Overview

Dataset statistics

Number of variables15
Number of observations469
Missing cells950
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.6 KiB
Average record size in memory95.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 247 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 (7.0%) 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 (2.3%) missing values Missing
Kilpailukykyinen has 15 (3.2%) missing values Missing
Työpaikka has 362 (77.2%) missing values Missing
Vapaa sana has 433 (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 started2021-02-22 18:36:27.381405
Analysis finished2021-02-22 18:36:34.433732
Duration7.05 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct469
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-22 18:58:45.951000
2021-02-22T18:36:34.539347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:34.775758image/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
237 
Tampere
109 
Turku
46 
Oulu
25 
Jyväskylä
 
18
Other values (20)
30 

Length

Max length15
Median length8
Mean length7.240860215
Min length2

Characters and Unicode

Total characters3367
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-Seutu237
50.5%
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%
2021-02-22T18:36:35.364972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu237
50.5%
tampere109
23.2%
turku46
 
9.8%
oulu25
 
5.3%
jyväskylä18
 
3.8%
kuopio5
 
1.1%
lontoo2
 
0.4%
tallinna2
 
0.4%
vaasa2
 
0.4%
eu2
 
0.4%
Other values (19)21
 
4.5%

Most occurring characters

ValueCountFrequency (%)
u628
18.7%
e466
13.8%
K245
 
7.3%
t243
 
7.2%
P240
 
7.1%
-239
 
7.1%
S239
 
7.1%
r161
 
4.8%
T157
 
4.7%
a132
 
3.9%
Other values (29)617
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2178
64.7%
Uppercase Letter945
28.1%
Dash Punctuation239
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u628
28.8%
e466
21.4%
t243
 
11.2%
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 (%)
K245
25.9%
P240
25.4%
S239
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 (%)
-239
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3123
92.8%
Common244
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u628
20.1%
e466
14.9%
K245
 
7.8%
t243
 
7.8%
P240
 
7.7%
S239
 
7.7%
r161
 
5.2%
T157
 
5.0%
a132
 
4.2%
p115
 
3.7%
Other values (26)497
15.9%
ValueCountFrequency (%)
-239
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3323
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u628
18.9%
e466
14.0%
K245
 
7.4%
t243
 
7.3%
P240
 
7.2%
-239
 
7.2%
S239
 
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%
Mean33.82441113
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2021-02-22T18:36:35.522911image/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 deviation6.085513953
Coefficient of variation (CV)0.1799148529
Kurtosis0.2085110154
Mean33.82441113
Median Absolute Deviation (MAD)5
Skewness0.4703117635
Sum15796
Variance37.03348007
MonotocityNot monotonic
2021-02-22T18:36:35.666473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33156
33.3%
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%
33156
33.3%
38102
21.7%
4352
 
11.1%
ValueCountFrequency (%)
536
 
1.3%
487
 
1.5%
4352
 
11.1%
38102
21.7%
33156
33.3%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing33
Missing (%)7.0%
Memory size729.0 B
mies
393 
nainen
 
35
muu
 
8

Length

Max length6
Median length4
Mean length4.142201835
Min length3

Characters and Unicode

Total characters1806
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 (%)
mies393
83.8%
nainen35
 
7.5%
muu8
 
1.7%
(Missing)33
 
7.0%
2021-02-22T18:36:36.066221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T18:36:36.211703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies393
90.1%
nainen35
 
8.0%
muu8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i428
23.7%
e428
23.7%
m401
22.2%
s393
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1806
100.0%

Most frequent character per category

ValueCountFrequency (%)
i428
23.7%
e428
23.7%
m401
22.2%
s393
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1806
100.0%

Most frequent character per script

ValueCountFrequency (%)
i428
23.7%
e428
23.7%
m401
22.2%
s393
21.8%
n105
 
5.8%
a35
 
1.9%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1806
100.0%

Most frequent character per block

ValueCountFrequency (%)
i428
23.7%
e428
23.7%
m401
22.2%
s393
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%
Mean9.653763441
Minimum0
Maximum30
Zeros4
Zeros (%)0.9%
Memory size3.8 KiB
2021-02-22T18:36:36.343012image/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 deviation6.078377434
Coefficient of variation (CV)0.6296381169
Kurtosis-0.05949309688
Mean9.653763441
Median Absolute Deviation (MAD)4
Skewness0.7106661906
Sum4489
Variance36.94667223
MonotocityNot monotonic
2021-02-22T18:36:36.519227image/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%
1527
 
5.8%
2027
 
5.8%
325
 
5.3%
1325
 
5.3%
624
 
5.1%
224
 
5.1%
Other values (17)165
35.2%
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
418 
Freelancer
 
25
Yrittäjä
 
25

Length

Max length24
Median length24
Mean length22.3974359
Min length8

Characters and Unicode

Total characters10482
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 / palkollinen418
89.1%
Freelancer25
 
5.3%
Yrittäjä25
 
5.3%
(Missing)1
 
0.2%
2021-02-22T18:36:36.925416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T18:36:37.058950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
418
32.1%
palkollinen418
32.1%
työntekijä418
32.1%
yrittäjä25
 
1.9%
freelancer25
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1279
12.2%
l1279
12.2%
e911
 
8.7%
i861
 
8.2%
k836
 
8.0%
836
 
8.0%
t468
 
4.5%
ä468
 
4.5%
j443
 
4.2%
a443
 
4.2%
Other values (10)2658
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8760
83.6%
Space Separator836
 
8.0%
Uppercase Letter468
 
4.5%
Other Punctuation418
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1279
14.6%
l1279
14.6%
e911
10.4%
i861
9.8%
k836
9.5%
t468
 
5.3%
ä468
 
5.3%
j443
 
5.1%
a443
 
5.1%
y418
 
4.8%
Other values (5)1354
15.5%
ValueCountFrequency (%)
T418
89.3%
Y25
 
5.3%
F25
 
5.3%
ValueCountFrequency (%)
836
100.0%
ValueCountFrequency (%)
/418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9228
88.0%
Common1254
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1279
13.9%
l1279
13.9%
e911
9.9%
i861
9.3%
k836
9.1%
t468
 
5.1%
ä468
 
5.1%
j443
 
4.8%
a443
 
4.8%
T418
 
4.5%
Other values (8)1822
19.7%
ValueCountFrequency (%)
836
66.7%
/418
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9596
91.5%
None886
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1279
13.3%
l1279
13.3%
e911
9.5%
i861
9.0%
k836
8.7%
836
8.7%
t468
 
4.9%
j443
 
4.6%
a443
 
4.6%
T418
 
4.4%
Other values (8)1822
19.0%
ValueCountFrequency (%)
ä468
52.8%
ö418
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)4.1%
Memory size3.8 KiB
1.0
423 
0.8
 
23
0.5
 
2
0.6
 
1
0.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1350
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 (%)
1.0423
90.2%
0.823
 
4.9%
0.52
 
0.4%
0.61
 
0.2%
0.71
 
0.2%
(Missing)19
 
4.1%
2021-02-22T18:36:37.365048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T18:36:37.472990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0423
94.0%
0.823
 
5.1%
0.52
 
0.4%
0.61
 
0.2%
0.71
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.450
33.3%
0450
33.3%
1423
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number900
66.7%
Other Punctuation450
33.3%

Most frequent character per category

ValueCountFrequency (%)
0450
50.0%
1423
47.0%
823
 
2.6%
52
 
0.2%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1350
100.0%

Most frequent character per script

ValueCountFrequency (%)
.450
33.3%
0450
33.3%
1423
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1350
100.0%

Most frequent character per block

ValueCountFrequency (%)
.450
33.3%
0450
33.3%
1423
31.3%
823
 
1.7%
52
 
0.1%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct247
Distinct (%)54.0%
Missing12
Missing (%)2.6%
Memory size3.8 KiB
Ohjelmistokehittäjä
38 
full-stack
 
33
Full-stack
 
23
ohjelmistokehittäjä
 
16
Arkkitehti
 
15
Other values (242)
332 

Length

Max length67
Median length18
Mean length19.11597374
Min length2

Characters and Unicode

Total characters8736
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

Unique200 ?
Unique (%)43.8%

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 (237)300
64.0%
(Missing)12
 
2.6%
2021-02-22T18:36:37.977658image/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 (179)363
43.3%

Most occurring characters

ValueCountFrequency (%)
t913
 
10.5%
e803
 
9.2%
i639
 
7.3%
l637
 
7.3%
k486
 
5.6%
o456
 
5.2%
a422
 
4.8%
s410
 
4.7%
388
 
4.4%
h351
 
4.0%
Other values (47)3231
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7589
86.9%
Uppercase Letter442
 
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 (%)
t913
12.0%
e803
 
10.6%
i639
 
8.4%
l637
 
8.4%
k486
 
6.4%
o456
 
6.0%
a422
 
5.6%
s410
 
5.4%
h351
 
4.6%
j330
 
4.3%
Other values (16)2142
28.2%
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 (%)
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 (%)
Latin8031
91.9%
Common705
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t913
 
11.4%
e803
 
10.0%
i639
 
8.0%
l637
 
7.9%
k486
 
6.1%
o456
 
5.7%
a422
 
5.3%
s410
 
5.1%
h351
 
4.4%
j330
 
4.1%
Other values (37)2584
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 (%)
ASCII8403
96.2%
None333
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t913
 
10.9%
e803
 
9.6%
i639
 
7.6%
l637
 
7.6%
k486
 
5.8%
o456
 
5.4%
a422
 
5.0%
s410
 
4.9%
388
 
4.6%
h351
 
4.2%
Other values (44)2898
34.5%
ValueCountFrequency (%)
ä316
94.9%
ö16
 
4.8%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size729.0 B
Etä
197 
Toimisto
159 
50/50
110 

Length

Max length8
Median length5
Mean length5.178111588
Min length3

Characters and Unicode

Total characters2413
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
42.0%
Toimisto159
33.9%
50/50110
23.5%
(Missing)3
 
0.6%
2021-02-22T18:36:38.513869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T18:36:38.636867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä197
42.3%
toimisto159
34.1%
50/50110
23.6%

Most occurring characters

ValueCountFrequency (%)
t356
14.8%
o318
13.2%
i318
13.2%
5220
9.1%
0220
9.1%
E197
8.2%
ä197
8.2%
T159
6.6%
m159
6.6%
s159
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1507
62.5%
Decimal Number440
 
18.2%
Uppercase Letter356
 
14.8%
Other Punctuation110
 
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 (%)
5220
50.0%
0220
50.0%
ValueCountFrequency (%)
E197
55.3%
T159
44.7%
ValueCountFrequency (%)
/110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1863
77.2%
Common550
 
22.8%

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 (%)
5220
40.0%
0220
40.0%
/110
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2216
91.8%
None197
 
8.2%

Most frequent character per block

ValueCountFrequency (%)
t356
16.1%
o318
14.4%
i318
14.4%
5220
9.9%
0220
9.9%
E197
8.9%
T159
7.2%
m159
7.2%
s159
7.2%
/110
 
5.0%
ValueCountFrequency (%)
ä197
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct125
Distinct (%)29.1%
Missing40
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean4713.440559
Minimum1666
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2021-02-22T18:36:38.796440image/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 deviation1447.162357
Coefficient of variation (CV)0.307028876
Kurtosis8.050690351
Mean4713.440559
Median Absolute Deviation (MAD)800
Skewness1.71261771
Sum2022066
Variance2094278.887
MonotocityNot monotonic
2021-02-22T18:36:39.034219image/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
 
2.3%
300011
 
2.3%
700011
 
2.3%
Other values (115)276
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%
120002
0.4%
93001
0.2%
85002
0.4%
82001
0.2%

Vuositulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct180
Distinct (%)39.3%
Missing11
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean66280.82205
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2021-02-22T18:36:39.276225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34947.5
Q150000
median59687.5
Q375000
95-th percentile125750
Maximum300000
Range300000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation32064.73508
Coefficient of variation (CV)0.4837709323
Kurtosis11.79606981
Mean66280.82205
Median Absolute Deviation (MAD)12187.5
Skewness2.666013584
Sum30356616.5
Variance1028147236
MonotocityNot monotonic
2021-02-22T18:36:39.494399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.8%
7500016
 
3.4%
5000016
 
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
71.6%
(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
140 
(Missing)
 
15
ValueCountFrequency (%)
True314
67.0%
False140
29.9%
(Missing)15
 
3.2%
2021-02-22T18:36:39.651669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct72
Distinct (%)67.3%
Missing362
Missing (%)77.2%
Memory size3.8 KiB
Gofore
11 
Vincit
 
6
Futurice
 
5
Mavericks
 
4
Fraktio
 
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%
Mavericks4
 
0.9%
Fraktio4
 
0.9%
Pankki3
 
0.6%
Arado3
 
0.6%
KVTES-alainen kunnan omistama oy 2
 
0.4%
Qvik2
 
0.4%
Compile Oy2
 
0.4%
Other values (62)65
 
13.9%
(Missing)362
77.2%
2021-02-22T18:36:40.037001image/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%
futurice5
 
2.9%
oyj5
 
2.9%
fraktio4
 
2.4%
siili4
 
2.4%
pankki3
 
1.8%
omistama3
 
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%
Missing433
Missing (%)92.3%
Memory size3.8 KiB
palkan lisänä lounas- ja virkistysetu
 
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
Ei sinänsä liity suoraan palkkoihin, mutta olisi mielenkiintoista tietää miten palkka vaikuttaa työpaikan vaihtoon. Eli esim. Oletko vaihtanut/vaihtamassa/miettinyt vaihtamista, koska toisaalla maksetaan enemmän?
 
1
Sijainti Pori, mutta etätöitä 100%. Varsinainen positio Tampere - Helsinki. Edut aika huonot, perusjutut. Työ itsessään aika masentavaa. Seuraavaksi varmaan freelance/yrittäjyys.
 
1
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
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%
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%
Ei sinänsä liity suoraan palkkoihin, mutta olisi mielenkiintoista tietää miten palkka vaikuttaa työpaikan vaihtoon. Eli esim. Oletko vaihtanut/vaihtamassa/miettinyt vaihtamista, koska toisaalla maksetaan enemmän?1
 
0.2%
Sijainti Pori, mutta etätöitä 100%. Varsinainen positio Tampere - Helsinki. Edut aika huonot, perusjutut. Työ itsessään aika masentavaa. Seuraavaksi varmaan freelance/yrittäjyys.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%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
Bonukset riippuu firman tuloksesta. Palkka olisi varmastikin enemmän muualla mutta uskoakseni linjassa kollegoideni kanssa.1
 
0.2%
Ilmaset kaffet, safkat, salit jne.1
 
0.2%
Palkka perustuu osittain laskutukseen, joten vuositulot vaihtelevat hieman.1
 
0.2%
startup, palkan lisäksi optiopaketti.1
 
0.2%
Other values (25)25
 
5.3%
(Missing)433
92.3%
2021-02-22T18:36:40.489022image/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%
ihan4
 
0.9%
joten4
 
0.9%
nyt4
 
0.9%
firman4
 
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 (%)39.3%
Missing11
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean5523.401838
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2021-02-22T18:36:40.699376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2912.291667
Q14166.666667
median4973.958333
Q36250
95-th percentile10479.16667
Maximum25000
Range25000
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2672.061256
Coefficient of variation (CV)0.4837709323
Kurtosis11.79606981
Mean5523.401838
Median Absolute Deviation (MAD)1015.625
Skewness2.666013584
Sum2529718.042
Variance7139911.358
MonotocityNot monotonic
2021-02-22T18:36:41.071261image/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
 
2.3%
5208.33333310
 
2.1%
5416.66666710
 
2.1%
6666.6666679
 
1.9%
31259
 
1.9%
3333.3333339
 
1.9%
Other values (170)336
71.6%
(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

2021-02-22T18:36:28.764400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:28.956517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:29.144197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:29.335534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:29.510098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:29.713028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:29.903407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:30.108593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:30.292287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:30.486471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:30.679782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:30.978423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:31.172825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:31.352095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:31.538612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:31.734483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:31.918543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:32.110513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:32.287116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T18:36:32.478663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-22T18:36:41.272563image/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.
2021-02-22T18:36:41.503048image/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.
2021-02-22T18:36:41.747416image/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.
2021-02-22T18:36:41.984027image/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

2021-02-22T18:36:32.852692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-22T18:36:33.351293image/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.
2021-02-22T18:36:33.805178image/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.
2021-02-22T18:36:34.213811image/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
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
4682021-02-22 18:58:45.951PK-Seutu43mies15.0Työntekijä / palkollinen1.0TeknologiajohtajaToimisto12000.0220000.0TrueNaNNaN18333.333333