
r 32
 
4.5%
è 32
 
4.5%
Other values (10) 113
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
101
14.3%
s 99
14.1%
t 68
9.7%
a 67
9.5%
i 66
9.4%
4 60
8.5%
- 33
 
4.7%
f 33
 
4.7%
r 32
 
4.5%
è 32
 
4.5%
Other values (10) 113
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
101
14.3%
s 99
14.1%
t 68
9.7%
a 67
9.5%
i 66
9.4%
4 60
8.5%
- 33
 
4.7%
f 33
 
4.7%
r 32
 
4.5%
è 32
 
4.5%
Other values (10) 113
16.1%

Quel est votre revenu ANNUEL NET imposable en 2022 ?
Real number (ℝ)

Missing 

Distinct85
Distinct (%)81.0%
Missing31
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean29556.38095
Minimum10000
Maximum74000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:31.930075image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile16277.8
Q122117
median26400
Q335000
95-th percentile52827
Maximum74000
Range64000
Interquartile range (IQR)12883

Descriptive statistics

Standard deviation11672.32284
Coefficient of variation (CV)0.3949171875
Kurtosis2.328163893
Mean29556.38095
Median Absolute Deviation (MAD)5940
Skewness1.399463574
Sum3103420
Variance136243120.4
MonotonicityNot monotonic
2025-03-21T07:31:31.965184image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38000 4
 
2.9%
24000 3
 
2.2%
36000 3
 
2.2%
26400 3
 
2.2%
45000 3
 
2.2%
30000 2
 
1.5%
34000 2
 
1.5%
23280 2
 
1.5%
39000 2
 
1.5%
20000 2
 
1.5%
Other values (75) 79
58.1%
(Missing) 31
 
22.8%
ValueCountFrequency (%)
10000 1
0.7%
14019 1
0.7%
14574 1
0.7%
15000 1
0.7%
16015 1
0.7%
ValueCountFrequency (%)
74000 1
0.7%
68737 1
0.7%
60391 1
0.7%
58000 1
0.7%
54000 2
1.5%

Revenu salarial annuel net imposable : somme de tous les salaires perçus par un individu au cours d'une année donnée, nets de toutes cotisations sociales, y compris contribution sociale généralisée (CSG) et contribution au remboursement de la dette sociale (CRDS).
Text

Constant  Missing 

Distinct1
Distinct (%)0.8%
Missing16
Missing (%)11.8%
Memory size1.2 KiB
2025-03-21T07:31:31.990574image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters120
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX
ValueCountFrequency (%)
x 120
100.0%
2025-03-21T07:31:32.041595image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 120
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 120
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 120
100.0%

Revenu d’activité annuel : rémunération issue de l’activité non salariée, déduction faite des cotisations sociales payées dans l’année mais pas des contributions sociales (CSG non déductible, CRDS). Il est calculé à partir du revenu professionnel imposable auquel sont réintégrés certains allègements fiscaux et cotisations sociales facultatives : chiffre d’affaires des micro-entrepreneurs après abattement pour frais professionnels, bénéfice net des entrepreneurs individuels classiques, rémunération des gérants majoritaires (incluant une partie des dividendes).
Text

Constant  Missing 

Distinct1
Distinct (%)5.6%
Missing118
Missing (%)86.8%
Memory size1.2 KiB
2025-03-21T07:31:32.059099image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX
ValueCountFrequency (%)
x 18
100.0%
2025-03-21T07:31:32.114013image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 18
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 18
100.0%

Quel est votre salaire ANNUEL NET imposable en 2022 ?
Real number (ℝ)

Missing 

Distinct91
Distinct (%)86.7%
Missing31
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean29219.18095
Minimum1
Maximum400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:32.147763image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1940
Q119386
median25000
Q332722
95-th percentile45920
Maximum400000
Range399999
Interquartile range (IQR)13336

Descriptive statistics

Standard deviation38649.17311
Coefficient of variation (CV)1.322732939
Kurtosis83.24502521
Mean29219.18095
Median Absolute Deviation (MAD)6000
Skewness8.635406824
Sum3068014
Variance1493758582
MonotonicityNot monotonic
2025-03-21T07:31:32.190350image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38000 4
 
2.9%
24000 4
 
2.9%
25600 3
 
2.2%
25200 2
 
1.5%
25000 2
 
1.5%
27600 2
 
1.5%
45000 2
 
1.5%
19000 2
 
1.5%
1940 2
 
1.5%
24811 1
 
0.7%
Other values (81) 81
59.6%
(Missing) 31
 
22.8%
ValueCountFrequency (%)
1 1
0.7%
30 1
0.7%
1688 1
0.7%
1800 1
0.7%
1850 1
0.7%
ValueCountFrequency (%)
400000 1
0.7%
69700 1
0.7%
58000 1
0.7%
54000 1
0.7%
48000 1
0.7%

Quel est votre revenu d’activité ANNUEL en 2022 ?
Real number (ℝ)

Missing 

Distinct16
Distinct (%)94.1%
Missing119
Missing (%)87.5%
Infinite0
Infinite (%)0.0%
Mean27600.41176
Minimum2720
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:32.220067image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum2720
5-th percentile2944
Q120000
median26000
Q335000
95-th percentile55200
Maximum60000
Range57280
Interquartile range (IQR)15000

Descriptive statistics

Standard deviation15019.81861
Coefficient of variation (CV)0.5441882076
Kurtosis0.6271764964
Mean27600.41176
Median Absolute Deviation (MAD)7280
Skewness0.4893198725
Sum469207
Variance225594951
MonotonicityNot monotonic
2025-03-21T07:31:32.244241image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
18720 2
 
1.5%
24000 1
 
0.7%
23000 1
 
0.7%
26045 1
 
0.7%
60000 1
 
0.7%
3000 1
 
0.7%
32487 1
 
0.7%
26400 1
 
0.7%
20000 1
 
0.7%
26000 1
 
0.7%
Other values (6) 6
 
4.4%
(Missing) 119
87.5%
ValueCountFrequency (%)
2720 1
0.7%
3000 1
0.7%
18720 2
1.5%
20000 1
0.7%
21115 1
0.7%
ValueCountFrequency (%)
60000 1
0.7%
54000 1
0.7%
40000 1
0.7%
38000 1
0.7%
35000 1
0.7%

Quels sont vos avantages ?
Text

Missing 

Distinct82
Distinct (%)86.3%
Missing41
Missing (%)30.1%
Memory size1.2 KiB
2025-03-21T07:31:32.352239image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length127
Median length73
Mean length32.84210526
Min length1

Characters and Unicode

Total characters3120
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)77.9%

Sample

1st rowtickets restaurants 10€
2nd rowTicket restaurant + voiture de fonction
3rd rowRemboursement de notes de frais, frais de comptabilité & assurance décennale d'ores-et-déjà déduits avant salaire net
4th rowTickets restaurants
5th rowPrime bi annuelle et cheques vacances
ValueCountFrequency (%)
tickets 34
 
7.6%
de 27
 
6.0%
rtt 25
 
5.6%
restaurants 20
 
4.5%
restaurant 14
 
3.1%
voiture 12
 
2.7%
ticket 11
 
2.5%
aucun 9
 
2.0%
fonction 9
 
2.0%
chèques 9
 
2.0%
Other values (163) 278
62.1%
2025-03-21T07:31:32.519370image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
377
 
12.1%
e 306
 
9.8%
t 274
 
8.8%
a 209
 
6.7%
r 206
 
6.6%
s 203
 
6.5%
n 178
 
5.7%
i 156
 
5.0%
c 130
 
4.2%
u 128
 
4.1%
Other values (56) 953
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
377
 
12.1%
e 306
 
9.8%
t 274
 
8.8%
a 209
 
6.7%
r 206
 
6.6%
s 203
 
6.5%
n 178
 
5.7%
i 156
 
5.0%
c 130
 
4.2%
u 128
 
4.1%
Other values (56) 953
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
377
 
12.1%
e 306
 
9.8%
t 274
 
8.8%
a 209
 
6.7%
r 206
 
6.6%
s 203
 
6.5%
n 178
 
5.7%
i 156
 
5.0%
c 130
 
4.2%
u 128
 
4.1%
Other values (56) 953
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
377
 
12.1%
e 306
 
9.8%
t 274
 
8.8%
a 209
 
6.7%
r 206
 
6.6%
s 203
 
6.5%
n 178
 
5.7%
i 156
 
5.0%
c 130
 
4.2%
u 128
 
4.1%
Other values (56) 953
30.5%

Etes-vous satisfait-e de votre rémunération ?
Text

Distinct6
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:32.556868image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length25
Median length1
Mean length3.661764706
Min length1

Characters and Unicode

Total characters498
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3
ValueCountFrequency (%)
3 47
22.8%
4 36
17.5%
2 25
12.1%
18
 
8.7%
satisfait 18
 
8.7%
1 10
 
4.9%
5 10
 
4.9%
très 10
 
4.9%
0 8
 
3.9%
pas 8
 
3.9%
Other values (2) 16
 
7.8%
2025-03-21T07:31:32.613311image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
14.1%
s 54
10.8%
t 52
10.4%
3 47
9.4%
a 44
8.8%
4 36
 
7.2%
i 36
 
7.2%
2 25
 
5.0%
- 18
 
3.6%
f 18
 
3.6%
Other values (10) 98
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70
14.1%
s 54
10.8%
t 52
10.4%
3 47
9.4%
a 44
8.8%
4 36
 
7.2%
i 36
 
7.2%
2 25
 
5.0%
- 18
 
3.6%
f 18
 
3.6%
Other values (10) 98
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70
14.1%
s 54
10.8%
t 52
10.4%
3 47
9.4%
a 44
8.8%
4 36
 
7.2%
i 36
 
7.2%
2 25
 
5.0%
- 18
 
3.6%
f 18
 
3.6%
Other values (10) 98
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70
14.1%
s 54
10.8%
t 52
10.4%
3 47
9.4%
a 44
8.8%
4 36
 
7.2%
i 36
 
7.2%
2 25
 
5.0%
- 18
 
3.6%
f 18
 
3.6%
Other values (10) 98
19.7%

Pensez-vous que la loi MOP permet une juste rémunération des paysagistes ?
Text

Missing 

Distinct6
Distinct (%)5.0%
Missing16
Missing (%)11.8%
Memory size1.2 KiB
2025-03-21T07:31:32.656008image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length25
Median length1
Mean length4.741666667
Min length1

Characters and Unicode

Total characters569
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
2 34
16.0%
3 34
16.0%
1 32
15.0%
19
8.9%
satisfait 19
8.9%
0 18
8.5%
pas 18
8.5%
du 18
8.5%
tout 18
8.5%
5 1
 
0.5%
Other values (2) 2
 
0.9%
2025-03-21T07:31:32.721850image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93
16.3%
t 74
13.0%
s 57
10.0%
a 56
9.8%
i 38
 
6.7%
u 36
 
6.3%
3 34
 
6.0%
2 34
 
6.0%
1 32
 
5.6%
- 19
 
3.3%
Other values (10) 96
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
93
16.3%
t 74
13.0%
s 57
10.0%
a 56
9.8%
i 38
 
6.7%
u 36
 
6.3%
3 34
 
6.0%
2 34
 
6.0%
1 32
 
5.6%
- 19
 
3.3%
Other values (10) 96
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
93
16.3%
t 74
13.0%
s 57
10.0%
a 56
9.8%
i 38
 
6.7%
u 36
 
6.3%
3 34
 
6.0%
2 34
 
6.0%
1 32
 
5.6%
- 19
 
3.3%
Other values (10) 96
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
93
16.3%
t 74
13.0%
s 57
10.0%
a 56
9.8%
i 38
 
6.7%
u 36
 
6.3%
3 34
 
6.0%
2 34
 
6.0%
1 32
 
5.6%
- 19
 
3.3%
Other values (10) 96
16.9%

Nombre d'années depuis diplôme
Real number (ℝ)

Zeros 

Distinct26
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.022058824
Minimum0
Maximum33
Zeros3
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2025-03-21T07:31:32.754070image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q312
95-th percentile20
Maximum33
Range33
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.59906493
Coefficient of variation (CV)0.8226148767
Kurtosis2.782191208
Mean8.022058824
Median Absolute Deviation (MAD)4
Skewness1.501322036
Sum1091
Variance43.54765795
MonotonicityNot monotonic
2025-03-21T07:31:32.793233image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4 16
11.8%
2 14
10.3%
6 13
9.6%
5 11
 
8.1%
12 11
 
8.1%
1 10
 
7.4%
3 9
 
6.6%
11 9
 
6.6%
10 7
 
5.1%
14 5
 
3.7%
Other values (16) 31
22.8%
ValueCountFrequency (%)
0 3
 
2.2%
1 10
7.4%
2 14
10.3%
3 9
6.6%
4 16
11.8%
ValueCountFrequency (%)
33 1
0.7%
32 1
0.7%
30 1
0.7%
29 1
0.7%
25 1
0.7%

Experience
Categorical

Missing 

Distinct4
Distinct (%)3.0%
Missing3
Missing (%)2.2%
Memory size472.0 B
11+
44 
4-6
40 
0-3
33 
7-10
16 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4-6
2nd row4-6
3rd row11+
4th row7-10
5th row11+

Common Values

ValueCountFrequency (%)
11+ 44
32.4%
4-6 40
29.4%
0-3 33
24.3%
7-10 16
 
11.8%
(Missing) 3
 
2.2%

Common Values (Plot)

2025-03-21T07:31:32.826741image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/