Overview

Dataset statistics

Number of variables32
Number of observations952
Missing cells10497
Missing cells (%)34.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory190.1 KiB
Average record size in memory204.5 B

Variable types

DateTime1
Categorical10
Numeric11
Text8
Unsupported2

Alerts

Työkokemus alalta (vuosina) is highly overall correlated with Kuukausipalkka and 3 other fieldsHigh correlation
Montako vuotta olet tehnyt laskuttavaa työtä alalla? is highly overall correlated with Palkansaaja vai laskuttaja and 1 other fieldsHigh correlation
Tuntilaskutus (ALV 0%, euroina) is highly overall correlated with Vuosilaskutus (ALV 0%, euroina) and 4 other fieldsHigh correlation
Vuosilaskutus (ALV 0%, euroina) is highly overall correlated with Tuntilaskutus (ALV 0%, euroina) and 2 other fieldsHigh correlation
Työaika is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Kuinka suuren osan ajasta teet lähityönä toimistolla? is highly overall correlated with Palkansaaja vai laskuttajaHigh correlation
Kuukausipalkka is highly overall correlated with Työkokemus alalta (vuosina) and 4 other fieldsHigh correlation
Vuositulot is highly overall correlated with Työkokemus alalta (vuosina) and 4 other fieldsHigh correlation
Kk-tulot (laskennallinen) is highly overall correlated with Työkokemus alalta (vuosina) and 4 other fieldsHigh correlation
Kk-tulot (laskennallinen, normalisoitu) is highly overall correlated with Työkokemus alalta (vuosina) and 5 other fieldsHigh correlation
Palkansaaja vai laskuttaja is highly overall correlated with Montako vuotta olet tehnyt laskuttavaa työtä alalla? and 13 other fieldsHigh correlation
Sukupuoli is highly overall correlated with Tuntilaskutus (ALV 0%, euroina)High correlation
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? is highly overall correlated with Palkansaaja vai laskuttaja and 3 other fieldsHigh correlation
Mistä asiakkaat ovat? is highly overall correlated with Palkansaaja vai laskuttaja and 3 other fieldsHigh correlation
Kaupunki is highly overall correlated with Kk-tulot (laskennallinen, normalisoitu) and 1 other fieldsHigh correlation
Millaisessa yrityksessä työskentelet? is highly overall correlated with Palkansaaja vai laskuttaja and 1 other fieldsHigh correlation
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? is highly overall correlated with Montako vuotta olet tehnyt laskuttavaa työtä alalla? and 5 other fieldsHigh correlation
Vastauskieli is highly overall correlated with Tuntilaskutus (ALV 0%, euroina) and 3 other fieldsHigh correlation
Oletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2022 jälkeen? is highly imbalanced (81.0%)Imbalance
Sukupuoli is highly imbalanced (66.0%)Imbalance
Mistä asiakkaat ovat? is highly imbalanced (55.9%)Imbalance
Kaupunki is highly imbalanced (59.4%)Imbalance
Vastauskieli is highly imbalanced (67.3%)Imbalance
Sukupuoli has 69 (7.2%) missing valuesMissing
Koulutustaustasi has 58 (6.1%) missing valuesMissing
Tulojen muutos viime vuodesta (%) has 74 (7.8%) missing valuesMissing
Montako vuotta olet tehnyt laskuttavaa työtä alalla? has 835 (87.7%) missing valuesMissing
Palvelut has 835 (87.7%) missing valuesMissing
Tuntilaskutus (ALV 0%, euroina) has 842 (88.4%) missing valuesMissing
Vuosilaskutus (ALV 0%, euroina) has 852 (89.5%) missing valuesMissing
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita? has 835 (87.7%) missing valuesMissing
Mistä asiakkaat ovat? has 835 (87.7%) missing valuesMissing
Työpaikka has 735 (77.2%) missing valuesMissing
Kaupunki has 136 (14.3%) missing valuesMissing
Millaisessa yrityksessä työskentelet? has 127 (13.3%) missing valuesMissing
Työaika has 119 (12.5%) missing valuesMissing
Kuinka suuren osan ajasta teet lähityönä toimistolla? has 124 (13.0%) missing valuesMissing
Rooli has 154 (16.2%) missing valuesMissing
Kuukausipalkka has 120 (12.6%) missing valuesMissing
Vuositulot has 122 (12.8%) missing valuesMissing
Vapaa kuvaus kokonaiskompensaatiomallista has 660 (69.3%) missing valuesMissing
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? (muut vastaukset) has 900 (94.5%) missing valuesMissing
Vapaa sana has 901 (94.6%) missing valuesMissing
Palaute has 908 (95.4%) missing valuesMissing
Kk-tulot (laskennallinen) has 122 (12.8%) missing valuesMissing
Kk-tulot (laskennallinen, normalisoitu) has 124 (13.0%) missing valuesMissing
Tulojen muutos viime vuodesta (%) is highly skewed (γ1 = 20.58555446)Skewed
Timestamp has unique valuesUnique
Vastaustunniste has unique valuesUnique
Vapaa kuvaus kokonaiskompensaatiomallista is an unsupported type, check if it needs cleaning or further analysisUnsupported
Vapaa sana is an unsupported type, check if it needs cleaning or further analysisUnsupported
Tulojen muutos viime vuodesta (%) has 175 (18.4%) zerosZeros
Kuinka suuren osan ajasta teet lähityönä toimistolla? has 131 (13.8%) zerosZeros
Vuositulot has 14 (1.5%) zerosZeros
Kk-tulot (laskennallinen) has 14 (1.5%) zerosZeros
Kk-tulot (laskennallinen, normalisoitu) has 14 (1.5%) zerosZeros

Reproduction

Analysis started2023-09-28 13:49:40.338340
Analysis finished2023-09-28 13:49:57.589147
Duration17.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Timestamp
Date

UNIQUE 

Distinct952
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
Minimum2023-09-03 12:02:04.465000
Maximum2023-09-24 23:45:23.790000
2023-09-28T13:49:57.680507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:57.850622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Palkansaaja vai laskuttaja
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
Palkansaaja
835 
Laskuttaja
117 

Length

Max length11
Median length11
Mean length10.877101
Min length10

Characters and Unicode

Total characters10355
Distinct characters10
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowPalkansaaja
2nd rowPalkansaaja
3rd rowLaskuttaja
4th rowPalkansaaja
5th rowLaskuttaja

Common Values

ValueCountFrequency (%)
Palkansaaja 835
87.7%
Laskuttaja 117
 
12.3%

Length

2023-09-28T13:49:58.009793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-28T13:49:58.124300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
palkansaaja 835
87.7%
laskuttaja 117
 
12.3%

Most occurring characters

ValueCountFrequency (%)
a 4526
43.7%
k 952
 
9.2%
s 952
 
9.2%
j 952
 
9.2%
P 835
 
8.1%
l 835
 
8.1%
n 835
 
8.1%
t 234
 
2.3%
L 117
 
1.1%
u 117
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9403
90.8%
Uppercase Letter 952
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4526
48.1%
k 952
 
10.1%
s 952
 
10.1%
j 952
 
10.1%
l 835
 
8.9%
n 835
 
8.9%
t 234
 
2.5%
u 117
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
P 835
87.7%
L 117
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 10355
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4526
43.7%
k 952
 
9.2%
s 952
 
9.2%
j 952
 
9.2%
P 835
 
8.1%
l 835
 
8.1%
n 835
 
8.1%
t 234
 
2.3%
L 117
 
1.1%
u 117
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4526
43.7%
k 952
 
9.2%
s 952
 
9.2%
j 952
 
9.2%
P 835
 
8.1%
l 835
 
8.1%
n 835
 
8.1%
t 234
 
2.3%
L 117
 
1.1%
u 117
 
1.1%
Distinct3
Distinct (%)0.3%
Missing6
Missing (%)0.6%
Memory size8.5 KiB
Ei
904 
palkansaaja → laskuttaja
 
30
laskuttaja → palkansaaja
 
12

Length

Max length24
Median length2
Mean length2.9767442
Min length2

Characters and Unicode

Total characters2816
Distinct characters13
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 rowEi
2nd rowEi
3rd rowpalkansaaja → laskuttaja
4th rowEi
5th rowEi

Common Values

ValueCountFrequency (%)
Ei 904
95.0%
palkansaaja → laskuttaja 30
 
3.2%
laskuttaja → palkansaaja 12
 
1.3%
(Missing) 6
 
0.6%

Length

2023-09-28T13:49:58.241184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-28T13:49:58.350559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
ei 904
87.8%
palkansaaja 42
 
4.1%
42
 
4.1%
laskuttaja 42
 
4.1%

Most occurring characters

ValueCountFrequency (%)
E 904
32.1%
i 904
32.1%
a 336
 
11.9%
l 84
 
3.0%
k 84
 
3.0%
s 84
 
3.0%
j 84
 
3.0%
84
 
3.0%
t 84
 
3.0%
p 42
 
1.5%
Other values (3) 126
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1786
63.4%
Uppercase Letter 904
32.1%
Space Separator 84
 
3.0%
Math Symbol 42
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 904
50.6%
a 336
 
18.8%
l 84
 
4.7%
k 84
 
4.7%
s 84
 
4.7%
j 84
 
4.7%
t 84
 
4.7%
p 42
 
2.4%
n 42
 
2.4%
u 42
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
E 904
100.0%
Space Separator
ValueCountFrequency (%)
84
100.0%
Math Symbol
ValueCountFrequency (%)
42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2690
95.5%
Common 126
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 904
33.6%
i 904
33.6%
a 336
 
12.5%
l 84
 
3.1%
k 84
 
3.1%
s 84
 
3.1%
j 84
 
3.1%
t 84
 
3.1%
p 42
 
1.6%
n 42
 
1.6%
Common
ValueCountFrequency (%)
84
66.7%
42
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2774
98.5%
Arrows 42
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 904
32.6%
i 904
32.6%
a 336
 
12.1%
l 84
 
3.0%
k 84
 
3.0%
s 84
 
3.0%
j 84
 
3.0%
84
 
3.0%
t 84
 
3.0%
p 42
 
1.5%
Other values (2) 84
 
3.0%
Arrows
ValueCountFrequency (%)
42
100.0%

Ikä
Categorical

Distinct10
Distinct (%)1.1%
Missing1
Missing (%)0.1%
Memory size8.7 KiB
31-35
261 
36-40
245 
26-30
178 
41-45
162 
46-50
50 
21-25
39 
51-55
 
9
15-20
 
3
< 15v
 
2
> 55v
 
2

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4755
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
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 row36-40
2nd row26-30
3rd row31-35
4th row31-35
5th row36-40

Common Values

ValueCountFrequency (%)
31-35 261
27.4%
36-40 245
25.7%
26-30 178
18.7%
41-45 162
17.0%
46-50 50
 
5.3%
21-25 39
 
4.1%
51-55 9
 
0.9%
15-20 3
 
0.3%
< 15v 2
 
0.2%
> 55v 2
 
0.2%
(Missing) 1
 
0.1%

Length

2023-09-28T13:49:58.478953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-28T13:49:58.616280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
31-35 261
27.3%
36-40 245
25.7%
26-30 178
18.6%
41-45 162
17.0%
46-50 50
 
5.2%
21-25 39
 
4.1%
51-55 9
 
0.9%
4
 
0.4%
15-20 3
 
0.3%
15v 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
- 947
19.9%
3 945
19.9%
4 619
13.0%
5 548
11.5%
1 476
10.0%
0 476
10.0%
6 473
9.9%
2 259
 
5.4%
4
 
0.1%
v 4
 
0.1%
Other values (2) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3796
79.8%
Dash Punctuation 947
 
19.9%
Space Separator 4
 
0.1%
Lowercase Letter 4
 
0.1%
Math Symbol 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 945
24.9%
4 619
16.3%
5 548
14.4%
1 476
12.5%
0 476
12.5%
6 473
12.5%
2 259
 
6.8%
Math Symbol
ValueCountFrequency (%)
< 2
50.0%
> 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 947
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
v 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4751
99.9%
Latin 4
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 947
19.9%
3 945
19.9%
4 619
13.0%
5 548
11.5%
1 476
10.0%
0 476
10.0%
6 473
10.0%
2 259
 
5.5%
4
 
0.1%
< 2
 
< 0.1%
Latin
ValueCountFrequency (%)
v 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 947
19.9%
3 945
19.9%
4 619
13.0%
5 548
11.5%
1 476
10.0%
0 476
10.0%
6 473
9.9%
2 259
 
5.4%
4
 
0.1%
v 4
 
0.1%
Other values (2) 4
 
0.1%

Sukupuoli
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.3%
Missing69
Missing (%)7.2%
Memory size8.5 KiB
mies
784 
nainen
94 
muu
 
5

Length

Max length6
Median length4
Mean length4.207248
Min length3

Characters and Unicode

Total characters3715
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

Common Values

ValueCountFrequency (%)
mies 784
82.4%
nainen 94
 
9.9%
muu 5
 
0.5%
(Missing) 69
 
7.2%

Length

2023-09-28T13:49:58.784353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-28T13:49:58.911825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
mies 784
88.8%
nainen 94
 
10.6%
muu 5
 
0.6%

Most occurring characters

ValueCountFrequency (%)
i 878
23.6%
e 878
23.6%
m 789
21.2%
s 784
21.1%
n 282
 
7.6%
a 94
 
2.5%
u 10
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3715
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 878
23.6%
e 878
23.6%
m 789
21.2%
s 784
21.1%
n 282
 
7.6%
a 94
 
2.5%
u 10
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3715
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 878
23.6%
e 878
23.6%
m 789
21.2%
s 784
21.1%
n 282
 
7.6%
a 94
 
2.5%
u 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 878
23.6%
e 878
23.6%
m 789
21.2%
s 784
21.1%
n 282
 
7.6%
a 94
 
2.5%
u 10
 
0.3%

Työkokemus alalta (vuosina)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)3.5%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean10.85353
Minimum0
Maximum37
Zeros8
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:49:59.045376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median10
Q315
95-th percentile23.6
Maximum37
Range37
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.6647128
Coefficient of variation (CV)0.61405947
Kurtosis-0.087298737
Mean10.85353
Median Absolute Deviation (MAD)5
Skewness0.67946639
Sum10300
Variance44.418397
MonotonicityNot monotonic
2023-09-28T13:49:59.197142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
15 79
 
8.3%
5 74
 
7.8%
10 69
 
7.2%
8 66
 
6.9%
6 65
 
6.8%
7 58
 
6.1%
20 51
 
5.4%
4 50
 
5.3%
12 45
 
4.7%
9 41
 
4.3%
Other values (23) 351
36.9%
ValueCountFrequency (%)
0 8
 
0.8%
1 24
 
2.5%
2 38
4.0%
3 39
4.1%
4 50
5.3%
5 74
7.8%
6 65
6.8%
7 58
6.1%
8 66
6.9%
9 41
4.3%
ValueCountFrequency (%)
37 1
 
0.1%
33 1
 
0.1%
32 1
 
0.1%
30 4
 
0.4%
29 1
 
0.1%
28 3
 
0.3%
26 7
 
0.7%
25 26
2.7%
24 4
 
0.4%
23 16
1.7%

Koulutustaustasi
Text

MISSING 

Distinct418
Distinct (%)46.8%
Missing58
Missing (%)6.1%
Memory size14.9 KiB
2023-09-28T13:49:59.500580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length153
Median length59
Mean length17.618568
Min length1

Characters and Unicode

Total characters15751
Distinct characters65
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

Unique334 ?
Unique (%)37.4%

Sample

1st rowAmmattikoulu
2nd rowTietojenkäsittelyn tradenomi
3rd rowDI
4th rowFilosofian kandidaatti, tietojenkäsittelytiede
5th rowMelkein DI
ValueCountFrequency (%)
amk 121
 
7.3%
di 112
 
6.8%
diplomi-insinööri 76
 
4.6%
insinööri 71
 
4.3%
maisteri 59
 
3.6%
tietotekniikan 48
 
2.9%
ylioppilas 43
 
2.6%
fm 39
 
2.4%
tradenomi 37
 
2.2%
filosofian 36
 
2.2%
Other values (306) 1007
61.1%
2023-09-28T13:49:59.980546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2217
14.1%
t 1353
 
8.6%
e 1210
 
7.7%
n 1186
 
7.5%
o 1063
 
6.7%
a 913
 
5.8%
k 816
 
5.2%
774
 
4.9%
s 632
 
4.0%
l 613
 
3.9%
Other values (55) 4974
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13015
82.6%
Uppercase Letter 1502
 
9.5%
Space Separator 774
 
4.9%
Other Punctuation 148
 
0.9%
Dash Punctuation 137
 
0.9%
Close Punctuation 80
 
0.5%
Open Punctuation 79
 
0.5%
Math Symbol 9
 
0.1%
Decimal Number 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2217
17.0%
t 1353
10.4%
e 1210
9.3%
n 1186
9.1%
o 1063
8.2%
a 913
 
7.0%
k 816
 
6.3%
s 632
 
4.9%
l 613
 
4.7%
r 498
 
3.8%
Other values (16) 2514
19.3%
Uppercase Letter
ValueCountFrequency (%)
M 220
14.6%
T 206
13.7%
D 200
13.3%
A 196
13.0%
I 187
12.5%
K 162
10.8%
Y 83
 
5.5%
F 76
 
5.1%
L 46
 
3.1%
S 37
 
2.5%
Other values (10) 89
5.9%
Other Punctuation
ValueCountFrequency (%)
, 103
69.6%
. 21
 
14.2%
' 8
 
5.4%
/ 8
 
5.4%
: 2
 
1.4%
" 2
 
1.4%
@ 1
 
0.7%
& 1
 
0.7%
% 1
 
0.7%
! 1
 
0.7%
Decimal Number
ValueCountFrequency (%)
2 4
57.1%
5 1
 
14.3%
0 1
 
14.3%
8 1
 
14.3%
Space Separator
ValueCountFrequency (%)
774
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14517
92.2%
Common 1234
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2217
15.3%
t 1353
 
9.3%
e 1210
 
8.3%
n 1186
 
8.2%
o 1063
 
7.3%
a 913
 
6.3%
k 816
 
5.6%
s 632
 
4.4%
l 613
 
4.2%
r 498
 
3.4%
Other values (36) 4016
27.7%
Common
ValueCountFrequency (%)
774
62.7%
- 137
 
11.1%
, 103
 
8.3%
) 80
 
6.5%
( 79
 
6.4%
. 21
 
1.7%
+ 9
 
0.7%
' 8
 
0.6%
/ 8
 
0.6%
2 4
 
0.3%
Other values (9) 11
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15323
97.3%
None 428
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2217
14.5%
t 1353
 
8.8%
e 1210
 
7.9%
n 1186
 
7.7%
o 1063
 
6.9%
a 913
 
6.0%
k 816
 
5.3%
774
 
5.1%
s 632
 
4.1%
l 613
 
4.0%
Other values (53) 4546
29.7%
None
ValueCountFrequency (%)
ö 318
74.3%
ä 110
 
25.7%

Tulojen muutos viime vuodesta (%)
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct131
Distinct (%)14.9%
Missing74
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean10.035769
Minimum-40
Maximum1000
Zeros175
Zeros (%)18.4%
Negative33
Negative (%)3.5%
Memory size14.9 KiB
2023-09-28T13:50:00.169929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-40
5-th percentile0
Q11
median4
Q310
95-th percentile33
Maximum1000
Range1040
Interquartile range (IQR)9

Descriptive statistics

Standard deviation38.116149
Coefficient of variation (CV)3.7980298
Kurtosis521.66913
Mean10.035769
Median Absolute Deviation (MAD)4
Skewness20.585554
Sum8811.405
Variance1452.8408
MonotonicityNot monotonic
2023-09-28T13:50:00.338691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175
18.4%
3 103
 
10.8%
5 63
 
6.6%
10 52
 
5.5%
4 42
 
4.4%
2 29
 
3.0%
15 22
 
2.3%
8 22
 
2.3%
11 21
 
2.2%
7 21
 
2.2%
Other values (121) 328
34.5%
(Missing) 74
 
7.8%
ValueCountFrequency (%)
-40 1
 
0.1%
-38 1
 
0.1%
-25 2
 
0.2%
-21 1
 
0.1%
-20 2
 
0.2%
-18 1
 
0.1%
-15 4
0.4%
-12 1
 
0.1%
-10 6
0.6%
-9 1
 
0.1%
ValueCountFrequency (%)
1000 1
 
0.1%
200 1
 
0.1%
190 1
 
0.1%
180 1
 
0.1%
133 1
 
0.1%
122 1
 
0.1%
120 1
 
0.1%
105 1
 
0.1%
100 3
0.3%
90 1
 
0.1%

Montako vuotta olet tehnyt laskuttavaa työtä alalla?
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)23.1%
Missing835
Missing (%)87.7%
Infinite0
Infinite (%)0.0%
Mean3.8025641
Minimum0
Maximum26
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:00.494414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.48
Q11.5
median3
Q34.5
95-th percentile12.2
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.1105722
Coefficient of variation (CV)1.0810001
Kurtosis9.1804791
Mean3.8025641
Median Absolute Deviation (MAD)1.5
Skewness2.697707
Sum444.9
Variance16.896804
MonotonicityNot monotonic
2023-09-28T13:50:00.638860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2 24
 
2.5%
3 19
 
2.0%
1 15
 
1.6%
5 10
 
1.1%
4 9
 
0.9%
0.5 6
 
0.6%
1.5 5
 
0.5%
0 4
 
0.4%
10 3
 
0.3%
7 3
 
0.3%
Other values (17) 19
 
2.0%
(Missing) 835
87.7%
ValueCountFrequency (%)
0 4
 
0.4%
0.2 1
 
0.1%
0.4 1
 
0.1%
0.5 6
 
0.6%
1 15
1.6%
1.5 5
 
0.5%
2 24
2.5%
2.5 1
 
0.1%
2.8 1
 
0.1%
3 19
2.0%
ValueCountFrequency (%)
26 1
 
0.1%
20 1
 
0.1%
16 1
 
0.1%
15 1
 
0.1%
14 1
 
0.1%
13 1
 
0.1%
12 2
0.2%
10 3
0.3%
9 1
 
0.1%
8 1
 
0.1%

Palvelut
Text

MISSING 

Distinct94
Distinct (%)80.3%
Missing835
Missing (%)87.7%
Memory size14.9 KiB
2023-09-28T13:50:01.113785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length81
Median length46
Mean length26.145299
Min length3

Characters and Unicode

Total characters3059
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

Unique88 ?
Unique (%)75.2%

Sample

1st rowFrontend
2nd rowFull-stack lead dev
3rd rowFull-stack devausta
4th rowFull stack
5th rowFull stack softadevaus
ValueCountFrequency (%)
stack 52
 
14.1%
full 51
 
13.8%
data 12
 
3.2%
backend 11
 
3.0%
arkkitehtuuri 11
 
3.0%
devops 11
 
3.0%
devausta 9
 
2.4%
full-stack 9
 
2.4%
fullstack 9
 
2.4%
mobiili 7
 
1.9%
Other values (127) 188
50.8%
2023-09-28T13:50:01.641269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 276
 
9.0%
t 265
 
8.7%
254
 
8.3%
l 208
 
6.8%
e 202
 
6.6%
i 183
 
6.0%
k 178
 
5.8%
u 166
 
5.4%
s 160
 
5.2%
n 129
 
4.2%
Other values (46) 1038
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2497
81.6%
Space Separator 254
 
8.3%
Uppercase Letter 169
 
5.5%
Other Punctuation 108
 
3.5%
Dash Punctuation 18
 
0.6%
Math Symbol 4
 
0.1%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 276
11.1%
t 265
10.6%
l 208
 
8.3%
e 202
 
8.1%
i 183
 
7.3%
k 178
 
7.1%
u 166
 
6.6%
s 160
 
6.4%
n 129
 
5.2%
o 122
 
4.9%
Other values (16) 608
24.3%
Uppercase Letter
ValueCountFrequency (%)
F 69
40.8%
S 16
 
9.5%
D 15
 
8.9%
B 11
 
6.5%
A 8
 
4.7%
O 7
 
4.1%
C 7
 
4.1%
M 6
 
3.6%
P 6
 
3.6%
L 5
 
3.0%
Other values (10) 19
 
11.2%
Other Punctuation
ValueCountFrequency (%)
, 98
90.7%
. 5
 
4.6%
/ 4
 
3.7%
& 1
 
0.9%
Space Separator
ValueCountFrequency (%)
254
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2666
87.2%
Common 393
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 276
 
10.4%
t 265
 
9.9%
l 208
 
7.8%
e 202
 
7.6%
i 183
 
6.9%
k 178
 
6.7%
u 166
 
6.2%
s 160
 
6.0%
n 129
 
4.8%
o 122
 
4.6%
Other values (36) 777
29.1%
Common
ValueCountFrequency (%)
254
64.6%
, 98
 
24.9%
- 18
 
4.6%
. 5
 
1.3%
/ 4
 
1.0%
+ 4
 
1.0%
) 4
 
1.0%
( 4
 
1.0%
& 1
 
0.3%
3 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3030
99.1%
None 29
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 276
 
9.1%
t 265
 
8.7%
254
 
8.4%
l 208
 
6.9%
e 202
 
6.7%
i 183
 
6.0%
k 178
 
5.9%
u 166
 
5.5%
s 160
 
5.3%
n 129
 
4.3%
Other values (44) 1009
33.3%
None
ValueCountFrequency (%)
ä 25
86.2%
ö 4
 
13.8%

Tuntilaskutus (ALV 0%, euroina)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)34.5%
Missing842
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean93.138182
Minimum0
Maximum240
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:01.819183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile65.45
Q180
median90
Q3100
95-th percentile130
Maximum240
Range240
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.866584
Coefficient of variation (CV)0.27772267
Kurtosis10.339206
Mean93.138182
Median Absolute Deviation (MAD)10
Skewness1.5895882
Sum10245.2
Variance669.08018
MonotonicityNot monotonic
2023-09-28T13:50:01.975866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
90 13
 
1.4%
80 12
 
1.3%
95 10
 
1.1%
85 10
 
1.1%
120 6
 
0.6%
100 6
 
0.6%
110 4
 
0.4%
75 3
 
0.3%
76 3
 
0.3%
88 3
 
0.3%
Other values (28) 40
 
4.2%
(Missing) 842
88.4%
ValueCountFrequency (%)
0 1
 
0.1%
30 1
 
0.1%
54 1
 
0.1%
65 3
0.3%
66 1
 
0.1%
70 2
0.2%
72 2
0.2%
73 2
0.2%
74 1
 
0.1%
75 3
0.3%
ValueCountFrequency (%)
240 1
 
0.1%
160 1
 
0.1%
150 2
 
0.2%
140 1
 
0.1%
130 2
 
0.2%
126 1
 
0.1%
125 2
 
0.2%
120 6
0.6%
114 1
 
0.1%
110 4
0.4%

Vuosilaskutus (ALV 0%, euroina)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)51.0%
Missing852
Missing (%)89.5%
Infinite0
Infinite (%)0.0%
Mean134861.37
Minimum0
Maximum300000
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:02.133837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29850
Q1120000
median140000
Q3160000
95-th percentile217500
Maximum300000
Range300000
Interquartile range (IQR)40000

Descriptive statistics

Standard deviation53389.44
Coefficient of variation (CV)0.39588386
Kurtosis1.3518031
Mean134861.37
Median Absolute Deviation (MAD)20000
Skewness-0.30328108
Sum13486137
Variance2.8504323 × 109
MonotonicityNot monotonic
2023-09-28T13:50:02.302779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150000 13
 
1.4%
120000 9
 
0.9%
160000 6
 
0.6%
140000 5
 
0.5%
125000 4
 
0.4%
200000 4
 
0.4%
100000 4
 
0.4%
130000 4
 
0.4%
105000 3
 
0.3%
152000 2
 
0.2%
Other values (41) 46
 
4.8%
(Missing) 852
89.5%
ValueCountFrequency (%)
0 1
0.1%
115 1
0.1%
130 1
0.1%
10000 1
0.1%
27000 1
0.1%
30000 2
0.2%
32000 1
0.1%
50000 2
0.2%
52800 1
0.1%
65200 1
0.1%
ValueCountFrequency (%)
300000 1
 
0.1%
260000 1
 
0.1%
240000 1
 
0.1%
230000 1
 
0.1%
227000 1
 
0.1%
217000 1
 
0.1%
205000 1
 
0.1%
200000 4
0.4%
190000 2
0.2%
180000 2
0.2%
Distinct10
Distinct (%)8.5%
Missing835
Missing (%)87.7%
Memory size14.9 KiB
Itse
42 
Käytän välitysfirmoja
40 
Itse, Käytän välitysfirmoja
27 
Myself
 
2
En hanki, sivutoimisena teen vain muutamalle asiakkaalle
 
1
Itse, Verkostot / kumppaniy välittää liidejä
 
1
Kaksi isoa sopimuskumppania
 
1
Itse, Käytän välitysfirmoja,
 
1
Agencies
 
1
Myself, Agencies
 
1

Length

Max length56
Median length44
Mean length16.487179
Min length4

Characters and Unicode

Total characters1929
Distinct characters31
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

Unique6 ?
Unique (%)5.1%

Sample

1st rowItse
2nd rowItse
3rd rowItse, Käytän välitysfirmoja
4th rowItse
5th rowKäytän välitysfirmoja

Common Values

ValueCountFrequency (%)
Itse 42
 
4.4%
Käytän välitysfirmoja 40
 
4.2%
Itse, Käytän välitysfirmoja 27
 
2.8%
Myself 2
 
0.2%
En hanki, sivutoimisena teen vain muutamalle asiakkaalle 1
 
0.1%
Itse, Verkostot / kumppaniy välittää liidejä 1
 
0.1%
Kaksi isoa sopimuskumppania 1
 
0.1%
Itse, Käytän välitysfirmoja, 1
 
0.1%
Agencies 1
 
0.1%
Myself, Agencies 1
 
0.1%
(Missing) 835
87.7%

Length

2023-09-28T13:50:02.464509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-28T13:50:02.607875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
itse 71
31.3%
käytän 68
30.0%
välitysfirmoja 68
30.0%
myself 3
 
1.3%
agencies 2
 
0.9%
hanki 1
 
0.4%
sivutoimisena 1
 
0.4%
teen 1
 
0.4%
vain 1
 
0.4%
muutamalle 1
 
0.4%
Other values (10) 10
 
4.4%

Most occurring characters

ValueCountFrequency (%)
t 214
 
11.1%
ä 208
 
10.8%
s 152
 
7.9%
i 152
 
7.9%
y 140
 
7.3%
111
 
5.8%
e 85
 
4.4%
a 82
 
4.3%
n 77
 
4.0%
l 77
 
4.0%
Other values (21) 631
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1638
84.9%
Uppercase Letter 147
 
7.6%
Space Separator 111
 
5.8%
Other Punctuation 33
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 214
13.1%
ä 208
12.7%
s 152
9.3%
i 152
9.3%
y 140
 
8.5%
e 85
 
5.2%
a 82
 
5.0%
n 77
 
4.7%
l 77
 
4.7%
m 74
 
4.5%
Other values (12) 377
23.0%
Uppercase Letter
ValueCountFrequency (%)
I 71
48.3%
K 69
46.9%
M 3
 
2.0%
A 2
 
1.4%
E 1
 
0.7%
V 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 32
97.0%
/ 1
 
3.0%
Space Separator
ValueCountFrequency (%)
111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1785
92.5%
Common 144
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 214
12.0%
ä 208
 
11.7%
s 152
 
8.5%
i 152
 
8.5%
y 140
 
7.8%
e 85
 
4.8%
a 82
 
4.6%
n 77
 
4.3%
l 77
 
4.3%
m 74
 
4.1%
Other values (18) 524
29.4%
Common
ValueCountFrequency (%)
111
77.1%
, 32
 
22.2%
/ 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1721
89.2%
None 208
 
10.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 214
 
12.4%
s 152
 
8.8%
i 152
 
8.8%
y 140
 
8.1%
111
 
6.4%
e 85
 
4.9%
a 82
 
4.8%
n 77
 
4.5%
l 77
 
4.5%
m 74
 
4.3%
Other values (20) 557
32.4%
None
ValueCountFrequency (%)
ä 208
100.0%

Mistä asiakkaat ovat?
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)5.1%
Missing835
Missing (%)87.7%
Memory size8.6 KiB
Suomesta
90 
Suomesta, Ulkomailta
13 
Ulkomailta
11 
Abroad
 
1
Ainostaan Yhdysvalloista. No exceptions.
 
1
Finland, Abroad
 
1

Length

Max length40
Median length8
Mean length9.8376068
Min length6

Characters and Unicode

Total characters1151
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)2.6%

Sample

1st rowSuomesta
2nd rowSuomesta
3rd rowSuomesta
4th rowSuomesta
5th rowSuomesta

Common Values

ValueCountFrequency (%)
Suomesta 90
 
9.5%
Suomesta, Ulkomailta 13
 
1.4%
Ulkomailta 11
 
1.2%
Abroad 1
 
0.1%
Ainostaan Yhdysvalloista. No exceptions. 1
 
0.1%
Finland, Abroad 1
 
0.1%
(Missing) 835
87.7%

Length

2023-09-28T13:50:02.795813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-28T13:50:02.933818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
suomesta 103
76.9%
ulkomailta 24
 
17.9%
abroad 2
 
1.5%
ainostaan 1
 
0.7%
yhdysvalloista 1
 
0.7%
no 1
 
0.7%
exceptions 1
 
0.7%
finland 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 158
13.7%
o 133
11.6%
t 130
11.3%
m 127
11.0%
s 107
9.3%
e 105
9.1%
S 103
8.9%
u 103
8.9%
l 51
 
4.4%
i 28
 
2.4%
Other values (19) 106
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 985
85.6%
Uppercase Letter 133
 
11.6%
Space Separator 17
 
1.5%
Other Punctuation 16
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 158
16.0%
o 133
13.5%
t 130
13.2%
m 127
12.9%
s 107
10.9%
e 105
10.7%
u 103
10.5%
l 51
 
5.2%
i 28
 
2.8%
k 24
 
2.4%
Other values (10) 19
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
S 103
77.4%
U 24
 
18.0%
A 3
 
2.3%
Y 1
 
0.8%
N 1
 
0.8%
F 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 14
87.5%
. 2
 
12.5%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1118
97.1%
Common 33
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 158
14.1%
o 133
11.9%
t 130
11.6%
m 127
11.4%
s 107
9.6%
e 105
9.4%
S 103
9.2%
u 103
9.2%
l 51
 
4.6%
i 28
 
2.5%
Other values (16) 73
6.5%
Common
ValueCountFrequency (%)
17
51.5%
, 14
42.4%
. 2
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 158
13.7%
o 133
11.6%
t 130
11.3%
m 127
11.0%
s 107
9.3%
e 105
9.1%
S 103
8.9%
u 103
8.9%
l 51
 
4.4%
i 28
 
2.4%
Other values (19) 106
9.2%

Työpaikka
Text

MISSING 

Distinct107
Distinct (%)49.3%
Missing735
Missing (%)77.2%
Memory size14.9 KiB
2023-09-28T13:50:03.214432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length42
Median length33
Mean length9.0138249
Min length0

Characters and Unicode

Total characters1956
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)35.9%

Sample

1st rowMavericks
2nd rowGofore
3rd rowArado
4th rowVerkkokauppa.comj
5th rowWunderdog
ValueCountFrequency (%)
vincit 26
 
9.5%
siili 11
 
4.0%
gofore 10
 
3.6%
reaktor 10
 
3.6%
solita 9
 
3.3%
compile 8
 
2.9%
futurice 7
 
2.6%
mavericks 7
 
2.6%
mehiläinen 7
 
2.6%
wolt 7
 
2.6%
Other values (124) 172
62.8%
2023-09-28T13:50:03.681075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 254
 
13.0%
o 167
 
8.5%
t 146
 
7.5%
e 140
 
7.2%
a 131
 
6.7%
n 123
 
6.3%
l 108
 
5.5%
r 91
 
4.7%
s 62
 
3.2%
62
 
3.2%
Other values (51) 672
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1604
82.0%
Uppercase Letter 275
 
14.1%
Space Separator 62
 
3.2%
Other Punctuation 7
 
0.4%
Dash Punctuation 5
 
0.3%
Decimal Number 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 254
15.8%
o 167
10.4%
t 146
9.1%
e 140
8.7%
a 131
8.2%
n 123
 
7.7%
l 108
 
6.7%
r 91
 
5.7%
s 62
 
3.9%
u 55
 
3.4%
Other values (18) 327
20.4%
Uppercase Letter
ValueCountFrequency (%)
S 45
16.4%
V 36
13.1%
M 24
 
8.7%
C 20
 
7.3%
R 16
 
5.8%
E 12
 
4.4%
F 12
 
4.4%
P 12
 
4.4%
W 11
 
4.0%
L 11
 
4.0%
Other values (15) 76
27.6%
Other Punctuation
ValueCountFrequency (%)
. 5
71.4%
! 1
 
14.3%
, 1
 
14.3%
Decimal Number
ValueCountFrequency (%)
8 1
33.3%
3 1
33.3%
0 1
33.3%
Space Separator
ValueCountFrequency (%)
62
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1879
96.1%
Common 77
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 254
13.5%
o 167
 
8.9%
t 146
 
7.8%
e 140
 
7.5%
a 131
 
7.0%
n 123
 
6.5%
l 108
 
5.7%
r 91
 
4.8%
s 62
 
3.3%
u 55
 
2.9%
Other values (43) 602
32.0%
Common
ValueCountFrequency (%)
62
80.5%
. 5
 
6.5%
- 5
 
6.5%
8 1
 
1.3%
! 1
 
1.3%
, 1
 
1.3%
3 1
 
1.3%
0 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1945
99.4%
None 11
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 254
 
13.1%
o 167
 
8.6%
t 146
 
7.5%
e 140
 
7.2%
a 131
 
6.7%
n 123
 
6.3%
l 108
 
5.6%
r 91
 
4.7%
s 62
 
3.2%
62
 
3.2%
Other values (49) 661
34.0%
None
ValueCountFrequency (%)
ä 9
81.8%
ö 2
 
18.2%

Kaupunki
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct37
Distinct (%)4.5%
Missing136
Missing (%)14.3%
Memory size9.7 KiB
PK-seutu
469 
Tampere
157 
Turku
79 
Oulu
 
33
Jyväskylä
 
31
Kuopio
 
7
Joensuu
 
4
Vaasa
 
4
Pori
 
3
Seinäjoki
 
2
No HQ
 
1
Remote
 
1
Piilaakso
 
1
Mikkeli
 
1
San Francisco
 
1
Täysi etätyö, ei toimistoa
 
1
Ulkomaat
 
1
työpaikka hajautettu
 
1
Muu
 
1
Amsterdam
 
1
Other values (17)
 
17

Length

Max length47
Median length8
Mean length7.4816176
Min length2

Characters and Unicode

Total characters6105
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)3.3%

Sample

1st rowTurku
2nd rowTurku
3rd rowPK-seutu
4th rowPK-seutu
5th rowPK-seutu

Common Values

ValueCountFrequency (%)
PK-seutu 469
49.3%
Tampere 157
 
16.5%
Turku 79
 
8.3%
Oulu 33
 
3.5%
Jyväskylä 31
 
3.3%
Kuopio 7
 
0.7%
Joensuu 4
 
0.4%
Vaasa 4
 
0.4%
Pori 3
 
0.3%
Seinäjoki 2
 
0.2%
Other values (27) 27
 
2.8%
(Missing) 136
 
14.3%

Length

2023-09-28T13:50:03.862725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pk-seutu 469
55.8%
tampere 157
 
18.7%
turku 80
 
9.5%
oulu 33
 
3.9%
jyväskylä 31
 
3.7%
kuopio 7
 
0.8%
joensuu 4
 
0.5%
vaasa 4
 
0.5%
pori 3
 
0.4%
remote 2
 
0.2%
Other values (46) 50
 
6.0%

Most occurring characters

ValueCountFrequency (%)
u 1191
19.5%
e 808
13.2%
s 516
8.5%
t 490
8.0%
K 480
7.9%
P 473
 
7.7%
- 470
 
7.7%
r 253
 
4.1%
T 238
 
3.9%
a 199
 
3.3%
Other values (41) 987
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4303
70.5%
Uppercase Letter 1300
 
21.3%
Dash Punctuation 470
 
7.7%
Space Separator 26
 
0.4%
Other Punctuation 4
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 1191
27.7%
e 808
18.8%
s 516
12.0%
t 490
11.4%
r 253
 
5.9%
a 199
 
4.6%
m 172
 
4.0%
p 169
 
3.9%
k 123
 
2.9%
l 77
 
1.8%
Other values (15) 305
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
K 480
36.9%
P 473
36.4%
T 238
18.3%
J 35
 
2.7%
O 33
 
2.5%
S 5
 
0.4%
U 4
 
0.3%
V 4
 
0.3%
H 4
 
0.3%
A 3
 
0.2%
Other values (11) 21
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 470
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5603
91.8%
Common 502
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 1191
21.3%
e 808
14.4%
s 516
9.2%
t 490
8.7%
K 480
8.6%
P 473
 
8.4%
r 253
 
4.5%
T 238
 
4.2%
a 199
 
3.6%
m 172
 
3.1%
Other values (36) 783
14.0%
Common
ValueCountFrequency (%)
- 470
93.6%
26
 
5.2%
, 4
 
0.8%
( 1
 
0.2%
) 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6035
98.9%
None 70
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 1191
19.7%
e 808
13.4%
s 516
8.6%
t 490
8.1%
K 480
8.0%
P 473
 
7.8%
- 470
 
7.8%
r 253
 
4.2%
T 238
 
3.9%
a 199
 
3.3%
Other values (39) 917
15.2%
None
ValueCountFrequency (%)
ä 68
97.1%
ö 2
 
2.9%

Millaisessa yrityksessä työskentelet?
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)1.7%
Missing127
Missing (%)13.3%
Memory size9.0 KiB
Konsulttitalossa
397 
Tuotetalossa, jonka core-bisnes on softa
238 
Yrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)
111 
Consulting
 
27
Julkinen tai kolmas sektori
 
23
Product company with software as their core business
 
17
A company where software is support role (for example banks or healthcare)
 
5
.
 
1
Alihankkija, konsultointi
 
1
Digitoimisto
 
1
Service provider
 
1
Tuotetalo, jolla myös konsultointia
 
1
Tuotetalossa, jossa softa ja rauta muodostavat yhdessä tuotteen
 
1
startup
 
1

Length

Max length74
Median length63
Mean length31.987879
Min length1

Characters and Unicode

Total characters26390
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.8%

Sample

1st rowKonsulttitalossa
2nd rowKonsulttitalossa
3rd rowYrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)
4th rowYrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)
5th rowTuotetalossa, jonka core-bisnes on softa

Common Values

ValueCountFrequency (%)
Konsulttitalossa 397
41.7%
Tuotetalossa, jonka core-bisnes on softa 238
25.0%
Yrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms) 111
 
11.7%
Consulting 27
 
2.8%
Julkinen tai kolmas sektori 23
 
2.4%
Product company with software as their core business 17
 
1.8%
A company where software is support role (for example banks or healthcare) 5
 
0.5%
. 1
 
0.1%
Alihankkija, konsultointi 1
 
0.1%
Digitoimisto 1
 
0.1%
Other values (4) 4
 
0.4%
(Missing) 127
 
13.3%

Length

2023-09-28T13:50:04.015396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
konsulttitalossa 397
13.1%
softa 350
11.5%
on 349
11.5%
tuotetalossa 239
 
7.9%
jonka 238
 
7.9%
core-bisnes 238
 
7.9%
jossa 112
 
3.7%
esim 111
 
3.7%
yms 111
 
3.7%
terveysala 111
 
3.7%
Other values (43) 775
25.6%

Most occurring characters

ValueCountFrequency (%)
s 3571
13.5%
o 2965
11.2%
t 2857
10.8%
a 2554
9.7%
2206
 
8.4%
n 1594
 
6.0%
e 1431
 
5.4%
i 1354
 
5.1%
l 1238
 
4.7%
u 843
 
3.2%
Other values (31) 5777
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22316
84.6%
Space Separator 2206
 
8.4%
Uppercase Letter 823
 
3.1%
Other Punctuation 575
 
2.2%
Dash Punctuation 238
 
0.9%
Close Punctuation 116
 
0.4%
Open Punctuation 116
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 3571
16.0%
o 2965
13.3%
t 2857
12.8%
a 2554
11.4%
n 1594
7.1%
e 1431
6.4%
i 1354
 
6.1%
l 1238
 
5.5%
u 843
 
3.8%
k 649
 
2.9%
Other values (16) 3260
14.6%
Uppercase Letter
ValueCountFrequency (%)
K 397
48.2%
T 240
29.2%
Y 111
 
13.5%
C 27
 
3.3%
J 23
 
2.8%
P 17
 
2.1%
A 6
 
0.7%
D 1
 
0.1%
S 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 574
99.8%
. 1
 
0.2%
Space Separator
ValueCountFrequency (%)
2206
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 238
100.0%
Close Punctuation
ValueCountFrequency (%)
) 116
100.0%
Open Punctuation
ValueCountFrequency (%)
( 116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23139
87.7%
Common 3251
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 3571
15.4%
o 2965
12.8%
t 2857
12.3%
a 2554
11.0%
n 1594
 
6.9%
e 1431
 
6.2%
i 1354
 
5.9%
l 1238
 
5.4%
u 843
 
3.6%
k 649
 
2.8%
Other values (25) 4083
17.6%
Common
ValueCountFrequency (%)
2206
67.9%
, 574
 
17.7%
- 238
 
7.3%
) 116
 
3.6%
( 116
 
3.6%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26277
99.6%
None 113
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 3571
13.6%
o 2965
11.3%
t 2857
10.9%
a 2554
9.7%
2206
 
8.4%
n 1594
 
6.1%
e 1431
 
5.4%
i 1354
 
5.2%
l 1238
 
4.7%
u 843
 
3.2%
Other values (29) 5664
21.6%
None
ValueCountFrequency (%)
ä 112
99.1%
ö 1
 
0.9%

Työaika
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)2.6%
Missing119
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean0.98405894
Minimum0.15
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:04.156436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.8
Q11
median1
Q31
95-th percentile1
Maximum1.4
Range1.25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.080982899
Coefficient of variation (CV)0.082294765
Kurtosis34.590744
Mean0.98405894
Median Absolute Deviation (MAD)0
Skewness-4.7954811
Sum819.7211
Variance0.0065582299
MonotonicityNot monotonic
2023-09-28T13:50:04.299268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 762
80.0%
0.8 32
 
3.4%
0.9 7
 
0.7%
0.5 5
 
0.5%
0.6 4
 
0.4%
0.7 2
 
0.2%
1.2 2
 
0.2%
0.95 2
 
0.2%
1.1 2
 
0.2%
0.987 2
 
0.2%
Other values (12) 13
 
1.4%
(Missing) 119
 
12.5%
ValueCountFrequency (%)
0.15 1
 
0.1%
0.3 1
 
0.1%
0.4 1
 
0.1%
0.5 5
 
0.5%
0.5001 1
 
0.1%
0.6 4
 
0.4%
0.7 2
 
0.2%
0.8 32
3.4%
0.85 1
 
0.1%
0.9 7
 
0.7%
ValueCountFrequency (%)
1.4 1
 
0.1%
1.33 1
 
0.1%
1.2 2
 
0.2%
1.15 1
 
0.1%
1.1 2
 
0.2%
1.06 1
 
0.1%
1 762
80.0%
0.99 2
 
0.2%
0.987 2
 
0.2%
0.967 1
 
0.1%

Kuinka suuren osan ajasta teet lähityönä toimistolla?
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct31
Distinct (%)3.7%
Missing124
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean0.32639493
Minimum0
Maximum1
Zeros131
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:04.454793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.2
Q30.6
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.32840066
Coefficient of variation (CV)1.0061451
Kurtosis-0.80914562
Mean0.32639493
Median Absolute Deviation (MAD)0.2
Skewness0.77690798
Sum270.255
Variance0.10784699
MonotonicityNot monotonic
2023-09-28T13:50:04.610150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 131
13.8%
0.2 98
10.3%
0.1 84
8.8%
0.05 73
 
7.7%
0.5 51
 
5.4%
0.8 45
 
4.7%
0.4 43
 
4.5%
0.9 42
 
4.4%
1 37
 
3.9%
0.6 36
 
3.8%
Other values (21) 188
19.7%
(Missing) 124
13.0%
ValueCountFrequency (%)
0 131
13.8%
0.01 27
 
2.8%
0.02 13
 
1.4%
0.025 1
 
0.1%
0.03 9
 
0.9%
0.04 3
 
0.3%
0.05 73
7.7%
0.08 2
 
0.2%
0.1 84
8.8%
0.15 23
 
2.4%
ValueCountFrequency (%)
1 37
3.9%
0.99 1
 
0.1%
0.95 16
 
1.7%
0.9 42
4.4%
0.85 2
 
0.2%
0.82 1
 
0.1%
0.8 45
4.7%
0.75 21
2.2%
0.7 7
 
0.7%
0.67 1
 
0.1%

Rooli
Text

MISSING 

Distinct390
Distinct (%)48.9%
Missing154
Missing (%)16.2%
Memory size14.9 KiB
2023-09-28T13:50:04.857260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length168
Median length70
Mean length20.992481
Min length2

Characters and Unicode

Total characters16752
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique310 ?
Unique (%)38.8%

Sample

1st rowFull stack developer
2nd rowFull-stack Developer
3rd rowFull-stack developer
4th rowLead Developer (fronttipainotus)
5th rowFull-stack developer
ValueCountFrequency (%)
developer 435
21.0%
software 205
 
9.9%
engineer 122
 
5.9%
senior 120
 
5.8%
full 90
 
4.3%
stack 88
 
4.2%
lead 71
 
3.4%
fullstack 68
 
3.3%
49
 
2.4%
architect 47
 
2.3%
Other values (250) 776
37.5%
2023-09-28T13:50:05.311883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2596
15.5%
1290
 
7.7%
r 1217
 
7.3%
l 1073
 
6.4%
o 1067
 
6.4%
t 1009
 
6.0%
a 924
 
5.5%
n 823
 
4.9%
i 668
 
4.0%
d 590
 
3.5%
Other values (58) 5495
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13816
82.5%
Uppercase Letter 1422
 
8.5%
Space Separator 1291
 
7.7%
Other Punctuation 113
 
0.7%
Dash Punctuation 67
 
0.4%
Open Punctuation 16
 
0.1%
Close Punctuation 16
 
0.1%
Math Symbol 8
 
< 0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2596
18.8%
r 1217
 
8.8%
l 1073
 
7.8%
o 1067
 
7.7%
t 1009
 
7.3%
a 924
 
6.7%
n 823
 
6.0%
i 668
 
4.8%
d 590
 
4.3%
p 543
 
3.9%
Other values (17) 3306
23.9%
Uppercase Letter
ValueCountFrequency (%)
S 391
27.5%
F 233
16.4%
D 204
14.3%
E 100
 
7.0%
O 69
 
4.9%
C 60
 
4.2%
A 59
 
4.1%
L 55
 
3.9%
T 44
 
3.1%
P 43
 
3.0%
Other values (15) 164
11.5%
Other Punctuation
ValueCountFrequency (%)
/ 50
44.2%
, 40
35.4%
& 14
 
12.4%
. 8
 
7.1%
: 1
 
0.9%
Decimal Number
ValueCountFrequency (%)
6 1
33.3%
5 1
33.3%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
1290
99.9%
  1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 66
98.5%
1
 
1.5%
Math Symbol
ValueCountFrequency (%)
+ 7
87.5%
> 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15238
91.0%
Common 1514
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2596
17.0%
r 1217
 
8.0%
l 1073
 
7.0%
o 1067
 
7.0%
t 1009
 
6.6%
a 924
 
6.1%
n 823
 
5.4%
i 668
 
4.4%
d 590
 
3.9%
p 543
 
3.6%
Other values (42) 4728
31.0%
Common
ValueCountFrequency (%)
1290
85.2%
- 66
 
4.4%
/ 50
 
3.3%
, 40
 
2.6%
( 16
 
1.1%
) 16
 
1.1%
& 14
 
0.9%
. 8
 
0.5%
+ 7
 
0.5%
6 1
 
0.1%
Other values (6) 6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16684
99.6%
None 67
 
0.4%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2596
15.6%
1290
 
7.7%
r 1217
 
7.3%
l 1073
 
6.4%
o 1067
 
6.4%
t 1009
 
6.0%
a 924
 
5.5%
n 823
 
4.9%
i 668
 
4.0%
d 590
 
3.5%
Other values (54) 5427
32.5%
None
ValueCountFrequency (%)
ä 55
82.1%
ö 11
 
16.4%
  1
 
1.5%
Punctuation
ValueCountFrequency (%)
1
100.0%

Kuukausipalkka
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct294
Distinct (%)35.3%
Missing120
Missing (%)12.6%
Infinite0
Infinite (%)0.0%
Mean5497.7209
Minimum0
Maximum13750
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:05.486958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3027.5
Q14300
median5300
Q36453
95-th percentile8500
Maximum13750
Range13750
Interquartile range (IQR)2153

Descriptive statistics

Standard deviation1785.2289
Coefficient of variation (CV)0.32472163
Kurtosis2.7033476
Mean5497.7209
Median Absolute Deviation (MAD)1000
Skewness1.0521378
Sum4574103.8
Variance3187042.2
MonotonicityNot monotonic
2023-09-28T13:50:05.656379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000 33
 
3.5%
4500 29
 
3.0%
7000 29
 
3.0%
6500 28
 
2.9%
5500 23
 
2.4%
5000 23
 
2.4%
5300 18
 
1.9%
4200 16
 
1.7%
5700 16
 
1.7%
4000 16
 
1.7%
Other values (284) 601
63.1%
(Missing) 120
 
12.6%
ValueCountFrequency (%)
0 1
 
0.1%
1125 1
 
0.1%
1179 1
 
0.1%
1200 1
 
0.1%
1240 1
 
0.1%
1800 1
 
0.1%
2000 3
0.3%
2200 1
 
0.1%
2340 1
 
0.1%
2400 2
0.2%
ValueCountFrequency (%)
13750 1
0.1%
13390 1
0.1%
13333 1
0.1%
13300 1
0.1%
12500 1
0.1%
12100 1
0.1%
12000 1
0.1%
11900 1
0.1%
11300 1
0.1%
11250 1
0.1%

Vuositulot
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct316
Distinct (%)38.1%
Missing122
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean67985.769
Minimum0
Maximum300000
Zeros14
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:05.822833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14366.25
Q152500
median66000
Q381250
95-th percentile115550
Maximum300000
Range300000
Interquartile range (IQR)28750

Descriptive statistics

Standard deviation31340.032
Coefficient of variation (CV)0.4609793
Kurtosis8.5106547
Mean67985.769
Median Absolute Deviation (MAD)14000
Skewness1.5197233
Sum56428188
Variance9.821976 × 108
MonotonicityNot monotonic
2023-09-28T13:50:05.989335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70000 25
 
2.6%
60000 24
 
2.5%
100000 22
 
2.3%
65000 22
 
2.3%
75000 21
 
2.2%
72000 20
 
2.1%
55000 17
 
1.8%
80000 16
 
1.7%
54000 14
 
1.5%
0 14
 
1.5%
Other values (306) 635
66.7%
(Missing) 122
 
12.8%
ValueCountFrequency (%)
0 14
1.5%
500 3
 
0.3%
1000 4
 
0.4%
1020 1
 
0.1%
1500 2
 
0.2%
2000 2
 
0.2%
2500 1
 
0.1%
3600 1
 
0.1%
3700 2
 
0.2%
4100 1
 
0.1%
ValueCountFrequency (%)
300000 1
 
0.1%
270000 1
 
0.1%
250000 1
 
0.1%
210000 2
0.2%
199000 1
 
0.1%
198000 1
 
0.1%
180000 1
 
0.1%
168714 1
 
0.1%
160000 3
0.3%
155000 1
 
0.1%

Vapaa kuvaus kokonaiskompensaatiomallista
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing660
Missing (%)69.3%
Memory size14.9 KiB
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
Kyllä
544 
Ei
212 
Muu
196 

Length

Max length5
Median length5
Mean length3.9201681
Min length2

Characters and Unicode

Total characters3732
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
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 rowKyllä
2nd rowKyllä
3rd rowMuu
4th rowKyllä
5th rowMuu

Common Values

ValueCountFrequency (%)
Kyllä 544
57.1%
Ei 212
 
22.3%
Muu 196
 
20.6%

Length

2023-09-28T13:50:06.150690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-28T13:50:06.269457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
kyllä 544
57.1%
ei 212
 
22.3%
muu 196
 
20.6%

Most occurring characters

ValueCountFrequency (%)
l 1088
29.2%
K 544
14.6%
y 544
14.6%
ä 544
14.6%
u 392
 
10.5%
E 212
 
5.7%
i 212
 
5.7%
M 196
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2780
74.5%
Uppercase Letter 952
 
25.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 1088
39.1%
y 544
19.6%
ä 544
19.6%
u 392
 
14.1%
i 212
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
K 544
57.1%
E 212
 
22.3%
M 196
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3732
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 1088
29.2%
K 544
14.6%
y 544
14.6%
ä 544
14.6%
u 392
 
10.5%
E 212
 
5.7%
i 212
 
5.7%
M 196
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3188
85.4%
None 544
 
14.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 1088
34.1%
K 544
17.1%
y 544
17.1%
u 392
 
12.3%
E 212
 
6.6%
i 212
 
6.6%
M 196
 
6.1%
None
ValueCountFrequency (%)
ä 544
100.0%
Distinct47
Distinct (%)90.4%
Missing900
Missing (%)94.5%
Memory size14.9 KiB
2023-09-28T13:50:06.556007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length211
Median length73.5
Mean length54.692308
Min length3

Characters and Unicode

Total characters2844
Distinct characters58
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)80.8%

Sample

1st rowEn tiedä
2nd rowMaksaa paremmin kuin moni muu, mutta osa kilpailijoista maksaa vielä enemmän
3rd rowEOS
4th rowyrityksen sisällä ei ole kilpailukykyinen. Samoja tehtäviä tekevät kollegat ansaitsevat enemmän
5th rowKompensaatio kilpailijoihin kilpailukykyinen, mutta inflaatiotarkistuksia ei ole vielä implementoitu täysimääräisesti. Myös firman sisällä eurooppalaisen etäduunarin liksa on halvemmasta päästä.
ValueCountFrequency (%)
ei 14
 
3.6%
en 13
 
3.3%
mutta 10
 
2.5%
enemmän 10
 
2.5%
on 8
 
2.0%
ole 7
 
1.8%
palkka 6
 
1.5%
i 5
 
1.3%
kilpailukykyinen 5
 
1.3%
saisin 5
 
1.3%
Other values (241) 311
78.9%
2023-09-28T13:50:07.052833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
343
12.1%
a 327
11.5%
i 242
 
8.5%
s 196
 
6.9%
e 194
 
6.8%
n 182
 
6.4%
t 174
 
6.1%
o 149
 
5.2%
l 144
 
5.1%
k 130
 
4.6%
Other values (48) 763
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2359
82.9%
Space Separator 343
 
12.1%
Uppercase Letter 64
 
2.3%
Other Punctuation 50
 
1.8%
Decimal Number 19
 
0.7%
Currency Symbol 3
 
0.1%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Dash Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 327
13.9%
i 242
10.3%
s 196
 
8.3%
e 194
 
8.2%
n 182
 
7.7%
t 174
 
7.4%
o 149
 
6.3%
l 144
 
6.1%
k 130
 
5.5%
m 104
 
4.4%
Other values (15) 517
21.9%
Uppercase Letter
ValueCountFrequency (%)
E 14
21.9%
S 14
21.9%
K 7
10.9%
I 7
10.9%
P 5
 
7.8%
T 4
 
6.2%
M 3
 
4.7%
V 2
 
3.1%
D 2
 
3.1%
J 1
 
1.6%
Other values (5) 5
 
7.8%
Decimal Number
ValueCountFrequency (%)
0 9
47.4%
1 3
 
15.8%
7 2
 
10.5%
2 2
 
10.5%
3 1
 
5.3%
4 1
 
5.3%
5 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 21
42.0%
. 18
36.0%
' 7
 
14.0%
/ 3
 
6.0%
% 1
 
2.0%
Space Separator
ValueCountFrequency (%)
343
100.0%
Currency Symbol
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2423
85.2%
Common 421
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 327
13.5%
i 242
10.0%
s 196
 
8.1%
e 194
 
8.0%
n 182
 
7.5%
t 174
 
7.2%
o 149
 
6.1%
l 144
 
5.9%
k 130
 
5.4%
m 104
 
4.3%
Other values (30) 581
24.0%
Common
ValueCountFrequency (%)
343
81.5%
, 21
 
5.0%
. 18
 
4.3%
0 9
 
2.1%
' 7
 
1.7%
1 3
 
0.7%
3
 
0.7%
/ 3
 
0.7%
) 2
 
0.5%
7 2
 
0.5%
Other values (8) 10
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2758
97.0%
None 83
 
2.9%
Currency Symbols 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
343
12.4%
a 327
11.9%
i 242
 
8.8%
s 196
 
7.1%
e 194
 
7.0%
n 182
 
6.6%
t 174
 
6.3%
o 149
 
5.4%
l 144
 
5.2%
k 130
 
4.7%
Other values (45) 677
24.5%
None
ValueCountFrequency (%)
ä 77
92.8%
ö 6
 
7.2%
Currency Symbols
ValueCountFrequency (%)
3
100.0%

Vapaa sana
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing901
Missing (%)94.6%
Memory size14.9 KiB

Palaute
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing908
Missing (%)95.4%
Memory size14.9 KiB
2023-09-28T13:50:07.372838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length587
Median length98.5
Mean length129.04545
Min length1

Characters and Unicode

Total characters5678
Distinct characters71
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st rowKiinnostaisi nähdä miten eri teknologioiden kanssa puuhastelu vaikuttaa palkkaan
2nd rowVuoden odotetuin ja tärkein kysely! Uskon että tästä on konkreettista hyötyä IT alan työntekijöille
3rd rowKiitos!
4th rowYrityksen koko olisi kiinnostava tieto myös
5th rowLaskuttaja ja työntekijä vaihtoehdot ei soveltunut koodaavalle softayrittäjälle.
ValueCountFrequency (%)
ja 15
 
2.1%
on 15
 
2.1%
voisi 11
 
1.6%
hyvä 8
 
1.1%
the 8
 
1.1%
että 7
 
1.0%
ei 6
 
0.8%
olisi 6
 
0.8%
kysely 5
 
0.7%
olla 5
 
0.7%
Other values (509) 623
87.9%
2023-09-28T13:50:08.066226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
665
11.7%
a 569
 
10.0%
i 493
 
8.7%
t 463
 
8.2%
s 379
 
6.7%
e 354
 
6.2%
n 339
 
6.0%
o 291
 
5.1%
k 272
 
4.8%
l 262
 
4.6%
Other values (61) 1591
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4742
83.5%
Space Separator 665
 
11.7%
Other Punctuation 139
 
2.4%
Uppercase Letter 91
 
1.6%
Dash Punctuation 12
 
0.2%
Open Punctuation 7
 
0.1%
Close Punctuation 7
 
0.1%
Final Punctuation 4
 
0.1%
Decimal Number 4
 
0.1%
Control 3
 
0.1%
Other values (2) 4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 569
12.0%
i 493
10.4%
t 463
9.8%
s 379
 
8.0%
e 354
 
7.5%
n 339
 
7.1%
o 291
 
6.1%
k 272
 
5.7%
l 262
 
5.5%
ä 201
 
4.2%
Other values (17) 1119
23.6%
Uppercase Letter
ValueCountFrequency (%)
H 9
 
9.9%
K 8
 
8.8%
S 7
 
7.7%
I 7
 
7.7%
E 6
 
6.6%
A 6
 
6.6%
V 6
 
6.6%
J 5
 
5.5%
O 5
 
5.5%
T 5
 
5.5%
Other values (14) 27
29.7%
Other Punctuation
ValueCountFrequency (%)
, 48
34.5%
. 38
27.3%
! 15
 
10.8%
" 12
 
8.6%
? 9
 
6.5%
/ 8
 
5.8%
: 5
 
3.6%
' 2
 
1.4%
% 1
 
0.7%
1
 
0.7%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
5 2
50.0%
Space Separator
ValueCountFrequency (%)
665
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Final Punctuation
ValueCountFrequency (%)
4
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Currency Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4833
85.1%
Common 845
 
14.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 569
11.8%
i 493
10.2%
t 463
 
9.6%
s 379
 
7.8%
e 354
 
7.3%
n 339
 
7.0%
o 291
 
6.0%
k 272
 
5.6%
l 262
 
5.4%
ä 201
 
4.2%
Other values (41) 1210
25.0%
Common
ValueCountFrequency (%)
665
78.7%
, 48
 
5.7%
. 38
 
4.5%
! 15
 
1.8%
- 12
 
1.4%
" 12
 
1.4%
? 9
 
1.1%
/ 8
 
0.9%
( 7
 
0.8%
) 7
 
0.8%
Other values (10) 24
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5443
95.9%
None 229
 
4.0%
Punctuation 5
 
0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
665
12.2%
a 569
10.5%
i 493
 
9.1%
t 463
 
8.5%
s 379
 
7.0%
e 354
 
6.5%
n 339
 
6.2%
o 291
 
5.3%
k 272
 
5.0%
l 262
 
4.8%
Other values (55) 1356
24.9%
None
ValueCountFrequency (%)
ä 201
87.8%
ö 27
 
11.8%
Ä 1
 
0.4%
Punctuation
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

Vastauskieli
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
fi
895 
en
 
57

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1904
Distinct characters4
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 rowfi
2nd rowfi
3rd rowfi
4th rowfi
5th rowfi

Common Values

ValueCountFrequency (%)
fi 895
94.0%
en 57
 
6.0%

Length

2023-09-28T13:50:08.233911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-28T13:50:08.340819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
fi 895
94.0%
en 57
 
6.0%

Most occurring characters

ValueCountFrequency (%)
f 895
47.0%
i 895
47.0%
e 57
 
3.0%
n 57
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1904
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 895
47.0%
i 895
47.0%
e 57
 
3.0%
n 57
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1904
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 895
47.0%
i 895
47.0%
e 57
 
3.0%
n 57
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 895
47.0%
i 895
47.0%
e 57
 
3.0%
n 57
 
3.0%

Vastaustunniste
Text

UNIQUE 

Distinct952
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:08.514202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters15232
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique952 ?
Unique (%)100.0%

Sample

1st row4002e856f911fb75
2nd row9f98e866e048193e
3rd row5edd9c2a607af530
4th rowf7b97a4ffaef7dd3
5th row64ba71cf1135e704
ValueCountFrequency (%)
4002e856f911fb75 1
 
0.1%
8e3c97e7849cf0e9 1
 
0.1%
eedc8e905c4ef95b 1
 
0.1%
5edd9c2a607af530 1
 
0.1%
f7b97a4ffaef7dd3 1
 
0.1%
64ba71cf1135e704 1
 
0.1%
ef5546d052e475af 1
 
0.1%
95a6b376691faa1e 1
 
0.1%
a92b72d161f644d7 1
 
0.1%
7dbd455f3d4628d8 1
 
0.1%
Other values (942) 942
98.9%
2023-09-28T13:50:08.875063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 1004
 
6.6%
a 976
 
6.4%
3 975
 
6.4%
d 973
 
6.4%
2 966
 
6.3%
4 963
 
6.3%
5 959
 
6.3%
f 959
 
6.3%
8 946
 
6.2%
b 945
 
6.2%
Other values (6) 5566
36.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9544
62.7%
Lowercase Letter 5688
37.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1004
10.5%
3 975
10.2%
2 966
10.1%
4 963
10.1%
5 959
10.0%
8 946
9.9%
6 936
9.8%
9 935
9.8%
0 930
9.7%
1 930
9.7%
Lowercase Letter
ValueCountFrequency (%)
a 976
17.2%
d 973
17.1%
f 959
16.9%
b 945
16.6%
c 918
16.1%
e 917
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9544
62.7%
Latin 5688
37.3%

Most frequent character per script

Common
ValueCountFrequency (%)
7 1004
10.5%
3 975
10.2%
2 966
10.1%
4 963
10.1%
5 959
10.0%
8 946
9.9%
6 936
9.8%
9 935
9.8%
0 930
9.7%
1 930
9.7%
Latin
ValueCountFrequency (%)
a 976
17.2%
d 973
17.1%
f 959
16.9%
b 945
16.6%
c 918
16.1%
e 917
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 1004
 
6.6%
a 976
 
6.4%
3 975
 
6.4%
d 973
 
6.4%
2 966
 
6.3%
4 963
 
6.3%
5 959
 
6.3%
f 959
 
6.3%
8 946
 
6.2%
b 945
 
6.2%
Other values (6) 5566
36.5%
Distinct357
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:09.124807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length168
Median length96
Mean length17.618697
Min length0

Characters and Unicode

Total characters16773
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique292 ?
Unique (%)30.7%

Sample

1st row*Full-stack Developer
2nd row*Full-stack Developer
3rd row
4th row*Full-stack Developer
5th row
ValueCountFrequency (%)
developer 445
22.5%
software 176
 
8.9%
full-stack 161
 
8.1%
senior 120
 
6.1%
engineer 120
 
6.1%
lead 71
 
3.6%
49
 
2.5%
architect 47
 
2.4%
frontend 47
 
2.4%
devops 38
 
1.9%
Other values (248) 703
35.6%
2023-09-28T13:50:09.582726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2581
15.4%
r 1196
 
7.1%
1191
 
7.1%
l 1082
 
6.5%
o 1056
 
6.3%
t 981
 
5.8%
a 898
 
5.4%
n 823
 
4.9%
i 663
 
4.0%
p 551
 
3.3%
Other values (59) 5751
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13517
80.6%
Uppercase Letter 1530
 
9.1%
Space Separator 1192
 
7.1%
Other Punctuation 308
 
1.8%
Dash Punctuation 183
 
1.1%
Open Punctuation 16
 
0.1%
Close Punctuation 16
 
0.1%
Math Symbol 8
 
< 0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2581
19.1%
r 1196
 
8.8%
l 1082
 
8.0%
o 1056
 
7.8%
t 981
 
7.3%
a 898
 
6.6%
n 823
 
6.1%
i 663
 
4.9%
p 551
 
4.1%
v 534
 
4.0%
Other values (17) 3152
23.3%
Uppercase Letter
ValueCountFrequency (%)
S 360
23.5%
D 350
22.9%
F 234
15.3%
E 100
 
6.5%
C 64
 
4.2%
O 60
 
3.9%
A 59
 
3.9%
L 55
 
3.6%
T 44
 
2.9%
P 43
 
2.8%
Other values (15) 161
10.5%
Other Punctuation
ValueCountFrequency (%)
* 197
64.0%
/ 50
 
16.2%
, 38
 
12.3%
& 14
 
4.5%
. 8
 
2.6%
: 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
6 1
33.3%
5 1
33.3%
Space Separator
ValueCountFrequency (%)
1191
99.9%
  1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 182
99.5%
1
 
0.5%
Math Symbol
ValueCountFrequency (%)
+ 7
87.5%
> 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15047
89.7%
Common 1726
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2581
17.2%
r 1196
 
7.9%
l 1082
 
7.2%
o 1056
 
7.0%
t 981
 
6.5%
a 898
 
6.0%
n 823
 
5.5%
i 663
 
4.4%
p 551
 
3.7%
v 534
 
3.5%
Other values (42) 4682
31.1%
Common
ValueCountFrequency (%)
1191
69.0%
* 197
 
11.4%
- 182
 
10.5%
/ 50
 
2.9%
, 38
 
2.2%
( 16
 
0.9%
) 16
 
0.9%
& 14
 
0.8%
. 8
 
0.5%
+ 7
 
0.4%
Other values (7) 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16705
99.6%
None 67
 
0.4%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2581
15.5%
r 1196
 
7.2%
1191
 
7.1%
l 1082
 
6.5%
o 1056
 
6.3%
t 981
 
5.9%
a 898
 
5.4%
n 823
 
4.9%
i 663
 
4.0%
p 551
 
3.3%
Other values (55) 5683
34.0%
None
ValueCountFrequency (%)
ä 55
82.1%
ö 11
 
16.4%
  1
 
1.5%
Punctuation
ValueCountFrequency (%)
1
100.0%

Kk-tulot (laskennallinen)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct316
Distinct (%)38.1%
Missing122
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean5665.4807
Minimum0
Maximum25000
Zeros14
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:09.766046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1197.1875
Q14375
median5500
Q36770.8333
95-th percentile9629.1667
Maximum25000
Range25000
Interquartile range (IQR)2395.8333

Descriptive statistics

Standard deviation2611.6693
Coefficient of variation (CV)0.4609793
Kurtosis8.5106547
Mean5665.4807
Median Absolute Deviation (MAD)1166.6667
Skewness1.5197233
Sum4702349
Variance6820816.7
MonotonicityNot monotonic
2023-09-28T13:50:09.936178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5833.333333 25
 
2.6%
5000 24
 
2.5%
8333.333333 22
 
2.3%
5416.666667 22
 
2.3%
6250 21
 
2.2%
6000 20
 
2.1%
4583.333333 17
 
1.8%
6666.666667 16
 
1.7%
4500 14
 
1.5%
0 14
 
1.5%
Other values (306) 635
66.7%
(Missing) 122
 
12.8%
ValueCountFrequency (%)
0 14
1.5%
41.66666667 3
 
0.3%
83.33333333 4
 
0.4%
85 1
 
0.1%
125 2
 
0.2%
166.6666667 2
 
0.2%
208.3333333 1
 
0.1%
300 1
 
0.1%
308.3333333 2
 
0.2%
341.6666667 1
 
0.1%
ValueCountFrequency (%)
25000 1
 
0.1%
22500 1
 
0.1%
20833.33333 1
 
0.1%
17500 2
0.2%
16583.33333 1
 
0.1%
16500 1
 
0.1%
15000 1
 
0.1%
14059.5 1
 
0.1%
13333.33333 3
0.3%
12916.66667 1
 
0.1%

Kk-tulot (laskennallinen, normalisoitu)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct339
Distinct (%)40.9%
Missing124
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean5772.3208
Minimum0
Maximum28125
Zeros14
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-09-28T13:50:10.093748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1625.1645
Q14476.0417
median5568.4167
Q36791.6667
95-th percentile9709.6181
Maximum28125
Range28125
Interquartile range (IQR)2315.625

Descriptive statistics

Standard deviation2711.0464
Coefficient of variation (CV)0.46966316
Kurtosis12.402105
Mean5772.3208
Median Absolute Deviation (MAD)1151.75
Skewness1.9935415
Sum4779481.6
Variance7349772.7
MonotonicityNot monotonic
2023-09-28T13:50:10.255552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5833.333333 25
 
2.6%
5000 23
 
2.4%
5416.666667 22
 
2.3%
8333.333333 21
 
2.2%
6250 21
 
2.2%
6000 18
 
1.9%
4583.333333 17
 
1.8%
6666.666667 16
 
1.7%
0 14
 
1.5%
7500 12
 
1.3%
Other values (329) 639
67.1%
(Missing) 124
 
13.0%
ValueCountFrequency (%)
0 14
1.5%
41.66666667 3
 
0.3%
83.33333333 4
 
0.4%
85 1
 
0.1%
125 1
 
0.1%
138.8888889 1
 
0.1%
166.6666667 2
 
0.2%
208.3333333 1
 
0.1%
300 1
 
0.1%
308.3333333 2
 
0.2%
ValueCountFrequency (%)
28125 1
0.1%
25000 1
0.1%
22500 1
0.1%
20833.33333 1
0.1%
17500 2
0.2%
16583.33333 1
0.1%
16500 1
0.1%
15000 1
0.1%
14059.5 1
0.1%
13888.88889 2
0.2%

Interactions

2023-09-28T13:49:54.612629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:42.487537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.743577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.087323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.160789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.224097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.299786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:49.515813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:50.916674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.143958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.354120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:54.729855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:42.618356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.865756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.191297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.262151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.331338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.420094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:49.644520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.038783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.262137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.474500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:54.844116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:42.732980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.974303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.296747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.367654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.443665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.533799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:49.759143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.153648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.375216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.587075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:54.936497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:42.833468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:44.078308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.397886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.480077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.546440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.627537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:49.856915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.243899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.468964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.679654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:55.028468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:42.933292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:44.299749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.505318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.576711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.648632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.718977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:50.111322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.334843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.557990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.772020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:55.122057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.038275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:44.406524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.609756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.680340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.751492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.813219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:50.203390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.429214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.646657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.862250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:55.238393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.156737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:44.520010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.699290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.770436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.840543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.928214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:50.320910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.549624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.766875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.988019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:55.357662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.272966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:44.633312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.793317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.863256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.935505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:49.044713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:50.444687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.669683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.884381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:54.114979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:55.482191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.394202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:44.752293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.884988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.954104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.026380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:49.165814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:50.567605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.791164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.008314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:54.242253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:55.600325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.510181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:44.865447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:45.975219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.041389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.118302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:49.281854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:50.684953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:51.909922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.123073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:54.370203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:55.717261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:43.629339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:44.976602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:46.068328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:47.130805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:48.208648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:49.401038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:50.800920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:52.027210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:53.239576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-28T13:49:54.499840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-09-28T13:50:10.394297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Työkokemus alalta (vuosina)Tulojen muutos viime vuodesta (%)Montako vuotta olet tehnyt laskuttavaa työtä alalla?Tuntilaskutus (ALV 0%, euroina)Vuosilaskutus (ALV 0%, euroina)TyöaikaKuinka suuren osan ajasta teet lähityönä toimistolla?KuukausipalkkaVuositulotKk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)Palkansaaja vai laskuttajaOletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2022 jälkeen?IkäSukupuoliHankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?Mistä asiakkaat ovat?KaupunkiMillaisessa yrityksessä työskentelet?Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?Vastauskieli
Työkokemus alalta (vuosina)1.000-0.2290.2010.0820.0660.0900.0020.5710.5240.5240.5240.1700.1030.3860.1790.0000.0000.0000.0000.0760.026
Tulojen muutos viime vuodesta (%)-0.2291.000-0.1690.0970.073-0.0300.0430.0280.0370.0370.0500.0000.1710.0000.0000.0000.0000.0000.1510.0000.000
Montako vuotta olet tehnyt laskuttavaa työtä alalla?0.201-0.1691.0000.2030.088NaNNaNNaNNaNNaNNaN1.0000.4540.3060.1250.0850.0000.0000.0001.0000.080
Tuntilaskutus (ALV 0%, euroina)0.0820.0970.2031.0000.528NaNNaNNaNNaNNaNNaN1.0000.1560.3800.5600.4490.0000.0000.0001.0000.660
Vuosilaskutus (ALV 0%, euroina)0.0660.0730.0880.5281.000NaNNaNNaNNaNNaNNaN1.0000.0000.1160.3190.2890.4290.0000.0001.0000.489
Työaika0.090-0.030NaNNaNNaN1.000-0.0480.2430.1970.1970.0211.0000.0000.0800.0910.0000.0000.4830.1500.0750.000
Kuinka suuren osan ajasta teet lähityönä toimistolla?0.0020.043NaNNaNNaN-0.0481.000-0.054-0.042-0.042-0.0361.0000.0770.0100.0000.0000.0000.0000.0000.0350.000
Kuukausipalkka0.5710.028NaNNaNNaN0.243-0.0541.0000.8880.8880.8481.0000.0400.1880.1630.0000.0000.2940.1560.2650.076
Vuositulot0.5240.037NaNNaNNaN0.197-0.0420.8881.0001.0000.9671.0000.0800.1430.1100.0000.0000.4350.0990.2390.069
Kk-tulot (laskennallinen)0.5240.037NaNNaNNaN0.197-0.0420.8881.0001.0000.9671.0000.0800.1430.1100.0000.0000.4350.0990.2390.069
Kk-tulot (laskennallinen, normalisoitu)0.5240.050NaNNaNNaN0.021-0.0360.8480.9670.9671.0001.0000.0750.1190.0750.0000.0000.5180.0560.2440.086
Palkansaaja vai laskuttaja0.1700.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.4630.0210.0861.0001.0001.0001.0000.7340.009
Oletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2022 jälkeen?0.1030.1710.4540.1560.0000.0000.0770.0400.0800.0800.0750.4631.0000.0880.0000.2120.2220.0190.1880.2360.000
Ikä0.3860.0000.3060.3800.1160.0800.0100.1880.1430.1430.1190.0210.0881.0000.0000.2800.0980.0000.2240.0440.063
Sukupuoli0.1790.0000.1250.5600.3190.0910.0000.1630.1100.1100.0750.0860.0000.0001.0000.0000.0000.0000.0600.0670.096
Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?0.0000.0000.0850.4490.2890.0000.0000.0000.0000.0000.0001.0000.2120.2800.0001.0000.5330.0000.0001.0000.965
Mistä asiakkaat ovat?0.0000.0000.0000.0000.4290.0000.0000.0000.0000.0000.0001.0000.2220.0980.0000.5331.0000.0000.0001.0000.674
Kaupunki0.0000.0000.0000.0000.0000.4830.0000.2940.4350.4350.5181.0000.0190.0000.0000.0000.0001.0000.0000.1710.100
Millaisessa yrityksessä työskentelet?0.0000.1510.0000.0000.0000.1500.0000.1560.0990.0990.0561.0000.1880.2240.0600.0000.0000.0001.0000.0780.962
Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?0.0760.0001.0001.0001.0000.0750.0350.2650.2390.2390.2440.7340.2360.0440.0671.0001.0000.1710.0781.0000.000
Vastauskieli0.0260.0000.0800.6600.4890.0000.0000.0760.0690.0690.0860.0090.0000.0630.0960.9650.6740.1000.9620.0001.000

Missing values

2023-09-28T13:49:56.104641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-28T13:49:56.607017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-28T13:49:57.110290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TimestampPalkansaaja vai laskuttajaOletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2022 jälkeen?IkäSukupuoliTyökokemus alalta (vuosina)KoulutustaustasiTulojen muutos viime vuodesta (%)Montako vuotta olet tehnyt laskuttavaa työtä alalla?PalvelutTuntilaskutus (ALV 0%, euroina)Vuosilaskutus (ALV 0%, euroina)Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?Mistä asiakkaat ovat?TyöpaikkaKaupunkiMillaisessa yrityksessä työskentelet?TyöaikaKuinka suuren osan ajasta teet lähityönä toimistolla?RooliKuukausipalkkaVuositulotVapaa kuvaus kokonaiskompensaatiomallistaOnko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? (muut vastaukset)Vapaa sanaPalauteVastauskieliVastaustunnisteRooli (normalisoitu)Kk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)
12023-09-04 09:23:56.606PalkansaajaEi36-40mies13.0Ammattikoulu5.0NaNNaNNaNNaNNaNNaNMavericksTurkuKonsulttitalossa1.00.20Full stack developer8000.0100000.0Osa laskutuksestaKylläNoneNaNNaNfi4002e856f911fb75*Full-stack Developer8333.3333338333.333333
22023-09-04 09:26:51.993PalkansaajaEi26-30mies9.0Tietojenkäsittelyn tradenomi0.0NaNNaNNaNNaNNaNNaNNaNTurkuKonsulttitalossa1.00.00Full-stack Developer7000.085000.0NaNKylläNoneNaNNaNfi9f98e866e048193e*Full-stack Developer7083.3333337083.333333
32023-09-04 09:27:26.367Laskuttajapalkansaaja → laskuttaja31-35mies11.0DINaN0.5Frontend95.0NaNItseSuomestaNaNNaNNaNNaNNaNNaNNaNNaNNaNMuuNaNNaNNaNfi5edd9c2a607af530NaNNaN
42023-09-04 09:28:10.769PalkansaajaEi31-35mies5.0Filosofian kandidaatti, tietojenkäsittelytiedeNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuYrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)1.00.35Full-stack developer5200.063000.0NaNKylläNoneNaNNaNfif7b97a4ffaef7dd3*Full-stack Developer5250.0000005250.000000
52023-09-04 09:28:56.952LaskuttajaEi36-40mies15.0Melkein DI0.02.0Full-stack lead devNaN150000.0ItseSuomestaNaNNaNNaNNaNNaNNaNNaNNaNNaNMuuNaNNaNNaNfi64ba71cf1135e704NaNNaN
62023-09-04 09:29:09.717PalkansaajaEi41-45mies22.0tradenomi5.0NaNNaNNaNNaNNaNNaNNaNPK-seutuYrityksessä, jossa softa on tukeva toiminto (esim pankit, terveysala, yms)1.00.75Lead Developer (fronttipainotus)6300.080000.0Ei muuta kuin palkkaKylläNoneNaNNaNfief5546d052e475afLead Developer (fronttipainotus)6666.6666676666.666667
72023-09-04 09:29:34.963LaskuttajaEi31-35mies10.0IT-tradenomi10.02.0Full-stack devausta80.0130000.0Itse, Käytän välitysfirmojaSuomestaNaNNaNNaNNaNNaNNaNNaNNaNNaNMuuNaNNaNNaNfi95a6b376691faa1eNaNNaN
82023-09-04 09:29:45.706PalkansaajaEi21-25mies7.0Amis0.0NaNNaNNaNNaNNaNNaNNaNPK-seutuTuotetalossa, jonka core-bisnes on softa1.00.10Full-stack developer4350.054375.0OptiotaKylläNoneTyön vaativuuteen & vastuihin nähden palkkaus on hyvä vaikkakin euromääräisesti saisi muualta enemmänNaNfia92b72d161f644d7*Full-stack Developer4531.2500004531.250000
92023-09-04 09:30:01.402PalkansaajaEi21-25mies2.0Tietotekniikan kandidaatin tutkinto133.0NaNNaNNaNNaNNaNNaNNaNJyväskyläKonsulttitalossa1.00.80Full Stack-kehittäjä3000.037500.0NaNKylläNoneNaNNaNfi7dbd455f3d4628d8Full Stack-kehittäjä3125.0000003125.000000
102023-09-04 09:30:36.236PalkansaajaEi31-35mies7.0Amis + keskeytetty AMK9.0NaNNaNNaNNaNNaNNaNGoforeTurkuKonsulttitalossa1.00.10Cloud Specialist4600.057500.0NaNEiNoneNaNNaNfi11f273cf7c5f141fCloud Specialist4791.6666674791.666667
TimestampPalkansaaja vai laskuttajaOletko siirtynyt palkansaajasta laskuttajaksi tai päinvastoin 1.10.2022 jälkeen?IkäSukupuoliTyökokemus alalta (vuosina)KoulutustaustasiTulojen muutos viime vuodesta (%)Montako vuotta olet tehnyt laskuttavaa työtä alalla?PalvelutTuntilaskutus (ALV 0%, euroina)Vuosilaskutus (ALV 0%, euroina)Hankitko asiakkaasi itse suoraan vai käytätkö välitysfirmojen palveluita?Mistä asiakkaat ovat?TyöpaikkaKaupunkiMillaisessa yrityksessä työskentelet?TyöaikaKuinka suuren osan ajasta teet lähityönä toimistolla?RooliKuukausipalkkaVuositulotVapaa kuvaus kokonaiskompensaatiomallistaOnko palkkasi nykyroolissasi mielestäsi kilpailukykyinen?Onko palkkasi nykyroolissasi mielestäsi kilpailukykyinen? (muut vastaukset)Vapaa sanaPalauteVastauskieliVastaustunnisteRooli (normalisoitu)Kk-tulot (laskennallinen)Kk-tulot (laskennallinen, normalisoitu)
9452023-09-22 07:37:56.656PalkansaajaEi36-40nainen12.0Bachelor of engineering7.0NaNNaNNaNNaNNaNNaNNaNPK-seutuConsulting1.00.10Senior web developer, accessibility specialist4400.053000.0NaNMuuDon't knowNaNNaNene15f8f90dfb21f6dSenior web developer, accessibility specialist4416.6666674416.666667
9462023-09-22 08:23:41.855PalkansaajaEi36-40nainen9.0PhD13.0NaNNaNNaNNaNNaNNaNNaNPK-seutuConsulting0.80.60Senior web developer, Techical competence manager5200.067600.0NaNEiNoneNaNNaNenfebb1f96fbd945dfSenior web developer, Techical competence manager5633.3333337041.666667
9472023-09-22 08:27:22.314PalkansaajaEi46-50NaNNaNNaN6.5NaNNaNNaNNaNNaNNaNExovePK-seutuConsulting1.00.10Senior developer4920.062000.0NaNMuuNaNNaNNaNen568b1f66e9bb9df1*Senior Developer5166.6666675166.666667
9482023-09-22 08:31:34.030PalkansaajaEi26-30nainen7.0Master of science in IT0.0NaNNaNNaNNaNNaNNaNNaNOuluConsulting1.00.30Full stack software developer4900.062500.0NaNKylläNoneNaNNaNen89cb1ba7ce1f4d0bFull stack software developer5208.3333335208.333333
9492023-09-22 08:33:02.297PalkansaajaEi26-30mies8.0Vocational college + some university8.0NaNNaNNaNNaNNaNNaNNaNOuluConsulting1.00.90Senior Developer / Manager5000.068000.0NaNKylläNoneNaNNaNendb789ae07b9335cfSenior Developer / Manager5666.6666675666.666667
9502023-09-22 10:58:37.431PalkansaajaEi26-30mies4.0Bachelor's degree in media engineeering19.0NaNNaNNaNNaNNaNNaNNaNPK-seutuConsulting1.00.60Support Developer3100.040000.0NaNKylläNoneNaNNaNeneac033cd827855ffSupport Developer3333.3333333333.333333
9512023-09-22 11:29:22.044PalkansaajaEi41-45nainen0.0Trade School certificate in relevant industry, Bachelor degree in other disciplineNaNNaNNaNNaNNaNNaNNaNNaNPK-seutuProduct company with software as their core business1.00.60Fullstack Web Developer3000.0NaNNaNEiNoneNaNNaNen7f95f8371025e08e*Full-stack DeveloperNaNNaN
9522023-09-22 13:23:28.220PalkansaajaEi31-35mies8.0Bachelor in IT9.0NaNNaNNaNNaNNaNNaNNaNOuluConsulting1.00.05Software Developer4600.058000.0Just the salaryMuuI belive it's in the middle. I could probably earn a bit more, or a bit less elsewhere with the same salary model. Naturally it's not competitive wrt. provisiopalkka, but it's not really fair to compare the two.NaNNaNen6a25fcf09d34bf24Software Developer4833.3333334833.333333
9532023-09-22 14:19:12.388PalkansaajaEi26-30mies4.0Bachelor of engineering0.0NaNNaNNaNNaNNaNNaNExovePK-seutuProduct company with software as their core business1.00.05Support developer3700.03700.0100% salaryKylläNoneNaNNaNen5e2117c3d44e3a3fSupport developer308.333333308.333333
9542023-09-22 14:20:34.855LaskuttajaEi41-45NaN22.0Industrial EngineeringNaN5.0Front end, back end, fullstack, web, mobile, Integrations, maintenance0.00.0Myself, AgenciesSuomestaNaNNaNNaNNaNNaNNaNNaNNaNNaNMuuNaNNaNNaNenbe713ab7df96851cNaNNaN