Overview

Dataset statistics

Number of variables15
Number of observations457
Missing cells929
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.5 KiB
Average record size in memory95.3 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 238 distinct values High cardinality
Työpaikka has a high cardinality: 71 distinct values High cardinality
Vuositulot is highly correlated with Kk-tulotHigh correlation
Kk-tulot is highly correlated with VuositulotHigh correlation
Vapaa sana is highly correlated with Työpaikka and 1 other fieldsHigh correlation
Työpaikka is highly correlated with Vapaa sanaHigh correlation
Kilpailukykyinen is highly correlated with Vapaa sanaHigh correlation
Sukupuoli has 33 (7.2%) missing values Missing
Työaika has 19 (4.2%) missing values Missing
Rooli has 12 (2.6%) missing values Missing
Kuukausipalkka has 40 (8.8%) missing values Missing
Vuositulot has 11 (2.4%) missing values Missing
Kilpailukykyinen has 15 (3.3%) missing values Missing
Työpaikka has 351 (76.8%) missing values Missing
Vapaa sana has 423 (92.6%) missing values Missing
Kk-tulot has 11 (2.4%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-22 08:36:33.713622
Analysis finished2021-02-22 08:36:38.590516
Duration4.88 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct457
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-22 10:02:50.113000
2021-02-22T08:36:38.662196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:38.820929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

Distinct25
Distinct (%)5.5%
Missing4
Missing (%)0.9%
Memory size1.3 KiB
PK-Seutu
231 
Tampere
106 
Turku
46 
Oulu
 
23
Jyväskylä
 
18
Other values (20)
29 

Length

Max length15
Median length8
Mean length7.258278146
Min length2

Characters and Unicode

Total characters3288
Distinct characters39
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

Unique14 ?
Unique (%)3.1%

Sample

1st rowPK-Seutu
2nd rowTurku
3rd rowPK-Seutu
4th rowTampere
5th rowPK-Seutu
ValueCountFrequency (%)
PK-Seutu231
50.5%
Tampere106
23.2%
Turku46
 
10.1%
Oulu23
 
5.0%
Jyväskylä18
 
3.9%
Kuopio5
 
1.1%
Pori2
 
0.4%
Lontoo2
 
0.4%
Vaasa2
 
0.4%
Hämeenlinna2
 
0.4%
Other values (15)16
 
3.5%
(Missing)4
 
0.9%
2021-02-22T08:36:39.251291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu231
50.5%
tampere106
23.2%
turku46
 
10.1%
oulu23
 
5.0%
jyväskylä18
 
3.9%
kuopio5
 
1.1%
tallinna2
 
0.4%
pori2
 
0.4%
vaasa2
 
0.4%
lontoo2
 
0.4%
Other values (19)20
 
4.4%

Most occurring characters

ValueCountFrequency (%)
u612
18.6%
e454
13.8%
K239
 
7.3%
t237
 
7.2%
P234
 
7.1%
-233
 
7.1%
S233
 
7.1%
r158
 
4.8%
T154
 
4.7%
a129
 
3.9%
Other values (29)605
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2130
64.8%
Uppercase Letter920
28.0%
Dash Punctuation233
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u612
28.7%
e454
21.3%
t237
 
11.1%
r158
 
7.4%
a129
 
6.1%
p112
 
5.3%
m111
 
5.2%
k65
 
3.1%
l52
 
2.4%
ä44
 
2.1%
Other values (10)156
 
7.3%
ValueCountFrequency (%)
K239
26.0%
P234
25.4%
S233
25.3%
T154
16.7%
O23
 
2.5%
J19
 
2.1%
L4
 
0.4%
E3
 
0.3%
V3
 
0.3%
H2
 
0.2%
Other values (6)6
 
0.7%
ValueCountFrequency (%)
-233
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3050
92.8%
Common238
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u612
20.1%
e454
14.9%
K239
 
7.8%
t237
 
7.8%
P234
 
7.7%
S233
 
7.6%
r158
 
5.2%
T154
 
5.0%
a129
 
4.2%
p112
 
3.7%
Other values (26)488
16.0%
ValueCountFrequency (%)
-233
97.9%
4
 
1.7%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3244
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u612
18.9%
e454
14.0%
K239
 
7.4%
t237
 
7.3%
P234
 
7.2%
-233
 
7.2%
S233
 
7.2%
r158
 
4.9%
T154
 
4.7%
a129
 
4.0%
Other values (28)561
17.3%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.5%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean49.68351648
Minimum34
Maximum78
Zeros0
Zeros (%)0.0%
Memory size3.7 KiB
2021-02-22T08:36:39.369076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile34
Q141
median48
Q356
95-th percentile64
Maximum78
Range44
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.199984004
Coefficient of variation (CV)0.1851717562
Kurtosis0.1077620309
Mean49.68351648
Median Absolute Deviation (MAD)7
Skewness0.5420950479
Sum22606
Variance84.63970567
MonotocityNot monotonic
2021-02-22T08:36:39.476475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
48153
33.5%
41109
23.9%
5699
21.7%
6451
 
11.2%
3430
 
6.6%
717
 
1.5%
786
 
1.3%
(Missing)2
 
0.4%
ValueCountFrequency (%)
3430
 
6.6%
41109
23.9%
48153
33.5%
5699
21.7%
6451
 
11.2%
ValueCountFrequency (%)
786
 
1.3%
717
 
1.5%
6451
 
11.2%
5699
21.7%
48153
33.5%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing33
Missing (%)7.2%
Memory size717.0 B
mies
383 
nainen
 
33
muu
 
8

Length

Max length6
Median length4
Mean length4.136792453
Min length3

Characters and Unicode

Total characters1754
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 (%)
mies383
83.8%
nainen33
 
7.2%
muu8
 
1.8%
(Missing)33
 
7.2%
2021-02-22T08:36:39.759573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T08:36:39.852331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies383
90.3%
nainen33
 
7.8%
muu8
 
1.9%

Most occurring characters

ValueCountFrequency (%)
i416
23.7%
e416
23.7%
m391
22.3%
s383
21.8%
n99
 
5.6%
a33
 
1.9%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1754
100.0%

Most frequent character per category

ValueCountFrequency (%)
i416
23.7%
e416
23.7%
m391
22.3%
s383
21.8%
n99
 
5.6%
a33
 
1.9%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1754
100.0%

Most frequent character per script

ValueCountFrequency (%)
i416
23.7%
e416
23.7%
m391
22.3%
s383
21.8%
n99
 
5.6%
a33
 
1.9%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1754
100.0%

Most frequent character per block

ValueCountFrequency (%)
i416
23.7%
e416
23.7%
m391
22.3%
s383
21.8%
n99
 
5.6%
a33
 
1.9%
u16
 
0.9%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)6.0%
Missing4
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean9.664459161
Minimum0
Maximum30
Zeros3
Zeros (%)0.7%
Memory size3.7 KiB
2021-02-22T08:36:39.948544image/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 deviation6.097474777
Coefficient of variation (CV)0.6309173307
Kurtosis-0.0611176499
Mean9.664459161
Median Absolute Deviation (MAD)4
Skewness0.7155313977
Sum4378
Variance37.17919866
MonotocityNot monotonic
2021-02-22T08:36:40.072950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
548
 
10.5%
1037
 
8.1%
430
 
6.6%
728
 
6.1%
2026
 
5.7%
325
 
5.5%
1325
 
5.5%
1525
 
5.5%
624
 
5.3%
224
 
5.3%
Other values (17)161
35.2%
ValueCountFrequency (%)
03
 
0.7%
116
3.5%
224
5.3%
325
5.5%
430
6.6%
ValueCountFrequency (%)
302
 
0.4%
256
1.3%
243
0.7%
234
0.9%
225
1.1%
Distinct3
Distinct (%)0.7%
Missing1
Missing (%)0.2%
Memory size3.7 KiB
Työntekijä / palkollinen
407 
Freelancer
 
25
Yrittäjä
 
24

Length

Max length24
Median length24
Mean length22.39035088
Min length8

Characters and Unicode

Total characters10210
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 / palkollinen407
89.1%
Freelancer25
 
5.5%
Yrittäjä24
 
5.3%
(Missing)1
 
0.2%
2021-02-22T08:36:40.369710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T08:36:40.470034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
407
32.0%
palkollinen407
32.0%
työntekijä407
32.0%
freelancer25
 
2.0%
yrittäjä24
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1246
12.2%
l1246
12.2%
e889
 
8.7%
i838
 
8.2%
k814
 
8.0%
814
 
8.0%
t455
 
4.5%
ä455
 
4.5%
a432
 
4.2%
j431
 
4.2%
Other values (10)2590
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8533
83.6%
Space Separator814
 
8.0%
Uppercase Letter456
 
4.5%
Other Punctuation407
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1246
14.6%
l1246
14.6%
e889
10.4%
i838
9.8%
k814
9.5%
t455
 
5.3%
ä455
 
5.3%
a432
 
5.1%
j431
 
5.1%
y407
 
4.8%
Other values (5)1320
15.5%
ValueCountFrequency (%)
T407
89.3%
F25
 
5.5%
Y24
 
5.3%
ValueCountFrequency (%)
814
100.0%
ValueCountFrequency (%)
/407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8989
88.0%
Common1221
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1246
13.9%
l1246
13.9%
e889
9.9%
i838
9.3%
k814
9.1%
t455
 
5.1%
ä455
 
5.1%
a432
 
4.8%
j431
 
4.8%
T407
 
4.5%
Other values (8)1776
19.8%
ValueCountFrequency (%)
814
66.7%
/407
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9348
91.6%
None862
 
8.4%

Most frequent character per block

ValueCountFrequency (%)
n1246
13.3%
l1246
13.3%
e889
9.5%
i838
9.0%
k814
8.7%
814
8.7%
t455
 
4.9%
a432
 
4.6%
j431
 
4.6%
T407
 
4.4%
Other values (8)1776
19.0%
ValueCountFrequency (%)
ä455
52.8%
ö407
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)4.2%
Memory size3.7 KiB
1.0
411 
0.8
 
23
0.5
 
2
0.7
 
1
0.6
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1314
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 (%)0.5%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0411
89.9%
0.823
 
5.0%
0.52
 
0.4%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
4.2%
2021-02-22T08:36:40.696923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T08:36:40.777468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0411
93.8%
0.823
 
5.3%
0.52
 
0.5%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.438
33.3%
0438
33.3%
1411
31.3%
823
 
1.8%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number876
66.7%
Other Punctuation438
33.3%

Most frequent character per category

ValueCountFrequency (%)
0438
50.0%
1411
46.9%
823
 
2.6%
52
 
0.2%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1314
100.0%

Most frequent character per script

ValueCountFrequency (%)
.438
33.3%
0438
33.3%
1411
31.3%
823
 
1.8%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1314
100.0%

Most frequent character per block

ValueCountFrequency (%)
.438
33.3%
0438
33.3%
1411
31.3%
823
 
1.8%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct238
Distinct (%)53.5%
Missing12
Missing (%)2.6%
Memory size3.7 KiB
Ohjelmistokehittäjä
37 
full-stack
33 
Full-stack
 
23
ohjelmistokehittäjä
 
16
Arkkitehti
 
15
Other values (233)
321 

Length

Max length67
Median length18
Mean length19.14382022
Min length2

Characters and Unicode

Total characters8519
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

Unique192 ?
Unique (%)43.1%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä37
 
8.1%
full-stack33
 
7.2%
Full-stack23
 
5.0%
ohjelmistokehittäjä16
 
3.5%
Arkkitehti15
 
3.3%
Full-stack ohjelmistokehittäjä8
 
1.8%
full-stack ohjelmistokehittäjä7
 
1.5%
Frontend6
 
1.3%
arkkitehti6
 
1.3%
Full-stack kehittäjä5
 
1.1%
Other values (228)289
63.2%
(Missing)12
 
2.6%
2021-02-22T08:36:41.119390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack135
 
16.5%
ohjelmistokehittäjä107
 
13.1%
developer54
 
6.6%
arkkitehti34
 
4.2%
32
 
3.9%
lead31
 
3.8%
frontend25
 
3.1%
senior18
 
2.2%
kehittäjä16
 
2.0%
software14
 
1.7%
Other values (171)350
42.9%

Most occurring characters

ValueCountFrequency (%)
t895
 
10.5%
e780
 
9.2%
l625
 
7.3%
i620
 
7.3%
k473
 
5.6%
o442
 
5.2%
a408
 
4.8%
s405
 
4.8%
376
 
4.4%
h343
 
4.0%
Other values (47)3152
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7401
86.9%
Uppercase Letter428
 
5.0%
Space Separator377
 
4.4%
Dash Punctuation163
 
1.9%
Other Punctuation92
 
1.1%
Open Punctuation25
 
0.3%
Close Punctuation25
 
0.3%
Math Symbol8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t895
12.1%
e780
 
10.5%
l625
 
8.4%
i620
 
8.4%
k473
 
6.4%
o442
 
6.0%
a408
 
5.5%
s405
 
5.5%
h343
 
4.6%
j322
 
4.4%
Other values (16)2088
28.2%
ValueCountFrequency (%)
F98
22.9%
O88
20.6%
S48
11.2%
D39
 
9.1%
A26
 
6.1%
T24
 
5.6%
L18
 
4.2%
C16
 
3.7%
P11
 
2.6%
E11
 
2.6%
Other values (11)49
11.4%
ValueCountFrequency (%)
,51
55.4%
/37
40.2%
&3
 
3.3%
.1
 
1.1%
ValueCountFrequency (%)
376
99.7%
 1
 
0.3%
ValueCountFrequency (%)
-163
100.0%
ValueCountFrequency (%)
(25
100.0%
ValueCountFrequency (%)
)25
100.0%
ValueCountFrequency (%)
+8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7829
91.9%
Common690
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t895
 
11.4%
e780
 
10.0%
l625
 
8.0%
i620
 
7.9%
k473
 
6.0%
o442
 
5.6%
a408
 
5.2%
s405
 
5.2%
h343
 
4.4%
j322
 
4.1%
Other values (37)2516
32.1%
ValueCountFrequency (%)
376
54.5%
-163
23.6%
,51
 
7.4%
/37
 
5.4%
(25
 
3.6%
)25
 
3.6%
+8
 
1.2%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8195
96.2%
None324
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t895
 
10.9%
e780
 
9.5%
l625
 
7.6%
i620
 
7.6%
k473
 
5.8%
o442
 
5.4%
a408
 
5.0%
s405
 
4.9%
376
 
4.6%
h343
 
4.2%
Other values (44)2828
34.5%
ValueCountFrequency (%)
ä308
95.1%
ö15
 
4.6%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.7%
Missing3
Missing (%)0.7%
Memory size717.0 B
Etä
195 
Toimisto
151 
50/50
108 

Length

Max length8
Median length5
Mean length5.13876652
Min length3

Characters and Unicode

Total characters2333
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 (%)
Etä195
42.7%
Toimisto151
33.0%
50/50108
23.6%
(Missing)3
 
0.7%
2021-02-22T08:36:41.506624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T08:36:41.596346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä195
43.0%
toimisto151
33.3%
50/50108
23.8%

Most occurring characters

ValueCountFrequency (%)
t346
14.8%
o302
12.9%
i302
12.9%
5216
9.3%
0216
9.3%
E195
8.4%
ä195
8.4%
T151
6.5%
m151
6.5%
s151
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1447
62.0%
Decimal Number432
 
18.5%
Uppercase Letter346
 
14.8%
Other Punctuation108
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
t346
23.9%
o302
20.9%
i302
20.9%
ä195
13.5%
m151
10.4%
s151
10.4%
ValueCountFrequency (%)
5216
50.0%
0216
50.0%
ValueCountFrequency (%)
E195
56.4%
T151
43.6%
ValueCountFrequency (%)
/108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1793
76.9%
Common540
 
23.1%

Most frequent character per script

ValueCountFrequency (%)
t346
19.3%
o302
16.8%
i302
16.8%
E195
10.9%
ä195
10.9%
T151
8.4%
m151
8.4%
s151
8.4%
ValueCountFrequency (%)
5216
40.0%
0216
40.0%
/108
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2138
91.6%
None195
 
8.4%

Most frequent character per block

ValueCountFrequency (%)
t346
16.2%
o302
14.1%
i302
14.1%
5216
10.1%
0216
10.1%
E195
9.1%
T151
7.1%
m151
7.1%
s151
7.1%
/108
 
5.1%
ValueCountFrequency (%)
ä195
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct122
Distinct (%)29.3%
Missing40
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean4700.978417
Minimum1666
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.7 KiB
2021-02-22T08:36:41.705034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1666
5-th percentile2800
Q13800
median4500
Q35500
95-th percentile7000
Maximum15000
Range13334
Interquartile range (IQR)1700

Descriptive statistics

Standard deviation1402.092786
Coefficient of variation (CV)0.298255525
Kurtosis8.064090583
Mean4700.978417
Median Absolute Deviation (MAD)800
Skewness1.607063414
Sum1960308
Variance1965864.18
MonotocityNot monotonic
2021-02-22T08:36:41.866675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400023
 
5.0%
450021
 
4.6%
600017
 
3.7%
500017
 
3.7%
550015
 
3.3%
480011
 
2.4%
700011
 
2.4%
420011
 
2.4%
430011
 
2.4%
380010
 
2.2%
Other values (112)270
59.1%
(Missing)40
 
8.8%
ValueCountFrequency (%)
16661
0.2%
17001
0.2%
18001
0.2%
21001
0.2%
22751
0.2%
ValueCountFrequency (%)
150001
 
0.2%
120001
 
0.2%
93001
 
0.2%
85002
 
0.4%
80006
1.3%

Vuositulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct176
Distinct (%)39.5%
Missing11
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean66161.75224
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.7 KiB
2021-02-22T08:36:42.027323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35000
Q150000
median60000
Q375000
95-th percentile123750
Maximum300000
Range300000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation31484.56811
Coefficient of variation (CV)0.475872646
Kurtosis12.08890518
Mean66161.75224
Median Absolute Deviation (MAD)12500
Skewness2.645578444
Sum29508141.5
Variance991278028.8
MonotocityNot monotonic
2021-02-22T08:36:42.177084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.9%
7500016
 
3.5%
5000014
 
3.1%
6000014
 
3.1%
8500011
 
2.4%
6250010
 
2.2%
6500010
 
2.2%
800009
 
2.0%
400009
 
2.0%
700008
 
1.8%
Other values (166)327
71.6%
(Missing)11
 
2.4%
ValueCountFrequency (%)
02
0.4%
40001
0.2%
61001
0.2%
75001
0.2%
200001
0.2%
ValueCountFrequency (%)
3000001
 
0.2%
2500001
 
0.2%
2000004
0.9%
1900001
 
0.2%
1800001
 
0.2%

Kilpailukykyinen
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.5%
Missing15
Missing (%)3.3%
Memory size3.7 KiB
True
306 
False
136 
(Missing)
 
15
ValueCountFrequency (%)
True306
67.0%
False136
29.8%
(Missing)15
 
3.3%
2021-02-22T08:36:42.292249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct71
Distinct (%)67.0%
Missing351
Missing (%)76.8%
Memory size3.7 KiB
Gofore
11 
Vincit
 
6
Futurice
 
5
Mavericks
 
4
Fraktio
 
4
Other values (66)
76 

Length

Max length132
Median length8
Mean length10.66037736
Min length2

Characters and Unicode

Total characters1130
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

Unique58 ?
Unique (%)54.7%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.4%
Vincit6
 
1.3%
Futurice5
 
1.1%
Mavericks4
 
0.9%
Fraktio4
 
0.9%
Arado3
 
0.7%
Pankki3
 
0.7%
Compile Oy2
 
0.4%
KVTES-alainen kunnan omistama oy 2
 
0.4%
Qvik2
 
0.4%
Other values (61)64
 
14.0%
(Missing)351
76.8%
2021-02-22T08:36:42.574798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore13
 
7.7%
oy12
 
7.1%
vincit6
 
3.6%
mavericks6
 
3.6%
futurice5
 
3.0%
oyj5
 
3.0%
siili4
 
2.4%
fraktio4
 
2.4%
arado3
 
1.8%
pankki3
 
1.8%
Other values (91)108
63.9%

Most occurring characters

ValueCountFrequency (%)
i115
 
10.2%
a86
 
7.6%
o85
 
7.5%
e79
 
7.0%
t78
 
6.9%
66
 
5.8%
r61
 
5.4%
n52
 
4.6%
l46
 
4.1%
u44
 
3.9%
Other values (44)418
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter914
80.9%
Uppercase Letter144
 
12.7%
Space Separator66
 
5.8%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i115
12.6%
a86
 
9.4%
o85
 
9.3%
e79
 
8.6%
t78
 
8.5%
r61
 
6.7%
n52
 
5.7%
l46
 
5.0%
u44
 
4.8%
k42
 
4.6%
Other values (16)226
24.7%
ValueCountFrequency (%)
O17
11.8%
G14
 
9.7%
S14
 
9.7%
V12
 
8.3%
F10
 
6.9%
K8
 
5.6%
C7
 
4.9%
A7
 
4.9%
M7
 
4.9%
P6
 
4.2%
Other values (15)42
29.2%
ValueCountFrequency (%)
66
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1058
93.6%
Common72
 
6.4%

Most frequent character per script

ValueCountFrequency (%)
i115
 
10.9%
a86
 
8.1%
o85
 
8.0%
e79
 
7.5%
t78
 
7.4%
r61
 
5.8%
n52
 
4.9%
l46
 
4.3%
u44
 
4.2%
k42
 
4.0%
Other values (41)370
35.0%
ValueCountFrequency (%)
66
91.7%
.3
 
4.2%
-3
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1118
98.9%
None12
 
1.1%

Most frequent character per block

ValueCountFrequency (%)
i115
 
10.3%
a86
 
7.7%
o85
 
7.6%
e79
 
7.1%
t78
 
7.0%
66
 
5.9%
r61
 
5.5%
n52
 
4.7%
l46
 
4.1%
u44
 
3.9%
Other values (42)406
36.3%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct33
Distinct (%)97.1%
Missing423
Missing (%)92.6%
Memory size3.7 KiB
palkan lisänä lounas- ja virkistysetu
 
2
+ merkittävä optiopaketti
 
1
olen sekä päivätyöläinen että friikku. jospa nyt kuitenki vois valita monta?
 
1
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
Startup
 
1
Other values (28)
28 

Length

Max length286
Median length73
Mean length95.41176471
Min length7

Characters and Unicode

Total characters3244
Distinct characters55
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

Unique32 ?
Unique (%)94.1%

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%
olen sekä päivätyöläinen että friikku. jospa nyt kuitenki vois valita monta?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%
Startup1
 
0.2%
Teen 80% työaikaa jotta ehtisin harrastaa kaikenlaista työnteon lisäksi1
 
0.2%
Vastasin kysymyksiin läpällä. Summat on enemmän sitä minkä verran yrittäjänä haluaa sykkiä ja mennä "raha edellä". 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%
Korona-aika on lisännyt etätyön määrää. Aiemmin pari päivää viikossa etänä, nyt kokonaan. Paluuta vanhaan ei varmaankaan ole, ehkä päivä viikossa konttorilla ihan sosiaalisten kontaktien takia.1
 
0.2%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
Other values (23)23
 
5.0%
(Missing)423
92.6%
2021-02-22T08:36:42.890556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ei11
 
2.7%
palkka10
 
2.4%
on9
 
2.2%
ja8
 
2.0%
mutta7
 
1.7%
ole6
 
1.5%
ihan4
 
1.0%
nyt4
 
1.0%
palkan4
 
1.0%
firman4
 
1.0%
Other values (289)343
83.7%

Most occurring characters

ValueCountFrequency (%)
379
11.7%
a344
10.6%
i277
 
8.5%
t254
 
7.8%
n220
 
6.8%
s210
 
6.5%
e207
 
6.4%
k188
 
5.8%
l163
 
5.0%
o149
 
4.6%
Other values (45)853
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2697
83.1%
Space Separator379
 
11.7%
Other Punctuation77
 
2.4%
Uppercase Letter49
 
1.5%
Decimal Number27
 
0.8%
Dash Punctuation6
 
0.2%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%
Math Symbol3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a344
12.8%
i277
10.3%
t254
9.4%
n220
 
8.2%
s210
 
7.8%
e207
 
7.7%
k188
 
7.0%
l163
 
6.0%
o149
 
5.5%
u120
 
4.4%
Other values (14)565
20.9%
ValueCountFrequency (%)
P9
18.4%
T7
14.3%
O6
12.2%
E6
12.2%
V6
12.2%
S4
8.2%
K3
 
6.1%
I2
 
4.1%
H2
 
4.1%
R1
 
2.0%
Other values (3)3
 
6.1%
ValueCountFrequency (%)
015
55.6%
13
 
11.1%
52
 
7.4%
22
 
7.4%
82
 
7.4%
62
 
7.4%
31
 
3.7%
ValueCountFrequency (%)
.40
51.9%
,24
31.2%
/5
 
6.5%
%4
 
5.2%
"2
 
2.6%
?2
 
2.6%
ValueCountFrequency (%)
379
100.0%
ValueCountFrequency (%)
(3
100.0%
ValueCountFrequency (%)
)3
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2746
84.6%
Common498
 
15.4%

Most frequent character per script

ValueCountFrequency (%)
a344
12.5%
i277
10.1%
t254
9.2%
n220
 
8.0%
s210
 
7.6%
e207
 
7.5%
k188
 
6.8%
l163
 
5.9%
o149
 
5.4%
u120
 
4.4%
Other values (27)614
22.4%
ValueCountFrequency (%)
379
76.1%
.40
 
8.0%
,24
 
4.8%
015
 
3.0%
-6
 
1.2%
/5
 
1.0%
%4
 
0.8%
(3
 
0.6%
)3
 
0.6%
+3
 
0.6%
Other values (8)16
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3103
95.7%
None141
 
4.3%

Most frequent character per block

ValueCountFrequency (%)
379
12.2%
a344
11.1%
i277
 
8.9%
t254
 
8.2%
n220
 
7.1%
s210
 
6.8%
e207
 
6.7%
k188
 
6.1%
l163
 
5.3%
o149
 
4.8%
Other values (43)712
22.9%
ValueCountFrequency (%)
ä117
83.0%
ö24
 
17.0%

Kk-tulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct176
Distinct (%)39.5%
Missing11
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean5513.479354
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.7 KiB
2021-02-22T08:36:43.037398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2916.666667
Q14166.666667
median5000
Q36250
95-th percentile10312.5
Maximum25000
Range25000
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2623.714009
Coefficient of variation (CV)0.475872646
Kurtosis12.08890518
Mean5513.479354
Median Absolute Deviation (MAD)1041.666667
Skewness2.645578444
Sum2459011.792
Variance6883875.2
MonotocityNot monotonic
2021-02-22T08:36:43.323863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4583.33333318
 
3.9%
625016
 
3.5%
4166.66666714
 
3.1%
500014
 
3.1%
7083.33333311
 
2.4%
5208.33333310
 
2.2%
5416.66666710
 
2.2%
6666.6666679
 
2.0%
3333.3333339
 
2.0%
31258
 
1.8%
Other values (166)327
71.6%
(Missing)11
 
2.4%
ValueCountFrequency (%)
02
0.4%
333.33333331
0.2%
508.33333331
0.2%
6251
0.2%
1666.6666671
0.2%
ValueCountFrequency (%)
250001
 
0.2%
20833.333331
 
0.2%
16666.666674
0.9%
15833.333331
 
0.2%
150001
 
0.2%

Interactions

2021-02-22T08:36:34.675197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:34.798339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:34.918239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:35.042908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:35.161167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:35.282381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:35.421359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:35.557209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:35.685056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:35.810727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:35.942489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:36.181690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:36.314604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:36.444844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:36.582356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:36.720207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:36.852938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:36.975600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:37.108308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T08:36:37.238992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-22T08:36:43.467058image/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.
2021-02-22T08:36:43.644949image/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.
2021-02-22T08:36:43.821994image/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.
2021-02-22T08:36:44.005135image/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

2021-02-22T08:36:37.497553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-22T08:36:37.832628image/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.
2021-02-22T08:36:38.150238image/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.
2021-02-22T08:36:38.443747image/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-Seutu48NaN10.0Työntekijä / palkollinen1.0Arkkitehti50/506500.083000.0TrueNaNNaN6916.666667
12021-02-15 11:57:19.676Turku48mies14.0Työntekijä / palkollinen1.0full-stackEtä5000.062500.0TrueNaNNaN5208.333333
22021-02-15 11:58:03.592PK-Seutu41mies2.0Työntekijä / palkollinen1.0Full-stack ohjelmistokehittäjäEtä2475.030000.0FalseNaNNaN2500.000000
32021-02-15 11:58:15.261Tampere48mies22.0Yrittäjä1.0web-arkkitehtiEtä4300.0100000.0TrueNaNNaN8333.333333
42021-02-15 11:58:16.983PK-Seutu41mies2.0Työntekijä / palkollinen1.0OhjelmistokehittäjäEtä3000.037500.0FalseNaNNaN3125.000000
52021-02-15 11:58:49.454PK-Seutu64mies23.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto8000.0100000.0TrueNaNNaN8333.333333
62021-02-15 12:00:03.771PK-Seutu48mies10.0Freelancer1.0OhjelmistokehittäjäEtä6000.0140000.0TrueNaNNaN11666.666667
72021-02-15 12:00:04.655Tampere48NaN10.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto4250.054000.0TrueNaNNaN4500.000000
82021-02-15 12:01:00.769Tampere48mies6.0Työntekijä / palkollinen1.0Lead developerToimisto4000.050000.0FalseNaNNaN4166.666667
92021-02-15 12:02:03.577Tallinna48mies12.0Freelancer1.0NaNEtäNaN200000.0TrueQuestradeNaN16666.666667

Last rows

TimestampKaupunkiIkäSukupuoliTyökokemusTyösuhteen luonneTyöaikaRooliEtäKuukausipalkkaVuositulotKilpailukykyinenTyöpaikkaVapaa sanaKk-tulot
4472021-02-20 22:54:48.513PK-Seutu56nainen7.0Työntekijä / palkollinen1.0Project manager50/503800.047500.0FalseNaNNaN3958.333333
4482021-02-21 11:49:58.888Tampere48mies5.0Työntekijä / palkollinen1.0full-stackEtä5100.064000.0TrueNaNNaN5333.333333
4492021-02-21 13:18:18.719Tampere64mies10.0Työntekijä / palkollinen1.0Cloud ArchitectEtä5000.062500.0FalseKonsulttitaloNaN5208.333333
4502021-02-21 17:09:05.499PK-Seutu48mies10.0Työntekijä / palkollinen1.0data engineering, team leadEtä5300.071500.0FalseNaNNaN5958.333333
4512021-02-21 18:34:07.903PK-Seutu34mies1.0Työntekijä / palkollinen1.0FrontendToimisto2600.031200.0FalseNaNNaN2600.000000
4522021-02-21 23:03:57.647PK-Seutu56mies22.0Yrittäjä1.0Full-stackToimisto5000.085000.0TrueNaNNaN7083.333333
4532021-02-22 07:33:10.449Hämeenlinna48NaN5.0Työntekijä / palkollinen0.8OhjelmistokehittäjäEtä2400.025000.0FalseNaNNaN2083.333333
4542021-02-22 07:47:19.579PK-Seutu56mies12.0Työntekijä / palkollinen1.0SovelluskehittäjäToimisto6000.075000.0FalseNaNPieni firma ja paljon hattuja päässä. Palkka on hyvä, mutta ei korvaa stressiä ja painetta.6250.000000
4552021-02-22 09:49:11.345Lontoo56mies17.0Työntekijä / palkollinen1.0CTOEtä8500.0200000.0TrueNaNNaN16666.666667
4562021-02-22 10:02:50.113PK-Seutu48mies3.0Työntekijä / palkollinen1.0OhjelmistokehittäjäEtä3200.040000.0FalseSiili Solutions OyjNaN3333.333333