Overview

Dataset statistics

Number of variables15
Number of observations479
Missing cells972
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.0 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 34 (7.1%) 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 370 (77.2%) missing values Missing
Vapaa sana has 442 (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 16:18:43.335177
Analysis finished2021-02-23 16:18:47.678604
Duration4.34 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct479
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-23 16:18:52.379000
2021-02-23T16:18:47.745803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:47.890537image/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
111 
Turku
47 
Oulu
25 
Jyväskylä
 
18
Other values (21)
32 

Length

Max length15
Median length8
Mean length7.242105263
Min length2

Characters and Unicode

Total characters3440
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
50.5%
Tampere111
23.2%
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%
2021-02-23T16:18:48.314140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu242
50.5%
tampere111
23.2%
turku47
 
9.8%
oulu25
 
5.2%
jyväskylä18
 
3.8%
kuopio5
 
1.0%
tallinna2
 
0.4%
eu2
 
0.4%
pori2
 
0.4%
hämeenlinna2
 
0.4%
Other values (20)23
 
4.8%

Most occurring characters

ValueCountFrequency (%)
u640
18.6%
e475
13.8%
K250
 
7.3%
t249
 
7.2%
P245
 
7.1%
-244
 
7.1%
S244
 
7.1%
r164
 
4.8%
T160
 
4.7%
a137
 
4.0%
Other values (29)632
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2226
64.7%
Uppercase Letter965
28.1%
Dash Punctuation244
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u640
28.8%
e475
21.3%
t249
 
11.2%
r164
 
7.4%
a137
 
6.2%
m117
 
5.3%
p117
 
5.3%
k67
 
3.0%
l55
 
2.5%
ä44
 
2.0%
Other values (10)161
 
7.2%
ValueCountFrequency (%)
K250
25.9%
P245
25.4%
S244
25.3%
T160
16.6%
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 (%)
Latin3191
92.8%
Common249
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u640
20.1%
e475
14.9%
K250
 
7.8%
t249
 
7.8%
P245
 
7.7%
S244
 
7.6%
r164
 
5.1%
T160
 
5.0%
a137
 
4.3%
m117
 
3.7%
Other values (26)510
16.0%
ValueCountFrequency (%)
-244
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3396
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u640
18.8%
e475
14.0%
K250
 
7.4%
t249
 
7.3%
P245
 
7.2%
-244
 
7.2%
S244
 
7.2%
r164
 
4.8%
T160
 
4.7%
a137
 
4.0%
Other values (28)588
17.3%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.5%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean33.81932773
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2021-02-23T16:18:48.414214image/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
2021-02-23T16:18:48.503752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33159
33.2%
28117
24.4%
38104
21.7%
4353
 
11.1%
2330
 
6.3%
487
 
1.5%
536
 
1.3%
(Missing)3
 
0.6%
ValueCountFrequency (%)
2330
 
6.3%
28117
24.4%
33159
33.2%
38104
21.7%
4353
 
11.1%
ValueCountFrequency (%)
536
 
1.3%
487
 
1.5%
4353
 
11.1%
38104
21.7%
33159
33.2%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing34
Missing (%)7.1%
Memory size739.0 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
83.7%
nainen36
 
7.5%
muu8
 
1.7%
(Missing)34
 
7.1%
2021-02-23T16:18:48.802803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T16:18:48.884543image/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%
Mean9.593684211
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size3.9 KiB
2021-02-23T16:18:48.970297image/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 deviation6.04912747
Coefficient of variation (CV)0.6305322687
Kurtosis-0.02508619952
Mean9.593684211
Median Absolute Deviation (MAD)4
Skewness0.7255989633
Sum4557
Variance36.59194315
MonotocityNot monotonic
2021-02-23T16:18:49.081474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
553
 
11.1%
1039
 
8.1%
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
427 
Freelancer
 
26
Yrittäjä
 
25

Length

Max length24
Median length24
Mean length22.40167364
Min length8

Characters and Unicode

Total characters10708
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 / palkollinen427
89.1%
Freelancer26
 
5.4%
Yrittäjä25
 
5.2%
(Missing)1
 
0.2%
2021-02-23T16:18:49.342777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T16:18:49.429796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
427
32.1%
palkollinen427
32.1%
työntekijä427
32.1%
freelancer26
 
2.0%
yrittäjä25
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1307
12.2%
l1307
12.2%
e932
 
8.7%
i879
 
8.2%
k854
 
8.0%
854
 
8.0%
t477
 
4.5%
ä477
 
4.5%
a453
 
4.2%
j452
 
4.2%
Other values (10)2716
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8949
83.6%
Space Separator854
 
8.0%
Uppercase Letter478
 
4.5%
Other Punctuation427
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1307
14.6%
l1307
14.6%
e932
10.4%
i879
9.8%
k854
9.5%
t477
 
5.3%
ä477
 
5.3%
a453
 
5.1%
j452
 
5.1%
y427
 
4.8%
Other values (5)1384
15.5%
ValueCountFrequency (%)
T427
89.3%
F26
 
5.4%
Y25
 
5.2%
ValueCountFrequency (%)
854
100.0%
ValueCountFrequency (%)
/427
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9427
88.0%
Common1281
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1307
13.9%
l1307
13.9%
e932
9.9%
i879
9.3%
k854
9.1%
t477
 
5.1%
ä477
 
5.1%
a453
 
4.8%
j452
 
4.8%
T427
 
4.5%
Other values (8)1862
19.8%
ValueCountFrequency (%)
854
66.7%
/427
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9804
91.6%
None904
 
8.4%

Most frequent character per block

ValueCountFrequency (%)
n1307
13.3%
l1307
13.3%
e932
9.5%
i879
9.0%
k854
8.7%
854
8.7%
t477
 
4.9%
a453
 
4.6%
j452
 
4.6%
T427
 
4.4%
Other values (8)1862
19.0%
ValueCountFrequency (%)
ä477
52.8%
ö427
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)4.0%
Memory size3.9 KiB
1.0
432 
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 characters1380
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 (%)
1.0432
90.2%
0.823
 
4.8%
0.53
 
0.6%
0.61
 
0.2%
0.71
 
0.2%
(Missing)19
 
4.0%
2021-02-23T16:18:49.637962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T16:18:49.708336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0432
93.9%
0.823
 
5.0%
0.53
 
0.7%
0.61
 
0.2%
0.71
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.460
33.3%
0460
33.3%
1432
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number920
66.7%
Other Punctuation460
33.3%

Most frequent character per category

ValueCountFrequency (%)
0460
50.0%
1432
47.0%
823
 
2.5%
53
 
0.3%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1380
100.0%

Most frequent character per script

ValueCountFrequency (%)
.460
33.3%
0460
33.3%
1432
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1380
100.0%

Most frequent character per block

ValueCountFrequency (%)
.460
33.3%
0460
33.3%
1432
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct253
Distinct (%)54.2%
Missing12
Missing (%)2.5%
Memory size3.9 KiB
Ohjelmistokehittäjä
40 
full-stack
34 
Full-stack
 
23
ohjelmistokehittäjä
 
17
Arkkitehti
 
15
Other values (248)
338 

Length

Max length67
Median length18
Mean length19.14561028
Min length2

Characters and Unicode

Total characters8941
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 (%)44.1%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä40
 
8.4%
full-stack34
 
7.1%
Full-stack23
 
4.8%
ohjelmistokehittäjä17
 
3.5%
Arkkitehti15
 
3.1%
Full-stack ohjelmistokehittäjä8
 
1.7%
full-stack ohjelmistokehittäjä7
 
1.5%
arkkitehti6
 
1.3%
Frontend6
 
1.3%
CTO5
 
1.0%
Other values (243)306
63.9%
(Missing)12
 
2.5%
2021-02-23T16:18:50.020968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack138
 
16.1%
ohjelmistokehittäjä111
 
13.0%
developer59
 
6.9%
arkkitehti35
 
4.1%
34
 
4.0%
lead32
 
3.7%
frontend25
 
2.9%
senior21
 
2.5%
backend16
 
1.9%
kehittäjä16
 
1.9%
Other values (184)369
43.1%

Most occurring characters

ValueCountFrequency (%)
t933
 
10.4%
e826
 
9.2%
l654
 
7.3%
i651
 
7.3%
k495
 
5.5%
o471
 
5.3%
a428
 
4.8%
s422
 
4.7%
395
 
4.4%
h359
 
4.0%
Other values (47)3307
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7771
86.9%
Uppercase Letter449
 
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 (%)
t933
12.0%
e826
 
10.6%
l654
 
8.4%
i651
 
8.4%
k495
 
6.4%
o471
 
6.1%
a428
 
5.5%
s422
 
5.4%
h359
 
4.6%
j338
 
4.3%
Other values (16)2194
28.2%
ValueCountFrequency (%)
F100
22.3%
O92
20.5%
S50
11.1%
D41
9.1%
T27
 
6.0%
A26
 
5.8%
L20
 
4.5%
C17
 
3.8%
P11
 
2.4%
E11
 
2.4%
Other values (11)54
12.0%
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 (%)
Latin8220
91.9%
Common721
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t933
 
11.4%
e826
 
10.0%
l654
 
8.0%
i651
 
7.9%
k495
 
6.0%
o471
 
5.7%
a428
 
5.2%
s422
 
5.1%
h359
 
4.4%
j338
 
4.1%
Other values (37)2643
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 (%)
ASCII8600
96.2%
None341
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t933
 
10.8%
e826
 
9.6%
l654
 
7.6%
i651
 
7.6%
k495
 
5.8%
o471
 
5.5%
a428
 
5.0%
s422
 
4.9%
395
 
4.6%
h359
 
4.2%
Other values (44)2966
34.5%
ValueCountFrequency (%)
ä324
95.0%
ö16
 
4.7%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size739.0 B
Etä
200 
Toimisto
164 
50/50
112 

Length

Max length8
Median length5
Mean length5.193277311
Min length3

Characters and Unicode

Total characters2472
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%
Toimisto164
34.2%
50/50112
23.4%
(Missing)3
 
0.6%
2021-02-23T16:18:50.392235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T16:18:50.470344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä200
42.0%
toimisto164
34.5%
50/50112
23.5%

Most occurring characters

ValueCountFrequency (%)
t364
14.7%
o328
13.3%
i328
13.3%
5224
9.1%
0224
9.1%
E200
8.1%
ä200
8.1%
T164
6.6%
m164
6.6%
s164
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1548
62.6%
Decimal Number448
 
18.1%
Uppercase Letter364
 
14.7%
Other Punctuation112
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t364
23.5%
o328
21.2%
i328
21.2%
ä200
12.9%
m164
10.6%
s164
10.6%
ValueCountFrequency (%)
5224
50.0%
0224
50.0%
ValueCountFrequency (%)
E200
54.9%
T164
45.1%
ValueCountFrequency (%)
/112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1912
77.3%
Common560
 
22.7%

Most frequent character per script

ValueCountFrequency (%)
t364
19.0%
o328
17.2%
i328
17.2%
E200
10.5%
ä200
10.5%
T164
8.6%
m164
8.6%
s164
8.6%
ValueCountFrequency (%)
5224
40.0%
0224
40.0%
/112
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2272
91.9%
None200
 
8.1%

Most frequent character per block

ValueCountFrequency (%)
t364
16.0%
o328
14.4%
i328
14.4%
5224
9.9%
0224
9.9%
E200
8.8%
T164
7.2%
m164
7.2%
s164
7.2%
/112
 
4.9%
ValueCountFrequency (%)
ä200
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct127
Distinct (%)29.0%
Missing41
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean4696.214612
Minimum1100
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2021-02-23T16:18:50.567878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile2795.5
Q13800
median4500
Q35500
95-th percentile7000
Maximum15000
Range13900
Interquartile range (IQR)1700

Descriptive statistics

Standard deviation1445.551973
Coefficient of variation (CV)0.3078121621
Kurtosis8.040654277
Mean4696.214612
Median Absolute Deviation (MAD)765.5
Skewness1.680538294
Sum2056942
Variance2089620.508
MonotocityNot monotonic
2021-02-23T16:18:50.697195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400026
 
5.4%
450023
 
4.8%
600017
 
3.5%
500017
 
3.5%
550016
 
3.3%
480012
 
2.5%
420012
 
2.5%
430012
 
2.5%
380011
 
2.3%
300011
 
2.3%
Other values (117)281
58.7%
(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%
Mean65962.2409
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.9 KiB
2021-02-23T16:18:50.839623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34755
Q150000
median59000
Q375000
95-th percentile123500
Maximum300000
Range300000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation31898.1388
Coefficient of variation (CV)0.4835817941
Kurtosis11.93661581
Mean65962.2409
Median Absolute Deviation (MAD)11625
Skewness2.674195426
Sum30804366.5
Variance1017491259
MonotocityNot monotonic
2021-02-23T16:18:50.975180image/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%
6250010
 
2.1%
6500010
 
2.1%
7000010
 
2.1%
475009
 
1.9%
540009
 
1.9%
Other values (171)342
71.4%
(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
319 
False
145 
(Missing)
 
15
ValueCountFrequency (%)
True319
66.6%
False145
30.3%
(Missing)15
 
3.1%
2021-02-23T16:18:51.077870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)67.0%
Missing370
Missing (%)77.2%
Memory size3.9 KiB
Gofore
11 
Vincit
 
7
Futurice
 
5
Mavericks
 
4
Fraktio
 
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%
Mavericks4
 
0.8%
Fraktio4
 
0.8%
Arado3
 
0.6%
Pankki3
 
0.6%
KVTES-alainen kunnan omistama oy 2
 
0.4%
Siili2
 
0.4%
Compile Oy2
 
0.4%
Other values (63)66
 
13.8%
(Missing)370
77.2%
2021-02-23T16:18:51.322426image/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%
pankki3
 
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%
Missing442
Missing (%)92.3%
Memory size3.9 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Ennen koronaa oli osittainen etätyö, koronan jälkeen 100%
 
1
Pakettiin kuuluu reilu määrä optioita ja palkka nousee (ja laskee) firman liikevaihdon myötä.
 
1
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
Osittain laskutukseen perustuva palkka joten vaihtelee.
 
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%
Ennen koronaa oli osittainen etätyö, koronan jälkeen 100%1
 
0.2%
Pakettiin kuuluu reilu määrä optioita ja palkka nousee (ja laskee) firman liikevaihdon myötä.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%
Osittain laskutukseen perustuva palkka joten vaihtelee.1
 
0.2%
Työskentelen opintojen ohella, ensimmäisessä frontend devaajan työssä. Olen opiskellut reilu 2 vuotta yliopistossa. Palkkani on mielestäni nyt ihan ok, mutta tarkoituksena nostaa sitä 3000e /kk loppukesään mennessä. 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%
hyvä kysely1
 
0.2%
Ilmaset kaffet, safkat, salit jne.1
 
0.2%
Vaikea vastata henkilönä joka tekee yrityksen kautta yhdelle ulkomaalaiselle yritykselle töitä (jolla ei ole entiteettiä suomessa). Vastasin nyt ikään kuin olisin yrittäjä vaikka käytännössä tämä on sama kuin olisin palkkaduunissa.1
 
0.2%
Other values (26)26
 
5.4%
(Missing)442
92.3%
2021-02-23T16:18:51.606050image/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%
ihan4
 
0.9%
olen4
 
0.9%
nyt4
 
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%
Mean5496.853408
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.9 KiB
2021-02-23T16:18:51.734977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2896.25
Q14166.666667
median4916.666667
Q36250
95-th percentile10291.66667
Maximum25000
Range25000
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2658.178233
Coefficient of variation (CV)0.4835817941
Kurtosis11.93661581
Mean5496.853408
Median Absolute Deviation (MAD)968.75
Skewness2.674195426
Sum2567030.542
Variance7065911.52
MonotocityNot monotonic
2021-02-23T16:18:52.008533image/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)342
71.4%
(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

2021-02-23T16:18:44.091209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:44.211546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:44.329296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:44.447643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:44.562026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:44.679516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:44.802013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:44.924510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:45.043145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:45.161537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:45.283151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:45.514179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:45.632880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:45.749867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:45.871350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:45.993744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:46.112993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:46.224984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:46.340339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T16:18:46.457016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-23T16:18:52.127633image/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.
2021-02-23T16:18:52.283757image/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.
2021-02-23T16:18:52.439853image/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.
2021-02-23T16:18:52.602478image/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

2021-02-23T16:18:46.686853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-23T16:18:46.986212image/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.
2021-02-23T16:18:47.273337image/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.
2021-02-23T16:18:47.543682image/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
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
4782021-02-23 16:18:52.379TampereNaNNaN10.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto4250.054000.0TrueNaNNaN4500.000000