Overview

Dataset statistics

Number of variables15
Number of observations493
Missing cells1001
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.3 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 Työpaikka and 1 other fieldsHigh correlation
Työpaikka is highly correlated with Vapaa sanaHigh correlation
Kilpailukykyinen is highly correlated with Vapaa sanaHigh 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 (77.5%) missing values Missing
Vapaa sana has 455 (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 10:20:20.639753
Analysis finished2021-02-26 10:20:25.749619
Duration5.11 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-26 12:16:19.696000
2021-02-26T10:20:25.827586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:25.994617image/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
114 
Turku
47 
Oulu
26 
Jyväskylä
 
18
Other values (23)
36 

Length

Max length15
Median length8
Mean length7.231557377
Min length2

Characters and Unicode

Total characters3529
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
50.1%
Tampere114
23.1%
Turku47
 
9.5%
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%
2021-02-26T10:20:26.419559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu247
50.2%
tampere114
23.2%
turku47
 
9.6%
oulu26
 
5.3%
jyväskylä18
 
3.7%
kuopio7
 
1.4%
pori2
 
0.4%
hämeenlinna2
 
0.4%
vaasa2
 
0.4%
lontoo2
 
0.4%
Other values (22)25
 
5.1%

Most occurring characters

ValueCountFrequency (%)
u654
18.5%
e487
13.8%
K257
 
7.3%
t254
 
7.2%
P250
 
7.1%
-249
 
7.1%
S249
 
7.1%
r167
 
4.7%
T163
 
4.6%
a141
 
4.0%
Other values (30)658
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2287
64.8%
Uppercase Letter988
28.0%
Dash Punctuation249
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u654
28.6%
e487
21.3%
t254
 
11.1%
r167
 
7.3%
a141
 
6.2%
p122
 
5.3%
m120
 
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%
T163
16.5%
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 (%)
Latin3275
92.8%
Common254
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u654
20.0%
e487
14.9%
K257
 
7.8%
t254
 
7.8%
P250
 
7.6%
S249
 
7.6%
r167
 
5.1%
T163
 
5.0%
a141
 
4.3%
p122
 
3.7%
Other values (27)531
16.2%
ValueCountFrequency (%)
-249
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3485
98.8%
None44
 
1.2%

Most frequent character per block

ValueCountFrequency (%)
u654
18.8%
e487
14.0%
K257
 
7.4%
t254
 
7.3%
P250
 
7.2%
-249
 
7.1%
S249
 
7.1%
r167
 
4.8%
T163
 
4.7%
a141
 
4.0%
Other values (29)614
17.6%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.4%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean33.76530612
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-26T10:20:26.534250image/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 deviation6.081923702
Coefficient of variation (CV)0.1801234581
Kurtosis0.2085691786
Mean33.76530612
Median Absolute Deviation (MAD)5
Skewness0.4780608832
Sum16545
Variance36.98979592
MonotocityNot monotonic
2021-02-26T10:20:26.637756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33164
33.3%
28121
24.5%
38106
21.5%
4353
 
10.8%
2332
 
6.5%
488
 
1.6%
536
 
1.2%
(Missing)3
 
0.6%
ValueCountFrequency (%)
2332
 
6.5%
28121
24.5%
33164
33.3%
38106
21.5%
4353
 
10.8%
ValueCountFrequency (%)
536
 
1.2%
488
 
1.6%
4353
 
10.8%
38106
21.5%
33164
33.3%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing35
Missing (%)7.1%
Memory size753.0 B
mies
413 
nainen
 
36
muu
 
9

Length

Max length6
Median length4
Mean length4.137554585
Min length3

Characters and Unicode

Total characters1895
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 (%)
mies413
83.8%
nainen36
 
7.3%
muu9
 
1.8%
(Missing)35
 
7.1%
2021-02-26T10:20:26.928456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T10:20:27.022470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies413
90.2%
nainen36
 
7.9%
muu9
 
2.0%

Most occurring characters

ValueCountFrequency (%)
i449
23.7%
e449
23.7%
m422
22.3%
s413
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1895
100.0%

Most frequent character per category

ValueCountFrequency (%)
i449
23.7%
e449
23.7%
m422
22.3%
s413
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1895
100.0%

Most frequent character per script

ValueCountFrequency (%)
i449
23.7%
e449
23.7%
m422
22.3%
s413
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1895
100.0%

Most frequent character per block

ValueCountFrequency (%)
i449
23.7%
e449
23.7%
m422
22.3%
s413
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
0.9%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)5.5%
Missing4
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean9.525562372
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size4.0 KiB
2021-02-26T10:20:27.120699image/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 deviation6.054386445
Coefficient of variation (CV)0.6355935963
Kurtosis-0.03215552626
Mean9.525562372
Median Absolute Deviation (MAD)4
Skewness0.730374291
Sum4658
Variance36.65559523
MonotocityNot monotonic
2021-02-26T10:20:27.248136image/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%
1529
 
5.9%
228
 
5.7%
327
 
5.5%
2027
 
5.5%
627
 
5.5%
825
 
5.1%
Other values (17)171
34.7%
ValueCountFrequency (%)
04
 
0.8%
117
3.4%
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
440 
Freelancer
 
26
Yrittäjä
 
26

Length

Max length24
Median length24
Mean length22.41463415
Min length8

Characters and Unicode

Total characters11028
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 / palkollinen440
89.2%
Freelancer26
 
5.3%
Yrittäjä26
 
5.3%
(Missing)1
 
0.2%
2021-02-26T10:20:27.517185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T10:20:27.609537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
440
32.1%
palkollinen440
32.1%
työntekijä440
32.1%
yrittäjä26
 
1.9%
freelancer26
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1346
12.2%
l1346
12.2%
e958
 
8.7%
i906
 
8.2%
k880
 
8.0%
880
 
8.0%
t492
 
4.5%
ä492
 
4.5%
j466
 
4.2%
a466
 
4.2%
Other values (10)2796
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9216
83.6%
Space Separator880
 
8.0%
Uppercase Letter492
 
4.5%
Other Punctuation440
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1346
14.6%
l1346
14.6%
e958
10.4%
i906
9.8%
k880
9.5%
t492
 
5.3%
ä492
 
5.3%
j466
 
5.1%
a466
 
5.1%
y440
 
4.8%
Other values (5)1424
15.5%
ValueCountFrequency (%)
T440
89.4%
Y26
 
5.3%
F26
 
5.3%
ValueCountFrequency (%)
880
100.0%
ValueCountFrequency (%)
/440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9708
88.0%
Common1320
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1346
13.9%
l1346
13.9%
e958
9.9%
i906
9.3%
k880
9.1%
t492
 
5.1%
ä492
 
5.1%
j466
 
4.8%
a466
 
4.8%
T440
 
4.5%
Other values (8)1916
19.7%
ValueCountFrequency (%)
880
66.7%
/440
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10096
91.5%
None932
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1346
13.3%
l1346
13.3%
e958
9.5%
i906
9.0%
k880
8.7%
880
8.7%
t492
 
4.9%
j466
 
4.6%
a466
 
4.6%
T440
 
4.4%
Other values (8)1916
19.0%
ValueCountFrequency (%)
ä492
52.8%
ö440
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)3.9%
Memory size4.0 KiB
1.0
445 
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 characters1422
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 (%)
1.0445
90.3%
0.823
 
4.7%
0.54
 
0.8%
0.61
 
0.2%
0.71
 
0.2%
(Missing)19
 
3.9%
2021-02-26T10:20:27.839181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T10:20:27.920390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0445
93.9%
0.823
 
4.9%
0.54
 
0.8%
0.61
 
0.2%
0.71
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.474
33.3%
0474
33.3%
1445
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number948
66.7%
Other Punctuation474
33.3%

Most frequent character per category

ValueCountFrequency (%)
0474
50.0%
1445
46.9%
823
 
2.4%
54
 
0.4%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1422
100.0%

Most frequent character per script

ValueCountFrequency (%)
.474
33.3%
0474
33.3%
1445
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1422
100.0%

Most frequent character per block

ValueCountFrequency (%)
.474
33.3%
0474
33.3%
1445
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct259
Distinct (%)54.0%
Missing13
Missing (%)2.6%
Memory size4.0 KiB
Ohjelmistokehittäjä
41 
full-stack
35 
Full-stack
 
24
ohjelmistokehittäjä
 
17
Arkkitehti
 
15
Other values (254)
348 

Length

Max length67
Median length18.5
Mean length19.28958333
Min length2

Characters and Unicode

Total characters9259
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 (%)44.0%

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
 
3.4%
Arkkitehti15
 
3.0%
Full-stack ohjelmistokehittäjä8
 
1.6%
full-stack ohjelmistokehittäjä7
 
1.4%
Frontend6
 
1.2%
arkkitehti6
 
1.2%
Full-stack kehittäjä5
 
1.0%
Other values (249)316
64.1%
(Missing)13
 
2.6%
2021-02-26T10:20:28.275931image/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%
backend16
 
1.8%
kehittäjä16
 
1.8%
Other values (196)390
43.8%

Most occurring characters

ValueCountFrequency (%)
t963
 
10.4%
e854
 
9.2%
l674
 
7.3%
i672
 
7.3%
k510
 
5.5%
o485
 
5.2%
s442
 
4.8%
a442
 
4.8%
416
 
4.5%
h370
 
4.0%
Other values (48)3431
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8037
86.8%
Uppercase Letter469
 
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 (%)
t963
12.0%
e854
 
10.6%
l674
 
8.4%
i672
 
8.4%
k510
 
6.3%
o485
 
6.0%
s442
 
5.5%
a442
 
5.5%
h370
 
4.6%
j350
 
4.4%
Other values (16)2275
28.3%
ValueCountFrequency (%)
F105
22.4%
O97
20.7%
S52
11.1%
D42
 
9.0%
T28
 
6.0%
A27
 
5.8%
L21
 
4.5%
C18
 
3.8%
E12
 
2.6%
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 (%)
-174
100.0%
ValueCountFrequency (%)
(27
100.0%
ValueCountFrequency (%)
)27
100.0%
ValueCountFrequency (%)
+8
100.0%
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8506
91.9%
Common753
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t963
 
11.3%
e854
 
10.0%
l674
 
7.9%
i672
 
7.9%
k510
 
6.0%
o485
 
5.7%
s442
 
5.2%
a442
 
5.2%
h370
 
4.3%
j350
 
4.1%
Other values (37)2744
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 (%)
ASCII8907
96.2%
None352
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t963
 
10.8%
e854
 
9.6%
l674
 
7.6%
i672
 
7.5%
k510
 
5.7%
o485
 
5.4%
s442
 
5.0%
a442
 
5.0%
416
 
4.7%
h370
 
4.2%
Other values (45)3079
34.6%
ValueCountFrequency (%)
ä335
95.2%
ö16
 
4.5%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size753.0 B
Etä
205 
Toimisto
171 
50/50
114 

Length

Max length8
Median length5
Mean length5.210204082
Min length3

Characters and Unicode

Total characters2553
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
41.6%
Toimisto171
34.7%
50/50114
23.1%
(Missing)3
 
0.6%
2021-02-26T10:20:28.672263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-26T10:20:28.763603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä205
41.8%
toimisto171
34.9%
50/50114
23.3%

Most occurring characters

ValueCountFrequency (%)
t376
14.7%
o342
13.4%
i342
13.4%
5228
8.9%
0228
8.9%
E205
8.0%
ä205
8.0%
T171
6.7%
m171
6.7%
s171
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1607
62.9%
Decimal Number456
 
17.9%
Uppercase Letter376
 
14.7%
Other Punctuation114
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t376
23.4%
o342
21.3%
i342
21.3%
ä205
12.8%
m171
10.6%
s171
10.6%
ValueCountFrequency (%)
5228
50.0%
0228
50.0%
ValueCountFrequency (%)
E205
54.5%
T171
45.5%
ValueCountFrequency (%)
/114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1983
77.7%
Common570
 
22.3%

Most frequent character per script

ValueCountFrequency (%)
t376
19.0%
o342
17.2%
i342
17.2%
E205
10.3%
ä205
10.3%
T171
8.6%
m171
8.6%
s171
8.6%
ValueCountFrequency (%)
5228
40.0%
0228
40.0%
/114
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2348
92.0%
None205
 
8.0%

Most frequent character per block

ValueCountFrequency (%)
t376
16.0%
o342
14.6%
i342
14.6%
5228
9.7%
0228
9.7%
E205
8.7%
T171
7.3%
m171
7.3%
s171
7.3%
/114
 
4.9%
ValueCountFrequency (%)
ä205
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct129
Distinct (%)28.6%
Missing42
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean4674.019956
Minimum1081
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-26T10:20:28.873915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1081
5-th percentile2785
Q13800
median4500
Q35485
95-th percentile7000
Maximum15000
Range13919
Interquartile range (IQR)1685

Descriptive statistics

Standard deviation1445.075001
Coefficient of variation (CV)0.3091717653
Kurtosis7.937375566
Mean4674.019956
Median Absolute Deviation (MAD)775
Skewness1.630212835
Sum2107983
Variance2088241.757
MonotocityNot monotonic
2021-02-26T10:20:29.024740image/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
 
3.4%
600017
 
3.4%
430013
 
2.6%
420012
 
2.4%
480012
 
2.4%
300012
 
2.4%
380011
 
2.2%
Other values (119)289
58.6%
(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%
Mean65447.9553
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-26T10:20:29.189432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33750
Q149375
median58750
Q375000
95-th percentile120000
Maximum300000
Range300000
Interquartile range (IQR)25625

Descriptive statistics

Standard deviation31694.09337
Coefficient of variation (CV)0.4842640724
Kurtosis12.07232086
Mean65447.9553
Median Absolute Deviation (MAD)11750
Skewness2.675413248
Sum31480466.5
Variance1004515554
MonotocityNot monotonic
2021-02-26T10:20:29.345269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.7%
5000018
 
3.7%
7500016
 
3.2%
6000014
 
2.8%
8500011
 
2.2%
7000011
 
2.2%
6250010
 
2.0%
6500010
 
2.0%
3750010
 
2.0%
475009
 
1.8%
Other values (174)354
71.8%
(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
153 
(Missing)
 
15
ValueCountFrequency (%)
True325
65.9%
False153
31.0%
(Missing)15
 
3.0%
2021-02-26T10:20:29.459903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)65.8%
Missing382
Missing (%)77.5%
Memory size4.0 KiB
Gofore
12 
Vincit
 
7
Futurice
 
5
Fraktio
 
4
Mavericks
 
4
Other values (68)
79 

Length

Max length132
Median length7
Mean length10.23423423
Min length2

Characters and Unicode

Total characters1136
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 (%)53.2%

Sample

1st rowQuestrade
2nd rowDigiaj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore12
 
2.4%
Vincit7
 
1.4%
Futurice5
 
1.0%
Fraktio4
 
0.8%
Mavericks4
 
0.8%
Pankki3
 
0.6%
Arado3
 
0.6%
If2
 
0.4%
Compile2
 
0.4%
Columbia Road2
 
0.4%
Other values (63)67
 
13.6%
(Missing)382
77.5%
2021-02-26T10:20:29.748013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore12
 
7.5%
vincit7
 
4.4%
mavericks6
 
3.8%
futurice5
 
3.1%
siili4
 
2.5%
fraktio4
 
2.5%
omistama3
 
1.9%
konsulttitalo3
 
1.9%
pankki3
 
1.9%
arado3
 
1.9%
Other values (96)109
68.6%

Most occurring characters

ValueCountFrequency (%)
i123
 
10.8%
a89
 
7.8%
o89
 
7.8%
e86
 
7.6%
t81
 
7.1%
r63
 
5.5%
n58
 
5.1%
51
 
4.5%
k49
 
4.3%
l46
 
4.0%
Other values (44)401
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter946
83.3%
Uppercase Letter133
 
11.7%
Space Separator51
 
4.5%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i123
13.0%
a89
9.4%
o89
9.4%
e86
 
9.1%
t81
 
8.6%
r63
 
6.7%
n58
 
6.1%
k49
 
5.2%
l46
 
4.9%
u45
 
4.8%
Other values (16)217
22.9%
ValueCountFrequency (%)
G15
 
11.3%
S14
 
10.5%
V13
 
9.8%
F10
 
7.5%
K8
 
6.0%
A7
 
5.3%
M7
 
5.3%
C6
 
4.5%
P6
 
4.5%
T6
 
4.5%
Other values (15)41
30.8%
ValueCountFrequency (%)
51
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1079
95.0%
Common57
 
5.0%

Most frequent character per script

ValueCountFrequency (%)
i123
 
11.4%
a89
 
8.2%
o89
 
8.2%
e86
 
8.0%
t81
 
7.5%
r63
 
5.8%
n58
 
5.4%
k49
 
4.5%
l46
 
4.3%
u45
 
4.2%
Other values (41)350
32.4%
ValueCountFrequency (%)
51
89.5%
.3
 
5.3%
-3
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1124
98.9%
None12
 
1.1%

Most frequent character per block

ValueCountFrequency (%)
i123
 
10.9%
a89
 
7.9%
o89
 
7.9%
e86
 
7.7%
t81
 
7.2%
r63
 
5.6%
n58
 
5.2%
51
 
4.5%
k49
 
4.4%
l46
 
4.1%
Other values (42)389
34.6%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct37
Distinct (%)97.4%
Missing455
Missing (%)92.3%
Memory size4.0 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Korona-aika on lisännyt etätyön määrää. Aiemmin pari päivää viikossa etänä, nyt kokonaan. Paluuta vanhaan ei varmaankaan ole, ehkä päivä viikossa konttorilla ihan sosiaalisten kontaktien takia.
 
1
startup, palkan lisäksi optiopaketti.
 
1
hyvä kysely
 
1
+ merkittävä optiopaketti
 
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%
Korona-aika on lisännyt etätyön määrää. Aiemmin pari päivää viikossa etänä, nyt kokonaan. Paluuta vanhaan ei varmaankaan ole, ehkä päivä viikossa konttorilla ihan sosiaalisten kontaktien takia.1
 
0.2%
startup, palkan lisäksi optiopaketti.1
 
0.2%
hyvä kysely1
 
0.2%
+ merkittävä optiopaketti1
 
0.2%
Startup1
 
0.2%
Pakettiin kuuluu reilu määrä optioita ja palkka nousee (ja laskee) firman liikevaihdon myötä.1
 
0.2%
Palkka perustuu osittain laskutukseen, joten vuositulot vaihtelevat hieman.1
 
0.2%
Työskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.1
 
0.2%
Ihan OK. Edut myös kovat.1
 
0.2%
Other values (27)27
 
5.5%
(Missing)455
92.3%
2021-02-26T10:20:30.076329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ei11
 
2.4%
ja11
 
2.4%
on10
 
2.2%
palkka10
 
2.2%
mutta9
 
2.0%
ole6
 
1.3%
nyt5
 
1.1%
olen4
 
0.9%
ihan4
 
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%
Mean5453.996275
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-26T10:20:30.226528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2812.5
Q14114.583333
median4895.833333
Q36250
95-th percentile10000
Maximum25000
Range25000
Interquartile range (IQR)2135.416667

Descriptive statistics

Standard deviation2641.174447
Coefficient of variation (CV)0.4842640724
Kurtosis12.07232086
Mean5453.996275
Median Absolute Deviation (MAD)979.1666667
Skewness2.675413248
Sum2623372.208
Variance6975802.461
MonotocityNot monotonic
2021-02-26T10:20:30.517876image/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
 
3.2%
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)354
71.8%
(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

2021-02-26T10:20:21.595964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:21.735427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:21.872755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:22.009568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:22.142690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:22.278163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:22.420128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:22.562581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:22.701124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:22.837397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:22.977722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:23.226508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:23.364382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:23.500322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:23.638801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:23.778905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:23.917472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:24.046363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:24.178713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-26T10:20:24.313074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-26T10:20:30.663249image/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.
2021-02-26T10:20:30.840429image/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.
2021-02-26T10:20:31.016413image/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.
2021-02-26T10:20:31.202274image/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

2021-02-26T10:20:24.578207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-26T10:20:24.925617image/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.
2021-02-26T10:20:25.280652image/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.
2021-02-26T10:20:25.592543image/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
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
4922021-02-26 12:16:19.696Tampere38mies15.0Työntekijä / palkollinen1.0OhjelmistosuunnittelijaToimisto4300.053750.0FalseGoforeNaN4479.166667