Overview

Dataset statistics

Number of variables15
Number of observations497
Missing cells1012
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.6 KiB
Average record size in memory96.1 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 259 distinct values High cardinality
Työpaikka has a high cardinality: 73 distinct values High cardinality
Vuositulot is highly correlated with Kk-tulotHigh correlation
Kk-tulot is highly correlated with VuositulotHigh correlation
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
Kaupunki has 5 (1.0%) missing values Missing
Sukupuoli has 35 (7.0%) missing values Missing
Työkokemus has 5 (1.0%) missing values Missing
Työaika has 19 (3.8%) missing values Missing
Rooli has 13 (2.6%) missing values Missing
Kuukausipalkka has 44 (8.9%) missing values Missing
Vuositulot has 13 (2.6%) missing values Missing
Kilpailukykyinen has 15 (3.0%) missing values Missing
Työpaikka has 384 (77.3%) missing values Missing
Vapaa sana has 459 (92.4%) missing values Missing
Kk-tulot has 13 (2.6%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-26 13:14:09.536509
Analysis finished2021-02-26 13:14:15.544465
Duration6.01 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct497
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-26 13:24:35.647000
2021-02-26T13:14:15.637895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:15.841279image/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
250 
Tampere
115 
Turku
47 
Oulu
26 
Jyväskylä
 
18
Other values (23)
36 

Length

Max length15
Median length8
Mean length7.235772358
Min length2

Characters and Unicode

Total characters3560
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 (%)3.0%

Sample

1st rowPK-Seutu
2nd rowTurku
3rd rowPK-Seutu
4th rowTampere
5th rowPK-Seutu
ValueCountFrequency (%)
PK-Seutu250
50.3%
Tampere115
23.1%
Turku47
 
9.5%
Oulu26
 
5.2%
Jyväskylä18
 
3.6%
Kuopio7
 
1.4%
Lontoo2
 
0.4%
Vaasa2
 
0.4%
Tallinna2
 
0.4%
Pori2
 
0.4%
Other values (18)21
 
4.2%
(Missing)5
 
1.0%
2021-02-26T13:14:16.327172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu250
50.4%
tampere115
23.2%
turku47
 
9.5%
oulu26
 
5.2%
jyväskylä18
 
3.6%
kuopio7
 
1.4%
eu2
 
0.4%
vaasa2
 
0.4%
lahti2
 
0.4%
tallinna2
 
0.4%
Other values (22)25
 
5.0%

Most occurring characters

ValueCountFrequency (%)
u660
18.5%
e492
13.8%
K260
 
7.3%
t257
 
7.2%
P253
 
7.1%
-252
 
7.1%
S252
 
7.1%
r168
 
4.7%
T164
 
4.6%
a142
 
4.0%
Other values (30)660
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2305
64.7%
Uppercase Letter998
28.0%
Dash Punctuation252
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u660
28.6%
e492
21.3%
t257
 
11.1%
r168
 
7.3%
a142
 
6.2%
p123
 
5.3%
m121
 
5.2%
k70
 
3.0%
l57
 
2.5%
ä44
 
1.9%
Other values (10)171
 
7.4%
ValueCountFrequency (%)
K260
26.1%
P253
25.4%
S252
25.3%
T164
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 (%)
-252
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3303
92.8%
Common257
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u660
20.0%
e492
14.9%
K260
 
7.9%
t257
 
7.8%
P253
 
7.7%
S252
 
7.6%
r168
 
5.1%
T164
 
5.0%
a142
 
4.3%
p123
 
3.7%
Other values (27)532
16.1%
ValueCountFrequency (%)
-252
98.1%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3516
98.8%
None44
 
1.2%

Most frequent character per block

ValueCountFrequency (%)
u660
18.8%
e492
14.0%
K260
 
7.4%
t257
 
7.3%
P253
 
7.2%
-252
 
7.2%
S252
 
7.2%
r168
 
4.8%
T164
 
4.7%
a142
 
4.0%
Other values (29)616
17.5%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.4%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean33.75910931
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-26T13:14:16.466197image/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 deviation6.057589411
Coefficient of variation (CV)0.1794357
Kurtosis0.235945693
Mean33.75910931
Median Absolute Deviation (MAD)5
Skewness0.4829687447
Sum16677
Variance36.69438947
MonotocityNot monotonic
2021-02-26T13:14:16.599128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33168
33.8%
28121
24.3%
38106
21.3%
4353
 
10.7%
2332
 
6.4%
488
 
1.6%
536
 
1.2%
(Missing)3
 
0.6%
ValueCountFrequency (%)
2332
 
6.4%
28121
24.3%
33168
33.8%
38106
21.3%
4353
 
10.7%
ValueCountFrequency (%)
536
 
1.2%
488
 
1.6%
4353
 
10.7%
38106
21.3%
33168
33.8%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.6%
Missing35
Missing (%)7.0%
Memory size757.0 B
mies
416 
nainen
 
37
muu
 
9

Length

Max length6
Median length4
Mean length4.140692641
Min length3

Characters and Unicode

Total characters1913
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 (%)
mies416
83.7%
nainen37
 
7.4%
muu9
 
1.8%
(Missing)35
 
7.0%
2021-02-26T13:14:16.963281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T13:14:17.082122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies416
90.0%
nainen37
 
8.0%
muu9
 
1.9%

Most occurring characters

ValueCountFrequency (%)
i453
23.7%
e453
23.7%
m425
22.2%
s416
21.7%
n111
 
5.8%
a37
 
1.9%
u18
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1913
100.0%

Most frequent character per category

ValueCountFrequency (%)
i453
23.7%
e453
23.7%
m425
22.2%
s416
21.7%
n111
 
5.8%
a37
 
1.9%
u18
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1913
100.0%

Most frequent character per script

ValueCountFrequency (%)
i453
23.7%
e453
23.7%
m425
22.2%
s416
21.7%
n111
 
5.8%
a37
 
1.9%
u18
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1913
100.0%

Most frequent character per block

ValueCountFrequency (%)
i453
23.7%
e453
23.7%
m425
22.2%
s416
21.7%
n111
 
5.8%
a37
 
1.9%
u18
 
0.9%

Työkokemus
Real number (ℝ≥0)

MISSING

Distinct27
Distinct (%)5.5%
Missing5
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean9.518292683
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size4.0 KiB
2021-02-26T13:14:17.199688image/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 deviation6.043770314
Coefficient of variation (CV)0.6349636973
Kurtosis-0.02325054833
Mean9.518292683
Median Absolute Deviation (MAD)4
Skewness0.7308340323
Sum4683
Variance36.5271596
MonotocityNot monotonic
2021-02-26T13:14:17.360284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
554
 
10.9%
1040
 
8.0%
431
 
6.2%
730
 
6.0%
1529
 
5.8%
328
 
5.6%
228
 
5.6%
2027
 
5.4%
627
 
5.4%
825
 
5.0%
Other values (17)173
34.8%
ValueCountFrequency (%)
04
 
0.8%
117
3.4%
228
5.6%
328
5.6%
431
6.2%
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
443 
Freelancer
 
27
Yrittäjä
 
26

Length

Max length24
Median length24
Mean length22.39919355
Min length8

Characters and Unicode

Total characters11110
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 / palkollinen443
89.1%
Freelancer27
 
5.4%
Yrittäjä26
 
5.2%
(Missing)1
 
0.2%
2021-02-26T13:14:17.694442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T13:14:17.810951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
443
32.1%
työntekijä443
32.1%
palkollinen443
32.1%
freelancer27
 
2.0%
yrittäjä26
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1356
12.2%
l1356
12.2%
e967
 
8.7%
i912
 
8.2%
k886
 
8.0%
886
 
8.0%
t495
 
4.5%
ä495
 
4.5%
a470
 
4.2%
j469
 
4.2%
Other values (10)2818
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9285
83.6%
Space Separator886
 
8.0%
Uppercase Letter496
 
4.5%
Other Punctuation443
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1356
14.6%
l1356
14.6%
e967
10.4%
i912
9.8%
k886
9.5%
t495
 
5.3%
ä495
 
5.3%
a470
 
5.1%
j469
 
5.1%
y443
 
4.8%
Other values (5)1436
15.5%
ValueCountFrequency (%)
T443
89.3%
F27
 
5.4%
Y26
 
5.2%
ValueCountFrequency (%)
886
100.0%
ValueCountFrequency (%)
/443
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9781
88.0%
Common1329
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1356
13.9%
l1356
13.9%
e967
9.9%
i912
9.3%
k886
9.1%
t495
 
5.1%
ä495
 
5.1%
a470
 
4.8%
j469
 
4.8%
T443
 
4.5%
Other values (8)1932
19.8%
ValueCountFrequency (%)
886
66.7%
/443
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10172
91.6%
None938
 
8.4%

Most frequent character per block

ValueCountFrequency (%)
n1356
13.3%
l1356
13.3%
e967
9.5%
i912
9.0%
k886
8.7%
886
8.7%
t495
 
4.9%
a470
 
4.6%
j469
 
4.6%
T443
 
4.4%
Other values (8)1932
19.0%
ValueCountFrequency (%)
ä495
52.8%
ö443
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.0%
Missing19
Missing (%)3.8%
Memory size4.0 KiB
1.0
449 
0.8
 
23
0.5
 
4
0.6
 
1
0.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1434
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 (%)
1.0449
90.3%
0.823
 
4.6%
0.54
 
0.8%
0.61
 
0.2%
0.71
 
0.2%
(Missing)19
 
3.8%
2021-02-26T13:14:18.084455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T13:14:18.188004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0449
93.9%
0.823
 
4.8%
0.54
 
0.8%
0.61
 
0.2%
0.71
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.478
33.3%
0478
33.3%
1449
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number956
66.7%
Other Punctuation478
33.3%

Most frequent character per category

ValueCountFrequency (%)
0478
50.0%
1449
47.0%
823
 
2.4%
54
 
0.4%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1434
100.0%

Most frequent character per script

ValueCountFrequency (%)
.478
33.3%
0478
33.3%
1449
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1434
100.0%

Most frequent character per block

ValueCountFrequency (%)
.478
33.3%
0478
33.3%
1449
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct259
Distinct (%)53.5%
Missing13
Missing (%)2.6%
Memory size4.0 KiB
Ohjelmistokehittäjä
42 
full-stack
35 
Full-stack
 
25
ohjelmistokehittäjä
 
17
Arkkitehti
 
16
Other values (254)
349 

Length

Max length67
Median length18
Mean length19.22727273
Min length2

Characters and Unicode

Total characters9306
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

Unique211 ?
Unique (%)43.6%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä42
 
8.5%
full-stack35
 
7.0%
Full-stack25
 
5.0%
ohjelmistokehittäjä17
 
3.4%
Arkkitehti16
 
3.2%
Full-stack ohjelmistokehittäjä8
 
1.6%
full-stack ohjelmistokehittäjä7
 
1.4%
Frontend6
 
1.2%
frontend6
 
1.2%
arkkitehti6
 
1.2%
Other values (249)316
63.6%
(Missing)13
 
2.6%
2021-02-26T13:14:18.599770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack143
 
16.0%
ohjelmistokehittäjä115
 
12.9%
developer61
 
6.8%
arkkitehti36
 
4.0%
35
 
3.9%
lead33
 
3.7%
frontend28
 
3.1%
senior21
 
2.3%
backend16
 
1.8%
kehittäjä16
 
1.8%
Other values (196)390
43.6%

Most occurring characters

ValueCountFrequency (%)
t970
 
10.4%
e858
 
9.2%
l677
 
7.3%
i676
 
7.3%
k514
 
5.5%
o487
 
5.2%
s444
 
4.8%
a443
 
4.8%
416
 
4.5%
h373
 
4.0%
Other values (48)3448
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8080
86.8%
Uppercase Letter472
 
5.1%
Space Separator417
 
4.5%
Dash Punctuation175
 
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 (%)
t970
12.0%
e858
 
10.6%
l677
 
8.4%
i676
 
8.4%
k514
 
6.4%
o487
 
6.0%
s444
 
5.5%
a443
 
5.5%
h373
 
4.6%
j352
 
4.4%
Other values (16)2286
28.3%
ValueCountFrequency (%)
F106
22.5%
O98
20.8%
S52
11.0%
D42
 
8.9%
A28
 
5.9%
T28
 
5.9%
L21
 
4.4%
C18
 
3.8%
E12
 
2.5%
P11
 
2.3%
Other values (11)56
11.9%
ValueCountFrequency (%)
,53
53.5%
/42
42.4%
&3
 
3.0%
.1
 
1.0%
ValueCountFrequency (%)
416
99.8%
 1
 
0.2%
ValueCountFrequency (%)
-175
100.0%
ValueCountFrequency (%)
(27
100.0%
ValueCountFrequency (%)
)27
100.0%
ValueCountFrequency (%)
+8
100.0%
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8552
91.9%
Common754
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t970
 
11.3%
e858
 
10.0%
l677
 
7.9%
i676
 
7.9%
k514
 
6.0%
o487
 
5.7%
s444
 
5.2%
a443
 
5.2%
h373
 
4.4%
j352
 
4.1%
Other values (37)2758
32.2%
ValueCountFrequency (%)
416
55.2%
-175
23.2%
,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 (%)
ASCII8952
96.2%
None354
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t970
 
10.8%
e858
 
9.6%
l677
 
7.6%
i676
 
7.6%
k514
 
5.7%
o487
 
5.4%
s444
 
5.0%
a443
 
4.9%
416
 
4.6%
h373
 
4.2%
Other values (45)3094
34.6%
ValueCountFrequency (%)
ä337
95.2%
ö16
 
4.5%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size757.0 B
Etä
206 
Toimisto
172 
50/50
116 

Length

Max length8
Median length5
Mean length5.210526316
Min length3

Characters and Unicode

Total characters2574
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 (%)
Etä206
41.4%
Toimisto172
34.6%
50/50116
23.3%
(Missing)3
 
0.6%
2021-02-26T13:14:19.054444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T13:14:19.168828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä206
41.7%
toimisto172
34.8%
50/50116
23.5%

Most occurring characters

ValueCountFrequency (%)
t378
14.7%
o344
13.4%
i344
13.4%
5232
9.0%
0232
9.0%
E206
8.0%
ä206
8.0%
T172
6.7%
m172
6.7%
s172
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1616
62.8%
Decimal Number464
 
18.0%
Uppercase Letter378
 
14.7%
Other Punctuation116
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t378
23.4%
o344
21.3%
i344
21.3%
ä206
12.7%
m172
10.6%
s172
10.6%
ValueCountFrequency (%)
5232
50.0%
0232
50.0%
ValueCountFrequency (%)
E206
54.5%
T172
45.5%
ValueCountFrequency (%)
/116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1994
77.5%
Common580
 
22.5%

Most frequent character per script

ValueCountFrequency (%)
t378
19.0%
o344
17.3%
i344
17.3%
E206
10.3%
ä206
10.3%
T172
8.6%
m172
8.6%
s172
8.6%
ValueCountFrequency (%)
5232
40.0%
0232
40.0%
/116
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2368
92.0%
None206
 
8.0%

Most frequent character per block

ValueCountFrequency (%)
t378
16.0%
o344
14.5%
i344
14.5%
5232
9.8%
0232
9.8%
E206
8.7%
T172
7.3%
m172
7.3%
s172
7.3%
/116
 
4.9%
ValueCountFrequency (%)
ä206
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct129
Distinct (%)28.5%
Missing44
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean4676.121413
Minimum1081
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-26T13:14:19.297460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1081
5-th percentile2788
Q13800
median4500
Q35500
95-th percentile7000
Maximum15000
Range13919
Interquartile range (IQR)1700

Descriptive statistics

Standard deviation1445.013477
Coefficient of variation (CV)0.3090196659
Kurtosis7.886674619
Mean4676.121413
Median Absolute Deviation (MAD)775
Skewness1.622784862
Sum2118283
Variance2088063.948
MonotocityNot monotonic
2021-02-26T13:14:19.482402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400026
 
5.2%
450024
 
4.8%
500018
 
3.6%
550017
 
3.4%
600017
 
3.4%
430013
 
2.6%
420012
 
2.4%
480012
 
2.4%
380012
 
2.4%
300012
 
2.4%
Other values (119)290
58.4%
(Missing)44
 
8.9%
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

Distinct185
Distinct (%)38.2%
Missing13
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean65690.11674
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-26T13:14:19.697783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33885
Q149656.25
median58875
Q375000
95-th percentile124250
Maximum300000
Range300000
Interquartile range (IQR)25343.75

Descriptive statistics

Standard deviation31880.88687
Coefficient of variation (CV)0.4853224267
Kurtosis11.6925282
Mean65690.11674
Median Absolute Deviation (MAD)11875
Skewness2.639413056
Sum31794016.5
Variance1016390947
MonotocityNot monotonic
2021-02-26T13:14:19.892963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000018
 
3.6%
5500018
 
3.6%
7500017
 
3.4%
6000014
 
2.8%
8500011
 
2.2%
7000011
 
2.2%
6250010
 
2.0%
6500010
 
2.0%
3750010
 
2.0%
800009
 
1.8%
Other values (175)356
71.6%
(Missing)13
 
2.6%
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
328 
False
154 
(Missing)
 
15
ValueCountFrequency (%)
True328
66.0%
False154
31.0%
(Missing)15
 
3.0%
2021-02-26T13:14:20.039597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)64.6%
Missing384
Missing (%)77.3%
Memory size4.0 KiB
Gofore
12 
Vincit
 
8
Futurice
 
5
Fraktio
 
4
Mavericks
 
4
Other values (68)
80 

Length

Max length132
Median length7
Mean length10.15044248
Min length2

Characters and Unicode

Total characters1147
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 (%)52.2%

Sample

1st rowQuestrade
2nd rowDigiaj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore12
 
2.4%
Vincit8
 
1.6%
Futurice5
 
1.0%
Fraktio4
 
0.8%
Mavericks4
 
0.8%
Arado3
 
0.6%
Pankki3
 
0.6%
Siili3
 
0.6%
Goforej2
 
0.4%
KVTES-alainen kunnan omistama 2
 
0.4%
Other values (63)67
 
13.5%
(Missing)384
77.3%
2021-02-26T13:14:20.396180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore12
 
7.5%
vincit8
 
5.0%
mavericks6
 
3.7%
futurice5
 
3.1%
siili5
 
3.1%
fraktio4
 
2.5%
pankki3
 
1.9%
arado3
 
1.9%
if3
 
1.9%
omistama3
 
1.9%
Other values (96)109
67.7%

Most occurring characters

ValueCountFrequency (%)
i128
 
11.2%
a89
 
7.8%
o89
 
7.8%
e86
 
7.5%
t82
 
7.1%
r63
 
5.5%
n59
 
5.1%
51
 
4.4%
k49
 
4.3%
l47
 
4.1%
Other values (44)404
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter955
83.3%
Uppercase Letter135
 
11.8%
Space Separator51
 
4.4%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i128
13.4%
a89
9.3%
o89
9.3%
e86
 
9.0%
t82
 
8.6%
r63
 
6.6%
n59
 
6.2%
k49
 
5.1%
l47
 
4.9%
u45
 
4.7%
Other values (16)218
22.8%
ValueCountFrequency (%)
G15
 
11.1%
S15
 
11.1%
V14
 
10.4%
F10
 
7.4%
K8
 
5.9%
A7
 
5.2%
M7
 
5.2%
C6
 
4.4%
P6
 
4.4%
T6
 
4.4%
Other values (15)41
30.4%
ValueCountFrequency (%)
51
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1090
95.0%
Common57
 
5.0%

Most frequent character per script

ValueCountFrequency (%)
i128
 
11.7%
a89
 
8.2%
o89
 
8.2%
e86
 
7.9%
t82
 
7.5%
r63
 
5.8%
n59
 
5.4%
k49
 
4.5%
l47
 
4.3%
u45
 
4.1%
Other values (41)353
32.4%
ValueCountFrequency (%)
51
89.5%
.3
 
5.3%
-3
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1135
99.0%
None12
 
1.0%

Most frequent character per block

ValueCountFrequency (%)
i128
 
11.3%
a89
 
7.8%
o89
 
7.8%
e86
 
7.6%
t82
 
7.2%
r63
 
5.6%
n59
 
5.2%
51
 
4.5%
k49
 
4.3%
l47
 
4.1%
Other values (42)392
34.5%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct37
Distinct (%)97.4%
Missing459
Missing (%)92.4%
Memory size4.0 KiB
palkan lisänä lounas- ja virkistysetu
 
2
hyvä kysely
 
1
Osittain laskutukseen perustuva palkka joten vaihtelee.
 
1
Sijainti Pori, mutta etätöitä 100%. Varsinainen positio Tampere - Helsinki. Edut aika huonot, perusjutut. Työ itsessään aika masentavaa. Seuraavaksi varmaan freelance/yrittäjyys.
 
1
Työskentelen opintojen ohella, ensimmäisessä frontend devaajan työssä. Olen opiskellut reilu 2 vuotta yliopistossa. Palkkani on mielestäni nyt ihan ok, mutta tarkoituksena nostaa sitä 3000e /kk loppukesään mennessä.
 
1
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%
hyvä kysely1
 
0.2%
Osittain laskutukseen perustuva palkka joten vaihtelee.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 opintojen ohella, ensimmäisessä frontend devaajan työssä. Olen opiskellut reilu 2 vuotta yliopistossa. Palkkani on mielestäni nyt ihan ok, mutta tarkoituksena nostaa sitä 3000e /kk loppukesään mennessä. 1
 
0.2%
Työskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.1
 
0.2%
Vastasin kysymyksiin läpällä. Summat on enemmän sitä minkä verran yrittäjänä haluaa sykkiä ja mennä "raha edellä". 1
 
0.2%
Vuositulot pitää sisällään myös sivutoimisena tehtyä pientä laskutusta.1
 
0.2%
Pakettiin kuuluu reilu määrä optioita ja palkka nousee (ja laskee) firman liikevaihdon myötä.1
 
0.2%
saispa lisää liksaa1
 
0.2%
Other values (27)27
 
5.4%
(Missing)459
92.4%
2021-02-26T13:14:20.797599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ja11
 
2.4%
ei11
 
2.4%
palkka10
 
2.2%
on10
 
2.2%
mutta9
 
2.0%
ole6
 
1.3%
nyt5
 
1.1%
firman4
 
0.9%
olen4
 
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

Distinct185
Distinct (%)38.2%
Missing13
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean5474.176395
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-26T13:14:20.983898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2823.75
Q14138.020833
median4906.25
Q36250
95-th percentile10354.16667
Maximum25000
Range25000
Interquartile range (IQR)2111.979167

Descriptive statistics

Standard deviation2656.740572
Coefficient of variation (CV)0.4853224267
Kurtosis11.6925282
Mean5474.176395
Median Absolute Deviation (MAD)989.5833333
Skewness2.639413056
Sum2649501.375
Variance7058270.468
MonotocityNot monotonic
2021-02-26T13:14:21.299812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4166.66666718
 
3.6%
4583.33333318
 
3.6%
625017
 
3.4%
500014
 
2.8%
5833.33333311
 
2.2%
7083.33333311
 
2.2%
312510
 
2.0%
5416.66666710
 
2.0%
5208.33333310
 
2.0%
4333.3333339
 
1.8%
Other values (175)356
71.6%
(Missing)13
 
2.6%
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

2021-02-26T13:14:10.783532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:10.940229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:11.089548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:11.250849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:11.399141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:11.557078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:11.724128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:11.892699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:12.052624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:12.210947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:12.368947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:12.635153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:12.797180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:12.957326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:13.113891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:13.272586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:13.430746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:13.573044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:13.723584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T13:14:13.892851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-26T13:14:21.476754image/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.
2021-02-26T13:14:21.672954image/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.
2021-02-26T13:14:21.862935image/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.
2021-02-26T13:14:22.068178image/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

2021-02-26T13:14:14.190501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-26T13:14:14.614439image/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.
2021-02-26T13:14:15.004056image/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.
2021-02-26T13:14:15.364567image/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
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
4922021-02-26 12:16:19.696Tampere38mies15.0Työntekijä / palkollinen1.0OhjelmistosuunnittelijaToimisto4300.053750.0FalseGoforeNaN4479.166667
4932021-02-26 12:21:52.296Tampere33mies11.0Freelancer1.0frontendEtäNaN157300.0TrueNaNNaN13108.333333
4942021-02-26 12:46:37.404PK-Seutu33mies11.0Työntekijä / palkollinen1.0ArkkitehtiToimisto6500.081250.0TrueSiiliNaN6770.833333
4952021-02-26 12:47:26.116PK-Seutu33nainen3.0Työntekijä / palkollinen1.0Full-stack50/503800.0NaNFalseNaNNaNNaN
4962021-02-26 13:24:35.647PK-Seutu33miesNaNTyöntekijä / palkollinen1.0Ohjelmistokehittäjä50/50NaN75000.0TrueVincitNaN6250.000000