Overview

Dataset statistics

Number of variables56
Number of observations682
Missing cells11078
Missing cells (%)29.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory268.7 KiB
Average record size in memory403.4 B

Variable types

DateTime1
Categorical19
Numeric20
Unsupported2
Text14

Alerts

Bonus (EUR) is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 3 other fieldsHigh correlation
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? is highly overall correlated with Bonus (EUR) and 7 other fieldsHigh correlation
Kaupunki is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Kk-tulot (laskennallinen) is highly overall correlated with Kk-tulot (laskennallinen, normalisoitu) and 6 other fieldsHigh correlation
Kk-tulot (laskennallinen, normalisoitu) is highly overall correlated with Kk-tulot (laskennallinen) and 5 other fieldsHigh correlation
Kuinka suuren osan ajasta teet lähityönä toimistolla? is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Kuukausipalkka is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 7 other fieldsHigh correlation
Laskutettavat tunnit viikossa is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Laskutustavat is highly overall correlated with Bonus (EUR) and 7 other fieldsHigh correlation
Lomaraha (EUR) is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 8 other fieldsHigh correlation
Luontoisedut is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Millaisessa yrityksessä työskentelet? is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Mistä asiakkaat ovat? is highly overall correlated with Bonus (EUR) and 9 other fieldsHigh correlation
Montako vuotta olet tehnyt laskuttavaa työtä alalla? is highly overall correlated with Palkansaaja vai laskuttaja and 2 other fieldsHigh correlation
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Osakkeet/optiot (EUR) is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 3 other fieldsHigh correlation
Palkansaaja vai laskuttaja is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 21 other fieldsHigh correlation
Provisio (kk, brutto) is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 3 other fieldsHigh correlation
Seniority is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Siirtynyt palkansaaja/laskuttaja is highly overall correlated with Montako vuotta olet tehnyt laskuttavaa työtä alalla?High correlation
Sopimuksen pituus is highly overall correlated with Bonus (EUR) and 6 other fieldsHigh correlation
Tulojen muutos viime vuodesta (%) is highly overall correlated with Mistä asiakkaat ovat?High correlation
Tuntilaskutus (ALV 0%, euroina) is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Työaika is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Työkokemus alalta (vuosina) is highly overall correlated with Kk-tulot (laskennallinen) and 4 other fieldsHigh correlation
Työpaikkojen lukumäärä is highly overall correlated with Kk-tulot (laskennallinen) and 2 other fieldsHigh correlation
Viikot ilman laskutusta is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Virallinen senioriteetti is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Vuosia nykyisellä työnantajalla is highly overall correlated with Montako vuotta olet tehnyt laskuttavaa työtä alalla?High correlation
Vuosilaskutus (ALV 0%, euroina) is highly overall correlated with Palkansaaja vai laskuttaja and 1 other fieldsHigh correlation
Vuosittaiset verovapaat edut (EUR) is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Vuositulot is highly overall correlated with Kk-tulot (laskennallinen) and 6 other fieldsHigh correlation
Siirtynyt palkansaaja/laskuttaja is highly imbalanced (79.4%)Imbalance
Sukupuoli is highly imbalanced (67.2%)Imbalance
Suomen kielen taito is highly imbalanced (88.8%)Imbalance
Työkieli is highly imbalanced (50.3%)Imbalance
Opintoala is highly imbalanced (69.1%)Imbalance
Laskutustavat is highly imbalanced (56.8%)Imbalance
Kaupunki is highly imbalanced (55.1%)Imbalance
Vuosia nykyisellä työnantajalla has 9 (1.3%) missing valuesMissing
Työpaikkojen lukumäärä has 12 (1.8%) missing valuesMissing
Opintoala has 36 (5.3%) missing valuesMissing
Tulojen muutos viime vuodesta (%) has 111 (16.3%) missing valuesMissing
Montako vuotta olet tehnyt laskuttavaa työtä alalla? has 591 (86.7%) missing valuesMissing
Palvelut has 595 (87.2%) missing valuesMissing
Tuntilaskutus (ALV 0%, euroina) has 600 (88.0%) missing valuesMissing
Vuosilaskutus (ALV 0%, euroina) has 602 (88.3%) missing valuesMissing
Laskutettavat tunnit viikossa has 598 (87.7%) missing valuesMissing
Viikot ilman laskutusta has 599 (87.8%) missing valuesMissing
Laskutustavat has 592 (86.8%) missing valuesMissing
Sopimuksen pituus has 593 (87.0%) missing valuesMissing
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? has 594 (87.1%) missing valuesMissing
Mistä asiakkaat ovat? has 592 (86.8%) missing valuesMissing
Työpaikka has 534 (78.3%) missing valuesMissing
Kaupunki has 100 (14.7%) missing valuesMissing
Millaisessa yrityksessä työskentelet? has 97 (14.2%) missing valuesMissing
Työaika has 97 (14.2%) missing valuesMissing
Kuinka suuren osan ajasta teet lähityönä toimistolla? has 97 (14.2%) missing valuesMissing
Rooli has 95 (13.9%) missing valuesMissing
Seniority has 100 (14.7%) missing valuesMissing
Virallinen senioriteetti has 108 (15.8%) missing valuesMissing
Vapaa kuvaus kokonaiskompensaatiomallista has 591 (86.7%) missing valuesMissing
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? has 100 (14.7%) missing valuesMissing
Bonukset (kuvaus) has 131 (19.2%) missing valuesMissing
Edut (ei luontoisedut) has 104 (15.2%) missing valuesMissing
Vuosittaiset verovapaat edut (EUR) has 318 (46.6%) missing valuesMissing
Luontoisedut has 177 (26.0%) missing valuesMissing
Käyttöjärjestelmä has 9 (1.3%) missing valuesMissing
Ohjelmointikieli has 63 (9.2%) missing valuesMissing
Web-kehykset has 195 (28.6%) missing valuesMissing
Data & ML has 252 (37.0%) missing valuesMissing
DevOps & pilvi has 73 (10.7%) missing valuesMissing
Tietokannat has 131 (19.2%) missing valuesMissing
Vapaa sana has 647 (94.9%) missing valuesMissing
Palaute has 633 (92.8%) missing valuesMissing
Vuositulot has 94 (13.8%) missing valuesMissing
Kk-tulot (laskennallinen) has 94 (13.8%) missing valuesMissing
Kk-tulot (laskennallinen, normalisoitu) has 99 (14.5%) missing valuesMissing
Provisio (kk, brutto) is highly skewed (γ1 = 23.75138568)Skewed
Timestamp has unique valuesUnique
Vastaustunniste has unique valuesUnique
Yrityksen koko is an unsupported type, check if it needs cleaning or further analysisUnsupported
Vapaa kuvaus kokonaiskompensaatiomallista is an unsupported type, check if it needs cleaning or further analysisUnsupported
Vuosia nykyisellä työnantajalla has 55 (8.1%) zerosZeros
Tulojen muutos viime vuodesta (%) has 127 (18.6%) zerosZeros
Montako vuotta olet tehnyt laskuttavaa työtä alalla? has 7 (1.0%) zerosZeros
Kuinka suuren osan ajasta teet lähityönä toimistolla? has 71 (10.4%) zerosZeros
Kuukausipalkka has 94 (13.8%) zerosZeros
Provisio (kk, brutto) has 606 (88.9%) zerosZeros
Lomaraha (EUR) has 235 (34.5%) zerosZeros
Bonus (EUR) has 517 (75.8%) zerosZeros
Osakkeet/optiot (EUR) has 620 (90.9%) zerosZeros
Vuosittaiset verovapaat edut (EUR) has 29 (4.3%) zerosZeros

Reproduction

Analysis started2026-03-12 15:15:13.619477
Analysis finished2026-03-12 15:15:41.746329
Duration28.13 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Timestamp
Date

Unique 

Distinct682
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
Minimum2026-02-09 08:08:48.457000
Maximum2026-03-03 16:36:20.497000
Invalid dates0
Invalid dates (%)0.0%
2026-03-12T15:15:41.807069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:41.895421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Palkansaaja vai laskuttaja
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Palkansaaja
590 
Laskuttaja
92 

Length

Max length11
Median length11
Mean length10.865103
Min length10

Characters and Unicode

Total characters7410
Distinct characters10
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 rowLaskuttaja
2nd rowPalkansaaja
3rd rowPalkansaaja
4th rowPalkansaaja
5th rowPalkansaaja

Common Values

ValueCountFrequency (%)
Palkansaaja590
86.5%
Laskuttaja92
 
13.5%

Length

2026-03-12T15:15:41.975523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:42.018851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
palkansaaja590
86.5%
laskuttaja92
 
13.5%

Most occurring characters

ValueCountFrequency (%)
a3226
43.5%
k682
 
9.2%
j682
 
9.2%
s682
 
9.2%
P590
 
8.0%
l590
 
8.0%
n590
 
8.0%
t184
 
2.5%
L92
 
1.2%
u92
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)7410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a3226
43.5%
k682
 
9.2%
j682
 
9.2%
s682
 
9.2%
P590
 
8.0%
l590
 
8.0%
n590
 
8.0%
t184
 
2.5%
L92
 
1.2%
u92
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a3226
43.5%
k682
 
9.2%
j682
 
9.2%
s682
 
9.2%
P590
 
8.0%
l590
 
8.0%
n590
 
8.0%
t184
 
2.5%
L92
 
1.2%
u92
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a3226
43.5%
k682
 
9.2%
j682
 
9.2%
s682
 
9.2%
P590
 
8.0%
l590
 
8.0%
n590
 
8.0%
t184
 
2.5%
L92
 
1.2%
u92
 
1.2%

Siirtynyt palkansaaja/laskuttaja
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size6.1 KiB
Ei
648 
palkansaaja → laskuttaja
 
20
laskuttaja → palkansaaja
 
13

Length

Max length24
Median length2
Mean length3.0660793
Min length2

Characters and Unicode

Total characters2088
Distinct characters13
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 rowEi
2nd rowEi
3rd rowEi
4th rowEi
5th rowEi

Common Values

ValueCountFrequency (%)
Ei648
95.0%
palkansaaja → laskuttaja20
 
2.9%
laskuttaja → palkansaaja13
 
1.9%
(Missing)1
 
0.1%

Length

2026-03-12T15:15:42.074762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:42.116430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ei648
86.7%
palkansaaja33
 
4.4%
33
 
4.4%
laskuttaja33
 
4.4%

Most occurring characters

ValueCountFrequency (%)
E648
31.0%
i648
31.0%
a264
12.6%
l66
 
3.2%
k66
 
3.2%
t66
 
3.2%
s66
 
3.2%
j66
 
3.2%
66
 
3.2%
p33
 
1.6%
Other values (3)99
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E648
31.0%
i648
31.0%
a264
12.6%
l66
 
3.2%
k66
 
3.2%
t66
 
3.2%
s66
 
3.2%
j66
 
3.2%
66
 
3.2%
p33
 
1.6%
Other values (3)99
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E648
31.0%
i648
31.0%
a264
12.6%
l66
 
3.2%
k66
 
3.2%
t66
 
3.2%
s66
 
3.2%
j66
 
3.2%
66
 
3.2%
p33
 
1.6%
Other values (3)99
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E648
31.0%
i648
31.0%
a264
12.6%
l66
 
3.2%
k66
 
3.2%
t66
 
3.2%
s66
 
3.2%
j66
 
3.2%
66
 
3.2%
p33
 
1.6%
Other values (3)99
 
4.7%

Ikä
Categorical

Distinct9
Distinct (%)1.3%
Missing1
Missing (%)0.1%
Memory size6.4 KiB
38
197 
33
164 
43
131 
28
80 
48
68 
53
20 
23
 
15
> 55v
 
4
18
 
2

Length

Max length5
Median length2
Mean length2.0176211
Min length2

Characters and Unicode

Total characters1374
Distinct characters9
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 row38
2nd row48
3rd row38
4th row28
5th row43

Common Values

ValueCountFrequency (%)
38197
28.9%
33164
24.0%
43131
19.2%
2880
11.7%
4868
 
10.0%
5320
 
2.9%
2315
 
2.2%
> 55v4
 
0.6%
182
 
0.3%
(Missing)1
 
0.1%

Length

2026-03-12T15:15:42.174268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:42.236059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
38197
28.8%
33164
23.9%
43131
19.1%
2880
11.7%
4868
 
9.9%
5320
 
2.9%
2315
 
2.2%
4
 
0.6%
55v4
 
0.6%
182
 
0.3%

Most occurring characters

ValueCountFrequency (%)
3691
50.3%
8347
25.3%
4199
 
14.5%
295
 
6.9%
528
 
2.0%
>4
 
0.3%
4
 
0.3%
v4
 
0.3%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3691
50.3%
8347
25.3%
4199
 
14.5%
295
 
6.9%
528
 
2.0%
>4
 
0.3%
4
 
0.3%
v4
 
0.3%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3691
50.3%
8347
25.3%
4199
 
14.5%
295
 
6.9%
528
 
2.0%
>4
 
0.3%
4
 
0.3%
v4
 
0.3%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3691
50.3%
8347
25.3%
4199
 
14.5%
295
 
6.9%
528
 
2.0%
>4
 
0.3%
4
 
0.3%
v4
 
0.3%
12
 
0.1%

Sukupuoli
Categorical

Imbalance 

Distinct4
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
mies
592 
nainen
76 
Prefer not to say
 
8
muu
 
5

Length

Max length17
Median length4
Mean length4.3685756
Min length3

Characters and Unicode

Total characters2975
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
mies592
86.8%
nainen76
 
11.1%
Prefer not to say8
 
1.2%
muu5
 
0.7%
(Missing)1
 
0.1%

Length

2026-03-12T15:15:42.319111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:42.367150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mies592
84.0%
nainen76
 
10.8%
prefer8
 
1.1%
not8
 
1.1%
to8
 
1.1%
say8
 
1.1%
muu5
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e684
23.0%
i668
22.5%
s600
20.2%
m597
20.1%
n236
 
7.9%
a84
 
2.8%
24
 
0.8%
r16
 
0.5%
t16
 
0.5%
o16
 
0.5%
Other values (4)34
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e684
23.0%
i668
22.5%
s600
20.2%
m597
20.1%
n236
 
7.9%
a84
 
2.8%
24
 
0.8%
r16
 
0.5%
t16
 
0.5%
o16
 
0.5%
Other values (4)34
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e684
23.0%
i668
22.5%
s600
20.2%
m597
20.1%
n236
 
7.9%
a84
 
2.8%
24
 
0.8%
r16
 
0.5%
t16
 
0.5%
o16
 
0.5%
Other values (4)34
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e684
23.0%
i668
22.5%
s600
20.2%
m597
20.1%
n236
 
7.9%
a84
 
2.8%
24
 
0.8%
r16
 
0.5%
t16
 
0.5%
o16
 
0.5%
Other values (4)34
 
1.1%

Suomen kielen taito
Categorical

Imbalance 

Distinct3
Distinct (%)0.4%
Missing3
Missing (%)0.4%
Memory size10.7 KiB
Yes
662 
No
 
16
Prefer not to say
 
1

Length

Max length17
Median length3
Mean length2.9970545
Min length2

Characters and Unicode

Total characters2035
Distinct characters13
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 (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
Yes662
97.1%
No16
 
2.3%
Prefer not to say1
 
0.1%
(Missing)3
 
0.4%

Length

2026-03-12T15:15:42.430592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:42.482622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes662
97.1%
no16
 
2.3%
prefer1
 
0.1%
not1
 
0.1%
to1
 
0.1%
say1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e664
32.6%
s663
32.6%
Y662
32.5%
o18
 
0.9%
N16
 
0.8%
3
 
0.1%
r2
 
0.1%
t2
 
0.1%
P1
 
< 0.1%
f1
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2035
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e664
32.6%
s663
32.6%
Y662
32.5%
o18
 
0.9%
N16
 
0.8%
3
 
0.1%
r2
 
0.1%
t2
 
0.1%
P1
 
< 0.1%
f1
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2035
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e664
32.6%
s663
32.6%
Y662
32.5%
o18
 
0.9%
N16
 
0.8%
3
 
0.1%
r2
 
0.1%
t2
 
0.1%
P1
 
< 0.1%
f1
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2035
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e664
32.6%
s663
32.6%
Y662
32.5%
o18
 
0.9%
N16
 
0.8%
3
 
0.1%
r2
 
0.1%
t2
 
0.1%
P1
 
< 0.1%
f1
 
< 0.1%
Other values (3)3
 
0.1%

Työkieli
Categorical

Imbalance 

Distinct10
Distinct (%)1.5%
Missing2
Missing (%)0.3%
Memory size10.7 KiB
Both
288 
Finnish
203 
English
182 
Organisation’s official language is English, but with customers I mainly use Finnish
 
1
Norwegian
 
1
Swedish, finnish
 
1
Finnish, English both
 
1
swedish
 
1
I don't understand what this question is trying to ask
 
1
Swedish
 
1

Length

Max length84
Median length7
Mean length5.9485294
Min length4

Characters and Unicode

Total characters4045
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

Unique7 ?
Unique (%)1.0%

Sample

1st rowBoth
2nd rowBoth
3rd rowBoth
4th rowBoth
5th rowFinnish

Common Values

ValueCountFrequency (%)
Both288
42.2%
Finnish203
29.8%
English182
26.7%
Organisation’s official language is English, but with customers I mainly use Finnish1
 
0.1%
Norwegian1
 
0.1%
Swedish, finnish1
 
0.1%
Finnish, English both1
 
0.1%
swedish1
 
0.1%
I don't understand what this question is trying to ask1
 
0.1%
Swedish1
 
0.1%
(Missing)2
 
0.3%

Length

2026-03-12T15:15:42.542609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:42.611344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
both289
41.1%
finnish206
29.3%
english184
26.2%
swedish3
 
0.4%
is2
 
0.3%
i2
 
0.3%
organisation’s1
 
0.1%
language1
 
0.1%
official1
 
0.1%
with1
 
0.1%
Other values (13)13
 
1.8%

Most occurring characters

ValueCountFrequency (%)
h685
16.9%
i611
15.1%
n606
15.0%
s405
10.0%
t300
7.4%
o296
7.3%
B288
7.1%
F205
 
5.1%
g189
 
4.7%
l187
 
4.6%
Other values (22)273
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)4045
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h685
16.9%
i611
15.1%
n606
15.0%
s405
10.0%
t300
7.4%
o296
7.3%
B288
7.1%
F205
 
5.1%
g189
 
4.7%
l187
 
4.6%
Other values (22)273
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4045
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h685
16.9%
i611
15.1%
n606
15.0%
s405
10.0%
t300
7.4%
o296
7.3%
B288
7.1%
F205
 
5.1%
g189
 
4.7%
l187
 
4.6%
Other values (22)273
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4045
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h685
16.9%
i611
15.1%
n606
15.0%
s405
10.0%
t300
7.4%
o296
7.3%
B288
7.1%
F205
 
5.1%
g189
 
4.7%
l187
 
4.6%
Other values (22)273
 
6.7%

Työkokemus alalta (vuosina)
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)5.0%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean13.080882
Minimum0
Maximum42
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:42.705362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median12
Q318
95-th percentile26
Maximum42
Range42
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.0715418
Coefficient of variation (CV)0.54060128
Kurtosis-0.19620868
Mean13.080882
Median Absolute Deviation (MAD)5
Skewness0.64833142
Sum8895
Variance50.006703
MonotonicityNot monotonic
2026-03-12T15:15:42.770945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1060
 
8.8%
1559
 
8.7%
850
 
7.3%
2049
 
7.2%
742
 
6.2%
639
 
5.7%
537
 
5.4%
1235
 
5.1%
930
 
4.4%
427
 
4.0%
Other values (24)252
37.0%
ValueCountFrequency (%)
01
 
0.1%
11
 
0.1%
29
 
1.3%
315
 
2.2%
427
4.0%
537
5.4%
639
5.7%
742
6.2%
850
7.3%
930
4.4%
ValueCountFrequency (%)
421
 
0.1%
321
 
0.1%
311
 
0.1%
3010
1.5%
292
 
0.3%
289
 
1.3%
278
 
1.2%
2614
2.1%
2524
3.5%
245
 
0.7%

Vuosia nykyisellä työnantajalla
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct22
Distinct (%)3.3%
Missing9
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean4.3640416
Minimum0
Maximum26
Zeros55
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:42.832452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8062859
Coefficient of variation (CV)0.87219285
Kurtosis3.5907079
Mean4.3640416
Median Absolute Deviation (MAD)2
Skewness1.6478622
Sum2937
Variance14.487812
MonotonicityNot monotonic
2026-03-12T15:15:42.901422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3109
16.0%
1106
15.5%
494
13.8%
573
10.7%
267
9.8%
055
8.1%
832
 
4.7%
732
 
4.7%
625
 
3.7%
1024
 
3.5%
Other values (12)56
8.2%
ValueCountFrequency (%)
055
8.1%
1106
15.5%
267
9.8%
3109
16.0%
494
13.8%
573
10.7%
625
 
3.7%
732
 
4.7%
832
 
4.7%
98
 
1.2%
ValueCountFrequency (%)
261
 
0.1%
202
 
0.3%
191
 
0.1%
185
0.7%
173
 
0.4%
162
 
0.3%
153
 
0.4%
146
0.9%
139
1.3%
127
1.0%

Työpaikkojen lukumäärä
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)2.2%
Missing12
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean3.9761194
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:42.957424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile8
Maximum20
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1739104
Coefficient of variation (CV)0.54674172
Kurtosis5.9487015
Mean3.9761194
Median Absolute Deviation (MAD)1
Skewness1.6094011
Sum2664
Variance4.7258863
MonotonicityNot monotonic
2026-03-12T15:15:43.017182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3141
20.7%
4135
19.8%
2123
18.0%
593
13.6%
653
 
7.8%
151
 
7.5%
734
 
5.0%
818
 
2.6%
1012
 
1.8%
94
 
0.6%
Other values (5)6
 
0.9%
(Missing)12
 
1.8%
ValueCountFrequency (%)
151
 
7.5%
2123
18.0%
3141
20.7%
4135
19.8%
593
13.6%
653
 
7.8%
734
 
5.0%
818
 
2.6%
94
 
0.6%
1012
 
1.8%
ValueCountFrequency (%)
201
 
0.1%
151
 
0.1%
141
 
0.1%
121
 
0.1%
112
 
0.3%
1012
 
1.8%
94
 
0.6%
818
 
2.6%
734
5.0%
653
7.8%

Yrityksen koko
Unsupported

Rejected  Unsupported 

Missing4
Missing (%)0.6%
Memory size10.7 KiB

Koulutustaustasi
Categorical

Distinct9
Distinct (%)1.3%
Missing1
Missing (%)0.1%
Memory size10.7 KiB
Master’s degree – university (FM, KTM, DI, etc.)
299 
Bachelor’s degree – AMK (insinööri, tradenomi, etc.)
161 
Bachelor’s degree – university (kandidaatti / alempi korkeakoulututkinto)
82 
Upper secondary – general (lukio / high school equivalent)
68 
Upper secondary – vocational (ammatillinen perustutkinto)
33 
Master’s degree – AMK (YAMK)
 
18
Doctorate (PhD / tohtori)
 
14
Basic education (peruskoulu / compulsory education)
 
4
Licentiate degree (lisensiaatti)
 
2

Length

Max length73
Median length58
Mean length52.359765
Min length25

Characters and Unicode

Total characters35657
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowBachelor’s degree – AMK (insinööri, tradenomi, etc.)
2nd rowBachelor’s degree – AMK (insinööri, tradenomi, etc.)
3rd rowMaster’s degree – university (FM, KTM, DI, etc.)
4th rowMaster’s degree – university (FM, KTM, DI, etc.)
5th rowMaster’s degree – university (FM, KTM, DI, etc.)

Common Values

ValueCountFrequency (%)
Master’s degree – university (FM, KTM, DI, etc.)299
43.8%
Bachelor’s degree – AMK (insinööri, tradenomi, etc.)161
23.6%
Bachelor’s degree – university (kandidaatti / alempi korkeakoulututkinto)82
 
12.0%
Upper secondary – general (lukio / high school equivalent)68
 
10.0%
Upper secondary – vocational (ammatillinen perustutkinto)33
 
4.8%
Master’s degree – AMK (YAMK)18
 
2.6%
Doctorate (PhD / tohtori)14
 
2.1%
Basic education (peruskoulu / compulsory education)4
 
0.6%
Licentiate degree (lisensiaatti)2
 
0.3%
(Missing)1
 
0.1%

Length

2026-03-12T15:15:43.087642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:43.152426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
661
12.8%
degree562
 
10.9%
etc460
 
8.9%
university381
 
7.4%
master’s317
 
6.1%
fm299
 
5.8%
ktm299
 
5.8%
di299
 
5.8%
bachelor’s243
 
4.7%
amk179
 
3.5%
Other values (26)1461
28.3%

Most occurring characters

ValueCountFrequency (%)
4480
 
12.6%
e3984
 
11.2%
r2246
 
6.3%
i2106
 
5.9%
t2034
 
5.7%
s1637
 
4.6%
a1532
 
4.3%
n1409
 
4.0%
,1219
 
3.4%
o1130
 
3.2%
Other values (31)13880
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)35657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4480
 
12.6%
e3984
 
11.2%
r2246
 
6.3%
i2106
 
5.9%
t2034
 
5.7%
s1637
 
4.6%
a1532
 
4.3%
n1409
 
4.0%
,1219
 
3.4%
o1130
 
3.2%
Other values (31)13880
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)35657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4480
 
12.6%
e3984
 
11.2%
r2246
 
6.3%
i2106
 
5.9%
t2034
 
5.7%
s1637
 
4.6%
a1532
 
4.3%
n1409
 
4.0%
,1219
 
3.4%
o1130
 
3.2%
Other values (31)13880
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)35657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4480
 
12.6%
e3984
 
11.2%
r2246
 
6.3%
i2106
 
5.9%
t2034
 
5.7%
s1637
 
4.6%
a1532
 
4.3%
n1409
 
4.0%
,1219
 
3.4%
o1130
 
3.2%
Other values (31)13880
38.9%

Opintoala
Categorical

Imbalance  Missing 

Distinct18
Distinct (%)2.8%
Missing36
Missing (%)5.3%
Memory size10.7 KiB
Information and Communication Technologies (ICT / Computer Science)
517 
Engineering, Manufacturing, and Construction
 
40
Business, Administration, and Law
 
28
Natural Sciences, Mathematics, and Statistics
 
21
Arts and Humanities
 
17
Social Sciences, Journalism, and Information
 
7
Health and Welfare
 
3
Education and Teaching
 
2
Agriculture, Forestry, Fisheries, and Veterinary
 
2
Don't want to tell
 
1
No completed degree
 
1
Communications Engineering
 
1
Information Systems Science
 
1
Design
 
1
Lukio
 
1
Two master's decrees: MSc & MBA
 
1
none
 
1
Restonomi, alempi ammattikoulu tutkinto tieto- ja viestintätekniikan perustutkinto
 
1

Length

Max length82
Median length67
Mean length60.852941
Min length4

Characters and Unicode

Total characters39311
Distinct characters49
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

Unique9 ?
Unique (%)1.4%

Sample

1st rowArts and Humanities
2nd rowInformation and Communication Technologies (ICT / Computer Science)
3rd rowInformation and Communication Technologies (ICT / Computer Science)
4th rowInformation and Communication Technologies (ICT / Computer Science)
5th rowInformation and Communication Technologies (ICT / Computer Science)

Common Values

ValueCountFrequency (%)
Information and Communication Technologies (ICT / Computer Science)517
75.8%
Engineering, Manufacturing, and Construction40
 
5.9%
Business, Administration, and Law28
 
4.1%
Natural Sciences, Mathematics, and Statistics21
 
3.1%
Arts and Humanities17
 
2.5%
Social Sciences, Journalism, and Information7
 
1.0%
Health and Welfare3
 
0.4%
Education and Teaching2
 
0.3%
Agriculture, Forestry, Fisheries, and Veterinary2
 
0.3%
Don't want to tell1
 
0.1%
Other values (8)8
 
1.2%
(Missing)36
 
5.3%

Length

2026-03-12T15:15:43.266545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and637
13.7%
information525
11.3%
518
11.1%
science518
11.1%
communication517
11.1%
ict517
11.1%
technologies517
11.1%
computer517
11.1%
engineering41
 
0.9%
manufacturing40
 
0.9%
Other values (45)306
6.6%

Most occurring characters

ValueCountFrequency (%)
n4194
 
10.7%
4007
 
10.2%
o3776
 
9.6%
i3033
 
7.7%
e2833
 
7.2%
c2265
 
5.8%
m2158
 
5.5%
a1971
 
5.0%
t1928
 
4.9%
C1592
 
4.0%
Other values (39)11554
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)39311
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n4194
 
10.7%
4007
 
10.2%
o3776
 
9.6%
i3033
 
7.7%
e2833
 
7.2%
c2265
 
5.8%
m2158
 
5.5%
a1971
 
5.0%
t1928
 
4.9%
C1592
 
4.0%
Other values (39)11554
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)39311
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n4194
 
10.7%
4007
 
10.2%
o3776
 
9.6%
i3033
 
7.7%
e2833
 
7.2%
c2265
 
5.8%
m2158
 
5.5%
a1971
 
5.0%
t1928
 
4.9%
C1592
 
4.0%
Other values (39)11554
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)39311
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n4194
 
10.7%
4007
 
10.2%
o3776
 
9.6%
i3033
 
7.7%
e2833
 
7.2%
c2265
 
5.8%
m2158
 
5.5%
a1971
 
5.0%
t1928
 
4.9%
C1592
 
4.0%
Other values (39)11554
29.4%

Tulojen muutos viime vuodesta (%)
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct165
Distinct (%)28.9%
Missing111
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean6.9099176
Minimum-93
Maximum170
Zeros127
Zeros (%)18.6%
Negative32
Negative (%)4.7%
Memory size10.7 KiB
2026-03-12T15:15:43.346919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-93
5-th percentile-2.705
Q10
median3
Q38.25
95-th percentile26.5
Maximum170
Range263
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation18.652233
Coefficient of variation (CV)2.6993424
Kurtosis25.791833
Mean6.9099176
Median Absolute Deviation (MAD)3
Skewness3.6411197
Sum3945.5629
Variance347.90581
MonotonicityNot monotonic
2026-03-12T15:15:43.427487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0127
18.6%
244
 
6.5%
522
 
3.2%
1021
 
3.1%
319
 
2.8%
2.517
 
2.5%
117
 
2.5%
416
 
2.3%
1513
 
1.9%
2010
 
1.5%
Other values (155)265
38.9%
(Missing)111
16.3%
ValueCountFrequency (%)
-931
 
0.1%
-611
 
0.1%
-503
0.4%
-241
 
0.1%
-201
 
0.1%
-172
0.3%
-153
0.4%
-111
 
0.1%
-103
0.4%
-8.421
 
0.1%
ValueCountFrequency (%)
1701
 
0.1%
1252
0.3%
1111
 
0.1%
1102
0.3%
1051
 
0.1%
1003
0.4%
701
 
0.1%
651
 
0.1%
571
 
0.1%
52.191
 
0.1%

Montako vuotta olet tehnyt laskuttavaa työtä alalla?
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct16
Distinct (%)17.6%
Missing591
Missing (%)86.7%
Infinite0
Infinite (%)0.0%
Mean5.043956
Minimum0
Maximum28
Zeros7
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:43.488609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile15
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7793818
Coefficient of variation (CV)0.94754629
Kurtosis5.9057627
Mean5.043956
Median Absolute Deviation (MAD)2
Skewness2.0819647
Sum459
Variance22.842491
MonotonicityNot monotonic
2026-03-12T15:15:43.542288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
415
 
2.2%
315
 
2.2%
113
 
1.9%
510
 
1.5%
07
 
1.0%
67
 
1.0%
25
 
0.7%
104
 
0.6%
153
 
0.4%
93
 
0.4%
Other values (6)9
 
1.3%
(Missing)591
86.7%
ValueCountFrequency (%)
07
1.0%
113
1.9%
25
 
0.7%
315
2.2%
415
2.2%
510
1.5%
67
1.0%
83
 
0.4%
93
 
0.4%
104
 
0.6%
ValueCountFrequency (%)
281
 
0.1%
191
 
0.1%
181
 
0.1%
153
0.4%
132
 
0.3%
121
 
0.1%
104
0.6%
93
0.4%
83
0.4%
67
1.0%

Palvelut
Text

Missing 

Distinct72
Distinct (%)82.8%
Missing595
Missing (%)87.2%
Memory size10.7 KiB
2026-03-12T15:15:43.658709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length98
Median length53
Mean length31.873563
Min length3

Characters and Unicode

Total characters2773
Distinct characters51
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

Unique66 ?
Unique (%)75.9%

Sample

1st rowFullstack development with backend focus
2nd rowSoftware development full-stack
3rd rowFull-stack
4th rowFull-stack
5th rowFull-stack lead developer so all of the above.
ValueCountFrequency (%)
full-stack34
 
10.6%
development32
 
10.0%
software19
 
5.9%
architecture18
 
5.6%
ai12
 
3.7%
backend11
 
3.4%
data11
 
3.4%
devops10
 
3.1%
management7
 
2.2%
stack6
 
1.9%
Other values (97)161
50.2%
2026-03-12T15:15:43.879388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e311
 
11.2%
236
 
8.5%
t213
 
7.7%
a190
 
6.9%
l171
 
6.2%
n154
 
5.6%
c142
 
5.1%
s127
 
4.6%
o115
 
4.1%
r109
 
3.9%
Other values (41)1005
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2773
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e311
 
11.2%
236
 
8.5%
t213
 
7.7%
a190
 
6.9%
l171
 
6.2%
n154
 
5.6%
c142
 
5.1%
s127
 
4.6%
o115
 
4.1%
r109
 
3.9%
Other values (41)1005
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2773
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e311
 
11.2%
236
 
8.5%
t213
 
7.7%
a190
 
6.9%
l171
 
6.2%
n154
 
5.6%
c142
 
5.1%
s127
 
4.6%
o115
 
4.1%
r109
 
3.9%
Other values (41)1005
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2773
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e311
 
11.2%
236
 
8.5%
t213
 
7.7%
a190
 
6.9%
l171
 
6.2%
n154
 
5.6%
c142
 
5.1%
s127
 
4.6%
o115
 
4.1%
r109
 
3.9%
Other values (41)1005
36.2%

Tuntilaskutus (ALV 0%, euroina)
Real number (ℝ)

High correlation  Missing 

Distinct33
Distinct (%)40.2%
Missing600
Missing (%)88.0%
Infinite0
Infinite (%)0.0%
Mean86.067073
Minimum0
Maximum150
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:43.946384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile52.4
Q178.5
median85.5
Q395
95-th percentile128.55
Maximum150
Range150
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation20.724644
Coefficient of variation (CV)0.24079643
Kurtosis4.8745366
Mean86.067073
Median Absolute Deviation (MAD)9.5
Skewness-0.13895172
Sum7057.5
Variance429.51088
MonotonicityNot monotonic
2026-03-12T15:15:44.018442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
8013
 
1.9%
9010
 
1.5%
959
 
1.3%
756
 
0.9%
855
 
0.7%
704
 
0.6%
1004
 
0.6%
1292
 
0.3%
1502
 
0.3%
922
 
0.3%
Other values (23)25
 
3.7%
(Missing)600
88.0%
ValueCountFrequency (%)
01
 
0.1%
401
 
0.1%
502
 
0.3%
521
 
0.1%
601
 
0.1%
651
 
0.1%
704
0.6%
722
 
0.3%
741
 
0.1%
756
0.9%
ValueCountFrequency (%)
1502
0.3%
1301
 
0.1%
1292
0.3%
1201
 
0.1%
1021
 
0.1%
1004
0.6%
98.51
 
0.1%
981
 
0.1%
971
 
0.1%
961
 
0.1%

Vuosilaskutus (ALV 0%, euroina)
Real number (ℝ)

High correlation  Missing 

Distinct49
Distinct (%)61.3%
Missing602
Missing (%)88.3%
Infinite0
Infinite (%)0.0%
Mean114454.66
Minimum0
Maximum203000
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:44.096837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28800
Q190000
median124500
Q3150000
95-th percentile180350
Maximum203000
Range203000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation48146.86
Coefficient of variation (CV)0.42066316
Kurtosis-0.21612277
Mean114454.66
Median Absolute Deviation (MAD)25500
Skewness-0.57087716
Sum9156373
Variance2.3181201 × 109
MonotonicityNot monotonic
2026-03-12T15:15:44.186312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1000008
 
1.2%
1500007
 
1.0%
1600004
 
0.6%
900003
 
0.4%
1300003
 
0.4%
1200003
 
0.4%
1350003
 
0.4%
2030002
 
0.3%
1100002
 
0.3%
700002
 
0.3%
Other values (39)43
 
6.3%
(Missing)602
88.3%
ValueCountFrequency (%)
02
0.3%
37001
0.1%
250001
0.1%
290001
0.1%
300002
0.3%
320001
0.1%
430001
0.1%
500001
0.1%
550001
0.1%
600002
0.3%
ValueCountFrequency (%)
2030002
0.3%
1900001
 
0.1%
1870001
 
0.1%
1800001
 
0.1%
1760001
 
0.1%
1750001
 
0.1%
1720001
 
0.1%
1600004
0.6%
1570001
 
0.1%
1564171
 
0.1%

Laskutettavat tunnit viikossa
Real number (ℝ)

High correlation  Missing 

Distinct23
Distinct (%)27.4%
Missing598
Missing (%)87.7%
Infinite0
Infinite (%)0.0%
Mean34.327381
Minimum0
Maximum138
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:44.253520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.875
Q130
median37.5
Q337.5
95-th percentile41.7
Maximum138
Range138
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation15.425002
Coefficient of variation (CV)0.44934981
Kurtosis24.717776
Mean34.327381
Median Absolute Deviation (MAD)1
Skewness3.166739
Sum2883.5
Variance237.93069
MonotonicityNot monotonic
2026-03-12T15:15:44.313685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
37.532
 
4.7%
409
 
1.3%
306
 
0.9%
386
 
0.9%
374
 
0.6%
153
 
0.4%
353
 
0.4%
203
 
0.4%
432
 
0.3%
252
 
0.3%
Other values (13)14
 
2.1%
(Missing)598
87.7%
ValueCountFrequency (%)
02
0.3%
11
 
0.1%
51
 
0.1%
12.51
 
0.1%
153
0.4%
181
 
0.1%
203
0.4%
211
 
0.1%
231
 
0.1%
252
0.3%
ValueCountFrequency (%)
1381
 
0.1%
601
 
0.1%
432
 
0.3%
421
 
0.1%
409
 
1.3%
386
 
0.9%
37.532
4.7%
374
 
0.6%
361
 
0.1%
353
 
0.4%

Viikot ilman laskutusta
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)20.5%
Missing599
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean8.3614458
Minimum0
Maximum52
Zeros4
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:44.373441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median6
Q38
95-th percentile30
Maximum52
Range52
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.123246
Coefficient of variation (CV)1.0911087
Kurtosis10.780383
Mean8.3614458
Median Absolute Deviation (MAD)2
Skewness3.2280151
Sum694
Variance83.233617
MonotonicityNot monotonic
2026-03-12T15:15:44.433079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
619
 
2.8%
512
 
1.8%
811
 
1.6%
106
 
0.9%
36
 
0.9%
75
 
0.7%
45
 
0.7%
94
 
0.6%
04
 
0.6%
23
 
0.4%
Other values (7)8
 
1.2%
(Missing)599
87.8%
ValueCountFrequency (%)
04
 
0.6%
23
 
0.4%
36
 
0.9%
45
 
0.7%
512
1.8%
619
2.8%
75
 
0.7%
811
1.6%
94
 
0.6%
106
 
0.9%
ValueCountFrequency (%)
521
 
0.1%
451
 
0.1%
401
 
0.1%
341
 
0.1%
302
 
0.3%
141
 
0.1%
121
 
0.1%
106
0.9%
94
 
0.6%
811
1.6%

Laskutustavat
Categorical

High correlation  Imbalance  Missing 

Distinct7
Distinct (%)7.8%
Missing592
Missing (%)86.8%
Memory size10.7 KiB
Hourly billing
69 
Monthly billing
11 
Fixed price / project-based
 
6
I don't do consulting
 
1
All above and bug bounties
 
1
Yearly subscription fee
 
1
Hourly + Fixed/project-based
 
1

Length

Max length28
Median length14
Mean length15.466667
Min length14

Characters and Unicode

Total characters1392
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

Unique4 ?
Unique (%)4.4%

Sample

1st rowHourly billing
2nd rowHourly billing
3rd rowHourly billing
4th rowHourly billing
5th rowMonthly billing

Common Values

ValueCountFrequency (%)
Hourly billing69
 
10.1%
Monthly billing11
 
1.6%
Fixed price / project-based6
 
0.9%
I don't do consulting1
 
0.1%
All above and bug bounties1
 
0.1%
Yearly subscription fee1
 
0.1%
Hourly + Fixed/project-based1
 
0.1%
(Missing)592
86.8%

Length

2026-03-12T15:15:44.500607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:44.561718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
billing80
40.2%
hourly70
35.2%
monthly11
 
5.5%
7
 
3.5%
fixed6
 
3.0%
price6
 
3.0%
project-based6
 
3.0%
i1
 
0.5%
don't1
 
0.5%
do1
 
0.5%
Other values (10)10
 
5.0%

Most occurring characters

ValueCountFrequency (%)
l245
17.6%
i177
12.7%
110
7.9%
n97
 
7.0%
o94
 
6.8%
b91
 
6.5%
r85
 
6.1%
y82
 
5.9%
g82
 
5.9%
u74
 
5.3%
Other values (22)255
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l245
17.6%
i177
12.7%
110
7.9%
n97
 
7.0%
o94
 
6.8%
b91
 
6.5%
r85
 
6.1%
y82
 
5.9%
g82
 
5.9%
u74
 
5.3%
Other values (22)255
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l245
17.6%
i177
12.7%
110
7.9%
n97
 
7.0%
o94
 
6.8%
b91
 
6.5%
r85
 
6.1%
y82
 
5.9%
g82
 
5.9%
u74
 
5.3%
Other values (22)255
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l245
17.6%
i177
12.7%
110
7.9%
n97
 
7.0%
o94
 
6.8%
b91
 
6.5%
r85
 
6.1%
y82
 
5.9%
g82
 
5.9%
u74
 
5.3%
Other values (22)255
18.3%

Sopimuksen pituus
Categorical

High correlation  Missing 

Distinct8
Distinct (%)9.0%
Missing593
Missing (%)87.0%
Memory size10.7 KiB
6-12 months
23 
Over 24 months
22 
Open-ended
14 
12-24 months
12 
3-6 months
Varies significantly
1-3 months
I don't do consulting
 
1

Length

Max length21
Median length20
Mean length12.314607
Min length10

Characters and Unicode

Total characters1096
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

Unique1 ?
Unique (%)1.1%

Sample

1st row3-6 months
2nd row6-12 months
3rd rowOver 24 months
4th rowOver 24 months
5th rowOpen-ended

Common Values

ValueCountFrequency (%)
6-12 months23
 
3.4%
Over 24 months22
 
3.2%
Open-ended14
 
2.1%
12-24 months12
 
1.8%
3-6 months6
 
0.9%
Varies significantly6
 
0.9%
1-3 months5
 
0.7%
I don't do consulting1
 
0.1%
(Missing)593
87.0%

Length

2026-03-12T15:15:44.814409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:44.871871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
months68
36.2%
6-1223
 
12.2%
over22
 
11.7%
2422
 
11.7%
open-ended14
 
7.4%
12-2412
 
6.4%
3-66
 
3.2%
varies6
 
3.2%
significantly6
 
3.2%
1-35
 
2.7%
Other values (4)4
 
2.1%

Most occurring characters

ValueCountFrequency (%)
n111
 
10.1%
99
 
9.0%
s81
 
7.4%
t76
 
6.9%
o71
 
6.5%
e70
 
6.4%
269
 
6.3%
h68
 
6.2%
m68
 
6.2%
-60
 
5.5%
Other values (20)323
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n111
 
10.1%
99
 
9.0%
s81
 
7.4%
t76
 
6.9%
o71
 
6.5%
e70
 
6.4%
269
 
6.3%
h68
 
6.2%
m68
 
6.2%
-60
 
5.5%
Other values (20)323
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n111
 
10.1%
99
 
9.0%
s81
 
7.4%
t76
 
6.9%
o71
 
6.5%
e70
 
6.4%
269
 
6.3%
h68
 
6.2%
m68
 
6.2%
-60
 
5.5%
Other values (20)323
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n111
 
10.1%
99
 
9.0%
s81
 
7.4%
t76
 
6.9%
o71
 
6.5%
e70
 
6.4%
269
 
6.3%
h68
 
6.2%
m68
 
6.2%
-60
 
5.5%
Other values (20)323
29.5%
Distinct9
Distinct (%)10.2%
Missing594
Missing (%)87.1%
Memory size10.7 KiB
Myself, Agencies
34 
Myself
25 
Agencies
23 
I don't do consulting
 
1
Myself, Partner companies, bug bounties
 
1
One major client with an ongoing contract
 
1
Myself, Agencies, Ohjelmistofriikit network
 
1
Agencies, Industry connections
 
1
Own sales
 
1

Length

Max length43
Median length41
Mean length12.056818
Min length6

Characters and Unicode

Total characters1061
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

Unique6 ?
Unique (%)6.8%

Sample

1st rowMyself, Agencies
2nd rowAgencies
3rd rowAgencies
4th rowMyself, Agencies
5th rowMyself

Common Values

ValueCountFrequency (%)
Myself, Agencies34
 
5.0%
Myself25
 
3.7%
Agencies23
 
3.4%
I don't do consulting1
 
0.1%
Myself, Partner companies, bug bounties1
 
0.1%
One major client with an ongoing contract1
 
0.1%
Myself, Agencies, Ohjelmistofriikit network1
 
0.1%
Agencies, Industry connections1
 
0.1%
Own sales1
 
0.1%
(Missing)594
87.1%

Length

2026-03-12T15:15:44.956371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:45.021001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
myself61
43.3%
agencies59
41.8%
i1
 
0.7%
don't1
 
0.7%
do1
 
0.7%
consulting1
 
0.7%
partner1
 
0.7%
companies1
 
0.7%
bug1
 
0.7%
bounties1
 
0.7%
Other values (13)13
 
9.2%

Most occurring characters

ValueCountFrequency (%)
e188
17.7%
s128
12.1%
n77
 
7.3%
i70
 
6.6%
c66
 
6.2%
l65
 
6.1%
g63
 
5.9%
y62
 
5.8%
f62
 
5.8%
M61
 
5.7%
Other values (20)219
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1061
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e188
17.7%
s128
12.1%
n77
 
7.3%
i70
 
6.6%
c66
 
6.2%
l65
 
6.1%
g63
 
5.9%
y62
 
5.8%
f62
 
5.8%
M61
 
5.7%
Other values (20)219
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1061
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e188
17.7%
s128
12.1%
n77
 
7.3%
i70
 
6.6%
c66
 
6.2%
l65
 
6.1%
g63
 
5.9%
y62
 
5.8%
f62
 
5.8%
M61
 
5.7%
Other values (20)219
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1061
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e188
17.7%
s128
12.1%
n77
 
7.3%
i70
 
6.6%
c66
 
6.2%
l65
 
6.1%
g63
 
5.9%
y62
 
5.8%
f62
 
5.8%
M61
 
5.7%
Other values (20)219
20.6%

Mistä asiakkaat ovat?
Categorical

High correlation  Missing 

Distinct4
Distinct (%)4.4%
Missing592
Missing (%)86.8%
Memory size6.2 KiB
Suomesta
66 
Finland, Abroad
15 
Abroad
I don't do consulting
 
1

Length

Max length21
Median length8
Mean length9.1333333
Min length6

Characters and Unicode

Total characters822
Distinct characters22
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 (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
Suomesta66
 
9.7%
Finland, Abroad15
 
2.2%
Abroad8
 
1.2%
I don't do consulting1
 
0.1%
(Missing)592
86.8%

Length

2026-03-12T15:15:45.114545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:45.164149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
suomesta66
61.1%
abroad23
 
21.3%
finland15
 
13.9%
i1
 
0.9%
don't1
 
0.9%
do1
 
0.9%
consulting1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
a104
12.7%
o92
11.2%
t68
8.3%
u67
8.2%
s67
8.2%
S66
8.0%
e66
8.0%
m66
8.0%
d40
 
4.9%
n33
 
4.0%
Other values (12)153
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a104
12.7%
o92
11.2%
t68
8.3%
u67
8.2%
s67
8.2%
S66
8.0%
e66
8.0%
m66
8.0%
d40
 
4.9%
n33
 
4.0%
Other values (12)153
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a104
12.7%
o92
11.2%
t68
8.3%
u67
8.2%
s67
8.2%
S66
8.0%
e66
8.0%
m66
8.0%
d40
 
4.9%
n33
 
4.0%
Other values (12)153
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a104
12.7%
o92
11.2%
t68
8.3%
u67
8.2%
s67
8.2%
S66
8.0%
e66
8.0%
m66
8.0%
d40
 
4.9%
n33
 
4.0%
Other values (12)153
18.6%

Työpaikka
Text

Missing 

Distinct89
Distinct (%)60.1%
Missing534
Missing (%)78.3%
Memory size10.7 KiB
2026-03-12T15:15:45.327564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length22
Mean length7.8310811
Min length2

Characters and Unicode

Total characters1159
Distinct characters57
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

Unique62 ?
Unique (%)41.9%

Sample

1st rowMirantis
2nd rowReaktor
3rd rowValtio
4th rowMapbox
5th rowKaiku Crew
ValueCountFrequency (%)
reaktor15
 
8.5%
nitor7
 
4.0%
vincit7
 
4.0%
solita6
 
3.4%
gofore6
 
3.4%
wolt4
 
2.3%
tieto4
 
2.3%
futurice3
 
1.7%
op3
 
1.7%
visma3
 
1.7%
Other values (94)119
67.2%
2026-03-12T15:15:45.580815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o115
 
9.9%
i98
 
8.5%
e95
 
8.2%
a94
 
8.1%
t90
 
7.8%
r83
 
7.2%
n58
 
5.0%
l54
 
4.7%
s35
 
3.0%
29
 
2.5%
Other values (47)408
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1159
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o115
 
9.9%
i98
 
8.5%
e95
 
8.2%
a94
 
8.1%
t90
 
7.8%
r83
 
7.2%
n58
 
5.0%
l54
 
4.7%
s35
 
3.0%
29
 
2.5%
Other values (47)408
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1159
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o115
 
9.9%
i98
 
8.5%
e95
 
8.2%
a94
 
8.1%
t90
 
7.8%
r83
 
7.2%
n58
 
5.0%
l54
 
4.7%
s35
 
3.0%
29
 
2.5%
Other values (47)408
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1159
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o115
 
9.9%
i98
 
8.5%
e95
 
8.2%
a94
 
8.1%
t90
 
7.8%
r83
 
7.2%
n58
 
5.0%
l54
 
4.7%
s35
 
3.0%
29
 
2.5%
Other values (47)408
35.2%

Kaupunki
Categorical

High correlation  Imbalance  Missing 

Distinct22
Distinct (%)3.8%
Missing100
Missing (%)14.7%
Memory size6.7 KiB
PK-seutu
349 
Tampere
110 
Turku
50 
Oulu
 
18
Jyväskylä
 
13
Etätyö
 
12
Kuopio
 
7
Joensuu
 
3
Ulkomaat
 
3
Vaasa
 
3
Lappeenranta
 
2
Hämeenlinna
 
2
Copenhagen
 
1
Dubai
 
1
Kokkola
 
1
Rovaniemi
 
1
Lahti
 
1
Oslo
 
1
Salo
 
1
Tallinna
 
1
Other values (2)
 
2

Length

Max length12
Median length8
Mean length7.362543
Min length2

Characters and Unicode

Total characters4285
Distinct characters37
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

Unique10 ?
Unique (%)1.7%

Sample

1st rowPK-seutu
2nd rowTurku
3rd rowTampere
4th rowPK-seutu
5th rowTampere

Common Values

ValueCountFrequency (%)
PK-seutu349
51.2%
Tampere110
 
16.1%
Turku50
 
7.3%
Oulu18
 
2.6%
Jyväskylä13
 
1.9%
Etätyö12
 
1.8%
Kuopio7
 
1.0%
Joensuu3
 
0.4%
Ulkomaat3
 
0.4%
Vaasa3
 
0.4%
Other values (12)14
 
2.1%
(Missing)100
 
14.7%

Length

2026-03-12T15:15:45.660206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu349
60.0%
tampere110
 
18.9%
turku50
 
8.6%
oulu18
 
3.1%
jyväskylä13
 
2.2%
etätyö12
 
2.1%
kuopio7
 
1.2%
joensuu3
 
0.5%
ulkomaat3
 
0.5%
vaasa3
 
0.5%
Other values (12)14
 
2.4%

Most occurring characters

ValueCountFrequency (%)
u848
19.8%
e584
13.6%
t379
8.8%
s369
8.6%
K357
8.3%
-349
8.1%
P349
8.1%
r162
 
3.8%
T161
 
3.8%
a141
 
3.3%
Other values (27)586
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)4285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u848
19.8%
e584
13.6%
t379
8.8%
s369
8.6%
K357
8.3%
-349
8.1%
P349
8.1%
r162
 
3.8%
T161
 
3.8%
a141
 
3.3%
Other values (27)586
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u848
19.8%
e584
13.6%
t379
8.8%
s369
8.6%
K357
8.3%
-349
8.1%
P349
8.1%
r162
 
3.8%
T161
 
3.8%
a141
 
3.3%
Other values (27)586
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u848
19.8%
e584
13.6%
t379
8.8%
s369
8.6%
K357
8.3%
-349
8.1%
P349
8.1%
r162
 
3.8%
T161
 
3.8%
a141
 
3.3%
Other values (27)586
13.7%

Millaisessa yrityksessä työskentelet?
Categorical

High correlation  Missing 

Distinct8
Distinct (%)1.4%
Missing97
Missing (%)14.2%
Memory size6.3 KiB
Konsultointi
284 
Tuotetalossa, jonka core-bisnes on softa
200 
Yritys, jossa softa tukirooli
75 
Julkinen/kolmas sektori
 
22
Consulting and product
 
1
Digitoimisto
 
1
Mostly consulting, but also own product
 
1
Software maintenance company
 
1

Length

Max length40
Median length39
Mean length24.25641
Min length12

Characters and Unicode

Total characters14190
Distinct characters33
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

Unique4 ?
Unique (%)0.7%

Sample

1st rowTuotetalossa, jonka core-bisnes on softa
2nd rowTuotetalossa, jonka core-bisnes on softa
3rd rowKonsultointi
4th rowYritys, jossa softa tukirooli
5th rowTuotetalossa, jonka core-bisnes on softa

Common Values

ValueCountFrequency (%)
Konsultointi284
41.6%
Tuotetalossa, jonka core-bisnes on softa200
29.3%
Yritys, jossa softa tukirooli75
 
11.0%
Julkinen/kolmas sektori22
 
3.2%
Consulting and product1
 
0.1%
Digitoimisto1
 
0.1%
Mostly consulting, but also own product1
 
0.1%
Software maintenance company1
 
0.1%
(Missing)97
 
14.2%

Length

2026-03-12T15:15:45.727787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:45.785814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
konsultointi284
17.3%
softa275
16.8%
tuotetalossa200
12.2%
jonka200
12.2%
core-bisnes200
12.2%
on200
12.2%
yritys75
 
4.6%
jossa75
 
4.6%
tukirooli75
 
4.6%
julkinen/kolmas22
 
1.3%
Other values (12)35
 
2.1%

Most occurring characters

ValueCountFrequency (%)
o2123
15.0%
s1633
11.5%
t1425
10.0%
n1222
8.6%
1056
 
7.4%
i1044
 
7.4%
a978
 
6.9%
e647
 
4.6%
l607
 
4.3%
u586
 
4.1%
Other values (23)2869
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)14190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o2123
15.0%
s1633
11.5%
t1425
10.0%
n1222
8.6%
1056
 
7.4%
i1044
 
7.4%
a978
 
6.9%
e647
 
4.6%
l607
 
4.3%
u586
 
4.1%
Other values (23)2869
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o2123
15.0%
s1633
11.5%
t1425
10.0%
n1222
8.6%
1056
 
7.4%
i1044
 
7.4%
a978
 
6.9%
e647
 
4.6%
l607
 
4.3%
u586
 
4.1%
Other values (23)2869
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o2123
15.0%
s1633
11.5%
t1425
10.0%
n1222
8.6%
1056
 
7.4%
i1044
 
7.4%
a978
 
6.9%
e647
 
4.6%
l607
 
4.3%
u586
 
4.1%
Other values (23)2869
20.2%

Työaika
Real number (ℝ)

High correlation  Missing 

Distinct20
Distinct (%)3.4%
Missing97
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean0.99375043
Minimum0.6
Maximum1.8666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:45.865651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.8
Q11
median1
Q31
95-th percentile1.0666667
Maximum1.8666667
Range1.2666667
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.070579721
Coefficient of variation (CV)0.071023588
Kurtosis47.047733
Mean0.99375043
Median Absolute Deviation (MAD)0
Skewness1.4779721
Sum581.344
Variance0.004981497
MonotonicityNot monotonic
2026-03-12T15:15:45.935904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1480
70.4%
1.06666666727
 
4.0%
0.825
 
3.7%
0.986666666710
 
1.5%
1.0133333339
 
1.3%
0.96666666677
 
1.0%
1.26
 
0.9%
0.90666666673
 
0.4%
0.63
 
0.4%
0.93333333333
 
0.4%
Other values (10)12
 
1.8%
(Missing)97
 
14.2%
ValueCountFrequency (%)
0.63
 
0.4%
0.69333333331
 
0.1%
0.71
 
0.1%
0.78666666671
 
0.1%
0.825
3.7%
0.86666666671
 
0.1%
0.90666666673
 
0.4%
0.93333333333
 
0.4%
0.95333333331
 
0.1%
0.96666666677
 
1.0%
ValueCountFrequency (%)
1.8666666671
 
0.1%
1.26
 
0.9%
1.122
 
0.3%
1.0933333331
 
0.1%
1.06666666727
 
4.0%
1.042
 
0.3%
1.0133333339
 
1.3%
1480
70.4%
0.99066666671
 
0.1%
0.986666666710
 
1.5%

Kuinka suuren osan ajasta teet lähityönä toimistolla?
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct30
Distinct (%)5.1%
Missing97
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean0.36869231
Minimum0
Maximum1
Zeros71
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:46.002297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.2
Q30.6
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.33152028
Coefficient of variation (CV)0.89917871
Kurtosis-1.0498851
Mean0.36869231
Median Absolute Deviation (MAD)0.2
Skewness0.58759385
Sum215.685
Variance0.10990569
MonotonicityNot monotonic
2026-03-12T15:15:46.069237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.283
12.2%
071
10.4%
0.455
8.1%
0.0555
8.1%
0.148
7.0%
0.838
 
5.6%
0.537
 
5.4%
0.933
 
4.8%
0.633
 
4.8%
127
 
4.0%
Other values (20)105
15.4%
(Missing)97
14.2%
ValueCountFrequency (%)
071
10.4%
0.0112
 
1.8%
0.028
 
1.2%
0.032
 
0.3%
0.043
 
0.4%
0.0555
8.1%
0.091
 
0.1%
0.148
7.0%
0.1512
 
1.8%
0.283
12.2%
ValueCountFrequency (%)
127
4.0%
0.991
 
0.1%
0.981
 
0.1%
0.9518
2.6%
0.933
4.8%
0.861
 
0.1%
0.852
 
0.3%
0.838
5.6%
0.759
 
1.3%
0.74
 
0.6%

Rooli
Text

Missing 

Distinct57
Distinct (%)9.7%
Missing95
Missing (%)13.9%
Memory size10.7 KiB
2026-03-12T15:15:46.186153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length89
Median length68
Mean length22.870528
Min length3

Characters and Unicode

Total characters13425
Distinct characters51
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

Unique32 ?
Unique (%)5.5%

Sample

1st rowFounder, Technology or Otherwise
2nd rowAI/ML Engineer
3rd rowDeveloper, Backend
4th rowDeveloper, Full-Stack
5th rowDeveloper, QA or Test
ValueCountFrequency (%)
developer361
23.8%
full-stack259
17.1%
or113
 
7.4%
engineer68
 
4.5%
software53
 
3.5%
solutions51
 
3.4%
architect50
 
3.3%
manager38
 
2.5%
backend38
 
2.5%
professional30
 
2.0%
Other values (87)456
30.1%
2026-03-12T15:15:46.383686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1809
 
13.5%
l1039
 
7.7%
r941
 
7.0%
931
 
6.9%
o860
 
6.4%
t729
 
5.4%
a591
 
4.4%
c533
 
4.0%
n523
 
3.9%
,443
 
3.3%
Other values (41)5026
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)13425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1809
 
13.5%
l1039
 
7.7%
r941
 
7.0%
931
 
6.9%
o860
 
6.4%
t729
 
5.4%
a591
 
4.4%
c533
 
4.0%
n523
 
3.9%
,443
 
3.3%
Other values (41)5026
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1809
 
13.5%
l1039
 
7.7%
r941
 
7.0%
931
 
6.9%
o860
 
6.4%
t729
 
5.4%
a591
 
4.4%
c533
 
4.0%
n523
 
3.9%
,443
 
3.3%
Other values (41)5026
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1809
 
13.5%
l1039
 
7.7%
r941
 
7.0%
931
 
6.9%
o860
 
6.4%
t729
 
5.4%
a591
 
4.4%
c533
 
4.0%
n523
 
3.9%
,443
 
3.3%
Other values (41)5026
37.4%

Seniority
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.9%
Missing100
Missing (%)14.7%
Memory size10.7 KiB
Senior
299 
Mid-level
118 
Staff / Principal / Lead
115 
Manager or People Lead
42 
Junior / Entry-level
 
8

Length

Max length24
Median length6
Mean length11.512027
Min length6

Characters and Unicode

Total characters6700
Distinct characters25
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 rowManager or People Lead
2nd rowStaff / Principal / Lead
3rd rowSenior
4th rowSenior
5th rowSenior

Common Values

ValueCountFrequency (%)
Senior299
43.8%
Mid-level118
 
17.3%
Staff / Principal / Lead115
 
16.9%
Manager or People Lead42
 
6.2%
Junior / Entry-level8
 
1.2%
(Missing)100
 
14.7%

Length

2026-03-12T15:15:46.460193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:46.510693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
senior299
25.3%
238
20.1%
lead157
13.3%
mid-level118
 
10.0%
staff115
 
9.7%
principal115
 
9.7%
manager42
 
3.5%
or42
 
3.5%
people42
 
3.5%
junior8
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e834
12.4%
i655
 
9.8%
602
 
9.0%
r514
 
7.7%
n472
 
7.0%
a471
 
7.0%
S414
 
6.2%
l409
 
6.1%
o391
 
5.8%
d275
 
4.1%
Other values (15)1663
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)6700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e834
12.4%
i655
 
9.8%
602
 
9.0%
r514
 
7.7%
n472
 
7.0%
a471
 
7.0%
S414
 
6.2%
l409
 
6.1%
o391
 
5.8%
d275
 
4.1%
Other values (15)1663
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e834
12.4%
i655
 
9.8%
602
 
9.0%
r514
 
7.7%
n472
 
7.0%
a471
 
7.0%
S414
 
6.2%
l409
 
6.1%
o391
 
5.8%
d275
 
4.1%
Other values (15)1663
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e834
12.4%
i655
 
9.8%
602
 
9.0%
r514
 
7.7%
n472
 
7.0%
a471
 
7.0%
S414
 
6.2%
l409
 
6.1%
o391
 
5.8%
d275
 
4.1%
Other values (15)1663
24.8%

Virallinen senioriteetti
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.5%
Missing108
Missing (%)15.8%
Memory size10.7 KiB
Yes
337 
No
191 
Not sure
46 

Length

Max length8
Median length3
Mean length3.0679443
Min length2

Characters and Unicode

Total characters1761
Distinct characters9
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 rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Yes337
49.4%
No191
28.0%
Not sure46
 
6.7%
(Missing)108
 
15.8%

Length

2026-03-12T15:15:46.578829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:46.622357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes337
54.4%
no191
30.8%
not46
 
7.4%
sure46
 
7.4%

Most occurring characters

ValueCountFrequency (%)
e383
21.7%
s383
21.7%
Y337
19.1%
N237
13.5%
o237
13.5%
t46
 
2.6%
46
 
2.6%
u46
 
2.6%
r46
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e383
21.7%
s383
21.7%
Y337
19.1%
N237
13.5%
o237
13.5%
t46
 
2.6%
46
 
2.6%
u46
 
2.6%
r46
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e383
21.7%
s383
21.7%
Y337
19.1%
N237
13.5%
o237
13.5%
t46
 
2.6%
46
 
2.6%
u46
 
2.6%
r46
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e383
21.7%
s383
21.7%
Y337
19.1%
N237
13.5%
o237
13.5%
t46
 
2.6%
46
 
2.6%
u46
 
2.6%
r46
 
2.6%

Kuukausipalkka
Real number (ℝ)

High correlation  Zeros 

Distinct286
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6320.3407
Minimum0
Maximum110000
Zeros94
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:46.685331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14400
median5477.5
Q36700
95-th percentile9500
Maximum110000
Range110000
Interquartile range (IQR)2300

Descriptive statistics

Standard deviation10430.329
Coefficient of variation (CV)1.6502796
Kurtosis66.970027
Mean6320.3407
Median Absolute Deviation (MAD)1125
Skewness7.9166789
Sum4310472.4
Variance1.0879177 × 108
MonotonicityNot monotonic
2026-03-12T15:15:46.765961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
094
 
13.8%
600025
 
3.7%
650018
 
2.6%
500014
 
2.1%
530014
 
2.1%
450012
 
1.8%
540011
 
1.6%
700011
 
1.6%
580010
 
1.5%
560010
 
1.5%
Other values (276)463
67.9%
ValueCountFrequency (%)
094
13.8%
20091
 
0.1%
21151
 
0.1%
23201
 
0.1%
25002
 
0.3%
26601
 
0.1%
2678.41
 
0.1%
27361
 
0.1%
28001
 
0.1%
29001
 
0.1%
ValueCountFrequency (%)
1100002
0.3%
950001
0.1%
928811
0.1%
920001
0.1%
910001
0.1%
900002
0.3%
561171
0.1%
300001
0.1%
175001
0.1%
158001
0.1%

Provisio (kk, brutto)
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct52
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean367.58815
Minimum0
Maximum92000
Zeros606
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:46.850492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1800
Maximum92000
Range92000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3630.5634
Coefficient of variation (CV)9.8767151
Kurtosis598.2462
Mean367.58815
Median Absolute Deviation (MAD)0
Skewness23.751386
Sum250695.12
Variance13180991
MonotonicityNot monotonic
2026-03-12T15:15:46.932491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0606
88.9%
40004
 
0.6%
1004
 
0.6%
22004
 
0.6%
10003
 
0.4%
16003
 
0.4%
20003
 
0.4%
35003
 
0.4%
2003
 
0.4%
5003
 
0.4%
Other values (42)46
 
6.7%
ValueCountFrequency (%)
0606
88.9%
1004
 
0.6%
1401
 
0.1%
2003
 
0.4%
2501
 
0.1%
3002
 
0.3%
3501
 
0.1%
3601
 
0.1%
4001
 
0.1%
4501
 
0.1%
ValueCountFrequency (%)
920001
0.1%
75002
0.3%
72001
0.1%
65001
0.1%
64001
0.1%
50001
0.1%
47001
0.1%
46001
0.1%
45501
0.1%
40211
0.1%

Lomaraha (EUR)
Real number (ℝ)

High correlation  Zeros 

Distinct234
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2193.4604
Minimum0
Maximum9360
Zeros235
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:47.011891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2666
Q33534.6
95-th percentile4677.7
Maximum9360
Range9360
Interquartile range (IQR)3534.6

Descriptive statistics

Standard deviation1803.5138
Coefficient of variation (CV)0.82222309
Kurtosis-0.71833951
Mean2193.4604
Median Absolute Deviation (MAD)1302
Skewness0.10145026
Sum1495940
Variance3252661.9
MonotonicityNot monotonic
2026-03-12T15:15:47.089945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0235
34.5%
300025
 
3.7%
350022
 
3.2%
360016
 
2.3%
400013
 
1.9%
250011
 
1.6%
39008
 
1.2%
42008
 
1.2%
33608
 
1.2%
20007
 
1.0%
Other values (224)329
48.2%
ValueCountFrequency (%)
0235
34.5%
11
 
0.1%
531.931
 
0.1%
5871
 
0.1%
8031
 
0.1%
8171
 
0.1%
9101
 
0.1%
10001
 
0.1%
1071.811
 
0.1%
11301
 
0.1%
ValueCountFrequency (%)
93601
 
0.1%
77001
 
0.1%
75601
 
0.1%
70003
0.4%
69681
 
0.1%
65001
 
0.1%
6472.81
 
0.1%
62401
 
0.1%
57001
 
0.1%
55501
 
0.1%

Bonus (EUR)
Real number (ℝ)

High correlation  Zeros 

Distinct103
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2065.5944
Minimum0
Maximum200000
Zeros517
Zeros (%)75.8%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:47.174048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9000
Maximum200000
Range200000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11115.823
Coefficient of variation (CV)5.381416
Kurtosis191.33345
Mean2065.5944
Median Absolute Deviation (MAD)0
Skewness12.700925
Sum1408735.4
Variance1.2356151 × 108
MonotonicityNot monotonic
2026-03-12T15:15:47.259875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0517
75.8%
100011
 
1.6%
200010
 
1.5%
50006
 
0.9%
60006
 
0.9%
30004
 
0.6%
5004
 
0.6%
100004
 
0.6%
120004
 
0.6%
140003
 
0.4%
Other values (93)113
 
16.6%
ValueCountFrequency (%)
0517
75.8%
11
 
0.1%
701
 
0.1%
801
 
0.1%
1001
 
0.1%
2002
 
0.3%
3001
 
0.1%
3301
 
0.1%
3601
 
0.1%
4001
 
0.1%
ValueCountFrequency (%)
2000001
0.1%
1370001
0.1%
940001
0.1%
900001
0.1%
425001
0.1%
360001
0.1%
270001
0.1%
222001
0.1%
220001
0.1%
200001
0.1%

Osakkeet/optiot (EUR)
Real number (ℝ)

High correlation  Zeros 

Distinct44
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8685.2375
Minimum0
Maximum2183297
Zeros620
Zeros (%)90.9%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:47.342544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9900
Maximum2183297
Range2183297
Interquartile range (IQR)0

Descriptive statistics

Standard deviation99206.384
Coefficient of variation (CV)11.422415
Kurtosis370.86862
Mean8685.2375
Median Absolute Deviation (MAD)0
Skewness18.328662
Sum5923332
Variance9.8419066 × 109
MonotonicityNot monotonic
2026-03-12T15:15:47.419544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0620
90.9%
200006
 
0.9%
20005
 
0.7%
80003
 
0.4%
120002
 
0.3%
8002
 
0.3%
10002
 
0.3%
5002
 
0.3%
500002
 
0.3%
300002
 
0.3%
Other values (34)36
 
5.3%
ValueCountFrequency (%)
0620
90.9%
301
 
0.1%
601
 
0.1%
5002
 
0.3%
8002
 
0.3%
8541
 
0.1%
10002
 
0.3%
16001
 
0.1%
17001
 
0.1%
20005
 
0.7%
ValueCountFrequency (%)
21832971
0.1%
12000001
0.1%
4500001
0.1%
4000001
0.1%
3000001
0.1%
1300002
0.3%
1072001
0.1%
1000001
0.1%
836001
0.1%
800001
0.1%

Vapaa kuvaus kokonaiskompensaatiomallista
Unsupported

Missing  Rejected  Unsupported 

Missing591
Missing (%)86.7%
Memory size10.7 KiB

Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.7%
Missing100
Missing (%)14.7%
Memory size6.2 KiB
Markkinataso
269 
Alle markkinatason
147 
Yli markkinatason
127 
En osaa sanoa
39 

Length

Max length18
Median length17
Mean length14.67354
Min length12

Characters and Unicode

Total characters8540
Distinct characters16
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 rowAlle markkinatason
2nd rowAlle markkinatason
3rd rowMarkkinataso
4th rowMarkkinataso
5th rowMarkkinataso

Common Values

ValueCountFrequency (%)
Markkinataso269
39.4%
Alle markkinatason147
21.6%
Yli markkinatason127
18.6%
En osaa sanoa39
 
5.7%
(Missing)100
 
14.7%

Length

2026-03-12T15:15:47.490725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:47.546144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
markkinatason274
29.3%
markkinataso269
28.8%
alle147
15.7%
yli127
13.6%
en39
 
4.2%
osaa39
 
4.2%
sanoa39
 
4.2%

Most occurring characters

ValueCountFrequency (%)
a1785
20.9%
k1086
12.7%
n895
10.5%
i670
 
7.8%
s621
 
7.3%
o621
 
7.3%
t543
 
6.4%
r543
 
6.4%
l421
 
4.9%
352
 
4.1%
Other values (6)1003
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)8540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1785
20.9%
k1086
12.7%
n895
10.5%
i670
 
7.8%
s621
 
7.3%
o621
 
7.3%
t543
 
6.4%
r543
 
6.4%
l421
 
4.9%
352
 
4.1%
Other values (6)1003
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1785
20.9%
k1086
12.7%
n895
10.5%
i670
 
7.8%
s621
 
7.3%
o621
 
7.3%
t543
 
6.4%
r543
 
6.4%
l421
 
4.9%
352
 
4.1%
Other values (6)1003
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1785
20.9%
k1086
12.7%
n895
10.5%
i670
 
7.8%
s621
 
7.3%
o621
 
7.3%
t543
 
6.4%
r543
 
6.4%
l421
 
4.9%
352
 
4.1%
Other values (6)1003
11.7%

Bonukset (kuvaus)
Text

Missing 

Distinct138
Distinct (%)25.0%
Missing131
Missing (%)19.2%
Memory size10.7 KiB
2026-03-12T15:15:47.637026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length286
Median length227
Mean length86.034483
Min length14

Characters and Unicode

Total characters47405
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

Unique71 ?
Unique (%)12.9%

Sample

1st rowLomaraha (Holiday bonus)
2nd rowStock options / equity
3rd rowLomaraha (Holiday bonus), Individual performance bonus, Company performance bonus
4th rowLomaraha (Holiday bonus), Company performance bonus, Referral bonus (for successful hires), On-call compensation (standby pay)
5th rowLomaraha (Holiday bonus), Company performance bonus, Overtime extra pay (e.g. 1.5x or 2x pay rate), Referral bonus (for successful hires)
ValueCountFrequency (%)
bonus1104
17.8%
lomaraha485
 
7.8%
holiday485
 
7.8%
performance320
 
5.2%
pay292
 
4.7%
for276
 
4.5%
successful275
 
4.4%
referral274
 
4.4%
hires274
 
4.4%
company217
 
3.5%
Other values (76)2184
35.3%
2026-03-12T15:15:47.822201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5635
 
11.9%
o4055
 
8.6%
a3882
 
8.2%
s2951
 
6.2%
r2870
 
6.1%
e2807
 
5.9%
n2548
 
5.4%
u1946
 
4.1%
i1750
 
3.7%
m1424
 
3.0%
Other values (37)17537
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)47405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5635
 
11.9%
o4055
 
8.6%
a3882
 
8.2%
s2951
 
6.2%
r2870
 
6.1%
e2807
 
5.9%
n2548
 
5.4%
u1946
 
4.1%
i1750
 
3.7%
m1424
 
3.0%
Other values (37)17537
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)47405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5635
 
11.9%
o4055
 
8.6%
a3882
 
8.2%
s2951
 
6.2%
r2870
 
6.1%
e2807
 
5.9%
n2548
 
5.4%
u1946
 
4.1%
i1750
 
3.7%
m1424
 
3.0%
Other values (37)17537
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)47405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5635
 
11.9%
o4055
 
8.6%
a3882
 
8.2%
s2951
 
6.2%
r2870
 
6.1%
e2807
 
5.9%
n2548
 
5.4%
u1946
 
4.1%
i1750
 
3.7%
m1424
 
3.0%
Other values (37)17537
37.0%
Distinct399
Distinct (%)69.0%
Missing104
Missing (%)15.2%
Memory size10.7 KiB
2026-03-12T15:15:47.947778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length545
Median length288
Mean length186.20415
Min length11

Characters and Unicode

Total characters107626
Distinct characters62
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

Unique312 ?
Unique (%)54.0%

Sample

1st rowHealth insurance, Work laptop, Free gym membership, Fixed-line internet to your home, Monitors for home office
2nd rowHealth insurance, Work laptop, Monitors for home office
3rd rowHealth insurance, Work laptop, Training/Courses/Education budget, Other tools like high-end keyboard, mouse etc, Monitors for home office, Snacks and beverages in office
4th rowWork laptop, Free office parking (during work hours), Monitors for home office
5th rowHealth insurance, Work remotely from abroad (1 week or more), Work laptop, Mobile phone with paid plan (Select only if NO 20 € puhelinetu on payslip), Home office equipment budget (chair, desk, etc.), Fixed-line internet to your home, Other tools like high-end keyboard, mouse etc, Monitors for home office
ValueCountFrequency (%)
work953
 
6.2%
office916
 
5.9%
laptop554
 
3.6%
home518
 
3.4%
insurance463
 
3.0%
health461
 
3.0%
budget407
 
2.6%
etc387
 
2.5%
and382
 
2.5%
in379
 
2.5%
Other values (147)10038
64.9%
2026-03-12T15:15:48.168096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14882
 
13.8%
e10333
 
9.6%
o7692
 
7.1%
r6331
 
5.9%
i6177
 
5.7%
n5630
 
5.2%
a5184
 
4.8%
t4921
 
4.6%
s3699
 
3.4%
,3289
 
3.1%
Other values (52)39488
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)107626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14882
 
13.8%
e10333
 
9.6%
o7692
 
7.1%
r6331
 
5.9%
i6177
 
5.7%
n5630
 
5.2%
a5184
 
4.8%
t4921
 
4.6%
s3699
 
3.4%
,3289
 
3.1%
Other values (52)39488
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)107626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14882
 
13.8%
e10333
 
9.6%
o7692
 
7.1%
r6331
 
5.9%
i6177
 
5.7%
n5630
 
5.2%
a5184
 
4.8%
t4921
 
4.6%
s3699
 
3.4%
,3289
 
3.1%
Other values (52)39488
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)107626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14882
 
13.8%
e10333
 
9.6%
o7692
 
7.1%
r6331
 
5.9%
i6177
 
5.7%
n5630
 
5.2%
a5184
 
4.8%
t4921
 
4.6%
s3699
 
3.4%
,3289
 
3.1%
Other values (52)39488
36.7%

Vuosittaiset verovapaat edut (EUR)
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct47
Distinct (%)12.9%
Missing318
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean585.78022
Minimum0
Maximum7200
Zeros29
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:48.244752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median400
Q3700
95-th percentile1285
Maximum7200
Range7200
Interquartile range (IQR)400

Descriptive statistics

Standard deviation672.40617
Coefficient of variation (CV)1.1478813
Kurtosis33.811512
Mean585.78022
Median Absolute Deviation (MAD)200
Skewness4.7987587
Sum213224
Variance452130.05
MonotonicityNot monotonic
2026-03-12T15:15:48.324259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
40099
 
14.5%
60033
 
4.8%
029
 
4.3%
30028
 
4.1%
20028
 
4.1%
80026
 
3.8%
100022
 
3.2%
50017
 
2.5%
7009
 
1.3%
12006
 
0.9%
Other values (37)67
 
9.8%
(Missing)318
46.6%
ValueCountFrequency (%)
029
4.3%
501
 
0.1%
841
 
0.1%
1003
 
0.4%
1202
 
0.3%
1504
 
0.6%
20028
4.1%
2202
 
0.3%
2403
 
0.4%
2505
 
0.7%
ValueCountFrequency (%)
72001
 
0.1%
48001
 
0.1%
40002
0.3%
30002
0.3%
27001
 
0.1%
26401
 
0.1%
25761
 
0.1%
25003
0.4%
20004
0.6%
16601
 
0.1%

Luontoisedut
Categorical

High correlation  Missing 

Distinct35
Distinct (%)6.9%
Missing177
Missing (%)26.0%
Memory size10.7 KiB
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)
199 
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction)
72 
Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)
54 
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed)
46 
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)
40 
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed)
 
17
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Garage (outside work hours)
 
12
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Limited car benefit
 
8
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit)
 
7
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed), Limited car benefit
 
5
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Employer-subsidised commuter ticket (taxed)
 
4
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Employer-subsidised commuter ticket (taxed)
 
4
Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed)
 
4
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Limited car benefit
 
4
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Limited car benefit, Garage (outside work hours)
 
3
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Garage (outside work hours)
 
3
Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Garage (outside work hours)
 
2
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed), Unlimited car benefit
 
2
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Garage (outside work hours)
 
2
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Limited car benefit
 
2
Other values (15)
 
15

Length

Max length281
Median length258
Mean length131.45149
Min length12

Characters and Unicode

Total characters66383
Distinct characters49
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

Unique15 ?
Unique (%)3.0%

Sample

1st rowLimited car benefit
2nd rowFull Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)
3rd rowPartial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)
4th rowPartial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)
5th rowPartial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction)

Common Values

ValueCountFrequency (%)
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)199
29.2%
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction)72
 
10.6%
Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)54
 
7.9%
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed)46
 
6.7%
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)40
 
5.9%
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed)17
 
2.5%
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Garage (outside work hours)12
 
1.8%
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Limited car benefit8
 
1.2%
Full Lunch benefit employer pays 100% (luontoisetu/fringe benefit)7
 
1.0%
Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed), Limited car benefit5
 
0.7%
Other values (25)45
 
6.6%
(Missing)177
26.0%

Length

2026-03-12T15:15:48.412599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
benefit550
 
5.9%
lunch444
 
4.7%
employer444
 
4.7%
pays444
 
4.7%
paid406
 
4.3%
with406
 
4.3%
phone406
 
4.3%
mobile406
 
4.3%
if406
 
4.3%
only406
 
4.3%
Other values (34)5057
53.9%

Most occurring characters

ValueCountFrequency (%)
8870
 
13.4%
e5830
 
8.8%
n5016
 
7.6%
l4303
 
6.5%
i4225
 
6.4%
p3411
 
5.1%
o3218
 
4.8%
t3066
 
4.6%
a2935
 
4.4%
u2817
 
4.2%
Other values (39)22692
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)66383
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8870
 
13.4%
e5830
 
8.8%
n5016
 
7.6%
l4303
 
6.5%
i4225
 
6.4%
p3411
 
5.1%
o3218
 
4.8%
t3066
 
4.6%
a2935
 
4.4%
u2817
 
4.2%
Other values (39)22692
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)66383
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8870
 
13.4%
e5830
 
8.8%
n5016
 
7.6%
l4303
 
6.5%
i4225
 
6.4%
p3411
 
5.1%
o3218
 
4.8%
t3066
 
4.6%
a2935
 
4.4%
u2817
 
4.2%
Other values (39)22692
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)66383
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8870
 
13.4%
e5830
 
8.8%
n5016
 
7.6%
l4303
 
6.5%
i4225
 
6.4%
p3411
 
5.1%
o3218
 
4.8%
t3066
 
4.6%
a2935
 
4.4%
u2817
 
4.2%
Other values (39)22692
34.2%

Käyttöjärjestelmä
Categorical

Missing 

Distinct6
Distinct (%)0.9%
Missing9
Missing (%)1.3%
Memory size10.7 KiB
macOS
433 
Windows
81 
Linux (bare metal / native)
80 
Windows + WSL
72 
Windows + Linux VM
 
6
All of them
 
1

Length

Max length27
Median length5
Mean length8.8365527
Min length5

Characters and Unicode

Total characters5947
Distinct characters31
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 (%)0.1%

Sample

1st rowmacOS
2nd rowmacOS
3rd rowmacOS
4th rowWindows
5th rowmacOS

Common Values

ValueCountFrequency (%)
macOS433
63.5%
Windows81
 
11.9%
Linux (bare metal / native)80
 
11.7%
Windows + WSL72
 
10.6%
Windows + Linux VM6
 
0.9%
All of them1
 
0.1%
(Missing)9
 
1.3%

Length

2026-03-12T15:15:48.480552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-12T15:15:48.531723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
macos433
37.4%
windows159
 
13.7%
158
 
13.7%
linux86
 
7.4%
bare80
 
6.9%
metal80
 
6.9%
native80
 
6.9%
wsl72
 
6.2%
vm6
 
0.5%
all1
 
0.1%
Other values (2)2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a673
 
11.3%
m514
 
8.6%
S505
 
8.5%
484
 
8.1%
c433
 
7.3%
O433
 
7.3%
i325
 
5.5%
n325
 
5.5%
e241
 
4.1%
W231
 
3.9%
Other values (21)1783
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a673
 
11.3%
m514
 
8.6%
S505
 
8.5%
484
 
8.1%
c433
 
7.3%
O433
 
7.3%
i325
 
5.5%
n325
 
5.5%
e241
 
4.1%
W231
 
3.9%
Other values (21)1783
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a673
 
11.3%
m514
 
8.6%
S505
 
8.5%
484
 
8.1%
c433
 
7.3%
O433
 
7.3%
i325
 
5.5%
n325
 
5.5%
e241
 
4.1%
W231
 
3.9%
Other values (21)1783
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a673
 
11.3%
m514
 
8.6%
S505
 
8.5%
484
 
8.1%
c433
 
7.3%
O433
 
7.3%
i325
 
5.5%
n325
 
5.5%
e241
 
4.1%
W231
 
3.9%
Other values (21)1783
30.0%

Ohjelmointikieli
Text

Missing 

Distinct166
Distinct (%)26.8%
Missing63
Missing (%)9.2%
Memory size10.7 KiB
2026-03-12T15:15:48.628258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length32
Mean length18.962843
Min length2

Characters and Unicode

Total characters11738
Distinct characters51
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

Unique91 ?
Unique (%)14.7%

Sample

1st rowTypeScript, Python, Go
2nd rowTypeScript
3rd rowTypeScript, Python
4th rowTypeScript, C#
5th rowTypeScript, Java, PHP
ValueCountFrequency (%)
typescript427
29.5%
python259
17.9%
javascript211
14.6%
c98
 
6.8%
java94
 
6.5%
bash87
 
6.0%
go60
 
4.1%
php50
 
3.5%
kotlin43
 
3.0%
clojure19
 
1.3%
Other values (32)100
 
6.9%
2026-03-12T15:15:48.799327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p1073
 
9.1%
t990
 
8.4%
831
 
7.1%
,823
 
7.0%
a733
 
6.2%
i708
 
6.0%
r707
 
6.0%
y696
 
5.9%
S672
 
5.7%
c653
 
5.6%
Other values (41)3852
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)11738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p1073
 
9.1%
t990
 
8.4%
831
 
7.1%
,823
 
7.0%
a733
 
6.2%
i708
 
6.0%
r707
 
6.0%
y696
 
5.9%
S672
 
5.7%
c653
 
5.6%
Other values (41)3852
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p1073
 
9.1%
t990
 
8.4%
831
 
7.1%
,823
 
7.0%
a733
 
6.2%
i708
 
6.0%
r707
 
6.0%
y696
 
5.9%
S672
 
5.7%
c653
 
5.6%
Other values (41)3852
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p1073
 
9.1%
t990
 
8.4%
831
 
7.1%
,823
 
7.0%
a733
 
6.2%
i708
 
6.0%
r707
 
6.0%
y696
 
5.9%
S672
 
5.7%
c653
 
5.6%
Other values (41)3852
32.8%

Web-kehykset
Text

Missing 

Distinct144
Distinct (%)29.6%
Missing195
Missing (%)28.6%
Memory size10.7 KiB
2026-03-12T15:15:48.887394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length30
Mean length18.147844
Min length3

Characters and Unicode

Total characters8838
Distinct characters55
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

Unique88 ?
Unique (%)18.1%

Sample

1st rowReact, Svelte
2nd rowReact, Next.js, Django
3rd rowNext.js, ASP.NET Core
4th rowReact, Next.js, Spring Boot
5th rowReact, Spring Boot
ValueCountFrequency (%)
react372
29.6%
node.js207
16.5%
next.js111
 
8.8%
express62
 
4.9%
spring61
 
4.9%
boot61
 
4.9%
fastapi56
 
4.5%
asp.net43
 
3.4%
core43
 
3.4%
django41
 
3.3%
Other values (54)199
15.8%
2026-03-12T15:15:49.054476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e917
 
10.4%
770
 
8.7%
t655
 
7.4%
,644
 
7.3%
s592
 
6.7%
a567
 
6.4%
o442
 
5.0%
j401
 
4.5%
.401
 
4.5%
R390
 
4.4%
Other values (45)3059
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)8838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e917
 
10.4%
770
 
8.7%
t655
 
7.4%
,644
 
7.3%
s592
 
6.7%
a567
 
6.4%
o442
 
5.0%
j401
 
4.5%
.401
 
4.5%
R390
 
4.4%
Other values (45)3059
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e917
 
10.4%
770
 
8.7%
t655
 
7.4%
,644
 
7.3%
s592
 
6.7%
a567
 
6.4%
o442
 
5.0%
j401
 
4.5%
.401
 
4.5%
R390
 
4.4%
Other values (45)3059
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e917
 
10.4%
770
 
8.7%
t655
 
7.4%
,644
 
7.3%
s592
 
6.7%
a567
 
6.4%
o442
 
5.0%
j401
 
4.5%
.401
 
4.5%
R390
 
4.4%
Other values (45)3059
34.6%

Data & ML
Text

Missing 

Distinct76
Distinct (%)17.7%
Missing252
Missing (%)37.0%
Memory size10.7 KiB
2026-03-12T15:15:49.148168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length56
Median length3
Mean length12.311628
Min length3

Characters and Unicode

Total characters5294
Distinct characters55
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

Unique46 ?
Unique (%)10.7%

Sample

1st rowSQL, Kafka, scikit-learn
2nd rowSQL
3rd rowSQL, Pandas / NumPy
4th rowSQL, Apache Spark, Airflow
5th rowSQL
ValueCountFrequency (%)
sql388
38.8%
143
 
14.3%
kafka64
 
6.4%
pandas57
 
5.7%
numpy57
 
5.7%
snowflake42
 
4.2%
bigquery42
 
4.2%
redshift42
 
4.2%
airflow23
 
2.3%
pytorch23
 
2.3%
Other values (34)120
 
12.0%
2026-03-12T15:15:49.324555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
572
 
10.8%
S450
 
8.5%
Q435
 
8.2%
L395
 
7.5%
a360
 
6.8%
,261
 
4.9%
e189
 
3.6%
f176
 
3.3%
i156
 
2.9%
r148
 
2.8%
Other values (45)2152
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)5294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
572
 
10.8%
S450
 
8.5%
Q435
 
8.2%
L395
 
7.5%
a360
 
6.8%
,261
 
4.9%
e189
 
3.6%
f176
 
3.3%
i156
 
2.9%
r148
 
2.8%
Other values (45)2152
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
572
 
10.8%
S450
 
8.5%
Q435
 
8.2%
L395
 
7.5%
a360
 
6.8%
,261
 
4.9%
e189
 
3.6%
f176
 
3.3%
i156
 
2.9%
r148
 
2.8%
Other values (45)2152
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
572
 
10.8%
S450
 
8.5%
Q435
 
8.2%
L395
 
7.5%
a360
 
6.8%
,261
 
4.9%
e189
 
3.6%
f176
 
3.3%
i156
 
2.9%
r148
 
2.8%
Other values (45)2152
40.6%

DevOps & pilvi
Text

Missing 

Distinct110
Distinct (%)18.1%
Missing73
Missing (%)10.7%
Memory size10.7 KiB
2026-03-12T15:15:49.421737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length85
Median length70
Mean length47.059113
Min length3

Characters and Unicode

Total characters28659
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

Unique39 ?
Unique (%)6.4%

Sample

1st rowCI/CD (GitHub Actions, GitLab CI, Jenkins), AWS
2nd rowCI/CD (GitHub Actions, GitLab CI, Jenkins), Google Cloud Platform (GCP)
3rd rowDocker, Kubernetes, CI/CD (GitHub Actions, GitLab CI, Jenkins)
4th rowTerraform, CI/CD (GitHub Actions, GitLab CI, Jenkins), Microsoft Azure
5th rowDocker, Google Cloud Platform (GCP)
ValueCountFrequency (%)
ci/cd343
 
8.8%
github342
 
8.8%
actions342
 
8.8%
gitlab342
 
8.8%
jenkins342
 
8.8%
ci342
 
8.8%
docker334
 
8.6%
aws203
 
5.2%
server175
 
4.5%
terraform158
 
4.1%
Other values (26)975
25.0%
2026-03-12T15:15:49.619791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3289
 
11.5%
e1889
 
6.6%
i1725
 
6.0%
,1698
 
5.9%
r1678
 
5.9%
o1466
 
5.1%
t1399
 
4.9%
n1384
 
4.8%
s1212
 
4.2%
C1185
 
4.1%
Other values (35)11734
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)28659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3289
 
11.5%
e1889
 
6.6%
i1725
 
6.0%
,1698
 
5.9%
r1678
 
5.9%
o1466
 
5.1%
t1399
 
4.9%
n1384
 
4.8%
s1212
 
4.2%
C1185
 
4.1%
Other values (35)11734
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3289
 
11.5%
e1889
 
6.6%
i1725
 
6.0%
,1698
 
5.9%
r1678
 
5.9%
o1466
 
5.1%
t1399
 
4.9%
n1384
 
4.8%
s1212
 
4.2%
C1185
 
4.1%
Other values (35)11734
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3289
 
11.5%
e1889
 
6.6%
i1725
 
6.0%
,1698
 
5.9%
r1678
 
5.9%
o1466
 
5.1%
t1399
 
4.9%
n1384
 
4.8%
s1212
 
4.2%
C1185
 
4.1%
Other values (35)11734
40.9%

Tietokannat
Text

Missing 

Distinct100
Distinct (%)18.1%
Missing131
Missing (%)19.2%
Memory size10.7 KiB
2026-03-12T15:15:49.707221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length41
Mean length16.163339
Min length3

Characters and Unicode

Total characters8906
Distinct characters53
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

Unique62 ?
Unique (%)11.3%

Sample

1st rowPostgreSQL, Tiger Data (Timescale)
2nd rowPostgreSQL, Firestore
3rd rowPostgreSQL
4th rowPostgreSQL
5th rowMySQL
ValueCountFrequency (%)
postgresql411
38.2%
redis136
 
12.6%
mysql114
 
10.6%
sql84
 
7.8%
server83
 
7.7%
dynamodb77
 
7.2%
mongodb58
 
5.4%
oracle21
 
2.0%
sqlite13
 
1.2%
influxdb7
 
0.7%
Other values (33)72
 
6.7%
2026-03-12T15:15:49.875956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e805
 
9.0%
S707
 
7.9%
r645
 
7.2%
o626
 
7.0%
Q621
 
7.0%
L619
 
7.0%
s588
 
6.6%
530
 
6.0%
g480
 
5.4%
t451
 
5.1%
Other values (43)2834
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e805
 
9.0%
S707
 
7.9%
r645
 
7.2%
o626
 
7.0%
Q621
 
7.0%
L619
 
7.0%
s588
 
6.6%
530
 
6.0%
g480
 
5.4%
t451
 
5.1%
Other values (43)2834
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e805
 
9.0%
S707
 
7.9%
r645
 
7.2%
o626
 
7.0%
Q621
 
7.0%
L619
 
7.0%
s588
 
6.6%
530
 
6.0%
g480
 
5.4%
t451
 
5.1%
Other values (43)2834
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e805
 
9.0%
S707
 
7.9%
r645
 
7.2%
o626
 
7.0%
Q621
 
7.0%
L619
 
7.0%
s588
 
6.6%
530
 
6.0%
g480
 
5.4%
t451
 
5.1%
Other values (43)2834
31.8%

Vapaa sana
Text

Missing 

Distinct35
Distinct (%)100.0%
Missing647
Missing (%)94.9%
Memory size10.7 KiB
2026-03-12T15:15:50.064575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length484
Median length81
Mean length86.628571
Min length3

Characters and Unicode

Total characters3032
Distinct characters74
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

Unique35 ?
Unique (%)100.0%

Sample

1st row"Number of change negotiations in your company during the last two calendar years" or something to showcase the volatility of the market. We've had two.
2nd rowIs your company hiring?: yes, join us at kaikucrew.com
3rd rowStartup status; feelings checks; AI tools mandates
4th rowOletko tekoälyänkyrä
5th rowAmount of days need to be at customer office
ValueCountFrequency (%)
in11
 
2.2%
a11
 
2.2%
the11
 
2.2%
i10
 
2.0%
you10
 
2.0%
for9
 
1.8%
ai7
 
1.4%
to7
 
1.4%
work7
 
1.4%
of7
 
1.4%
Other values (298)419
82.3%
2026-03-12T15:15:50.350253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
468
15.4%
e249
 
8.2%
o221
 
7.3%
a211
 
7.0%
t198
 
6.5%
i166
 
5.5%
n162
 
5.3%
r154
 
5.1%
s140
 
4.6%
l109
 
3.6%
Other values (64)954
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
468
15.4%
e249
 
8.2%
o221
 
7.3%
a211
 
7.0%
t198
 
6.5%
i166
 
5.5%
n162
 
5.3%
r154
 
5.1%
s140
 
4.6%
l109
 
3.6%
Other values (64)954
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
468
15.4%
e249
 
8.2%
o221
 
7.3%
a211
 
7.0%
t198
 
6.5%
i166
 
5.5%
n162
 
5.3%
r154
 
5.1%
s140
 
4.6%
l109
 
3.6%
Other values (64)954
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
468
15.4%
e249
 
8.2%
o221
 
7.3%
a211
 
7.0%
t198
 
6.5%
i166
 
5.5%
n162
 
5.3%
r154
 
5.1%
s140
 
4.6%
l109
 
3.6%
Other values (64)954
31.5%

Palaute
Text

Missing 

Distinct49
Distinct (%)100.0%
Missing633
Missing (%)92.8%
Memory size10.7 KiB
2026-03-12T15:15:50.523444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length396
Median length83
Mean length90.367347
Min length2

Characters and Unicode

Total characters4428
Distinct characters71
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

Unique49 ?
Unique (%)100.0%

Sample

1st rowKiitos, tämä on tärkeä kysely!
2nd rowAI usage would be maybe interesting info maybe. Holiday bonus could have option as "bonus as days off" or something if one chooses to rather have 6/12 extra vacation days instead of money if company allows it.
3rd rowExcellent survey.
4th rowEn jaksa alkaa laskea kaikkia summia whatever.
5th rowai tools? Primary editor? :) Good job!
ValueCountFrequency (%)
the25
 
3.3%
to23
 
3.0%
of15
 
2.0%
a14
 
1.8%
in12
 
1.6%
be12
 
1.6%
would11
 
1.5%
for11
 
1.5%
10
 
1.3%
survey10
 
1.3%
Other values (382)614
81.1%
2026-03-12T15:15:50.793529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
715
16.1%
e418
 
9.4%
t316
 
7.1%
o285
 
6.4%
a255
 
5.8%
s248
 
5.6%
i242
 
5.5%
n207
 
4.7%
r206
 
4.7%
l174
 
3.9%
Other values (61)1362
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
715
16.1%
e418
 
9.4%
t316
 
7.1%
o285
 
6.4%
a255
 
5.8%
s248
 
5.6%
i242
 
5.5%
n207
 
4.7%
r206
 
4.7%
l174
 
3.9%
Other values (61)1362
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
715
16.1%
e418
 
9.4%
t316
 
7.1%
o285
 
6.4%
a255
 
5.8%
s248
 
5.6%
i242
 
5.5%
n207
 
4.7%
r206
 
4.7%
l174
 
3.9%
Other values (61)1362
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
715
16.1%
e418
 
9.4%
t316
 
7.1%
o285
 
6.4%
a255
 
5.8%
s248
 
5.6%
i242
 
5.5%
n207
 
4.7%
r206
 
4.7%
l174
 
3.9%
Other values (61)1362
30.8%

Vastaustunniste
Text

Unique 

Distinct682
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:50.905710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters10912
Distinct characters16
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

Unique682 ?
Unique (%)100.0%

Sample

1st row2a27944c9ee50d0c
2nd row0ccb0b24699bc2c0
3rd row16a59d6a893866f2
4th row282df8e8786fc5e9
5th row6f3bb4f81f79603d
ValueCountFrequency (%)
2a27944c9ee50d0c1
 
0.1%
0ccb0b24699bc2c01
 
0.1%
16a59d6a893866f21
 
0.1%
282df8e8786fc5e91
 
0.1%
6f3bb4f81f79603d1
 
0.1%
03d8b27b951c041b1
 
0.1%
0934d5fe86a3b35b1
 
0.1%
b03e28d1db6f71dc1
 
0.1%
df9e0d7f7c623cb61
 
0.1%
92e582269ffe7a6d1
 
0.1%
Other values (672)672
98.5%
2026-03-12T15:15:51.092434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6728
 
6.7%
7718
 
6.6%
c716
 
6.6%
e714
 
6.5%
f711
 
6.5%
8697
 
6.4%
5696
 
6.4%
2681
 
6.2%
d674
 
6.2%
0662
 
6.1%
Other values (6)3915
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6728
 
6.7%
7718
 
6.6%
c716
 
6.6%
e714
 
6.5%
f711
 
6.5%
8697
 
6.4%
5696
 
6.4%
2681
 
6.2%
d674
 
6.2%
0662
 
6.1%
Other values (6)3915
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6728
 
6.7%
7718
 
6.6%
c716
 
6.6%
e714
 
6.5%
f711
 
6.5%
8697
 
6.4%
5696
 
6.4%
2681
 
6.2%
d674
 
6.2%
0662
 
6.1%
Other values (6)3915
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6728
 
6.7%
7718
 
6.6%
c716
 
6.6%
e714
 
6.5%
f711
 
6.5%
8697
 
6.4%
5696
 
6.4%
2681
 
6.2%
d674
 
6.2%
0662
 
6.1%
Other values (6)3915
35.9%

Vuositulot
Real number (ℝ)

High correlation  Missing 

Distinct477
Distinct (%)81.1%
Missing94
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean102982.44
Minimum24911
Maximum2680297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:51.183994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24911
5-th percentile46086
Q161113.75
median73150
Q387600
95-th percentile157132.18
Maximum2680297
Range2655386
Interquartile range (IQR)26486.25

Descriptive statistics

Standard deviation181937.4
Coefficient of variation (CV)1.7666837
Kurtosis88.309051
Mean102982.44
Median Absolute Deviation (MAD)13170
Skewness8.4697036
Sum60553676
Variance3.3101219 × 1010
MonotonicityNot monotonic
2026-03-12T15:15:51.269008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
756009
 
1.3%
600008
 
1.2%
780007
 
1.0%
819007
 
1.0%
630006
 
0.9%
705606
 
0.9%
648005
 
0.7%
504004
 
0.6%
720004
 
0.6%
876004
 
0.6%
Other values (467)528
77.4%
(Missing)94
 
13.8%
ValueCountFrequency (%)
249111
0.1%
253801
0.1%
278401
0.1%
300002
0.3%
319201
0.1%
336001
0.1%
33687.81
0.1%
34473.871
0.1%
348001
0.1%
376001
0.1%
ValueCountFrequency (%)
26802971
0.1%
13475001
0.1%
13400001
0.1%
13200001
0.1%
12000001
0.1%
11597501
0.1%
11182821
0.1%
11090001
0.1%
11049801
0.1%
11000001
0.1%
Distinct58
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:51.390745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length89
Median length68
Mean length19.684751
Min length0

Characters and Unicode

Total characters13425
Distinct characters51
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

Unique32 ?
Unique (%)4.7%

Sample

1st row
2nd rowFounder, Technology or Otherwise
3rd rowAI/ML Engineer
4th rowDeveloper, Backend
5th rowDeveloper, Full-Stack
ValueCountFrequency (%)
developer361
23.8%
full-stack259
17.1%
or113
 
7.4%
engineer68
 
4.5%
software53
 
3.5%
solutions51
 
3.4%
architect50
 
3.3%
manager38
 
2.5%
backend38
 
2.5%
professional30
 
2.0%
Other values (87)456
30.1%
2026-03-12T15:15:51.592402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1809
 
13.5%
l1039
 
7.7%
r941
 
7.0%
931
 
6.9%
o860
 
6.4%
t729
 
5.4%
a591
 
4.4%
c533
 
4.0%
n523
 
3.9%
,443
 
3.3%
Other values (41)5026
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)13425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1809
 
13.5%
l1039
 
7.7%
r941
 
7.0%
931
 
6.9%
o860
 
6.4%
t729
 
5.4%
a591
 
4.4%
c533
 
4.0%
n523
 
3.9%
,443
 
3.3%
Other values (41)5026
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1809
 
13.5%
l1039
 
7.7%
r941
 
7.0%
931
 
6.9%
o860
 
6.4%
t729
 
5.4%
a591
 
4.4%
c533
 
4.0%
n523
 
3.9%
,443
 
3.3%
Other values (41)5026
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1809
 
13.5%
l1039
 
7.7%
r941
 
7.0%
931
 
6.9%
o860
 
6.4%
t729
 
5.4%
a591
 
4.4%
c533
 
4.0%
n523
 
3.9%
,443
 
3.3%
Other values (41)5026
37.4%

Kk-tulot (laskennallinen)
Real number (ℝ)

High correlation  Missing 

Distinct477
Distinct (%)81.1%
Missing94
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean8581.8701
Minimum2075.9167
Maximum223358.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:51.669391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2075.9167
5-th percentile3840.5
Q15092.8125
median6095.8333
Q37300
95-th percentile13094.348
Maximum223358.08
Range221282.17
Interquartile range (IQR)2207.1875

Descriptive statistics

Standard deviation15161.45
Coefficient of variation (CV)1.7666837
Kurtosis88.309051
Mean8581.8701
Median Absolute Deviation (MAD)1097.5
Skewness8.4697036
Sum5046139.6
Variance2.2986958 × 108
MonotonicityNot monotonic
2026-03-12T15:15:51.757840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63009
 
1.3%
50008
 
1.2%
65007
 
1.0%
68257
 
1.0%
52506
 
0.9%
58806
 
0.9%
54005
 
0.7%
42004
 
0.6%
60004
 
0.6%
73004
 
0.6%
Other values (467)528
77.4%
(Missing)94
 
13.8%
ValueCountFrequency (%)
2075.9166671
0.1%
21151
0.1%
23201
0.1%
25002
0.3%
26601
0.1%
28001
0.1%
2807.3166671
0.1%
2872.82251
0.1%
29001
0.1%
3133.3333331
0.1%
ValueCountFrequency (%)
223358.08331
0.1%
112291.66671
0.1%
111666.66671
0.1%
1100001
0.1%
1000001
0.1%
96645.833331
0.1%
93190.166671
0.1%
92416.666671
0.1%
92081.666671
0.1%
91666.666671
0.1%

Kk-tulot (laskennallinen, normalisoitu)
Real number (ℝ)

High correlation  Missing 

Distinct494
Distinct (%)84.7%
Missing99
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean8569.5348
Minimum2289.4737
Maximum209398.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2026-03-12T15:15:51.834463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2289.4737
5-th percentile3952.9167
Q15171.4583
median6110
Q37290.8333
95-th percentile12364.764
Maximum209398.2
Range207108.73
Interquartile range (IQR)2119.375

Descriptive statistics

Standard deviation14704.182
Coefficient of variation (CV)1.715867
Kurtosis80.037862
Mean8569.5348
Median Absolute Deviation (MAD)1073.3333
Skewness8.1230236
Sum4996038.8
Variance2.1621297 × 108
MonotonicityNot monotonic
2026-03-12T15:15:51.916023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63009
 
1.3%
50007
 
1.0%
58806
 
0.9%
68256
 
0.9%
65005
 
0.7%
52505
 
0.7%
56704
 
0.6%
55654
 
0.6%
47254
 
0.6%
60004
 
0.6%
Other values (484)529
77.6%
(Missing)99
 
14.5%
ValueCountFrequency (%)
2289.4736841
0.1%
25002
0.3%
26251
0.1%
2695.9459461
0.1%
2872.82251
0.1%
29001
0.1%
2937.51
0.1%
2965.5952381
0.1%
32751
0.1%
33601
0.1%
ValueCountFrequency (%)
209398.20311
0.1%
111666.66671
0.1%
105273.43751
0.1%
1031251
0.1%
96645.833331
0.1%
937501
0.1%
93190.166671
0.1%
92416.666671
0.1%
92081.666671
0.1%
91666.666671
0.1%

Interactions

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2026-03-12T15:15:16.772512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:18.027996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:19.233993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:20.535631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:21.710375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:22.778759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:23.985383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:25.108350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:26.180569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:27.193165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:28.315398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:29.639138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:30.794948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:31.930073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:33.044177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:34.280423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:35.546330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-03-12T15:15:19.295568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-03-12T15:15:22.833272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-03-12T15:15:31.984260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:33.114289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-03-12T15:15:35.603242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:36.739048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:37.849365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-03-12T15:15:25.226701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-03-12T15:15:20.472488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:21.652112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:22.729008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:23.937896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:25.060814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:26.126556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:27.144555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:28.256757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:29.571135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:30.730797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:31.868328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:32.989524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:34.217995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:35.490568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:36.623059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:37.738085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-12T15:15:38.842549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-03-12T15:15:52.157799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Bonus (EUR)Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?IkäKaupunkiKk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)KoulutustaustasiKuinka suuren osan ajasta teet lähityönä toimistolla?KuukausipalkkaKäyttöjärjestelmäLaskutettavat tunnit viikossaLaskutustavatLomaraha (EUR)LuontoisedutMillaisessa yrityksessä työskentelet?Mistä asiakkaat ovat?Montako vuotta olet tehnyt laskuttavaa työtä alalla?Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?OpintoalaOsakkeet/optiot (EUR)Palkansaaja vai laskuttajaProvisio (kk, brutto)SenioritySiirtynyt palkansaaja/laskuttajaSopimuksen pituusSukupuoliSuomen kielen taitoTulojen muutos viime vuodesta (%)Tuntilaskutus (ALV 0%, euroina)TyöaikaTyökieliTyökokemus alalta (vuosina)Työpaikkojen lukumääräViikot ilman laskutustaVirallinen senioriteettiVuosia nykyisellä työnantajallaVuosilaskutus (ALV 0%, euroina)Vuosittaiset verovapaat edut (EUR)Vuositulot
Bonus (EUR)1.0001.0000.1030.4370.1900.2040.0000.0290.2050.000NaN1.0000.2510.0000.0001.000NaN0.0650.0000.0320.000-0.0400.0730.0001.0000.0000.0000.040NaN0.0070.0000.016-0.040NaN0.0780.072NaN-0.0040.190
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?1.0001.0000.0000.0000.0000.0000.0000.0001.0000.1660.0000.6381.0000.0000.0000.6190.0000.0000.2121.0001.0001.0000.0000.1670.3930.0000.0000.3260.0000.0000.0000.0000.0000.2340.0000.0000.1890.0000.000
Ikä0.1030.0001.0000.0000.0570.0000.1290.0350.0580.0700.1990.0000.1060.0510.0000.0000.2610.0710.0000.0000.0960.4870.2090.0000.0750.0580.0000.0000.1520.0750.0000.4390.0980.4160.0000.1110.2890.0000.057
Kaupunki0.4370.0000.0001.0000.4690.3840.1550.0080.4770.0910.0000.0000.1500.1480.0000.0000.0000.0960.0000.4651.0000.0000.0450.1630.0000.0000.0000.3760.0000.1120.4540.0000.2060.0000.0000.0000.0000.0000.469
Kk-tulot (laskennallinen)0.1900.0000.0570.4691.0000.9720.034-0.0920.9770.000NaN0.0000.5210.0000.0000.000NaN0.0450.0000.3151.0000.2060.0550.0000.0000.0000.149-0.048NaN0.2320.0000.6120.505NaN0.0000.077NaN0.0451.000
Kk-tulot (laskennallinen, normalisoitu)0.2040.0000.0000.3840.9721.0000.032-0.0760.9450.000NaN0.0000.5260.0000.0000.000NaN0.0570.0000.3261.0000.2140.0300.0000.0000.0000.282-0.048NaN0.0730.0000.6080.500NaN0.0640.097NaN0.0880.972
Koulutustaustasi0.0000.0000.1290.1550.0340.0321.0000.0590.0430.0000.1340.0000.0000.0000.0000.0000.1900.0510.2440.1280.0000.0000.1060.1040.1190.0280.0000.1560.4290.1220.0000.0500.0000.4850.0600.0290.2310.0000.034
Kuinka suuren osan ajasta teet lähityönä toimistolla?0.0290.0000.0350.008-0.092-0.0760.0591.000-0.0750.000NaN0.000-0.1080.0880.0000.000NaN0.0210.000-0.0011.000-0.0660.0000.0000.0000.0950.0610.051NaN-0.0140.074-0.006-0.082NaN0.0910.080NaN0.055-0.092
Kuukausipalkka0.2051.0000.0580.4770.9770.9450.043-0.0751.0000.000NaN1.0000.5820.0000.0701.000NaN0.0560.0000.2620.0000.2670.0350.0791.0000.0000.2330.059NaN0.2460.0000.3820.253NaN0.0570.079NaN0.0240.977
Käyttöjärjestelmä0.0000.1660.0700.0910.0000.0000.0000.0000.0001.0000.0000.1510.0000.1820.0000.0000.1370.0850.1410.0000.0000.0600.0900.0560.0000.0730.0000.0000.2150.0000.1780.1160.0400.1610.0530.1300.2040.3130.000
Laskutettavat tunnit viikossaNaN0.0000.1990.000NaNNaN0.134NaNNaN0.0001.0000.196NaN0.0000.0000.154-0.0440.0000.000NaN1.000NaN0.0000.0000.0000.1320.000-0.016-0.042NaN0.0000.2930.075-0.3690.000-0.1150.441NaNNaN
Laskutustavat1.0000.6380.0000.0000.0000.0000.0000.0001.0000.1510.1961.0001.0000.0000.0000.6300.0980.0000.0001.0001.0001.0000.0000.2150.4180.0000.0000.3660.0000.0000.0000.0000.0000.0490.0000.0000.1270.0000.000
Lomaraha (EUR)0.2511.0000.1060.1500.5210.5260.000-0.1080.5820.000NaN1.0001.0000.0000.0001.000NaN0.2320.0000.0960.5220.1140.2380.1671.0000.0580.0000.052NaN0.0090.0000.2280.057NaN0.0390.163NaN0.0950.521
Luontoisedut0.0000.0000.0510.1480.0000.0000.0000.0880.0000.1820.0000.0000.0001.0000.0270.0000.0000.0360.0000.0001.0000.0000.2130.0000.0000.0000.0000.0000.0000.2710.0000.0240.2800.0000.0750.0000.0000.0950.000
Millaisessa yrityksessä työskentelet?0.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0271.0000.0000.0000.0000.0000.0001.0000.0000.0990.0000.0000.0000.0230.0000.0000.1730.1100.0320.0450.0000.0000.0590.0000.0000.000
Mistä asiakkaat ovat?1.0000.6190.0000.0000.0000.0000.0000.0001.0000.0000.1540.6301.0000.0000.0001.0000.3350.0000.0001.0001.0001.0000.0000.1450.5860.0000.0000.5280.0000.0000.1970.2850.0000.0000.0000.0000.0730.0000.000
Montako vuotta olet tehnyt laskuttavaa työtä alalla?NaN0.0000.2610.000NaNNaN0.190NaNNaN0.137-0.0440.098NaN0.0000.0000.3351.0000.0000.254NaN1.000NaN0.0000.7270.1830.3360.000-0.0970.086NaN0.0000.4630.137-0.0370.0000.6950.233NaNNaN
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?0.0650.0000.0710.0960.0450.0570.0510.0210.0560.0850.0000.0000.2320.0360.0000.0000.0001.0000.0790.0891.0000.0000.1300.0000.0000.0850.1000.0710.0000.0420.1360.1240.1650.0000.0350.1070.0000.0000.045
Opintoala0.0000.2120.0000.0000.0000.0000.2440.0000.0000.1410.0000.0000.0000.0000.0000.0000.2540.0791.0000.0000.0000.0000.1980.1410.0000.1760.0000.0590.2280.0000.0000.1040.1400.3600.0000.0000.2840.1370.000
Osakkeet/optiot (EUR)0.0321.0000.0000.4650.3150.3260.128-0.0010.2620.000NaN1.0000.0960.0000.0001.000NaN0.0890.0001.0000.0000.0270.0900.0001.0000.0000.0000.134NaN0.0970.0000.0570.097NaN0.0000.011NaN0.0760.315
Palkansaaja vai laskuttaja0.0001.0000.0961.0001.0001.0000.0001.0000.0000.0001.0001.0000.5221.0001.0001.0001.0001.0000.0000.0001.0000.0001.0000.4141.0000.1160.0840.1031.0001.0000.1010.1540.2231.0001.0000.0001.0001.0001.000
Provisio (kk, brutto)-0.0401.0000.4870.0000.2060.2140.000-0.0660.2670.060NaN1.0000.1140.0000.0001.000NaN0.0000.0000.0270.0001.0000.0000.0001.0000.0000.000-0.075NaN0.0150.0000.0810.009NaN0.0000.057NaN0.1510.206
Seniority0.0730.0000.2090.0450.0550.0300.1060.0000.0350.0900.0000.0000.2380.2130.0990.0000.0000.1300.1980.0901.0000.0001.0000.0000.0000.1230.0250.1250.0000.1020.0000.3510.1880.0000.0900.1000.0000.0000.055
Siirtynyt palkansaaja/laskuttaja0.0000.1670.0000.1630.0000.0000.1040.0000.0790.0560.0000.2150.1670.0000.0000.1450.7270.0000.1410.0000.4140.0000.0001.0000.2580.0530.0000.3480.0000.0000.1090.0000.0630.0000.0000.1620.1030.1510.000
Sopimuksen pituus1.0000.3930.0750.0000.0000.0000.1190.0001.0000.0000.0000.4181.0000.0000.0000.5860.1830.0000.0001.0001.0001.0000.0000.2581.0000.1330.0000.4230.1940.0000.0000.0000.0000.3340.0000.0000.2230.0000.000
Sukupuoli0.0000.0000.0580.0000.0000.0000.0280.0950.0000.0730.1320.0000.0580.0000.0000.0000.3360.0850.1760.0000.1160.0000.1230.0530.1331.0000.0000.0000.0000.1070.0000.1370.0820.3040.0000.2000.1160.0000.000
Suomen kielen taito0.0000.0000.0000.0000.1490.2820.0000.0610.2330.0000.0000.0000.0000.0000.0230.0000.0000.1000.0000.0000.0840.0000.0250.0000.0000.0001.0000.2410.3640.0000.1490.0000.1570.0000.0000.0000.0000.3230.149
Tulojen muutos viime vuodesta (%)0.0400.3260.0000.376-0.048-0.0480.1560.0510.0590.000-0.0160.3660.0520.0000.0000.528-0.0970.0710.0590.1340.103-0.0750.1250.3480.4230.0000.2411.0000.1590.0160.000-0.243-0.132-0.0300.000-0.2330.1190.038-0.048
Tuntilaskutus (ALV 0%, euroina)NaN0.0000.1520.000NaNNaN0.429NaNNaN0.215-0.0420.000NaN0.0000.0000.0000.0860.0000.228NaN1.000NaN0.0000.0000.1940.0000.3640.1591.000NaN0.0000.4270.2210.0650.0000.0030.383NaNNaN
Työaika0.0070.0000.0750.1120.2320.0730.122-0.0140.2460.000NaN0.0000.0090.2710.1730.000NaN0.0420.0000.0971.0000.0150.1020.0000.0000.1070.0000.016NaN1.0000.0000.0740.113NaN0.000-0.128NaN-0.0570.232
Työkieli0.0000.0000.0000.4540.0000.0000.0000.0740.0000.1780.0000.0000.0000.0000.1100.1970.0000.1360.0000.0000.1010.0000.0000.1090.0000.0000.1490.0000.0000.0001.0000.0000.0000.0000.0390.0000.1260.0000.000
Työkokemus alalta (vuosina)0.0160.0000.4390.0000.6120.6080.050-0.0060.3820.1160.2930.0000.2280.0240.0320.2850.4630.1240.1040.0570.1540.0810.3510.0000.0000.1370.000-0.2430.4270.0740.0001.0000.555-0.2420.0890.2950.5400.1020.612
Työpaikkojen lukumäärä-0.0400.0000.0980.2060.5050.5000.000-0.0820.2530.0400.0750.0000.0570.2800.0450.0000.1370.1650.1400.0970.2230.0090.1880.0630.0000.0820.157-0.1320.2210.1130.0000.5551.000-0.0300.068-0.2280.3170.0450.505
Viikot ilman laskutustaNaN0.2340.4160.000NaNNaN0.485NaNNaN0.161-0.3690.049NaN0.0000.0000.000-0.0370.0000.360NaN1.000NaN0.0000.0000.3340.3040.000-0.0300.065NaN0.000-0.242-0.0301.0000.0000.032-0.377NaNNaN
Virallinen senioriteetti0.0780.0000.0000.0000.0000.0640.0600.0910.0570.0530.0000.0000.0390.0750.0000.0000.0000.0350.0000.0001.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0390.0890.0680.0001.0000.0000.0000.0700.000
Vuosia nykyisellä työnantajalla0.0720.0000.1110.0000.0770.0970.0290.0800.0790.130-0.1150.0000.1630.0000.0590.0000.6950.1070.0000.0110.0000.0570.1000.1620.0000.2000.000-0.2330.003-0.1280.0000.295-0.2280.0320.0001.0000.1550.0890.077
Vuosilaskutus (ALV 0%, euroina)NaN0.1890.2890.000NaNNaN0.231NaNNaN0.2040.4410.127NaN0.0000.0000.0730.2330.0000.284NaN1.000NaN0.0000.1030.2230.1160.0000.1190.383NaN0.1260.5400.317-0.3770.0000.1551.000NaNNaN
Vuosittaiset verovapaat edut (EUR)-0.0040.0000.0000.0000.0450.0880.0000.0550.0240.313NaN0.0000.0950.0950.0000.000NaN0.0000.1370.0761.0000.1510.0000.1510.0000.0000.3230.038NaN-0.0570.0000.1020.045NaN0.0700.089NaN1.0000.045
Vuositulot0.1900.0000.0570.4691.0000.9720.034-0.0920.9770.000NaN0.0000.5210.0000.0000.000NaN0.0450.0000.3151.0000.2060.0550.0000.0000.0000.149-0.048NaN0.2320.0000.6120.505NaN0.0000.077NaN0.0451.000

Missing values

2026-03-12T15:15:40.127711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-03-12T15:15:40.556496image/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.
2026-03-12T15:15:41.073807image/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

TimestampPalkansaaja vai laskuttajaSiirtynyt palkansaaja/laskuttajaIkäSukupuoliSuomen kielen taitoTyökieliTyökokemus alalta (vuosina)Vuosia nykyisellä työnantajallaTyöpaikkojen lukumääräYrityksen kokoKoulutustaustasiOpintoalaTulojen muutos viime vuodesta (%)Montako vuotta olet tehnyt laskuttavaa työtä alalla?PalvelutTuntilaskutus (ALV 0%, euroina)Vuosilaskutus (ALV 0%, euroina)Laskutettavat tunnit viikossaViikot ilman laskutustaLaskutustavatSopimuksen pituusHankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?Mistä asiakkaat ovat?TyöpaikkaKaupunkiMillaisessa yrityksessä työskentelet?TyöaikaKuinka suuren osan ajasta teet lähityönä toimistolla?RooliSeniorityVirallinen senioriteettiKuukausipalkkaProvisio (kk, brutto)Lomaraha (EUR)Bonus (EUR)Osakkeet/optiot (EUR)Vapaa kuvaus kokonaiskompensaatiomallistaOnko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?Bonukset (kuvaus)Edut (ei luontoisedut)Vuosittaiset verovapaat edut (EUR)LuontoisedutKäyttöjärjestelmäOhjelmointikieliWeb-kehyksetData & MLDevOps & pilviTietokannatVapaa sanaPalauteVastaustunnisteVuositulotRooli (normalisoitu)Kk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)
02026-02-09 08:08:48.457LaskuttajaEi38miesYesBoth10.04.04.01Bachelor’s degree – AMK (insinööri, tradenomi, etc.)Arts and Humanities-5.004.0Fullstack development with backend focus85.0100000.030.09.0Hourly billing3-6 monthsMyself, AgenciesSuomestaNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.00.00.0NaNNaNNaNNaNNaNNaNmacOSTypeScript, Python, GoNaNSQL, Kafka, scikit-learnCI/CD (GitHub Actions, GitLab CI, Jenkins), AWSPostgreSQL, Tiger Data (Timescale)NaNNaN2a27944c9ee50d0cNaNNaNNaN
12026-02-09 08:10:51.389PalkansaajaEi48miesYesBoth20.04.05.011-50Bachelor’s degree – AMK (insinööri, tradenomi, etc.)Information and Communication Technologies (ICT / Computer Science)21.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuTuotetalossa, jonka core-bisnes on softa1.200.10Founder, Technology or OtherwiseManager or People LeadYes7000.00.03500.00.00.0NaNAlle markkinatasonLomaraha (Holiday bonus)Health insurance, Work laptop, Free gym membership, Fixed-line internet to your home, Monitors for home officeNaNLimited car benefitmacOSTypeScriptReact, SvelteNaNCI/CD (GitHub Actions, GitLab CI, Jenkins), Google Cloud Platform (GCP)PostgreSQL, FirestoreNaNNaN0ccb0b24699bc2c087500.0Founder, Technology or Otherwise7291.6666676076.388889
22026-02-09 08:13:13.891PalkansaajaEi38miesYesBoth14.03.05.03-5Master’s degree – university (FM, KTM, DI, etc.)Information and Communication Technologies (ICT / Computer Science)0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTurkuTuotetalossa, jonka core-bisnes on softa1.120.20AI/ML EngineerStaff / Principal / LeadNo6000.00.03100.00.00.0NaNAlle markkinatasonStock options / equityHealth insurance, Work laptop, Monitors for home officeNaNFull Lunch benefit employer pays 100% (luontoisetu/fringe benefit), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)macOSTypeScript, PythonReact, Next.js, DjangoNaNDocker, Kubernetes, CI/CD (GitHub Actions, GitLab CI, Jenkins)PostgreSQLNaNKiitos, tämä on tärkeä kysely!16a59d6a893866f275100.0AI/ML Engineer6258.3333335587.797619
32026-02-09 08:14:20.316PalkansaajaEi28miesYesBoth6.04.03.0501-1000Master’s degree – university (FM, KTM, DI, etc.)Information and Communication Technologies (ICT / Computer Science)0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTampereKonsultointi1.000.05Developer, BackendSeniorYes4900.00.02940.09805.00.0NaNMarkkinatasoLomaraha (Holiday bonus), Individual performance bonus, Company performance bonusHealth insurance, Work laptop, Training/Courses/Education budget, Other tools like high-end keyboard, mouse etc, Monitors for home office, Snacks and beverages in office1000.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)WindowsTypeScript, C#Next.js, ASP.NET CoreSQLTerraform, CI/CD (GitHub Actions, GitLab CI, Jenkins), Microsoft AzurePostgreSQLNaNNaN282df8e8786fc5e971545.0Developer, Backend5962.0833335962.083333
42026-02-09 08:14:32.538PalkansaajaEi43miesYesFinnish20.010.04.0101-500Master’s degree – university (FM, KTM, DI, etc.)Information and Communication Technologies (ICT / Computer Science)1.25NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuYritys, jossa softa tukirooli1.000.20Developer, Full-StackSeniorNo7000.00.05040.05400.00.0NaNMarkkinatasoLomaraha (Holiday bonus), Company performance bonus, Referral bonus (for successful hires), On-call compensation (standby pay)Work laptop, Free office parking (during work hours), Monitors for home officeNaNPartial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)macOSTypeScript, Java, PHPReact, Next.js, Spring BootNaNDocker, Google Cloud Platform (GCP)MySQLNaNNaN6f3bb4f81f79603d94440.0Developer, Full-Stack7870.0000007870.000000
52026-02-09 08:14:41.435PalkansaajaEi43miesYesFinnish20.03.06.051-100Bachelor’s degree – AMK (insinööri, tradenomi, etc.)Business, Administration, and Law5.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTampereTuotetalossa, jonka core-bisnes on softa1.000.00Developer, QA or TestSeniorNo5500.00.03000.02000.00.0NaNMarkkinatasoLomaraha (Holiday bonus), Company performance bonus, Overtime extra pay (e.g. 1.5x or 2x pay rate), Referral bonus (for successful hires)Health insurance, Work remotely from abroad (1 week or more), Work laptop, Mobile phone with paid plan (Select only if NO 20 € puhelinetu on payslip), Home office equipment budget (chair, desk, etc.), Fixed-line internet to your home, Other tools like high-end keyboard, mouse etc, Monitors for home office600.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction)macOSJavaScript, PythonNaNNaNDocker, CI/CD (GitHub Actions, GitLab CI, Jenkins)PostgreSQLNaNNaN03d8b27b951c041b71000.0Developer, QA or Test5916.6666675916.666667
62026-02-09 08:14:43.034LaskuttajaEi38miesYesBoth13.03.04.01Master’s degree – university (FM, KTM, DI, etc.)Information and Communication Technologies (ICT / Computer Science)0.003.0Software development full-stack74.0124000.037.55.0Hourly billing6-12 monthsAgenciesSuomestaNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.00.00.0NaNNaNNaNNaNNaNNaNWindowsJavaScript, JavaReact, Spring BootNaNDocker, Kubernetes, CI/CD (GitHub Actions, GitLab CI, Jenkins)Db2NaNNaN0934d5fe86a3b35bNaNNaNNaN
72026-02-09 08:16:30.363PalkansaajaEi43miesYesEnglish15.08.04.01001-5000Bachelor’s degree – AMK (insinööri, tradenomi, etc.)Information and Communication Technologies (ICT / Computer Science)0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuYritys, jossa softa tukirooli1.000.40Developer, Full-StackSeniorYes4950.00.02970.01800.00.0NaNAlle markkinatasonLomaraha (Holiday bonus), Company performance bonusHealth insurance, Work remotely from abroad (1 week or more), Work laptop250.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)macOSJavaScript, TypeScriptReact, Node.jsNaNCI/CD (GitHub Actions, GitLab CI, Jenkins), AWSDynamoDBNaNAI usage would be maybe interesting info maybe. Holiday bonus could have option as "bonus as days off" or something if one chooses to rather have 6/12 extra vacation days instead of money if company allows it.b03e28d1db6f71dc64170.0Developer, Full-Stack5347.5000005347.500000
82026-02-09 08:19:04.143PalkansaajaEi28miesYesBoth5.04.02.0101-500Master’s degree – university (FM, KTM, DI, etc.)Engineering, Manufacturing, and Construction9.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuKonsultointi1.000.90Developer, QA or TestSeniorNo4925.00.02955.00.00.0NaNMarkkinatasoLomaraha (Holiday bonus), Referral bonus (for successful hires)Health insurance, Work remotely from abroad (1 week or more), Work laptop, Free gym membership, Training/Courses/Education budget, Snacks and beverages in office, Public transport subsidy (tax-free)800.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)Windows + WSLJavaScript, Python, Robot FrameworkReact, Node.js, DjangoSQL, Pandas / NumPyDocker, Kubernetes, CI/CD (GitHub Actions, GitLab CI, Jenkins)NaN"Number of change negotiations in your company during the last two calendar years" or something to showcase the volatility of the market. We've had two.Excellent survey.df9e0d7f7c623cb662055.0Developer, QA or Test5171.2500005171.250000
92026-02-09 08:21:01.882PalkansaajaEi33miesYesBoth15.03.03.051-100Doctorate (PhD / tohtori)Natural Sciences, Mathematics, and Statistics0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTampereKonsultointi1.200.10AI/ML EngineerStaff / Principal / LeadYes7200.04000.00.00.00.0NaNMarkkinatasoLomaraha (Holiday bonus), Stock options / equity, Profit sharing, Commission (sales or performance-based), Referral bonus (for successful hires)Health insurance, Work remotely from abroad (1 week or more), Work laptop, Mobile phone with paid plan (Select only if NO 20 € puhelinetu on payslip), Free office parking (during work hours), Monitors for home office, Snacks and beverages in office200.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)macOSTypeScript, Python, C++React, Next.js, FastAPISQL, Apache Spark, AirflowDocker, CI/CD (GitHub Actions, GitLab CI, Jenkins), Microsoft AzurePostgreSQL, Redis, InfluxDBNaNNaN92e582269ffe7a6d86400.0AI/ML Engineer7200.0000006000.000000
TimestampPalkansaaja vai laskuttajaSiirtynyt palkansaaja/laskuttajaIkäSukupuoliSuomen kielen taitoTyökieliTyökokemus alalta (vuosina)Vuosia nykyisellä työnantajallaTyöpaikkojen lukumääräYrityksen kokoKoulutustaustasiOpintoalaTulojen muutos viime vuodesta (%)Montako vuotta olet tehnyt laskuttavaa työtä alalla?PalvelutTuntilaskutus (ALV 0%, euroina)Vuosilaskutus (ALV 0%, euroina)Laskutettavat tunnit viikossaViikot ilman laskutustaLaskutustavatSopimuksen pituusHankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?Mistä asiakkaat ovat?TyöpaikkaKaupunkiMillaisessa yrityksessä työskentelet?TyöaikaKuinka suuren osan ajasta teet lähityönä toimistolla?RooliSeniorityVirallinen senioriteettiKuukausipalkkaProvisio (kk, brutto)Lomaraha (EUR)Bonus (EUR)Osakkeet/optiot (EUR)Vapaa kuvaus kokonaiskompensaatiomallistaOnko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?Bonukset (kuvaus)Edut (ei luontoisedut)Vuosittaiset verovapaat edut (EUR)LuontoisedutKäyttöjärjestelmäOhjelmointikieliWeb-kehyksetData & MLDevOps & pilviTietokannatVapaa sanaPalauteVastaustunnisteVuositulotRooli (normalisoitu)Kk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)
6732026-03-02 10:46:07.611PalkansaajaEi43miesYesBoth26.011.07.0101-500Master’s degree – university (FM, KTM, DI, etc.)Information and Communication Technologies (ICT / Computer Science)2.74NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuTuotetalossa, jonka core-bisnes on softa1.0400000.30Architect, Software or SolutionsStaff / Principal / LeadYes10788.00.06472.80.00.0NaNYli markkinatasonLomaraha (Holiday bonus), Individual performance bonus, Sign-on bonus, Overtime extra pay (e.g. 1.5x or 2x pay rate), Stock options / equity, Commission (sales or performance-based), Referral bonus (for successful hires), On-call compensation (standby pay)Health insurance, Work remotely from abroad (1 week or more), Work laptop, Training/Courses/Education budget, Fixed-line internet to your home, Other tools like high-end keyboard, mouse etc, Monitors for home office, Snacks and beverages in office, Public transport subsidy (tax-free)NaNPartial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip), Employer-subsidised commuter ticket (taxed)macOSGo, Bash,NaNSQL, KafkaLinux server, Docker, AnsiblePostgreSQL, MySQLDirect reporteers, commercial responsibilities, travel daysNaN6ec137a9b0a09028135928.8Architect, Software or Solutions11327.40000010891.730769
6742026-03-02 10:52:18.927PalkansaajaEi33miesYesFinnish8.08.02.011-50Bachelor’s degree – AMK (insinööri, tradenomi, etc.)Information and Communication Technologies (ICT / Computer Science)3.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTurkuKonsultointi1.0000000.90Architect, Software or SolutionsManager or People LeadYes6100.00.03000.0500.00.0NaNMarkkinatasoLomaraha (Holiday bonus), Company performance bonus, Referral bonus (for successful hires)Health insurance, Work laptop, Home office equipment budget (chair, desk, etc.), Free office parking (during work hours), Free gym membership, Training/Courses/Education budget, Other tools like high-end keyboard, mouse etc, Snacks and beverages in office400.0NaNWindows + WSLTypeScript, PythonReact, Node.js, DjangoSQLTerraform, CI/CD (GitHub Actions, GitLab CI, Jenkins), Microsoft AzurePostgreSQL, MySQL, RedisNaNNaN0d48cbe29d3e0b6976700.0Architect, Software or Solutions6391.6666676391.666667
6752026-03-02 11:11:22.208PalkansaajaEi28miesYesBoth6.08.01.010000+Master’s degree – university (FM, KTM, DI, etc.)Information and Communication Technologies (ICT / Computer Science)4.43NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNOuluTuotetalossa, jonka core-bisnes on softa1.0000000.50Developer, BackendMid-levelYes4008.00.02402.06663.02100.0Salary + bonus (perf driven, monetary) + stock bonusMarkkinatasoLomaraha (Holiday bonus), Individual performance bonus, Company performance bonus, Overtime extra pay (e.g. 1.5x or 2x pay rate), Stock options / equity, Profit sharing, Referral bonus (for successful hires), On-call compensation (standby pay)Health insurance, Work laptop, Home office equipment budget (chair, desk, etc.), Free office parking (during work hours), Free gym membership, Monitors for home office, Public transport subsidy (tax-free)400.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)Windows + WSLPython, BashDjango, Flask, FastAPIKafka, Pandas / NumPy,Linux server, Docker, CI/CD (GitHub Actions, GitLab CI, Jenkins)MySQL, Redis, ElasticsearchNaNNaN369055fe35eab2e259261.0Developer, Backend4938.4166674938.416667
6762026-03-02 11:59:28.546LaskuttajaEi48miesYesBoth25.05.04.03-5Master’s degree – university (FM, KTM, DI, etc.)Information and Communication Technologies (ICT / Computer Science)10.005.0Own SaaS applicationNaNNaNNaNNaNFixed price / project-based6-12 monthsOwn salesFinland, AbroadNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.00.00.0NaNNaNNaNNaNNaNNaNmacOSTypeScriptReact, Next.js, Node.jsSQLDocker, CI/CD (GitHub Actions, GitLab CI, Jenkins), AWSPostgreSQL, DynamoDBQuestions does not work at all for SaaS companyNaN56a2c9a1390877d9NaNNaNNaN
6772026-03-02 12:16:16.031PalkansaajaEi48miesYesEnglish20.04.06.010000+Master’s degree – university (FM, KTM, DI, etc.)Information and Communication Technologies (ICT / Computer Science)2.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuYritys, jossa softa tukirooli1.0000000.05Architect, Software or SolutionsSeniorYes7000.00.03500.012000.00.0NaNYli markkinatasonLomaraha (Holiday bonus), Company performance bonusWork laptop, Snacks and beverages in office, Public transport subsidy (tax-free)600.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Mobile phone with paid plan (Select only if 20 € puhelinetu on payslip)macOSTypeScript, BashNaNNaNTerraform, CI/CD (GitHub Actions, GitLab CI, Jenkins), AWSPostgreSQLNaNNaN7580c8f8369e332699500.0Architect, Software or Solutions8291.6666678291.666667
6782026-03-02 13:05:21.541LaskuttajaEi33miesYesFinnish8.08.01.02Bachelor’s degree – AMK (insinööri, tradenomi, etc.)Information and Communication Technologies (ICT / Computer Science)15.008.0EverythingNaN172000.00.00.0Monthly billingOpen-endedMyselfSuomestaNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.00.00.0NaNNaNNaNNaNNaNNaNmacOSJavaScript, TypeScript, PHPReact, Node.jsSQLLinux server, DockerMySQL, RedisNaNNaN2a79718b69271466NaNNaNNaN
6792026-03-02 18:08:12.657LaskuttajaEi38miesYesFinnish14.05.04.01Upper secondary – vocational (ammatillinen perustutkinto)Information and Communication Technologies (ICT / Computer Science)0.005.0Full stack development and maintenance services80.0138000.037.56.0Hourly billingOver 24 monthsMyself, AgenciesSuomestaNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.00.00.0NaNNaNNaNNaNNaNNaNmacOSJavaScript, TypeScript, PHPReact, Next.js, Node.jsNaNLinux server, Docker, CI/CD (GitHub Actions, GitLab CI, Jenkins)PostgreSQL, MySQLNaNNaN08bd722ced435fddNaNNaNNaN
6802026-03-02 18:51:52.716PalkansaajaEi33miesYesBoth9.0NaN3.010000+Master’s degree – university (FM, KTM, DI, etc.)Engineering, Manufacturing, and ConstructionNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuTuotetalossa, jonka core-bisnes on softa1.0000000.50Applied ScientistSeniorYes10000.00.00.00.0450000.0NaNEn osaa sanoaLomaraha (Holiday bonus), Stock options / equityHealth insurance, Work laptop, Monitors for home office0.0NaNWindows + WSLPython, C++NaNPyTorchLinux server, Docker, CI/CD (GitHub Actions, GitLab CI, Jenkins)NaNNaNNaN6f7540b517e6cc24570000.0Applied Scientist47500.00000047500.000000
6812026-03-03 08:55:47.076PalkansaajaEi33nainenYesBoth1.01.02.0501-1000Upper secondary – general (lukio / high school equivalent)NaN40.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuYritys, jossa softa tukirooli0.9866670.05Developer, QA or TestJunior / Entry-levelYes2660.00.00.00.00.0NaNMarkkinatasoNaNWork laptop, Snacks and beverages in office300.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction)macOSNaNNaNNaNNaNNaNNaNNaNa611c95eb3fd08bf31920.0Developer, QA or Test2660.0000002695.945946
6822026-03-03 16:36:20.497PalkansaajaEi23nainenYesBoth6.05.04.0501-1000Upper secondary – general (lukio / high school equivalent)NaN4.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTampereKonsultointi0.6000000.20Developer, Full-StackMid-levelYes3410.00.01416.01360.0854.0NaNAlle markkinatasonLomaraha (Holiday bonus), Company performance bonus, Overtime extra pay (e.g. 1.5x or 2x pay rate), Stock options / equity, Profit sharing, Commission (sales or performance-based), Referral bonus (for successful hires), On-call compensation (standby pay)Health insurance, Ryhmäeläkevakuutus (Additional employer pension contribution), Work laptop, Free office parking (during work hours), Training/Courses/Education budget, Other tools like high-end keyboard, mouse etc, Monitors for home office, Snacks and beverages in office400.0Partial Lunch benefit employer pays 25% (lounasvähennys/lunch deduction), Garage (outside work hours)macOSTypeScript, C#React, Next.js, ASP.NET CoreNaNWindows server, Docker, Microsoft AzureSQL Server, MongoDB, RedisNaNNaN57657a75edded78d44550.0Developer, Full-Stack3712.5000006187.500000