Overview

Dataset statistics

Number of variables15
Number of observations498
Missing cells1014
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.7 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 (8.8%) missing values Missing
Vuositulot has 13 (2.6%) missing values Missing
Kilpailukykyinen has 15 (3.0%) missing values Missing
Työpaikka has 385 (77.3%) missing values Missing
Vapaa sana has 460 (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 started2021-02-27 00:27:48.389162
Analysis finished2021-02-27 00:27:54.016741
Duration5.63 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct498
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-26 16:28:30.010000
2021-02-27T00:27:54.098558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:54.283930image/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
116 
Turku
47 
Oulu
26 
Jyväskylä
 
18
Other values (23)
36 

Length

Max length15
Median length8
Mean length7.235294118
Min length2

Characters and Unicode

Total characters3567
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
50.2%
Tampere116
23.3%
Turku47
 
9.4%
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%
2021-02-27T00:27:54.719196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu250
50.3%
tampere116
23.3%
turku47
 
9.5%
oulu26
 
5.2%
jyväskylä18
 
3.6%
kuopio7
 
1.4%
vaasa2
 
0.4%
lontoo2
 
0.4%
pori2
 
0.4%
hämeenlinna2
 
0.4%
Other values (22)25
 
5.0%

Most occurring characters

ValueCountFrequency (%)
u660
18.5%
e494
13.8%
K260
 
7.3%
t257
 
7.2%
P253
 
7.1%
-252
 
7.1%
S252
 
7.1%
r169
 
4.7%
T165
 
4.6%
a143
 
4.0%
Other values (30)662
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2311
64.8%
Uppercase Letter999
28.0%
Dash Punctuation252
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u660
28.6%
e494
21.4%
t257
 
11.1%
r169
 
7.3%
a143
 
6.2%
p124
 
5.4%
m122
 
5.3%
k70
 
3.0%
l57
 
2.5%
ä44
 
1.9%
Other values (10)171
 
7.4%
ValueCountFrequency (%)
K260
26.0%
P253
25.3%
S252
25.2%
T165
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 (%)
-252
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3310
92.8%
Common257
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u660
19.9%
e494
14.9%
K260
 
7.9%
t257
 
7.8%
P253
 
7.6%
S252
 
7.6%
r169
 
5.1%
T165
 
5.0%
a143
 
4.3%
p124
 
3.7%
Other values (27)533
16.1%
ValueCountFrequency (%)
-252
98.1%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3523
98.8%
None44
 
1.2%

Most frequent character per block

ValueCountFrequency (%)
u660
18.7%
e494
14.0%
K260
 
7.4%
t257
 
7.3%
P253
 
7.2%
-252
 
7.2%
S252
 
7.2%
r169
 
4.8%
T165
 
4.7%
a143
 
4.1%
Other values (29)618
17.5%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.4%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean33.77777778
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-27T00:27:54.840296image/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 deviation6.065692259
Coefficient of variation (CV)0.1795764156
Kurtosis0.2170449208
Mean33.77777778
Median Absolute Deviation (MAD)5
Skewness0.4779742182
Sum16720
Variance36.79262258
MonotocityNot monotonic
2021-02-27T00:27:54.949031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33168
33.7%
28121
24.3%
38106
21.3%
4354
 
10.8%
2332
 
6.4%
488
 
1.6%
536
 
1.2%
(Missing)3
 
0.6%
ValueCountFrequency (%)
2332
 
6.4%
28121
24.3%
33168
33.7%
38106
21.3%
4354
 
10.8%
ValueCountFrequency (%)
536
 
1.2%
488
 
1.6%
4354
 
10.8%
38106
21.3%
33168
33.7%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.6%
Missing35
Missing (%)7.0%
Memory size758.0 B
mies
417 
nainen
 
37
muu
 
9

Length

Max length6
Median length4
Mean length4.140388769
Min length3

Characters and Unicode

Total characters1917
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 (%)
mies417
83.7%
nainen37
 
7.4%
muu9
 
1.8%
(Missing)35
 
7.0%
2021-02-27T00:27:55.253377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-27T00:27:55.354855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies417
90.1%
nainen37
 
8.0%
muu9
 
1.9%

Most occurring characters

ValueCountFrequency (%)
i454
23.7%
e454
23.7%
m426
22.2%
s417
21.8%
n111
 
5.8%
a37
 
1.9%
u18
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1917
100.0%

Most frequent character per category

ValueCountFrequency (%)
i454
23.7%
e454
23.7%
m426
22.2%
s417
21.8%
n111
 
5.8%
a37
 
1.9%
u18
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1917
100.0%

Most frequent character per script

ValueCountFrequency (%)
i454
23.7%
e454
23.7%
m426
22.2%
s417
21.8%
n111
 
5.8%
a37
 
1.9%
u18
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1917
100.0%

Most frequent character per block

ValueCountFrequency (%)
i454
23.7%
e454
23.7%
m426
22.2%
s417
21.8%
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%
Mean9.539553753
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size4.0 KiB
2021-02-27T00:27:55.457670image/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 deviation6.056052321
Coefficient of variation (CV)0.6348360184
Kurtosis-0.04542368738
Mean9.539553753
Median Absolute Deviation (MAD)4
Skewness0.7249200448
Sum4703
Variance36.67576972
MonotocityNot monotonic
2021-02-27T00:27:55.590251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
554
 
10.8%
1040
 
8.0%
431
 
6.2%
730
 
6.0%
1529
 
5.8%
328
 
5.6%
228
 
5.6%
2028
 
5.6%
627
 
5.4%
825
 
5.0%
Other values (17)173
34.7%
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
444 
Freelancer
 
27
Yrittäjä
 
26

Length

Max length24
Median length24
Mean length22.40241449
Min length8

Characters and Unicode

Total characters11134
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 / palkollinen444
89.2%
Freelancer27
 
5.4%
Yrittäjä26
 
5.2%
(Missing)1
 
0.2%
2021-02-27T00:27:55.870146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-27T00:27:55.967141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
444
32.1%
palkollinen444
32.1%
työntekijä444
32.1%
freelancer27
 
1.9%
yrittäjä26
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1359
12.2%
l1359
12.2%
e969
 
8.7%
i914
 
8.2%
k888
 
8.0%
888
 
8.0%
t496
 
4.5%
ä496
 
4.5%
a471
 
4.2%
j470
 
4.2%
Other values (10)2824
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9305
83.6%
Space Separator888
 
8.0%
Uppercase Letter497
 
4.5%
Other Punctuation444
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1359
14.6%
l1359
14.6%
e969
10.4%
i914
9.8%
k888
9.5%
t496
 
5.3%
ä496
 
5.3%
a471
 
5.1%
j470
 
5.1%
y444
 
4.8%
Other values (5)1439
15.5%
ValueCountFrequency (%)
T444
89.3%
F27
 
5.4%
Y26
 
5.2%
ValueCountFrequency (%)
888
100.0%
ValueCountFrequency (%)
/444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9802
88.0%
Common1332
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1359
13.9%
l1359
13.9%
e969
9.9%
i914
9.3%
k888
9.1%
t496
 
5.1%
ä496
 
5.1%
a471
 
4.8%
j470
 
4.8%
T444
 
4.5%
Other values (8)1936
19.8%
ValueCountFrequency (%)
888
66.7%
/444
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10194
91.6%
None940
 
8.4%

Most frequent character per block

ValueCountFrequency (%)
n1359
13.3%
l1359
13.3%
e969
9.5%
i914
9.0%
k888
8.7%
888
8.7%
t496
 
4.9%
a471
 
4.6%
j470
 
4.6%
T444
 
4.4%
Other values (8)1936
19.0%
ValueCountFrequency (%)
ä496
52.8%
ö444
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.0%
Missing19
Missing (%)3.8%
Memory size4.0 KiB
1.0
450 
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 characters1437
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 (%)
1.0450
90.4%
0.823
 
4.6%
0.54
 
0.8%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
3.8%
2021-02-27T00:27:56.209330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-27T00:27:56.296050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0450
93.9%
0.823
 
4.8%
0.54
 
0.8%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.479
33.3%
0479
33.3%
1450
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number958
66.7%
Other Punctuation479
33.3%

Most frequent character per category

ValueCountFrequency (%)
0479
50.0%
1450
47.0%
823
 
2.4%
54
 
0.4%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.479
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1437
100.0%

Most frequent character per script

ValueCountFrequency (%)
.479
33.3%
0479
33.3%
1450
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1437
100.0%

Most frequent character per block

ValueCountFrequency (%)
.479
33.3%
0479
33.3%
1450
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct259
Distinct (%)53.4%
Missing13
Missing (%)2.6%
Memory size4.0 KiB
Ohjelmistokehittäjä
42 
full-stack
36 
Full-stack
 
25
ohjelmistokehittäjä
 
17
Arkkitehti
 
16
Other values (254)
349 

Length

Max length67
Median length18
Mean length19.20824742
Min length2

Characters and Unicode

Total characters9316
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 (%)43.5%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä42
 
8.4%
full-stack36
 
7.2%
Full-stack25
 
5.0%
ohjelmistokehittäjä17
 
3.4%
Arkkitehti16
 
3.2%
Full-stack ohjelmistokehittäjä8
 
1.6%
full-stack ohjelmistokehittäjä7
 
1.4%
frontend6
 
1.2%
arkkitehti6
 
1.2%
Frontend6
 
1.2%
Other values (249)316
63.5%
(Missing)13
 
2.6%
2021-02-27T00:27:56.647464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack144
 
16.1%
ohjelmistokehittäjä115
 
12.8%
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 (%)
t971
 
10.4%
e858
 
9.2%
l679
 
7.3%
i676
 
7.3%
k515
 
5.5%
o487
 
5.2%
s445
 
4.8%
a444
 
4.8%
416
 
4.5%
h373
 
4.0%
Other values (48)3452
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8089
86.8%
Uppercase Letter472
 
5.1%
Space Separator417
 
4.5%
Dash Punctuation176
 
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 (%)
t971
12.0%
e858
 
10.6%
l679
 
8.4%
i676
 
8.4%
k515
 
6.4%
o487
 
6.0%
s445
 
5.5%
a444
 
5.5%
h373
 
4.6%
j352
 
4.4%
Other values (16)2289
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 (%)
-176
100.0%
ValueCountFrequency (%)
(27
100.0%
ValueCountFrequency (%)
)27
100.0%
ValueCountFrequency (%)
+8
100.0%
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8561
91.9%
Common755
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t971
 
11.3%
e858
 
10.0%
l679
 
7.9%
i676
 
7.9%
k515
 
6.0%
o487
 
5.7%
s445
 
5.2%
a444
 
5.2%
h373
 
4.4%
j352
 
4.1%
Other values (37)2761
32.3%
ValueCountFrequency (%)
416
55.1%
-176
23.3%
,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 (%)
ASCII8962
96.2%
None354
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t971
 
10.8%
e858
 
9.6%
l679
 
7.6%
i676
 
7.5%
k515
 
5.7%
o487
 
5.4%
s445
 
5.0%
a444
 
5.0%
416
 
4.6%
h373
 
4.2%
Other values (45)3098
34.6%
ValueCountFrequency (%)
ä337
95.2%
ö16
 
4.5%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size758.0 B
Etä
206 
Toimisto
173 
50/50
116 

Length

Max length8
Median length5
Mean length5.216161616
Min length3

Characters and Unicode

Total characters2582
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%
Toimisto173
34.7%
50/50116
23.3%
(Missing)3
 
0.6%
2021-02-27T00:27:57.053841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-27T00:27:57.148798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä206
41.6%
toimisto173
34.9%
50/50116
23.4%

Most occurring characters

ValueCountFrequency (%)
t379
14.7%
o346
13.4%
i346
13.4%
5232
9.0%
0232
9.0%
E206
8.0%
ä206
8.0%
T173
6.7%
m173
6.7%
s173
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1623
62.9%
Decimal Number464
 
18.0%
Uppercase Letter379
 
14.7%
Other Punctuation116
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t379
23.4%
o346
21.3%
i346
21.3%
ä206
12.7%
m173
10.7%
s173
10.7%
ValueCountFrequency (%)
5232
50.0%
0232
50.0%
ValueCountFrequency (%)
E206
54.4%
T173
45.6%
ValueCountFrequency (%)
/116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2002
77.5%
Common580
 
22.5%

Most frequent character per script

ValueCountFrequency (%)
t379
18.9%
o346
17.3%
i346
17.3%
E206
10.3%
ä206
10.3%
T173
8.6%
m173
8.6%
s173
8.6%
ValueCountFrequency (%)
5232
40.0%
0232
40.0%
/116
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2376
92.0%
None206
 
8.0%

Most frequent character per block

ValueCountFrequency (%)
t379
16.0%
o346
14.6%
i346
14.6%
5232
9.8%
0232
9.8%
E206
8.7%
T173
7.3%
m173
7.3%
s173
7.3%
/116
 
4.9%
ValueCountFrequency (%)
ä206
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct129
Distinct (%)28.4%
Missing44
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean4676.394273
Minimum1081
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-27T00:27:57.267406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1081
5-th percentile2789.5
Q13800
median4500
Q35492.5
95-th percentile7000
Maximum15000
Range13919
Interquartile range (IQR)1692.5

Descriptive statistics

Standard deviation1443.429367
Coefficient of variation (CV)0.308662889
Kurtosis7.908934975
Mean4676.394273
Median Absolute Deviation (MAD)765.5
Skewness1.623955367
Sum2123083
Variance2083488.336
MonotocityNot monotonic
2021-02-27T00:27:57.425617image/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%
480013
 
2.6%
430013
 
2.6%
380012
 
2.4%
300012
 
2.4%
420012
 
2.4%
Other values (119)290
58.2%
(Missing)44
 
8.8%
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 (%)38.1%
Missing13
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean65680.44639
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-27T00:27:57.600524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33930
Q149750
median59000
Q375000
95-th percentile124000
Maximum300000
Range300000
Interquartile range (IQR)25250

Descriptive statistics

Standard deviation31848.64709
Coefficient of variation (CV)0.4849030242
Kurtosis11.72456543
Mean65680.44639
Median Absolute Deviation (MAD)12000
Skewness2.642852192
Sum31855016.5
Variance1014336321
MonotocityNot monotonic
2021-02-27T00:27:57.766696image/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%
6500010
 
2.0%
6250010
 
2.0%
3750010
 
2.0%
400009
 
1.8%
Other values (175)357
71.7%
(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
329 
False
154 
(Missing)
 
15
ValueCountFrequency (%)
True329
66.1%
False154
30.9%
(Missing)15
 
3.0%
2021-02-27T00:27:57.889202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)64.6%
Missing385
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%
Pankki3
 
0.6%
Arado3
 
0.6%
Siili3
 
0.6%
Goforej2
 
0.4%
If2
 
0.4%
Other values (63)67
 
13.5%
(Missing)385
77.3%
2021-02-27T00:27:58.192130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore12
 
7.5%
vincit8
 
5.0%
mavericks6
 
3.7%
siili5
 
3.1%
futurice5
 
3.1%
fraktio4
 
2.5%
pankki3
 
1.9%
arado3
 
1.9%
konsulttitalo3
 
1.9%
if3
 
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%
Missing460
Missing (%)92.4%
Memory size4.0 KiB
palkan lisänä lounas- ja virkistysetu
 
2
saispa lisää liksaa
 
1
Johtajasopimus, ei työaikaa
 
1
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
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.
 
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%
saispa lisää liksaa1
 
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%
Vastasin kysymyksiin läpällä. Summat on enemmän sitä minkä verran yrittäjänä haluaa sykkiä ja mennä "raha edellä". 1
 
0.2%
Startup1
 
0.2%
Vaikea vastata henkilönä joka tekee yrityksen kautta yhdelle ulkomaalaiselle yritykselle töitä (jolla ei ole entiteettiä suomessa). Vastasin nyt ikään kuin olisin yrittäjä vaikka käytännössä tämä on sama kuin olisin palkkaduunissa.1
 
0.2%
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%
Työskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.1
 
0.2%
Other values (27)27
 
5.4%
(Missing)460
92.4%
2021-02-27T00:27:58.535583image/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%
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

Distinct185
Distinct (%)38.1%
Missing13
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean5473.370533
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-27T00:27:58.692142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2827.5
Q14145.833333
median4916.666667
Q36250
95-th percentile10333.33333
Maximum25000
Range25000
Interquartile range (IQR)2104.166667

Descriptive statistics

Standard deviation2654.053924
Coefficient of variation (CV)0.4849030242
Kurtosis11.72456543
Mean5473.370533
Median Absolute Deviation (MAD)1000
Skewness2.642852192
Sum2654584.708
Variance7044002.23
MonotocityNot monotonic
2021-02-27T00:27:58.990729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4583.33333318
 
3.6%
4166.66666718
 
3.6%
625017
 
3.4%
500014
 
2.8%
7083.33333311
 
2.2%
5833.33333311
 
2.2%
5208.33333310
 
2.0%
312510
 
2.0%
5416.66666710
 
2.0%
6666.6666679
 
1.8%
Other values (175)357
71.7%
(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

2021-02-27T00:27:49.663785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:49.811258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:49.956012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:50.101422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:50.239801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:50.384189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:50.531619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:50.684096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:50.830554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:50.975543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:51.125826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:51.381982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:51.527137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:51.669064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:51.815316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:51.963239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:52.112027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:52.248193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:52.389304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T00:27:52.535117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-27T00:27:59.135651image/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.
2021-02-27T00:27:59.322745image/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.
2021-02-27T00:27:59.510217image/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.
2021-02-27T00:27:59.705572image/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

2021-02-27T00:27:52.812285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-27T00:27:53.175007image/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.
2021-02-27T00:27:53.523937image/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.
2021-02-27T00:27:53.852742image/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
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
4972021-02-26 16:28:30.010Tampere43mies20.0Työntekijä / palkollinen1.0full-stackToimisto4800.061000.0TrueNaNNaN5083.333333