Overview

Dataset statistics

Number of variables15
Number of observations478
Missing cells968
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.9 KiB
Average record size in memory96.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 253 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
Kilpailukykyinen is highly correlated with Vapaa sanaHigh correlation
Vapaa sana is highly correlated with Työpaikka and 1 other fieldsHigh correlation
Sukupuoli has 33 (6.9%) 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 (3.1%) missing values Missing
Työpaikka has 369 (77.2%) missing values Missing
Vapaa sana has 441 (92.3%) 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 13:13:36.118973
Analysis finished2021-02-23 13:13:41.961950
Duration5.84 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct478
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-23 14:00:37.170000
2021-02-23T13:13:42.061419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:42.278842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

Distinct26
Distinct (%)5.5%
Missing4
Missing (%)0.8%
Memory size1.8 KiB
PK-Seutu
242 
Tampere
110 
Turku
47 
Oulu
25 
Jyväskylä
 
18
Other values (21)
32 

Length

Max length15
Median length8
Mean length7.242616034
Min length2

Characters and Unicode

Total characters3433
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

Unique13 ?
Unique (%)2.7%

Sample

1st rowPK-Seutu
2nd rowTurku
3rd rowPK-Seutu
4th rowTampere
5th rowPK-Seutu
ValueCountFrequency (%)
PK-Seutu242
50.6%
Tampere110
23.0%
Turku47
 
9.8%
Oulu25
 
5.2%
Jyväskylä18
 
3.8%
Kuopio5
 
1.0%
Lontoo2
 
0.4%
Vaasa2
 
0.4%
Tallinna2
 
0.4%
Pori2
 
0.4%
Other values (16)19
 
4.0%
(Missing)4
 
0.8%
2021-02-23T13:13:42.830657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu242
50.6%
tampere110
23.0%
turku47
 
9.8%
oulu25
 
5.2%
jyväskylä18
 
3.8%
kuopio5
 
1.0%
tallinna2
 
0.4%
hämeenlinna2
 
0.4%
lahti2
 
0.4%
vaasa2
 
0.4%
Other values (20)23
 
4.8%

Most occurring characters

ValueCountFrequency (%)
u640
18.6%
e473
13.8%
K250
 
7.3%
t249
 
7.3%
P245
 
7.1%
-244
 
7.1%
S244
 
7.1%
r163
 
4.7%
T159
 
4.6%
a136
 
4.0%
Other values (29)630
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2220
64.7%
Uppercase Letter964
28.1%
Dash Punctuation244
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u640
28.8%
e473
21.3%
t249
 
11.2%
r163
 
7.3%
a136
 
6.1%
m116
 
5.2%
p116
 
5.2%
k67
 
3.0%
l55
 
2.5%
ä44
 
2.0%
Other values (10)161
 
7.3%
ValueCountFrequency (%)
K250
25.9%
P245
25.4%
S244
25.3%
T159
16.5%
O25
 
2.6%
J19
 
2.0%
L5
 
0.5%
E4
 
0.4%
V3
 
0.3%
H3
 
0.3%
Other values (6)7
 
0.7%
ValueCountFrequency (%)
-244
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3184
92.7%
Common249
 
7.3%

Most frequent character per script

ValueCountFrequency (%)
u640
20.1%
e473
14.9%
K250
 
7.9%
t249
 
7.8%
P245
 
7.7%
S244
 
7.7%
r163
 
5.1%
T159
 
5.0%
a136
 
4.3%
m116
 
3.6%
Other values (26)509
16.0%
ValueCountFrequency (%)
-244
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3389
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u640
18.9%
e473
14.0%
K250
 
7.4%
t249
 
7.3%
P245
 
7.2%
-244
 
7.2%
S244
 
7.2%
r163
 
4.8%
T159
 
4.7%
a136
 
4.0%
Other values (28)586
17.3%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.5%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean33.81932773
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2021-02-23T13:13:42.984039image/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 deviation6.066380554
Coefficient of variation (CV)0.1793761426
Kurtosis0.2062209488
Mean33.81932773
Median Absolute Deviation (MAD)5
Skewness0.4713319492
Sum16098
Variance36.80097302
MonotocityNot monotonic
2021-02-23T13:13:43.117142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33159
33.3%
28117
24.5%
38104
21.8%
4353
 
11.1%
2330
 
6.3%
487
 
1.5%
536
 
1.3%
(Missing)2
 
0.4%
ValueCountFrequency (%)
2330
 
6.3%
28117
24.5%
33159
33.3%
38104
21.8%
4353
 
11.1%
ValueCountFrequency (%)
536
 
1.3%
487
 
1.5%
4353
 
11.1%
38104
21.8%
33159
33.3%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing33
Missing (%)6.9%
Memory size738.0 B
mies
401 
nainen
 
36
muu
 
8

Length

Max length6
Median length4
Mean length4.143820225
Min length3

Characters and Unicode

Total characters1844
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 (%)
mies401
83.9%
nainen36
 
7.5%
muu8
 
1.7%
(Missing)33
 
6.9%
2021-02-23T13:13:43.473556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T13:13:43.591595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies401
90.1%
nainen36
 
8.1%
muu8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i437
23.7%
e437
23.7%
m409
22.2%
s401
21.7%
n108
 
5.9%
a36
 
2.0%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1844
100.0%

Most frequent character per category

ValueCountFrequency (%)
i437
23.7%
e437
23.7%
m409
22.2%
s401
21.7%
n108
 
5.9%
a36
 
2.0%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1844
100.0%

Most frequent character per script

ValueCountFrequency (%)
i437
23.7%
e437
23.7%
m409
22.2%
s401
21.7%
n108
 
5.9%
a36
 
2.0%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1844
100.0%

Most frequent character per block

ValueCountFrequency (%)
i437
23.7%
e437
23.7%
m409
22.2%
s401
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%
Mean9.592827004
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size3.9 KiB
2021-02-23T13:13:43.717345image/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 deviation6.05548964
Coefficient of variation (CV)0.6312518341
Kurtosis-0.03089223169
Mean9.592827004
Median Absolute Deviation (MAD)4
Skewness0.7252758301
Sum4547
Variance36.66895478
MonotocityNot monotonic
2021-02-23T13:13:43.878424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
553
 
11.1%
1038
 
7.9%
431
 
6.5%
729
 
6.1%
1527
 
5.6%
2027
 
5.6%
326
 
5.4%
225
 
5.2%
1325
 
5.2%
825
 
5.2%
Other values (17)168
35.1%
ValueCountFrequency (%)
04
 
0.8%
116
3.3%
225
5.2%
326
5.4%
431
6.5%
ValueCountFrequency (%)
302
 
0.4%
256
1.3%
243
0.6%
234
0.8%
225
1.0%
Distinct3
Distinct (%)0.6%
Missing1
Missing (%)0.2%
Memory size3.9 KiB
Työntekijä / palkollinen
426 
Freelancer
 
26
Yrittäjä
 
25

Length

Max length24
Median length24
Mean length22.39832285
Min length8

Characters and Unicode

Total characters10684
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 / palkollinen426
89.1%
Freelancer26
 
5.4%
Yrittäjä25
 
5.2%
(Missing)1
 
0.2%
2021-02-23T13:13:44.245747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T13:13:44.371583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
426
32.1%
työntekijä426
32.1%
palkollinen426
32.1%
freelancer26
 
2.0%
yrittäjä25
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1304
12.2%
l1304
12.2%
e930
 
8.7%
i877
 
8.2%
k852
 
8.0%
852
 
8.0%
t476
 
4.5%
ä476
 
4.5%
a452
 
4.2%
j451
 
4.2%
Other values (10)2710
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8929
83.6%
Space Separator852
 
8.0%
Uppercase Letter477
 
4.5%
Other Punctuation426
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1304
14.6%
l1304
14.6%
e930
10.4%
i877
9.8%
k852
9.5%
t476
 
5.3%
ä476
 
5.3%
a452
 
5.1%
j451
 
5.1%
y426
 
4.8%
Other values (5)1381
15.5%
ValueCountFrequency (%)
T426
89.3%
F26
 
5.5%
Y25
 
5.2%
ValueCountFrequency (%)
852
100.0%
ValueCountFrequency (%)
/426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9406
88.0%
Common1278
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1304
13.9%
l1304
13.9%
e930
9.9%
i877
9.3%
k852
9.1%
t476
 
5.1%
ä476
 
5.1%
a452
 
4.8%
j451
 
4.8%
T426
 
4.5%
Other values (8)1858
19.8%
ValueCountFrequency (%)
852
66.7%
/426
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9782
91.6%
None902
 
8.4%

Most frequent character per block

ValueCountFrequency (%)
n1304
13.3%
l1304
13.3%
e930
9.5%
i877
9.0%
k852
8.7%
852
8.7%
t476
 
4.9%
a452
 
4.6%
j451
 
4.6%
T426
 
4.4%
Other values (8)1858
19.0%
ValueCountFrequency (%)
ä476
52.8%
ö426
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)4.0%
Memory size3.9 KiB
1.0
431 
0.8
 
23
0.5
 
3
0.6
 
1
0.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1377
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 (%)
1.0431
90.2%
0.823
 
4.8%
0.53
 
0.6%
0.61
 
0.2%
0.71
 
0.2%
(Missing)19
 
4.0%
2021-02-23T13:13:44.655360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T13:13:44.753724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0431
93.9%
0.823
 
5.0%
0.53
 
0.7%
0.61
 
0.2%
0.71
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.459
33.3%
0459
33.3%
1431
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number918
66.7%
Other Punctuation459
33.3%

Most frequent character per category

ValueCountFrequency (%)
0459
50.0%
1431
46.9%
823
 
2.5%
53
 
0.3%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1377
100.0%

Most frequent character per script

ValueCountFrequency (%)
.459
33.3%
0459
33.3%
1431
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1377
100.0%

Most frequent character per block

ValueCountFrequency (%)
.459
33.3%
0459
33.3%
1431
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct253
Distinct (%)54.3%
Missing12
Missing (%)2.5%
Memory size3.9 KiB
Ohjelmistokehittäjä
39 
full-stack
34 
Full-stack
 
23
ohjelmistokehittäjä
 
17
Arkkitehti
 
15
Other values (248)
338 

Length

Max length67
Median length18
Mean length19.14592275
Min length2

Characters and Unicode

Total characters8922
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

Unique206 ?
Unique (%)44.2%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä39
 
8.2%
full-stack34
 
7.1%
Full-stack23
 
4.8%
ohjelmistokehittäjä17
 
3.6%
Arkkitehti15
 
3.1%
Full-stack ohjelmistokehittäjä8
 
1.7%
full-stack ohjelmistokehittäjä7
 
1.5%
Frontend6
 
1.3%
arkkitehti6
 
1.3%
Full-stack kehittäjä5
 
1.0%
Other values (243)306
64.0%
(Missing)12
 
2.5%
2021-02-23T13:13:45.189992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack138
 
16.1%
ohjelmistokehittäjä110
 
12.9%
developer59
 
6.9%
arkkitehti35
 
4.1%
34
 
4.0%
lead32
 
3.7%
frontend25
 
2.9%
senior21
 
2.5%
kehittäjä16
 
1.9%
backend16
 
1.9%
Other values (184)369
43.2%

Most occurring characters

ValueCountFrequency (%)
t930
 
10.4%
e824
 
9.2%
l653
 
7.3%
i649
 
7.3%
k494
 
5.5%
o470
 
5.3%
a428
 
4.8%
s421
 
4.7%
395
 
4.4%
h357
 
4.0%
Other values (47)3301
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7753
86.9%
Uppercase Letter448
 
5.0%
Space Separator396
 
4.4%
Dash Punctuation168
 
1.9%
Other Punctuation97
 
1.1%
Open Punctuation26
 
0.3%
Close Punctuation26
 
0.3%
Math Symbol8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t930
12.0%
e824
 
10.6%
l653
 
8.4%
i649
 
8.4%
k494
 
6.4%
o470
 
6.1%
a428
 
5.5%
s421
 
5.4%
h357
 
4.6%
j336
 
4.3%
Other values (16)2191
28.3%
ValueCountFrequency (%)
F100
22.3%
O91
20.3%
S50
11.2%
D41
9.2%
T27
 
6.0%
A26
 
5.8%
L20
 
4.5%
C17
 
3.8%
P11
 
2.5%
E11
 
2.5%
Other values (11)54
12.1%
ValueCountFrequency (%)
,52
53.6%
/41
42.3%
&3
 
3.1%
.1
 
1.0%
ValueCountFrequency (%)
395
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 (%)
Latin8201
91.9%
Common721
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t930
 
11.3%
e824
 
10.0%
l653
 
8.0%
i649
 
7.9%
k494
 
6.0%
o470
 
5.7%
a428
 
5.2%
s421
 
5.1%
h357
 
4.4%
j336
 
4.1%
Other values (37)2639
32.2%
ValueCountFrequency (%)
395
54.8%
-168
23.3%
,52
 
7.2%
/41
 
5.7%
(26
 
3.6%
)26
 
3.6%
+8
 
1.1%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8583
96.2%
None339
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t930
 
10.8%
e824
 
9.6%
l653
 
7.6%
i649
 
7.6%
k494
 
5.8%
o470
 
5.5%
a428
 
5.0%
s421
 
4.9%
395
 
4.6%
h357
 
4.2%
Other values (44)2962
34.5%
ValueCountFrequency (%)
ä322
95.0%
ö16
 
4.7%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size738.0 B
Etä
200 
Toimisto
163 
50/50
112 

Length

Max length8
Median length5
Mean length5.187368421
Min length3

Characters and Unicode

Total characters2464
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 (%)
Etä200
41.8%
Toimisto163
34.1%
50/50112
23.4%
(Missing)3
 
0.6%
2021-02-23T13:13:45.636302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T13:13:45.745127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä200
42.1%
toimisto163
34.3%
50/50112
23.6%

Most occurring characters

ValueCountFrequency (%)
t363
14.7%
o326
13.2%
i326
13.2%
5224
9.1%
0224
9.1%
E200
8.1%
ä200
8.1%
T163
6.6%
m163
6.6%
s163
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1541
62.5%
Decimal Number448
 
18.2%
Uppercase Letter363
 
14.7%
Other Punctuation112
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t363
23.6%
o326
21.2%
i326
21.2%
ä200
13.0%
m163
10.6%
s163
10.6%
ValueCountFrequency (%)
5224
50.0%
0224
50.0%
ValueCountFrequency (%)
E200
55.1%
T163
44.9%
ValueCountFrequency (%)
/112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1904
77.3%
Common560
 
22.7%

Most frequent character per script

ValueCountFrequency (%)
t363
19.1%
o326
17.1%
i326
17.1%
E200
10.5%
ä200
10.5%
T163
8.6%
m163
8.6%
s163
8.6%
ValueCountFrequency (%)
5224
40.0%
0224
40.0%
/112
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2264
91.9%
None200
 
8.1%

Most frequent character per block

ValueCountFrequency (%)
t363
16.0%
o326
14.4%
i326
14.4%
5224
9.9%
0224
9.9%
E200
8.8%
T163
7.2%
m163
7.2%
s163
7.2%
/112
 
4.9%
ValueCountFrequency (%)
ä200
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct127
Distinct (%)29.1%
Missing41
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean4697.235698
Minimum1100
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2021-02-23T13:13:45.872366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile2794
Q13800
median4500
Q35500
95-th percentile7000
Maximum15000
Range13900
Interquartile range (IQR)1700

Descriptive statistics

Standard deviation1447.050622
Coefficient of variation (CV)0.3080642988
Kurtosis8.015666001
Mean4697.235698
Median Absolute Deviation (MAD)775
Skewness1.677123352
Sum2052692
Variance2093955.502
MonotocityNot monotonic
2021-02-23T13:13:46.050922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400026
 
5.4%
450023
 
4.8%
600017
 
3.6%
500017
 
3.6%
550016
 
3.3%
480012
 
2.5%
420012
 
2.5%
430012
 
2.5%
380011
 
2.3%
300011
 
2.3%
Other values (117)280
58.6%
(Missing)41
 
8.6%
ValueCountFrequency (%)
11001
0.2%
16661
0.2%
17001
0.2%
18001
0.2%
21001
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

Distinct181
Distinct (%)38.8%
Missing12
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean65987.91094
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.9 KiB
2021-02-23T13:13:46.247952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34737.5
Q150000
median59000
Q375000
95-th percentile123750
Maximum300000
Range300000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation31927.59027
Coefficient of variation (CV)0.4838399914
Kurtosis11.9052155
Mean65987.91094
Median Absolute Deviation (MAD)11812.5
Skewness2.670252748
Sum30750366.5
Variance1019371020
MonotocityNot monotonic
2021-02-23T13:13:46.441186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.8%
5000018
 
3.8%
7500016
 
3.3%
6000014
 
2.9%
8500011
 
2.3%
7000010
 
2.1%
6250010
 
2.1%
6500010
 
2.1%
400009
 
1.9%
520009
 
1.9%
Other values (171)341
71.3%
(Missing)12
 
2.5%
ValueCountFrequency (%)
02
0.4%
40001
0.2%
61001
0.2%
75001
0.2%
137501
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 (%)3.1%
Memory size3.9 KiB
True
318 
False
145 
(Missing)
 
15
ValueCountFrequency (%)
True318
66.5%
False145
30.3%
(Missing)15
 
3.1%
2021-02-23T13:13:46.587458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)67.0%
Missing369
Missing (%)77.2%
Memory size3.9 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
 
1.0%
Fraktio4
 
0.8%
Mavericks4
 
0.8%
Arado3
 
0.6%
Pankki3
 
0.6%
KVTES-alainen kunnan omistama oy 2
 
0.4%
Siili2
 
0.4%
Gofore Oyj2
 
0.4%
Other values (63)66
 
13.8%
(Missing)369
77.2%
2021-02-23T13:13:46.923642image/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%
futurice5
 
2.9%
oyj5
 
2.9%
fraktio4
 
2.3%
siili4
 
2.3%
omistama3
 
1.7%
pankki3
 
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%
Missing441
Missing (%)92.3%
Memory size3.9 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Pieni firma ja paljon hattuja päässä. Palkka on hyvä, mutta ei korvaa stressiä ja painetta.
 
1
Bonukset riippuu firman tuloksesta. Palkka olisi varmastikin enemmän muualla mutta uskoakseni linjassa kollegoideni kanssa.
 
1
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi
 
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
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%
Pieni firma ja paljon hattuja päässä. Palkka on hyvä, mutta ei korvaa stressiä ja painetta.1
 
0.2%
Bonukset riippuu firman tuloksesta. Palkka olisi varmastikin enemmän muualla mutta uskoakseni linjassa kollegoideni kanssa.1
 
0.2%
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi1
 
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%
Ilmaset kaffet, safkat, salit jne.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%
Opiskelija1
 
0.2%
Rahapalkan päälle tulee vielä kohtuullinen optiopotti, mutta se toki on lähinnä arpalippu1
 
0.2%
Pakettiin kuuluu reilu määrä optioita ja palkka nousee (ja laskee) firman liikevaihdon myötä.1
 
0.2%
Other values (26)26
 
5.4%
(Missing)441
92.3%
2021-02-23T13:13:47.334636image/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%
ihan4
 
0.9%
olen4
 
0.9%
firman4
 
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

Distinct181
Distinct (%)38.8%
Missing12
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean5498.992579
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.9 KiB
2021-02-23T13:13:47.539187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2894.791667
Q14166.666667
median4916.666667
Q36250
95-th percentile10312.5
Maximum25000
Range25000
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2660.632522
Coefficient of variation (CV)0.4838399914
Kurtosis11.9052155
Mean5498.992579
Median Absolute Deviation (MAD)984.375
Skewness2.670252748
Sum2562530.542
Variance7078965.418
MonotocityNot monotonic
2021-02-23T13:13:47.859117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4583.33333318
 
3.8%
4166.66666718
 
3.8%
625016
 
3.3%
500014
 
2.9%
7083.33333311
 
2.3%
5833.33333310
 
2.1%
5416.66666710
 
2.1%
5208.33333310
 
2.1%
4333.3333339
 
1.9%
6666.6666679
 
1.9%
Other values (171)341
71.3%
(Missing)12
 
2.5%
ValueCountFrequency (%)
02
0.4%
333.33333331
0.2%
508.33333331
0.2%
6251
0.2%
1145.8333331
0.2%
ValueCountFrequency (%)
250001
 
0.2%
20833.333331
 
0.2%
18333.333331
 
0.2%
16666.666674
0.8%
15833.333331
 
0.2%

Interactions

2021-02-23T13:13:37.058357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:37.219317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:37.374753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:37.536228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:37.687459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:37.839103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:38.003288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:38.176738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:38.338415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:38.510226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:38.675247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:38.939721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:39.102156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:39.259358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:39.433323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:39.602753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:39.762533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:39.922055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:40.079967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T13:13:40.240329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-23T13:13:48.023660image/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.
2021-02-23T13:13:48.217845image/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.
2021-02-23T13:13:48.416632image/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.
2021-02-23T13:13:48.629976image/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

2021-02-23T13:13:40.525038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-23T13:13:40.956454image/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.
2021-02-23T13:13:41.384382image/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.
2021-02-23T13:13:41.771367image/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
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
4742021-02-23 12:41:17.548Turku28mies5.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto4500.056250.0TrueNaNNaN4687.500000
4752021-02-23 12:51:35.493Hanksalmi28mies2.0Freelancer0.5ohjelmistokehittäjäEtä1100.013750.0FalseNaNNaN1145.833333
4762021-02-23 13:07:40.896PK-Seutu38mies4.0Työntekijä / palkollinen1.0FullstaxkEtä4300.058000.0TrueNaNNaN4833.333333
4772021-02-23 14:00:37.170Tampere28mies5.0Työntekijä / palkollinen1.0Senior/Lead developerToimisto4000.052000.0FalseNaNNaN4333.333333