Overview

Dataset statistics

Number of variables15
Number of observations492
Missing cells1000
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.2 KiB
Average record size in memory96.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 259 distinct values High cardinality
Työpaikka has a high cardinality: 74 distinct values High cardinality
Vuositulot is highly correlated with Kk-tulotHigh correlation
Kk-tulot is highly correlated with VuositulotHigh correlation
Kilpailukykyinen is highly correlated with Vapaa sanaHigh correlation
Työpaikka is highly correlated with Vapaa sanaHigh correlation
Vapaa sana is highly correlated with Kilpailukykyinen and 1 other fieldsHigh correlation
Kaupunki has 5 (1.0%) missing values Missing
Sukupuoli has 35 (7.1%) missing values Missing
Työaika has 19 (3.9%) missing values Missing
Rooli has 13 (2.6%) missing values Missing
Kuukausipalkka has 42 (8.5%) missing values Missing
Vuositulot has 12 (2.4%) missing values Missing
Kilpailukykyinen has 15 (3.0%) missing values Missing
Työpaikka has 382 (77.6%) missing values Missing
Vapaa sana has 454 (92.3%) missing values Missing
Kk-tulot has 12 (2.4%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-26 10:09:51.652132
Analysis finished2021-02-26 10:09:57.038527
Duration5.39 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct492
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-26 09:32:59.778000
2021-02-26T10:09:57.118333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:57.289273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

MISSING

Distinct28
Distinct (%)5.7%
Missing5
Missing (%)1.0%
Memory size1.9 KiB
PK-Seutu
247 
Tampere
113 
Turku
47 
Oulu
26 
Jyväskylä
 
18
Other values (23)
36 

Length

Max length15
Median length8
Mean length7.232032854
Min length2

Characters and Unicode

Total characters3522
Distinct characters40
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

Unique15 ?
Unique (%)3.1%

Sample

1st rowPK-Seutu
2nd rowTurku
3rd rowPK-Seutu
4th rowTampere
5th rowPK-Seutu
ValueCountFrequency (%)
PK-Seutu247
50.2%
Tampere113
23.0%
Turku47
 
9.6%
Oulu26
 
5.3%
Jyväskylä18
 
3.7%
Kuopio7
 
1.4%
Lontoo2
 
0.4%
Vaasa2
 
0.4%
Tallinna2
 
0.4%
Pori2
 
0.4%
Other values (18)21
 
4.3%
(Missing)5
 
1.0%
2021-02-26T10:09:57.715369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu247
50.3%
tampere113
23.0%
turku47
 
9.6%
oulu26
 
5.3%
jyväskylä18
 
3.7%
kuopio7
 
1.4%
hämeenlinna2
 
0.4%
pori2
 
0.4%
vaasa2
 
0.4%
lontoo2
 
0.4%
Other values (22)25
 
5.1%

Most occurring characters

ValueCountFrequency (%)
u654
18.6%
e485
13.8%
K257
 
7.3%
t254
 
7.2%
P250
 
7.1%
-249
 
7.1%
S249
 
7.1%
r166
 
4.7%
T162
 
4.6%
a140
 
4.0%
Other values (30)656
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2281
64.8%
Uppercase Letter987
28.0%
Dash Punctuation249
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u654
28.7%
e485
21.3%
t254
 
11.1%
r166
 
7.3%
a140
 
6.1%
p121
 
5.3%
m119
 
5.2%
k70
 
3.1%
l57
 
2.5%
ä44
 
1.9%
Other values (10)171
 
7.5%
ValueCountFrequency (%)
K257
26.0%
P250
25.3%
S249
25.2%
T162
16.4%
O26
 
2.6%
J19
 
1.9%
L5
 
0.5%
E4
 
0.4%
V3
 
0.3%
H3
 
0.3%
Other values (7)9
 
0.9%
ValueCountFrequency (%)
-249
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3268
92.8%
Common254
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u654
20.0%
e485
14.8%
K257
 
7.9%
t254
 
7.8%
P250
 
7.6%
S249
 
7.6%
r166
 
5.1%
T162
 
5.0%
a140
 
4.3%
p121
 
3.7%
Other values (27)530
16.2%
ValueCountFrequency (%)
-249
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3478
98.8%
None44
 
1.2%

Most frequent character per block

ValueCountFrequency (%)
u654
18.8%
e485
13.9%
K257
 
7.4%
t254
 
7.3%
P250
 
7.2%
-249
 
7.2%
S249
 
7.2%
r166
 
4.8%
T162
 
4.7%
a140
 
4.0%
Other values (29)612
17.6%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.4%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean33.75664622
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-26T10:09:57.832588image/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 deviation6.085127142
Coefficient of variation (CV)0.1802645649
Kurtosis0.2106879698
Mean33.75664622
Median Absolute Deviation (MAD)5
Skewness0.4818818306
Sum16507
Variance37.02877234
MonotocityNot monotonic
2021-02-26T10:09:57.938106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33164
33.3%
28121
24.6%
38105
21.3%
4353
 
10.8%
2332
 
6.5%
488
 
1.6%
536
 
1.2%
(Missing)3
 
0.6%
ValueCountFrequency (%)
2332
 
6.5%
28121
24.6%
33164
33.3%
38105
21.3%
4353
 
10.8%
ValueCountFrequency (%)
536
 
1.2%
488
 
1.6%
4353
 
10.8%
38105
21.3%
33164
33.3%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing35
Missing (%)7.1%
Memory size752.0 B
mies
412 
nainen
 
36
muu
 
9

Length

Max length6
Median length4
Mean length4.13785558
Min length3

Characters and Unicode

Total characters1891
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 (%)
mies412
83.7%
nainen36
 
7.3%
muu9
 
1.8%
(Missing)35
 
7.1%
2021-02-26T10:09:58.238081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T10:09:58.338176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies412
90.2%
nainen36
 
7.9%
muu9
 
2.0%

Most occurring characters

ValueCountFrequency (%)
i448
23.7%
e448
23.7%
m421
22.3%
s412
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1891
100.0%

Most frequent character per category

ValueCountFrequency (%)
i448
23.7%
e448
23.7%
m421
22.3%
s412
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1891
100.0%

Most frequent character per script

ValueCountFrequency (%)
i448
23.7%
e448
23.7%
m421
22.3%
s412
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1891
100.0%

Most frequent character per block

ValueCountFrequency (%)
i448
23.7%
e448
23.7%
m421
22.3%
s412
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
1.0%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)5.5%
Missing4
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean9.514344262
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size4.0 KiB
2021-02-26T10:09:58.439981image/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 deviation6.05550975
Coefficient of variation (CV)0.6364610722
Kurtosis-0.02411999715
Mean9.514344262
Median Absolute Deviation (MAD)4
Skewness0.7355300636
Sum4643
Variance36.66919834
MonotocityNot monotonic
2021-02-26T10:09:58.570598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
554
 
11.0%
1040
 
8.1%
431
 
6.3%
730
 
6.1%
228
 
5.7%
1528
 
5.7%
327
 
5.5%
2027
 
5.5%
627
 
5.5%
825
 
5.1%
Other values (17)171
34.8%
ValueCountFrequency (%)
04
 
0.8%
117
3.5%
228
5.7%
327
5.5%
431
6.3%
ValueCountFrequency (%)
302
 
0.4%
256
1.2%
243
0.6%
234
0.8%
225
1.0%
Distinct3
Distinct (%)0.6%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
Työntekijä / palkollinen
439 
Yrittäjä
 
26
Freelancer
 
26

Length

Max length24
Median length24
Mean length22.4114053
Min length8

Characters and Unicode

Total characters11004
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 / palkollinen439
89.2%
Yrittäjä26
 
5.3%
Freelancer26
 
5.3%
(Missing)1
 
0.2%
2021-02-26T10:09:58.846266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T10:09:58.943472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
439
32.1%
työntekijä439
32.1%
palkollinen439
32.1%
yrittäjä26
 
1.9%
freelancer26
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1343
12.2%
l1343
12.2%
e956
 
8.7%
i904
 
8.2%
k878
 
8.0%
878
 
8.0%
t491
 
4.5%
ä491
 
4.5%
j465
 
4.2%
a465
 
4.2%
Other values (10)2790
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9196
83.6%
Space Separator878
 
8.0%
Uppercase Letter491
 
4.5%
Other Punctuation439
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1343
14.6%
l1343
14.6%
e956
10.4%
i904
9.8%
k878
9.5%
t491
 
5.3%
ä491
 
5.3%
j465
 
5.1%
a465
 
5.1%
y439
 
4.8%
Other values (5)1421
15.5%
ValueCountFrequency (%)
T439
89.4%
Y26
 
5.3%
F26
 
5.3%
ValueCountFrequency (%)
878
100.0%
ValueCountFrequency (%)
/439
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9687
88.0%
Common1317
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1343
13.9%
l1343
13.9%
e956
9.9%
i904
9.3%
k878
9.1%
t491
 
5.1%
ä491
 
5.1%
j465
 
4.8%
a465
 
4.8%
T439
 
4.5%
Other values (8)1912
19.7%
ValueCountFrequency (%)
878
66.7%
/439
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10074
91.5%
None930
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1343
13.3%
l1343
13.3%
e956
9.5%
i904
9.0%
k878
8.7%
878
8.7%
t491
 
4.9%
j465
 
4.6%
a465
 
4.6%
T439
 
4.4%
Other values (8)1912
19.0%
ValueCountFrequency (%)
ä491
52.8%
ö439
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)3.9%
Memory size4.0 KiB
1.0
444 
0.8
 
23
0.5
 
4
0.7
 
1
0.6
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1419
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 (%)
1.0444
90.2%
0.823
 
4.7%
0.54
 
0.8%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
3.9%
2021-02-26T10:09:59.179969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T10:09:59.263427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0444
93.9%
0.823
 
4.9%
0.54
 
0.8%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.473
33.3%
0473
33.3%
1444
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number946
66.7%
Other Punctuation473
33.3%

Most frequent character per category

ValueCountFrequency (%)
0473
50.0%
1444
46.9%
823
 
2.4%
54
 
0.4%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.473
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1419
100.0%

Most frequent character per script

ValueCountFrequency (%)
.473
33.3%
0473
33.3%
1444
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1419
100.0%

Most frequent character per block

ValueCountFrequency (%)
.473
33.3%
0473
33.3%
1444
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct259
Distinct (%)54.1%
Missing13
Missing (%)2.6%
Memory size4.0 KiB
Ohjelmistokehittäjä
41 
full-stack
35 
Full-stack
 
24
ohjelmistokehittäjä
 
17
Arkkitehti
 
15
Other values (254)
347 

Length

Max length67
Median length18
Mean length19.28183716
Min length2

Characters and Unicode

Total characters9236
Distinct characters58
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

Unique212 ?
Unique (%)44.3%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä41
 
8.3%
full-stack35
 
7.1%
Full-stack24
 
4.9%
ohjelmistokehittäjä17
 
3.5%
Arkkitehti15
 
3.0%
Full-stack ohjelmistokehittäjä8
 
1.6%
full-stack ohjelmistokehittäjä7
 
1.4%
Frontend6
 
1.2%
arkkitehti6
 
1.2%
Full-stack developer5
 
1.0%
Other values (249)315
64.0%
(Missing)13
 
2.6%
2021-02-26T10:09:59.632393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack142
 
16.0%
ohjelmistokehittäjä114
 
12.8%
developer61
 
6.9%
35
 
3.9%
arkkitehti35
 
3.9%
lead33
 
3.7%
frontend27
 
3.0%
senior21
 
2.4%
kehittäjä16
 
1.8%
backend16
 
1.8%
Other values (196)389
43.8%

Most occurring characters

ValueCountFrequency (%)
t960
 
10.4%
e852
 
9.2%
l672
 
7.3%
i669
 
7.2%
k510
 
5.5%
o484
 
5.2%
a441
 
4.8%
s440
 
4.8%
416
 
4.5%
h369
 
4.0%
Other values (48)3423
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8015
86.8%
Uppercase Letter468
 
5.1%
Space Separator417
 
4.5%
Dash Punctuation174
 
1.9%
Other Punctuation99
 
1.1%
Open Punctuation27
 
0.3%
Close Punctuation27
 
0.3%
Math Symbol8
 
0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t960
12.0%
e852
 
10.6%
l672
 
8.4%
i669
 
8.3%
k510
 
6.4%
o484
 
6.0%
a441
 
5.5%
s440
 
5.5%
h369
 
4.6%
j348
 
4.3%
Other values (16)2270
28.3%
ValueCountFrequency (%)
F105
22.4%
O96
20.5%
S52
11.1%
D42
 
9.0%
T28
 
6.0%
A27
 
5.8%
L21
 
4.5%
C18
 
3.8%
E12
 
2.6%
P11
 
2.4%
Other values (11)56
12.0%
ValueCountFrequency (%)
,53
53.5%
/42
42.4%
&3
 
3.0%
.1
 
1.0%
ValueCountFrequency (%)
416
99.8%
 1
 
0.2%
ValueCountFrequency (%)
-174
100.0%
ValueCountFrequency (%)
(27
100.0%
ValueCountFrequency (%)
)27
100.0%
ValueCountFrequency (%)
+8
100.0%
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8483
91.8%
Common753
 
8.2%

Most frequent character per script

ValueCountFrequency (%)
t960
 
11.3%
e852
 
10.0%
l672
 
7.9%
i669
 
7.9%
k510
 
6.0%
o484
 
5.7%
a441
 
5.2%
s440
 
5.2%
h369
 
4.3%
j348
 
4.1%
Other values (37)2738
32.3%
ValueCountFrequency (%)
416
55.2%
-174
23.1%
,53
 
7.0%
/42
 
5.6%
(27
 
3.6%
)27
 
3.6%
+8
 
1.1%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8884
96.2%
None352
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t960
 
10.8%
e852
 
9.6%
l672
 
7.6%
i669
 
7.5%
k510
 
5.7%
o484
 
5.4%
a441
 
5.0%
s440
 
5.0%
416
 
4.7%
h369
 
4.2%
Other values (45)3071
34.6%
ValueCountFrequency (%)
ä335
95.2%
ö16
 
4.5%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size752.0 B
Etä
205 
Toimisto
170 
50/50
114 

Length

Max length8
Median length5
Mean length5.204498978
Min length3

Characters and Unicode

Total characters2545
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 (%)
Etä205
41.7%
Toimisto170
34.6%
50/50114
23.2%
(Missing)3
 
0.6%
2021-02-26T10:10:00.024422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T10:10:00.117248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä205
41.9%
toimisto170
34.8%
50/50114
23.3%

Most occurring characters

ValueCountFrequency (%)
t375
14.7%
o340
13.4%
i340
13.4%
5228
9.0%
0228
9.0%
E205
8.1%
ä205
8.1%
T170
6.7%
m170
6.7%
s170
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1600
62.9%
Decimal Number456
 
17.9%
Uppercase Letter375
 
14.7%
Other Punctuation114
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t375
23.4%
o340
21.2%
i340
21.2%
ä205
12.8%
m170
10.6%
s170
10.6%
ValueCountFrequency (%)
5228
50.0%
0228
50.0%
ValueCountFrequency (%)
E205
54.7%
T170
45.3%
ValueCountFrequency (%)
/114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1975
77.6%
Common570
 
22.4%

Most frequent character per script

ValueCountFrequency (%)
t375
19.0%
o340
17.2%
i340
17.2%
E205
10.4%
ä205
10.4%
T170
8.6%
m170
8.6%
s170
8.6%
ValueCountFrequency (%)
5228
40.0%
0228
40.0%
/114
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2340
91.9%
None205
 
8.1%

Most frequent character per block

ValueCountFrequency (%)
t375
16.0%
o340
14.5%
i340
14.5%
5228
9.7%
0228
9.7%
E205
8.8%
T170
7.3%
m170
7.3%
s170
7.3%
/114
 
4.9%
ValueCountFrequency (%)
ä205
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct129
Distinct (%)28.7%
Missing42
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean4674.851111
Minimum1081
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-26T10:10:00.232204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1081
5-th percentile2783.5
Q13800
median4500
Q35492.5
95-th percentile7000
Maximum15000
Range13919
Interquartile range (IQR)1692.5

Descriptive statistics

Standard deviation1446.575396
Coefficient of variation (CV)0.3094377471
Kurtosis7.912783439
Mean4674.851111
Median Absolute Deviation (MAD)787.5
Skewness1.627088368
Sum2103683
Variance2092580.376
MonotocityNot monotonic
2021-02-26T10:10:00.385565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400026
 
5.3%
450024
 
4.9%
500018
 
3.7%
550017
 
3.5%
600017
 
3.5%
480012
 
2.4%
300012
 
2.4%
420012
 
2.4%
430012
 
2.4%
380011
 
2.2%
Other values (119)289
58.7%
(Missing)42
 
8.5%
ValueCountFrequency (%)
10811
0.2%
11001
0.2%
16661
0.2%
17001
0.2%
18001
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

Distinct184
Distinct (%)38.3%
Missing12
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean65472.32604
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-26T10:10:00.553345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33692.5
Q149281.25
median58750
Q375000
95-th percentile120250
Maximum300000
Range300000
Interquartile range (IQR)25718.75

Descriptive statistics

Standard deviation31722.64784
Coefficient of variation (CV)0.4845199454
Kurtosis12.04148297
Mean65472.32604
Median Absolute Deviation (MAD)11750
Skewness2.671579449
Sum31426716.5
Variance1006326386
MonotocityNot monotonic
2021-02-26T10:10:00.717581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.7%
5000018
 
3.7%
7500016
 
3.3%
6000014
 
2.8%
8500011
 
2.2%
7000011
 
2.2%
6250010
 
2.0%
6500010
 
2.0%
3750010
 
2.0%
475009
 
1.8%
Other values (174)353
71.7%
(Missing)12
 
2.4%
ValueCountFrequency (%)
02
0.4%
40001
0.2%
61001
0.2%
75001
0.2%
137501
0.2%
ValueCountFrequency (%)
3000001
 
0.2%
2500001
 
0.2%
2200001
 
0.2%
2000004
0.8%
1900001
 
0.2%

Kilpailukykyinen
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing15
Missing (%)3.0%
Memory size4.0 KiB
True
325 
False
152 
(Missing)
 
15
ValueCountFrequency (%)
True325
66.1%
False152
30.9%
(Missing)15
 
3.0%
2021-02-26T10:10:00.836757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)67.3%
Missing382
Missing (%)77.6%
Memory size4.0 KiB
Gofore
11 
Vincit
 
7
Futurice
 
5
Fraktio
 
4
Mavericks
 
4
Other values (69)
79 

Length

Max length132
Median length8
Mean length10.76363636
Min length2

Characters and Unicode

Total characters1184
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

Unique61 ?
Unique (%)55.5%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.2%
Vincit7
 
1.4%
Futurice5
 
1.0%
Fraktio4
 
0.8%
Mavericks4
 
0.8%
Arado3
 
0.6%
Pankki3
 
0.6%
If2
 
0.4%
Compile Oy2
 
0.4%
Siili2
 
0.4%
Other values (64)67
 
13.6%
(Missing)382
77.6%
2021-02-26T10:10:01.125005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore13
 
7.4%
oy13
 
7.4%
vincit7
 
4.0%
mavericks6
 
3.4%
futurice5
 
2.8%
oyj5
 
2.8%
siili4
 
2.3%
fraktio4
 
2.3%
pankki3
 
1.7%
arado3
 
1.7%
Other values (96)113
64.2%

Most occurring characters

ValueCountFrequency (%)
i123
 
10.4%
a89
 
7.5%
o89
 
7.5%
e85
 
7.2%
t81
 
6.8%
69
 
5.8%
r62
 
5.2%
n58
 
4.9%
k49
 
4.1%
l46
 
3.9%
Other values (44)433
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter961
81.2%
Uppercase Letter148
 
12.5%
Space Separator69
 
5.8%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i123
12.8%
a89
 
9.3%
o89
 
9.3%
e85
 
8.8%
t81
 
8.4%
r62
 
6.5%
n58
 
6.0%
k49
 
5.1%
l46
 
4.8%
u45
 
4.7%
Other values (16)234
24.3%
ValueCountFrequency (%)
O18
 
12.2%
G14
 
9.5%
S14
 
9.5%
V13
 
8.8%
F10
 
6.8%
K8
 
5.4%
A7
 
4.7%
M7
 
4.7%
C6
 
4.1%
P6
 
4.1%
Other values (15)45
30.4%
ValueCountFrequency (%)
69
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1109
93.7%
Common75
 
6.3%

Most frequent character per script

ValueCountFrequency (%)
i123
 
11.1%
a89
 
8.0%
o89
 
8.0%
e85
 
7.7%
t81
 
7.3%
r62
 
5.6%
n58
 
5.2%
k49
 
4.4%
l46
 
4.1%
u45
 
4.1%
Other values (41)382
34.4%
ValueCountFrequency (%)
69
92.0%
.3
 
4.0%
-3
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1172
99.0%
None12
 
1.0%

Most frequent character per block

ValueCountFrequency (%)
i123
 
10.5%
a89
 
7.6%
o89
 
7.6%
e85
 
7.3%
t81
 
6.9%
69
 
5.9%
r62
 
5.3%
n58
 
4.9%
k49
 
4.2%
l46
 
3.9%
Other values (42)421
35.9%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct37
Distinct (%)97.4%
Missing454
Missing (%)92.3%
Memory size4.0 KiB
palkan lisänä lounas- ja virkistysetu
 
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
Rahapalkan päälle tulee vielä kohtuullinen optiopotti, mutta se toki on lähinnä arpalippu
 
1
olen sekä päivätyöläinen että friikku. jospa nyt kuitenki vois valita monta?
 
1
Johtajasopimus, ei työaikaa
 
1
Other values (32)
32 

Length

Max length286
Median length73
Mean length95.57894737
Min length7

Characters and Unicode

Total characters3632
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

Unique36 ?
Unique (%)94.7%

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%
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%
Rahapalkan päälle tulee vielä kohtuullinen optiopotti, mutta se toki on lähinnä arpalippu1
 
0.2%
olen sekä päivätyöläinen että friikku. jospa nyt kuitenki vois valita monta?1
 
0.2%
Johtajasopimus, ei työaikaa1
 
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%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
Startup1
 
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%
+ merkittävä optiopaketti1
 
0.2%
Other values (27)27
 
5.5%
(Missing)454
92.3%
2021-02-26T10:10:01.463073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ei11
 
2.4%
ja11
 
2.4%
palkka10
 
2.2%
on10
 
2.2%
mutta9
 
2.0%
ole6
 
1.3%
nyt5
 
1.1%
olen4
 
0.9%
firman4
 
0.9%
joten4
 
0.9%
Other values (321)383
83.8%

Most occurring characters

ValueCountFrequency (%)
422
11.6%
a383
 
10.5%
i311
 
8.6%
t284
 
7.8%
n245
 
6.7%
s237
 
6.5%
e228
 
6.3%
k206
 
5.7%
l183
 
5.0%
o169
 
4.7%
Other values (46)964
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3025
83.3%
Space Separator422
 
11.6%
Other Punctuation85
 
2.3%
Uppercase Letter53
 
1.5%
Decimal Number28
 
0.8%
Dash Punctuation8
 
0.2%
Open Punctuation4
 
0.1%
Close Punctuation4
 
0.1%
Math Symbol3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a383
12.7%
i311
10.3%
t284
9.4%
n245
 
8.1%
s237
 
7.8%
e228
 
7.5%
k206
 
6.8%
l183
 
6.0%
o169
 
5.6%
u140
 
4.6%
Other values (14)639
21.1%
ValueCountFrequency (%)
P9
17.0%
T7
13.2%
O7
13.2%
E6
11.3%
V6
11.3%
K5
9.4%
S4
7.5%
I2
 
3.8%
J2
 
3.8%
H2
 
3.8%
Other values (3)3
 
5.7%
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 (%)
.44
51.8%
,28
32.9%
/5
 
5.9%
%4
 
4.7%
"2
 
2.4%
?2
 
2.4%
ValueCountFrequency (%)
422
100.0%
ValueCountFrequency (%)
(4
100.0%
ValueCountFrequency (%)
)4
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3078
84.7%
Common554
 
15.3%

Most frequent character per script

ValueCountFrequency (%)
a383
12.4%
i311
10.1%
t284
9.2%
n245
 
8.0%
s237
 
7.7%
e228
 
7.4%
k206
 
6.7%
l183
 
5.9%
o169
 
5.5%
u140
 
4.5%
Other values (27)692
22.5%
ValueCountFrequency (%)
422
76.2%
.44
 
7.9%
,28
 
5.1%
015
 
2.7%
-8
 
1.4%
/5
 
0.9%
(4
 
0.7%
)4
 
0.7%
%4
 
0.7%
+3
 
0.5%
Other values (9)17
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3479
95.8%
None153
 
4.2%

Most frequent character per block

ValueCountFrequency (%)
422
12.1%
a383
11.0%
i311
 
8.9%
t284
 
8.2%
n245
 
7.0%
s237
 
6.8%
e228
 
6.6%
k206
 
5.9%
l183
 
5.3%
o169
 
4.9%
Other values (44)811
23.3%
ValueCountFrequency (%)
ä126
82.4%
ö27
 
17.6%

Kk-tulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct184
Distinct (%)38.3%
Missing12
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean5456.02717
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-26T10:10:01.625724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2807.708333
Q14106.770833
median4895.833333
Q36250
95-th percentile10020.83333
Maximum25000
Range25000
Interquartile range (IQR)2143.229167

Descriptive statistics

Standard deviation2643.553987
Coefficient of variation (CV)0.4845199454
Kurtosis12.04148297
Mean5456.02717
Median Absolute Deviation (MAD)979.1666667
Skewness2.671579449
Sum2618893.042
Variance6988377.68
MonotocityNot monotonic
2021-02-26T10:10:01.907628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4166.66666718
 
3.7%
4583.33333318
 
3.7%
625016
 
3.3%
500014
 
2.8%
5833.33333311
 
2.2%
7083.33333311
 
2.2%
312510
 
2.0%
5208.33333310
 
2.0%
5416.66666710
 
2.0%
6666.6666679
 
1.8%
Other values (174)353
71.7%
(Missing)12
 
2.4%
ValueCountFrequency (%)
02
0.4%
333.33333331
0.2%
508.33333331
0.2%
6251
0.2%
1145.8333331
0.2%
ValueCountFrequency (%)
250001
 
0.2%
20833.333331
 
0.2%
18333.333331
 
0.2%
16666.666674
0.8%
15833.333331
 
0.2%

Interactions

2021-02-26T10:09:52.751255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:52.894441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:53.034209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:53.175364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:53.311751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:53.450361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:53.598177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:53.743876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:53.885449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:54.025071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:54.169339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:54.413839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:54.555525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:54.694703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:54.838913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:54.986132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:55.128463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:55.261582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:55.399505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:09:55.538845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-26T10:10:02.054013image/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.
2021-02-26T10:10:02.239329image/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.
2021-02-26T10:10:02.420515image/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.
2021-02-26T10:10:02.611738image/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

2021-02-26T10:09:55.809530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-26T10:09:56.168557image/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.
2021-02-26T10:09:56.550469image/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.
2021-02-26T10:09:56.877377image/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
4822021-02-24 17:28:57.097Kuopio23mies2.0Työntekijä / palkollinen1.0frontend50/502900.036000.0FalseNaNNaN3000.000000
4832021-02-24 23:49:22.242Tampere28mies2.0Työntekijä / palkollinen1.0Ohjelmistokehittäjä (frontend)Etä2860.035850.0FalseNaNNaN2987.500000
4842021-02-25 09:34:48.368Kuopio33mies6.0Työntekijä / palkollinen1.0Ohjelmistokehittäjä, Tech LeadToimisto4500.056250.0TrueNaNNaN4687.500000
4852021-02-25 10:53:41.881PK-Seutu28mies6.0Työntekijä / palkollinen1.0full-stack50/505100.064000.0FalseNaNNaN5333.333333
4862021-02-25 11:09:16.999PK-Seutu28mies3.0Työntekijä / palkollinen1.0Fullstack ja pientä devops tunkkiaToimisto3500.044000.0FalseNaNNaN3666.666667
4872021-02-25 11:10:42.322NaN33NaN15.0Työntekijä / palkollinen1.0NaNToimisto5200.068000.0FalseNaNNaN5666.666667
4882021-02-25 12:33:58.490PK-Seutu28mies5.0Työntekijä / palkollinen1.0Full-stack developerToimisto5500.068000.0TrueNaNNaN5666.666667
4892021-02-25 14:10:32.597Tampere23muu1.0Työntekijä / palkollinen0.5Systems Administrator ja firmän sisäinen 1st line -tukihessuToimisto1081.014000.0TrueNaNKk-palkkani on varsinkin vaihteleva, koska riippuu vuorolisistä (mahdollisista pyhä- ja yövuoroista ja tuurauksista). Jonkinlaisen oletuksen nyt yritin lyödä vuositulolle, mutta taitaa jäädä todellisuudessa hivenen sen alle.1166.666667
4902021-02-25 21:17:36.323PK-Seutu33mies10.0Työntekijä / palkollinen1.0Full-stack ohjemistokehittäjäToimisto4600.058000.0TrueNaNNaN4833.333333
4912021-02-26 09:32:59.778Oulu48mies21.0Työntekijä / palkollinen1.0Backend-koodariEtä5000.070000.0TrueNokiaNaN5833.333333