Overview

Dataset statistics

Number of variables15
Number of observations430
Missing cells874
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.1 KiB
Average record size in memory95.5 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 229 distinct values High cardinality
Työpaikka has a high cardinality: 67 distinct values High cardinality
Vuositulot is highly correlated with Kk-tulotHigh correlation
Kk-tulot is highly correlated with VuositulotHigh correlation
Työpaikka is highly correlated with Vapaa sanaHigh correlation
Vapaa sana is highly correlated with Työpaikka and 1 other fieldsHigh correlation
Kilpailukykyinen is highly correlated with Vapaa sanaHigh correlation
Sukupuoli has 32 (7.4%) missing values Missing
Työaika has 16 (3.7%) missing values Missing
Rooli has 10 (2.3%) missing values Missing
Kuukausipalkka has 38 (8.8%) missing values Missing
Vuositulot has 11 (2.6%) missing values Missing
Kilpailukykyinen has 14 (3.3%) missing values Missing
Työpaikka has 331 (77.0%) missing values Missing
Vapaa sana has 397 (92.3%) missing values Missing
Kk-tulot has 11 (2.6%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-19 16:58:28.076285
Analysis finished2021-02-19 16:58:34.728921
Duration6.65 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct430
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-19 18:34:24.007000
2021-02-19T16:58:34.832831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:35.060762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

Distinct25
Distinct (%)5.9%
Missing4
Missing (%)0.9%
Memory size1.3 KiB
PK-Seutu
218 
Tampere
99 
Turku
44 
Oulu
23 
Jyväskylä
 
17
Other values (20)
25 

Length

Max length15
Median length8
Mean length7.244131455
Min length2

Characters and Unicode

Total characters3086
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)4.2%

Sample

1st rowPK-Seutu
2nd rowTurku
3rd rowPK-Seutu
4th rowTampere
5th rowPK-Seutu
ValueCountFrequency (%)
PK-Seutu218
50.7%
Tampere99
23.0%
Turku44
 
10.2%
Oulu23
 
5.3%
Jyväskylä17
 
4.0%
Kuopio5
 
1.2%
Pori2
 
0.5%
Ruotsi1
 
0.2%
Wien1
 
0.2%
Viimsi1
 
0.2%
Other values (15)15
 
3.5%
(Missing)4
 
0.9%
2021-02-19T16:58:35.617163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu218
50.7%
tampere99
23.0%
turku44
 
10.2%
oulu23
 
5.3%
jyväskylä17
 
4.0%
kuopio5
 
1.2%
pori2
 
0.5%
europe1
 
0.2%
hämeenlinna1
 
0.2%
viimsi1
 
0.2%
Other values (19)19
 
4.4%

Most occurring characters

ValueCountFrequency (%)
u582
18.9%
e425
13.8%
K226
 
7.3%
t223
 
7.2%
P221
 
7.2%
-220
 
7.1%
S220
 
7.1%
r149
 
4.8%
T144
 
4.7%
a116
 
3.8%
Other values (29)560
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1994
64.6%
Uppercase Letter867
28.1%
Dash Punctuation220
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u582
29.2%
e425
21.3%
t223
 
11.2%
r149
 
7.5%
a116
 
5.8%
p105
 
5.3%
m103
 
5.2%
k62
 
3.1%
l48
 
2.4%
ä41
 
2.1%
Other values (10)140
 
7.0%
ValueCountFrequency (%)
K226
26.1%
P221
25.5%
S220
25.4%
T144
16.6%
O23
 
2.7%
J18
 
2.1%
E3
 
0.3%
L3
 
0.3%
V2
 
0.2%
W1
 
0.1%
Other values (6)6
 
0.7%
ValueCountFrequency (%)
-220
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2861
92.7%
Common225
 
7.3%

Most frequent character per script

ValueCountFrequency (%)
u582
20.3%
e425
14.9%
K226
 
7.9%
t223
 
7.8%
P221
 
7.7%
S220
 
7.7%
r149
 
5.2%
T144
 
5.0%
a116
 
4.1%
p105
 
3.7%
Other values (26)450
15.7%
ValueCountFrequency (%)
-220
97.8%
4
 
1.8%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3045
98.7%
None41
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u582
19.1%
e425
14.0%
K226
 
7.4%
t223
 
7.3%
P221
 
7.3%
-220
 
7.2%
S220
 
7.2%
r149
 
4.9%
T144
 
4.7%
a116
 
3.8%
Other values (28)519
17.0%
ValueCountFrequency (%)
ä41
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.6%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean49.64018692
Minimum34
Maximum78
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-02-19T16:58:35.775721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile34
Q141
median48
Q356
95-th percentile64
Maximum78
Range44
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.122711292
Coefficient of variation (CV)0.1837767313
Kurtosis0.08057182662
Mean49.64018692
Median Absolute Deviation (MAD)7
Skewness0.53911042
Sum21246
Variance83.22386132
MonotocityNot monotonic
2021-02-19T16:58:35.911900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
48143
33.3%
41105
24.4%
5694
21.9%
6447
 
10.9%
3427
 
6.3%
717
 
1.6%
785
 
1.2%
(Missing)2
 
0.5%
ValueCountFrequency (%)
3427
 
6.3%
41105
24.4%
48143
33.3%
5694
21.9%
6447
 
10.9%
ValueCountFrequency (%)
785
 
1.2%
717
 
1.6%
6447
 
10.9%
5694
21.9%
48143
33.3%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.8%
Missing32
Missing (%)7.4%
Memory size690.0 B
mies
360 
nainen
 
30
muu
 
8

Length

Max length6
Median length4
Mean length4.130653266
Min length3

Characters and Unicode

Total characters1644
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 rowmies
5th rowmies
ValueCountFrequency (%)
mies360
83.7%
nainen30
 
7.0%
muu8
 
1.9%
(Missing)32
 
7.4%
2021-02-19T16:58:36.272608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-19T16:58:36.394641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies360
90.5%
nainen30
 
7.5%
muu8
 
2.0%

Most occurring characters

ValueCountFrequency (%)
i390
23.7%
e390
23.7%
m368
22.4%
s360
21.9%
n90
 
5.5%
a30
 
1.8%
u16
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1644
100.0%

Most frequent character per category

ValueCountFrequency (%)
i390
23.7%
e390
23.7%
m368
22.4%
s360
21.9%
n90
 
5.5%
a30
 
1.8%
u16
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1644
100.0%

Most frequent character per script

ValueCountFrequency (%)
i390
23.7%
e390
23.7%
m368
22.4%
s360
21.9%
n90
 
5.5%
a30
 
1.8%
u16
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1644
100.0%

Most frequent character per block

ValueCountFrequency (%)
i390
23.7%
e390
23.7%
m368
22.4%
s360
21.9%
n90
 
5.5%
a30
 
1.8%
u16
 
1.0%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)6.3%
Missing4
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean9.633802817
Minimum0
Maximum30
Zeros3
Zeros (%)0.7%
Memory size3.5 KiB
2021-02-19T16:58:36.524404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median9
Q313
95-th percentile21
Maximum30
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0474347
Coefficient of variation (CV)0.6277307948
Kurtosis-0.03089875099
Mean9.633802817
Median Absolute Deviation (MAD)4
Skewness0.7105708099
Sum4104
Variance36.57146645
MonotocityNot monotonic
2021-02-19T16:58:36.694405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
544
 
10.2%
1034
 
7.9%
429
 
6.7%
726
 
6.0%
1525
 
5.8%
2025
 
5.8%
324
 
5.6%
1323
 
5.3%
622
 
5.1%
222
 
5.1%
Other values (17)152
35.3%
ValueCountFrequency (%)
03
 
0.7%
115
3.5%
222
5.1%
324
5.6%
429
6.7%
ValueCountFrequency (%)
302
 
0.5%
255
1.2%
242
 
0.5%
234
0.9%
224
0.9%
Distinct3
Distinct (%)0.7%
Missing1
Missing (%)0.2%
Memory size3.5 KiB
Työntekijä / palkollinen
384 
Freelancer
 
23
Yrittäjä
 
22

Length

Max length24
Median length24
Mean length22.42890443
Min length8

Characters and Unicode

Total characters9622
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 rowTyöntekijä / palkollinen
2nd rowTyöntekijä / palkollinen
3rd rowTyöntekijä / palkollinen
4th rowYrittäjä
5th rowTyöntekijä / palkollinen
ValueCountFrequency (%)
Työntekijä / palkollinen384
89.3%
Freelancer23
 
5.3%
Yrittäjä22
 
5.1%
(Missing)1
 
0.2%
2021-02-19T16:58:37.078739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-19T16:58:37.216817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
384
32.1%
työntekijä384
32.1%
palkollinen384
32.1%
freelancer23
 
1.9%
yrittäjä22
 
1.8%

Most occurring characters

ValueCountFrequency (%)
n1175
12.2%
l1175
12.2%
e837
 
8.7%
i790
 
8.2%
k768
 
8.0%
768
 
8.0%
t428
 
4.4%
ä428
 
4.4%
a407
 
4.2%
j406
 
4.2%
Other values (10)2440
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8041
83.6%
Space Separator768
 
8.0%
Uppercase Letter429
 
4.5%
Other Punctuation384
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1175
14.6%
l1175
14.6%
e837
10.4%
i790
9.8%
k768
9.6%
t428
 
5.3%
ä428
 
5.3%
a407
 
5.1%
j406
 
5.0%
y384
 
4.8%
Other values (5)1243
15.5%
ValueCountFrequency (%)
T384
89.5%
F23
 
5.4%
Y22
 
5.1%
ValueCountFrequency (%)
768
100.0%
ValueCountFrequency (%)
/384
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8470
88.0%
Common1152
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1175
13.9%
l1175
13.9%
e837
9.9%
i790
9.3%
k768
9.1%
t428
 
5.1%
ä428
 
5.1%
a407
 
4.8%
j406
 
4.8%
T384
 
4.5%
Other values (8)1672
19.7%
ValueCountFrequency (%)
768
66.7%
/384
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8810
91.6%
None812
 
8.4%

Most frequent character per block

ValueCountFrequency (%)
n1175
13.3%
l1175
13.3%
e837
9.5%
i790
9.0%
k768
8.7%
768
8.7%
t428
 
4.9%
a407
 
4.6%
j406
 
4.6%
T384
 
4.4%
Other values (8)1672
19.0%
ValueCountFrequency (%)
ä428
52.7%
ö384
47.3%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.2%
Missing16
Missing (%)3.7%
Memory size3.5 KiB
1.0
390 
0.8
 
20
0.5
 
2
0.6
 
1
0.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1242
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0390
90.7%
0.820
 
4.7%
0.52
 
0.5%
0.61
 
0.2%
0.71
 
0.2%
(Missing)16
 
3.7%
2021-02-19T16:58:37.558491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-19T16:58:37.674794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0390
94.2%
0.820
 
4.8%
0.52
 
0.5%
0.61
 
0.2%
0.71
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.414
33.3%
0414
33.3%
1390
31.4%
820
 
1.6%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number828
66.7%
Other Punctuation414
33.3%

Most frequent character per category

ValueCountFrequency (%)
0414
50.0%
1390
47.1%
820
 
2.4%
52
 
0.2%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.414
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1242
100.0%

Most frequent character per script

ValueCountFrequency (%)
.414
33.3%
0414
33.3%
1390
31.4%
820
 
1.6%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1242
100.0%

Most frequent character per block

ValueCountFrequency (%)
.414
33.3%
0414
33.3%
1390
31.4%
820
 
1.6%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct229
Distinct (%)54.5%
Missing10
Missing (%)2.3%
Memory size3.5 KiB
Ohjelmistokehittäjä
33 
full-stack
31 
Full-stack
 
21
ohjelmistokehittäjä
 
15
Arkkitehti
 
15
Other values (224)
305 

Length

Max length67
Median length18
Mean length19.20238095
Min length2

Characters and Unicode

Total characters8065
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique185 ?
Unique (%)44.0%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä33
 
7.7%
full-stack31
 
7.2%
Full-stack21
 
4.9%
ohjelmistokehittäjä15
 
3.5%
Arkkitehti15
 
3.5%
Full-stack ohjelmistokehittäjä8
 
1.9%
full-stack ohjelmistokehittäjä6
 
1.4%
arkkitehti6
 
1.4%
DevOps5
 
1.2%
Frontend5
 
1.2%
Other values (219)275
64.0%
(Missing)10
 
2.3%
2021-02-19T16:58:38.148987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack128
 
16.6%
ohjelmistokehittäjä100
 
13.0%
developer52
 
6.7%
arkkitehti34
 
4.4%
30
 
3.9%
lead28
 
3.6%
frontend24
 
3.1%
senior18
 
2.3%
kehittäjä15
 
1.9%
backend14
 
1.8%
Other values (166)329
42.6%

Most occurring characters

ValueCountFrequency (%)
t845
 
10.5%
e738
 
9.2%
i595
 
7.4%
l588
 
7.3%
k453
 
5.6%
o419
 
5.2%
a387
 
4.8%
s386
 
4.8%
357
 
4.4%
h322
 
4.0%
Other values (47)2975
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7013
87.0%
Uppercase Letter397
 
4.9%
Space Separator358
 
4.4%
Dash Punctuation154
 
1.9%
Other Punctuation87
 
1.1%
Open Punctuation24
 
0.3%
Close Punctuation24
 
0.3%
Math Symbol8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t845
12.0%
e738
 
10.5%
i595
 
8.5%
l588
 
8.4%
k453
 
6.5%
o419
 
6.0%
a387
 
5.5%
s386
 
5.5%
h322
 
4.6%
j303
 
4.3%
Other values (16)1977
28.2%
ValueCountFrequency (%)
F90
22.7%
O80
20.2%
S45
11.3%
D39
9.8%
A24
 
6.0%
T21
 
5.3%
L17
 
4.3%
C13
 
3.3%
E11
 
2.8%
P10
 
2.5%
Other values (11)47
11.8%
ValueCountFrequency (%)
,50
57.5%
/33
37.9%
&3
 
3.4%
.1
 
1.1%
ValueCountFrequency (%)
357
99.7%
 1
 
0.3%
ValueCountFrequency (%)
-154
100.0%
ValueCountFrequency (%)
(24
100.0%
ValueCountFrequency (%)
)24
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7410
91.9%
Common655
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t845
 
11.4%
e738
 
10.0%
i595
 
8.0%
l588
 
7.9%
k453
 
6.1%
o419
 
5.7%
a387
 
5.2%
s386
 
5.2%
h322
 
4.3%
j303
 
4.1%
Other values (37)2374
32.0%
ValueCountFrequency (%)
357
54.5%
-154
23.5%
,50
 
7.6%
/33
 
5.0%
(24
 
3.7%
)24
 
3.7%
+8
 
1.2%
&3
 
0.5%
.1
 
0.2%
 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7761
96.2%
None304
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t845
 
10.9%
e738
 
9.5%
i595
 
7.7%
l588
 
7.6%
k453
 
5.8%
o419
 
5.4%
a387
 
5.0%
s386
 
5.0%
357
 
4.6%
h322
 
4.1%
Other values (44)2671
34.4%
ValueCountFrequency (%)
ä288
94.7%
ö15
 
4.9%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.7%
Missing3
Missing (%)0.7%
Memory size690.0 B
Etä
182 
Toimisto
143 
50/50
102 

Length

Max length8
Median length5
Mean length5.152224824
Min length3

Characters and Unicode

Total characters2200
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row50/50
2nd rowEtä
3rd rowEtä
4th rowEtä
5th rowEtä
ValueCountFrequency (%)
Etä182
42.3%
Toimisto143
33.3%
50/50102
23.7%
(Missing)3
 
0.7%
2021-02-19T16:58:38.697156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-19T16:58:38.816564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä182
42.6%
toimisto143
33.5%
50/50102
23.9%

Most occurring characters

ValueCountFrequency (%)
t325
14.8%
o286
13.0%
i286
13.0%
5204
9.3%
0204
9.3%
E182
8.3%
ä182
8.3%
T143
6.5%
m143
6.5%
s143
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1365
62.0%
Decimal Number408
 
18.5%
Uppercase Letter325
 
14.8%
Other Punctuation102
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
t325
23.8%
o286
21.0%
i286
21.0%
ä182
13.3%
m143
10.5%
s143
10.5%
ValueCountFrequency (%)
5204
50.0%
0204
50.0%
ValueCountFrequency (%)
E182
56.0%
T143
44.0%
ValueCountFrequency (%)
/102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1690
76.8%
Common510
 
23.2%

Most frequent character per script

ValueCountFrequency (%)
t325
19.2%
o286
16.9%
i286
16.9%
E182
10.8%
ä182
10.8%
T143
8.5%
m143
8.5%
s143
8.5%
ValueCountFrequency (%)
5204
40.0%
0204
40.0%
/102
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2018
91.7%
None182
 
8.3%

Most frequent character per block

ValueCountFrequency (%)
t325
16.1%
o286
14.2%
i286
14.2%
5204
10.1%
0204
10.1%
E182
9.0%
T143
7.1%
m143
7.1%
s143
7.1%
/102
 
5.1%
ValueCountFrequency (%)
ä182
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct117
Distinct (%)29.8%
Missing38
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean4668.260204
Minimum1666
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-02-19T16:58:38.967485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1666
5-th percentile2811
Q13800
median4500
Q35500
95-th percentile7000
Maximum15000
Range13334
Interquartile range (IQR)1700

Descriptive statistics

Standard deviation1321.308438
Coefficient of variation (CV)0.2830408718
Kurtosis8.884793734
Mean4668.260204
Median Absolute Deviation (MAD)765.5
Skewness1.47805712
Sum1829958
Variance1745855.988
MonotocityNot monotonic
2021-02-19T16:58:39.190954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400023
 
5.3%
450020
 
4.7%
600016
 
3.7%
550015
 
3.5%
500015
 
3.5%
480011
 
2.6%
700011
 
2.6%
430011
 
2.6%
420010
 
2.3%
300010
 
2.3%
Other values (107)250
58.1%
(Missing)38
 
8.8%
ValueCountFrequency (%)
16661
0.2%
17001
0.2%
18001
0.2%
21001
0.2%
22751
0.2%
ValueCountFrequency (%)
150001
 
0.2%
85001
 
0.2%
80005
1.2%
75002
 
0.5%
72001
 
0.2%

Vuositulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct168
Distinct (%)40.1%
Missing11
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean65194.07279
Minimum0
Maximum250000
Zeros2
Zeros (%)0.5%
Memory size3.5 KiB
2021-02-19T16:58:39.402570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35000
Q150000
median60000
Q375000
95-th percentile120000
Maximum250000
Range250000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation28613.15468
Coefficient of variation (CV)0.4388919644
Kurtosis8.424708087
Mean65194.07279
Median Absolute Deviation (MAD)12500
Skewness2.17245918
Sum27316316.5
Variance818712620.6
MonotocityNot monotonic
2021-02-19T16:58:39.631487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500017
 
4.0%
7500015
 
3.5%
5000014
 
3.3%
6000014
 
3.3%
6500010
 
2.3%
850009
 
2.1%
625009
 
2.1%
800009
 
2.1%
700008
 
1.9%
375008
 
1.9%
Other values (158)306
71.2%
(Missing)11
 
2.6%
ValueCountFrequency (%)
02
0.5%
40001
0.2%
61001
0.2%
75001
0.2%
200001
0.2%
ValueCountFrequency (%)
2500001
 
0.2%
2000003
0.7%
1900001
 
0.2%
1800001
 
0.2%
1550001
 
0.2%

Kilpailukykyinen
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.5%
Missing14
Missing (%)3.3%
Memory size3.5 KiB
True
291 
False
125 
(Missing)
 
14
ValueCountFrequency (%)
True291
67.7%
False125
29.1%
(Missing)14
 
3.3%
2021-02-19T16:58:39.776695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct67
Distinct (%)67.7%
Missing331
Missing (%)77.0%
Memory size3.5 KiB
Gofore
11 
Vincit
 
6
Futurice
 
4
Fraktio
 
4
Pankki
 
3
Other values (62)
71 

Length

Max length132
Median length7
Mean length10.50505051
Min length2

Characters and Unicode

Total characters1040
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)55.6%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.6%
Vincit6
 
1.4%
Futurice4
 
0.9%
Fraktio4
 
0.9%
Pankki3
 
0.7%
Arado3
 
0.7%
Mavericks3
 
0.7%
Siili2
 
0.5%
Qvik2
 
0.5%
If2
 
0.5%
Other values (57)59
 
13.7%
(Missing)331
77.0%
2021-02-19T16:58:40.148894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore13
 
8.3%
oy9
 
5.8%
vincit6
 
3.8%
oyj4
 
2.6%
futurice4
 
2.6%
fraktio4
 
2.6%
mavericks4
 
2.6%
pankki3
 
1.9%
siili3
 
1.9%
omistama3
 
1.9%
Other values (89)103
66.0%

Most occurring characters

ValueCountFrequency (%)
i104
 
10.0%
a82
 
7.9%
o79
 
7.6%
e73
 
7.0%
t71
 
6.8%
60
 
5.8%
r57
 
5.5%
n50
 
4.8%
l41
 
3.9%
u40
 
3.8%
Other values (43)383
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter843
81.1%
Uppercase Letter131
 
12.6%
Space Separator60
 
5.8%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
G14
 
10.7%
O13
 
9.9%
V12
 
9.2%
S11
 
8.4%
F9
 
6.9%
A7
 
5.3%
K7
 
5.3%
C6
 
4.6%
P6
 
4.6%
E5
 
3.8%
Other values (15)41
31.3%
ValueCountFrequency (%)
i104
12.3%
a82
 
9.7%
o79
 
9.4%
e73
 
8.7%
t71
 
8.4%
r57
 
6.8%
n50
 
5.9%
l41
 
4.9%
u40
 
4.7%
k40
 
4.7%
Other values (15)206
24.4%
ValueCountFrequency (%)
60
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin974
93.7%
Common66
 
6.3%

Most frequent character per script

ValueCountFrequency (%)
i104
 
10.7%
a82
 
8.4%
o79
 
8.1%
e73
 
7.5%
t71
 
7.3%
r57
 
5.9%
n50
 
5.1%
l41
 
4.2%
u40
 
4.1%
k40
 
4.1%
Other values (40)337
34.6%
ValueCountFrequency (%)
60
90.9%
.3
 
4.5%
-3
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028
98.8%
None12
 
1.2%

Most frequent character per block

ValueCountFrequency (%)
i104
 
10.1%
a82
 
8.0%
o79
 
7.7%
e73
 
7.1%
t71
 
6.9%
60
 
5.8%
r57
 
5.5%
n50
 
4.9%
l41
 
4.0%
u40
 
3.9%
Other values (41)371
36.1%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct32
Distinct (%)97.0%
Missing397
Missing (%)92.3%
Memory size3.5 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Osittain laskutukseen perustuva palkka joten vaihtelee.
 
1
Ei sinänsä liity suoraan palkkoihin, mutta olisi mielenkiintoista tietää miten palkka vaikuttaa työpaikan vaihtoon. Eli esim. Oletko vaihtanut/vaihtamassa/miettinyt vaihtamista, koska toisaalla maksetaan enemmän?
 
1
Vastasin kysymyksiin läpällä. Summat on enemmän sitä minkä verran yrittäjänä haluaa sykkiä ja mennä "raha edellä".
 
1
Kuukausipalkkaan tulossa ihan juuri firman laajuinen pieni (muistaakseni 50 e) yleiskorotus + palkka nousee ainakin 2800 e/kk, kunhan valmistuisi.
 
1
Other values (27)
27 

Length

Max length286
Median length71
Mean length95.54545455
Min length7

Characters and Unicode

Total characters3153
Distinct characters55
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)93.9%

Sample

1st rowKuukausipalkkaan tulossa ihan juuri firman laajuinen pieni (muistaakseni 50 e) yleiskorotus + palkka nousee ainakin 2800 e/kk, kunhan valmistuisi.
2nd rowTyöskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.
3rd rowpalkan lisäksi kompensaatioon kuuluu varsin runsas ja suomen it-alalla uniikki etupaketti. pelkkä palkka ei välttämättä ole kilpailukykyinen, mutta koko kompensaatio yleisesti työstäni on ehdottomasti kilpailukykyinen.
4th rowRahapalkan päälle tulee vielä kohtuullinen optiopotti, mutta se toki on lähinnä arpalippu
5th rowOsittain laskutukseen perustuva palkka joten vaihtelee.
ValueCountFrequency (%)
palkan lisänä lounas- ja virkistysetu2
 
0.5%
Osittain laskutukseen perustuva palkka joten vaihtelee.1
 
0.2%
Ei sinänsä liity suoraan palkkoihin, mutta olisi mielenkiintoista tietää miten palkka vaikuttaa työpaikan vaihtoon. Eli esim. Oletko vaihtanut/vaihtamassa/miettinyt vaihtamista, koska toisaalla maksetaan enemmän?1
 
0.2%
Vastasin kysymyksiin läpällä. Summat on enemmän sitä minkä verran yrittäjänä haluaa sykkiä ja mennä "raha edellä". 1
 
0.2%
Kuukausipalkkaan tulossa ihan juuri firman laajuinen pieni (muistaakseni 50 e) yleiskorotus + palkka nousee ainakin 2800 e/kk, kunhan valmistuisi.1
 
0.2%
Vuositulot pitää sisällään myös sivutoimisena tehtyä pientä laskutusta.1
 
0.2%
Halpaa freelancer laskutusta oman tuotekehityksen sivussa1
 
0.2%
Ilmaset kaffet, safkat, salit jne.1
 
0.2%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
Vaikka merkitsin, että palkkani ei ole mielestäni kilpailukykyinen, se ei tarkoita ettenkö olisi siihen tyytyväinen. Tilanne yrittäjillä ei yleensä vastaa samaa kuin palkansaajilla, joten palkka ei ole yrittäjille monestikaan niin mustavalkoinen asia vaan kysymys on isommasta kuviosta.1
 
0.2%
Other values (22)22
 
5.1%
(Missing)397
92.3%
2021-02-19T16:58:40.565820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ei10
 
2.5%
palkka9
 
2.3%
on8
 
2.0%
mutta6
 
1.5%
ole6
 
1.5%
ja6
 
1.5%
firman4
 
1.0%
nyt4
 
1.0%
joten4
 
1.0%
palkan4
 
1.0%
Other values (281)334
84.6%

Most occurring characters

ValueCountFrequency (%)
365
11.6%
a331
10.5%
i271
 
8.6%
t247
 
7.8%
n216
 
6.9%
s205
 
6.5%
e203
 
6.4%
k185
 
5.9%
l161
 
5.1%
o146
 
4.6%
Other values (45)823
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2625
83.3%
Space Separator365
 
11.6%
Other Punctuation74
 
2.3%
Uppercase Letter47
 
1.5%
Decimal Number27
 
0.9%
Dash Punctuation6
 
0.2%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%
Math Symbol3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a331
12.6%
i271
10.3%
t247
9.4%
n216
 
8.2%
s205
 
7.8%
e203
 
7.7%
k185
 
7.0%
l161
 
6.1%
o146
 
5.6%
u118
 
4.5%
Other values (14)542
20.6%
ValueCountFrequency (%)
T7
14.9%
P7
14.9%
O6
12.8%
E6
12.8%
V6
12.8%
S4
8.5%
K3
6.4%
I2
 
4.3%
H2
 
4.3%
R1
 
2.1%
Other values (3)3
6.4%
ValueCountFrequency (%)
015
55.6%
13
 
11.1%
52
 
7.4%
22
 
7.4%
82
 
7.4%
62
 
7.4%
31
 
3.7%
ValueCountFrequency (%)
.38
51.4%
,23
31.1%
/5
 
6.8%
%4
 
5.4%
"2
 
2.7%
?2
 
2.7%
ValueCountFrequency (%)
365
100.0%
ValueCountFrequency (%)
(3
100.0%
ValueCountFrequency (%)
)3
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2672
84.7%
Common481
 
15.3%

Most frequent character per script

ValueCountFrequency (%)
a331
12.4%
i271
10.1%
t247
9.2%
n216
 
8.1%
s205
 
7.7%
e203
 
7.6%
k185
 
6.9%
l161
 
6.0%
o146
 
5.5%
u118
 
4.4%
Other values (27)589
22.0%
ValueCountFrequency (%)
365
75.9%
.38
 
7.9%
,23
 
4.8%
015
 
3.1%
-6
 
1.2%
/5
 
1.0%
%4
 
0.8%
(3
 
0.6%
)3
 
0.6%
+3
 
0.6%
Other values (8)16
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3017
95.7%
None136
 
4.3%

Most frequent character per block

ValueCountFrequency (%)
365
12.1%
a331
11.0%
i271
 
9.0%
t247
 
8.2%
n216
 
7.2%
s205
 
6.8%
e203
 
6.7%
k185
 
6.1%
l161
 
5.3%
o146
 
4.8%
Other values (43)687
22.8%
ValueCountFrequency (%)
ä112
82.4%
ö24
 
17.6%

Kk-tulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct168
Distinct (%)40.1%
Missing11
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean5432.839399
Minimum0
Maximum20833.33333
Zeros2
Zeros (%)0.5%
Memory size3.5 KiB
2021-02-19T16:58:40.778579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2916.666667
Q14166.666667
median5000
Q36250
95-th percentile10000
Maximum20833.33333
Range20833.33333
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2384.429556
Coefficient of variation (CV)0.4388919644
Kurtosis8.424708087
Mean5432.839399
Median Absolute Deviation (MAD)1041.666667
Skewness2.17245918
Sum2276359.708
Variance5685504.31
MonotocityNot monotonic
2021-02-19T16:58:41.099637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4583.33333317
 
4.0%
625015
 
3.5%
4166.66666714
 
3.3%
500014
 
3.3%
5416.66666710
 
2.3%
5208.3333339
 
2.1%
7083.3333339
 
2.1%
6666.6666679
 
2.1%
31258
 
1.9%
4333.3333338
 
1.9%
Other values (158)306
71.2%
(Missing)11
 
2.6%
ValueCountFrequency (%)
02
0.5%
333.33333331
0.2%
508.33333331
0.2%
6251
0.2%
1666.6666671
0.2%
ValueCountFrequency (%)
20833.333331
 
0.2%
16666.666673
0.7%
15833.333331
 
0.2%
150001
 
0.2%
12916.666671
 
0.2%

Interactions

2021-02-19T16:58:29.521125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:29.688957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:29.853022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:30.016214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:30.172820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:30.340816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:30.519732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:30.695962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:30.866420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:31.040744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:31.221986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:31.418863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:31.700242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:31.865785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:32.034827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:32.210669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:32.373760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:32.540753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:32.722353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-19T16:58:32.896804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-19T16:58:41.277346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-19T16:58:41.502734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-19T16:58:41.730187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-19T16:58:41.966684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-19T16:58:33.224119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-19T16:58:33.693624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-19T16:58:34.126611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-19T16:58:34.532407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

TimestampKaupunkiIkäSukupuoliTyökokemusTyösuhteen luonneTyöaikaRooliEtäKuukausipalkkaVuositulotKilpailukykyinenTyöpaikkaVapaa sanaKk-tulot
02021-02-15 11:57:08.316PK-Seutu48NaN10.0Työntekijä / palkollinen1.0Arkkitehti50/506500.083000.0TrueNaNNaN6916.666667
12021-02-15 11:57:19.676Turku48mies14.0Työntekijä / palkollinen1.0full-stackEtä5000.062500.0TrueNaNNaN5208.333333
22021-02-15 11:58:03.592PK-Seutu41mies2.0Työntekijä / palkollinen1.0Full-stack ohjelmistokehittäjäEtä2475.030000.0FalseNaNNaN2500.000000
32021-02-15 11:58:15.261Tampere48mies22.0Yrittäjä1.0web-arkkitehtiEtä4300.0100000.0TrueNaNNaN8333.333333
42021-02-15 11:58:16.983PK-Seutu41mies2.0Työntekijä / palkollinen1.0OhjelmistokehittäjäEtä3000.037500.0FalseNaNNaN3125.000000
52021-02-15 11:58:49.454PK-Seutu64mies23.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto8000.0100000.0TrueNaNNaN8333.333333
62021-02-15 12:00:03.771PK-Seutu48mies10.0Freelancer1.0OhjelmistokehittäjäEtä6000.0140000.0TrueNaNNaN11666.666667
72021-02-15 12:00:04.655Tampere48NaN10.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto4250.054000.0TrueNaNNaN4500.000000
82021-02-15 12:01:00.769Tampere48mies6.0Työntekijä / palkollinen1.0Lead developerToimisto4000.050000.0FalseNaNNaN4166.666667
92021-02-15 12:02:03.577Tallinna48mies12.0Freelancer1.0NaNEtäNaN200000.0TrueQuestradeNaN16666.666667

Last rows

TimestampKaupunkiIkäSukupuoliTyökokemusTyösuhteen luonneTyöaikaRooliEtäKuukausipalkkaVuositulotKilpailukykyinenTyöpaikkaVapaa sanaKk-tulot
4202021-02-19 16:34:07.545PK-Seutu48mies12.0Työntekijä / palkollinen1.0full-stackEtä8000.095000.0TrueMavericksNaN7916.666667
4212021-02-19 16:36:55.938Tampere64mies22.0Työntekijä / palkollinen0.8ohjelmistokehittäjä (backend) / arkkitehtiEtä4700.058750.0FalseNaNNaN4895.833333
4222021-02-19 16:38:41.403PK-Seutu56mies2.0Työntekijä / palkollinen1.0WordPress-kehittäjä50/503000.037500.0FalseNaNNaN3125.000000
4232021-02-19 16:39:14.831Tampere48mies5.0Työntekijä / palkollinen1.0Data scientistEtä4300.053750.0NaNWapiceNaN4479.166667
4242021-02-19 16:48:04.696PK-Seutu64mies15.0Työntekijä / palkollinen1.0ohjelmistokehittäjä50/50NaN100000.0TrueNaNNaN8333.333333
4252021-02-19 16:54:30.691Turku56mies13.0Työntekijä / palkollinen1.0Lead Software EngineerToimisto5500.075000.0TrueNaNNaN6250.000000
4262021-02-19 17:13:18.923PK-Seutu56mies15.0Työntekijä / palkollinen1.0full-stackEtä6000.076000.0TrueNaNNaN6333.333333
4272021-02-19 17:51:37.178PK-Seutu34nainen4.0Työntekijä / palkollinen1.0Frontend ohjelmistokehittäjäEtä4000.055000.0TrueNaNNaN4583.333333
4282021-02-19 18:20:45.185Oulu48nainen6.0Työntekijä / palkollinen1.0Tekninen asiantuntija/suunnittelijaEtä3250.042000.0FalseNaNNaN3500.000000
4292021-02-19 18:34:24.007Turku41mies12.0Työntekijä / palkollinen1.0full-stackEtä3000.037500.0FalseIfNaN3125.000000