Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations731
Missing cells8273
Missing cells (%)35.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory146.6 KiB
Average record size in memory205.4 B

Variable types

DateTime1
Categorical10
Numeric11
Text9
Unsupported1

Alerts

Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? is highly overall correlated with Mistä asiakkaat ovat? and 4 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 3 other fieldsHigh correlation
Kk-tulot (laskennallinen, normalisoitu) is highly overall correlated with Kk-tulot (laskennallinen) and 3 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 Kk-tulot (laskennallinen) and 4 other fieldsHigh correlation
Millaisessa yrityksessä työskentelet? is highly overall correlated with Palkansaaja vai laskuttaja and 1 other fieldsHigh correlation
Mistä asiakkaat ovat? is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 3 other fieldsHigh correlation
Montako vuotta olet tehnyt laskuttavaa työtä alalla? is highly overall correlated with Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? and 2 other fieldsHigh correlation
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 5 other fieldsHigh correlation
Palkansaaja vai laskuttaja is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 13 other fieldsHigh correlation
Sukupuoli is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 4 other fieldsHigh correlation
Tuntilaskutus (ALV 0%, euroina) is highly overall correlated with Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? and 3 other fieldsHigh correlation
Työaika is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Työkokemus alalta (vuosina) is highly overall correlated with KuukausipalkkaHigh correlation
Vastauskieli is highly overall correlated with Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? and 1 other fieldsHigh correlation
Vuosilaskutus (ALV 0%, euroina) is highly overall correlated with Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? and 3 other fieldsHigh correlation
Vuositulot is highly overall correlated with Kk-tulot (laskennallinen) and 3 other fieldsHigh correlation
Oletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2023 jälkeen? is highly imbalanced (88.4%) Imbalance
Sukupuoli is highly imbalanced (65.8%) Imbalance
Kaupunki is highly imbalanced (56.1%) Imbalance
Vastauskieli is highly imbalanced (63.5%) Imbalance
Oletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2023 jälkeen? has 8 (1.1%) missing values Missing
Sukupuoli has 58 (7.9%) missing values Missing
Koulutustaustasi has 44 (6.0%) missing values Missing
Tulojen muutos viime vuodesta (%) has 35 (4.8%) missing values Missing
Montako vuotta olet tehnyt laskuttavaa työtä alalla? has 638 (87.3%) missing values Missing
Palvelut has 638 (87.3%) missing values Missing
Tuntilaskutus (ALV 0%, euroina) has 644 (88.1%) missing values Missing
Vuosilaskutus (ALV 0%, euroina) has 647 (88.5%) missing values Missing
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? has 636 (87.0%) missing values Missing
Mistä asiakkaat ovat? has 635 (86.9%) missing values Missing
Työpaikka has 560 (76.6%) missing values Missing
Kaupunki has 115 (15.7%) missing values Missing
Millaisessa yrityksessä työskentelet? has 102 (14.0%) missing values Missing
Työaika has 100 (13.7%) missing values Missing
Kuinka suuren osan ajasta teet lähityönä toimistolla? has 102 (14.0%) missing values Missing
Rooli has 115 (15.7%) missing values Missing
Kuukausipalkka has 96 (13.1%) missing values Missing
Vuositulot has 174 (23.8%) missing values Missing
Vapaa kuvaus kokonaiskompensaatiomallista has 488 (66.8%) missing values Missing
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? (muut vastaukset) has 688 (94.1%) missing values Missing
Vapaa sana has 699 (95.6%) missing values Missing
Palaute has 692 (94.7%) missing values Missing
Kk-tulot (laskennallinen) has 174 (23.8%) missing values Missing
Kk-tulot (laskennallinen, normalisoitu) has 176 (24.1%) missing values Missing
Timestamp has unique values Unique
Vastaustunniste has unique values Unique
Vapaa kuvaus kokonaiskompensaatiomallista is an unsupported type, check if it needs cleaning or further analysis Unsupported
Tulojen muutos viime vuodesta (%) has 184 (25.2%) zeros Zeros
Kuinka suuren osan ajasta teet lähityönä toimistolla? has 104 (14.2%) zeros Zeros
Vuositulot has 15 (2.1%) zeros Zeros
Kk-tulot (laskennallinen) has 15 (2.1%) zeros Zeros
Kk-tulot (laskennallinen, normalisoitu) has 15 (2.1%) zeros Zeros

Reproduction

Analysis started2024-10-29 10:23:38.277264
Analysis finished2024-10-29 10:23:59.910761
Duration21.63 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Timestamp
Date

Unique 

Distinct731
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
Minimum2024-10-07 10:05:39.380000
Maximum2024-10-27 22:09:17.916000
2024-10-29T10:24:00.057910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:24:00.300112image/svg+xmlMatplotlib v3.7.5, 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 size6.5 KiB
Palkansaaja
635 
Laskuttaja
96 

Length

Max length11
Median length11
Mean length10.868673
Min length10

Characters and Unicode

Total characters7945
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 rowPalkansaaja
2nd rowPalkansaaja
3rd rowPalkansaaja
4th rowPalkansaaja
5th rowPalkansaaja

Common Values

ValueCountFrequency (%)
Palkansaaja 635
86.9%
Laskuttaja 96
 
13.1%

Length

2024-10-29T10:24:00.529981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T10:24:00.704979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
palkansaaja 635
86.9%
laskuttaja 96
 
13.1%

Most occurring characters

ValueCountFrequency (%)
a 3463
43.6%
k 731
 
9.2%
s 731
 
9.2%
j 731
 
9.2%
P 635
 
8.0%
l 635
 
8.0%
n 635
 
8.0%
t 192
 
2.4%
L 96
 
1.2%
u 96
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3463
43.6%
k 731
 
9.2%
s 731
 
9.2%
j 731
 
9.2%
P 635
 
8.0%
l 635
 
8.0%
n 635
 
8.0%
t 192
 
2.4%
L 96
 
1.2%
u 96
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3463
43.6%
k 731
 
9.2%
s 731
 
9.2%
j 731
 
9.2%
P 635
 
8.0%
l 635
 
8.0%
n 635
 
8.0%
t 192
 
2.4%
L 96
 
1.2%
u 96
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3463
43.6%
k 731
 
9.2%
s 731
 
9.2%
j 731
 
9.2%
P 635
 
8.0%
l 635
 
8.0%
n 635
 
8.0%
t 192
 
2.4%
L 96
 
1.2%
u 96
 
1.2%
Distinct3
Distinct (%)0.4%
Missing8
Missing (%)1.1%
Memory size6.6 KiB
Ei
706 
palkansaaja → laskuttaja
 
9
laskuttaja → palkansaaja
 
8

Length

Max length24
Median length2
Mean length2.5172891
Min length2

Characters and Unicode

Total characters1820
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 (%)
Ei 706
96.6%
palkansaaja → laskuttaja 9
 
1.2%
laskuttaja → palkansaaja 8
 
1.1%
(Missing) 8
 
1.1%

Length

2024-10-29T10:24:00.886274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T10:24:01.056346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ei 706
93.3%
palkansaaja 17
 
2.2%
17
 
2.2%
laskuttaja 17
 
2.2%

Most occurring characters

ValueCountFrequency (%)
E 706
38.8%
i 706
38.8%
a 136
 
7.5%
l 34
 
1.9%
k 34
 
1.9%
s 34
 
1.9%
j 34
 
1.9%
34
 
1.9%
t 34
 
1.9%
p 17
 
0.9%
Other values (3) 51
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 706
38.8%
i 706
38.8%
a 136
 
7.5%
l 34
 
1.9%
k 34
 
1.9%
s 34
 
1.9%
j 34
 
1.9%
34
 
1.9%
t 34
 
1.9%
p 17
 
0.9%
Other values (3) 51
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 706
38.8%
i 706
38.8%
a 136
 
7.5%
l 34
 
1.9%
k 34
 
1.9%
s 34
 
1.9%
j 34
 
1.9%
34
 
1.9%
t 34
 
1.9%
p 17
 
0.9%
Other values (3) 51
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 706
38.8%
i 706
38.8%
a 136
 
7.5%
l 34
 
1.9%
k 34
 
1.9%
s 34
 
1.9%
j 34
 
1.9%
34
 
1.9%
t 34
 
1.9%
p 17
 
0.9%
Other values (3) 51
 
2.8%

Ikä
Categorical

Distinct9
Distinct (%)1.2%
Missing2
Missing (%)0.3%
Memory size6.8 KiB
36-40
201 
31-35
194 
41-45
124 
26-30
116 
46-50
57 
21-25
 
20
51-55
 
11
15-20
 
4
> 55v
 
2

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3645
Distinct characters11
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 row46-50
2nd row31-35
3rd row36-40
4th row41-45
5th row41-45

Common Values

ValueCountFrequency (%)
36-40 201
27.5%
31-35 194
26.5%
41-45 124
17.0%
26-30 116
15.9%
46-50 57
 
7.8%
21-25 20
 
2.7%
51-55 11
 
1.5%
15-20 4
 
0.5%
> 55v 2
 
0.3%
(Missing) 2
 
0.3%

Length

2024-10-29T10:24:01.243183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T10:24:01.446251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
36-40 201
27.5%
31-35 194
26.5%
41-45 124
17.0%
26-30 116
15.9%
46-50 57
 
7.8%
21-25 20
 
2.7%
51-55 11
 
1.5%
15-20 4
 
0.5%
2
 
0.3%
55v 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
- 727
19.9%
3 705
19.3%
4 506
13.9%
5 436
12.0%
0 378
10.4%
6 374
10.3%
1 353
9.7%
2 160
 
4.4%
> 2
 
0.1%
2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 727
19.9%
3 705
19.3%
4 506
13.9%
5 436
12.0%
0 378
10.4%
6 374
10.3%
1 353
9.7%
2 160
 
4.4%
> 2
 
0.1%
2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 727
19.9%
3 705
19.3%
4 506
13.9%
5 436
12.0%
0 378
10.4%
6 374
10.3%
1 353
9.7%
2 160
 
4.4%
> 2
 
0.1%
2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 727
19.9%
3 705
19.3%
4 506
13.9%
5 436
12.0%
0 378
10.4%
6 374
10.3%
1 353
9.7%
2 160
 
4.4%
> 2
 
0.1%
2
 
0.1%

Sukupuoli
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.4%
Missing58
Missing (%)7.9%
Memory size6.6 KiB
mies
594 
nainen
77 
muu
 
2

Length

Max length6
Median length4
Mean length4.2258544
Min length3

Characters and Unicode

Total characters2844
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
mies 594
81.3%
nainen 77
 
10.5%
muu 2
 
0.3%
(Missing) 58
 
7.9%

Length

2024-10-29T10:24:01.684684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T10:24:01.861638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
mies 594
88.3%
nainen 77
 
11.4%
muu 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 671
23.6%
e 671
23.6%
m 596
21.0%
s 594
20.9%
n 231
 
8.1%
a 77
 
2.7%
u 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 671
23.6%
e 671
23.6%
m 596
21.0%
s 594
20.9%
n 231
 
8.1%
a 77
 
2.7%
u 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 671
23.6%
e 671
23.6%
m 596
21.0%
s 594
20.9%
n 231
 
8.1%
a 77
 
2.7%
u 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 671
23.6%
e 671
23.6%
m 596
21.0%
s 594
20.9%
n 231
 
8.1%
a 77
 
2.7%
u 4
 
0.1%

Työkokemus alalta (vuosina)
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)4.7%
Missing7
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean11.638122
Minimum0
Maximum35
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:02.042853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median10
Q316
95-th percentile25
Maximum35
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.6581399
Coefficient of variation (CV)0.57209747
Kurtosis-0.19408068
Mean11.638122
Median Absolute Deviation (MAD)5
Skewness0.6393077
Sum8426
Variance44.330827
MonotonicityNot monotonic
2024-10-29T10:24:02.264264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10 59
 
8.1%
15 55
 
7.5%
8 50
 
6.8%
6 46
 
6.3%
5 43
 
5.9%
12 41
 
5.6%
4 40
 
5.5%
7 37
 
5.1%
3 37
 
5.1%
20 34
 
4.7%
Other values (24) 282
38.6%
ValueCountFrequency (%)
0 1
 
0.1%
1 4
 
0.5%
2 22
3.0%
3 37
5.1%
4 40
5.5%
5 43
5.9%
6 46
6.3%
7 37
5.1%
8 50
6.8%
9 30
4.1%
ValueCountFrequency (%)
35 1
 
0.1%
32 1
 
0.1%
31 1
 
0.1%
30 3
 
0.4%
29 1
 
0.1%
28 3
 
0.4%
27 5
 
0.7%
26 5
 
0.7%
25 24
3.3%
24 7
 
1.0%

Koulutustaustasi
Text

Missing 

Distinct347
Distinct (%)50.5%
Missing44
Missing (%)6.0%
Memory size11.4 KiB
2024-10-29T10:24:02.614638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length106
Median length67
Mean length18.317322
Min length2

Characters and Unicode

Total characters12584
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

Unique279 ?
Unique (%)40.6%

Sample

1st rowtietotekniikan kandidaatti
2nd rowYlioppilas
3rd rowDI
4th rowIT-tradenomi
5th rowFM (Tietojenkäsittelytiede)
ValueCountFrequency (%)
amk 98
 
7.7%
di 76
 
5.9%
diplomi-insinööri 64
 
5.0%
insinööri 60
 
4.7%
maisteri 44
 
3.4%
tradenomi 38
 
3.0%
ylioppilas 37
 
2.9%
fm 35
 
2.7%
tietotekniikan 34
 
2.7%
kandidaatti 32
 
2.5%
Other values (264) 761
59.5%
2024-10-29T10:24:03.170777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1799
14.3%
t 1109
 
8.8%
e 1038
 
8.2%
n 976
 
7.8%
o 799
 
6.3%
a 693
 
5.5%
k 636
 
5.1%
600
 
4.8%
s 487
 
3.9%
l 475
 
3.8%
Other values (52) 3972
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1799
14.3%
t 1109
 
8.8%
e 1038
 
8.2%
n 976
 
7.8%
o 799
 
6.3%
a 693
 
5.5%
k 636
 
5.1%
600
 
4.8%
s 487
 
3.9%
l 475
 
3.8%
Other values (52) 3972
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1799
14.3%
t 1109
 
8.8%
e 1038
 
8.2%
n 976
 
7.8%
o 799
 
6.3%
a 693
 
5.5%
k 636
 
5.1%
600
 
4.8%
s 487
 
3.9%
l 475
 
3.8%
Other values (52) 3972
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1799
14.3%
t 1109
 
8.8%
e 1038
 
8.2%
n 976
 
7.8%
o 799
 
6.3%
a 693
 
5.5%
k 636
 
5.1%
600
 
4.8%
s 487
 
3.9%
l 475
 
3.8%
Other values (52) 3972
31.6%

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

Missing  Zeros 

Distinct101
Distinct (%)14.5%
Missing35
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean5.0661494
Minimum-100
Maximum100
Zeros184
Zeros (%)25.2%
Negative34
Negative (%)4.7%
Memory size11.4 KiB
2024-10-29T10:24:03.413296image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0
Q10
median3
Q37
95-th percentile21.775
Maximum100
Range200
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.692049
Coefficient of variation (CV)2.1104882
Kurtosis26.321643
Mean5.0661494
Median Absolute Deviation (MAD)3
Skewness0.8214737
Sum3526.04
Variance114.31991
MonotonicityNot monotonic
2024-10-29T10:24:03.668526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 184
25.2%
3 63
 
8.6%
5 53
 
7.3%
2 42
 
5.7%
10 35
 
4.8%
1 32
 
4.4%
4 21
 
2.9%
8 21
 
2.9%
20 18
 
2.5%
7 17
 
2.3%
Other values (91) 210
28.7%
(Missing) 35
 
4.8%
ValueCountFrequency (%)
-100 1
0.1%
-40 1
0.1%
-30 1
0.1%
-25 1
0.1%
-24 1
0.1%
-20 2
0.3%
-17 1
0.1%
-15 1
0.1%
-14.5 1
0.1%
-14 1
0.1%
ValueCountFrequency (%)
100 1
 
0.1%
60 2
0.3%
53 1
 
0.1%
50 3
0.4%
47 1
 
0.1%
42 1
 
0.1%
40 1
 
0.1%
38 1
 
0.1%
37 1
 
0.1%
32 1
 
0.1%

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

High correlation  Missing 

Distinct19
Distinct (%)20.4%
Missing638
Missing (%)87.3%
Infinite0
Infinite (%)0.0%
Mean4.0322581
Minimum0
Maximum22
Zeros3
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:03.880810image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile11
Maximum22
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4576683
Coefficient of variation (CV)0.85750173
Kurtosis7.4521378
Mean4.0322581
Median Absolute Deviation (MAD)1
Skewness2.2861027
Sum375
Variance11.95547
MonotonicityNot monotonic
2024-10-29T10:24:04.064760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 21
 
2.9%
2 21
 
2.9%
4 9
 
1.2%
6 7
 
1.0%
1 6
 
0.8%
5 4
 
0.5%
1.5 4
 
0.5%
10 4
 
0.5%
11 3
 
0.4%
0 3
 
0.4%
Other values (9) 11
 
1.5%
(Missing) 638
87.3%
ValueCountFrequency (%)
0 3
 
0.4%
0.5 1
 
0.1%
1 6
 
0.8%
1.5 4
 
0.5%
2 21
2.9%
2.5 2
 
0.3%
3 21
2.9%
3.5 1
 
0.1%
4 9
1.2%
5 4
 
0.5%
ValueCountFrequency (%)
22 1
 
0.1%
13 1
 
0.1%
12 1
 
0.1%
11 3
0.4%
10 4
0.5%
9 1
 
0.1%
8 1
 
0.1%
7 2
 
0.3%
6 7
1.0%
5 4
0.5%

Palvelut
Text

Missing 

Distinct73
Distinct (%)78.5%
Missing638
Missing (%)87.3%
Memory size11.4 KiB
2024-10-29T10:24:04.282026image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length73
Median length50
Mean length25.688172
Min length2

Characters and Unicode

Total characters2389
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

Unique69 ?
Unique (%)74.2%

Sample

1st rowFull stack, tech lead, AWS, pilvi, devops
2nd rowFull stack ja DevOps.
3rd rowOhjelmistokehitys, DevOps, Cloud
4th rowArkkitehtuuri, full stack
5th rowBackend, devops, yleisempää käsienheiluttelua ja speksaushommaa
ValueCountFrequency (%)
full 43
 
14.9%
stack 43
 
14.9%
devops 15
 
5.2%
backend 13
 
4.5%
mobiili 9
 
3.1%
softadevausta 7
 
2.4%
arkkitehtuuri 7
 
2.4%
frontend 7
 
2.4%
lead 6
 
2.1%
development 5
 
1.7%
Other values (87) 134
46.4%
2024-10-29T10:24:04.920383image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
200
 
8.4%
a 194
 
8.1%
t 189
 
7.9%
e 182
 
7.6%
l 166
 
6.9%
s 135
 
5.7%
u 131
 
5.5%
k 129
 
5.4%
i 126
 
5.3%
o 92
 
3.9%
Other values (43) 845
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
200
 
8.4%
a 194
 
8.1%
t 189
 
7.9%
e 182
 
7.6%
l 166
 
6.9%
s 135
 
5.7%
u 131
 
5.5%
k 129
 
5.4%
i 126
 
5.3%
o 92
 
3.9%
Other values (43) 845
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
200
 
8.4%
a 194
 
8.1%
t 189
 
7.9%
e 182
 
7.6%
l 166
 
6.9%
s 135
 
5.7%
u 131
 
5.5%
k 129
 
5.4%
i 126
 
5.3%
o 92
 
3.9%
Other values (43) 845
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
200
 
8.4%
a 194
 
8.1%
t 189
 
7.9%
e 182
 
7.6%
l 166
 
6.9%
s 135
 
5.7%
u 131
 
5.5%
k 129
 
5.4%
i 126
 
5.3%
o 92
 
3.9%
Other values (43) 845
35.4%

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

High correlation  Missing 

Distinct35
Distinct (%)40.2%
Missing644
Missing (%)88.1%
Infinite0
Infinite (%)0.0%
Mean93.313218
Minimum35
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:05.080812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile68.65
Q180
median90
Q396
95-th percentile123.5
Maximum500
Range465
Interquartile range (IQR)16

Descriptive statistics

Standard deviation47.342459
Coefficient of variation (CV)0.50734998
Kurtosis64.89106
Mean93.313218
Median Absolute Deviation (MAD)10
Skewness7.5171413
Sum8118.25
Variance2241.3085
MonotonicityNot monotonic
2024-10-29T10:24:05.228805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
90 11
 
1.5%
85 9
 
1.2%
95 9
 
1.2%
80 8
 
1.1%
100 7
 
1.0%
75 5
 
0.7%
70 4
 
0.5%
84 3
 
0.4%
83 3
 
0.4%
107 2
 
0.3%
Other values (25) 26
 
3.6%
(Missing) 644
88.1%
ValueCountFrequency (%)
35 1
 
0.1%
40 1
 
0.1%
54 1
 
0.1%
65 1
 
0.1%
68.5 1
 
0.1%
69 1
 
0.1%
70 4
0.5%
72.25 1
 
0.1%
74 1
 
0.1%
75 5
0.7%
ValueCountFrequency (%)
500 1
0.1%
150 1
0.1%
129 1
0.1%
126 1
0.1%
125 1
0.1%
120 2
0.3%
115 1
0.1%
110 1
0.1%
107 2
0.3%
101.5 1
0.1%

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

High correlation  Missing 

Distinct48
Distinct (%)57.1%
Missing647
Missing (%)88.5%
Infinite0
Infinite (%)0.0%
Mean133576.8
Minimum0
Maximum325000
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:05.377603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33000
Q1110000
median140000
Q3160000
95-th percentile197750
Maximum325000
Range325000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation49539.021
Coefficient of variation (CV)0.37086546
Kurtosis2.8204814
Mean133576.8
Median Absolute Deviation (MAD)20000
Skewness0.11556414
Sum11220451
Variance2.4541146 × 109
MonotonicityNot monotonic
2024-10-29T10:24:05.614506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
150000 10
 
1.4%
160000 9
 
1.2%
140000 7
 
1.0%
110000 3
 
0.4%
130000 3
 
0.4%
155000 3
 
0.4%
135000 3
 
0.4%
200000 2
 
0.3%
50000 2
 
0.3%
100000 2
 
0.3%
Other values (38) 40
 
5.5%
(Missing) 647
88.5%
ValueCountFrequency (%)
0 1
0.1%
24000 1
0.1%
25000 1
0.1%
28700 1
0.1%
30000 1
0.1%
50000 2
0.3%
60000 1
0.1%
65000 1
0.1%
70000 2
0.3%
86000 1
0.1%
ValueCountFrequency (%)
325000 1
0.1%
250000 1
0.1%
240000 1
0.1%
200000 2
0.3%
185000 1
0.1%
180000 2
0.3%
176000 1
0.1%
170000 1
0.1%
167000 1
0.1%
165000 1
0.1%
Distinct8
Distinct (%)8.4%
Missing636
Missing (%)87.0%
Memory size11.4 KiB
Käytän välitysfirmoja
33 
Itse
29 
Itse, Käytän välitysfirmoja
26 
Agencies
 
3
Itse, Alihankintana teknistä tietoturvaa, kun tutut tarvitsevat apukäsiä.
 
1
Itse, Aina sama asiakas.
 
1
Consulting companies that I know / know me
 
1
Company sales team.
 
1

Length

Max length73
Median length42
Mean length17.821053
Min length4

Characters and Unicode

Total characters1693
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

Unique4 ?
Unique (%)4.2%

Sample

1st rowItse, Käytän välitysfirmoja
2nd rowKäytän välitysfirmoja
3rd rowItse, Käytän välitysfirmoja
4th rowItse, Käytän välitysfirmoja
5th rowItse, Käytän välitysfirmoja

Common Values

ValueCountFrequency (%)
Käytän välitysfirmoja 33
 
4.5%
Itse 29
 
4.0%
Itse, Käytän välitysfirmoja 26
 
3.6%
Agencies 3
 
0.4%
Itse, Alihankintana teknistä tietoturvaa, kun tutut tarvitsevat apukäsiä. 1
 
0.1%
Itse, Aina sama asiakas. 1
 
0.1%
Consulting companies that I know / know me 1
 
0.1%
Company sales team. 1
 
0.1%
(Missing) 636
87.0%

Length

2024-10-29T10:24:05.851905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T10:24:06.051486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
käytän 59
29.6%
välitysfirmoja 59
29.6%
itse 57
28.6%
agencies 3
 
1.5%
know 2
 
1.0%
consulting 1
 
0.5%
sales 1
 
0.5%
company 1
 
0.5%
me 1
 
0.5%
1
 
0.5%
Other values (14) 14
 
7.0%

Most occurring characters

ValueCountFrequency (%)
t 191
 
11.3%
ä 180
 
10.6%
i 131
 
7.7%
s 129
 
7.6%
y 119
 
7.0%
104
 
6.1%
a 78
 
4.6%
n 74
 
4.4%
e 70
 
4.1%
o 65
 
3.8%
Other values (20) 552
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 191
 
11.3%
ä 180
 
10.6%
i 131
 
7.7%
s 129
 
7.6%
y 119
 
7.0%
104
 
6.1%
a 78
 
4.6%
n 74
 
4.4%
e 70
 
4.1%
o 65
 
3.8%
Other values (20) 552
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 191
 
11.3%
ä 180
 
10.6%
i 131
 
7.7%
s 129
 
7.6%
y 119
 
7.0%
104
 
6.1%
a 78
 
4.6%
n 74
 
4.4%
e 70
 
4.1%
o 65
 
3.8%
Other values (20) 552
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 191
 
11.3%
ä 180
 
10.6%
i 131
 
7.7%
s 129
 
7.6%
y 119
 
7.0%
104
 
6.1%
a 78
 
4.6%
n 74
 
4.4%
e 70
 
4.1%
o 65
 
3.8%
Other values (20) 552
32.6%

Mistä asiakkaat ovat?
Categorical

High correlation  Missing 

Distinct4
Distinct (%)4.2%
Missing635
Missing (%)86.9%
Memory size6.6 KiB
Suomesta
70 
Suomesta, Ulkomailta
15 
Ulkomailta
10 
Finland, Abroad
 
1

Length

Max length20
Median length8
Mean length10.15625
Min length8

Characters and Unicode

Total characters975
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
Suomesta 70
 
9.6%
Suomesta, Ulkomailta 15
 
2.1%
Ulkomailta 10
 
1.4%
Finland, Abroad 1
 
0.1%
(Missing) 635
86.9%

Length

2024-10-29T10:24:06.290883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T10:24:06.459616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
suomesta 85
75.9%
ulkomailta 25
 
22.3%
finland 1
 
0.9%
abroad 1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
a 137
14.1%
o 111
11.4%
m 110
11.3%
t 110
11.3%
S 85
8.7%
e 85
8.7%
s 85
8.7%
u 85
8.7%
l 51
 
5.2%
i 26
 
2.7%
Other values (10) 90
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 137
14.1%
o 111
11.4%
m 110
11.3%
t 110
11.3%
S 85
8.7%
e 85
8.7%
s 85
8.7%
u 85
8.7%
l 51
 
5.2%
i 26
 
2.7%
Other values (10) 90
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 137
14.1%
o 111
11.4%
m 110
11.3%
t 110
11.3%
S 85
8.7%
e 85
8.7%
s 85
8.7%
u 85
8.7%
l 51
 
5.2%
i 26
 
2.7%
Other values (10) 90
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 137
14.1%
o 111
11.4%
m 110
11.3%
t 110
11.3%
S 85
8.7%
e 85
8.7%
s 85
8.7%
u 85
8.7%
l 51
 
5.2%
i 26
 
2.7%
Other values (10) 90
9.2%

Työpaikka
Text

Missing 

Distinct101
Distinct (%)59.1%
Missing560
Missing (%)76.6%
Memory size11.4 KiB
2024-10-29T10:24:06.712010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length41
Median length23
Mean length8.871345
Min length2

Characters and Unicode

Total characters1517
Distinct characters65
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

Unique78 ?
Unique (%)45.6%

Sample

1st rowPieni tuotetalo
2nd rowCompile
3rd rowExove
4th rowFuturice
5th rowKnowit
ValueCountFrequency (%)
relex 17
 
7.9%
reaktor 12
 
5.6%
solutions 12
 
5.6%
solita 11
 
5.1%
futurice 7
 
3.2%
vincit 6
 
2.8%
compile 5
 
2.3%
mavericks 5
 
2.3%
tietoevry 5
 
2.3%
sok 4
 
1.9%
Other values (101) 132
61.1%
2024-10-29T10:24:07.236894image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 137
 
9.0%
e 134
 
8.8%
o 131
 
8.6%
t 111
 
7.3%
a 109
 
7.2%
l 94
 
6.2%
n 81
 
5.3%
r 74
 
4.9%
s 49
 
3.2%
S 48
 
3.2%
Other values (55) 549
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1517
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 137
 
9.0%
e 134
 
8.8%
o 131
 
8.6%
t 111
 
7.3%
a 109
 
7.2%
l 94
 
6.2%
n 81
 
5.3%
r 74
 
4.9%
s 49
 
3.2%
S 48
 
3.2%
Other values (55) 549
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1517
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 137
 
9.0%
e 134
 
8.8%
o 131
 
8.6%
t 111
 
7.3%
a 109
 
7.2%
l 94
 
6.2%
n 81
 
5.3%
r 74
 
4.9%
s 49
 
3.2%
S 48
 
3.2%
Other values (55) 549
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1517
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 137
 
9.0%
e 134
 
8.8%
o 131
 
8.6%
t 111
 
7.3%
a 109
 
7.2%
l 94
 
6.2%
n 81
 
5.3%
r 74
 
4.9%
s 49
 
3.2%
S 48
 
3.2%
Other values (55) 549
36.2%

Kaupunki
Categorical

High correlation  Imbalance  Missing 

Distinct28
Distinct (%)4.5%
Missing115
Missing (%)15.7%
Memory size7.7 KiB
PK-seutu
351 
Tampere
118 
Turku
69 
Oulu
 
23
Jyväskylä
 
16
Kuopio
 
5
Vaasa
 
4
Etätyöfirma
 
3
Seinäjoki
 
3
Ulkomaat
 
2
Pori
 
2
New York
 
2
Lahti
 
2
Joensuu
 
2
Naantali
 
1
Berlin
 
1
Lappeenranta
 
1
Kokkola
 
1
Salo
 
1
San Francisco
 
1
Other values (8)
 
8

Length

Max length18
Median length8
Mean length7.3262987
Min length4

Characters and Unicode

Total characters4513
Distinct characters43
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

Unique14 ?
Unique (%)2.3%

Sample

1st rowPK-seutu
2nd rowPK-seutu
3rd rowPK-seutu
4th rowTampere
5th rowHelsinki & Tampere

Common Values

ValueCountFrequency (%)
PK-seutu 351
48.0%
Tampere 118
 
16.1%
Turku 69
 
9.4%
Oulu 23
 
3.1%
Jyväskylä 16
 
2.2%
Kuopio 5
 
0.7%
Vaasa 4
 
0.5%
Etätyöfirma 3
 
0.4%
Seinäjoki 3
 
0.4%
Ulkomaat 2
 
0.3%
Other values (18) 22
 
3.0%
(Missing) 115
 
15.7%

Length

2024-10-29T10:24:07.476019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu 351
56.4%
tampere 119
 
19.1%
turku 69
 
11.1%
oulu 23
 
3.7%
jyväskylä 16
 
2.6%
kuopio 5
 
0.8%
vaasa 4
 
0.6%
etätyöfirma 3
 
0.5%
seinäjoki 3
 
0.5%
york 2
 
0.3%
Other values (22) 27
 
4.3%

Most occurring characters

ValueCountFrequency (%)
u 895
19.8%
e 603
13.4%
s 376
8.3%
t 364
8.1%
K 357
 
7.9%
P 353
 
7.8%
- 351
 
7.8%
r 201
 
4.5%
T 190
 
4.2%
a 156
 
3.5%
Other values (33) 667
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 895
19.8%
e 603
13.4%
s 376
8.3%
t 364
8.1%
K 357
 
7.9%
P 353
 
7.8%
- 351
 
7.8%
r 201
 
4.5%
T 190
 
4.2%
a 156
 
3.5%
Other values (33) 667
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 895
19.8%
e 603
13.4%
s 376
8.3%
t 364
8.1%
K 357
 
7.9%
P 353
 
7.8%
- 351
 
7.8%
r 201
 
4.5%
T 190
 
4.2%
a 156
 
3.5%
Other values (33) 667
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 895
19.8%
e 603
13.4%
s 376
8.3%
t 364
8.1%
K 357
 
7.9%
P 353
 
7.8%
- 351
 
7.8%
r 201
 
4.5%
T 190
 
4.2%
a 156
 
3.5%
Other values (33) 667
14.8%

Millaisessa yrityksessä työskentelet?
Categorical

High correlation  Missing 

Distinct15
Distinct (%)2.4%
Missing102
Missing (%)14.0%
Memory size7.1 KiB
Konsulttitalossa
264 
Tuotetalossa, jonka core-bisnes on softa
203 
Yrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)
91 
Product company with software as their core business
29 
Julkinen tai kolmas sektori
 
17
Consulting
 
15
A company where software is support role (for example banks or healthcare)
 
2
Digitoimisto
 
1
Konsulttitalo, jolla on omaa tuotebisnestä
 
1
Mainostoimisto
 
1
Oma softa, oma rauta ja niiden yhteispeli, ei toista ilman toista
 
1
Teollisuus
 
1
Tuotetalossa, jonka keskeinen tarjonta on ohjelmisto
 
1
Ylläpitotalossa
 
1
henkilöstövuokraus (aka konsulttitalo)
 
1

Length

Max length74
Median length65
Mean length34.325914
Min length10

Characters and Unicode

Total characters21591
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

Unique8 ?
Unique (%)1.3%

Sample

1st rowYrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)
2nd rowYrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)
3rd rowKonsulttitalossa
4th rowTuotetalossa, jonka core-bisnes on softa
5th rowTuotetalossa, jonka core-bisnes on softa

Common Values

ValueCountFrequency (%)
Konsulttitalossa 264
36.1%
Tuotetalossa, jonka core-bisnes on softa 203
27.8%
Yrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms) 91
 
12.4%
Product company with software as their core business 29
 
4.0%
Julkinen tai kolmas sektori 17
 
2.3%
Consulting 15
 
2.1%
A company where software is support role (for example banks or healthcare) 2
 
0.3%
Digitoimisto 1
 
0.1%
Konsulttitalo, jolla on omaa tuotebisnestä 1
 
0.1%
Mainostoimisto 1
 
0.1%
Other values (5) 5
 
0.7%
(Missing) 102
 
14.0%

Length

2024-10-29T10:24:07.696879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
on 296
11.6%
softa 295
11.5%
konsulttitalossa 264
 
10.3%
jonka 204
 
8.0%
tuotetalossa 204
 
8.0%
core-bisnes 203
 
7.9%
esim 91
 
3.6%
yms 91
 
3.6%
terveysala 91
 
3.6%
pankit 91
 
3.6%
Other values (48) 727
28.4%

Most occurring characters

ValueCountFrequency (%)
s 2849
13.2%
o 2406
11.1%
t 2237
10.4%
a 2044
9.5%
1928
 
8.9%
n 1286
 
6.0%
e 1243
 
5.8%
i 1100
 
5.1%
l 893
 
4.1%
u 659
 
3.1%
Other values (31) 4946
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 2849
13.2%
o 2406
11.1%
t 2237
10.4%
a 2044
9.5%
1928
 
8.9%
n 1286
 
6.0%
e 1243
 
5.8%
i 1100
 
5.1%
l 893
 
4.1%
u 659
 
3.1%
Other values (31) 4946
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 2849
13.2%
o 2406
11.1%
t 2237
10.4%
a 2044
9.5%
1928
 
8.9%
n 1286
 
6.0%
e 1243
 
5.8%
i 1100
 
5.1%
l 893
 
4.1%
u 659
 
3.1%
Other values (31) 4946
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 2849
13.2%
o 2406
11.1%
t 2237
10.4%
a 2044
9.5%
1928
 
8.9%
n 1286
 
6.0%
e 1243
 
5.8%
i 1100
 
5.1%
l 893
 
4.1%
u 659
 
3.1%
Other values (31) 4946
22.9%

Työaika
Real number (ℝ)

High correlation  Missing 

Distinct19
Distinct (%)3.0%
Missing100
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean0.97979391
Minimum0
Maximum1.15
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:07.885383image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q11
median1
Q31
95-th percentile1
Maximum1.15
Range1.15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.093186756
Coefficient of variation (CV)0.095108527
Kurtosis41.044076
Mean0.97979391
Median Absolute Deviation (MAD)0
Skewness-5.8586039
Sum618.24996
Variance0.0086837715
MonotonicityNot monotonic
2024-10-29T10:24:08.086495image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 579
79.2%
0.8 20
 
2.7%
0.4 5
 
0.7%
0.9 4
 
0.5%
0.97 3
 
0.4%
0.6 3
 
0.4%
0.5 2
 
0.3%
0.7 2
 
0.3%
1.05 2
 
0.3%
1.07 2
 
0.3%
Other values (9) 9
 
1.2%
(Missing) 100
 
13.7%
ValueCountFrequency (%)
0 1
 
0.1%
0.2 1
 
0.1%
0.4 5
 
0.7%
0.5 2
 
0.3%
0.6 3
 
0.4%
0.67 1
 
0.1%
0.7 2
 
0.3%
0.75 1
 
0.1%
0.7733 1
 
0.1%
0.8 20
2.7%
ValueCountFrequency (%)
1.15 1
 
0.1%
1.07 2
 
0.3%
1.05 2
 
0.3%
1 579
79.2%
0.97 3
 
0.4%
0.96666 1
 
0.1%
0.93 1
 
0.1%
0.9 4
 
0.5%
0.86 1
 
0.1%
0.8 20
 
2.7%

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

High correlation  Missing  Zeros 

Distinct28
Distinct (%)4.5%
Missing102
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean0.3248903
Minimum0
Maximum1
Zeros104
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:08.294223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.2
Q30.5
95-th percentile0.974
Maximum1
Range1
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.32921893
Coefficient of variation (CV)1.0133233
Kurtosis-0.76259956
Mean0.3248903
Median Absolute Deviation (MAD)0.2
Skewness0.7905172
Sum204.356
Variance0.1083851
MonotonicityNot monotonic
2024-10-29T10:24:08.511098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 104
14.2%
0.2 81
11.1%
0.05 65
8.9%
0.1 58
7.9%
0.4 44
 
6.0%
0.5 40
 
5.5%
0.8 32
 
4.4%
0.9 31
 
4.2%
1 27
 
3.7%
0.6 26
 
3.6%
Other values (18) 121
16.6%
(Missing) 102
14.0%
ValueCountFrequency (%)
0 104
14.2%
0.001 1
 
0.1%
0.01 23
 
3.1%
0.02 7
 
1.0%
0.025 1
 
0.1%
0.03 4
 
0.5%
0.05 65
8.9%
0.1 58
7.9%
0.15 12
 
1.6%
0.2 81
11.1%
ValueCountFrequency (%)
1 27
3.7%
0.99 5
 
0.7%
0.95 13
1.8%
0.9 31
4.2%
0.85 2
 
0.3%
0.8 32
4.4%
0.75 8
 
1.1%
0.7 10
 
1.4%
0.67 1
 
0.1%
0.6 26
3.6%

Rooli
Text

Missing 

Distinct330
Distinct (%)53.6%
Missing115
Missing (%)15.7%
Memory size11.4 KiB
2024-10-29T10:24:08.789268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length88
Median length61
Mean length21.188312
Min length3

Characters and Unicode

Total characters13052
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

Unique256 ?
Unique (%)41.6%

Sample

1st rowSite Reliability Engineer
2nd rowFullstack developer
3rd rowLead
4th rowSoftware developer, fullstack Clojure/script
5th rowTechnical project manager
ValueCountFrequency (%)
developer 310
19.2%
software 188
 
11.6%
senior 138
 
8.5%
engineer 114
 
7.1%
stack 60
 
3.7%
full 58
 
3.6%
fullstack 52
 
3.2%
lead 50
 
3.1%
full-stack 37
 
2.3%
architect 36
 
2.2%
Other values (195) 572
35.4%
2024-10-29T10:24:09.337612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2019
15.5%
1016
 
7.8%
r 995
 
7.6%
o 859
 
6.6%
l 782
 
6.0%
t 756
 
5.8%
a 739
 
5.7%
n 670
 
5.1%
i 557
 
4.3%
d 398
 
3.0%
Other values (52) 4261
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2019
15.5%
1016
 
7.8%
r 995
 
7.6%
o 859
 
6.6%
l 782
 
6.0%
t 756
 
5.8%
a 739
 
5.7%
n 670
 
5.1%
i 557
 
4.3%
d 398
 
3.0%
Other values (52) 4261
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2019
15.5%
1016
 
7.8%
r 995
 
7.6%
o 859
 
6.6%
l 782
 
6.0%
t 756
 
5.8%
a 739
 
5.7%
n 670
 
5.1%
i 557
 
4.3%
d 398
 
3.0%
Other values (52) 4261
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2019
15.5%
1016
 
7.8%
r 995
 
7.6%
o 859
 
6.6%
l 782
 
6.0%
t 756
 
5.8%
a 739
 
5.7%
n 670
 
5.1%
i 557
 
4.3%
d 398
 
3.0%
Other values (52) 4261
32.6%

Kuukausipalkka
Real number (ℝ)

High correlation  Missing 

Distinct253
Distinct (%)39.8%
Missing96
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean5682.4376
Minimum0
Maximum15000
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:09.572309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3177.3
Q14612.5
median5500
Q36500
95-th percentile8630
Maximum15000
Range15000
Interquartile range (IQR)1887.5

Descriptive statistics

Standard deviation1777.1336
Coefficient of variation (CV)0.31274141
Kurtosis3.8129866
Mean5682.4376
Median Absolute Deviation (MAD)1000
Skewness1.0059706
Sum3608347.9
Variance3158203.7
MonotonicityNot monotonic
2024-10-29T10:24:09.931610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500 27
 
3.7%
6000 26
 
3.6%
6500 23
 
3.1%
5000 18
 
2.5%
4500 17
 
2.3%
5700 15
 
2.1%
5800 15
 
2.1%
8000 12
 
1.6%
4000 12
 
1.6%
6300 12
 
1.6%
Other values (243) 458
62.7%
(Missing) 96
 
13.1%
ValueCountFrequency (%)
0 1
0.1%
560 1
0.1%
1200 1
0.1%
1360 1
0.1%
1432 1
0.1%
1557 1
0.1%
1560 1
0.1%
1700 1
0.1%
1890 1
0.1%
1936 1
0.1%
ValueCountFrequency (%)
15000 1
 
0.1%
14200 1
 
0.1%
13333 1
 
0.1%
13300 1
 
0.1%
12500 1
 
0.1%
12000 3
0.4%
11500 2
0.3%
11166 1
 
0.1%
10722 1
 
0.1%
10250 1
 
0.1%

Vuositulot
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct244
Distinct (%)43.8%
Missing174
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean72447.031
Minimum0
Maximum550000
Zeros15
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:10.163129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4160
Q155300
median70000
Q384000
95-th percentile121000
Maximum550000
Range550000
Interquartile range (IQR)28700

Descriptive statistics

Standard deviation41307.478
Coefficient of variation (CV)0.57017489
Kurtosis46.494541
Mean72447.031
Median Absolute Deviation (MAD)14500
Skewness4.6135736
Sum40352996
Variance1.7063077 × 109
MonotonicityNot monotonic
2024-10-29T10:24:10.392326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75000 21
 
2.9%
80000 17
 
2.3%
100000 16
 
2.2%
0 15
 
2.1%
60000 14
 
1.9%
78000 14
 
1.9%
70000 13
 
1.8%
72000 13
 
1.8%
90000 12
 
1.6%
55000 10
 
1.4%
Other values (234) 412
56.4%
(Missing) 174
23.8%
ValueCountFrequency (%)
0 15
2.1%
70 1
 
0.1%
72 1
 
0.1%
150 1
 
0.1%
200 2
 
0.3%
472 1
 
0.1%
500 1
 
0.1%
750 1
 
0.1%
1000 1
 
0.1%
2000 1
 
0.1%
ValueCountFrequency (%)
550000 1
0.1%
462000 1
0.1%
260000 1
0.1%
220000 1
0.1%
200000 2
0.3%
180000 1
0.1%
170000 2
0.3%
167000 1
0.1%
166000 1
0.1%
160000 2
0.3%

Vapaa kuvaus kokonaiskompensaatiomallista
Unsupported

Missing  Rejected  Unsupported 

Missing488
Missing (%)66.8%
Memory size11.4 KiB
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
Kyllä
413 
Ei
164 
Muu
154 

Length

Max length5
Median length5
Mean length3.9056088
Min length2

Characters and Unicode

Total characters2855
Distinct characters8
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 rowKyllä
2nd rowKyllä
3rd rowKyllä
4th rowKyllä
5th rowKyllä

Common Values

ValueCountFrequency (%)
Kyllä 413
56.5%
Ei 164
 
22.4%
Muu 154
 
21.1%

Length

2024-10-29T10:24:10.612776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T10:24:10.790638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
kyllä 413
56.5%
ei 164
 
22.4%
muu 154
 
21.1%

Most occurring characters

ValueCountFrequency (%)
l 826
28.9%
K 413
14.5%
y 413
14.5%
ä 413
14.5%
u 308
 
10.8%
E 164
 
5.7%
i 164
 
5.7%
M 154
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 826
28.9%
K 413
14.5%
y 413
14.5%
ä 413
14.5%
u 308
 
10.8%
E 164
 
5.7%
i 164
 
5.7%
M 154
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 826
28.9%
K 413
14.5%
y 413
14.5%
ä 413
14.5%
u 308
 
10.8%
E 164
 
5.7%
i 164
 
5.7%
M 154
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 826
28.9%
K 413
14.5%
y 413
14.5%
ä 413
14.5%
u 308
 
10.8%
E 164
 
5.7%
i 164
 
5.7%
M 154
 
5.4%
Distinct42
Distinct (%)97.7%
Missing688
Missing (%)94.1%
Memory size11.4 KiB
2024-10-29T10:24:11.048731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length119
Median length57
Mean length50.069767
Min length3

Characters and Unicode

Total characters2153
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)95.3%

Sample

1st rowSiinä ja siinä
2nd rowVarmaan saman kuin muut sisäisesti mutta vaihtamalla voisi parantua
3rd rowPerhe tulee toimeen, en tiedä mitä muut saavat enkä ole erityisen kiinnostunut
4th rowVarmaan sais muualla enemmän palakkaa, mutta stonksit ihan jees.
5th rowSamanlaisiin konsulttitaloihin verrattuna about sama, tuotetalossa saisin enemmän
ValueCountFrequency (%)
ei 10
 
3.4%
mutta 9
 
3.1%
en 7
 
2.4%
on 7
 
2.4%
ja 6
 
2.1%
verrattuna 6
 
2.1%
kuin 5
 
1.7%
enemmän 4
 
1.4%
sanoa 4
 
1.4%
kyllä 4
 
1.4%
Other values (196) 229
78.7%
2024-10-29T10:24:11.585532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
249
11.6%
a 244
11.3%
i 204
 
9.5%
t 157
 
7.3%
n 150
 
7.0%
e 137
 
6.4%
s 134
 
6.2%
o 109
 
5.1%
l 102
 
4.7%
k 90
 
4.2%
Other values (46) 577
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
249
11.6%
a 244
11.3%
i 204
 
9.5%
t 157
 
7.3%
n 150
 
7.0%
e 137
 
6.4%
s 134
 
6.2%
o 109
 
5.1%
l 102
 
4.7%
k 90
 
4.2%
Other values (46) 577
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
249
11.6%
a 244
11.3%
i 204
 
9.5%
t 157
 
7.3%
n 150
 
7.0%
e 137
 
6.4%
s 134
 
6.2%
o 109
 
5.1%
l 102
 
4.7%
k 90
 
4.2%
Other values (46) 577
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
249
11.6%
a 244
11.3%
i 204
 
9.5%
t 157
 
7.3%
n 150
 
7.0%
e 137
 
6.4%
s 134
 
6.2%
o 109
 
5.1%
l 102
 
4.7%
k 90
 
4.2%
Other values (46) 577
26.8%

Vapaa sana
Text

Missing 

Distinct32
Distinct (%)100.0%
Missing699
Missing (%)95.6%
Memory size11.4 KiB
2024-10-29T10:24:11.874938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length282
Median length87.5
Mean length90.75
Min length1

Characters and Unicode

Total characters2904
Distinct characters64
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 (%)100.0%

Sample

1st rowkiitos
2nd rowPenkki pelottaa
3rd rowMinulla on luultavasti tällä hetkellä poikkeuksellisen hyvä job fit, eikä esim. Suomesta luultavasti löydy juuri ketään minua paremmin tähän työnkuvaan sopivaa henkilöä.
4th rowRiippuu paljon serttei suorittaa, nii saa enempi bonareit
5th rowNykyinen työpaikka on kolmas oman alan työpaikka. Nykyisessä paikassa olen ollut viimeiset 9 vuotta. Tämä on saattanut vaikuttaa palkka kehitykseen negatiivisesti.
ValueCountFrequency (%)
ja 9
 
2.3%
on 9
 
2.3%
ei 5
 
1.3%
paljon 3
 
0.8%
en 3
 
0.8%
3
 
0.8%
was 3
 
0.8%
noin 3
 
0.8%
vuosi 3
 
0.8%
mitä 3
 
0.8%
Other values (307) 343
88.6%
2024-10-29T10:24:12.387793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
356
12.3%
a 285
 
9.8%
i 254
 
8.7%
t 213
 
7.3%
n 192
 
6.6%
e 185
 
6.4%
s 174
 
6.0%
l 154
 
5.3%
k 143
 
4.9%
o 143
 
4.9%
Other values (54) 805
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
356
12.3%
a 285
 
9.8%
i 254
 
8.7%
t 213
 
7.3%
n 192
 
6.6%
e 185
 
6.4%
s 174
 
6.0%
l 154
 
5.3%
k 143
 
4.9%
o 143
 
4.9%
Other values (54) 805
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
356
12.3%
a 285
 
9.8%
i 254
 
8.7%
t 213
 
7.3%
n 192
 
6.6%
e 185
 
6.4%
s 174
 
6.0%
l 154
 
5.3%
k 143
 
4.9%
o 143
 
4.9%
Other values (54) 805
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
356
12.3%
a 285
 
9.8%
i 254
 
8.7%
t 213
 
7.3%
n 192
 
6.6%
e 185
 
6.4%
s 174
 
6.0%
l 154
 
5.3%
k 143
 
4.9%
o 143
 
4.9%
Other values (54) 805
27.7%

Palaute
Text

Missing 

Distinct39
Distinct (%)100.0%
Missing692
Missing (%)94.7%
Memory size11.4 KiB
2024-10-29T10:24:12.642401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length449
Median length89
Mean length95.410256
Min length1

Characters and Unicode

Total characters3721
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st rowHyvä kysely, mukava pingi
2nd rowOlisi mukavaa lukea millaisia etuuksia rahan lisäksi työntekijät nauttivat
3rd rowHarmaa seuraava-nappi näyttää disabloidulta 😁
4th rowOlisin laittanut kuukausipalkka kysymyksen ennen kysymystä palkan muutoksesta, se olisi tuntunut loogisemmalta.
5th rowHyvä setti kyssäreitä!
ValueCountFrequency (%)
voisi 10
 
2.2%
ja 7
 
1.5%
olisi 6
 
1.3%
hyvä 5
 
1.1%
on 5
 
1.1%
5
 
1.1%
the 4
 
0.9%
be 4
 
0.9%
ei 4
 
0.9%
esim 4
 
0.9%
Other values (357) 409
88.3%
2024-10-29T10:24:13.125454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
428
11.5%
a 380
 
10.2%
i 323
 
8.7%
t 314
 
8.4%
s 258
 
6.9%
e 233
 
6.3%
n 223
 
6.0%
o 203
 
5.5%
k 172
 
4.6%
l 164
 
4.4%
Other values (56) 1023
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
428
11.5%
a 380
 
10.2%
i 323
 
8.7%
t 314
 
8.4%
s 258
 
6.9%
e 233
 
6.3%
n 223
 
6.0%
o 203
 
5.5%
k 172
 
4.6%
l 164
 
4.4%
Other values (56) 1023
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
428
11.5%
a 380
 
10.2%
i 323
 
8.7%
t 314
 
8.4%
s 258
 
6.9%
e 233
 
6.3%
n 223
 
6.0%
o 203
 
5.5%
k 172
 
4.6%
l 164
 
4.4%
Other values (56) 1023
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
428
11.5%
a 380
 
10.2%
i 323
 
8.7%
t 314
 
8.4%
s 258
 
6.9%
e 233
 
6.3%
n 223
 
6.0%
o 203
 
5.5%
k 172
 
4.6%
l 164
 
4.4%
Other values (56) 1023
27.5%

Vastauskieli
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
fi
680 
en
 
51

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1462
Distinct characters4
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 rowfi
2nd rowfi
3rd rowfi
4th rowfi
5th rowfi

Common Values

ValueCountFrequency (%)
fi 680
93.0%
en 51
 
7.0%

Length

2024-10-29T10:24:13.341960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T10:24:13.506142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
fi 680
93.0%
en 51
 
7.0%

Most occurring characters

ValueCountFrequency (%)
f 680
46.5%
i 680
46.5%
e 51
 
3.5%
n 51
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 680
46.5%
i 680
46.5%
e 51
 
3.5%
n 51
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 680
46.5%
i 680
46.5%
e 51
 
3.5%
n 51
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 680
46.5%
i 680
46.5%
e 51
 
3.5%
n 51
 
3.5%

Vastaustunniste
Text

Unique 

Distinct731
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:13.715768image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters11696
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

Unique731 ?
Unique (%)100.0%

Sample

1st row0cc7d7ce7d418c98
2nd row5930ded0f016a9ef
3rd row6a83c419393c1121
4th row2d2b3fbfcf27aa0b
5th row6b6dc4394a98cd4d
ValueCountFrequency (%)
0cc7d7ce7d418c98 1
 
0.1%
02f606aceff634e0 1
 
0.1%
999ea6df4bdaab5e 1
 
0.1%
6a83c419393c1121 1
 
0.1%
2d2b3fbfcf27aa0b 1
 
0.1%
6b6dc4394a98cd4d 1
 
0.1%
1a074cdc4fa2e773 1
 
0.1%
1296a91aa718e707 1
 
0.1%
e9678f135fec1969 1
 
0.1%
cbfd353e42764f94 1
 
0.1%
Other values (721) 721
98.6%
2024-10-29T10:24:14.167223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 761
 
6.5%
a 758
 
6.5%
8 756
 
6.5%
d 751
 
6.4%
2 744
 
6.4%
c 742
 
6.3%
6 734
 
6.3%
e 729
 
6.2%
4 726
 
6.2%
0 724
 
6.2%
Other values (6) 4271
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 761
 
6.5%
a 758
 
6.5%
8 756
 
6.5%
d 751
 
6.4%
2 744
 
6.4%
c 742
 
6.3%
6 734
 
6.3%
e 729
 
6.2%
4 726
 
6.2%
0 724
 
6.2%
Other values (6) 4271
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 761
 
6.5%
a 758
 
6.5%
8 756
 
6.5%
d 751
 
6.4%
2 744
 
6.4%
c 742
 
6.3%
6 734
 
6.3%
e 729
 
6.2%
4 726
 
6.2%
0 724
 
6.2%
Other values (6) 4271
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 761
 
6.5%
a 758
 
6.5%
8 756
 
6.5%
d 751
 
6.4%
2 744
 
6.4%
c 742
 
6.3%
6 734
 
6.3%
e 729
 
6.2%
4 726
 
6.2%
0 724
 
6.2%
Other values (6) 4271
36.5%
Distinct305
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:14.463890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length88
Median length70
Mean length17.560876
Min length0

Characters and Unicode

Total characters12837
Distinct characters63
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

Unique244 ?
Unique (%)33.4%

Sample

1st row
2nd rowSite Reliability Engineer
3rd row*Full-stack Developer
4th rowLead
5th rowSoftware developer, fullstack Clojure/script
ValueCountFrequency (%)
developer 316
20.5%
software 147
 
9.6%
senior 138
 
9.0%
engineer 113
 
7.3%
full-stack 98
 
6.4%
lead 50
 
3.3%
architect 36
 
2.3%
frontend 30
 
2.0%
fullstack 26
 
1.7%
stack 25
 
1.6%
Other values (195) 559
36.3%
2024-10-29T10:24:15.006844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1986
15.5%
r 959
 
7.5%
931
 
7.3%
o 823
 
6.4%
l 787
 
6.1%
t 712
 
5.5%
a 698
 
5.4%
n 668
 
5.2%
i 554
 
4.3%
p 385
 
3.0%
Other values (53) 4334
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1986
15.5%
r 959
 
7.5%
931
 
7.3%
o 823
 
6.4%
l 787
 
6.1%
t 712
 
5.5%
a 698
 
5.4%
n 668
 
5.2%
i 554
 
4.3%
p 385
 
3.0%
Other values (53) 4334
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1986
15.5%
r 959
 
7.5%
931
 
7.3%
o 823
 
6.4%
l 787
 
6.1%
t 712
 
5.5%
a 698
 
5.4%
n 668
 
5.2%
i 554
 
4.3%
p 385
 
3.0%
Other values (53) 4334
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1986
15.5%
r 959
 
7.5%
931
 
7.3%
o 823
 
6.4%
l 787
 
6.1%
t 712
 
5.5%
a 698
 
5.4%
n 668
 
5.2%
i 554
 
4.3%
p 385
 
3.0%
Other values (53) 4334
33.8%

Kk-tulot (laskennallinen)
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct244
Distinct (%)43.8%
Missing174
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean6037.2525
Minimum0
Maximum45833.333
Zeros15
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:15.242480image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile346.66667
Q14608.3333
median5833.3333
Q37000
95-th percentile10083.333
Maximum45833.333
Range45833.333
Interquartile range (IQR)2391.6667

Descriptive statistics

Standard deviation3442.2898
Coefficient of variation (CV)0.57017489
Kurtosis46.494541
Mean6037.2525
Median Absolute Deviation (MAD)1208.3333
Skewness4.6135736
Sum3362749.7
Variance11849359
MonotonicityNot monotonic
2024-10-29T10:24:15.475706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6250 21
 
2.9%
6666.666667 17
 
2.3%
8333.333333 16
 
2.2%
0 15
 
2.1%
5000 14
 
1.9%
6500 14
 
1.9%
5833.333333 13
 
1.8%
6000 13
 
1.8%
7500 12
 
1.6%
4583.333333 10
 
1.4%
Other values (234) 412
56.4%
(Missing) 174
23.8%
ValueCountFrequency (%)
0 15
2.1%
5.833333333 1
 
0.1%
6 1
 
0.1%
12.5 1
 
0.1%
16.66666667 2
 
0.3%
39.33333333 1
 
0.1%
41.66666667 1
 
0.1%
62.5 1
 
0.1%
83.33333333 1
 
0.1%
166.6666667 1
 
0.1%
ValueCountFrequency (%)
45833.33333 1
0.1%
38500 1
0.1%
21666.66667 1
0.1%
18333.33333 1
0.1%
16666.66667 2
0.3%
15000 1
0.1%
14166.66667 2
0.3%
13916.66667 1
0.1%
13833.33333 1
0.1%
13333.33333 2
0.3%

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

High correlation  Missing  Zeros 

Distinct251
Distinct (%)45.2%
Missing176
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean6025.8381
Minimum0
Maximum38500
Zeros15
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size11.4 KiB
2024-10-29T10:24:15.705503image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile345
Q14666.6667
median5900
Q37000
95-th percentile9766.6667
Maximum38500
Range38500
Interquartile range (IQR)2333.3333

Descriptive statistics

Standard deviation2985.3729
Coefficient of variation (CV)0.49542866
Kurtosis27.517303
Mean6025.8381
Median Absolute Deviation (MAD)1183.3333
Skewness2.9321302
Sum3344340.2
Variance8912451.3
MonotonicityNot monotonic
2024-10-29T10:24:15.956575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6250 22
 
3.0%
6666.666667 17
 
2.3%
0 15
 
2.1%
5833.333333 15
 
2.1%
8333.333333 15
 
2.1%
6500 14
 
1.9%
6000 13
 
1.8%
7500 12
 
1.6%
5000 12
 
1.6%
5416.666667 10
 
1.4%
Other values (241) 410
56.1%
(Missing) 176
24.1%
ValueCountFrequency (%)
0 15
2.1%
5.833333333 1
 
0.1%
6 1
 
0.1%
12.5 1
 
0.1%
16.66666667 2
 
0.3%
41.66666667 1
 
0.1%
62.5 1
 
0.1%
65.55555556 1
 
0.1%
83.33333333 1
 
0.1%
185.1851852 1
 
0.1%
ValueCountFrequency (%)
38500 1
0.1%
21666.66667 1
0.1%
18333.33333 1
0.1%
17777.77778 1
0.1%
16666.66667 2
0.3%
15000 1
0.1%
14166.66667 2
0.3%
13916.66667 1
0.1%
13333.33333 1
0.1%
13083.33333 1
0.1%

Interactions

2024-10-29T10:23:56.116741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:39.701725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:41.442697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:43.236806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:44.758529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:46.153071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:47.585512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:49.241941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:51.046964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:52.741362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:54.345044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:56.291262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:39.877898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:41.619076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:43.365452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:44.886271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:46.286993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:47.750239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:49.506455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:51.218565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:52.903794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:54.506343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:56.471756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:40.056910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:41.800756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:43.601658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:45.023900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:46.428421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:47.923616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:49.680123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:51.395673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:53.067177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:54.685081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:56.600457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:40.184927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:41.935173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:43.728376image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:45.154203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:46.564421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:48.049077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:49.810797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:51.523086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:53.192980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:54.812778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:56.724705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:40.311149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:42.068637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:43.858925image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:45.278848image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:46.698882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:48.171683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:49.937447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:51.644812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:53.315439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:54.936276image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:56.848908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:40.455109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:42.211607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:43.998825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:45.410900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:46.841223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:48.296092image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:50.066581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:51.768404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:53.439429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:55.060014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:57.006427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:40.619399image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:42.380087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:44.122275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:45.531826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:46.963942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:48.451443image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:50.226138image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:51.931412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:53.578672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:55.216258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:57.173941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:40.784843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:42.550181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:44.254955image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:45.659477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:47.091116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:48.609922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:50.382806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:52.096589image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:53.728808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:55.383936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:57.349158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:40.956781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:42.723879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:44.379291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:45.783613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:47.214138image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:48.773040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:50.551814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:52.261843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:53.890487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:55.655481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:57.503073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:41.114283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:42.876664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:44.504771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:45.904346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:47.337714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:48.910808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:50.697441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:52.417564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:54.034657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:55.801529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:57.682472image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:41.264587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:43.055437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:44.630838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:46.029018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:47.459978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:49.075902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:50.871872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:52.565932image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:54.178022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-29T10:23:55.939825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-10-29T10:24:16.154320image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?IkäKaupunkiKk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)Kuinka suuren osan ajasta teet lähityönä toimistolla?KuukausipalkkaMillaisessa yrityksessä työskentelet?Mistä asiakkaat ovat?Montako vuotta olet tehnyt laskuttavaa työtä alalla?Oletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2023 jälkeen?Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?Palkansaaja vai laskuttajaSukupuoliTulojen muutos viime vuodesta (%)Tuntilaskutus (ALV 0%, euroina)TyöaikaTyökokemus alalta (vuosina)VastauskieliVuosilaskutus (ALV 0%, euroina)Vuositulot
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?1.0000.1060.0000.0000.0000.0000.0000.0000.5990.2930.0001.0001.0001.0000.0000.0000.0000.2560.9670.2400.000
Ikä0.1061.0000.0700.1100.1040.0000.2020.0000.1060.1020.0000.1030.0500.0530.1380.1480.1600.4430.0640.0000.110
Kaupunki0.0000.0701.0000.1540.1710.0610.4310.2320.0000.0000.0000.1091.0000.0000.0000.0000.1210.0600.0000.0000.154
Kk-tulot (laskennallinen)0.0000.1100.1541.0000.989-0.0310.8700.0000.000NaN0.0690.2571.0000.063-0.060NaN0.2590.4880.000NaN1.000
Kk-tulot (laskennallinen, normalisoitu)0.0000.1040.1710.9891.000-0.0320.8570.0000.000NaN0.1550.2191.0000.109-0.059NaN0.1610.4990.000NaN0.989
Kuinka suuren osan ajasta teet lähityönä toimistolla?0.0000.0000.061-0.031-0.0321.000-0.0330.1100.000NaN0.0900.0001.0000.0860.019NaN-0.0280.0400.000NaN-0.031
Kuukausipalkka0.0000.2020.4310.8700.857-0.0331.0000.0000.000NaN0.0000.3051.0000.121-0.083NaN0.2890.5830.000NaN0.870
Millaisessa yrityksessä työskentelet?0.0000.0000.2320.0000.0000.1100.0001.0000.0000.0000.0000.0861.0000.0000.0500.0000.1060.0350.9900.0000.000
Mistä asiakkaat ovat?0.5990.1060.0000.0000.0000.0000.0000.0001.0000.3520.3301.0001.0001.0000.0000.2710.0000.3910.4180.1910.000
Montako vuotta olet tehnyt laskuttavaa työtä alalla?0.2930.1020.000NaNNaNNaNNaN0.0000.3521.0000.2351.0001.0001.000-0.0380.066NaN0.1560.3530.253NaN
Oletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2023 jälkeen?0.0000.0000.0000.0690.1550.0900.0000.0000.3300.2351.0000.1730.2880.0000.3350.3830.0000.0250.0650.0000.069
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?1.0000.1030.1090.2570.2190.0000.3050.0861.0001.0000.1731.0000.7510.0800.1951.0000.0840.1800.0171.0000.257
Palkansaaja vai laskuttaja1.0000.0501.0001.0001.0001.0001.0001.0001.0001.0000.2880.7511.0000.1310.2361.0001.0000.2060.0001.0001.000
Sukupuoli1.0000.0530.0000.0630.1090.0860.1210.0001.0001.0000.0000.0800.1311.0000.0001.0000.2160.1610.1091.0000.063
Tulojen muutos viime vuodesta (%)0.0000.1380.000-0.060-0.0590.019-0.0830.0500.000-0.0380.3350.1950.2360.0001.000-0.062-0.051-0.2680.0220.020-0.060
Tuntilaskutus (ALV 0%, euroina)0.0000.1480.000NaNNaNNaNNaN0.0000.2710.0660.3831.0001.0001.000-0.0621.000NaN0.2140.0000.521NaN
Työaika0.0000.1600.1210.2590.161-0.0280.2890.1060.000NaN0.0000.0841.0000.216-0.051NaN1.0000.1170.000NaN0.259
Työkokemus alalta (vuosina)0.2560.4430.0600.4880.4990.0400.5830.0350.3910.1560.0250.1800.2060.161-0.2680.2140.1171.0000.0560.1700.488
Vastauskieli0.9670.0640.0000.0000.0000.0000.0000.9900.4180.3530.0650.0170.0000.1090.0220.0000.0000.0561.0000.1100.000
Vuosilaskutus (ALV 0%, euroina)0.2400.0000.000NaNNaNNaNNaN0.0000.1910.2530.0001.0001.0001.0000.0200.521NaN0.1700.1101.000NaN
Vuositulot0.0000.1100.1541.0000.989-0.0310.8700.0000.000NaN0.0690.2571.0000.063-0.060NaN0.2590.4880.000NaN1.000

Missing values

2024-10-29T10:23:58.005983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-29T10:23:58.683089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-29T10:23:59.419633image/svg+xmlMatplotlib v3.7.5, 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 laskuttajaOletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2023 jälkeen?IkäSukupuoliTyökokemus alalta (vuosina)KoulutustaustasiTulojen muutos viime vuodesta (%)Montako vuotta olet tehnyt laskuttavaa työtä alalla?PalvelutTuntilaskutus (ALV 0%, euroina)Vuosilaskutus (ALV 0%, euroina)Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?Mistä asiakkaat ovat?TyöpaikkaKaupunkiMillaisessa yrityksessä työskentelet?TyöaikaKuinka suuren osan ajasta teet lähityönä toimistolla?RooliKuukausipalkkaVuositulotVapaa kuvaus kokonaiskompensaatiomallistaOnko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? (muut vastaukset)Vapaa sanaPalauteVastauskieliVastaustunnisteRooli (normalisoitu)Kk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)
02024-10-07 10:05:39.380PalkansaajaEi46-50NaN25.0NaN3.0NaNNaNNaNNaNNaNNaNNaNPK-seutuYrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)1.00.50NaN8750.0110000.0Palkka + vuosibonusKylläNoneNaNNaNfi0cc7d7ce7d418c989166.6666679166.666667
12024-10-07 10:06:12.205PalkansaajaEi31-35mies11.0tietotekniikan kandidaatti2.0NaNNaNNaNNaNNaNNaNNaNPK-seutuYrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)1.00.10Site Reliability Engineer5100.065200.0NaNKylläNoneNaNNaNfi5930ded0f016a9efSite Reliability Engineer5433.3333335433.333333
22024-10-07 10:07:00.564PalkansaajaEi36-40mies12.0Ylioppilas0.0NaNNaNNaNNaNNaNNaNNaNPK-seutuKonsulttitalossa1.00.05Fullstack developer5850.0105000.0Palkka+osinkoKylläNoneNaNNaNfi6a83c419393c1121*Full-stack Developer8750.0000008750.000000
32024-10-07 10:07:20.023PalkansaajaEi41-45mies15.0DINaNNaNNaNNaNNaNNaNNaNNaNTampereTuotetalossa, jonka core-bisnes on softa1.00.30Lead6800.081600.0RahapalkkaKylläNoneNaNNaNfi2d2b3fbfcf27aa0bLead6800.0000006800.000000
42024-10-07 10:07:35.416PalkansaajaEi41-45nainen9.0IT-tradenomi0.0NaNNaNNaNNaNNaNNaNNaNHelsinki & TampereTuotetalossa, jonka core-bisnes on softa1.00.20Software developer, fullstack Clojure/script4730.061500.0NaNKylläNoneNaNNaNfi6b6dc4394a98cd4dSoftware developer, fullstack Clojure/script5125.0000005125.000000
52024-10-07 10:07:54.251PalkansaajaEi41-45mies14.0NaN-5.0NaNNaNNaNNaNNaNNaNPieni tuotetaloPK-seutuTuotetalossa, jonka core-bisnes on softa1.00.50Technical project manager6660.083250.0NaNKylläNoneNaNNaNfi1a074cdc4fa2e773Technical project manager6937.5000006937.500000
62024-10-07 10:08:41.143PalkansaajaEi31-35mies13.0FM (Tietojenkäsittelytiede)0.0NaNNaNNaNNaNNaNNaNCompilePK-seutuKonsulttitalossa1.00.15Software Specialist7200.090000.0Henkilöstörahasto bonuksilleMuuSiinä ja siinäNaNNaNfi1296a91aa718e707Software Specialist7500.0000007500.000000
72024-10-07 10:08:48.660PalkansaajaEi36-40mies20.0DI-100.0NaNNaNNaNNaNNaNNaNNaNPK-seutuTuotetalossa, jonka core-bisnes on softa1.01.00CTO0.00.0omistajaEiNonekiitosNaNfie9678f135fec1969CTO0.0000000.000000
82024-10-07 10:08:54.790PalkansaajaEi36-40mies15.0Insinööri (AMK)0.0NaNNaNNaNNaNNaNNaNExovePK-seutuKonsulttitalossa1.00.01Senior Developer4300.0NaNNaNEiNoneNaNNaNficbfd353e42764f94*Senior DeveloperNaNNaN
92024-10-07 10:09:26.635LaskuttajaEi31-35mies10.0Lukio, alkaneet tietojenkäsittelytieteen yliopisto-opinnot mutta ei papereita-20.05.0Full stack, tech lead, AWS, pilvi, devops85.0NaNItse, Käytän välitysfirmojaSuomestaNaNNaNNaNNaNNaNNaNNaNNaNNaNMuuNaNNaNNaNfi5538dd6fa67da2c3NaNNaN
TimestampPalkansaaja vai laskuttajaOletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2023 jälkeen?IkäSukupuoliTyökokemus alalta (vuosina)KoulutustaustasiTulojen muutos viime vuodesta (%)Montako vuotta olet tehnyt laskuttavaa työtä alalla?PalvelutTuntilaskutus (ALV 0%, euroina)Vuosilaskutus (ALV 0%, euroina)Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?Mistä asiakkaat ovat?TyöpaikkaKaupunkiMillaisessa yrityksessä työskentelet?TyöaikaKuinka suuren osan ajasta teet lähityönä toimistolla?RooliKuukausipalkkaVuositulotVapaa kuvaus kokonaiskompensaatiomallistaOnko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? (muut vastaukset)Vapaa sanaPalauteVastauskieliVastaustunnisteRooli (normalisoitu)Kk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)
7232024-10-16 13:54:05.644PalkansaajaEi21-25NaN5.0BSc maths and computer science5.0NaNNaNNaNNaNNaNNaNNaNPK-seutuProduct company with software as their core business1.00.05Senior software developer5200.067600.0NaNMuuUnknownNaNNaNen0a439179d5ac5cef*Senior Developer5633.3333335633.333333
7242024-10-16 23:15:38.148PalkansaajaEi46-50mies19.0Beta science M.Sc.7.0NaNNaNNaNNaNNaNNaNNaNPK-seutuProduct company with software as their core business1.00.05Service delivery manager4566.058900.0Group level bonus 20% of 1 month salaryEiNoneNaNNaNenf73f7ab082ff3568Service delivery manager4908.3333334908.333333
7252024-10-21 09:26:01.660PalkansaajaEi36-40NaN10.0AMK0.0NaNNaNNaNNaNNaNNaNSanomajPK-seutuProduct company with software as their core business1.00.30IOS native developer6300.080000.0yearly optional perf bonusMuuNaNNaNNaNenefb0c3f33d9a799bIOS native developer6666.6666676666.666667
7262024-10-22 13:21:00.688PalkansaajaEi31-35nainen9.0IT-Tradenomi-30.0NaNNaNNaNNaNNaNNaNMavericks/WittedPK-seutuConsulting1.00.10Senior Mobile Developer1890.062680.0Commission-based salaryEiNoneNaNCould not add a minus in the percentage of salary change in the first page.en04967d62d23ef932Senior Mobile Developer5223.3333335223.333333
7272024-10-23 11:52:14.492PalkansaajaEi21-25mies2.0Bachelor of Science. Soon Master of Science10.0NaNNaNNaNNaNNaNNaNNaNTampereConsulting1.00.00Data Engineer3900.047200.0NaNEiNoneNaNNaNend2658f95965801dbData Engineer3933.3333333933.333333
7282024-10-23 11:59:41.857LaskuttajaEi31-35mies14.0BSc0.07.0Full-stack, AI, DevOps, architecture, cloud engineering, machine laerning95.0110000.0Company sales team.SuomestaNaNNaNNaNNaNNaNNaNNaNNaNNaNMuuNaNNaNNaNen9eb9c21bb24a20e7NaNNaN
7292024-10-23 11:59:43.865PalkansaajaEi31-35mies7.0University - Computer Science0.0NaNNaNNaNNaNNaNNaNNaNTampereConsulting1.00.90Fullstack developer5800.070000.0Base pay plus commission on billable hoursKylläNoneNaNNaNen3c994ce4487d3419*Full-stack Developer5833.3333335833.333333
7302024-10-23 13:45:09.504PalkansaajaEi26-30mies3.0Master of Science5.0NaNNaNNaNNaNNaNNaNNaNTampereConsulting1.00.90Software developer4670.055000.0Base salary + bonus per billable hour (consultancy)MuuYes, when working full time on a customer project, otherwise below averageNaNNaNene83d7c4a33da5c05Software developer4583.3333334583.333333
7312024-10-23 15:12:48.231PalkansaajaEi26-30mies3.0Master of Science in Biomedical Engineering5.0NaNNaNNaNNaNNaNNaNVeracellTampereConsulting1.00.90Data Engineer4700.058000.0Base monthly salary plus a provision part that is a fixed sum per billed working hour. The majority of the compensation comes from the base salary.KylläNoneNaNNaNen1060737903f8d7daData Engineer4833.3333334833.333333
7322024-10-25 17:35:10.523PalkansaajaEi31-35nainen4.0Amk (insinööri)0.0NaNNaNNaNNaNNaNNaNRoima IntelligencePK-seutuProduct company with software as their core business1.00.00Software developer3700.0NaNNaNEiNoneNaNNaNendbbc442636f62d81Software developerNaNNaN