Overview

Dataset statistics

Number of variables15
Number of observations488
Missing cells994
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.8 KiB
Average record size in memory96.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 256 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 (7.2%) missing values Missing
Työaika has 19 (3.9%) missing values Missing
Rooli has 13 (2.7%) missing values Missing
Kuukausipalkka has 42 (8.6%) missing values Missing
Vuositulot has 12 (2.5%) missing values Missing
Kilpailukykyinen has 15 (3.1%) missing values Missing
Työpaikka has 379 (77.7%) missing values Missing
Vapaa sana has 451 (92.4%) missing values Missing
Kk-tulot has 12 (2.5%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-25 10:14:02.269467
Analysis finished2021-02-25 10:14:07.278002
Duration5.01 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct488
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-25 11:10:42.322000
2021-02-25T10:14:07.352409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:07.493073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

MISSING

Distinct28
Distinct (%)5.8%
Missing5
Missing (%)1.0%
Memory size1.9 KiB
PK-Seutu
245 
Tampere
112 
Turku
47 
Oulu
25 
Jyväskylä
 
18
Other values (23)
36 

Length

Max length15
Median length8
Mean length7.236024845
Min length2

Characters and Unicode

Total characters3495
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-Seutu245
50.2%
Tampere112
23.0%
Turku47
 
9.6%
Oulu25
 
5.1%
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%
2021-02-25T10:14:07.860921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu245
50.3%
tampere112
23.0%
turku47
 
9.7%
oulu25
 
5.1%
jyväskylä18
 
3.7%
kuopio7
 
1.4%
lahti2
 
0.4%
eu2
 
0.4%
tallinna2
 
0.4%
pori2
 
0.4%
Other values (22)25
 
5.1%

Most occurring characters

ValueCountFrequency (%)
u648
18.5%
e481
13.8%
K255
 
7.3%
t252
 
7.2%
P248
 
7.1%
-247
 
7.1%
S247
 
7.1%
r165
 
4.7%
T161
 
4.6%
a139
 
4.0%
Other values (30)652
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2264
64.8%
Uppercase Letter979
28.0%
Dash Punctuation247
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u648
28.6%
e481
21.2%
t252
 
11.1%
r165
 
7.3%
a139
 
6.1%
p120
 
5.3%
m118
 
5.2%
k70
 
3.1%
l56
 
2.5%
ä44
 
1.9%
Other values (10)171
 
7.6%
ValueCountFrequency (%)
K255
26.0%
P248
25.3%
S247
25.2%
T161
16.4%
O25
 
2.6%
J19
 
1.9%
L5
 
0.5%
E4
 
0.4%
V3
 
0.3%
H3
 
0.3%
Other values (7)9
 
0.9%
ValueCountFrequency (%)
-247
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3243
92.8%
Common252
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u648
20.0%
e481
14.8%
K255
 
7.9%
t252
 
7.8%
P248
 
7.6%
S247
 
7.6%
r165
 
5.1%
T161
 
5.0%
a139
 
4.3%
p120
 
3.7%
Other values (27)527
16.3%
ValueCountFrequency (%)
-247
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3451
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u648
18.8%
e481
13.9%
K255
 
7.4%
t252
 
7.3%
P248
 
7.2%
-247
 
7.2%
S247
 
7.2%
r165
 
4.8%
T161
 
4.7%
a139
 
4.0%
Other values (29)608
17.6%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.4%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean33.7628866
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2021-02-25T10:14:07.962618image/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 deviation6.050362321
Coefficient of variation (CV)0.179201571
Kurtosis0.2241853077
Mean33.7628866
Median Absolute Deviation (MAD)5
Skewness0.4775796622
Sum16375
Variance36.60688421
MonotocityNot monotonic
2021-02-25T10:14:08.055975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33163
33.4%
28120
24.6%
38105
21.5%
4353
 
10.9%
2331
 
6.4%
487
 
1.4%
536
 
1.2%
(Missing)3
 
0.6%
ValueCountFrequency (%)
2331
 
6.4%
28120
24.6%
33163
33.4%
38105
21.5%
4353
 
10.9%
ValueCountFrequency (%)
536
 
1.2%
487
 
1.4%
4353
 
10.9%
38105
21.5%
33163
33.4%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing35
Missing (%)7.2%
Memory size748.0 B
mies
409 
nainen
 
36
muu
 
8

Length

Max length6
Median length4
Mean length4.141280353
Min length3

Characters and Unicode

Total characters1876
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 (%)
mies409
83.8%
nainen36
 
7.4%
muu8
 
1.6%
(Missing)35
 
7.2%
2021-02-25T10:14:08.314673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T10:14:08.400874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies409
90.3%
nainen36
 
7.9%
muu8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i445
23.7%
e445
23.7%
m417
22.2%
s409
21.8%
n108
 
5.8%
a36
 
1.9%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1876
100.0%

Most frequent character per category

ValueCountFrequency (%)
i445
23.7%
e445
23.7%
m417
22.2%
s409
21.8%
n108
 
5.8%
a36
 
1.9%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1876
100.0%

Most frequent character per script

ValueCountFrequency (%)
i445
23.7%
e445
23.7%
m417
22.2%
s409
21.8%
n108
 
5.8%
a36
 
1.9%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1876
100.0%

Most frequent character per block

ValueCountFrequency (%)
i445
23.7%
e445
23.7%
m417
22.2%
s409
21.8%
n108
 
5.8%
a36
 
1.9%
u16
 
0.9%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)5.6%
Missing4
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean9.516528926
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size3.9 KiB
2021-02-25T10:14:08.486217image/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 deviation6.042100016
Coefficient of variation (CV)0.6349058636
Kurtosis-0.009641267493
Mean9.516528926
Median Absolute Deviation (MAD)4
Skewness0.7379090849
Sum4606
Variance36.50697261
MonotocityNot monotonic
2021-02-25T10:14:08.596047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
553
 
10.9%
1039
 
8.0%
431
 
6.4%
730
 
6.1%
228
 
5.7%
1528
 
5.7%
327
 
5.5%
2027
 
5.5%
627
 
5.5%
825
 
5.1%
Other values (17)169
34.6%
ValueCountFrequency (%)
04
 
0.8%
116
3.3%
228
5.7%
327
5.5%
431
6.4%
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 size3.9 KiB
Työntekijä / palkollinen
435 
Yrittäjä
 
26
Freelancer
 
26

Length

Max length24
Median length24
Mean length22.39835729
Min length8

Characters and Unicode

Total characters10908
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 / palkollinen435
89.1%
Yrittäjä26
 
5.3%
Freelancer26
 
5.3%
(Missing)1
 
0.2%
2021-02-25T10:14:08.859682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T10:14:08.943617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
435
32.1%
työntekijä435
32.1%
palkollinen435
32.1%
freelancer26
 
1.9%
yrittäjä26
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1331
12.2%
l1331
12.2%
e948
 
8.7%
i896
 
8.2%
k870
 
8.0%
870
 
8.0%
t487
 
4.5%
ä487
 
4.5%
j461
 
4.2%
a461
 
4.2%
Other values (10)2766
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9116
83.6%
Space Separator870
 
8.0%
Uppercase Letter487
 
4.5%
Other Punctuation435
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1331
14.6%
l1331
14.6%
e948
10.4%
i896
9.8%
k870
9.5%
t487
 
5.3%
ä487
 
5.3%
j461
 
5.1%
a461
 
5.1%
y435
 
4.8%
Other values (5)1409
15.5%
ValueCountFrequency (%)
T435
89.3%
Y26
 
5.3%
F26
 
5.3%
ValueCountFrequency (%)
870
100.0%
ValueCountFrequency (%)
/435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9603
88.0%
Common1305
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1331
13.9%
l1331
13.9%
e948
9.9%
i896
9.3%
k870
9.1%
t487
 
5.1%
ä487
 
5.1%
j461
 
4.8%
a461
 
4.8%
T435
 
4.5%
Other values (8)1896
19.7%
ValueCountFrequency (%)
870
66.7%
/435
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9986
91.5%
None922
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1331
13.3%
l1331
13.3%
e948
9.5%
i896
9.0%
k870
8.7%
870
8.7%
t487
 
4.9%
j461
 
4.6%
a461
 
4.6%
T435
 
4.4%
Other values (8)1896
19.0%
ValueCountFrequency (%)
ä487
52.8%
ö435
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)3.9%
Memory size3.9 KiB
1.0
441 
0.8
 
23
0.5
 
3
0.7
 
1
0.6
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1407
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 (%)
1.0441
90.4%
0.823
 
4.7%
0.53
 
0.6%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
3.9%
2021-02-25T10:14:09.148279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T10:14:09.220251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0441
94.0%
0.823
 
4.9%
0.53
 
0.6%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.469
33.3%
0469
33.3%
1441
31.3%
823
 
1.6%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number938
66.7%
Other Punctuation469
33.3%

Most frequent character per category

ValueCountFrequency (%)
0469
50.0%
1441
47.0%
823
 
2.5%
53
 
0.3%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1407
100.0%

Most frequent character per script

ValueCountFrequency (%)
.469
33.3%
0469
33.3%
1441
31.3%
823
 
1.6%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1407
100.0%

Most frequent character per block

ValueCountFrequency (%)
.469
33.3%
0469
33.3%
1441
31.3%
823
 
1.6%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct256
Distinct (%)53.9%
Missing13
Missing (%)2.7%
Memory size3.9 KiB
Ohjelmistokehittäjä
41 
full-stack
35 
Full-stack
 
24
ohjelmistokehittäjä
 
17
Arkkitehti
 
15
Other values (251)
343 

Length

Max length67
Median length18
Mean length19.18315789
Min length2

Characters and Unicode

Total characters9112
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209 ?
Unique (%)44.0%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä41
 
8.4%
full-stack35
 
7.2%
Full-stack24
 
4.9%
ohjelmistokehittäjä17
 
3.5%
Arkkitehti15
 
3.1%
Full-stack ohjelmistokehittäjä8
 
1.6%
full-stack ohjelmistokehittäjä7
 
1.4%
Frontend6
 
1.2%
arkkitehti6
 
1.2%
frontend5
 
1.0%
Other values (246)311
63.7%
(Missing)13
 
2.7%
2021-02-25T10:14:09.535347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack140
 
16.0%
ohjelmistokehittäjä114
 
13.0%
developer60
 
6.8%
arkkitehti35
 
4.0%
35
 
4.0%
lead33
 
3.8%
frontend27
 
3.1%
senior21
 
2.4%
backend16
 
1.8%
kehittäjä16
 
1.8%
Other values (187)379
43.3%

Most occurring characters

ValueCountFrequency (%)
t950
 
10.4%
e842
 
9.2%
l666
 
7.3%
i659
 
7.2%
k504
 
5.5%
o478
 
5.2%
a435
 
4.8%
s429
 
4.7%
407
 
4.5%
h366
 
4.0%
Other values (47)3376
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7910
86.8%
Uppercase Letter463
 
5.1%
Space Separator408
 
4.5%
Dash Punctuation170
 
1.9%
Other Punctuation99
 
1.1%
Open Punctuation27
 
0.3%
Close Punctuation27
 
0.3%
Math Symbol8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t950
12.0%
e842
 
10.6%
l666
 
8.4%
i659
 
8.3%
k504
 
6.4%
o478
 
6.0%
a435
 
5.5%
s429
 
5.4%
h366
 
4.6%
j345
 
4.4%
Other values (16)2236
28.3%
ValueCountFrequency (%)
F103
22.2%
O96
20.7%
S51
11.0%
D42
9.1%
T28
 
6.0%
A26
 
5.6%
L21
 
4.5%
C18
 
3.9%
E12
 
2.6%
P11
 
2.4%
Other values (11)55
11.9%
ValueCountFrequency (%)
,53
53.5%
/42
42.4%
&3
 
3.0%
.1
 
1.0%
ValueCountFrequency (%)
407
99.8%
 1
 
0.2%
ValueCountFrequency (%)
-170
100.0%
ValueCountFrequency (%)
(27
100.0%
ValueCountFrequency (%)
)27
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8373
91.9%
Common739
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t950
 
11.3%
e842
 
10.1%
l666
 
8.0%
i659
 
7.9%
k504
 
6.0%
o478
 
5.7%
a435
 
5.2%
s429
 
5.1%
h366
 
4.4%
j345
 
4.1%
Other values (37)2699
32.2%
ValueCountFrequency (%)
407
55.1%
-170
23.0%
,53
 
7.2%
/42
 
5.7%
(27
 
3.7%
)27
 
3.7%
+8
 
1.1%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8764
96.2%
None348
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t950
 
10.8%
e842
 
9.6%
l666
 
7.6%
i659
 
7.5%
k504
 
5.8%
o478
 
5.5%
a435
 
5.0%
s429
 
4.9%
407
 
4.6%
h366
 
4.2%
Other values (44)3028
34.6%
ValueCountFrequency (%)
ä331
95.1%
ö16
 
4.6%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size748.0 B
Etä
204 
Toimisto
167 
50/50
114 

Length

Max length8
Median length5
Mean length5.191752577
Min length3

Characters and Unicode

Total characters2518
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 (%)
Etä204
41.8%
Toimisto167
34.2%
50/50114
23.4%
(Missing)3
 
0.6%
2021-02-25T10:14:09.884969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T10:14:09.962515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä204
42.1%
toimisto167
34.4%
50/50114
23.5%

Most occurring characters

ValueCountFrequency (%)
t371
14.7%
o334
13.3%
i334
13.3%
5228
9.1%
0228
9.1%
E204
8.1%
ä204
8.1%
T167
6.6%
m167
6.6%
s167
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1577
62.6%
Decimal Number456
 
18.1%
Uppercase Letter371
 
14.7%
Other Punctuation114
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t371
23.5%
o334
21.2%
i334
21.2%
ä204
12.9%
m167
10.6%
s167
10.6%
ValueCountFrequency (%)
5228
50.0%
0228
50.0%
ValueCountFrequency (%)
E204
55.0%
T167
45.0%
ValueCountFrequency (%)
/114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1948
77.4%
Common570
 
22.6%

Most frequent character per script

ValueCountFrequency (%)
t371
19.0%
o334
17.1%
i334
17.1%
E204
10.5%
ä204
10.5%
T167
8.6%
m167
8.6%
s167
8.6%
ValueCountFrequency (%)
5228
40.0%
0228
40.0%
/114
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2314
91.9%
None204
 
8.1%

Most frequent character per block

ValueCountFrequency (%)
t371
16.0%
o334
14.4%
i334
14.4%
5228
9.9%
0228
9.9%
E204
8.8%
T167
7.2%
m167
7.2%
s167
7.2%
/114
 
4.9%
ValueCountFrequency (%)
ä204
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct128
Distinct (%)28.7%
Missing42
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean4680.497758
Minimum1100
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2021-02-25T10:14:10.063090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile2800
Q13800
median4500
Q35492.5
95-th percentile7000
Maximum15000
Range13900
Interquartile range (IQR)1692.5

Descriptive statistics

Standard deviation1442.412467
Coefficient of variation (CV)0.308175015
Kurtosis8.025472396
Mean4680.497758
Median Absolute Deviation (MAD)787.5
Skewness1.678704706
Sum2087502
Variance2080553.725
MonotocityNot monotonic
2021-02-25T10:14:10.198238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400026
 
5.3%
450024
 
4.9%
600017
 
3.5%
500017
 
3.5%
550016
 
3.3%
480012
 
2.5%
300012
 
2.5%
420012
 
2.5%
430012
 
2.5%
380011
 
2.3%
Other values (118)287
58.8%
(Missing)42
 
8.6%
ValueCountFrequency (%)
11001
0.2%
16661
0.2%
17001
0.2%
18001
0.2%
21001
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

Distinct183
Distinct (%)38.4%
Missing12
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean65581.33718
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.9 KiB
2021-02-25T10:14:10.342071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34425
Q149281.25
median58750
Q375000
95-th percentile121250
Maximum300000
Range300000
Interquartile range (IQR)25718.75

Descriptive statistics

Standard deviation31765.34217
Coefficient of variation (CV)0.4843655761
Kurtosis12.03635083
Mean65581.33718
Median Absolute Deviation (MAD)11812.5
Skewness2.681999398
Sum31216716.5
Variance1009036963
MonotocityNot monotonic
2021-02-25T10:14:10.488204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.7%
5000018
 
3.7%
7500016
 
3.3%
6000014
 
2.9%
8500011
 
2.3%
6250010
 
2.0%
6500010
 
2.0%
3750010
 
2.0%
7000010
 
2.0%
475009
 
1.8%
Other values (173)350
71.7%
(Missing)12
 
2.5%
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 (%)3.1%
Memory size3.9 KiB
True
321 
False
152 
(Missing)
 
15
ValueCountFrequency (%)
True321
65.8%
False152
31.1%
(Missing)15
 
3.1%
2021-02-25T10:14:10.589725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)67.0%
Missing379
Missing (%)77.7%
Memory size3.9 KiB
Gofore
11 
Vincit
 
7
Futurice
 
5
Mavericks
 
4
Fraktio
 
4
Other values (68)
78 

Length

Max length132
Median length8
Mean length10.81651376
Min length2

Characters and Unicode

Total characters1179
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

Unique60 ?
Unique (%)55.0%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.3%
Vincit7
 
1.4%
Futurice5
 
1.0%
Mavericks4
 
0.8%
Fraktio4
 
0.8%
Pankki3
 
0.6%
Arado3
 
0.6%
KVTES-alainen kunnan omistama oy 2
 
0.4%
Gofore Oyj2
 
0.4%
If2
 
0.4%
Other values (63)66
 
13.5%
(Missing)379
77.7%
2021-02-25T10:14:10.837181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oy13
 
7.4%
gofore13
 
7.4%
vincit7
 
4.0%
mavericks6
 
3.4%
oyj5
 
2.9%
futurice5
 
2.9%
fraktio4
 
2.3%
siili4
 
2.3%
pankki3
 
1.7%
konsulttitalo3
 
1.7%
Other values (95)112
64.0%

Most occurring characters

ValueCountFrequency (%)
i122
 
10.3%
a88
 
7.5%
o88
 
7.5%
e85
 
7.2%
t81
 
6.9%
69
 
5.9%
r62
 
5.3%
n58
 
4.9%
k48
 
4.1%
l46
 
3.9%
Other values (44)432
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter957
81.2%
Uppercase Letter147
 
12.5%
Space Separator69
 
5.9%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i122
12.7%
a88
 
9.2%
o88
 
9.2%
e85
 
8.9%
t81
 
8.5%
r62
 
6.5%
n58
 
6.1%
k48
 
5.0%
l46
 
4.8%
u45
 
4.7%
Other values (16)234
24.5%
ValueCountFrequency (%)
O18
12.2%
G14
 
9.5%
S14
 
9.5%
V13
 
8.8%
F10
 
6.8%
K8
 
5.4%
A7
 
4.8%
M7
 
4.8%
C6
 
4.1%
P6
 
4.1%
Other values (15)44
29.9%
ValueCountFrequency (%)
69
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1104
93.6%
Common75
 
6.4%

Most frequent character per script

ValueCountFrequency (%)
i122
 
11.1%
a88
 
8.0%
o88
 
8.0%
e85
 
7.7%
t81
 
7.3%
r62
 
5.6%
n58
 
5.3%
k48
 
4.3%
l46
 
4.2%
u45
 
4.1%
Other values (41)381
34.5%
ValueCountFrequency (%)
69
92.0%
.3
 
4.0%
-3
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1167
99.0%
None12
 
1.0%

Most frequent character per block

ValueCountFrequency (%)
i122
 
10.5%
a88
 
7.5%
o88
 
7.5%
e85
 
7.3%
t81
 
6.9%
69
 
5.9%
r62
 
5.3%
n58
 
5.0%
k48
 
4.1%
l46
 
3.9%
Other values (42)420
36.0%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct36
Distinct (%)97.3%
Missing451
Missing (%)92.4%
Memory size3.9 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Kokemusta kokonaisuudessaan 7v, mutta siitä reilut kaksi vuotta lasten kanssa kotona koodaamatta.
 
1
Ilmaset kaffet, safkat, salit jne.
 
1
Ei sinänsä liity suoraan palkkoihin, mutta olisi mielenkiintoista tietää miten palkka vaikuttaa työpaikan vaihtoon. Eli esim. Oletko vaihtanut/vaihtamassa/miettinyt vaihtamista, koska toisaalla maksetaan enemmän?
 
1
Olen osakkaana startupissa, 5% osuus on osa kokonaiskompensaatiota. Edellisessä työssä bruttopalkka oli 6000 euroa kuukaudessa.
 
1
Other values (31)
31 

Length

Max length286
Median length71
Mean length92.10810811
Min length7

Characters and Unicode

Total characters3408
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

Unique35 ?
Unique (%)94.6%

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%
Kokemusta kokonaisuudessaan 7v, mutta siitä reilut kaksi vuotta lasten kanssa kotona koodaamatta.1
 
0.2%
Ilmaset kaffet, safkat, salit jne.1
 
0.2%
Ei sinänsä liity suoraan palkkoihin, mutta olisi mielenkiintoista tietää miten palkka vaikuttaa työpaikan vaihtoon. Eli esim. Oletko vaihtanut/vaihtamassa/miettinyt vaihtamista, koska toisaalla maksetaan enemmän?1
 
0.2%
Olen osakkaana startupissa, 5% osuus on osa kokonaiskompensaatiota. Edellisessä työssä bruttopalkka oli 6000 euroa kuukaudessa.1
 
0.2%
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi1
 
0.2%
Opiskelija1
 
0.2%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
startup, palkan lisäksi optiopaketti.1
 
0.2%
Olen firmaan, sen etuihin ja työilmapiiriin tyytyväinen.1
 
0.2%
Other values (26)26
 
5.3%
(Missing)451
92.4%
2021-02-25T10:14:11.128453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ei11
 
2.6%
palkka10
 
2.3%
on9
 
2.1%
ja9
 
2.1%
mutta8
 
1.9%
ole6
 
1.4%
ihan4
 
0.9%
nyt4
 
0.9%
joten4
 
0.9%
firman4
 
0.9%
Other values (303)362
84.0%

Most occurring characters

ValueCountFrequency (%)
397
11.6%
a363
10.7%
i291
 
8.5%
t269
 
7.9%
n232
 
6.8%
s221
 
6.5%
e216
 
6.3%
k197
 
5.8%
l168
 
4.9%
o157
 
4.6%
Other values (46)897
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2836
83.2%
Space Separator397
 
11.6%
Other Punctuation81
 
2.4%
Uppercase Letter51
 
1.5%
Decimal Number28
 
0.8%
Dash Punctuation6
 
0.2%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%
Math Symbol3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a363
12.8%
i291
10.3%
t269
9.5%
n232
 
8.2%
s221
 
7.8%
e216
 
7.6%
k197
 
6.9%
l168
 
5.9%
o157
 
5.5%
u127
 
4.5%
Other values (14)595
21.0%
ValueCountFrequency (%)
P9
17.6%
T7
13.7%
O7
13.7%
E6
11.8%
V6
11.8%
K4
7.8%
S4
7.8%
I2
 
3.9%
H2
 
3.9%
R1
 
2.0%
Other values (3)3
 
5.9%
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 (%)
.42
51.9%
,26
32.1%
/5
 
6.2%
%4
 
4.9%
"2
 
2.5%
?2
 
2.5%
ValueCountFrequency (%)
397
100.0%
ValueCountFrequency (%)
(3
100.0%
ValueCountFrequency (%)
)3
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2887
84.7%
Common521
 
15.3%

Most frequent character per script

ValueCountFrequency (%)
a363
12.6%
i291
10.1%
t269
9.3%
n232
 
8.0%
s221
 
7.7%
e216
 
7.5%
k197
 
6.8%
l168
 
5.8%
o157
 
5.4%
u127
 
4.4%
Other values (27)646
22.4%
ValueCountFrequency (%)
397
76.2%
.42
 
8.1%
,26
 
5.0%
015
 
2.9%
-6
 
1.2%
/5
 
1.0%
%4
 
0.8%
(3
 
0.6%
)3
 
0.6%
+3
 
0.6%
Other values (9)17
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3263
95.7%
None145
 
4.3%

Most frequent character per block

ValueCountFrequency (%)
397
12.2%
a363
11.1%
i291
 
8.9%
t269
 
8.2%
n232
 
7.1%
s221
 
6.8%
e216
 
6.6%
k197
 
6.0%
l168
 
5.1%
o157
 
4.8%
Other values (44)752
23.0%
ValueCountFrequency (%)
ä120
82.8%
ö25
 
17.2%

Kk-tulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct183
Distinct (%)38.4%
Missing12
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean5465.111432
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.9 KiB
2021-02-25T10:14:11.263481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2868.75
Q14106.770833
median4895.833333
Q36250
95-th percentile10104.16667
Maximum25000
Range25000
Interquartile range (IQR)2143.229167

Descriptive statistics

Standard deviation2647.111847
Coefficient of variation (CV)0.4843655761
Kurtosis12.03635083
Mean5465.111432
Median Absolute Deviation (MAD)984.375
Skewness2.681999398
Sum2601393.042
Variance7007201.131
MonotocityNot monotonic
2021-02-25T10:14:11.525912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4166.66666718
 
3.7%
4583.33333318
 
3.7%
625016
 
3.3%
500014
 
2.9%
7083.33333311
 
2.3%
5208.33333310
 
2.0%
5416.66666710
 
2.0%
5833.33333310
 
2.0%
312510
 
2.0%
6666.6666679
 
1.8%
Other values (173)350
71.7%
(Missing)12
 
2.5%
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

2021-02-25T10:14:03.657202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:03.784713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:03.901846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:04.015939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:04.130893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:04.252796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:04.379199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:04.499954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:04.611287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:04.730450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:04.854393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:05.068889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:05.187702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:05.309928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:05.439247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:05.563804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:05.680682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:05.789927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:05.906349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T10:14:06.020517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-25T10:14:11.656758image/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.
2021-02-25T10:14:11.810179image/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.
2021-02-25T10:14:11.960312image/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.
2021-02-25T10:14:12.115744image/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

2021-02-25T10:14:06.244614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-25T10:14:06.552294image/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.
2021-02-25T10:14:06.860083image/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.
2021-02-25T10:14:07.138187image/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
4782021-02-23 16:18:52.379TampereNaNNaN10.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto4250.054000.0TrueNaNNaN4500.000000
4792021-02-23 21:38:08.756Nokia38mies6.0Yrittäjä1.0Full Stack Web Developer / CEOEtäNaN27000.0FalseTuspe Design OyNaN2250.000000
4802021-02-24 15:19:46.521Mikkeli33mies7.0Työntekijä / palkollinen1.0Full-stackEtä3000.037500.0FalseNaNNaN3125.000000
4812021-02-24 16:09:32.939PK-Seutu33mies2.0Työntekijä / palkollinen1.0OhjelmistokehittäjäEtä3500.043750.0TrueNaNNaN3645.833333
4822021-02-24 17:28:57.097Kuopio23mies2.0Työntekijä / palkollinen1.0frontend50/502900.036000.0FalseNaNNaN3000.000000
4832021-02-24 23:49:22.242Tampere28mies2.0Työntekijä / palkollinen1.0Ohjelmistokehittäjä (frontend)Etä2860.035850.0FalseNaNNaN2987.500000
4842021-02-25 09:34:48.368Kuopio33mies6.0Työntekijä / palkollinen1.0Ohjelmistokehittäjä, Tech LeadToimisto4500.056250.0TrueNaNNaN4687.500000
4852021-02-25 10:53:41.881PK-Seutu28mies6.0Työntekijä / palkollinen1.0full-stack50/505100.064000.0FalseNaNNaN5333.333333
4862021-02-25 11:09:16.999PK-Seutu28mies3.0Työntekijä / palkollinen1.0Fullstack ja pientä devops tunkkiaToimisto3500.044000.0FalseNaNNaN3666.666667
4872021-02-25 11:10:42.322NaN33NaN15.0Työntekijä / palkollinen1.0NaNToimisto5200.068000.0FalseNaNNaN5666.666667