Overview

Dataset statistics

Number of variables15
Number of observations470
Missing cells952
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.7 KiB
Average record size in memory95.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 248 distinct values High cardinality
Työpaikka has a high cardinality: 72 distinct values High cardinality
Vuositulot is highly correlated with Kk-tulotHigh correlation
Kk-tulot is highly correlated with VuositulotHigh correlation
Vapaa sana is highly correlated with Työpaikka and 1 other fieldsHigh correlation
Työpaikka is highly correlated with Vapaa sanaHigh 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 (2.6%) missing values Missing
Kuukausipalkka has 40 (8.5%) missing values Missing
Vuositulot has 11 (2.3%) missing values Missing
Kilpailukykyinen has 15 (3.2%) missing values Missing
Työpaikka has 363 (77.2%) missing values Missing
Vapaa sana has 434 (92.3%) missing values Missing
Kk-tulot has 11 (2.3%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-23 00:27:50.554461
Analysis finished2021-02-23 00:27:56.999088
Duration6.44 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-22 23:53:12.243000
2021-02-23T00:27:57.108857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:57.342047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

Distinct25
Distinct (%)5.4%
Missing4
Missing (%)0.9%
Memory size1.3 KiB
PK-Seutu
238 
Tampere
109 
Turku
46 
Oulu
25 
Jyväskylä
 
18
Other values (20)
30 

Length

Max length15
Median length8
Mean length7.24248927
Min length2

Characters and Unicode

Total characters3375
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 (%)2.8%

Sample

1st rowPK-Seutu
2nd rowTurku
3rd rowPK-Seutu
4th rowTampere
5th rowPK-Seutu
ValueCountFrequency (%)
PK-Seutu238
50.6%
Tampere109
23.2%
Turku46
 
9.8%
Oulu25
 
5.3%
Jyväskylä18
 
3.8%
Kuopio5
 
1.1%
Pori2
 
0.4%
Tallinna2
 
0.4%
Vaasa2
 
0.4%
Hämeenlinna2
 
0.4%
Other values (15)17
 
3.6%
(Missing)4
 
0.9%
2021-02-23T00:27:57.862710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu238
50.6%
tampere109
23.2%
turku46
 
9.8%
oulu25
 
5.3%
jyväskylä18
 
3.8%
kuopio5
 
1.1%
pori2
 
0.4%
hämeenlinna2
 
0.4%
tallinna2
 
0.4%
lontoo2
 
0.4%
Other values (19)21
 
4.5%

Most occurring characters

ValueCountFrequency (%)
u630
18.7%
e467
13.8%
K246
 
7.3%
t244
 
7.2%
P241
 
7.1%
-240
 
7.1%
S240
 
7.1%
r161
 
4.8%
T157
 
4.7%
a132
 
3.9%
Other values (29)617
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2182
64.7%
Uppercase Letter948
28.1%
Dash Punctuation240
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u630
28.9%
e467
21.4%
t244
 
11.2%
r161
 
7.4%
a132
 
6.0%
p115
 
5.3%
m114
 
5.2%
k65
 
3.0%
l54
 
2.5%
ä44
 
2.0%
Other values (10)156
 
7.1%
ValueCountFrequency (%)
K246
25.9%
P241
25.4%
S240
25.3%
T157
16.6%
O25
 
2.6%
J19
 
2.0%
E4
 
0.4%
L4
 
0.4%
V3
 
0.3%
H2
 
0.2%
Other values (6)7
 
0.7%
ValueCountFrequency (%)
-240
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3130
92.7%
Common245
 
7.3%

Most frequent character per script

ValueCountFrequency (%)
u630
20.1%
e467
14.9%
K246
 
7.9%
t244
 
7.8%
P241
 
7.7%
S240
 
7.7%
r161
 
5.1%
T157
 
5.0%
a132
 
4.2%
p115
 
3.7%
Other values (26)497
15.9%
ValueCountFrequency (%)
-240
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3331
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u630
18.9%
e467
14.0%
K246
 
7.4%
t244
 
7.3%
P241
 
7.2%
-240
 
7.2%
S240
 
7.2%
r161
 
4.8%
T157
 
4.7%
a132
 
4.0%
Other values (28)573
17.2%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.5%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean33.82264957
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2021-02-23T00:27:58.009080image/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 deviation6.079114369
Coefficient of variation (CV)0.1797350132
Kurtosis0.2156878558
Mean33.82264957
Median Absolute Deviation (MAD)5
Skewness0.4716517264
Sum15829
Variance36.95563151
MonotocityNot monotonic
2021-02-23T00:27:58.136883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33157
33.4%
28114
24.3%
38102
21.7%
4352
 
11.1%
2330
 
6.4%
487
 
1.5%
536
 
1.3%
(Missing)2
 
0.4%
ValueCountFrequency (%)
2330
 
6.4%
28114
24.3%
33157
33.4%
38102
21.7%
4352
 
11.1%
ValueCountFrequency (%)
536
 
1.3%
487
 
1.5%
4352
 
11.1%
38102
21.7%
33157
33.4%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing33
Missing (%)7.0%
Memory size730.0 B
mies
394 
nainen
 
35
muu
 
8

Length

Max length6
Median length4
Mean length4.14187643
Min length3

Characters and Unicode

Total characters1810
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 (%)
mies394
83.8%
nainen35
 
7.4%
muu8
 
1.7%
(Missing)33
 
7.0%
2021-02-23T00:27:58.508428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T00:27:58.630325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies394
90.2%
nainen35
 
8.0%
muu8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i429
23.7%
e429
23.7%
m402
22.2%
s394
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1810
100.0%

Most frequent character per category

ValueCountFrequency (%)
i429
23.7%
e429
23.7%
m402
22.2%
s394
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1810
100.0%

Most frequent character per script

ValueCountFrequency (%)
i429
23.7%
e429
23.7%
m402
22.2%
s394
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1810
100.0%

Most frequent character per block

ValueCountFrequency (%)
i429
23.7%
e429
23.7%
m402
22.2%
s394
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)5.8%
Missing4
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean9.650214592
Minimum0
Maximum30
Zeros4
Zeros (%)0.9%
Memory size3.8 KiB
2021-02-23T00:27:58.757339image/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 deviation6.0723213
Coefficient of variation (CV)0.6292421005
Kurtosis-0.05240729566
Mean9.650214592
Median Absolute Deviation (MAD)4
Skewness0.712972362
Sum4497
Variance36.87308598
MonotocityNot monotonic
2021-02-23T00:27:58.945690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
551
 
10.9%
1038
 
8.1%
430
 
6.4%
729
 
6.2%
2027
 
5.7%
1527
 
5.7%
1325
 
5.3%
325
 
5.3%
824
 
5.1%
624
 
5.1%
Other values (17)166
35.3%
ValueCountFrequency (%)
04
 
0.9%
116
3.4%
224
5.1%
325
5.3%
430
6.4%
ValueCountFrequency (%)
302
 
0.4%
256
1.3%
243
0.6%
234
0.9%
225
1.1%
Distinct3
Distinct (%)0.6%
Missing1
Missing (%)0.2%
Memory size3.8 KiB
Työntekijä / palkollinen
419 
Yrittäjä
 
25
Freelancer
 
25

Length

Max length24
Median length24
Mean length22.40085288
Min length8

Characters and Unicode

Total characters10506
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 / palkollinen419
89.1%
Yrittäjä25
 
5.3%
Freelancer25
 
5.3%
(Missing)1
 
0.2%
2021-02-23T00:27:59.337797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T00:27:59.465087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
419
32.1%
palkollinen419
32.1%
työntekijä419
32.1%
yrittäjä25
 
1.9%
freelancer25
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1282
12.2%
l1282
12.2%
e913
 
8.7%
i863
 
8.2%
k838
 
8.0%
838
 
8.0%
t469
 
4.5%
ä469
 
4.5%
j444
 
4.2%
a444
 
4.2%
Other values (10)2664
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8780
83.6%
Space Separator838
 
8.0%
Uppercase Letter469
 
4.5%
Other Punctuation419
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1282
14.6%
l1282
14.6%
e913
10.4%
i863
9.8%
k838
9.5%
t469
 
5.3%
ä469
 
5.3%
j444
 
5.1%
a444
 
5.1%
y419
 
4.8%
Other values (5)1357
15.5%
ValueCountFrequency (%)
T419
89.3%
Y25
 
5.3%
F25
 
5.3%
ValueCountFrequency (%)
838
100.0%
ValueCountFrequency (%)
/419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9249
88.0%
Common1257
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1282
13.9%
l1282
13.9%
e913
9.9%
i863
9.3%
k838
9.1%
t469
 
5.1%
ä469
 
5.1%
j444
 
4.8%
a444
 
4.8%
T419
 
4.5%
Other values (8)1826
19.7%
ValueCountFrequency (%)
838
66.7%
/419
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9618
91.5%
None888
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1282
13.3%
l1282
13.3%
e913
9.5%
i863
9.0%
k838
8.7%
838
8.7%
t469
 
4.9%
j444
 
4.6%
a444
 
4.6%
T419
 
4.4%
Other values (8)1826
19.0%
ValueCountFrequency (%)
ä469
52.8%
ö419
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)4.0%
Memory size3.8 KiB
1.0
424 
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 characters1353
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 (%)
1.0424
90.2%
0.823
 
4.9%
0.52
 
0.4%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
4.0%
2021-02-23T00:27:59.740411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T00:27:59.840579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0424
94.0%
0.823
 
5.1%
0.52
 
0.4%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.451
33.3%
0451
33.3%
1424
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number902
66.7%
Other Punctuation451
33.3%

Most frequent character per category

ValueCountFrequency (%)
0451
50.0%
1424
47.0%
823
 
2.5%
52
 
0.2%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1353
100.0%

Most frequent character per script

ValueCountFrequency (%)
.451
33.3%
0451
33.3%
1424
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1353
100.0%

Most frequent character per block

ValueCountFrequency (%)
.451
33.3%
0451
33.3%
1424
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct248
Distinct (%)54.1%
Missing12
Missing (%)2.6%
Memory size3.8 KiB
Ohjelmistokehittäjä
38 
full-stack
 
33
Full-stack
 
23
ohjelmistokehittäjä
 
16
Arkkitehti
 
15
Other values (243)
333 

Length

Max length67
Median length18
Mean length19.12008734
Min length2

Characters and Unicode

Total characters8757
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

Unique201 ?
Unique (%)43.9%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä38
 
8.1%
full-stack33
 
7.0%
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%
Full-stack kehittäjä5
 
1.1%
Other values (238)301
64.0%
(Missing)12
 
2.6%
2021-02-23T00:28:00.310893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack135
 
16.0%
ohjelmistokehittäjä108
 
12.8%
developer57
 
6.8%
arkkitehti35
 
4.2%
34
 
4.0%
lead32
 
3.8%
frontend25
 
3.0%
senior20
 
2.4%
kehittäjä16
 
1.9%
backend16
 
1.9%
Other values (180)364
43.2%

Most occurring characters

ValueCountFrequency (%)
t913
 
10.4%
e808
 
9.2%
i640
 
7.3%
l638
 
7.3%
k486
 
5.5%
o458
 
5.2%
a423
 
4.8%
s411
 
4.7%
390
 
4.5%
h351
 
4.0%
Other values (47)3239
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7608
86.9%
Uppercase Letter442
 
5.0%
Space Separator391
 
4.5%
Dash Punctuation164
 
1.9%
Other Punctuation94
 
1.1%
Open Punctuation25
 
0.3%
Close Punctuation25
 
0.3%
Math Symbol8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t913
12.0%
e808
 
10.6%
i640
 
8.4%
l638
 
8.4%
k486
 
6.4%
o458
 
6.0%
a423
 
5.6%
s411
 
5.4%
h351
 
4.6%
j330
 
4.3%
Other values (16)2150
28.3%
ValueCountFrequency (%)
F98
22.2%
O90
20.4%
S49
11.1%
D40
9.0%
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 (%)
,51
54.3%
/39
41.5%
&3
 
3.2%
.1
 
1.1%
ValueCountFrequency (%)
390
99.7%
 1
 
0.3%
ValueCountFrequency (%)
-164
100.0%
ValueCountFrequency (%)
(25
100.0%
ValueCountFrequency (%)
)25
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8050
91.9%
Common707
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t913
 
11.3%
e808
 
10.0%
i640
 
8.0%
l638
 
7.9%
k486
 
6.0%
o458
 
5.7%
a423
 
5.3%
s411
 
5.1%
h351
 
4.4%
j330
 
4.1%
Other values (37)2592
32.2%
ValueCountFrequency (%)
390
55.2%
-164
23.2%
,51
 
7.2%
/39
 
5.5%
(25
 
3.5%
)25
 
3.5%
+8
 
1.1%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8424
96.2%
None333
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t913
 
10.8%
e808
 
9.6%
i640
 
7.6%
l638
 
7.6%
k486
 
5.8%
o458
 
5.4%
a423
 
5.0%
s411
 
4.9%
390
 
4.6%
h351
 
4.2%
Other values (44)2906
34.5%
ValueCountFrequency (%)
ä316
94.9%
ö16
 
4.8%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size730.0 B
Etä
197 
Toimisto
159 
50/50
111 

Length

Max length8
Median length5
Mean length5.177730193
Min length3

Characters and Unicode

Total characters2418
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 (%)
Etä197
41.9%
Toimisto159
33.8%
50/50111
23.6%
(Missing)3
 
0.6%
2021-02-23T00:28:00.789887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T00:28:00.907956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä197
42.2%
toimisto159
34.0%
50/50111
23.8%

Most occurring characters

ValueCountFrequency (%)
t356
14.7%
o318
13.2%
i318
13.2%
5222
9.2%
0222
9.2%
E197
8.1%
ä197
8.1%
T159
6.6%
m159
6.6%
s159
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1507
62.3%
Decimal Number444
 
18.4%
Uppercase Letter356
 
14.7%
Other Punctuation111
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
t356
23.6%
o318
21.1%
i318
21.1%
ä197
13.1%
m159
10.6%
s159
10.6%
ValueCountFrequency (%)
5222
50.0%
0222
50.0%
ValueCountFrequency (%)
E197
55.3%
T159
44.7%
ValueCountFrequency (%)
/111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1863
77.0%
Common555
 
23.0%

Most frequent character per script

ValueCountFrequency (%)
t356
19.1%
o318
17.1%
i318
17.1%
E197
10.6%
ä197
10.6%
T159
8.5%
m159
8.5%
s159
8.5%
ValueCountFrequency (%)
5222
40.0%
0222
40.0%
/111
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2221
91.9%
None197
 
8.1%

Most frequent character per block

ValueCountFrequency (%)
t356
16.0%
o318
14.3%
i318
14.3%
5222
10.0%
0222
10.0%
E197
8.9%
T159
7.2%
m159
7.2%
s159
7.2%
/111
 
5.0%
ValueCountFrequency (%)
ä197
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct125
Distinct (%)29.1%
Missing40
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean4711.781395
Minimum1666
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2021-02-23T00:28:01.053151image/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 deviation1445.884101
Coefficient of variation (CV)0.306865701
Kurtosis8.071766537
Mean4711.781395
Median Absolute Deviation (MAD)787.5
Skewness1.716319819
Sum2026066
Variance2090580.833
MonotocityNot monotonic
2021-02-23T00:28:01.275406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400025
 
5.3%
450022
 
4.7%
600017
 
3.6%
500017
 
3.6%
550016
 
3.4%
480012
 
2.6%
420012
 
2.6%
380011
 
2.3%
300011
 
2.3%
700011
 
2.3%
Other values (115)276
58.7%
(Missing)40
 
8.5%
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 (%)39.2%
Missing11
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean66245.35185
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2021-02-23T00:28:01.503268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34965
Q150000
median59375
Q375000
95-th percentile125500
Maximum300000
Range300000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation32038.72433
Coefficient of variation (CV)0.4836373184
Kurtosis11.82347203
Mean66245.35185
Median Absolute Deviation (MAD)11875
Skewness2.66969952
Sum30406616.5
Variance1026479857
MonotocityNot monotonic
2021-02-23T00:28:01.713575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.8%
5000017
 
3.6%
7500016
 
3.4%
6000014
 
3.0%
8500011
 
2.3%
6500010
 
2.1%
6250010
 
2.1%
375009
 
1.9%
475009
 
1.9%
700009
 
1.9%
Other values (170)336
71.5%
(Missing)11
 
2.3%
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
0.9%
1900001
 
0.2%

Kilpailukykyinen
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing15
Missing (%)3.2%
Memory size3.8 KiB
True
314 
False
141 
(Missing)
 
15
ValueCountFrequency (%)
True314
66.8%
False141
30.0%
(Missing)15
 
3.2%
2021-02-23T00:28:01.867402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct72
Distinct (%)67.3%
Missing363
Missing (%)77.2%
Memory size3.8 KiB
Gofore
11 
Vincit
 
6
Futurice
 
5
Fraktio
 
4
Mavericks
 
4
Other values (67)
77 

Length

Max length132
Median length8
Mean length10.62616822
Min length2

Characters and Unicode

Total characters1137
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

Unique59 ?
Unique (%)55.1%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.3%
Vincit6
 
1.3%
Futurice5
 
1.1%
Fraktio4
 
0.9%
Mavericks4
 
0.9%
Pankki3
 
0.6%
Arado3
 
0.6%
Gofore Oyj2
 
0.4%
KVTES-alainen kunnan omistama oy 2
 
0.4%
Compile Oy2
 
0.4%
Other values (62)65
 
13.8%
(Missing)363
77.2%
2021-02-23T00:28:02.241926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore13
 
7.6%
oy12
 
7.1%
mavericks6
 
3.5%
vincit6
 
3.5%
oyj5
 
2.9%
futurice5
 
2.9%
siili4
 
2.4%
fraktio4
 
2.4%
omistama3
 
1.8%
konsulttitalo3
 
1.8%
Other values (92)109
64.1%

Most occurring characters

ValueCountFrequency (%)
i115
 
10.1%
a87
 
7.7%
o86
 
7.6%
e81
 
7.1%
t78
 
6.9%
66
 
5.8%
r61
 
5.4%
n53
 
4.7%
l46
 
4.0%
u44
 
3.9%
Other values (44)420
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter920
80.9%
Uppercase Letter145
 
12.8%
Space Separator66
 
5.8%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i115
12.5%
a87
 
9.5%
o86
 
9.3%
e81
 
8.8%
t78
 
8.5%
r61
 
6.6%
n53
 
5.8%
l46
 
5.0%
u44
 
4.8%
k43
 
4.7%
Other values (16)226
24.6%
ValueCountFrequency (%)
O17
 
11.7%
G14
 
9.7%
S14
 
9.7%
V12
 
8.3%
F10
 
6.9%
K8
 
5.5%
C7
 
4.8%
A7
 
4.8%
M7
 
4.8%
P6
 
4.1%
Other values (15)43
29.7%
ValueCountFrequency (%)
66
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1065
93.7%
Common72
 
6.3%

Most frequent character per script

ValueCountFrequency (%)
i115
 
10.8%
a87
 
8.2%
o86
 
8.1%
e81
 
7.6%
t78
 
7.3%
r61
 
5.7%
n53
 
5.0%
l46
 
4.3%
u44
 
4.1%
k43
 
4.0%
Other values (41)371
34.8%
ValueCountFrequency (%)
66
91.7%
.3
 
4.2%
-3
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1125
98.9%
None12
 
1.1%

Most frequent character per block

ValueCountFrequency (%)
i115
 
10.2%
a87
 
7.7%
o86
 
7.6%
e81
 
7.2%
t78
 
6.9%
66
 
5.9%
r61
 
5.4%
n53
 
4.7%
l46
 
4.1%
u44
 
3.9%
Other values (42)408
36.3%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct35
Distinct (%)97.2%
Missing434
Missing (%)92.3%
Memory size3.8 KiB
palkan lisänä lounas- ja virkistysetu
 
2
saispa lisää liksaa
 
1
+ merkittävä optiopaketti
 
1
Ilmaset kaffet, safkat, salit jne.
 
1
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
Other values (30)
30 

Length

Max length286
Median length73
Mean length93.11111111
Min length7

Characters and Unicode

Total characters3352
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

Unique34 ?
Unique (%)94.4%

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%
saispa lisää liksaa1
 
0.2%
+ merkittävä optiopaketti1
 
0.2%
Ilmaset kaffet, safkat, salit jne.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%
Pieni firma ja paljon hattuja päässä. Palkka on hyvä, mutta ei korvaa stressiä ja painetta.1
 
0.2%
Osittain laskutukseen perustuva palkka joten vaihtelee.1
 
0.2%
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi1
 
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%
startup, palkan lisäksi optiopaketti.1
 
0.2%
Other values (25)25
 
5.3%
(Missing)434
92.3%
2021-02-23T00:28:02.675466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ei11
 
2.6%
palkka10
 
2.4%
on9
 
2.1%
ja8
 
1.9%
mutta8
 
1.9%
ole6
 
1.4%
nyt4
 
0.9%
ihan4
 
0.9%
firman4
 
0.9%
joten4
 
0.9%
Other values (299)356
84.0%

Most occurring characters

ValueCountFrequency (%)
391
11.7%
a359
10.7%
i282
 
8.4%
t265
 
7.9%
n225
 
6.7%
s220
 
6.6%
e212
 
6.3%
k197
 
5.9%
l166
 
5.0%
o157
 
4.7%
Other values (46)878
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2789
83.2%
Space Separator391
 
11.7%
Other Punctuation79
 
2.4%
Uppercase Letter50
 
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 (%)
a359
12.9%
i282
10.1%
t265
9.5%
n225
 
8.1%
s220
 
7.9%
e212
 
7.6%
k197
 
7.1%
l166
 
6.0%
o157
 
5.6%
u126
 
4.5%
Other values (14)580
20.8%
ValueCountFrequency (%)
P9
18.0%
T7
14.0%
O6
12.0%
E6
12.0%
V6
12.0%
K4
8.0%
S4
8.0%
I2
 
4.0%
H2
 
4.0%
R1
 
2.0%
Other values (3)3
 
6.0%
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 (%)
.41
51.9%
,25
31.6%
/5
 
6.3%
%4
 
5.1%
"2
 
2.5%
?2
 
2.5%
ValueCountFrequency (%)
391
100.0%
ValueCountFrequency (%)
(3
100.0%
ValueCountFrequency (%)
)3
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2839
84.7%
Common513
 
15.3%

Most frequent character per script

ValueCountFrequency (%)
a359
12.6%
i282
9.9%
t265
9.3%
n225
 
7.9%
s220
 
7.7%
e212
 
7.5%
k197
 
6.9%
l166
 
5.8%
o157
 
5.5%
u126
 
4.4%
Other values (27)630
22.2%
ValueCountFrequency (%)
391
76.2%
.41
 
8.0%
,25
 
4.9%
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 (%)
ASCII3209
95.7%
None143
 
4.3%

Most frequent character per block

ValueCountFrequency (%)
391
12.2%
a359
11.2%
i282
 
8.8%
t265
 
8.3%
n225
 
7.0%
s220
 
6.9%
e212
 
6.6%
k197
 
6.1%
l166
 
5.2%
o157
 
4.9%
Other values (44)735
22.9%
ValueCountFrequency (%)
ä119
83.2%
ö24
 
16.8%

Kk-tulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct180
Distinct (%)39.2%
Missing11
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean5520.445988
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2021-02-23T00:28:02.884524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2913.75
Q14166.666667
median4947.916667
Q36250
95-th percentile10458.33333
Maximum25000
Range25000
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2669.893694
Coefficient of variation (CV)0.4836373184
Kurtosis11.82347203
Mean5520.445988
Median Absolute Deviation (MAD)989.5833333
Skewness2.66969952
Sum2533884.708
Variance7128332.337
MonotocityNot monotonic
2021-02-23T00:28:03.236817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4583.33333318
 
3.8%
4166.66666717
 
3.6%
625016
 
3.4%
500014
 
3.0%
7083.33333311
 
2.3%
5208.33333310
 
2.1%
5416.66666710
 
2.1%
6666.6666679
 
1.9%
31259
 
1.9%
3333.3333339
 
1.9%
Other values (170)336
71.5%
(Missing)11
 
2.3%
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
0.9%
15833.333331
 
0.2%

Interactions

2021-02-23T00:27:51.705615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:51.890142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:52.066275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:52.232147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:52.405438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:52.583728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:52.763979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:52.945880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:53.124080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:53.299627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:53.483622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:53.765576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:53.944245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:54.118692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:54.292178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:54.467795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:54.649310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:54.812731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:54.983448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-23T00:27:55.159296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-23T00:28:03.421356image/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.
2021-02-23T00:28:03.637997image/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.
2021-02-23T00:28:03.853558image/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.
2021-02-23T00:28:04.099231image/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

2021-02-23T00:27:55.472008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-23T00:27:55.932708image/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.
2021-02-23T00:27:56.373396image/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.
2021-02-23T00:27:56.788524image/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
4602021-02-22 12:44:27.805Tampere38mies15.0Työntekijä / palkollinen1.0fullstack-ohjelmistokehittä / arkkitehti / pilviveikkoEtä5700.070000.0TrueNaNNaN5833.333333
4612021-02-22 12:44:41.634Oulu28mies7.0Työntekijä / palkollinen1.0BackendEtä3800.047500.0TrueNaNNaN3958.333333
4622021-02-22 12:49:30.713PK-Seutu28mies5.0Työntekijä / palkollinen1.0MobiilikehittäjäToimisto4500.056250.0TrueNaNNaN4687.500000
4632021-02-22 12:51:26.991Oulu28nainen5.0Työntekijä / palkollinen1.0Web developer50/503000.037500.0FalseNaNKokemusta kokonaisuudessaan 7v, mutta siitä reilut kaksi vuotta lasten kanssa kotona koodaamatta.3125.000000
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