Pandas Profiling Report

Dataset statistics

Number of variables	14
Number of observations	424
Missing cells	993
Missing cells (%)	16.7%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	36.2 KiB
Average record size in memory	87.5 B

Variable types

DateTime	1
Categorical	9
Numeric	3
Boolean	1

Warnings

`Rooli` has a high cardinality: 226 distinct values	High cardinality
`Työpaikka` has a high cardinality: 67 distinct values	High cardinality
`Kilpailukykyinen` is highly correlated with `Vapaa sana`	High correlation
`Vapaa sana` is highly correlated with `Kilpailukykyinen` and 1 other fields	High correlation
`Työpaikka` is highly correlated with `Vapaa sana`	High correlation
`Sukupuoli` has 32 (7.5%) missing values	Missing
`Työaika` has 16 (3.8%) missing values	Missing
`Rooli` has 10 (2.4%) missing values	Missing
`Etä` has 145 (34.2%) missing values	Missing
`Kuukausipalkka` has 37 (8.7%) missing values	Missing
`Vuositulot` has 11 (2.6%) missing values	Missing
`Kilpailukykyinen` has 14 (3.3%) missing values	Missing
`Työpaikka` has 326 (76.9%) missing values	Missing
`Vapaa sana` has 391 (92.2%) missing values	Missing
`Vapaa sana` is uniformly distributed	Uniform
`Timestamp` has unique values	Unique

Reproduction

Analysis started	2021-02-19 14:47:37.610890
Analysis finished	2021-02-19 14:47:41.080082
Duration	3.47 seconds
Software version	pandas-profiling v2.10.1
Download configuration	config.yaml

Timestamp
Date

UNIQUE

Distinct	424
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	3.4 KiB

Minimum	2021-02-15 11:57:08.316000
Maximum	2021-02-19 16:39:14.831000

Histogram

Histogram with fixed size bins (bins=50)

Kaupunki
Categorical

Distinct	25
Distinct (%)	6.0%
Missing	4
Missing (%)	0.9%
Memory size	1.3 KiB

PK-Seutu	215
Tampere	99
Turku	42
Oulu	22
Jyväskylä	17
Other values (20)	25

Length

Max length	15
Median length	8
Mean length	7.257142857
Min length	2

Characters and Unicode

Total characters	3048
Distinct characters	39
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	18 ?
Unique (%)	4.3%

Sample

1st row	PK-Seutu
2nd row	Turku
3rd row	PK-Seutu
4th row	Tampere
5th row	PK-Seutu

Value	Count	Frequency (%)
PK-Seutu	215	50.7%
Tampere	99	23.3%
Turku	42	9.9%
Oulu	22	5.2%
Jyväskylä	17	4.0%
Kuopio	5	1.2%
Pori	2	0.5%
Ruotsi	1	0.2%
Wien	1	0.2%
Viimsi	1	0.2%
Other values (15)	15	3.5%
(Missing)	4	0.9%

Histogram of lengths of the category

Value	Count	Frequency (%)
pk-seutu	215	50.7%
tampere	99	23.3%
turku	42	9.9%
oulu	22	5.2%
jyväskylä	17	4.0%
kuopio	5	1.2%
pori	2	0.5%
viimsi	1	0.2%
new	1	0.2%
francisco	1	0.2%
Other values (19)	19	4.5%

Most occurring characters

Value	Count	Frequency (%)
u	570	18.7%
e	422	13.8%
K	223	7.3%
t	220	7.2%
P	218	7.2%
-	217	7.1%
S	217	7.1%
r	147	4.8%
T	142	4.7%
a	116	3.8%
Other values (29)	556	18.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1971	64.7%
Uppercase Letter	855	28.1%
Dash Punctuation	217	7.1%
Space Separator	4	0.1%
Other Punctuation	1	< 0.1%

Most frequent character per category

Value	Count	Frequency (%)
u	570	28.9%
e	422	21.4%
t	220	11.2%
r	147	7.5%
a	116	5.9%
p	105	5.3%
m	103	5.2%
k	60	3.0%
l	47	2.4%
ä	41	2.1%
Other values (10)	140	7.1%

Value	Count	Frequency (%)
K	223	26.1%
P	218	25.5%
S	217	25.4%
T	142	16.6%
O	22	2.6%
J	18	2.1%
E	3	0.4%
L	3	0.4%
V	2	0.2%
W	1	0.1%
Other values (6)	6	0.7%

Value	Count	Frequency (%)
-	217	100.0%

Value	Count	Frequency (%)
	4	100.0%

Value	Count	Frequency (%)
,	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2826	92.7%
Common	222	7.3%

Most frequent character per script

Value	Count	Frequency (%)
u	570	20.2%
e	422	14.9%
K	223	7.9%
t	220	7.8%
P	218	7.7%
S	217	7.7%
r	147	5.2%
T	142	5.0%
a	116	4.1%
p	105	3.7%
Other values (26)	446	15.8%

Value	Count	Frequency (%)
-	217	97.7%
	4	1.8%
,	1	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3007	98.7%
None	41	1.3%

Most frequent character per block

Value	Count	Frequency (%)
u	570	19.0%
e	422	14.0%
K	223	7.4%
t	220	7.3%
P	218	7.2%
-	217	7.2%
S	217	7.2%
r	147	4.9%
T	142	4.7%
a	116	3.9%
Other values (28)	515	17.1%

Value	Count	Frequency (%)
ä	41	100.0%

Ikä
Categorical

Distinct	7
Distinct (%)	1.7%
Missing	2
Missing (%)	0.5%
Memory size	908.0 B

31-35 v	142
26-30 v	104
36-40 v	92
41-45 v	46
21-25 v	26
Other values (2)	12

Length

Max length	7
Median length	7
Mean length	7
Min length	7

Characters and Unicode

Total characters	2954
Distinct characters	10
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	31-35 v
2nd row	31-35 v
3rd row	26-30 v
4th row	31-35 v
5th row	26-30 v

Value	Count	Frequency (%)
31-35 v	142	33.5%
26-30 v	104	24.5%
36-40 v	92	21.7%
41-45 v	46	10.8%
21-25 v	26	6.1%
46-50 v	7	1.7%
51-55 v	5	1.2%
(Missing)	2	0.5%

Histogram of lengths of the category

Value	Count	Frequency (%)
v	422	50.0%
31-35	142	16.8%
26-30	104	12.3%
36-40	92	10.9%
41-45	46	5.5%
21-25	26	3.1%
46-50	7	0.8%
51-55	5	0.6%

Most occurring characters

Value	Count	Frequency (%)
3	480	16.2%
-	422	14.3%
	422	14.3%
v	422	14.3%
5	236	8.0%
1	219	7.4%
6	203	6.9%
0	203	6.9%
4	191	6.5%
2	156	5.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1688	57.1%
Dash Punctuation	422	14.3%
Space Separator	422	14.3%
Lowercase Letter	422	14.3%

Most frequent character per category

Value	Count	Frequency (%)
3	480	28.4%
5	236	14.0%
1	219	13.0%
6	203	12.0%
0	203	12.0%
4	191	11.3%
2	156	9.2%

Value	Count	Frequency (%)
-	422	100.0%

Value	Count	Frequency (%)
	422	100.0%

Value	Count	Frequency (%)
v	422	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2532	85.7%
Latin	422	14.3%

Most frequent character per script

Value	Count	Frequency (%)
3	480	19.0%
-	422	16.7%
	422	16.7%
5	236	9.3%
1	219	8.6%
6	203	8.0%
0	203	8.0%
4	191	7.5%
2	156	6.2%

Value	Count	Frequency (%)
v	422	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2954	100.0%

Most frequent character per block

Value	Count	Frequency (%)
3	480	16.2%
-	422	14.3%
	422	14.3%
v	422	14.3%
5	236	8.0%
1	219	7.4%
6	203	6.9%
0	203	6.9%
4	191	6.5%
2	156	5.3%

Sukupuoli
Categorical

MISSING

Distinct	3
Distinct (%)	0.8%
Missing	32
Missing (%)	7.5%
Memory size	684.0 B

mies	356
nainen	28
muu	8

Length

Max length	6
Median length	4
Mean length	4.12244898
Min length	3

Characters and Unicode

Total characters	1616
Distinct characters	7
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	mies
2nd row	mies
3rd row	mies
4th row	mies
5th row	mies

Value	Count	Frequency (%)
mies	356	84.0%
nainen	28	6.6%
muu	8	1.9%
(Missing)	32	7.5%

Histogram of lengths of the category

Value	Count	Frequency (%)
mies	356	90.8%
nainen	28	7.1%
muu	8	2.0%

Most occurring characters

Value	Count	Frequency (%)
i	384	23.8%
e	384	23.8%
m	364	22.5%
s	356	22.0%
n	84	5.2%
a	28	1.7%
u	16	1.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1616	100.0%

Most frequent character per category

Value	Count	Frequency (%)
i	384	23.8%
e	384	23.8%
m	364	22.5%
s	356	22.0%
n	84	5.2%
a	28	1.7%
u	16	1.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1616	100.0%

Most frequent character per script

Value	Count	Frequency (%)
i	384	23.8%
e	384	23.8%
m	364	22.5%
s	356	22.0%
n	84	5.2%
a	28	1.7%
u	16	1.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1616	100.0%

Most frequent character per block

Value	Count	Frequency (%)
i	384	23.8%
e	384	23.8%
m	364	22.5%
s	356	22.0%
n	84	5.2%
a	28	1.7%
u	16	1.0%

Työkokemus
Real number (ℝ_≥0)

Distinct	27
Distinct (%)	6.4%
Missing	4
Missing (%)	0.9%
Infinite	0
Infinite (%)	0.0%

Mean	9.616666667
Minimum	0
Maximum	30
Zeros	3
Zeros (%)	0.7%
Memory size	3.4 KiB

Quantile statistics

Minimum	0
5-th percentile	2
Q1	5
median	9
Q3	13
95-th percentile	21
Maximum	30
Range	30
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	6.067103545
Coefficient of variation (CV)	0.6308946494
Kurtosis	-0.02410700448
Mean	9.616666667
Median Absolute Deviation (MAD)	4
Skewness	0.720885956
Sum	4039
Variance	36.80974543
Monotocity	Not monotonic

Histogram with fixed size bins (bins=27)

Value	Count	Frequency (%)
5	44	10.4%
10	34	8.0%
4	28	6.6%
7	26	6.1%
20	25	5.9%
3	24	5.7%
15	23	5.4%
2	22	5.2%
11	22	5.2%
13	22	5.2%
Other values (17)	150	35.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	3	0.7%
1	15	3.5%
2	22	5.2%
3	24	5.7%
4	28	6.6%

Value	Count	Frequency (%)
30	2	0.5%
25	5	1.2%
24	2	0.5%
23	4	0.9%
22	4	0.9%

Työsuhteen luonne
Categorical

Distinct	3
Distinct (%)	0.7%
Missing	1
Missing (%)	0.2%
Memory size	3.4 KiB

Työntekijä / palkollinen	378
Freelancer	23
Yrittäjä	22

Length

Max length	24
Median length	24
Mean length	22.40661939
Min length	8

Characters and Unicode

Total characters	9478
Distinct characters	20
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Työntekijä / palkollinen
2nd row	Työntekijä / palkollinen
3rd row	Työntekijä / palkollinen
4th row	Yrittäjä
5th row	Työntekijä / palkollinen

Value	Count	Frequency (%)
Työntekijä / palkollinen	378	89.2%
Freelancer	23	5.4%
Yrittäjä	22	5.2%
(Missing)	1	0.2%

Histogram of lengths of the category

Value	Count	Frequency (%)
	378	32.1%
työntekijä	378	32.1%
palkollinen	378	32.1%
freelancer	23	2.0%
yrittäjä	22	1.9%

Most occurring characters

Value	Count	Frequency (%)
n	1157	12.2%
l	1157	12.2%
e	825	8.7%
i	778	8.2%
k	756	8.0%
	756	8.0%
t	422	4.5%
ä	422	4.5%
a	401	4.2%
j	400	4.2%
Other values (10)	2404	25.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7921	83.6%
Space Separator	756	8.0%
Uppercase Letter	423	4.5%
Other Punctuation	378	4.0%

Most frequent character per category

Value	Count	Frequency (%)
n	1157	14.6%
l	1157	14.6%
e	825	10.4%
i	778	9.8%
k	756	9.5%
t	422	5.3%
ä	422	5.3%
a	401	5.1%
j	400	5.0%
y	378	4.8%
Other values (5)	1225	15.5%

Value	Count	Frequency (%)
T	378	89.4%
F	23	5.4%
Y	22	5.2%

Value	Count	Frequency (%)
	756	100.0%

Value	Count	Frequency (%)
/	378	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8344	88.0%
Common	1134	12.0%

Most frequent character per script

Value	Count	Frequency (%)
n	1157	13.9%
l	1157	13.9%
e	825	9.9%
i	778	9.3%
k	756	9.1%
t	422	5.1%
ä	422	5.1%
a	401	4.8%
j	400	4.8%
T	378	4.5%
Other values (8)	1648	19.8%

Value	Count	Frequency (%)
	756	66.7%
/	378	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8678	91.6%
None	800	8.4%

Most frequent character per block

Value	Count	Frequency (%)
n	1157	13.3%
l	1157	13.3%
e	825	9.5%
i	778	9.0%
k	756	8.7%
	756	8.7%
t	422	4.9%
a	401	4.6%
j	400	4.6%
T	378	4.4%
Other values (8)	1648	19.0%

Value	Count	Frequency (%)
ä	422	52.8%
ö	378	47.2%

Työaika
Categorical

MISSING

Distinct	5
Distinct (%)	1.2%
Missing	16
Missing (%)	3.8%
Memory size	3.4 KiB

1.0	384
0.8	20
0.5	2
0.6	1
0.7	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	1224
Distinct characters	7
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.5%

Sample

1st row	1.0
2nd row	1.0
3rd row	1.0
4th row	1.0
5th row	1.0

Value	Count	Frequency (%)
1.0	384	90.6%
0.8	20	4.7%
0.5	2	0.5%
0.6	1	0.2%
0.7	1	0.2%
(Missing)	16	3.8%

Histogram of lengths of the category

Value	Count	Frequency (%)
1.0	384	94.1%
0.8	20	4.9%
0.5	2	0.5%
0.6	1	0.2%
0.7	1	0.2%

Most occurring characters

Value	Count	Frequency (%)
.	408	33.3%
0	408	33.3%
1	384	31.4%
8	20	1.6%
5	2	0.2%
7	1	0.1%
6	1	0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	816	66.7%
Other Punctuation	408	33.3%

Most frequent character per category

Value	Count	Frequency (%)
0	408	50.0%
1	384	47.1%
8	20	2.5%
5	2	0.2%
7	1	0.1%
6	1	0.1%

Value	Count	Frequency (%)
.	408	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	1224	100.0%

Most frequent character per script

Value	Count	Frequency (%)
.	408	33.3%
0	408	33.3%
1	384	31.4%
8	20	1.6%
5	2	0.2%
7	1	0.1%
6	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1224	100.0%

Most frequent character per block

Value	Count	Frequency (%)
.	408	33.3%
0	408	33.3%
1	384	31.4%
8	20	1.6%
5	2	0.2%
7	1	0.1%
6	1	0.1%

Rooli
Categorical

HIGH CARDINALITY
MISSING

Distinct	226
Distinct (%)	54.6%
Missing	10
Missing (%)	2.4%
Memory size	3.4 KiB

Ohjelmistokehittäjä	33
full-stack	29
Full-stack	21
Arkkitehti	15
ohjelmistokehittäjä	14
Other values (221)	302

Length

Max length	67
Median length	18
Mean length	19.17874396
Min length	2

Characters and Unicode

Total characters	7940
Distinct characters	57
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	182 ?
Unique (%)	44.0%

Sample

1st row	Arkkitehti
2nd row	full-stack
3rd row	Full-stack ohjelmistokehittäjä
4th row	web-arkkitehti
5th row	Ohjelmistokehittäjä

Value	Count	Frequency (%)
Ohjelmistokehittäjä	33	7.8%
full-stack	29	6.8%
Full-stack	21	5.0%
Arkkitehti	15	3.5%
ohjelmistokehittäjä	14	3.3%
Full-stack ohjelmistokehittäjä	8	1.9%
arkkitehti	6	1.4%
full-stack ohjelmistokehittäjä	6	1.4%
DevOps	5	1.2%
Frontend	5	1.2%
Other values (216)	272	64.2%
(Missing)	10	2.4%

Histogram of lengths of the category

Value	Count	Frequency (%)
full-stack	126	16.5%
ohjelmistokehittäjä	98	12.9%
developer	52	6.8%
arkkitehti	34	4.5%
	30	3.9%
lead	27	3.5%
frontend	23	3.0%
senior	18	2.4%
kehittäjä	15	2.0%
backend	14	1.8%
Other values (165)	325	42.7%

Most occurring characters

Value	Count	Frequency (%)
t	831	10.5%
e	726	9.1%
i	585	7.4%
l	581	7.3%
k	448	5.6%
o	413	5.2%
s	380	4.8%
a	379	4.8%
	352	4.4%
h	318	4.0%
Other values (47)	2927	36.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6901	86.9%
Uppercase Letter	392	4.9%
Space Separator	353	4.4%
Dash Punctuation	152	1.9%
Other Punctuation	86	1.1%
Open Punctuation	24	0.3%
Close Punctuation	24	0.3%
Math Symbol	8	0.1%

Most frequent character per category

Value	Count	Frequency (%)
t	831	12.0%
e	726	10.5%
i	585	8.5%
l	581	8.4%
k	448	6.5%
o	413	6.0%
s	380	5.5%
a	379	5.5%
h	318	4.6%
j	297	4.3%
Other values (16)	1943	28.2%

Value	Count	Frequency (%)
F	89	22.7%
O	80	20.4%
S	44	11.2%
D	39	9.9%
A	24	6.1%
T	20	5.1%
L	16	4.1%
C	13	3.3%
P	10	2.6%
E	10	2.6%
Other values (11)	47	12.0%

Value	Count	Frequency (%)
,	50	58.1%
/	32	37.2%
&	3	3.5%
.	1	1.2%

Value	Count	Frequency (%)
	352	99.7%
	1	0.3%

Value	Count	Frequency (%)
-	152	100.0%

Value	Count	Frequency (%)
(	24	100.0%

Value	Count	Frequency (%)
)	24	100.0%

Value	Count	Frequency (%)
+	8	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7293	91.9%
Common	647	8.1%

Most frequent character per script

Value	Count	Frequency (%)
t	831	11.4%
e	726	10.0%
i	585	8.0%
l	581	8.0%
k	448	6.1%
o	413	5.7%
s	380	5.2%
a	379	5.2%
h	318	4.4%
j	297	4.1%
Other values (37)	2335	32.0%

Value	Count	Frequency (%)
	352	54.4%
-	152	23.5%
,	50	7.7%
/	32	4.9%
(	24	3.7%
)	24	3.7%
+	8	1.2%
&	3	0.5%
.	1	0.2%
	1	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	7640	96.2%
None	300	3.8%

Most frequent character per block

Value	Count	Frequency (%)
t	831	10.9%
e	726	9.5%
i	585	7.7%
l	581	7.6%
k	448	5.9%
o	413	5.4%
s	380	5.0%
a	379	5.0%
	352	4.6%
h	318	4.2%
Other values (44)	2627	34.4%

Value	Count	Frequency (%)
ä	284	94.7%
ö	15	5.0%
	1	0.3%

Etä
Categorical

MISSING

Distinct	2
Distinct (%)	0.7%
Missing	145
Missing (%)	34.2%
Memory size	676.0 B

Etä	178
50/50	101

Length

Max length	5
Median length	3
Mean length	3.724014337
Min length	3

Characters and Unicode

Total characters	1039
Distinct characters	6
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	50/50
2nd row	Etä
3rd row	Etä
4th row	Etä
5th row	Etä

Value	Count	Frequency (%)
Etä	178	42.0%
50/50	101	23.8%
(Missing)	145	34.2%

Histogram of lengths of the category

Value	Count	Frequency (%)
etä	178	63.8%
50/50	101	36.2%

Most occurring characters

Value	Count	Frequency (%)
5	202	19.4%
0	202	19.4%
E	178	17.1%
t	178	17.1%
ä	178	17.1%
/	101	9.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	404	38.9%
Lowercase Letter	356	34.3%
Uppercase Letter	178	17.1%
Other Punctuation	101	9.7%

Most frequent character per category

Value	Count	Frequency (%)
5	202	50.0%
0	202	50.0%

Value	Count	Frequency (%)
t	178	50.0%
ä	178	50.0%

Value	Count	Frequency (%)
/	101	100.0%

Value	Count	Frequency (%)
E	178	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	534	51.4%
Common	505	48.6%

Most frequent character per script

Value	Count	Frequency (%)
5	202	40.0%
0	202	40.0%
/	101	20.0%

Value	Count	Frequency (%)
E	178	33.3%
t	178	33.3%
ä	178	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	861	82.9%
None	178	17.1%

Most frequent character per block

Value	Count	Frequency (%)
5	202	23.5%
0	202	23.5%
E	178	20.7%
t	178	20.7%
/	101	11.7%

Value	Count	Frequency (%)
ä	178	100.0%

Kuukausipalkka
Real number (ℝ_≥0)

MISSING

Distinct	117
Distinct (%)	30.2%
Missing	37
Missing (%)	8.7%
Infinite	0
Infinite (%)	0.0%

Mean	4672.372093
Minimum	1666
Maximum	15000
Zeros	0
Zeros (%)	0.0%
Memory size	3.4 KiB

Quantile statistics

Minimum	1666
5-th percentile	2806
Q1	3825
median	4500
Q3	5500
95-th percentile	7000
Maximum	15000
Range	13334
Interquartile range (IQR)	1675

Descriptive statistics

Standard deviation	1322.304375
Coefficient of variation (CV)	0.2830049382
Kurtosis	8.972606581
Mean	4672.372093
Median Absolute Deviation (MAD)	750
Skewness	1.49001451
Sum	1808208
Variance	1748488.861
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
4000	22	5.2%
4500	20	4.7%
6000	15	3.5%
5000	15	3.5%
5500	14	3.3%
4800	11	2.6%
7000	11	2.6%
4300	11	2.6%
4200	10	2.4%
4100	9	2.1%
Other values (107)	249	58.7%
(Missing)	37	8.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1666	1	0.2%
1700	1	0.2%
1800	1	0.2%
2100	1	0.2%
2275	1	0.2%

Value	Count	Frequency (%)
15000	1	0.2%
8500	1	0.2%
8000	5	1.2%
7500	2	0.5%
7200	1	0.2%

Vuositulot
Real number (ℝ_≥0)

MISSING

Distinct	168
Distinct (%)	40.7%
Missing	11
Missing (%)	2.6%
Infinite	0
Infinite (%)	0.0%

Mean	65207.78814
Minimum	0
Maximum	250000
Zeros	2
Zeros (%)	0.5%
Memory size	3.4 KiB

Quantile statistics

Minimum	0
5-th percentile	35000
Q1	50000
median	60000
Q3	75000
95-th percentile	120000
Maximum	250000
Range	250000
Interquartile range (IQR)	25000

Descriptive statistics

Standard deviation	28701.19644
Coefficient of variation (CV)	0.4401498236
Kurtosis	8.438798453
Mean	65207.78814
Median Absolute Deviation (MAD)	12000
Skewness	2.181597667
Sum	26930816.5
Variance	823758677.4
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
55000	16	3.8%
60000	14	3.3%
75000	14	3.3%
50000	14	3.3%
65000	10	2.4%
62500	9	2.1%
85000	9	2.1%
80000	9	2.1%
52000	8	1.9%
40000	8	1.9%
Other values (158)	302	71.2%
(Missing)	11	2.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2	0.5%
4000	1	0.2%
6100	1	0.2%
7500	1	0.2%
20000	1	0.2%

Value	Count	Frequency (%)
250000	1	0.2%
200000	3	0.7%
190000	1	0.2%
180000	1	0.2%
155000	1	0.2%

Kilpailukykyinen
Boolean

HIGH CORRELATION
MISSING

Distinct	2
Distinct (%)	0.5%
Missing	14
Missing (%)	3.3%
Memory size	3.4 KiB

True	287
False	123
(Missing)	14

Common Values
Chart

Value	Count	Frequency (%)
True	287	67.7%
False	123	29.0%
(Missing)	14	3.3%

Työpaikka
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	67
Distinct (%)	68.4%
Missing	326
Missing (%)	76.9%
Memory size	3.4 KiB

Gofore	11
Vincit	6
Fraktio	4
Futurice	4
Arado	3
Other values (62)	70

Length

Max length	132
Median length	7
Mean length	10.59183673
Min length	2

Characters and Unicode

Total characters	1038
Distinct characters	53
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	56 ?
Unique (%)	57.1%

Sample

1st row	Questrade
2nd row	Digia Oyj
3rd row	Gofore
4th row	Oura Health
5th row	Wirepas

Value	Count	Frequency (%)
Gofore	11	2.6%
Vincit	6	1.4%
Fraktio	4	0.9%
Futurice	4	0.9%
Arado	3	0.7%
Mavericks	3	0.7%
Pankki	3	0.7%
KVTES-alainen kunnan omistama oy	2	0.5%
Qvik	2	0.5%
Siili	2	0.5%
Other values (57)	58	13.7%
(Missing)	326	76.9%

Histogram of lengths of the category

Value	Count	Frequency (%)
gofore	13	8.4%
oy	9	5.8%
vincit	6	3.9%
fraktio	4	2.6%
oyj	4	2.6%
mavericks	4	2.6%
futurice	4	2.6%
pankki	3	1.9%
siili	3	1.9%
omistama	3	1.9%
Other values (89)	102	65.8%

Most occurring characters

Value	Count	Frequency (%)
i	104	10.0%
a	82	7.9%
o	79	7.6%
e	73	7.0%
t	71	6.8%
	60	5.8%
r	57	5.5%
n	50	4.8%
l	41	3.9%
u	40	3.9%
Other values (43)	381	36.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	842	81.1%
Uppercase Letter	130	12.5%
Space Separator	60	5.8%
Other Punctuation	3	0.3%
Dash Punctuation	3	0.3%

Most frequent character per category

Value	Count	Frequency (%)
G	14	10.8%
O	13	10.0%
V	12	9.2%
S	11	8.5%
F	9	6.9%
A	7	5.4%
K	7	5.4%
C	6	4.6%
P	6	4.6%
E	5	3.8%
Other values (15)	40	30.8%

Value	Count	Frequency (%)
i	104	12.4%
a	82	9.7%
o	79	9.4%
e	73	8.7%
t	71	8.4%
r	57	6.8%
n	50	5.9%
l	41	4.9%
u	40	4.8%
k	40	4.8%
Other values (15)	205	24.3%

Value	Count	Frequency (%)
	60	100.0%

Value	Count	Frequency (%)
.	3	100.0%

Value	Count	Frequency (%)
-	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	972	93.6%
Common	66	6.4%

Most frequent character per script

Value	Count	Frequency (%)
i	104	10.7%
a	82	8.4%
o	79	8.1%
e	73	7.5%
t	71	7.3%
r	57	5.9%
n	50	5.1%
l	41	4.2%
u	40	4.1%
k	40	4.1%
Other values (40)	335	34.5%

Value	Count	Frequency (%)
	60	90.9%
.	3	4.5%
-	3	4.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1026	98.8%
None	12	1.2%

Most frequent character per block

Value	Count	Frequency (%)
i	104	10.1%
a	82	8.0%
o	79	7.7%
e	73	7.1%
t	71	6.9%
	60	5.8%
r	57	5.6%
n	50	4.9%
l	41	4.0%
u	40	3.9%
Other values (41)	369	36.0%

Value	Count	Frequency (%)
ä	11	91.7%
ö	1	8.3%

Vapaa sana
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct	32
Distinct (%)	97.0%
Missing	391
Missing (%)	92.2%
Memory size	3.4 KiB

palkan lisänä lounas- ja virkistysetu	2
Ei sinänsä liity suoraan palkkoihin, mutta olisi mielenkiintoista tietää miten palkka vaikuttaa työpaikan vaihtoon. Eli esim. Oletko vaihtanut/vaihtamassa/miettinyt vaihtamista, koska toisaalla maksetaan enemmän?	1
saispa lisää liksaa	1
Työskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.	1
Ihan OK. Edut myös kovat.	1
Other values (27)	27

Length

Max length	286
Median length	71
Mean length	95.54545455
Min length	7

Characters and Unicode

Total characters	3153
Distinct characters	55
Distinct categories	9 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	31 ?
Unique (%)	93.9%

Sample

1st row	Kuukausipalkkaan tulossa ihan juuri firman laajuinen pieni (muistaakseni 50 e) yleiskorotus + palkka nousee ainakin 2800 e/kk, kunhan valmistuisi.
2nd row	Työskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.
3rd row	palkan lisäksi kompensaatioon kuuluu varsin runsas ja suomen it-alalla uniikki etupaketti. pelkkä palkka ei välttämättä ole kilpailukykyinen, mutta koko kompensaatio yleisesti työstäni on ehdottomasti kilpailukykyinen.
4th row	Rahapalkan päälle tulee vielä kohtuullinen optiopotti, mutta se toki on lähinnä arpalippu
5th row	Osittain laskutukseen perustuva palkka joten vaihtelee.

Value	Count	Frequency (%)
palkan lisänä lounas- ja virkistysetu	2	0.5%
Ei sinänsä liity suoraan palkkoihin, mutta olisi mielenkiintoista tietää miten palkka vaikuttaa työpaikan vaihtoon. Eli esim. Oletko vaihtanut/vaihtamassa/miettinyt vaihtamista, koska toisaalla maksetaan enemmän?	1	0.2%
saispa lisää liksaa	1	0.2%
Työskentelen toimistolla, koska täällä ei ole ketään muita. Työnantajan puolesta voisin työskennellä myös kotoa.	1	0.2%
Ihan OK. Edut myös kovat.	1	0.2%
Palkka perustuu osittain laskutukseen, joten vuositulot vaihtelevat hieman.	1	0.2%
Työskentelen opintojen ohella, ensimmäisessä frontend devaajan työssä. Olen opiskellut reilu 2 vuotta yliopistossa. Palkkani on mielestäni nyt ihan ok, mutta tarkoituksena nostaa sitä 3000e /kk loppukesään mennessä.	1	0.2%
Korona-aika on lisännyt etätyön määrää. Aiemmin pari päivää viikossa etänä, nyt kokonaan. Paluuta vanhaan ei varmaankaan ole, ehkä päivä viikossa konttorilla ihan sosiaalisten kontaktien takia.	1	0.2%
Halpaa freelancer laskutusta oman tuotekehityksen sivussa	1	0.2%
Bonukset riippuu firman tuloksesta. Palkka olisi varmastikin enemmän muualla mutta uskoakseni linjassa kollegoideni kanssa.	1	0.2%
Other values (22)	22	5.2%
(Missing)	391	92.2%

Histogram of lengths of the category

Value	Count	Frequency (%)
ei	10	2.5%
palkka	9	2.3%
on	8	2.0%
ole	6	1.5%
mutta	6	1.5%
ja	6	1.5%
joten	4	1.0%
palkan	4	1.0%
nyt	4	1.0%
firman	4	1.0%
Other values (281)	334	84.6%

Most occurring characters

Value	Count	Frequency (%)
	365	11.6%
a	331	10.5%
i	271	8.6%
t	247	7.8%
n	216	6.9%
s	205	6.5%
e	203	6.4%
k	185	5.9%
l	161	5.1%
o	146	4.6%
Other values (45)	823	26.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2625	83.3%
Space Separator	365	11.6%
Other Punctuation	74	2.3%
Uppercase Letter	47	1.5%
Decimal Number	27	0.9%
Dash Punctuation	6	0.2%
Open Punctuation	3	0.1%
Close Punctuation	3	0.1%
Math Symbol	3	0.1%

Most frequent character per category

Value	Count	Frequency (%)
a	331	12.6%
i	271	10.3%
t	247	9.4%
n	216	8.2%
s	205	7.8%
e	203	7.7%
k	185	7.0%
l	161	6.1%
o	146	5.6%
u	118	4.5%
Other values (14)	542	20.6%

Value	Count	Frequency (%)
T	7	14.9%
P	7	14.9%
O	6	12.8%
E	6	12.8%
V	6	12.8%
S	4	8.5%
K	3	6.4%
I	2	4.3%
H	2	4.3%
R	1	2.1%
Other values (3)	3	6.4%

Value	Count	Frequency (%)
0	15	55.6%
1	3	11.1%
5	2	7.4%
2	2	7.4%
8	2	7.4%
6	2	7.4%
3	1	3.7%

Value	Count	Frequency (%)
.	38	51.4%
,	23	31.1%
/	5	6.8%
%	4	5.4%
"	2	2.7%
?	2	2.7%

Value	Count	Frequency (%)
	365	100.0%

Value	Count	Frequency (%)
(	3	100.0%

Value	Count	Frequency (%)
)	3	100.0%

Value	Count	Frequency (%)
+	3	100.0%

Value	Count	Frequency (%)
-	6	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2672	84.7%
Common	481	15.3%

Most frequent character per script

Value	Count	Frequency (%)
a	331	12.4%
i	271	10.1%
t	247	9.2%
n	216	8.1%
s	205	7.7%
e	203	7.6%
k	185	6.9%
l	161	6.0%
o	146	5.5%
u	118	4.4%
Other values (27)	589	22.0%

Value	Count	Frequency (%)
	365	75.9%
.	38	7.9%
,	23	4.8%
0	15	3.1%
-	6	1.2%
/	5	1.0%
%	4	0.8%
(	3	0.6%
)	3	0.6%
+	3	0.6%
Other values (8)	16	3.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3017	95.7%
None	136	4.3%

Most frequent character per block

Value	Count	Frequency (%)
	365	12.1%
a	331	11.0%
i	271	9.0%
t	247	8.2%
n	216	7.2%
s	205	6.8%
e	203	6.7%
k	185	6.1%
l	161	5.3%
o	146	4.8%
Other values (43)	687	22.8%

Value	Count	Frequency (%)
ä	112	82.4%
ö	24	17.6%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	Timestamp	Kaupunki	Ikä	Sukupuoli	Työkokemus	Työsuhteen luonne	Työaika	Rooli	Etä	Kuukausipalkka	Vuositulot	Kilpailukykyinen	Työpaikka	Vapaa sana
0	2021-02-15 11:57:08.316	PK-Seutu	31-35 v	NaN	10.0	Työntekijä / palkollinen	1.0	Arkkitehti	50/50	6500.0	83000.0	True	NaN	NaN
1	2021-02-15 11:57:19.676	Turku	31-35 v	mies	14.0	Työntekijä / palkollinen	1.0	full-stack	Etä	5000.0	62500.0	True	NaN	NaN
2	2021-02-15 11:58:03.592	PK-Seutu	26-30 v	mies	2.0	Työntekijä / palkollinen	1.0	Full-stack ohjelmistokehittäjä	Etä	2475.0	30000.0	False	NaN	NaN
3	2021-02-15 11:58:15.261	Tampere	31-35 v	mies	22.0	Yrittäjä	1.0	web-arkkitehti	Etä	4300.0	100000.0	True	NaN	NaN
4	2021-02-15 11:58:16.983	PK-Seutu	26-30 v	mies	2.0	Työntekijä / palkollinen	1.0	Ohjelmistokehittäjä	Etä	3000.0	37500.0	False	NaN	NaN
5	2021-02-15 11:58:49.454	PK-Seutu	41-45 v	mies	23.0	Työntekijä / palkollinen	1.0	Ohjelmistokehittäjä	NaN	8000.0	100000.0	True	NaN	NaN
6	2021-02-15 12:00:03.771	PK-Seutu	31-35 v	mies	10.0	Freelancer	1.0	Ohjelmistokehittäjä	Etä	6000.0	140000.0	True	NaN	NaN
7	2021-02-15 12:00:04.655	Tampere	31-35 v	NaN	10.0	Työntekijä / palkollinen	1.0	Ohjelmistokehittäjä	NaN	4250.0	54000.0	True	NaN	NaN
8	2021-02-15 12:01:00.769	Tampere	31-35 v	mies	6.0	Työntekijä / palkollinen	1.0	Lead developer	NaN	4000.0	50000.0	False	NaN	NaN
9	2021-02-15 12:02:03.577	Tallinna	31-35 v	mies	12.0	Freelancer	1.0	NaN	Etä	NaN	200000.0	True	Questrade	NaN

Last rows

	Timestamp	Kaupunki	Ikä	Sukupuoli	Työkokemus	Työsuhteen luonne	Työaika	Rooli	Etä	Kuukausipalkka	Vuositulot	Kilpailukykyinen	Työpaikka	Vapaa sana
414	2021-02-19 15:34:53.741	Tampere	26-30 v	muu	7.0	Työntekijä / palkollinen	1.0	Full-stack developer	50/50	5550.0	69400.0	True	NaN	NaN
415	2021-02-19 15:40:16.336	PK-Seutu	26-30 v	mies	5.0	Työntekijä / palkollinen	0.8	Full-stack/mobiili/design	Etä	7000.0	90000.0	True	Mavericks	NaN
416	2021-02-19 16:04:50.348	Tampere	36-40 v	mies	16.0	Työntekijä / palkollinen	1.0	Ohjelmistokehittäjä	NaN	4800.0	65000.0	True	NaN	Bonukset riippuu firman tuloksesta. Palkka olisi varmastikin enemmän muualla mutta uskoakseni linjassa kollegoideni kanssa.
417	2021-02-19 16:17:29.891	PK-Seutu	36-40 v	nainen	8.0	Työntekijä / palkollinen	NaN	Product Owner	50/50	4500.0	56200.0	True	NaN	NaN
418	2021-02-19 16:26:32.700	PK-Seutu	36-40 v	mies	16.0	Työntekijä / palkollinen	1.0	Mobile SW	Etä	8000.0	95000.0	True	Mavericks	NaN
419	2021-02-19 16:33:27.762	PK-Seutu	31-35 v	mies	11.0	Työntekijä / palkollinen	1.0	Full stack	50/50	7000.0	87500.0	True	Mavericks	NaN
420	2021-02-19 16:34:07.545	PK-Seutu	31-35 v	mies	12.0	Työntekijä / palkollinen	1.0	full-stack	Etä	8000.0	95000.0	True	Mavericks	NaN
421	2021-02-19 16:36:55.938	Tampere	41-45 v	mies	22.0	Työntekijä / palkollinen	0.8	ohjelmistokehittäjä (backend) / arkkitehti	Etä	4700.0	58750.0	False	NaN	NaN
422	2021-02-19 16:38:41.403	PK-Seutu	36-40 v	mies	2.0	Työntekijä / palkollinen	1.0	WordPress-kehittäjä	50/50	3000.0	37500.0	False	NaN	NaN
423	2021-02-19 16:39:14.831	Tampere	31-35 v	mies	5.0	Työntekijä / palkollinen	1.0	Data scientist	Etä	4300.0	53750.0	NaN	Wapice	NaN

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Pearson's r

Spearman's ρ

Kendall's τ

Cramér's V (φc)

Missing values

Sample

First rows

Last rows