Overview

Dataset statistics

Number of variables15
Number of observations490
Missing cells997
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.0 KiB
Average record size in memory96.2 B

Variable types

DateTime1
Categorical8
Numeric5
Boolean1

Warnings

Rooli has a high cardinality: 257 distinct values High cardinality
Työpaikka has a high cardinality: 73 distinct values High cardinality
Vuositulot is highly correlated with Kk-tulotHigh correlation
Kk-tulot is highly correlated with VuositulotHigh correlation
Vapaa sana is highly correlated with Kilpailukykyinen and 1 other fieldsHigh correlation
Kilpailukykyinen is highly correlated with Vapaa sanaHigh correlation
Työpaikka is highly correlated with Vapaa sanaHigh correlation
Kaupunki has 5 (1.0%) missing values Missing
Sukupuoli has 35 (7.1%) missing values Missing
Työaika has 19 (3.9%) missing values Missing
Rooli has 13 (2.7%) missing values Missing
Kuukausipalkka has 42 (8.6%) missing values Missing
Vuositulot has 12 (2.4%) missing values Missing
Kilpailukykyinen has 15 (3.1%) missing values Missing
Työpaikka has 381 (77.8%) missing values Missing
Vapaa sana has 452 (92.2%) missing values Missing
Kk-tulot has 12 (2.4%) missing values Missing
Vapaa sana is uniformly distributed Uniform
Timestamp has unique values Unique

Reproduction

Analysis started2021-02-25 13:08:56.167864
Analysis finished2021-02-25 13:09:02.682763
Duration6.51 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Timestamp
Date

UNIQUE

Distinct490
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2021-02-15 11:57:08.316000
Maximum2021-02-25 14:10:32.597000
2021-02-25T13:09:02.783896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:09:02.983114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

MISSING

Distinct28
Distinct (%)5.8%
Missing5
Missing (%)1.0%
Memory size1.9 KiB
PK-Seutu
246 
Tampere
113 
Turku
47 
Oulu
25 
Jyväskylä
 
18
Other values (23)
36 

Length

Max length15
Median length8
Mean length7.237113402
Min length2

Characters and Unicode

Total characters3510
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-Seutu246
50.2%
Tampere113
23.1%
Turku47
 
9.6%
Oulu25
 
5.1%
Jyväskylä18
 
3.7%
Kuopio7
 
1.4%
Lontoo2
 
0.4%
Vaasa2
 
0.4%
Tallinna2
 
0.4%
Pori2
 
0.4%
Other values (18)21
 
4.3%
(Missing)5
 
1.0%
2021-02-25T13:09:03.484449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu246
50.3%
tampere113
23.1%
turku47
 
9.6%
oulu25
 
5.1%
jyväskylä18
 
3.7%
kuopio7
 
1.4%
lahti2
 
0.4%
vaasa2
 
0.4%
lontoo2
 
0.4%
tallinna2
 
0.4%
Other values (22)25
 
5.1%

Most occurring characters

ValueCountFrequency (%)
u650
18.5%
e484
13.8%
K256
 
7.3%
t253
 
7.2%
P249
 
7.1%
-248
 
7.1%
S248
 
7.1%
r166
 
4.7%
T162
 
4.6%
a140
 
4.0%
Other values (30)654
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2274
64.8%
Uppercase Letter983
28.0%
Dash Punctuation248
 
7.1%
Space Separator4
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
u650
28.6%
e484
21.3%
t253
 
11.1%
r166
 
7.3%
a140
 
6.2%
p121
 
5.3%
m119
 
5.2%
k70
 
3.1%
l56
 
2.5%
ä44
 
1.9%
Other values (10)171
 
7.5%
ValueCountFrequency (%)
K256
26.0%
P249
25.3%
S248
25.2%
T162
16.5%
O25
 
2.5%
J19
 
1.9%
L5
 
0.5%
E4
 
0.4%
V3
 
0.3%
H3
 
0.3%
Other values (7)9
 
0.9%
ValueCountFrequency (%)
-248
100.0%
ValueCountFrequency (%)
4
100.0%
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3257
92.8%
Common253
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
u650
20.0%
e484
14.9%
K256
 
7.9%
t253
 
7.8%
P249
 
7.6%
S248
 
7.6%
r166
 
5.1%
T162
 
5.0%
a140
 
4.3%
p121
 
3.7%
Other values (27)528
16.2%
ValueCountFrequency (%)
-248
98.0%
4
 
1.6%
,1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3466
98.7%
None44
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
u650
18.8%
e484
14.0%
K256
 
7.4%
t253
 
7.3%
P249
 
7.2%
-248
 
7.2%
S248
 
7.2%
r166
 
4.8%
T162
 
4.7%
a140
 
4.0%
Other values (29)610
17.6%
ValueCountFrequency (%)
ä44
100.0%

Ikä
Real number (ℝ≥0)

Distinct7
Distinct (%)1.4%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean33.72895277
Minimum23
Maximum53
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-25T13:09:03.622049image/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 deviation6.063148784
Coefficient of variation (CV)0.1797609557
Kurtosis0.2165391156
Mean33.72895277
Median Absolute Deviation (MAD)5
Skewness0.4761321633
Sum16426
Variance36.76177318
MonotocityNot monotonic
2021-02-25T13:09:03.748099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
33163
33.3%
28121
24.7%
38105
21.4%
4353
 
10.8%
2332
 
6.5%
487
 
1.4%
536
 
1.2%
(Missing)3
 
0.6%
ValueCountFrequency (%)
2332
 
6.5%
28121
24.7%
33163
33.3%
38105
21.4%
4353
 
10.8%
ValueCountFrequency (%)
536
 
1.2%
487
 
1.4%
4353
 
10.8%
38105
21.4%
33163
33.3%

Sukupuoli
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing35
Missing (%)7.1%
Memory size750.0 B
mies
410 
nainen
 
36
muu
 
9

Length

Max length6
Median length4
Mean length4.138461538
Min length3

Characters and Unicode

Total characters1883
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 (%)
mies410
83.7%
nainen36
 
7.3%
muu9
 
1.8%
(Missing)35
 
7.1%
2021-02-25T13:09:04.098599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T13:09:04.215351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mies410
90.1%
nainen36
 
7.9%
muu9
 
2.0%

Most occurring characters

ValueCountFrequency (%)
i446
23.7%
e446
23.7%
m419
22.3%
s410
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1883
100.0%

Most frequent character per category

ValueCountFrequency (%)
i446
23.7%
e446
23.7%
m419
22.3%
s410
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1883
100.0%

Most frequent character per script

ValueCountFrequency (%)
i446
23.7%
e446
23.7%
m419
22.3%
s410
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1883
100.0%

Most frequent character per block

ValueCountFrequency (%)
i446
23.7%
e446
23.7%
m419
22.3%
s410
21.8%
n108
 
5.7%
a36
 
1.9%
u18
 
1.0%

Työkokemus
Real number (ℝ≥0)

Distinct27
Distinct (%)5.6%
Missing4
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean9.489711934
Minimum0
Maximum30
Zeros4
Zeros (%)0.8%
Memory size4.0 KiB
2021-02-25T13:09:04.333514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8.5
Q313
95-th percentile21
Maximum30
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.04543767
Coefficient of variation (CV)0.6370517579
Kurtosis-0.006739826318
Mean9.489711934
Median Absolute Deviation (MAD)4.5
Skewness0.7403482802
Sum4612
Variance36.54731662
MonotocityNot monotonic
2021-02-25T13:09:04.489870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
554
 
11.0%
1039
 
8.0%
431
 
6.3%
730
 
6.1%
228
 
5.7%
1528
 
5.7%
327
 
5.5%
2027
 
5.5%
627
 
5.5%
825
 
5.1%
Other values (17)170
34.7%
ValueCountFrequency (%)
04
 
0.8%
117
3.5%
228
5.7%
327
5.5%
431
6.3%
ValueCountFrequency (%)
302
 
0.4%
256
1.2%
243
0.6%
234
0.8%
225
1.0%
Distinct3
Distinct (%)0.6%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
Työntekijä / palkollinen
437 
Freelancer
 
26
Yrittäjä
 
26

Length

Max length24
Median length24
Mean length22.40490798
Min length8

Characters and Unicode

Total characters10956
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 / palkollinen437
89.2%
Freelancer26
 
5.3%
Yrittäjä26
 
5.3%
(Missing)1
 
0.2%
2021-02-25T13:09:04.818375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T13:09:04.927419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
437
32.1%
työntekijä437
32.1%
palkollinen437
32.1%
freelancer26
 
1.9%
yrittäjä26
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n1337
12.2%
l1337
12.2%
e952
 
8.7%
i900
 
8.2%
k874
 
8.0%
874
 
8.0%
t489
 
4.5%
ä489
 
4.5%
j463
 
4.2%
a463
 
4.2%
Other values (10)2778
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9156
83.6%
Space Separator874
 
8.0%
Uppercase Letter489
 
4.5%
Other Punctuation437
 
4.0%

Most frequent character per category

ValueCountFrequency (%)
n1337
14.6%
l1337
14.6%
e952
10.4%
i900
9.8%
k874
9.5%
t489
 
5.3%
ä489
 
5.3%
j463
 
5.1%
a463
 
5.1%
y437
 
4.8%
Other values (5)1415
15.5%
ValueCountFrequency (%)
T437
89.4%
Y26
 
5.3%
F26
 
5.3%
ValueCountFrequency (%)
874
100.0%
ValueCountFrequency (%)
/437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9645
88.0%
Common1311
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
n1337
13.9%
l1337
13.9%
e952
9.9%
i900
9.3%
k874
9.1%
t489
 
5.1%
ä489
 
5.1%
j463
 
4.8%
a463
 
4.8%
T437
 
4.5%
Other values (8)1904
19.7%
ValueCountFrequency (%)
874
66.7%
/437
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10030
91.5%
None926
 
8.5%

Most frequent character per block

ValueCountFrequency (%)
n1337
13.3%
l1337
13.3%
e952
9.5%
i900
9.0%
k874
8.7%
874
8.7%
t489
 
4.9%
j463
 
4.6%
a463
 
4.6%
T437
 
4.4%
Other values (8)1904
19.0%
ValueCountFrequency (%)
ä489
52.8%
ö437
47.2%

Työaika
Categorical

MISSING

Distinct5
Distinct (%)1.1%
Missing19
Missing (%)3.9%
Memory size4.0 KiB
1.0
442 
0.8
 
23
0.5
 
4
0.7
 
1
0.6
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1413
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 (%)
1.0442
90.2%
0.823
 
4.7%
0.54
 
0.8%
0.71
 
0.2%
0.61
 
0.2%
(Missing)19
 
3.9%
2021-02-25T13:09:05.201149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T13:09:05.302398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0442
93.8%
0.823
 
4.9%
0.54
 
0.8%
0.71
 
0.2%
0.61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.471
33.3%
0471
33.3%
1442
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number942
66.7%
Other Punctuation471
33.3%

Most frequent character per category

ValueCountFrequency (%)
0471
50.0%
1442
46.9%
823
 
2.4%
54
 
0.4%
71
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
.471
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1413
100.0%

Most frequent character per script

ValueCountFrequency (%)
.471
33.3%
0471
33.3%
1442
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1413
100.0%

Most frequent character per block

ValueCountFrequency (%)
.471
33.3%
0471
33.3%
1442
31.3%
823
 
1.6%
54
 
0.3%
71
 
0.1%
61
 
0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct257
Distinct (%)53.9%
Missing13
Missing (%)2.7%
Memory size4.0 KiB
Ohjelmistokehittäjä
41 
full-stack
35 
Full-stack
 
24
ohjelmistokehittäjä
 
17
Arkkitehti
 
15
Other values (252)
345 

Length

Max length67
Median length18
Mean length19.27044025
Min length2

Characters and Unicode

Total characters9192
Distinct characters58
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)44.0%

Sample

1st rowArkkitehti
2nd rowfull-stack
3rd rowFull-stack ohjelmistokehittäjä
4th rowweb-arkkitehti
5th rowOhjelmistokehittäjä
ValueCountFrequency (%)
Ohjelmistokehittäjä41
 
8.4%
full-stack35
 
7.1%
Full-stack24
 
4.9%
ohjelmistokehittäjä17
 
3.5%
Arkkitehti15
 
3.1%
Full-stack ohjelmistokehittäjä8
 
1.6%
full-stack ohjelmistokehittäjä7
 
1.4%
Frontend6
 
1.2%
arkkitehti6
 
1.2%
CTO5
 
1.0%
Other values (247)313
63.9%
(Missing)13
 
2.7%
2021-02-25T13:09:05.735416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
full-stack141
 
15.9%
ohjelmistokehittäjä114
 
12.9%
developer61
 
6.9%
35
 
4.0%
arkkitehti35
 
4.0%
lead33
 
3.7%
frontend27
 
3.0%
senior21
 
2.4%
kehittäjä16
 
1.8%
backend16
 
1.8%
Other values (194)387
43.7%

Most occurring characters

ValueCountFrequency (%)
t956
 
10.4%
e849
 
9.2%
l670
 
7.3%
i666
 
7.2%
k506
 
5.5%
o480
 
5.2%
s438
 
4.8%
a438
 
4.8%
415
 
4.5%
h367
 
4.0%
Other values (48)3407
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7976
86.8%
Uppercase Letter466
 
5.1%
Space Separator416
 
4.5%
Dash Punctuation172
 
1.9%
Other Punctuation99
 
1.1%
Open Punctuation27
 
0.3%
Close Punctuation27
 
0.3%
Math Symbol8
 
0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t956
12.0%
e849
 
10.6%
l670
 
8.4%
i666
 
8.4%
k506
 
6.3%
o480
 
6.0%
s438
 
5.5%
a438
 
5.5%
h367
 
4.6%
j346
 
4.3%
Other values (16)2260
28.3%
ValueCountFrequency (%)
F104
22.3%
O96
20.6%
S52
11.2%
D42
9.0%
T28
 
6.0%
A27
 
5.8%
L21
 
4.5%
C18
 
3.9%
E12
 
2.6%
P11
 
2.4%
Other values (11)55
11.8%
ValueCountFrequency (%)
,53
53.5%
/42
42.4%
&3
 
3.0%
.1
 
1.0%
ValueCountFrequency (%)
415
99.8%
 1
 
0.2%
ValueCountFrequency (%)
-172
100.0%
ValueCountFrequency (%)
(27
100.0%
ValueCountFrequency (%)
)27
100.0%
ValueCountFrequency (%)
+8
100.0%
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8442
91.8%
Common750
 
8.2%

Most frequent character per script

ValueCountFrequency (%)
t956
 
11.3%
e849
 
10.1%
l670
 
7.9%
i666
 
7.9%
k506
 
6.0%
o480
 
5.7%
s438
 
5.2%
a438
 
5.2%
h367
 
4.3%
j346
 
4.1%
Other values (37)2726
32.3%
ValueCountFrequency (%)
415
55.3%
-172
22.9%
,53
 
7.1%
/42
 
5.6%
(27
 
3.6%
)27
 
3.6%
+8
 
1.1%
&3
 
0.4%
.1
 
0.1%
 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8842
96.2%
None350
 
3.8%

Most frequent character per block

ValueCountFrequency (%)
t956
 
10.8%
e849
 
9.6%
l670
 
7.6%
i666
 
7.5%
k506
 
5.7%
o480
 
5.4%
s438
 
5.0%
a438
 
5.0%
415
 
4.7%
h367
 
4.2%
Other values (45)3057
34.6%
ValueCountFrequency (%)
ä333
95.1%
ö16
 
4.6%
 1
 
0.3%

Etä
Categorical

Distinct3
Distinct (%)0.6%
Missing3
Missing (%)0.6%
Memory size750.0 B
Etä
204 
Toimisto
169 
50/50
114 

Length

Max length8
Median length5
Mean length5.203285421
Min length3

Characters and Unicode

Total characters2534
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50/50
2nd rowEtä
3rd rowEtä
4th rowEtä
5th rowEtä
ValueCountFrequency (%)
Etä204
41.6%
Toimisto169
34.5%
50/50114
23.3%
(Missing)3
 
0.6%
2021-02-25T13:09:06.185487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-25T13:09:06.291468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
etä204
41.9%
toimisto169
34.7%
50/50114
23.4%

Most occurring characters

ValueCountFrequency (%)
t373
14.7%
o338
13.3%
i338
13.3%
5228
9.0%
0228
9.0%
E204
8.1%
ä204
8.1%
T169
6.7%
m169
6.7%
s169
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1591
62.8%
Decimal Number456
 
18.0%
Uppercase Letter373
 
14.7%
Other Punctuation114
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
t373
23.4%
o338
21.2%
i338
21.2%
ä204
12.8%
m169
10.6%
s169
10.6%
ValueCountFrequency (%)
5228
50.0%
0228
50.0%
ValueCountFrequency (%)
E204
54.7%
T169
45.3%
ValueCountFrequency (%)
/114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1964
77.5%
Common570
 
22.5%

Most frequent character per script

ValueCountFrequency (%)
t373
19.0%
o338
17.2%
i338
17.2%
E204
10.4%
ä204
10.4%
T169
8.6%
m169
8.6%
s169
8.6%
ValueCountFrequency (%)
5228
40.0%
0228
40.0%
/114
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2330
91.9%
None204
 
8.1%

Most frequent character per block

ValueCountFrequency (%)
t373
16.0%
o338
14.5%
i338
14.5%
5228
9.8%
0228
9.8%
E204
8.8%
T169
7.3%
m169
7.3%
s169
7.3%
/114
 
4.9%
ValueCountFrequency (%)
ä204
100.0%

Kuukausipalkka
Real number (ℝ≥0)

MISSING

Distinct129
Distinct (%)28.8%
Missing42
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean4674.292411
Minimum1081
Maximum15000
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2021-02-25T13:09:06.425893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1081
5-th percentile2780.5
Q13800
median4500
Q35500
95-th percentile7000
Maximum15000
Range13919
Interquartile range (IQR)1700

Descriptive statistics

Standard deviation1449.72197
Coefficient of variation (CV)0.310147899
Kurtosis7.869748394
Mean4674.292411
Median Absolute Deviation (MAD)800
Skewness1.624918113
Sum2094083
Variance2101693.791
MonotocityNot monotonic
2021-02-25T13:09:06.603090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400026
 
5.3%
450024
 
4.9%
550017
 
3.5%
600017
 
3.5%
500017
 
3.5%
480012
 
2.4%
300012
 
2.4%
420012
 
2.4%
430012
 
2.4%
380011
 
2.2%
Other values (119)288
58.8%
(Missing)42
 
8.6%
ValueCountFrequency (%)
10811
0.2%
11001
0.2%
16661
0.2%
17001
0.2%
18001
0.2%
ValueCountFrequency (%)
150001
0.2%
120002
0.4%
93001
0.2%
85002
0.4%
82001
0.2%

Vuositulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct184
Distinct (%)38.5%
Missing12
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean65478.4864
Minimum0
Maximum300000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-25T13:09:06.799051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33577.5
Q149093.75
median58750
Q375000
95-th percentile120750
Maximum300000
Range300000
Interquartile range (IQR)25906.25

Descriptive statistics

Standard deviation31786.56498
Coefficient of variation (CV)0.4854505155
Kurtosis11.98196938
Mean65478.4864
Median Absolute Deviation (MAD)11812.5
Skewness2.666113866
Sum31298716.5
Variance1010385713
MonotocityNot monotonic
2021-02-25T13:09:07.007920image/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
 
2.2%
6250010
 
2.0%
6500010
 
2.0%
3750010
 
2.0%
7000010
 
2.0%
475009
 
1.8%
Other values (174)352
71.8%
(Missing)12
 
2.4%
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 size4.0 KiB
True
323 
False
152 
(Missing)
 
15
ValueCountFrequency (%)
True323
65.9%
False152
31.0%
(Missing)15
 
3.1%
2021-02-25T13:09:07.147234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)67.0%
Missing381
Missing (%)77.8%
Memory size4.0 KiB
Gofore
11 
Vincit
 
7
Futurice
 
5
Fraktio
 
4
Mavericks
 
4
Other values (68)
78 

Length

Max length132
Median length8
Mean length10.81651376
Min length2

Characters and Unicode

Total characters1179
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)55.0%

Sample

1st rowQuestrade
2nd rowDigia Oyj
3rd rowGofore
4th rowOura Health
5th rowWirepas
ValueCountFrequency (%)
Gofore11
 
2.2%
Vincit7
 
1.4%
Futurice5
 
1.0%
Fraktio4
 
0.8%
Mavericks4
 
0.8%
Pankki3
 
0.6%
Arado3
 
0.6%
Siili2
 
0.4%
Compile Oy2
 
0.4%
Gofore Oyj2
 
0.4%
Other values (63)66
 
13.5%
(Missing)381
77.8%
2021-02-25T13:09:07.477326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oy13
 
7.4%
gofore13
 
7.4%
vincit7
 
4.0%
mavericks6
 
3.4%
futurice5
 
2.9%
oyj5
 
2.9%
fraktio4
 
2.3%
siili4
 
2.3%
konsulttitalo3
 
1.7%
arado3
 
1.7%
Other values (95)112
64.0%

Most occurring characters

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

Most occurring categories

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

Most frequent character per category

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

Most occurring scripts

ValueCountFrequency (%)
Latin1104
93.6%
Common75
 
6.4%

Most frequent character per script

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

Most occurring blocks

ValueCountFrequency (%)
ASCII1167
99.0%
None12
 
1.0%

Most frequent character per block

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

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct37
Distinct (%)97.4%
Missing452
Missing (%)92.2%
Memory size4.0 KiB
palkan lisänä lounas- ja virkistysetu
 
2
Vuositulot pitää sisällään myös sivutoimisena tehtyä pientä laskutusta.
 
1
+ merkittävä optiopaketti
 
1
Kokemusta kokonaisuudessaan 7v, mutta siitä reilut kaksi vuotta lasten kanssa kotona koodaamatta.
 
1
Opiskelija
 
1
Other values (32)
32 

Length

Max length286
Median length73
Mean length95.57894737
Min length7

Characters and Unicode

Total characters3632
Distinct characters56
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)94.7%

Sample

1st rowKuukausipalkkaan tulossa ihan juuri firman laajuinen pieni (muistaakseni 50 e) yleiskorotus + palkka nousee ainakin 2800 e/kk, kunhan valmistuisi.
2nd rowTyöskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.
3rd rowpalkan lisäksi kompensaatioon kuuluu varsin runsas ja suomen it-alalla uniikki etupaketti. pelkkä palkka ei välttämättä ole kilpailukykyinen, mutta koko kompensaatio yleisesti työstäni on ehdottomasti kilpailukykyinen.
4th rowRahapalkan päälle tulee vielä kohtuullinen optiopotti, mutta se toki on lähinnä arpalippu
5th rowOsittain laskutukseen perustuva palkka joten vaihtelee.
ValueCountFrequency (%)
palkan lisänä lounas- ja virkistysetu2
 
0.4%
Vuositulot pitää sisällään myös sivutoimisena tehtyä pientä laskutusta.1
 
0.2%
+ merkittävä optiopaketti1
 
0.2%
Kokemusta kokonaisuudessaan 7v, mutta siitä reilut kaksi vuotta lasten kanssa kotona koodaamatta.1
 
0.2%
Opiskelija1
 
0.2%
Olen osakkaana startupissa, 5% osuus on osa kokonaiskompensaatiota. Edellisessä työssä bruttopalkka oli 6000 euroa kuukaudessa.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%
Johtajasopimus, ei työaikaa1
 
0.2%
Palkka riippuu osittain firman tuloksesta, joten vaikea sanoa tarkkaan.1
 
0.2%
Osittain laskutukseen perustuva palkka joten vaihtelee.1
 
0.2%
Other values (27)27
 
5.5%
(Missing)452
92.2%
2021-02-25T13:09:07.877522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ja11
 
2.4%
ei11
 
2.4%
palkka10
 
2.2%
on10
 
2.2%
mutta9
 
2.0%
ole6
 
1.3%
nyt5
 
1.1%
joten4
 
0.9%
palkan4
 
0.9%
olen4
 
0.9%
Other values (321)383
83.8%

Most occurring characters

ValueCountFrequency (%)
422
11.6%
a383
 
10.5%
i311
 
8.6%
t284
 
7.8%
n245
 
6.7%
s237
 
6.5%
e228
 
6.3%
k206
 
5.7%
l183
 
5.0%
o169
 
4.7%
Other values (46)964
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3025
83.3%
Space Separator422
 
11.6%
Other Punctuation85
 
2.3%
Uppercase Letter53
 
1.5%
Decimal Number28
 
0.8%
Dash Punctuation8
 
0.2%
Open Punctuation4
 
0.1%
Close Punctuation4
 
0.1%
Math Symbol3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a383
12.7%
i311
10.3%
t284
9.4%
n245
 
8.1%
s237
 
7.8%
e228
 
7.5%
k206
 
6.8%
l183
 
6.0%
o169
 
5.6%
u140
 
4.6%
Other values (14)639
21.1%
ValueCountFrequency (%)
P9
17.0%
T7
13.2%
O7
13.2%
E6
11.3%
V6
11.3%
K5
9.4%
S4
7.5%
I2
 
3.8%
J2
 
3.8%
H2
 
3.8%
Other values (3)3
 
5.7%
ValueCountFrequency (%)
015
53.6%
13
 
10.7%
52
 
7.1%
22
 
7.1%
82
 
7.1%
62
 
7.1%
31
 
3.6%
71
 
3.6%
ValueCountFrequency (%)
.44
51.8%
,28
32.9%
/5
 
5.9%
%4
 
4.7%
"2
 
2.4%
?2
 
2.4%
ValueCountFrequency (%)
422
100.0%
ValueCountFrequency (%)
(4
100.0%
ValueCountFrequency (%)
)4
100.0%
ValueCountFrequency (%)
+3
100.0%
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3078
84.7%
Common554
 
15.3%

Most frequent character per script

ValueCountFrequency (%)
a383
12.4%
i311
10.1%
t284
9.2%
n245
 
8.0%
s237
 
7.7%
e228
 
7.4%
k206
 
6.7%
l183
 
5.9%
o169
 
5.5%
u140
 
4.5%
Other values (27)692
22.5%
ValueCountFrequency (%)
422
76.2%
.44
 
7.9%
,28
 
5.1%
015
 
2.7%
-8
 
1.4%
/5
 
0.9%
(4
 
0.7%
)4
 
0.7%
%4
 
0.7%
+3
 
0.5%
Other values (9)17
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3479
95.8%
None153
 
4.2%

Most frequent character per block

ValueCountFrequency (%)
422
12.1%
a383
11.0%
i311
 
8.9%
t284
 
8.2%
n245
 
7.0%
s237
 
6.8%
e228
 
6.6%
k206
 
5.9%
l183
 
5.3%
o169
 
4.9%
Other values (44)811
23.3%
ValueCountFrequency (%)
ä126
82.4%
ö27
 
17.6%

Kk-tulot
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct184
Distinct (%)38.5%
Missing12
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean5456.540533
Minimum0
Maximum25000
Zeros2
Zeros (%)0.4%
Memory size4.0 KiB
2021-02-25T13:09:08.076188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2798.125
Q14091.145833
median4895.833333
Q36250
95-th percentile10062.5
Maximum25000
Range25000
Interquartile range (IQR)2158.854167

Descriptive statistics

Standard deviation2648.880415
Coefficient of variation (CV)0.4854505155
Kurtosis11.98196938
Mean5456.540533
Median Absolute Deviation (MAD)984.375
Skewness2.666113866
Sum2608226.375
Variance7016567.453
MonotocityNot monotonic
2021-02-25T13:09:08.404314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4166.66666718
 
3.7%
4583.33333318
 
3.7%
625016
 
3.3%
500014
 
2.9%
7083.33333311
 
2.2%
5208.33333310
 
2.0%
312510
 
2.0%
5416.66666710
 
2.0%
5833.33333310
 
2.0%
6666.6666679
 
1.8%
Other values (174)352
71.8%
(Missing)12
 
2.4%
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

2021-02-25T13:08:57.400469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:57.560656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:57.717700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:57.877550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:58.033092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:58.195119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:58.361872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:58.525007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:58.706226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:58.867285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:59.033969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:59.302885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:59.463192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:59.623466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:59.787786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:08:59.951288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:09:00.114894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:09:00.260415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:09:00.411315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-25T13:09:00.563349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-25T13:09:08.577940image/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.
2021-02-25T13:09:08.788747image/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.
2021-02-25T13:09:08.987930image/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.
2021-02-25T13:09:09.189584image/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

2021-02-25T13:09:00.871486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-25T13:09:01.492461image/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.
2021-02-25T13:09:02.132042image/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.
2021-02-25T13:09:02.502689image/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
4802021-02-24 15:19:46.521Mikkeli33mies7.0Työntekijä / palkollinen1.0Full-stackEtä3000.037500.0FalseNaNNaN3125.000000
4812021-02-24 16:09:32.939PK-Seutu33mies2.0Työntekijä / palkollinen1.0OhjelmistokehittäjäEtä3500.043750.0TrueNaNNaN3645.833333
4822021-02-24 17:28:57.097Kuopio23mies2.0Työntekijä / palkollinen1.0frontend50/502900.036000.0FalseNaNNaN3000.000000
4832021-02-24 23:49:22.242Tampere28mies2.0Työntekijä / palkollinen1.0Ohjelmistokehittäjä (frontend)Etä2860.035850.0FalseNaNNaN2987.500000
4842021-02-25 09:34:48.368Kuopio33mies6.0Työntekijä / palkollinen1.0Ohjelmistokehittäjä, Tech LeadToimisto4500.056250.0TrueNaNNaN4687.500000
4852021-02-25 10:53:41.881PK-Seutu28mies6.0Työntekijä / palkollinen1.0full-stack50/505100.064000.0FalseNaNNaN5333.333333
4862021-02-25 11:09:16.999PK-Seutu28mies3.0Työntekijä / palkollinen1.0Fullstack ja pientä devops tunkkiaToimisto3500.044000.0FalseNaNNaN3666.666667
4872021-02-25 11:10:42.322NaN33NaN15.0Työntekijä / palkollinen1.0NaNToimisto5200.068000.0FalseNaNNaN5666.666667
4882021-02-25 12:33:58.490PK-Seutu28mies5.0Työntekijä / palkollinen1.0Full-stack developerToimisto5500.068000.0TrueNaNNaN5666.666667
4892021-02-25 14:10:32.597Tampere23muu1.0Työntekijä / palkollinen0.5Systems Administrator ja firmän sisäinen 1st line -tukihessuToimisto1081.014000.0TrueNaNKk-palkkani on varsinkin vaihteleva, koska riippuu vuorolisistä (mahdollisista pyhä- ja yövuoroista ja tuurauksista). Jonkinlaisen oletuksen nyt yritin lyödä vuositulolle, mutta taitaa jäädä todellisuudessa hivenen sen alle.1166.666667