Overview

Dataset statistics

Number of variables15
Number of observations483
Missing cells980
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.4 KiB
Average record size in memory96.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 254 distinct values High cardinality
Työpaikka has a high cardinality: 74 distinct values High cardinality
Vuositulot is highly correlated with Kk-tulotHigh correlation
Kk-tulot is highly correlated with VuositulotHigh correlation
Kilpailukykyinen is highly correlated with Vapaa sanaHigh correlation
Vapaa sana is highly correlated with Kilpailukykyinen and 1 other fieldsHigh correlation
Työpaikka is highly correlated with Vapaa sanaHigh correlation
Sukupuoli has 34 (7.0%) missing values Missing
Työaika has 19 (3.9%) missing values Missing
Rooli has 12 (2.5%) missing values Missing
Kuukausipalkka has 42 (8.7%) missing values Missing
Vuositulot has 12 (2.5%) missing values Missing
Kilpailukykyinen has 15 (3.1%) missing values Missing
Työpaikka has 373 (77.2%) missing values Missing
Vapaa sana has 446 (92.3%) 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 started2021-02-24 16:21:44.269799
Analysis finished2021-02-24 16:21:50.726019
Duration6.46 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct483
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-24 17:28:57.097000
2021-02-24T16:21:50.827469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:51.049052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

Distinct28
Distinct (%)5.8%
Missing4
Missing (%)0.8%
Memory size1.9 KiB
PK-Seutu
243 
Tampere
111 
Turku
47 
Oulu
25 
Jyväskylä
 
18
Other values (23)
35 

Length

Max length15
Median length8
Mean length7.235908142
Min length2

Characters and Unicode

Total characters3466
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-Seutu243
50.3%
Tampere111
23.0%
Turku47
 
9.7%
Oulu25
 
5.2%
Jyväskylä18
 
3.7%
Kuopio6
 
1.2%
Lontoo2
 
0.4%
Vaasa2
 
0.4%
Tallinna2
 
0.4%
Pori2
 
0.4%
Other values (18)21
 
4.3%
(Missing)4
 
0.8%
2021-02-24T16:21:51.576933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu243
50.3%
tampere111
23.0%
turku47
 
9.7%
oulu25
 
5.2%
jyväskylä18
 
3.7%
kuopio6
 
1.2%
tallinna2
 
0.4%
lahti2
 
0.4%
eu2
 
0.4%
lontoo2
 
0.4%
Other values (22)25
 
5.2%

Most occurring characters

ValueCountFrequency (%)
u643
18.6%
e477
13.8%
K252
 
7.3%
t250
 
7.2%
P246
 
7.1%
-245
 
7.1%
S245
 
7.1%
r164
 
4.7%
T160
 
4.6%
a138
 
4.0%
Other values (30)646
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2245
64.8%
Uppercase Letter971
28.0%
Dash Punctuation245
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u643
28.6%
e477
21.2%
t250
 
11.1%
r164
 
7.3%
a138
 
6.1%
p118
 
5.3%
m117
 
5.2%
k70
 
3.1%
l56
 
2.5%
ä44
 
2.0%
Other values (10)168
 
7.5%
ValueCountFrequency (%)
K252
26.0%
P246
25.3%
S245
25.2%
T160
16.5%
O25
 
2.6%
J19
 
2.0%
L5
 
0.5%
E4
 
0.4%
V3
 
0.3%
H3
 
0.3%
Other values (7)9
 
0.9%
ValueCountFrequency (%)
-245
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3216
92.8%
Common250
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u643
20.0%
e477
14.8%
K252
 
7.8%
t250
 
7.8%
P246
 
7.6%
S245
 
7.6%
r164
 
5.1%
T160
 
5.0%
a138
 
4.3%
p118
 
3.7%
Other values (27)523
16.3%
ValueCountFrequency (%)
-245
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3422
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u643
18.8%
e477
13.9%
K252
 
7.4%
t250
 
7.3%
P246
 
7.2%
-245
 
7.2%
S245
 
7.2%
r164
 
4.8%
T160
 
4.7%
a138
 
4.0%
Other values (29)602
17.6%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.5%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean33.80208333
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2021-02-24T16:21:51.720439image/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 deviation6.064406908
Coefficient of variation (CV)0.1794092644
Kurtosis0.210456997
Mean33.80208333
Median Absolute Deviation (MAD)5
Skewness0.465174288
Sum16225
Variance36.77703114
MonotocityNot monotonic
2021-02-24T16:21:51.849905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33161
33.3%
28117
24.2%
38105
21.7%
4353
 
11.0%
2331
 
6.4%
487
 
1.4%
536
 
1.2%
(Missing)3
 
0.6%
ValueCountFrequency (%)
2331
 
6.4%
28117
24.2%
33161
33.3%
38105
21.7%
4353
 
11.0%
ValueCountFrequency (%)
536
 
1.2%
487
 
1.4%
4353
 
11.0%
38105
21.7%
33161
33.3%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing34
Missing (%)7.0%
Memory size743.0 B
mies
405 
nainen
 
36
muu
 
8

Length

Max length6
Median length4
Mean length4.142538976
Min length3

Characters and Unicode

Total characters1860
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 (%)
mies405
83.9%
nainen36
 
7.5%
muu8
 
1.7%
(Missing)34
 
7.0%
2021-02-24T16:21:52.215264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T16:21:52.338879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies405
90.2%
nainen36
 
8.0%
muu8
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i441
23.7%
e441
23.7%
m413
22.2%
s405
21.8%
n108
 
5.8%
a36
 
1.9%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1860
100.0%

Most frequent character per category

ValueCountFrequency (%)
i441
23.7%
e441
23.7%
m413
22.2%
s405
21.8%
n108
 
5.8%
a36
 
1.9%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
i441
23.7%
e441
23.7%
m413
22.2%
s405
21.8%
n108
 
5.8%
a36
 
1.9%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
i441
23.7%
e441
23.7%
m413
22.2%
s405
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%
Mean9.549060543
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size3.9 KiB
2021-02-24T16:21:52.466054image/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 deviation6.046991208
Coefficient of variation (CV)0.6332550915
Kurtosis-0.01372013853
Mean9.549060543
Median Absolute Deviation (MAD)4
Skewness0.7335686879
Sum4574
Variance36.56610267
MonotocityNot monotonic
2021-02-24T16:21:52.635007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
553
 
11.0%
1039
 
8.1%
431
 
6.4%
730
 
6.2%
1527
 
5.6%
227
 
5.6%
2027
 
5.6%
326
 
5.4%
1325
 
5.2%
825
 
5.2%
Other values (17)169
35.0%
ValueCountFrequency (%)
04
 
0.8%
116
3.3%
227
5.6%
326
5.4%
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
430 
Yrittäjä
 
26
Freelancer
 
26

Length

Max length24
Median length24
Mean length22.38174274
Min length8

Characters and Unicode

Total characters10788
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 / palkollinen430
89.0%
Yrittäjä26
 
5.4%
Freelancer26
 
5.4%
(Missing)1
 
0.2%
2021-02-24T16:21:53.024495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T16:21:53.142641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
430
32.0%
palkollinen430
32.0%
työntekijä430
32.0%
freelancer26
 
1.9%
yrittäjä26
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1316
12.2%
l1316
12.2%
e938
 
8.7%
i886
 
8.2%
k860
 
8.0%
860
 
8.0%
t482
 
4.5%
ä482
 
4.5%
j456
 
4.2%
a456
 
4.2%
Other values (10)2736
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9016
83.6%
Space Separator860
 
8.0%
Uppercase Letter482
 
4.5%
Other Punctuation430
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1316
14.6%
l1316
14.6%
e938
10.4%
i886
9.8%
k860
9.5%
t482
 
5.3%
ä482
 
5.3%
j456
 
5.1%
a456
 
5.1%
y430
 
4.8%
Other values (5)1394
15.5%
ValueCountFrequency (%)
T430
89.2%
Y26
 
5.4%
F26
 
5.4%
ValueCountFrequency (%)
860
100.0%
ValueCountFrequency (%)
/430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9498
88.0%
Common1290
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1316
13.9%
l1316
13.9%
e938
9.9%
i886
9.3%
k860
9.1%
t482
 
5.1%
ä482
 
5.1%
j456
 
4.8%
a456
 
4.8%
T430
 
4.5%
Other values (8)1876
19.8%
ValueCountFrequency (%)
860
66.7%
/430
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9876
91.5%
None912
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1316
13.3%
l1316
13.3%
e938
9.5%
i886
9.0%
k860
8.7%
860
8.7%
t482
 
4.9%
j456
 
4.6%
a456
 
4.6%
T430
 
4.4%
Other values (8)1876
19.0%
ValueCountFrequency (%)
ä482
52.9%
ö430
47.1%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)3.9%
Memory size3.9 KiB
1.0
436 
0.8
 
23
0.5
 
3
0.6
 
1
0.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1392
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 (%)
1.0436
90.3%
0.823
 
4.8%
0.53
 
0.6%
0.61
 
0.2%
0.71
 
0.2%
(Missing)19
 
3.9%
2021-02-24T16:21:53.426015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T16:21:53.529283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0436
94.0%
0.823
 
5.0%
0.53
 
0.6%
0.61
 
0.2%
0.71
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.464
33.3%
0464
33.3%
1436
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number928
66.7%
Other Punctuation464
33.3%

Most frequent character per category

ValueCountFrequency (%)
0464
50.0%
1436
47.0%
823
 
2.5%
53
 
0.3%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1392
100.0%

Most frequent character per script

ValueCountFrequency (%)
.464
33.3%
0464
33.3%
1436
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1392
100.0%

Most frequent character per block

ValueCountFrequency (%)
.464
33.3%
0464
33.3%
1436
31.3%
823
 
1.7%
53
 
0.2%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct254
Distinct (%)53.9%
Missing12
Missing (%)2.5%
Memory size3.9 KiB
Ohjelmistokehittäjä
41 
full-stack
 
34
Full-stack
 
24
ohjelmistokehittäjä
 
17
Arkkitehti
 
15
Other values (249)
340 

Length

Max length67
Median length18
Mean length19.12526539
Min length2

Characters and Unicode

Total characters9008
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

Unique207 ?
Unique (%)43.9%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä41
 
8.5%
full-stack34
 
7.0%
Full-stack24
 
5.0%
ohjelmistokehittäjä17
 
3.5%
Arkkitehti15
 
3.1%
Full-stack ohjelmistokehittäjä8
 
1.7%
full-stack ohjelmistokehittäjä7
 
1.4%
Frontend6
 
1.2%
arkkitehti6
 
1.2%
Full-stack kehittäjä5
 
1.0%
Other values (244)308
63.8%
(Missing)12
 
2.5%
2021-02-24T16:21:53.999274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack139
 
16.1%
ohjelmistokehittäjä112
 
12.9%
developer60
 
6.9%
35
 
4.0%
arkkitehti35
 
4.0%
lead32
 
3.7%
frontend26
 
3.0%
senior21
 
2.4%
backend16
 
1.8%
kehittäjä16
 
1.8%
Other values (185)373
43.1%

Most occurring characters

ValueCountFrequency (%)
t939
 
10.4%
e833
 
9.2%
l660
 
7.3%
i653
 
7.2%
k498
 
5.5%
o474
 
5.3%
a430
 
4.8%
s424
 
4.7%
400
 
4.4%
h361
 
4.0%
Other values (47)3336
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7822
86.8%
Uppercase Letter458
 
5.1%
Space Separator401
 
4.5%
Dash Punctuation169
 
1.9%
Other Punctuation98
 
1.1%
Open Punctuation26
 
0.3%
Close Punctuation26
 
0.3%
Math Symbol8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t939
12.0%
e833
 
10.6%
l660
 
8.4%
i653
 
8.3%
k498
 
6.4%
o474
 
6.1%
a430
 
5.5%
s424
 
5.4%
h361
 
4.6%
j340
 
4.3%
Other values (16)2210
28.3%
ValueCountFrequency (%)
F102
22.3%
O94
20.5%
S51
11.1%
D42
9.2%
T27
 
5.9%
A26
 
5.7%
L20
 
4.4%
C18
 
3.9%
E12
 
2.6%
P11
 
2.4%
Other values (11)55
12.0%
ValueCountFrequency (%)
,52
53.1%
/42
42.9%
&3
 
3.1%
.1
 
1.0%
ValueCountFrequency (%)
400
99.8%
 1
 
0.2%
ValueCountFrequency (%)
-169
100.0%
ValueCountFrequency (%)
(26
100.0%
ValueCountFrequency (%)
)26
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8280
91.9%
Common728
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t939
 
11.3%
e833
 
10.1%
l660
 
8.0%
i653
 
7.9%
k498
 
6.0%
o474
 
5.7%
a430
 
5.2%
s424
 
5.1%
h361
 
4.4%
j340
 
4.1%
Other values (37)2668
32.2%
ValueCountFrequency (%)
400
54.9%
-169
23.2%
,52
 
7.1%
/42
 
5.8%
(26
 
3.6%
)26
 
3.6%
+8
 
1.1%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8665
96.2%
None343
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t939
 
10.8%
e833
 
9.6%
l660
 
7.6%
i653
 
7.5%
k498
 
5.7%
o474
 
5.5%
a430
 
5.0%
s424
 
4.9%
400
 
4.6%
h361
 
4.2%
Other values (44)2993
34.5%
ValueCountFrequency (%)
ä326
95.0%
ö16
 
4.7%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size743.0 B
Etä
203 
Toimisto
164 
50/50
113 

Length

Max length8
Median length5
Mean length5.179166667
Min length3

Characters and Unicode

Total characters2486
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 (%)
Etä203
42.0%
Toimisto164
34.0%
50/50113
23.4%
(Missing)3
 
0.6%
2021-02-24T16:21:54.477459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-24T16:21:54.592628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä203
42.3%
toimisto164
34.2%
50/50113
23.5%

Most occurring characters

ValueCountFrequency (%)
t367
14.8%
o328
13.2%
i328
13.2%
5226
9.1%
0226
9.1%
E203
8.2%
ä203
8.2%
T164
6.6%
m164
6.6%
s164
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1554
62.5%
Decimal Number452
 
18.2%
Uppercase Letter367
 
14.8%
Other Punctuation113
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t367
23.6%
o328
21.1%
i328
21.1%
ä203
13.1%
m164
10.6%
s164
10.6%
ValueCountFrequency (%)
5226
50.0%
0226
50.0%
ValueCountFrequency (%)
E203
55.3%
T164
44.7%
ValueCountFrequency (%)
/113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1921
77.3%
Common565
 
22.7%

Most frequent character per script

ValueCountFrequency (%)
t367
19.1%
o328
17.1%
i328
17.1%
E203
10.6%
ä203
10.6%
T164
8.5%
m164
8.5%
s164
8.5%
ValueCountFrequency (%)
5226
40.0%
0226
40.0%
/113
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2283
91.8%
None203
 
8.2%

Most frequent character per block

ValueCountFrequency (%)
t367
16.1%
o328
14.4%
i328
14.4%
5226
9.9%
0226
9.9%
E203
8.9%
T164
7.2%
m164
7.2%
s164
7.2%
/113
 
4.9%
ValueCountFrequency (%)
ä203
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct127
Distinct (%)28.8%
Missing42
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean4685.582766
Minimum1100
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2021-02-24T16:21:54.743405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile2800
Q13800
median4500
Q35500
95-th percentile7000
Maximum15000
Range13900
Interquartile range (IQR)1700

Descriptive statistics

Standard deviation1446.507371
Coefficient of variation (CV)0.3087145065
Kurtosis7.995739302
Mean4685.582766
Median Absolute Deviation (MAD)800
Skewness1.678529937
Sum2066342
Variance2092383.576
MonotocityNot monotonic
2021-02-24T16:21:54.940119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400026
 
5.4%
450023
 
4.8%
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 (117)283
58.6%
(Missing)42
 
8.7%
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

Distinct182
Distinct (%)38.6%
Missing12
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean65708.31529
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.9 KiB
2021-02-24T16:21:55.152524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34200
Q149562.5
median58750
Q375000
95-th percentile122500
Maximum300000
Range300000
Interquartile range (IQR)25437.5

Descriptive statistics

Standard deviation31885.42762
Coefficient of variation (CV)0.4852571167
Kurtosis11.92530247
Mean65708.31529
Median Absolute Deviation (MAD)11875
Skewness2.670591419
Sum30948616.5
Variance1016680494
MonotocityNot monotonic
2021-02-24T16:21:55.372735image/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
 
2.1%
6500010
 
2.1%
3750010
 
2.1%
7000010
 
2.1%
475009
 
1.9%
Other values (172)345
71.4%
(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
320 
False
148 
(Missing)
 
15
ValueCountFrequency (%)
True320
66.3%
False148
30.6%
(Missing)15
 
3.1%
2021-02-24T16:21:55.523516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)67.3%
Missing373
Missing (%)77.2%
Memory size3.9 KiB
Gofore
11 
Vincit
 
7
Futurice
 
5
Mavericks
 
4
Fraktio
 
4
Other values (69)
79 

Length

Max length132
Median length8
Mean length10.78181818
Min length2

Characters and Unicode

Total characters1186
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)55.5%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.3%
Vincit7
 
1.4%
Futurice5
 
1.0%
Mavericks4
 
0.8%
Fraktio4
 
0.8%
Arado3
 
0.6%
Pankki3
 
0.6%
Siili2
 
0.4%
KVTES-alainen kunnan omistama oy 2
 
0.4%
Gofore Oyj2
 
0.4%
Other values (64)67
 
13.9%
(Missing)373
77.2%
2021-02-24T16:21:55.881737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore13
 
7.4%
oy13
 
7.4%
vincit7
 
4.0%
mavericks6
 
3.4%
oyj5
 
2.8%
futurice5
 
2.8%
siili4
 
2.3%
fraktio4
 
2.3%
omistama3
 
1.7%
pankki3
 
1.7%
Other values (96)113
64.2%

Most occurring characters

ValueCountFrequency (%)
i122
 
10.3%
o89
 
7.5%
a88
 
7.4%
e85
 
7.2%
t81
 
6.8%
69
 
5.8%
r62
 
5.2%
n58
 
4.9%
k48
 
4.0%
l47
 
4.0%
Other values (44)437
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter962
81.1%
Uppercase Letter149
 
12.6%
Space Separator69
 
5.8%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i122
12.7%
o89
 
9.3%
a88
 
9.1%
e85
 
8.8%
t81
 
8.4%
r62
 
6.4%
n58
 
6.0%
k48
 
5.0%
l47
 
4.9%
u46
 
4.8%
Other values (16)236
24.5%
ValueCountFrequency (%)
O18
 
12.1%
G14
 
9.4%
S14
 
9.4%
V13
 
8.7%
F10
 
6.7%
K8
 
5.4%
C7
 
4.7%
A7
 
4.7%
M7
 
4.7%
P6
 
4.0%
Other values (15)45
30.2%
ValueCountFrequency (%)
69
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1111
93.7%
Common75
 
6.3%

Most frequent character per script

ValueCountFrequency (%)
i122
 
11.0%
o89
 
8.0%
a88
 
7.9%
e85
 
7.7%
t81
 
7.3%
r62
 
5.6%
n58
 
5.2%
k48
 
4.3%
l47
 
4.2%
u46
 
4.1%
Other values (41)385
34.7%
ValueCountFrequency (%)
69
92.0%
.3
 
4.0%
-3
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1174
99.0%
None12
 
1.0%

Most frequent character per block

ValueCountFrequency (%)
i122
 
10.4%
o89
 
7.6%
a88
 
7.5%
e85
 
7.2%
t81
 
6.9%
69
 
5.9%
r62
 
5.3%
n58
 
4.9%
k48
 
4.1%
l47
 
4.0%
Other values (42)425
36.2%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct36
Distinct (%)97.3%
Missing446
Missing (%)92.3%
Memory size3.9 KiB
palkan lisänä lounas- ja virkistysetu
 
2
+ merkittävä optiopaketti
 
1
hyvä kysely
 
1
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi
 
1
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.
 
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%
+ merkittävä optiopaketti1
 
0.2%
hyvä kysely1
 
0.2%
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi1
 
0.2%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
Sijainti Pori, mutta etätöitä 100%. Varsinainen positio Tampere - Helsinki. Edut aika huonot, perusjutut. Työ itsessään aika masentavaa. Seuraavaksi varmaan freelance/yrittäjyys.1
 
0.2%
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%
Ilmaset kaffet, safkat, salit jne.1
 
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%
Johtajasopimus, ei työaikaa1
 
0.2%
Other values (26)26
 
5.4%
(Missing)446
92.3%
2021-02-24T16:21:56.326238image/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%
firman4
 
0.9%
joten4
 
0.9%
palkan4
 
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

Distinct182
Distinct (%)38.6%
Missing12
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean5475.692941
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.9 KiB
2021-02-24T16:21:56.518280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2850
Q14130.208333
median4895.833333
Q36250
95-th percentile10208.33333
Maximum25000
Range25000
Interquartile range (IQR)2119.791667

Descriptive statistics

Standard deviation2657.118968
Coefficient of variation (CV)0.4852571167
Kurtosis11.92530247
Mean5475.692941
Median Absolute Deviation (MAD)989.5833333
Skewness2.670591419
Sum2579051.375
Variance7060281.21
MonotocityNot monotonic
2021-02-24T16:21:56.866695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4583.33333318
 
3.7%
4166.66666718
 
3.7%
625016
 
3.3%
500014
 
2.9%
7083.33333311
 
2.3%
5416.66666710
 
2.1%
5833.33333310
 
2.1%
312510
 
2.1%
5208.33333310
 
2.1%
3333.3333339
 
1.9%
Other values (172)345
71.4%
(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

2021-02-24T16:21:45.540442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:45.712046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:45.883393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:46.056759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:46.222074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:46.389510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:46.561384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:46.737815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:46.907922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:47.081862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:47.255419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:47.530256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:47.703486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:47.869399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:48.044519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:48.219836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:48.391759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:48.549494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:48.712947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-24T16:21:48.883755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-24T16:21:57.078332image/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.
2021-02-24T16:21:57.301927image/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.
2021-02-24T16:21:57.519146image/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.
2021-02-24T16:21:57.755541image/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

2021-02-24T16:21:49.204305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-24T16:21:49.644150image/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.
2021-02-24T16:21:50.139651image/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.
2021-02-24T16:21:50.533640image/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
4732021-02-23 10:43:09.487PK-Seutu43mies9.0Työntekijä / palkollinen1.0ohjelmistokehittäjä/konsultti (senior, full-stack)50/503926.050000.0FalseVincitOlen firmaan, sen etuihin ja työilmapiiriin tyytyväinen.4166.666667
4742021-02-23 12:41:17.548Turku28mies5.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto4500.056250.0TrueNaNNaN4687.500000
4752021-02-23 12:51:35.493Hanksalmi28mies2.0Freelancer0.5ohjelmistokehittäjäEtä1100.013750.0FalseNaNNaN1145.833333
4762021-02-23 13:07:40.896PK-Seutu38mies4.0Työntekijä / palkollinen1.0FullstaxkEtä4300.058000.0TrueNaNNaN4833.333333
4772021-02-23 14:00:37.170Tampere28mies5.0Työntekijä / palkollinen1.0Senior/Lead developerToimisto4000.052000.0FalseNaNNaN4333.333333
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