Overview

Dataset statistics

Number of variables15
Number of observations460
Missing cells933
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.8 KiB
Average record size in memory95.3 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 239 distinct values High cardinality
Työpaikka has a high cardinality: 72 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 (4.1%) missing values Missing
Rooli has 12 (2.6%) missing values Missing
Kuukausipalkka has 40 (8.7%) missing values Missing
Vuositulot has 11 (2.4%) missing values Missing
Kilpailukykyinen has 15 (3.3%) missing values Missing
Työpaikka has 353 (76.7%) missing values Missing
Vapaa sana has 425 (92.4%) 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 10:06:26.977016
Analysis finished2021-02-22 10:06:31.388209
Duration4.41 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-22 11:05:29.788000
2021-02-22T10:06:31.455017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:31.595038image/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
233 
Tampere
107 
Turku
46 
Oulu
 
23
Jyväskylä
 
18
Other values (20)
29 

Length

Max length15
Median length8
Mean length7.260964912
Min length2

Characters and Unicode

Total characters3311
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-Seutu233
50.7%
Tampere107
23.3%
Turku46
 
10.0%
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%
2021-02-22T10:06:32.007689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu233
50.7%
tampere107
23.3%
turku46
 
10.0%
oulu23
 
5.0%
jyväskylä18
 
3.9%
kuopio5
 
1.1%
hämeenlinna2
 
0.4%
tallinna2
 
0.4%
lontoo2
 
0.4%
pori2
 
0.4%
Other values (19)20
 
4.3%

Most occurring characters

ValueCountFrequency (%)
u616
18.6%
e458
13.8%
K241
 
7.3%
t239
 
7.2%
P236
 
7.1%
-235
 
7.1%
S235
 
7.1%
r159
 
4.8%
T155
 
4.7%
a130
 
3.9%
Other values (29)607
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2144
64.8%
Uppercase Letter927
28.0%
Dash Punctuation235
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u616
28.7%
e458
21.4%
t239
 
11.1%
r159
 
7.4%
a130
 
6.1%
p113
 
5.3%
m112
 
5.2%
k65
 
3.0%
l52
 
2.4%
ä44
 
2.1%
Other values (10)156
 
7.3%
ValueCountFrequency (%)
K241
26.0%
P236
25.5%
S235
25.4%
T155
16.7%
O23
 
2.5%
J19
 
2.0%
L4
 
0.4%
E3
 
0.3%
V3
 
0.3%
H2
 
0.2%
Other values (6)6
 
0.6%
ValueCountFrequency (%)
-235
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3071
92.8%
Common240
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u616
20.1%
e458
14.9%
K241
 
7.8%
t239
 
7.8%
P236
 
7.7%
S235
 
7.7%
r159
 
5.2%
T155
 
5.0%
a130
 
4.2%
p113
 
3.7%
Other values (26)489
15.9%
ValueCountFrequency (%)
-235
97.9%
4
 
1.7%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3267
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u616
18.9%
e458
14.0%
K241
 
7.4%
t239
 
7.3%
P236
 
7.2%
-235
 
7.2%
S235
 
7.2%
r159
 
4.9%
T155
 
4.7%
a130
 
4.0%
Other values (28)563
17.2%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.5%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean49.70742358
Minimum34
Maximum78
Zeros0
Zeros (%)0.0%
Memory size3.7 KiB
2021-02-22T10:06:32.109958image/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 deviation9.179559964
Coefficient of variation (CV)0.1846718116
Kurtosis0.1101608357
Mean49.70742358
Median Absolute Deviation (MAD)7
Skewness0.5356945197
Sum22766
Variance84.26432114
MonotocityNot monotonic
2021-02-22T10:06:32.201435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
48154
33.5%
41109
23.7%
56101
22.0%
6451
 
11.1%
3430
 
6.5%
717
 
1.5%
786
 
1.3%
(Missing)2
 
0.4%
ValueCountFrequency (%)
3430
 
6.5%
41109
23.7%
48154
33.5%
56101
22.0%
6451
 
11.1%
ValueCountFrequency (%)
786
 
1.3%
717
 
1.5%
6451
 
11.1%
56101
22.0%
48154
33.5%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing33
Missing (%)7.2%
Memory size720.0 B
mies
385 
nainen
 
34
muu
 
8

Length

Max length6
Median length4
Mean length4.140515222
Min length3

Characters and Unicode

Total characters1768
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 (%)
mies385
83.7%
nainen34
 
7.4%
muu8
 
1.7%
(Missing)33
 
7.2%
2021-02-22T10:06:32.449317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T10:06:32.530900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies385
90.2%
nainen34
 
8.0%
muu8
 
1.9%

Most occurring characters

ValueCountFrequency (%)
i419
23.7%
e419
23.7%
m393
22.2%
s385
21.8%
n102
 
5.8%
a34
 
1.9%
u16
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1768
100.0%

Most frequent character per category

ValueCountFrequency (%)
i419
23.7%
e419
23.7%
m393
22.2%
s385
21.8%
n102
 
5.8%
a34
 
1.9%
u16
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1768
100.0%

Most frequent character per script

ValueCountFrequency (%)
i419
23.7%
e419
23.7%
m393
22.2%
s385
21.8%
n102
 
5.8%
a34
 
1.9%
u16
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1768
100.0%

Most frequent character per block

ValueCountFrequency (%)
i419
23.7%
e419
23.7%
m393
22.2%
s385
21.8%
n102
 
5.8%
a34
 
1.9%
u16
 
0.9%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)5.9%
Missing4
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean9.692982456
Minimum0
Maximum30
Zeros3
Zeros (%)0.7%
Memory size3.7 KiB
2021-02-22T10:06:32.616336image/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 deviation6.097561672
Coefficient of variation (CV)0.6290697109
Kurtosis-0.07597670086
Mean9.692982456
Median Absolute Deviation (MAD)4
Skewness0.7075420145
Sum4420
Variance37.18025834
MonotocityNot monotonic
2021-02-22T10:06:32.726407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
548
 
10.4%
1038
 
8.3%
430
 
6.5%
728
 
6.1%
2027
 
5.9%
325
 
5.4%
1325
 
5.4%
1525
 
5.4%
624
 
5.2%
224
 
5.2%
Other values (17)162
35.2%
ValueCountFrequency (%)
03
 
0.7%
116
3.5%
224
5.2%
325
5.4%
430
6.5%
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
409 
Freelancer
 
25
Yrittäjä
 
25

Length

Max length24
Median length24
Mean length22.36601307
Min length8

Characters and Unicode

Total characters10266
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 / palkollinen409
88.9%
Freelancer25
 
5.4%
Yrittäjä25
 
5.4%
(Missing)1
 
0.2%
2021-02-22T10:06:32.984919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T10:06:33.071720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
409
32.0%
palkollinen409
32.0%
työntekijä409
32.0%
yrittäjä25
 
2.0%
freelancer25
 
2.0%

Most occurring characters

ValueCountFrequency (%)
n1252
12.2%
l1252
12.2%
e893
 
8.7%
i843
 
8.2%
k818
 
8.0%
818
 
8.0%
t459
 
4.5%
ä459
 
4.5%
j434
 
4.2%
a434
 
4.2%
Other values (10)2604
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8580
83.6%
Space Separator818
 
8.0%
Uppercase Letter459
 
4.5%
Other Punctuation409
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1252
14.6%
l1252
14.6%
e893
10.4%
i843
9.8%
k818
9.5%
t459
 
5.3%
ä459
 
5.3%
j434
 
5.1%
a434
 
5.1%
y409
 
4.8%
Other values (5)1327
15.5%
ValueCountFrequency (%)
T409
89.1%
Y25
 
5.4%
F25
 
5.4%
ValueCountFrequency (%)
818
100.0%
ValueCountFrequency (%)
/409
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9039
88.0%
Common1227
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1252
13.9%
l1252
13.9%
e893
9.9%
i843
9.3%
k818
9.0%
t459
 
5.1%
ä459
 
5.1%
j434
 
4.8%
a434
 
4.8%
T409
 
4.5%
Other values (8)1786
19.8%
ValueCountFrequency (%)
818
66.7%
/409
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9398
91.5%
None868
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1252
13.3%
l1252
13.3%
e893
9.5%
i843
9.0%
k818
8.7%
818
8.7%
t459
 
4.9%
j434
 
4.6%
a434
 
4.6%
T409
 
4.4%
Other values (8)1786
19.0%
ValueCountFrequency (%)
ä459
52.9%
ö409
47.1%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)4.1%
Memory size3.7 KiB
1.0
414 
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 characters1323
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 (%)
1.0414
90.0%
0.823
 
5.0%
0.52
 
0.4%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
4.1%
2021-02-22T10:06:33.272171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T10:06:33.343269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0414
93.9%
0.823
 
5.2%
0.52
 
0.5%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.441
33.3%
0441
33.3%
1414
31.3%
823
 
1.7%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number882
66.7%
Other Punctuation441
33.3%

Most frequent character per category

ValueCountFrequency (%)
0441
50.0%
1414
46.9%
823
 
2.6%
52
 
0.2%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.441
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1323
100.0%

Most frequent character per script

ValueCountFrequency (%)
.441
33.3%
0441
33.3%
1414
31.3%
823
 
1.7%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1323
100.0%

Most frequent character per block

ValueCountFrequency (%)
.441
33.3%
0441
33.3%
1414
31.3%
823
 
1.7%
52
 
0.2%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct239
Distinct (%)53.3%
Missing12
Missing (%)2.6%
Memory size3.7 KiB
Ohjelmistokehittäjä
38 
full-stack
33 
Full-stack
 
23
ohjelmistokehittäjä
 
16
Arkkitehti
 
15
Other values (234)
323 

Length

Max length67
Median length18
Mean length19.10714286
Min length2

Characters and Unicode

Total characters8560
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

Unique193 ?
Unique (%)43.1%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä38
 
8.3%
full-stack33
 
7.2%
Full-stack23
 
5.0%
ohjelmistokehittäjä16
 
3.5%
Arkkitehti15
 
3.3%
Full-stack ohjelmistokehittäjä8
 
1.7%
full-stack ohjelmistokehittäjä7
 
1.5%
arkkitehti6
 
1.3%
Frontend6
 
1.3%
DevOps5
 
1.1%
Other values (229)291
63.3%
(Missing)12
 
2.6%
2021-02-22T10:06:33.647863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack135
 
16.5%
ohjelmistokehittäjä108
 
13.2%
developer54
 
6.6%
arkkitehti34
 
4.1%
32
 
3.9%
lead31
 
3.8%
frontend25
 
3.0%
senior18
 
2.2%
kehittäjä16
 
2.0%
backend14
 
1.7%
Other values (173)353
43.0%

Most occurring characters

ValueCountFrequency (%)
t900
 
10.5%
e782
 
9.1%
l626
 
7.3%
i623
 
7.3%
k474
 
5.5%
o443
 
5.2%
a412
 
4.8%
s408
 
4.8%
378
 
4.4%
h345
 
4.0%
Other values (47)3169
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7435
86.9%
Uppercase Letter433
 
5.1%
Space Separator379
 
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 (%)
t900
12.1%
e782
 
10.5%
l626
 
8.4%
i623
 
8.4%
k474
 
6.4%
o443
 
6.0%
a412
 
5.5%
s408
 
5.5%
h345
 
4.6%
j324
 
4.4%
Other values (16)2098
28.2%
ValueCountFrequency (%)
F98
22.6%
O90
20.8%
S48
11.1%
D39
 
9.0%
A26
 
6.0%
T25
 
5.8%
L18
 
4.2%
C17
 
3.9%
P11
 
2.5%
E11
 
2.5%
Other values (11)50
11.5%
ValueCountFrequency (%)
,51
55.4%
/37
40.2%
&3
 
3.3%
.1
 
1.1%
ValueCountFrequency (%)
378
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 (%)
Latin7868
91.9%
Common692
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
t900
 
11.4%
e782
 
9.9%
l626
 
8.0%
i623
 
7.9%
k474
 
6.0%
o443
 
5.6%
a412
 
5.2%
s408
 
5.2%
h345
 
4.4%
j324
 
4.1%
Other values (37)2531
32.2%
ValueCountFrequency (%)
378
54.6%
-163
23.6%
,51
 
7.4%
/37
 
5.3%
(25
 
3.6%
)25
 
3.6%
+8
 
1.2%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8233
96.2%
None327
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t900
 
10.9%
e782
 
9.5%
l626
 
7.6%
i623
 
7.6%
k474
 
5.8%
o443
 
5.4%
a412
 
5.0%
s408
 
5.0%
378
 
4.6%
h345
 
4.2%
Other values (44)2842
34.5%
ValueCountFrequency (%)
ä311
95.1%
ö15
 
4.6%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.7%
Missing3
Missing (%)0.7%
Memory size720.0 B
Etä
195 
Toimisto
153 
50/50
109 

Length

Max length8
Median length5
Mean length5.150984683
Min length3

Characters and Unicode

Total characters2354
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
42.4%
Toimisto153
33.3%
50/50109
23.7%
(Missing)3
 
0.7%
2021-02-22T10:06:34.015839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-22T10:06:34.094468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä195
42.7%
toimisto153
33.5%
50/50109
23.9%

Most occurring characters

ValueCountFrequency (%)
t348
14.8%
o306
13.0%
i306
13.0%
5218
9.3%
0218
9.3%
E195
8.3%
ä195
8.3%
T153
6.5%
m153
6.5%
s153
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1461
62.1%
Decimal Number436
 
18.5%
Uppercase Letter348
 
14.8%
Other Punctuation109
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
t348
23.8%
o306
20.9%
i306
20.9%
ä195
13.3%
m153
10.5%
s153
10.5%
ValueCountFrequency (%)
5218
50.0%
0218
50.0%
ValueCountFrequency (%)
E195
56.0%
T153
44.0%
ValueCountFrequency (%)
/109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1809
76.8%
Common545
 
23.2%

Most frequent character per script

ValueCountFrequency (%)
t348
19.2%
o306
16.9%
i306
16.9%
E195
10.8%
ä195
10.8%
T153
8.5%
m153
8.5%
s153
8.5%
ValueCountFrequency (%)
5218
40.0%
0218
40.0%
/109
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2159
91.7%
None195
 
8.3%

Most frequent character per block

ValueCountFrequency (%)
t348
16.1%
o306
14.2%
i306
14.2%
5218
10.1%
0218
10.1%
E195
9.0%
T153
7.1%
m153
7.1%
s153
7.1%
/109
 
5.0%
ValueCountFrequency (%)
ä195
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct124
Distinct (%)29.5%
Missing40
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean4705.633333
Minimum1666
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size3.7 KiB
2021-02-22T10:06:34.191152image/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 deviation1408.494121
Coefficient of variation (CV)0.2993208398
Kurtosis7.856277484
Mean4705.633333
Median Absolute Deviation (MAD)787.5
Skewness1.599840128
Sum1976366
Variance1983855.689
MonotocityNot monotonic
2021-02-22T10:06:34.335006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400024
 
5.2%
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 (114)272
59.1%
(Missing)40
 
8.7%
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%
82001
0.2%

Vuositulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct177
Distinct (%)39.4%
Missing11
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean66161.17261
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size3.7 KiB
2021-02-22T10:06:34.476880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35000
Q150000
median60000
Q375000
95-th percentile123000
Maximum300000
Range300000
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation31440.37295
Coefficient of variation (CV)0.4752088228
Kurtosis12.07469529
Mean66161.17261
Median Absolute Deviation (MAD)12500
Skewness2.641010429
Sum29706366.5
Variance988497051.3
MonotocityNot monotonic
2021-02-22T10:06:34.611212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5500018
 
3.9%
7500016
 
3.5%
5000015
 
3.3%
6000014
 
3.0%
8500011
 
2.4%
6500010
 
2.2%
6250010
 
2.2%
800009
 
2.0%
400009
 
2.0%
375008
 
1.7%
Other values (167)329
71.5%
(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 (%)0.4%
Missing15
Missing (%)3.3%
Memory size3.7 KiB
True
308 
False
137 
(Missing)
 
15
ValueCountFrequency (%)
True308
67.0%
False137
29.8%
(Missing)15
 
3.3%
2021-02-22T10:06:34.710259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct72
Distinct (%)67.3%
Missing353
Missing (%)76.7%
Memory size3.7 KiB
Gofore
11 
Vincit
 
6
Futurice
 
5
Fraktio
 
4
Mavericks
 
4
Other values (67)
77 

Length

Max length132
Median length8
Mean length10.62616822
Min length2

Characters and Unicode

Total characters1137
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)55.1%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.4%
Vincit6
 
1.3%
Futurice5
 
1.1%
Fraktio4
 
0.9%
Mavericks4
 
0.9%
Pankki3
 
0.7%
Arado3
 
0.7%
If2
 
0.4%
Compile Oy2
 
0.4%
Qvik2
 
0.4%
Other values (62)65
 
14.1%
(Missing)353
76.7%
2021-02-22T10:06:34.953900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gofore13
 
7.6%
oy12
 
7.1%
mavericks6
 
3.5%
vincit6
 
3.5%
oyj5
 
2.9%
futurice5
 
2.9%
siili4
 
2.4%
fraktio4
 
2.4%
konsulttitalo3
 
1.8%
if3
 
1.8%
Other values (92)109
64.1%

Most occurring characters

ValueCountFrequency (%)
i115
 
10.1%
a87
 
7.7%
o86
 
7.6%
e81
 
7.1%
t78
 
6.9%
66
 
5.8%
r61
 
5.4%
n53
 
4.7%
l46
 
4.0%
u44
 
3.9%
Other values (44)420
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter920
80.9%
Uppercase Letter145
 
12.8%
Space Separator66
 
5.8%
Other Punctuation3
 
0.3%
Dash Punctuation3
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
i115
12.5%
a87
 
9.5%
o86
 
9.3%
e81
 
8.8%
t78
 
8.5%
r61
 
6.6%
n53
 
5.8%
l46
 
5.0%
u44
 
4.8%
k43
 
4.7%
Other values (16)226
24.6%
ValueCountFrequency (%)
O17
 
11.7%
G14
 
9.7%
S14
 
9.7%
V12
 
8.3%
F10
 
6.9%
K8
 
5.5%
C7
 
4.8%
A7
 
4.8%
M7
 
4.8%
P6
 
4.1%
Other values (15)43
29.7%
ValueCountFrequency (%)
66
100.0%
ValueCountFrequency (%)
.3
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1065
93.7%
Common72
 
6.3%

Most frequent character per script

ValueCountFrequency (%)
i115
 
10.8%
a87
 
8.2%
o86
 
8.1%
e81
 
7.6%
t78
 
7.3%
r61
 
5.7%
n53
 
5.0%
l46
 
4.3%
u44
 
4.1%
k43
 
4.0%
Other values (41)371
34.8%
ValueCountFrequency (%)
66
91.7%
.3
 
4.2%
-3
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1125
98.9%
None12
 
1.1%

Most frequent character per block

ValueCountFrequency (%)
i115
 
10.2%
a87
 
7.7%
o86
 
7.6%
e81
 
7.2%
t78
 
6.9%
66
 
5.9%
r61
 
5.4%
n53
 
4.7%
l46
 
4.1%
u44
 
3.9%
Other values (42)408
36.3%
ValueCountFrequency (%)
ä11
91.7%
ö1
 
8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct34
Distinct (%)97.1%
Missing425
Missing (%)92.4%
Memory size3.7 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.
 
1
Halpaa freelancer laskutusta oman tuotekehityksen sivussa
 
1
hyvä kysely
 
1
palkan lisäksi kompensaatioon kuuluu varsin runsas ja suomen it-alalla uniikki etupaketti. pelkkä palkka ei välttämättä ole kilpailukykyinen, mutta koko kompensaatio yleisesti työstäni on ehdottomasti kilpailukykyinen.
 
1
Other values (29)
29 

Length

Max length286
Median length71
Mean length93
Min length7

Characters and Unicode

Total characters3255
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

Unique33 ?
Unique (%)94.3%

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%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
Halpaa freelancer laskutusta oman tuotekehityksen sivussa1
 
0.2%
hyvä kysely1
 
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%
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%
+ merkittävä optiopaketti1
 
0.2%
Ennen koronaa oli osittainen etätyö, koronan jälkeen 100%1
 
0.2%
Startup1
 
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%
Other values (24)24
 
5.2%
(Missing)425
92.4%
2021-02-22T10:06:35.233240image/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
 
1.9%
mutta7
 
1.7%
ole6
 
1.5%
firman4
 
1.0%
palkan4
 
1.0%
ihan4
 
1.0%
nyt4
 
1.0%
Other values (290)345
83.7%

Most occurring characters

ValueCountFrequency (%)
380
11.7%
a344
10.6%
i277
 
8.5%
t254
 
7.8%
n220
 
6.8%
s211
 
6.5%
e208
 
6.4%
k189
 
5.8%
l164
 
5.0%
o149
 
4.6%
Other values (45)859
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2707
83.2%
Space Separator380
 
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
12.7%
i277
10.2%
t254
9.4%
n220
 
8.1%
s211
 
7.8%
e208
 
7.7%
k189
 
7.0%
l164
 
6.1%
o149
 
5.5%
u120
 
4.4%
Other values (14)571
21.1%
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 (%)
380
100.0%
ValueCountFrequency (%)
(3
100.0%
ValueCountFrequency (%)
)3
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2756
84.7%
Common499
 
15.3%

Most frequent character per script

ValueCountFrequency (%)
a344
12.5%
i277
10.1%
t254
9.2%
n220
 
8.0%
s211
 
7.7%
e208
 
7.5%
k189
 
6.9%
l164
 
6.0%
o149
 
5.4%
u120
 
4.4%
Other values (27)620
22.5%
ValueCountFrequency (%)
380
76.2%
.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 (%)
ASCII3113
95.6%
None142
 
4.4%

Most frequent character per block

ValueCountFrequency (%)
380
12.2%
a344
11.1%
i277
 
8.9%
t254
 
8.2%
n220
 
7.1%
s211
 
6.8%
e208
 
6.7%
k189
 
6.1%
l164
 
5.3%
o149
 
4.8%
Other values (43)717
23.0%
ValueCountFrequency (%)
ä118
83.1%
ö24
 
16.9%

Kk-tulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct177
Distinct (%)39.4%
Missing11
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean5513.43105
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size3.7 KiB
2021-02-22T10:06:35.366732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2916.666667
Q14166.666667
median5000
Q36250
95-th percentile10250
Maximum25000
Range25000
Interquartile range (IQR)2083.333333

Descriptive statistics

Standard deviation2620.031079
Coefficient of variation (CV)0.4752088228
Kurtosis12.07469529
Mean5513.43105
Median Absolute Deviation (MAD)1041.666667
Skewness2.641010429
Sum2475530.542
Variance6864562.856
MonotocityNot monotonic
2021-02-22T10:06:35.640320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4583.33333318
 
3.9%
625016
 
3.5%
4166.66666715
 
3.3%
500014
 
3.0%
7083.33333311
 
2.4%
5208.33333310
 
2.2%
5416.66666710
 
2.2%
6666.6666679
 
2.0%
3333.3333339
 
2.0%
31258
 
1.7%
Other values (167)329
71.5%
(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

2021-02-22T10:06:27.838729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:27.955863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.069956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.184353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.294166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.407520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.527876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.648667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.765530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.878442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:28.998630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:29.232104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:29.349572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:29.461819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:29.580412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:29.700707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:29.818740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:29.925764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:30.038972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-22T10:06:30.154261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-22T10:06:35.764350image/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.
2021-02-22T10:06:35.919432image/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.
2021-02-22T10:06:36.074188image/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.
2021-02-22T10:06:36.237500image/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

2021-02-22T10:06:30.379855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-22T10:06:30.682098image/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.
2021-02-22T10:06:30.980798image/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.
2021-02-22T10:06:31.253959image/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
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
4572021-02-22 10:36:42.074PK-Seutu48mies20.0Yrittäjä1.0CTOToimisto4000.050000.0FalseNaNhyvä kysely4166.666667
4582021-02-22 11:03:33.749Tampere56mies10.0Työntekijä / palkollinen1.0OhjelmistokehittäjäToimisto3858.048225.0TrueWakeoneNaN4018.750000
4592021-02-22 11:05:29.788PK-Seutu56nainen12.0Työntekijä / palkollinen1.0Myynnistä vastaava50/508200.0100000.0TrueNaNNaN8333.333333