Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations105
Missing cells89
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.4 KiB
Average record size in memory637.5 B

Variable types

DateTime1
Text5
Categorical3

Alerts

Qu'est-ce qui vous décrit le mieux ? has 2 (1.9%) missing values Missing
Avez-vous déjà travaillé dans le domaine de la data ? has 3 (2.9%) missing values Missing
Comment avez-vous entendu parler de cette formation ? has 13 (12.4%) missing values Missing
Commentaires has 71 (67.6%) missing values Missing
Horodateur has unique values Unique

Reproduction

Analysis started2025-05-03 20:46:12.229265
Analysis finished2025-05-03 20:46:13.493321
Duration1.26 second
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Horodateur
Date

Unique 

Distinct105
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size972.0 B
Minimum2025-04-29 09:45:01.430000
Maximum2025-05-02 17:37:06.025000
Invalid dates0
Invalid dates (%)0.0%
2025-05-03T20:46:13.649397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-03T20:46:13.835411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Quel est votre nom ?
Text

Distinct103
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2025-05-03T20:46:14.202411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length14
Mean length7.6952381
Min length4

Characters and Unicode

Total characters808
Distinct characters45
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

Unique101 ?
Unique (%)96.2%

Sample

1st rowKANFITINE
2nd rowAMUZUVI
3rd rowAMAGLO-SEMENU
4th rowSETODJI
5th rowHOUNSI
ValueCountFrequency (%)
setodji 2
 
1.8%
akpo 2
 
1.8%
bitori 2
 
1.8%
amaglo-semenu 1
 
0.9%
hounsi 1
 
0.9%
amah-tchoutchoui 1
 
0.9%
kanfitine 1
 
0.9%
segla 1
 
0.9%
agbofoati 1
 
0.9%
possian 1
 
0.9%
Other values (97) 97
88.2%
2025-05-03T20:46:14.668978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 107
 
13.2%
O 84
 
10.4%
62
 
7.7%
I 48
 
5.9%
E 47
 
5.8%
N 45
 
5.6%
T 38
 
4.7%
U 35
 
4.3%
D 32
 
4.0%
K 32
 
4.0%
Other values (35) 278
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 107
 
13.2%
O 84
 
10.4%
62
 
7.7%
I 48
 
5.9%
E 47
 
5.8%
N 45
 
5.6%
T 38
 
4.7%
U 35
 
4.3%
D 32
 
4.0%
K 32
 
4.0%
Other values (35) 278
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 107
 
13.2%
O 84
 
10.4%
62
 
7.7%
I 48
 
5.9%
E 47
 
5.8%
N 45
 
5.6%
T 38
 
4.7%
U 35
 
4.3%
D 32
 
4.0%
K 32
 
4.0%
Other values (35) 278
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 107
 
13.2%
O 84
 
10.4%
62
 
7.7%
I 48
 
5.9%
E 47
 
5.8%
N 45
 
5.6%
T 38
 
4.7%
U 35
 
4.3%
D 32
 
4.0%
K 32
 
4.0%
Other values (35) 278
34.4%

Quel est votre prénom ?
Text

Distinct104
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2025-05-03T20:46:14.953169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length19
Mean length11.447619
Min length4

Characters and Unicode

Total characters1202
Distinct characters56
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

Unique103 ?
Unique (%)98.1%

Sample

1st rowBOUAME PRISCA
2nd rowEyram Rose Belinda
3rd rowKoami Walter
4th rowValère
5th rowAntoine
ValueCountFrequency (%)
essoham 4
 
2.3%
koffi 4
 
2.3%
kodjo 3
 
1.7%
kossi 3
 
1.7%
emmanuel 3
 
1.7%
sylvain 3
 
1.7%
gracia 2
 
1.1%
josué 2
 
1.1%
komi 2
 
1.1%
bright 2
 
1.1%
Other values (142) 146
83.9%
2025-05-03T20:46:15.356628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127
 
10.6%
a 107
 
8.9%
o 87
 
7.2%
i 87
 
7.2%
e 71
 
5.9%
n 66
 
5.5%
s 60
 
5.0%
l 46
 
3.8%
m 39
 
3.2%
A 38
 
3.2%
Other values (46) 474
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
127
 
10.6%
a 107
 
8.9%
o 87
 
7.2%
i 87
 
7.2%
e 71
 
5.9%
n 66
 
5.5%
s 60
 
5.0%
l 46
 
3.8%
m 39
 
3.2%
A 38
 
3.2%
Other values (46) 474
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
127
 
10.6%
a 107
 
8.9%
o 87
 
7.2%
i 87
 
7.2%
e 71
 
5.9%
n 66
 
5.5%
s 60
 
5.0%
l 46
 
3.8%
m 39
 
3.2%
A 38
 
3.2%
Other values (46) 474
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
127
 
10.6%
a 107
 
8.9%
o 87
 
7.2%
i 87
 
7.2%
e 71
 
5.9%
n 66
 
5.5%
s 60
 
5.0%
l 46
 
3.8%
m 39
 
3.2%
A 38
 
3.2%
Other values (46) 474
39.4%

Votre adresse mail
Text

Distinct104
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
2025-05-03T20:46:15.629053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length29
Mean length23.219048
Min length5

Characters and Unicode

Total characters2438
Distinct characters47
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

Unique103 ?
Unique (%)98.1%

Sample

1st rowBouameprisca@gmail.com
2nd rowamuzuvibelindac@gmail.com
3rd rowamaglowalter410@gmail.com
4th rowsetodji2001@gmail.com
5th rowantoinehounsi3@gmail.com
ValueCountFrequency (%)
bitoriessoham@gmail.com 2
 
1.9%
bouameprisca@gmail.com 1
 
1.0%
amaglowalter410@gmail.com 1
 
1.0%
amuzuvibelindac@gmail.com 1
 
1.0%
antoinehounsi3@gmail.com 1
 
1.0%
josherenamah@gmail.com 1
 
1.0%
kouraa355@gmail.com 1
 
1.0%
setodji2001@gmail.com 1
 
1.0%
agbofoatiablaruth@gmail.com 1
 
1.0%
josuepossian@gmail.com 1
 
1.0%
Other values (94) 94
89.5%
2025-05-03T20:46:16.007696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 279
 
11.4%
m 258
 
10.6%
o 228
 
9.4%
i 190
 
7.8%
l 167
 
6.8%
e 133
 
5.5%
g 131
 
5.4%
c 129
 
5.3%
. 108
 
4.4%
@ 104
 
4.3%
Other values (37) 711
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 279
 
11.4%
m 258
 
10.6%
o 228
 
9.4%
i 190
 
7.8%
l 167
 
6.8%
e 133
 
5.5%
g 131
 
5.4%
c 129
 
5.3%
. 108
 
4.4%
@ 104
 
4.3%
Other values (37) 711
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 279
 
11.4%
m 258
 
10.6%
o 228
 
9.4%
i 190
 
7.8%
l 167
 
6.8%
e 133
 
5.5%
g 131
 
5.4%
c 129
 
5.3%
. 108
 
4.4%
@ 104
 
4.3%
Other values (37) 711
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 279
 
11.4%
m 258
 
10.6%
o 228
 
9.4%
i 190
 
7.8%
l 167
 
6.8%
e 133
 
5.5%
g 131
 
5.4%
c 129
 
5.3%
. 108
 
4.4%
@ 104
 
4.3%
Other values (37) 711
29.2%

Qu'est-ce qui vous décrit le mieux ?
Categorical

Missing 

Distinct4
Distinct (%)3.9%
Missing2
Missing (%)1.9%
Memory size6.9 KiB
Etudiant
79 
Professionnel
13 
Chercheur
10 
Etudiant / Professionnel
 
1

Length

Max length24
Median length8
Mean length8.8834951
Min length8

Characters and Unicode

Total characters915
Distinct characters19
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 (%)1.0%

Sample

1st rowEtudiant
2nd rowEtudiant
3rd rowEtudiant
4th rowEtudiant
5th rowEtudiant

Common Values

ValueCountFrequency (%)
Etudiant 79
75.2%
Professionnel 13
 
12.4%
Chercheur 10
 
9.5%
Etudiant / Professionnel 1
 
1.0%
(Missing) 2
 
1.9%

Length

2025-05-03T20:46:16.133857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T20:46:16.221833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
etudiant 80
76.2%
professionnel 14
 
13.3%
chercheur 10
 
9.5%
1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
t 160
17.5%
n 108
11.8%
i 94
10.3%
u 90
9.8%
E 80
8.7%
d 80
8.7%
a 80
8.7%
e 48
 
5.2%
r 34
 
3.7%
s 28
 
3.1%
Other values (9) 113
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 160
17.5%
n 108
11.8%
i 94
10.3%
u 90
9.8%
E 80
8.7%
d 80
8.7%
a 80
8.7%
e 48
 
5.2%
r 34
 
3.7%
s 28
 
3.1%
Other values (9) 113
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 160
17.5%
n 108
11.8%
i 94
10.3%
u 90
9.8%
E 80
8.7%
d 80
8.7%
a 80
8.7%
e 48
 
5.2%
r 34
 
3.7%
s 28
 
3.1%
Other values (9) 113
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 160
17.5%
n 108
11.8%
i 94
10.3%
u 90
9.8%
E 80
8.7%
d 80
8.7%
a 80
8.7%
e 48
 
5.2%
r 34
 
3.7%
s 28
 
3.1%
Other values (9) 113
12.3%

Avez-vous déjà travaillé dans le domaine de la data ?
Categorical

Missing 

Distinct2
Distinct (%)2.0%
Missing3
Missing (%)2.9%
Memory size6.3 KiB
Non
56 
Oui
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters306
Distinct characters6
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 rowNon
2nd rowNon
3rd rowNon
4th rowOui
5th rowOui

Common Values

ValueCountFrequency (%)
Non 56
53.3%
Oui 46
43.8%
(Missing) 3
 
2.9%

Length

2025-05-03T20:46:16.316274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T20:46:16.632236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
non 56
54.9%
oui 46
45.1%

Most occurring characters

ValueCountFrequency (%)
N 56
18.3%
o 56
18.3%
n 56
18.3%
O 46
15.0%
u 46
15.0%
i 46
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 56
18.3%
o 56
18.3%
n 56
18.3%
O 46
15.0%
u 46
15.0%
i 46
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 56
18.3%
o 56
18.3%
n 56
18.3%
O 46
15.0%
u 46
15.0%
i 46
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 56
18.3%
o 56
18.3%
n 56
18.3%
O 46
15.0%
u 46
15.0%
i 46
15.0%

Comment allez-vous suivre la formation ?
Categorical

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Présentiel
58 
Distanciel
47 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1050
Distinct characters12
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 rowDistanciel
2nd rowDistanciel
3rd rowDistanciel
4th rowDistanciel
5th rowDistanciel

Common Values

ValueCountFrequency (%)
Présentiel 58
55.2%
Distanciel 47
44.8%

Length

2025-05-03T20:46:16.707898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-03T20:46:16.768430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
présentiel 58
55.2%
distanciel 47
44.8%

Most occurring characters

ValueCountFrequency (%)
e 163
15.5%
i 152
14.5%
s 105
10.0%
n 105
10.0%
l 105
10.0%
t 105
10.0%
P 58
 
5.5%
r 58
 
5.5%
é 58
 
5.5%
D 47
 
4.5%
Other values (2) 94
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 163
15.5%
i 152
14.5%
s 105
10.0%
n 105
10.0%
l 105
10.0%
t 105
10.0%
P 58
 
5.5%
r 58
 
5.5%
é 58
 
5.5%
D 47
 
4.5%
Other values (2) 94
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 163
15.5%
i 152
14.5%
s 105
10.0%
n 105
10.0%
l 105
10.0%
t 105
10.0%
P 58
 
5.5%
r 58
 
5.5%
é 58
 
5.5%
D 47
 
4.5%
Other values (2) 94
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 163
15.5%
i 152
14.5%
s 105
10.0%
n 105
10.0%
l 105
10.0%
t 105
10.0%
P 58
 
5.5%
r 58
 
5.5%
é 58
 
5.5%
D 47
 
4.5%
Other values (2) 94
9.0%

Comment avez-vous entendu parler de cette formation ?
Text

Missing 

Distinct70
Distinct (%)76.1%
Missing13
Missing (%)12.4%
Memory size9.9 KiB
2025-05-03T20:46:17.021121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length139
Median length61
Mean length23
Min length3

Characters and Unicode

Total characters2116
Distinct characters56
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

Unique60 ?
Unique (%)65.2%

Sample

1st rowRéseaux sociaux
2nd rowÀ travers les réseaux
3rd rowÀ travers notre délégué
4th rowLinkedin
5th rowSur linkedin
ValueCountFrequency (%)
linkedin 25
 
6.8%
par 23
 
6.2%
de 20
 
5.4%
un 19
 
5.2%
whatsapp 15
 
4.1%
sur 13
 
3.5%
ami 13
 
3.5%
le 12
 
3.3%
à 8
 
2.2%
d'un 8
 
2.2%
Other values (118) 212
57.6%
2025-05-03T20:46:17.448177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
325
15.4%
e 191
 
9.0%
a 160
 
7.6%
n 143
 
6.8%
u 117
 
5.5%
i 116
 
5.5%
r 115
 
5.4%
s 109
 
5.2%
t 74
 
3.5%
d 74
 
3.5%
Other values (46) 692
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
325
15.4%
e 191
 
9.0%
a 160
 
7.6%
n 143
 
6.8%
u 117
 
5.5%
i 116
 
5.5%
r 115
 
5.4%
s 109
 
5.2%
t 74
 
3.5%
d 74
 
3.5%
Other values (46) 692
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
325
15.4%
e 191
 
9.0%
a 160
 
7.6%
n 143
 
6.8%
u 117
 
5.5%
i 116
 
5.5%
r 115
 
5.4%
s 109
 
5.2%
t 74
 
3.5%
d 74
 
3.5%
Other values (46) 692
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
325
15.4%
e 191
 
9.0%
a 160
 
7.6%
n 143
 
6.8%
u 117
 
5.5%
i 116
 
5.5%
r 115
 
5.4%
s 109
 
5.2%
t 74
 
3.5%
d 74
 
3.5%
Other values (46) 692
32.7%

Commentaires
Text

Missing 

Distinct34
Distinct (%)100.0%
Missing71
Missing (%)67.6%
Memory size10.4 KiB
2025-05-03T20:46:17.814015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length246
Median length73
Mean length81.558824
Min length2

Characters and Unicode

Total characters2773
Distinct characters56
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

Unique34 ?
Unique (%)100.0%

Sample

1st rowRavis de cette initiative
2nd rowCa serai une super occasion de partager de la connaissance ensemble.
3rd rowJ'espère apprendre plein de choses sur la technologie IA
4th rowFormation, IA & Automatisation
5th rowRien à signaler
ValueCountFrequency (%)
de 21
 
4.8%
cette 17
 
3.9%
formation 15
 
3.4%
et 15
 
3.4%
pour 14
 
3.2%
je 13
 
3.0%
suis 9
 
2.1%
merci 8
 
1.8%
en 8
 
1.8%
à 8
 
1.8%
Other values (185) 307
70.6%
2025-05-03T20:46:18.287589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
411
14.8%
e 335
12.1%
i 197
 
7.1%
t 192
 
6.9%
r 185
 
6.7%
a 162
 
5.8%
n 159
 
5.7%
o 141
 
5.1%
s 134
 
4.8%
u 102
 
3.7%
Other values (46) 755
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2773
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
411
14.8%
e 335
12.1%
i 197
 
7.1%
t 192
 
6.9%
r 185
 
6.7%
a 162
 
5.8%
n 159
 
5.7%
o 141
 
5.1%
s 134
 
4.8%
u 102
 
3.7%
Other values (46) 755
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2773
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
411
14.8%
e 335
12.1%
i 197
 
7.1%
t 192
 
6.9%
r 185
 
6.7%
a 162
 
5.8%
n 159
 
5.7%
o 141
 
5.1%
s 134
 
4.8%
u 102
 
3.7%
Other values (46) 755
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2773
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
411
14.8%
e 335
12.1%
i 197
 
7.1%
t 192
 
6.9%
r 185
 
6.7%
a 162
 
5.8%
n 159
 
5.7%
o 141
 
5.1%
s 134
 
4.8%
u 102
 
3.7%
Other values (46) 755
27.2%

Correlations

2025-05-03T20:46:18.374459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Avez-vous déjà travaillé dans le domaine de la data ?Comment allez-vous suivre la formation ?Qu'est-ce qui vous décrit le mieux ?
Avez-vous déjà travaillé dans le domaine de la data ?1.0000.0000.000
Comment allez-vous suivre la formation ?0.0001.0000.143
Qu'est-ce qui vous décrit le mieux ?0.0000.1431.000

Missing values

2025-05-03T20:46:12.814357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-03T20:46:13.015470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-03T20:46:13.344497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

HorodateurQuel est votre nom ?Quel est votre prénom ?Votre adresse mailQu'est-ce qui vous décrit le mieux ?Avez-vous déjà travaillé dans le domaine de la data ?Comment allez-vous suivre la formation ?Comment avez-vous entendu parler de cette formation ?Commentaires
02025-04-29 09:45:01.430KANFITINEBOUAME PRISCABouameprisca@gmail.comEtudiantNonDistancielRéseaux sociauxNaN
12025-04-29 09:47:05.964AMUZUVIEyram Rose Belindaamuzuvibelindac@gmail.comEtudiantNonDistancielÀ travers les réseauxNaN
22025-04-29 09:48:55.820AMAGLO-SEMENUKoami Walteramaglowalter410@gmail.comEtudiantNonDistancielÀ travers notre déléguéNaN
32025-04-29 09:53:44.766SETODJIValèresetodji2001@gmail.comNaNNaNDistancielNaNNaN
42025-04-29 09:55:33.789HOUNSIAntoineantoinehounsi3@gmail.comEtudiantOuiDistancielNaNNaN
52025-04-29 10:22:13.534AMAH-TCHOUTCHOUIElikplim Josuéjosherenamah@gmail.comEtudiantOuiPrésentielLinkedinRavis de cette initiative
62025-04-29 10:26:16.750KOURATEAwè Augustinkouraa355@gmail.comEtudiantOuiPrésentielSur linkedinCa serai une super occasion de partager de la connaissance ensemble.
72025-04-29 10:36:43.044SEGLAKomi Samuelseglakomisamuel@gmail.comEtudiantNonDistancielPar un frèreJ'espère apprendre plein de choses sur la technologie IA
82025-04-29 10:48:12.646AGBOFOATIAbla Ruthagbofoatiablaruth@gmail.comEtudiantOuiDistancielJ'ai entendue parler de la formation par un post LinkedInNaN
92025-04-29 10:50:04.496POSSIANJosuéjosuepossian@gmail.comProfessionnelNonPrésentielLinkedInFormation, IA & Automatisation
HorodateurQuel est votre nom ?Quel est votre prénom ?Votre adresse mailQu'est-ce qui vous décrit le mieux ?Avez-vous déjà travaillé dans le domaine de la data ?Comment allez-vous suivre la formation ?Comment avez-vous entendu parler de cette formation ?Commentaires
952025-05-02 10:55:15.808SOKPOPelletiaabpelletia@gmail.comEtudiantNonPrésentielPar un amiNaN
962025-05-02 13:00:40.603KPARAGedeongedeonkpara@gmail.comEtudiantOuiPrésentielDans le group de la communauteNaN
972025-05-02 14:08:40.437KEZIREChareffchaffcryto@gmail.comChercheurNonPrésentielPar un frèreNaN
982025-05-02 14:15:33.793ADEWIEssoham Sylvainsylvainadewi5@gmail.comChercheurNonDistancielC'est un ami qui m'a envoyé le lienJ'espère apprendre beaucoup de choses au cours de cette formation
992025-05-02 15:34:09.830ZOKPODOAkossiwa Augustineaugustev005@gmail.comChercheurNonPrésentielGrâce à une amieNaN
1002025-05-02 15:54:47.994AKPOBertinakpobertin999@gmail.comEtudiantNonDistancielEn ligneJe serai content d'être sélectionné et participer à cette formation
1012025-05-02 16:04:34.964AKPOBertinakpobertin85@gmail.comEtudiantNonDistancielEn ligneJe suis ravie d'être avec vous
1022025-05-02 16:56:36.766ATCHONOUGLOYao Abelatchonouglomawuegnega@gmail.comEtudiantOuiPrésentielCamarades de classes.NaN
1032025-05-02 17:11:41.892MATCHATOMBalakibawibalakibawimatchatom@gmail.comEtudiantOuiPrésentielNaNNaN
1042025-05-02 17:37:06.025de SOUZAGraciagraciadesouza81@gmail.comEtudiantNonPrésentielNaNNaN