Overview

Dataset statistics

Number of variables15
Number of observations468
Missing cells948
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.5 KiB
Average record size in memory95.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 246 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 Kilpailukykyinen and 1 other fieldsHigh correlation
Kilpailukykyinen is highly correlated with Vapaa sanaHigh correlation
Työpaikka is highly correlated with Vapaa sanaHigh correlation
Sukupuoli has 33 (7.1%) missing values Missing
Työaika has 19 (4.1%) missing values Missing
Rooli has 12 (2.6%) missing values Missing
Kuukausipalkka has 40 (8.5%) missing values Missing
Vuositulot has 11 (2.4%) missing values Missing
Kilpailukykyinen has 15 (3.2%) missing values Missing
Työpaikka has 361 (77.1%) missing values Missing
Vapaa sana has 432 (92.3%) missing values Missing
Kk-tulot has 11 (2.4%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-22 12:53:15.338293
Analysis finished2021-02-22 12:53:20.320641
Duration4.98 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct468
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-22 14:11:08.271000
2021-02-22T12:53:20.398309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:20.561116image/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
236 
Tampere
109 
Turku
46 
Oulu
25 
Jyväskylä
 
18
Other values (20)
30 

Length

Max length15
Median length8
Mean length7.239224138
Min length2

Characters and Unicode

Total characters3359
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-Seutu236
50.4%
Tampere109
23.3%
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%
2021-02-22T12:53:21.000300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu236
50.4%
tampere109
23.3%
turku46
 
9.8%
oulu25
 
5.3%
jyväskylä18
 
3.8%
kuopio5
 
1.1%
vaasa2
 
0.4%
pori2
 
0.4%
tallinna2
 
0.4%
hämeenlinna2
 
0.4%
Other values (19)21
 
4.5%

Most occurring characters

ValueCountFrequency (%)
u626
18.6%
e465
13.8%
K244
 
7.3%
t242
 
7.2%
P239
 
7.1%
-238
 
7.1%
S238
 
7.1%
r161
 
4.8%
T157
 
4.7%
a132
 
3.9%
Other values (29)617
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2174
64.7%
Uppercase Letter942
28.0%
Dash Punctuation238
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u626
28.8%
e465
21.4%
t242
 
11.1%
r161
 
7.4%
a132
 
6.1%
p115
 
5.3%
m114
 
5.2%
k65
 
3.0%
l54
 
2.5%
ä44
 
2.0%
Other values (10)156
 
7.2%
ValueCountFrequency (%)
K244
25.9%
P239
25.4%
S238
25.3%
T157
16.7%
O25
 
2.7%
J19
 
2.0%
E4
 
0.4%
L4
 
0.4%
V3
 
0.3%
H2
 
0.2%
Other values (6)7
 
0.7%
ValueCountFrequency (%)
-238
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3116
92.8%
Common243
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u626
20.1%
e465
14.9%
K244
 
7.8%
t242
 
7.8%
P239
 
7.7%
S238
 
7.6%
r161
 
5.2%
T157
 
5.0%
a132
 
4.2%
p115
 
3.7%
Other values (26)497
15.9%
ValueCountFrequency (%)
-238
97.9%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3315
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u626
18.9%
e465
14.0%
K244
 
7.4%
t242
 
7.3%
P239
 
7.2%
-238
 
7.2%
S238
 
7.2%
r161
 
4.9%
T157
 
4.7%
a132
 
4.0%
Other values (28)573
17.3%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.5%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean33.80472103
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2021-02-22T12:53:21.112746image/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 deviation6.077143785
Coefficient of variation (CV)0.1797720437
Kurtosis0.2281917572
Mean33.80472103
Median Absolute Deviation (MAD)5
Skewness0.4756058088
Sum15753
Variance36.93167659
MonotocityNot monotonic
2021-02-22T12:53:21.211582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33156
33.3%
28114
24.4%
38102
21.8%
4351
 
10.9%
2330
 
6.4%
487
 
1.5%
536
 
1.3%
(Missing)2
 
0.4%
ValueCountFrequency (%)
2330
 
6.4%
28114
24.4%
33156
33.3%
38102
21.8%
4351
 
10.9%
ValueCountFrequency (%)
536
 
1.3%
487
 
1.5%
4351
 
10.9%
38102
21.8%
33156
33.3%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing33
Missing (%)7.1%
Memory size728.0 B
mies
392 
nainen
 
35
muu
 
8

Length

Max length6
Median length4
Mean length4.142528736
Min length3

Characters and Unicode

Total characters1802
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 (%)
mies392
83.8%
nainen35
 
7.5%
muu8
 
1.7%
(Missing)33
 
7.1%
2021-02-22T12:53:21.497632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T12:53:21.591195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies392
90.1%
nainen35
 
8.0%
muu8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i427
23.7%
e427
23.7%
m400
22.2%
s392
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1802
100.0%

Most frequent character per category

ValueCountFrequency (%)
i427
23.7%
e427
23.7%
m400
22.2%
s392
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1802
100.0%

Most frequent character per script

ValueCountFrequency (%)
i427
23.7%
e427
23.7%
m400
22.2%
s392
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1802
100.0%

Most frequent character per block

ValueCountFrequency (%)
i427
23.7%
e427
23.7%
m400
22.2%
s392
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%
Mean9.642241379
Minimum0
Maximum30
Zeros4
Zeros (%)0.9%
Memory size3.8 KiB
2021-02-22T12:53:21.686894image/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 deviation6.079852375
Coefficient of variation (CV)0.6305434738
Kurtosis-0.05179289756
Mean9.642241379
Median Absolute Deviation (MAD)4
Skewness0.7159246209
Sum4474
Variance36.9646049
MonotocityNot monotonic
2021-02-22T12:53:21.809590image/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
 
5.8%
1526
 
5.6%
325
 
5.3%
1325
 
5.3%
624
 
5.1%
224
 
5.1%
Other values (17)165
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
417 
Freelancer
 
25
Yrittäjä
 
25

Length

Max length24
Median length24
Mean length22.39400428
Min length8

Characters and Unicode

Total characters10458
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 / palkollinen417
89.1%
Freelancer25
 
5.3%
Yrittäjä25
 
5.3%
(Missing)1
 
0.2%
2021-02-22T12:53:22.103122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T12:53:22.199689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
417
32.1%
palkollinen417
32.1%
työntekijä417
32.1%
yrittäjä25
 
1.9%
freelancer25
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1276
12.2%
l1276
12.2%
e909
 
8.7%
i859
 
8.2%
k834
 
8.0%
834
 
8.0%
t467
 
4.5%
ä467
 
4.5%
j442
 
4.2%
a442
 
4.2%
Other values (10)2652
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8740
83.6%
Space Separator834
 
8.0%
Uppercase Letter467
 
4.5%
Other Punctuation417
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1276
14.6%
l1276
14.6%
e909
10.4%
i859
9.8%
k834
9.5%
t467
 
5.3%
ä467
 
5.3%
j442
 
5.1%
a442
 
5.1%
y417
 
4.8%
Other values (5)1351
15.5%
ValueCountFrequency (%)
T417
89.3%
Y25
 
5.4%
F25
 
5.4%
ValueCountFrequency (%)
834
100.0%
ValueCountFrequency (%)
/417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9207
88.0%
Common1251
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1276
13.9%
l1276
13.9%
e909
9.9%
i859
9.3%
k834
9.1%
t467
 
5.1%
ä467
 
5.1%
j442
 
4.8%
a442
 
4.8%
T417
 
4.5%
Other values (8)1818
19.7%
ValueCountFrequency (%)
834
66.7%
/417
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9574
91.5%
None884
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1276
13.3%
l1276
13.3%
e909
9.5%
i859
9.0%
k834
8.7%
834
8.7%
t467
 
4.9%
j442
 
4.6%
a442
 
4.6%
T417
 
4.4%
Other values (8)1818
19.0%
ValueCountFrequency (%)
ä467
52.8%
ö417
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)4.1%
Memory size3.8 KiB
1.0
422 
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 characters1347
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 (%)
1.0422
90.2%
0.823
 
4.9%
0.52
 
0.4%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
4.1%
2021-02-22T12:53:22.421366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T12:53:22.499094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0422
94.0%
0.823
 
5.1%
0.52
 
0.4%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.449
33.3%
0449
33.3%
1422
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number898
66.7%
Other Punctuation449
33.3%

Most frequent character per category

ValueCountFrequency (%)
0449
50.0%
1422
47.0%
823
 
2.6%
52
 
0.2%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1347
100.0%

Most frequent character per script

ValueCountFrequency (%)
.449
33.3%
0449
33.3%
1422
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1347
100.0%

Most frequent character per block

ValueCountFrequency (%)
.449
33.3%
0449
33.3%
1422
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct246
Distinct (%)53.9%
Missing12
Missing (%)2.6%
Memory size3.8 KiB
Ohjelmistokehittäjä
38 
full-stack
 
33
Full-stack
 
23
ohjelmistokehittäjä
 
16
Arkkitehti
 
15
Other values (241)
331 

Length

Max length67
Median length18
Mean length19.12061404
Min length2

Characters and Unicode

Total characters8719
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

Unique199 ?
Unique (%)43.6%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä38
 
8.1%
full-stack33
 
7.1%
Full-stack23
 
4.9%
ohjelmistokehittäjä16
 
3.4%
Arkkitehti15
 
3.2%
Full-stack ohjelmistokehittäjä8
 
1.7%
full-stack ohjelmistokehittäjä7
 
1.5%
Frontend6
 
1.3%
arkkitehti6
 
1.3%
Full-stack kehittäjä5
 
1.1%
Other values (236)299
63.9%
(Missing)12
 
2.6%
2021-02-22T12:53:22.840729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack135
 
16.1%
ohjelmistokehittäjä108
 
12.9%
developer56
 
6.7%
arkkitehti35
 
4.2%
34
 
4.1%
lead32
 
3.8%
frontend25
 
3.0%
senior19
 
2.3%
kehittäjä16
 
1.9%
backend16
 
1.9%
Other values (178)362
43.2%

Most occurring characters

ValueCountFrequency (%)
t912
 
10.5%
e802
 
9.2%
i638
 
7.3%
l636
 
7.3%
k485
 
5.6%
o453
 
5.2%
a419
 
4.8%
s410
 
4.7%
388
 
4.5%
h350
 
4.0%
Other values (47)3226
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7573
86.9%
Uppercase Letter441
 
5.1%
Space Separator389
 
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 (%)
t912
12.0%
e802
 
10.6%
i638
 
8.4%
l636
 
8.4%
k485
 
6.4%
o453
 
6.0%
a419
 
5.5%
s410
 
5.4%
h350
 
4.6%
j328
 
4.3%
Other values (16)2140
28.3%
ValueCountFrequency (%)
F98
22.2%
O90
20.4%
S49
11.1%
D40
9.1%
A26
 
5.9%
T26
 
5.9%
L19
 
4.3%
C17
 
3.9%
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 (%)
388
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 (%)
Latin8014
91.9%
Common705
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t912
 
11.4%
e802
 
10.0%
i638
 
8.0%
l636
 
7.9%
k485
 
6.1%
o453
 
5.7%
a419
 
5.2%
s410
 
5.1%
h350
 
4.4%
j328
 
4.1%
Other values (37)2581
32.2%
ValueCountFrequency (%)
388
55.0%
-164
23.3%
,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 (%)
ASCII8386
96.2%
None333
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t912
 
10.9%
e802
 
9.6%
i638
 
7.6%
l636
 
7.6%
k485
 
5.8%
o453
 
5.4%
a419
 
5.0%
s410
 
4.9%
388
 
4.6%
h350
 
4.2%
Other values (44)2893
34.5%
ValueCountFrequency (%)
ä316
94.9%
ö16
 
4.8%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size728.0 B
Etä
197 
Toimisto
158 
50/50
110 

Length

Max length8
Median length5
Mean length5.172043011
Min length3

Characters and Unicode

Total characters2405
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
42.1%
Toimisto158
33.8%
50/50110
23.5%
(Missing)3
 
0.6%
2021-02-22T12:53:23.232732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T12:53:23.321942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä197
42.4%
toimisto158
34.0%
50/50110
23.7%

Most occurring characters

ValueCountFrequency (%)
t355
14.8%
o316
13.1%
i316
13.1%
5220
9.1%
0220
9.1%
E197
8.2%
ä197
8.2%
T158
6.6%
m158
6.6%
s158
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1500
62.4%
Decimal Number440
 
18.3%
Uppercase Letter355
 
14.8%
Other Punctuation110
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
t355
23.7%
o316
21.1%
i316
21.1%
ä197
13.1%
m158
10.5%
s158
10.5%
ValueCountFrequency (%)
5220
50.0%
0220
50.0%
ValueCountFrequency (%)
E197
55.5%
T158
44.5%
ValueCountFrequency (%)
/110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1855
77.1%
Common550
 
22.9%

Most frequent character per script

ValueCountFrequency (%)
t355
19.1%
o316
17.0%
i316
17.0%
E197
10.6%
ä197
10.6%
T158
8.5%
m158
8.5%
s158
8.5%
ValueCountFrequency (%)
5220
40.0%
0220
40.0%
/110
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2208
91.8%
None197
 
8.2%

Most frequent character per block

ValueCountFrequency (%)
t355
16.1%
o316
14.3%
i316
14.3%
5220
10.0%
0220
10.0%
E197
8.9%
T158
7.2%
m158
7.2%
s158
7.2%
/110
 
5.0%
ValueCountFrequency (%)
ä197
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct125
Distinct (%)29.2%
Missing40
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean4696.415888
Minimum1666
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2021-02-22T12:53:23.431727image/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 deviation1405.187249
Coefficient of variation (CV)0.2992041766
Kurtosis7.822961659
Mean4696.415888
Median Absolute Deviation (MAD)787.5
Skewness1.583570749
Sum2010066
Variance1974551.204
MonotocityNot monotonic
2021-02-22T12:53:23.594337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400024
 
5.1%
450022
 
4.7%
600017
 
3.6%
500017
 
3.6%
550016
 
3.4%
480012
 
2.6%
420012
 
2.6%
380011
 
2.4%
300011
 
2.4%
700011
 
2.4%
Other values (115)275
58.8%
(Missing)40
 
8.5%
ValueCountFrequency (%)
16661
0.2%
17001
0.2%
18001
0.2%
21001
0.2%
22001
0.2%
ValueCountFrequency (%)
150001
0.2%
120001
0.2%
93001
0.2%
85002
0.4%
82001
0.2%

Vuositulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct179
Distinct (%)39.2%
Missing11
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean65944.45624
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2021-02-22T12:53:23.755885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34930
Q150000
median59375
Q375000
95-th percentile121000
Maximum300000
Range300000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation31280.49372
Coefficient of variation (CV)0.474346071
Kurtosis12.19334927
Mean65944.45624
Median Absolute Deviation (MAD)11875
Skewness2.64893291
Sum30136616.5
Variance978469287.5
MonotocityNot monotonic
2021-02-22T12:53:23.910276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.8%
5000016
 
3.4%
7500016
 
3.4%
6000014
 
3.0%
8500011
 
2.4%
6250010
 
2.1%
6500010
 
2.1%
700009
 
1.9%
400009
 
1.9%
475009
 
1.9%
Other values (169)335
71.6%
(Missing)11
 
2.4%
ValueCountFrequency (%)
02
0.4%
40001
0.2%
61001
0.2%
75001
0.2%
200001
0.2%
ValueCountFrequency (%)
3000001
 
0.2%
2500001
 
0.2%
2000004
0.9%
1900001
 
0.2%
1800001
 
0.2%

Kilpailukykyinen
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing15
Missing (%)3.2%
Memory size3.8 KiB
True
313 
False
140 
(Missing)
 
15
ValueCountFrequency (%)
True313
66.9%
False140
29.9%
(Missing)15
 
3.2%
2021-02-22T12:53:24.031456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct72
Distinct (%)67.3%
Missing361
Missing (%)77.1%
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
 
2.4%
Vincit6
 
1.3%
Futurice5
 
1.1%
Fraktio4
 
0.9%
Mavericks4
 
0.9%
Pankki3
 
0.6%
Arado3
 
0.6%
If2
 
0.4%
Gofore Oyj2
 
0.4%
Compile Oy2
 
0.4%
Other values (62)65
 
13.9%
(Missing)361
77.1%
2021-02-22T12:53:24.314002image/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%
Missing432
Missing (%)92.3%
Memory size3.8 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Ennen koronaa oli osittainen etätyö, koronan jälkeen 100%
 
1
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi
 
1
Ihan OK. Edut myös kovat.
 
1
Kokemusta kokonaisuudessaan 7v, mutta siitä reilut kaksi vuotta lasten kanssa kotona koodaamatta.
 
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%
Ennen koronaa oli osittainen etätyö, koronan jälkeen 100%1
 
0.2%
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi1
 
0.2%
Ihan OK. Edut myös kovat.1
 
0.2%
Kokemusta kokonaisuudessaan 7v, mutta siitä reilut kaksi vuotta lasten kanssa kotona koodaamatta.1
 
0.2%
Sijainti Pori, mutta etätöitä 100%. Varsinainen positio Tampere - Helsinki. Edut aika huonot, perusjutut. Työ itsessään aika masentavaa. Seuraavaksi varmaan freelance/yrittäjyys.1
 
0.2%
Työskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.1
 
0.2%
Olen osakkaana startupissa, 5% osuus on osa kokonaiskompensaatiota. Edellisessä työssä bruttopalkka oli 6000 euroa kuukaudessa.1
 
0.2%
Korona-aika on lisännyt etätyön määrää. Aiemmin pari päivää viikossa etänä, nyt kokonaan. Paluuta vanhaan ei varmaankaan ole, ehkä päivä viikossa konttorilla ihan sosiaalisten kontaktien takia.1
 
0.2%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
Other values (25)25
 
5.3%
(Missing)432
92.3%
2021-02-22T12:53:24.641503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ei11
 
2.6%
palkka10
 
2.4%
on9
 
2.1%
mutta8
 
1.9%
ja8
 
1.9%
ole6
 
1.4%
nyt4
 
0.9%
ihan4
 
0.9%
palkan4
 
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

Distinct179
Distinct (%)39.2%
Missing11
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean5495.371353
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2021-02-22T12:53:24.796785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2910.833333
Q14166.666667
median4947.916667
Q36250
95-th percentile10083.33333
Maximum25000
Range25000
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2606.70781
Coefficient of variation (CV)0.474346071
Kurtosis12.19334927
Mean5495.371353
Median Absolute Deviation (MAD)989.5833333
Skewness2.64893291
Sum2511384.708
Variance6794925.607
MonotocityNot monotonic
2021-02-22T12:53:25.072344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4583.33333318
 
3.8%
625016
 
3.4%
4166.66666716
 
3.4%
500014
 
3.0%
7083.33333311
 
2.4%
5208.33333310
 
2.1%
5416.66666710
 
2.1%
6666.6666679
 
1.9%
31259
 
1.9%
3333.3333339
 
1.9%
Other values (169)335
71.6%
(Missing)11
 
2.4%
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%
16666.666674
0.9%
15833.333331
 
0.2%
150001
 
0.2%

Interactions

2021-02-22T12:53:16.343544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:16.478407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:16.614181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:16.747672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:16.877175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:17.009685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:17.142828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:17.278109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:17.407224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:17.538179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:17.677597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:17.910755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:18.038014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:18.163086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:18.296399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:18.430799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:18.562037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:18.684666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:18.812210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T12:53:18.937960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-22T12:53:25.211272image/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.
2021-02-22T12:53:25.384149image/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.
2021-02-22T12:53:25.554167image/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.
2021-02-22T12:53:25.731152image/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

2021-02-22T12:53:19.184474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-22T12:53:19.525220image/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.
2021-02-22T12:53:19.857936image/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.
2021-02-22T12:53:20.166421image/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
4582021-02-22 11:03:33.749Tampere38mies10.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto3858.048225.0TrueWakeoneNaN4018.750000
4592021-02-22 11:05:29.788PK-Seutu38nainen12.0Työntekijä / palkollinen1.0Myynnistä vastaava50/508200.0100000.0TrueNaNNaN8333.333333
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