Overview

Dataset statistics

Number of variables15
Number of observations474
Missing cells960
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.1 KiB
Average record size in memory95.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 251 distinct values High cardinality
Työpaikka has a high cardinality: 73 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 33 (7.0%) missing values Missing
Työaika has 19 (4.0%) missing values Missing
Rooli has 12 (2.5%) missing values Missing
Kuukausipalkka has 41 (8.6%) missing values Missing
Vuositulot has 12 (2.5%) missing values Missing
Kilpailukykyinen has 15 (3.2%) missing values Missing
Työpaikka has 365 (77.0%) missing values Missing
Vapaa sana has 437 (92.2%) missing values Missing
Kk-tulot has 12 (2.5%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-23 10:07:33.650981
Analysis finished2021-02-23 10:07:39.467286
Duration5.82 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct474
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-23 10:43:09.487000
2021-02-23T10:07:39.553936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:39.750162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

Distinct25
Distinct (%)5.3%
Missing4
Missing (%)0.8%
Memory size1.3 KiB
PK-Seutu
241 
Tampere
109 
Turku
46 
Oulu
25 
Jyväskylä
 
18
Other values (20)
31 

Length

Max length15
Median length8
Mean length7.242553191
Min length2

Characters and Unicode

Total characters3404
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

Unique12 ?
Unique (%)2.6%

Sample

1st rowPK-Seutu
2nd rowTurku
3rd rowPK-Seutu
4th rowTampere
5th rowPK-Seutu
ValueCountFrequency (%)
PK-Seutu241
50.8%
Tampere109
23.0%
Turku46
 
9.7%
Oulu25
 
5.3%
Jyväskylä18
 
3.8%
Kuopio5
 
1.1%
Pori2
 
0.4%
Tallinna2
 
0.4%
Vaasa2
 
0.4%
Hämeenlinna2
 
0.4%
Other values (15)18
 
3.8%
(Missing)4
 
0.8%
2021-02-23T10:07:40.269953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu241
50.8%
tampere109
23.0%
turku46
 
9.7%
oulu25
 
5.3%
jyväskylä18
 
3.8%
kuopio5
 
1.1%
lontoo2
 
0.4%
hämeenlinna2
 
0.4%
eu2
 
0.4%
pori2
 
0.4%
Other values (19)22
 
4.6%

Most occurring characters

ValueCountFrequency (%)
u636
18.7%
e470
13.8%
K249
 
7.3%
t248
 
7.3%
P244
 
7.2%
-243
 
7.1%
S243
 
7.1%
r161
 
4.7%
T157
 
4.6%
a133
 
3.9%
Other values (29)620
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2198
64.6%
Uppercase Letter958
28.1%
Dash Punctuation243
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u636
28.9%
e470
21.4%
t248
 
11.3%
r161
 
7.3%
a133
 
6.1%
p115
 
5.2%
m114
 
5.2%
k65
 
3.0%
l54
 
2.5%
ä44
 
2.0%
Other values (10)158
 
7.2%
ValueCountFrequency (%)
K249
26.0%
P244
25.5%
S243
25.4%
T157
16.4%
O25
 
2.6%
J19
 
2.0%
L5
 
0.5%
E4
 
0.4%
V3
 
0.3%
H2
 
0.2%
Other values (6)7
 
0.7%
ValueCountFrequency (%)
-243
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3156
92.7%
Common248
 
7.3%

Most frequent character per script

ValueCountFrequency (%)
u636
20.2%
e470
14.9%
K249
 
7.9%
t248
 
7.9%
P244
 
7.7%
S243
 
7.7%
r161
 
5.1%
T157
 
5.0%
a133
 
4.2%
p115
 
3.6%
Other values (26)500
15.8%
ValueCountFrequency (%)
-243
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3360
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u636
18.9%
e470
14.0%
K249
 
7.4%
t248
 
7.4%
P244
 
7.3%
-243
 
7.2%
S243
 
7.2%
r161
 
4.8%
T157
 
4.7%
a133
 
4.0%
Other values (28)576
17.1%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.5%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean33.84745763
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2021-02-23T10:07:40.412798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile23
Q128
median33
Q338
95-th percentile43
Maximum53
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.071236167
Coefficient of variation (CV)0.1793705227
Kurtosis0.208649804
Mean33.84745763
Median Absolute Deviation (MAD)5
Skewness0.4651896891
Sum15976
Variance36.8599086
MonotocityNot monotonic
2021-02-23T10:07:40.535135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33159
33.5%
28114
24.1%
38103
21.7%
4353
 
11.2%
2330
 
6.3%
487
 
1.5%
536
 
1.3%
(Missing)2
 
0.4%
ValueCountFrequency (%)
2330
 
6.3%
28114
24.1%
33159
33.5%
38103
21.7%
4353
 
11.2%
ValueCountFrequency (%)
536
 
1.3%
487
 
1.5%
4353
 
11.2%
38103
21.7%
33159
33.5%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing33
Missing (%)7.0%
Memory size734.0 B
mies
397 
nainen
 
36
muu
 
8

Length

Max length6
Median length4
Mean length4.145124717
Min length3

Characters and Unicode

Total characters1828
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 (%)
mies397
83.8%
nainen36
 
7.6%
muu8
 
1.7%
(Missing)33
 
7.0%
2021-02-23T10:07:40.877584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T10:07:40.997072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies397
90.0%
nainen36
 
8.2%
muu8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i433
23.7%
e433
23.7%
m405
22.2%
s397
21.7%
n108
 
5.9%
a36
 
2.0%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1828
100.0%

Most frequent character per category

ValueCountFrequency (%)
i433
23.7%
e433
23.7%
m405
22.2%
s397
21.7%
n108
 
5.9%
a36
 
2.0%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1828
100.0%

Most frequent character per script

ValueCountFrequency (%)
i433
23.7%
e433
23.7%
m405
22.2%
s397
21.7%
n108
 
5.9%
a36
 
2.0%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1828
100.0%

Most frequent character per block

ValueCountFrequency (%)
i433
23.7%
e433
23.7%
m405
22.2%
s397
21.7%
n108
 
5.9%
a36
 
2.0%
u16
 
0.9%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)5.7%
Missing4
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean9.640425532
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size3.8 KiB
2021-02-23T10:07:41.121655image/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 deviation6.058040511
Coefficient of variation (CV)0.6283996999
Kurtosis-0.04167055875
Mean9.640425532
Median Absolute Deviation (MAD)4
Skewness0.7146588385
Sum4531
Variance36.69985483
MonotocityNot monotonic
2021-02-23T10:07:41.273692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
551
 
10.8%
1038
 
8.0%
430
 
6.3%
729
 
6.1%
2027
 
5.7%
1527
 
5.7%
326
 
5.5%
1325
 
5.3%
825
 
5.3%
624
 
5.1%
Other values (17)168
35.4%
ValueCountFrequency (%)
04
 
0.8%
116
3.4%
224
5.1%
326
5.5%
430
6.3%
ValueCountFrequency (%)
302
 
0.4%
256
1.3%
243
0.6%
234
0.8%
225
1.1%
Distinct3
Distinct (%)0.6%
Missing1
Missing (%)0.2%
Memory size3.8 KiB
Työntekijä / palkollinen
423 
Freelancer
 
25
Yrittäjä
 
25

Length

Max length24
Median length24
Mean length22.41437632
Min length8

Characters and Unicode

Total characters10602
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 / palkollinen423
89.2%
Freelancer25
 
5.3%
Yrittäjä25
 
5.3%
(Missing)1
 
0.2%
2021-02-23T10:07:41.621279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T10:07:41.736785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
423
32.1%
palkollinen423
32.1%
työntekijä423
32.1%
freelancer25
 
1.9%
yrittäjä25
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1294
12.2%
l1294
12.2%
e921
 
8.7%
i871
 
8.2%
k846
 
8.0%
846
 
8.0%
t473
 
4.5%
ä473
 
4.5%
j448
 
4.2%
a448
 
4.2%
Other values (10)2688
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8860
83.6%
Space Separator846
 
8.0%
Uppercase Letter473
 
4.5%
Other Punctuation423
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1294
14.6%
l1294
14.6%
e921
10.4%
i871
9.8%
k846
9.5%
t473
 
5.3%
ä473
 
5.3%
j448
 
5.1%
a448
 
5.1%
y423
 
4.8%
Other values (5)1369
15.5%
ValueCountFrequency (%)
T423
89.4%
Y25
 
5.3%
F25
 
5.3%
ValueCountFrequency (%)
846
100.0%
ValueCountFrequency (%)
/423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9333
88.0%
Common1269
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1294
13.9%
l1294
13.9%
e921
9.9%
i871
9.3%
k846
9.1%
t473
 
5.1%
ä473
 
5.1%
j448
 
4.8%
a448
 
4.8%
T423
 
4.5%
Other values (8)1842
19.7%
ValueCountFrequency (%)
846
66.7%
/423
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9706
91.5%
None896
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1294
13.3%
l1294
13.3%
e921
9.5%
i871
9.0%
k846
8.7%
846
8.7%
t473
 
4.9%
j448
 
4.6%
a448
 
4.6%
T423
 
4.4%
Other values (8)1842
19.0%
ValueCountFrequency (%)
ä473
52.8%
ö423
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)4.0%
Memory size3.8 KiB
1.0
428 
0.8
 
23
0.5
 
2
0.7
 
1
0.6
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1365
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 (%)0.4%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0428
90.3%
0.823
 
4.9%
0.52
 
0.4%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
4.0%
2021-02-23T10:07:42.001950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T10:07:42.094977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0428
94.1%
0.823
 
5.1%
0.52
 
0.4%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.455
33.3%
0455
33.3%
1428
31.4%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number910
66.7%
Other Punctuation455
33.3%

Most frequent character per category

ValueCountFrequency (%)
0455
50.0%
1428
47.0%
823
 
2.5%
52
 
0.2%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.455
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1365
100.0%

Most frequent character per script

ValueCountFrequency (%)
.455
33.3%
0455
33.3%
1428
31.4%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1365
100.0%

Most frequent character per block

ValueCountFrequency (%)
.455
33.3%
0455
33.3%
1428
31.4%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct251
Distinct (%)54.3%
Missing12
Missing (%)2.5%
Memory size3.8 KiB
Ohjelmistokehittäjä
38 
full-stack
34 
Full-stack
 
23
ohjelmistokehittäjä
 
16
Arkkitehti
 
15
Other values (246)
336 

Length

Max length67
Median length18
Mean length19.16450216
Min length2

Characters and Unicode

Total characters8854
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

Unique204 ?
Unique (%)44.2%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä38
 
8.0%
full-stack34
 
7.2%
Full-stack23
 
4.9%
ohjelmistokehittäjä16
 
3.4%
Arkkitehti15
 
3.2%
Full-stack ohjelmistokehittäjä8
 
1.7%
full-stack ohjelmistokehittäjä7
 
1.5%
arkkitehti6
 
1.3%
Frontend6
 
1.3%
DevOps5
 
1.1%
Other values (241)304
64.1%
(Missing)12
 
2.5%
2021-02-23T10:07:42.514536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack138
 
16.2%
ohjelmistokehittäjä108
 
12.7%
developer58
 
6.8%
arkkitehti35
 
4.1%
34
 
4.0%
lead32
 
3.8%
frontend25
 
2.9%
senior21
 
2.5%
kehittäjä16
 
1.9%
backend16
 
1.9%
Other values (182)367
43.2%

Most occurring characters

ValueCountFrequency (%)
t923
 
10.4%
e815
 
9.2%
l648
 
7.3%
i644
 
7.3%
k491
 
5.5%
o465
 
5.3%
a426
 
4.8%
s418
 
4.7%
394
 
4.4%
h353
 
4.0%
Other values (47)3277
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7691
86.9%
Uppercase Letter444
 
5.0%
Space Separator395
 
4.5%
Dash Punctuation168
 
1.9%
Other Punctuation96
 
1.1%
Open Punctuation26
 
0.3%
Close Punctuation26
 
0.3%
Math Symbol8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t923
12.0%
e815
 
10.6%
l648
 
8.4%
i644
 
8.4%
k491
 
6.4%
o465
 
6.0%
a426
 
5.5%
s418
 
5.4%
h353
 
4.6%
j332
 
4.3%
Other values (16)2176
28.3%
ValueCountFrequency (%)
F99
22.3%
O90
20.3%
S49
11.0%
D41
9.2%
T27
 
6.1%
A26
 
5.9%
L19
 
4.3%
C17
 
3.8%
P11
 
2.5%
E11
 
2.5%
Other values (11)54
12.2%
ValueCountFrequency (%)
,52
54.2%
/40
41.7%
&3
 
3.1%
.1
 
1.0%
ValueCountFrequency (%)
394
99.7%
 1
 
0.3%
ValueCountFrequency (%)
-168
100.0%
ValueCountFrequency (%)
(26
100.0%
ValueCountFrequency (%)
)26
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8135
91.9%
Common719
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t923
 
11.3%
e815
 
10.0%
l648
 
8.0%
i644
 
7.9%
k491
 
6.0%
o465
 
5.7%
a426
 
5.2%
s418
 
5.1%
h353
 
4.3%
j332
 
4.1%
Other values (37)2620
32.2%
ValueCountFrequency (%)
394
54.8%
-168
23.4%
,52
 
7.2%
/40
 
5.6%
(26
 
3.6%
)26
 
3.6%
+8
 
1.1%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8519
96.2%
None335
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t923
 
10.8%
e815
 
9.6%
l648
 
7.6%
i644
 
7.6%
k491
 
5.8%
o465
 
5.5%
a426
 
5.0%
s418
 
4.9%
394
 
4.6%
h353
 
4.1%
Other values (44)2942
34.5%
ValueCountFrequency (%)
ä318
94.9%
ö16
 
4.8%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size734.0 B
Etä
198 
Toimisto
161 
50/50
112 

Length

Max length8
Median length5
Mean length5.184713376
Min length3

Characters and Unicode

Total characters2442
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 (%)
Etä198
41.8%
Toimisto161
34.0%
50/50112
23.6%
(Missing)3
 
0.6%
2021-02-23T10:07:42.955870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T10:07:43.062154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä198
42.0%
toimisto161
34.2%
50/50112
23.8%

Most occurring characters

ValueCountFrequency (%)
t359
14.7%
o322
13.2%
i322
13.2%
5224
9.2%
0224
9.2%
E198
8.1%
ä198
8.1%
T161
6.6%
m161
6.6%
s161
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1523
62.4%
Decimal Number448
 
18.3%
Uppercase Letter359
 
14.7%
Other Punctuation112
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
t359
23.6%
o322
21.1%
i322
21.1%
ä198
13.0%
m161
10.6%
s161
10.6%
ValueCountFrequency (%)
5224
50.0%
0224
50.0%
ValueCountFrequency (%)
E198
55.2%
T161
44.8%
ValueCountFrequency (%)
/112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1882
77.1%
Common560
 
22.9%

Most frequent character per script

ValueCountFrequency (%)
t359
19.1%
o322
17.1%
i322
17.1%
E198
10.5%
ä198
10.5%
T161
8.6%
m161
8.6%
s161
8.6%
ValueCountFrequency (%)
5224
40.0%
0224
40.0%
/112
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2244
91.9%
None198
 
8.1%

Most frequent character per block

ValueCountFrequency (%)
t359
16.0%
o322
14.3%
i322
14.3%
5224
10.0%
0224
10.0%
E198
8.8%
T161
7.2%
m161
7.2%
s161
7.2%
/112
 
5.0%
ValueCountFrequency (%)
ä198
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct126
Distinct (%)29.1%
Missing41
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean4708.526559
Minimum1666
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2021-02-23T10:07:43.194578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1666
5-th percentile2800
Q13800
median4500
Q35500
95-th percentile7000
Maximum15000
Range13334
Interquartile range (IQR)1700

Descriptive statistics

Standard deviation1442.803388
Coefficient of variation (CV)0.3064235424
Kurtosis8.105265787
Mean4708.526559
Median Absolute Deviation (MAD)800
Skewness1.720489865
Sum2038792
Variance2081681.616
MonotocityNot monotonic
2021-02-23T10:07:43.399312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400025
 
5.3%
450022
 
4.6%
600017
 
3.6%
500017
 
3.6%
550016
 
3.4%
480012
 
2.5%
420012
 
2.5%
380011
 
2.3%
300011
 
2.3%
700011
 
2.3%
Other values (116)279
58.9%
(Missing)41
 
8.6%
ValueCountFrequency (%)
16661
0.2%
17001
0.2%
18001
0.2%
21001
0.2%
22001
0.2%
ValueCountFrequency (%)
150001
0.2%
120002
0.4%
93001
0.2%
85002
0.4%
82001
0.2%

Vuositulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct180
Distinct (%)39.0%
Missing12
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean66169.62446
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2021-02-23T10:07:43.599459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35000
Q150000
median59187.5
Q375000
95-th percentile124750
Maximum300000
Range300000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation31960.83472
Coefficient of variation (CV)0.4830136936
Kurtosis11.89626882
Mean66169.62446
Median Absolute Deviation (MAD)11937.5
Skewness2.677785851
Sum30570366.5
Variance1021494956
MonotocityNot monotonic
2021-02-23T10:07:43.788213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000018
 
3.8%
5500018
 
3.8%
7500016
 
3.4%
6000014
 
3.0%
8500011
 
2.3%
6500010
 
2.1%
7000010
 
2.1%
6250010
 
2.1%
800009
 
1.9%
400009
 
1.9%
Other values (170)337
71.1%
(Missing)12
 
2.5%
ValueCountFrequency (%)
02
0.4%
40001
0.2%
61001
0.2%
75001
0.2%
200001
0.2%
ValueCountFrequency (%)
3000001
 
0.2%
2500001
 
0.2%
2200001
 
0.2%
2000004
0.8%
1900001
 
0.2%

Kilpailukykyinen
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing15
Missing (%)3.2%
Memory size3.8 KiB
True
316 
False
143 
(Missing)
 
15
ValueCountFrequency (%)
True316
66.7%
False143
30.2%
(Missing)15
 
3.2%
2021-02-23T10:07:43.925318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)67.0%
Missing365
Missing (%)77.0%
Memory size3.8 KiB
Gofore
11 
Vincit
 
7
Futurice
 
5
Fraktio
 
4
Mavericks
 
4
Other values (68)
78 

Length

Max length132
Median length8
Mean length10.74311927
Min length2

Characters and Unicode

Total characters1171
Distinct characters54
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

Unique60 ?
Unique (%)55.0%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.3%
Vincit7
 
1.5%
Futurice5
 
1.1%
Fraktio4
 
0.8%
Mavericks4
 
0.8%
Pankki3
 
0.6%
Arado3
 
0.6%
Gofore Oyj2
 
0.4%
Siili2
 
0.4%
If2
 
0.4%
Other values (63)66
 
13.9%
(Missing)365
77.0%
2021-02-23T10:07:44.262228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore13
 
7.5%
oy12
 
6.9%
vincit7
 
4.0%
mavericks6
 
3.5%
oyj5
 
2.9%
futurice5
 
2.9%
fraktio4
 
2.3%
siili4
 
2.3%
omistama3
 
1.7%
arado3
 
1.7%
Other values (94)111
64.2%

Most occurring characters

ValueCountFrequency (%)
i121
 
10.3%
o89
 
7.6%
a88
 
7.5%
e83
 
7.1%
t81
 
6.9%
67
 
5.7%
r62
 
5.3%
n57
 
4.9%
k48
 
4.1%
l47
 
4.0%
Other values (44)428
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter952
81.3%
Uppercase Letter146
 
12.5%
Space Separator67
 
5.7%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i121
12.7%
o89
 
9.3%
a88
 
9.2%
e83
 
8.7%
t81
 
8.5%
r62
 
6.5%
n57
 
6.0%
k48
 
5.0%
l47
 
4.9%
u45
 
4.7%
Other values (16)231
24.3%
ValueCountFrequency (%)
O17
 
11.6%
G14
 
9.6%
S14
 
9.6%
V13
 
8.9%
F10
 
6.8%
K8
 
5.5%
C7
 
4.8%
A7
 
4.8%
M7
 
4.8%
P6
 
4.1%
Other values (15)43
29.5%
ValueCountFrequency (%)
67
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1098
93.8%
Common73
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
i121
 
11.0%
o89
 
8.1%
a88
 
8.0%
e83
 
7.6%
t81
 
7.4%
r62
 
5.6%
n57
 
5.2%
k48
 
4.4%
l47
 
4.3%
u45
 
4.1%
Other values (41)377
34.3%
ValueCountFrequency (%)
67
91.8%
.3
 
4.1%
-3
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1159
99.0%
None12
 
1.0%

Most frequent character per block

ValueCountFrequency (%)
i121
 
10.4%
o89
 
7.7%
a88
 
7.6%
e83
 
7.2%
t81
 
7.0%
67
 
5.8%
r62
 
5.3%
n57
 
4.9%
k48
 
4.1%
l47
 
4.1%
Other values (42)416
35.9%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct36
Distinct (%)97.3%
Missing437
Missing (%)92.2%
Memory size3.8 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Osittain laskutukseen perustuva palkka joten vaihtelee.
 
1
Olen firmaan, sen etuihin ja työilmapiiriin tyytyväinen.
 
1
Sijainti Pori, mutta etätöitä 100%. Varsinainen positio Tampere - Helsinki. Edut aika huonot, perusjutut. Työ itsessään aika masentavaa. Seuraavaksi varmaan freelance/yrittäjyys.
 
1
+ merkittävä optiopaketti
 
1
Other values (31)
31 

Length

Max length286
Median length71
Mean length92.10810811
Min length7

Characters and Unicode

Total characters3408
Distinct characters56
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

Unique35 ?
Unique (%)94.6%

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
 
0.4%
Osittain laskutukseen perustuva palkka joten vaihtelee.1
 
0.2%
Olen firmaan, sen etuihin ja työilmapiiriin tyytyväinen.1
 
0.2%
Sijainti Pori, mutta etätöitä 100%. Varsinainen positio Tampere - Helsinki. Edut aika huonot, perusjutut. Työ itsessään aika masentavaa. Seuraavaksi varmaan freelance/yrittäjyys.1
 
0.2%
+ merkittävä optiopaketti1
 
0.2%
Vastasin kysymyksiin läpällä. Summat on enemmän sitä minkä verran yrittäjänä haluaa sykkiä ja mennä "raha edellä". 1
 
0.2%
palkan 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. 1
 
0.2%
Johtajasopimus, ei työaikaa1
 
0.2%
it-ala 10+v koodaus 6v1
 
0.2%
Rahapalkan päälle tulee vielä kohtuullinen optiopotti, mutta se toki on lähinnä arpalippu1
 
0.2%
Other values (26)26
 
5.5%
(Missing)437
92.2%
2021-02-23T10:07:44.657616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ei11
 
2.6%
palkka10
 
2.3%
ja9
 
2.1%
on9
 
2.1%
mutta8
 
1.9%
ole6
 
1.4%
palkan4
 
0.9%
nyt4
 
0.9%
joten4
 
0.9%
olen4
 
0.9%
Other values (303)362
84.0%

Most occurring characters

ValueCountFrequency (%)
397
11.6%
a363
10.7%
i291
 
8.5%
t269
 
7.9%
n232
 
6.8%
s221
 
6.5%
e216
 
6.3%
k197
 
5.8%
l168
 
4.9%
o157
 
4.6%
Other values (46)897
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2836
83.2%
Space Separator397
 
11.6%
Other Punctuation81
 
2.4%
Uppercase Letter51
 
1.5%
Decimal Number28
 
0.8%
Dash Punctuation6
 
0.2%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%
Math Symbol3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a363
12.8%
i291
10.3%
t269
9.5%
n232
 
8.2%
s221
 
7.8%
e216
 
7.6%
k197
 
6.9%
l168
 
5.9%
o157
 
5.5%
u127
 
4.5%
Other values (14)595
21.0%
ValueCountFrequency (%)
P9
17.6%
T7
13.7%
O7
13.7%
E6
11.8%
V6
11.8%
K4
7.8%
S4
7.8%
I2
 
3.9%
H2
 
3.9%
R1
 
2.0%
Other values (3)3
 
5.9%
ValueCountFrequency (%)
015
53.6%
13
 
10.7%
52
 
7.1%
22
 
7.1%
82
 
7.1%
62
 
7.1%
31
 
3.6%
71
 
3.6%
ValueCountFrequency (%)
.42
51.9%
,26
32.1%
/5
 
6.2%
%4
 
4.9%
"2
 
2.5%
?2
 
2.5%
ValueCountFrequency (%)
397
100.0%
ValueCountFrequency (%)
(3
100.0%
ValueCountFrequency (%)
)3
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2887
84.7%
Common521
 
15.3%

Most frequent character per script

ValueCountFrequency (%)
a363
12.6%
i291
10.1%
t269
9.3%
n232
 
8.0%
s221
 
7.7%
e216
 
7.5%
k197
 
6.8%
l168
 
5.8%
o157
 
5.4%
u127
 
4.4%
Other values (27)646
22.4%
ValueCountFrequency (%)
397
76.2%
.42
 
8.1%
,26
 
5.0%
015
 
2.9%
-6
 
1.2%
/5
 
1.0%
%4
 
0.8%
(3
 
0.6%
)3
 
0.6%
+3
 
0.6%
Other values (9)17
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3263
95.7%
None145
 
4.3%

Most frequent character per block

ValueCountFrequency (%)
397
12.2%
a363
11.1%
i291
 
8.9%
t269
 
8.2%
n232
 
7.1%
s221
 
6.8%
e216
 
6.6%
k197
 
6.0%
l168
 
5.1%
o157
 
4.8%
Other values (44)752
23.0%
ValueCountFrequency (%)
ä120
82.8%
ö25
 
17.2%

Kk-tulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct180
Distinct (%)39.0%
Missing12
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean5514.135372
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2021-02-23T10:07:44.853883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2916.666667
Q14166.666667
median4932.291667
Q36250
95-th percentile10395.83333
Maximum25000
Range25000
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2663.402893
Coefficient of variation (CV)0.4830136936
Kurtosis11.89626882
Mean5514.135372
Median Absolute Deviation (MAD)994.7916667
Skewness2.677785851
Sum2547530.542
Variance7093714.97
MonotocityNot monotonic
2021-02-23T10:07:45.189258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4166.66666718
 
3.8%
4583.33333318
 
3.8%
625016
 
3.4%
500014
 
3.0%
7083.33333311
 
2.3%
5208.33333310
 
2.1%
5416.66666710
 
2.1%
5833.33333310
 
2.1%
6666.6666679
 
1.9%
31259
 
1.9%
Other values (170)337
71.1%
(Missing)12
 
2.5%
ValueCountFrequency (%)
02
0.4%
333.33333331
0.2%
508.33333331
0.2%
6251
0.2%
1666.6666671
0.2%
ValueCountFrequency (%)
250001
 
0.2%
20833.333331
 
0.2%
18333.333331
 
0.2%
16666.666674
0.8%
15833.333331
 
0.2%

Interactions

2021-02-23T10:07:34.678181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:34.847496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:35.010245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:35.182986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:35.346002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:35.501980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:35.679389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:35.849385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:36.017048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:36.182322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:36.353162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:36.627359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:36.785641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:36.938056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:37.101631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:37.260461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:37.413530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:37.557875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:37.700812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T10:07:37.845984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-23T10:07:45.356061image/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.
2021-02-23T10:07:45.566421image/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.
2021-02-23T10:07:45.765735image/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.
2021-02-23T10:07:45.991822image/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

2021-02-23T10:07:38.145476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-23T10:07:38.553854image/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.
2021-02-23T10:07:38.949529image/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.
2021-02-23T10:07:39.294969image/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-Seutu33NaN10.0Työntekijä / palkollinen1.0Arkkitehti50/506500.083000.0TrueNaNNaN6916.666667
12021-02-15 11:57:19.676Turku33mies14.0Työntekijä / palkollinen1.0full-stackEtä5000.062500.0TrueNaNNaN5208.333333
22021-02-15 11:58:03.592PK-Seutu28mies2.0Työntekijä / palkollinen1.0Full-stack ohjelmistokehittäjäEtä2475.030000.0FalseNaNNaN2500.000000
32021-02-15 11:58:15.261Tampere33mies22.0Yrittäjä1.0web-arkkitehtiEtä4300.0100000.0TrueNaNNaN8333.333333
42021-02-15 11:58:16.983PK-Seutu28mies2.0Työntekijä / palkollinen1.0OhjelmistokehittäjäEtä3000.037500.0FalseNaNNaN3125.000000
52021-02-15 11:58:49.454PK-Seutu43mies23.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto8000.0100000.0TrueNaNNaN8333.333333
62021-02-15 12:00:03.771PK-Seutu33mies10.0Freelancer1.0OhjelmistokehittäjäEtä6000.0140000.0TrueNaNNaN11666.666667
72021-02-15 12:00:04.655Tampere33NaN10.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto4250.054000.0TrueNaNNaN4500.000000
82021-02-15 12:01:00.769Tampere33mies6.0Työntekijä / palkollinen1.0Lead developerToimisto4000.050000.0FalseNaNNaN4166.666667
92021-02-15 12:02:03.577Tallinna33mies12.0Freelancer1.0NaNEtäNaN200000.0TrueQuestradeNaN16666.666667

Last rows

TimestampKaupunkiIkäSukupuoliTyökokemusTyösuhteen luonneTyöaikaRooliEtäKuukausipalkkaVuositulotKilpailukykyinenTyöpaikkaVapaa sanaKk-tulot
4642021-02-22 12:54:08.537PK-Seutu28mies9.0Työntekijä / palkollinen1.0TuotepäällikköToimisto5500.082500.0TrueNaNNaN6875.000000
4652021-02-22 13:03:17.260Tampere33mies5.0Työntekijä / palkollinen1.0Lead front end devToimisto4200.050000.0TrueNaNNaN4166.666667
4662021-02-22 13:33:47.981PK-Seutu28mies0.0Työntekijä / palkollinen1.0harjoittelijaToimisto2200.027500.0FalseNaNNaN2291.666667
4672021-02-22 14:11:08.271EU33mies8.0Työntekijä / palkollinen1.0Senior Backend DeveloperToimisto4800.059000.0FalseNaNNaN4916.666667
4682021-02-22 18:58:45.951PK-Seutu43mies15.0Työntekijä / palkollinen1.0TeknologiajohtajaToimisto12000.0220000.0TrueNaNNaN18333.333333
4692021-02-22 23:53:12.243PK-Seutu33mies8.0Työntekijä / palkollinen1.0senior game developer50/504000.050000.0FalseNaNNaN4166.666667
4702021-02-23 08:54:20.588PK-Seutu33nainen3.0Työntekijä / palkollinen1.0full-stackEtä3500.043750.0FalseNaNNaN3645.833333
4712021-02-23 09:56:27.496PK-Seutu33mies8.0Työntekijä / palkollinen1.0full-stack conslutToimisto5300.070000.0Truekeskikokoinen konsulttifirmaNaN5833.333333
4722021-02-23 10:12:27.163Lahti38mies14.0Työntekijä / palkollinen1.0Front-end DeveloperToimistoNaNNaNTrueNaNNaNNaN
4732021-02-23 10:43:09.487PK-Seutu43mies9.0Työntekijä / palkollinen1.0ohjelmistokehittäjä/konsultti (senior, full-stack)50/503926.050000.0FalseVincitOlen firmaan, sen etuihin ja työilmapiiriin tyytyväinen.4166.666667