Overview

Dataset statistics

Number of variables4
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory37.2 B

Variable types

Categorical1
Text2
Numeric1

Alerts

examin_ym has constant value ""Constant
respond_chartr_sdiv_id has unique valuesUnique
respond_chartr_sdiv_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:44:29.351820
Analysis finished2023-12-10 09:44:30.243984
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

examin_ym
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
202012
41 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202012
2nd row202012
3rd row202012
4th row202012
5th row202012

Common Values

ValueCountFrequency (%)
202012 41
100.0%

Length

2023-12-10T18:44:30.350945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:30.561984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202012 41
100.0%

respond_chartr_sdiv_id
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-10T18:44:30.937018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length9.6585366
Min length6

Characters and Unicode

Total characters396
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st rowSEXDSTN_M
2nd rowSEXDSTN_F
3rd rowAGRDE_20
4th rowAGRDE_30
5th rowAGRDE_40
ValueCountFrequency (%)
sexdstn_m 1
 
2.4%
oc_area_gn 1
 
2.4%
hshld_income_300 1
 
2.4%
hshld_income_300_500 1
 
2.4%
hshld_income_500_700 1
 
2.4%
hshld_income_700 1
 
2.4%
job_se 1
 
2.4%
job_sl 1
 
2.4%
job_gmw 1
 
2.4%
job_wc 1
 
2.4%
Other values (31) 31
75.6%
2023-12-10T18:44:31.769675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 67
16.9%
A 37
 
9.3%
E 30
 
7.6%
O 28
 
7.1%
R 28
 
7.1%
C 26
 
6.6%
S 20
 
5.1%
D 18
 
4.5%
0 17
 
4.3%
M 16
 
4.0%
Other values (19) 109
27.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 301
76.0%
Connector Punctuation 67
 
16.9%
Decimal Number 28
 
7.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 37
12.3%
E 30
10.0%
O 28
 
9.3%
R 28
 
9.3%
C 26
 
8.6%
S 20
 
6.6%
D 18
 
6.0%
M 16
 
5.3%
J 14
 
4.7%
G 14
 
4.7%
Other values (11) 70
23.3%
Decimal Number
ValueCountFrequency (%)
0 17
60.7%
3 3
 
10.7%
5 3
 
10.7%
7 2
 
7.1%
6 1
 
3.6%
4 1
 
3.6%
2 1
 
3.6%
Connector Punctuation
ValueCountFrequency (%)
_ 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 301
76.0%
Common 95
 
24.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 37
12.3%
E 30
10.0%
O 28
 
9.3%
R 28
 
9.3%
C 26
 
8.6%
S 20
 
6.6%
D 18
 
6.0%
M 16
 
5.3%
J 14
 
4.7%
G 14
 
4.7%
Other values (11) 70
23.3%
Common
ValueCountFrequency (%)
_ 67
70.5%
0 17
 
17.9%
3 3
 
3.2%
5 3
 
3.2%
7 2
 
2.1%
6 1
 
1.1%
4 1
 
1.1%
2 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 67
16.9%
A 37
 
9.3%
E 30
 
7.6%
O 28
 
7.1%
R 28
 
7.1%
C 26
 
6.6%
S 20
 
5.1%
D 18
 
4.5%
0 17
 
4.3%
M 16
 
4.0%
Other values (19) 109
27.5%

respond_chartr_sdiv_nm
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-10T18:44:32.181713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length12
Mean length5.7073171
Min length2

Characters and Unicode

Total characters234
Distinct characters87
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

Unique41 ?
Unique (%)100.0%

Sample

1st row남성
2nd row여성
3rd row20대
4th row30대
5th row40대
ValueCountFrequency (%)
자녀 4
 
7.3%
미만 3
 
5.5%
기타/무직 1
 
1.8%
500이상700만원 1
 
1.8%
700만원 1
 
1.8%
이상 1
 
1.8%
자영업 1
 
1.8%
판매/서비스직 1
 
1.8%
기능/숙련/일반작업직 1
 
1.8%
사무/기술직 1
 
1.8%
Other values (40) 40
72.7%
2023-12-10T18:44:33.038789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17
 
7.3%
14
 
6.0%
대 10
 
4.3%
/ 8
 
3.4%
만 7
 
3.0%
자 6
 
2.6%
원 6
 
2.6%
도 6
 
2.6%
( 6
 
2.6%
) 6
 
2.6%
Other values (77) 148
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
72.6%
Decimal Number 28
 
12.0%
Space Separator 14
 
6.0%
Other Punctuation 9
 
3.8%
Open Punctuation 6
 
2.6%
Close Punctuation 6
 
2.6%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 10
 
5.9%
만 7
 
4.1%
자 6
 
3.5%
원 6
 
3.5%
도 6
 
3.5%
전 6
 
3.5%
기 5
 
2.9%
상 5
 
2.9%
직 5
 
2.9%
녀 5
 
2.9%
Other values (64) 109
64.1%
Decimal Number
ValueCountFrequency (%)
0 17
60.7%
3 3
 
10.7%
5 3
 
10.7%
7 2
 
7.1%
4 1
 
3.6%
2 1
 
3.6%
6 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/ 8
88.9%
& 1
 
11.1%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
72.6%
Common 64
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 10
 
5.9%
만 7
 
4.1%
자 6
 
3.5%
원 6
 
3.5%
도 6
 
3.5%
전 6
 
3.5%
기 5
 
2.9%
상 5
 
2.9%
직 5
 
2.9%
녀 5
 
2.9%
Other values (64) 109
64.1%
Common
ValueCountFrequency (%)
0 17
26.6%
14
21.9%
/ 8
12.5%
( 6
 
9.4%
) 6
 
9.4%
3 3
 
4.7%
5 3
 
4.7%
7 2
 
3.1%
4 1
 
1.6%
& 1
 
1.6%
Other values (3) 3
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
72.6%
ASCII 64
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17
26.6%
14
21.9%
/ 8
12.5%
( 6
 
9.4%
) 6
 
9.4%
3 3
 
4.7%
5 3
 
4.7%
7 2
 
3.1%
4 1
 
1.6%
& 1
 
1.6%
Other values (3) 3
 
4.7%
Hangul
ValueCountFrequency (%)
대 10
 
5.9%
만 7
 
4.1%
자 6
 
3.5%
원 6
 
3.5%
도 6
 
3.5%
전 6
 
3.5%
기 5
 
2.9%
상 5
 
2.9%
직 5
 
2.9%
녀 5
 
2.9%
Other values (64) 109
64.1%

rspns_rate
Real number (ℝ)

Distinct40
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.631707
Minimum1.1
Maximum51.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-10T18:44:33.532706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.1
Q13.9
median7.9
Q321.8
95-th percentile49.1
Maximum51.6
Range50.5
Interquartile range (IQR)17.9

Descriptive statistics

Standard deviation14.164011
Coefficient of variation (CV)0.96803545
Kurtosis0.97653285
Mean14.631707
Median Absolute Deviation (MAD)5.4
Skewness1.3153349
Sum599.9
Variance200.61922
MonotonicityNot monotonic
2023-12-10T18:44:33.857749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.9 2
 
4.9%
5.9 1
 
2.4%
7.9 1
 
2.4%
24.3 1
 
2.4%
31.6 1
 
2.4%
36.3 1
 
2.4%
6.9 1
 
2.4%
3.1 1
 
2.4%
51.6 1
 
2.4%
11.0 1
 
2.4%
Other values (30) 30
73.2%
ValueCountFrequency (%)
1.1 1
2.4%
1.8 1
2.4%
2.1 1
2.4%
2.2 1
2.4%
2.5 1
2.4%
2.6 1
2.4%
2.9 1
2.4%
3.1 1
2.4%
3.4 1
2.4%
3.8 1
2.4%
ValueCountFrequency (%)
51.6 1
2.4%
50.9 1
2.4%
49.1 1
2.4%
36.3 1
2.4%
35.5 1
2.4%
31.6 1
2.4%
25.4 1
2.4%
25.0 1
2.4%
24.3 1
2.4%
23.4 1
2.4%

Interactions

2023-12-10T18:44:29.795705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:44:34.167699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
respond_chartr_sdiv_idrespond_chartr_sdiv_nmrspns_rate
respond_chartr_sdiv_id1.0001.0001.000
respond_chartr_sdiv_nm1.0001.0001.000
rspns_rate1.0001.0001.000

Missing values

2023-12-10T18:44:30.011076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:44:30.170048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

examin_ymrespond_chartr_sdiv_idrespond_chartr_sdiv_nmrspns_rate
0202012SEXDSTN_M남성49.1
1202012SEXDSTN_F여성50.9
2202012AGRDE_2020대18.2
3202012AGRDE_3030대25.0
4202012AGRDE_4040대21.4
5202012AGRDE_5050대20.0
6202012AGRDE_6060대15.4
7202012OC_AREA_SU서울23.4
8202012OC_AREA_BS부산7.4
9202012OC_AREA_DG대구5.2
examin_ymrespond_chartr_sdiv_idrespond_chartr_sdiv_nmrspns_rate
31202012JOB_BM경영/관리/전문직11.0
32202012JOB_HW전업주부8.9
33202012JOB_GS대학/대학원생5.0
34202012JOB_ETC기타/무직9.5
35202012MRST_UM미혼35.5
36202012MRST_NK신혼기(자녀 없음)10.7
37202012MRST_KD_EL자녀 유아&성장(막내 입학전~초등생)19.3
38202012MRST_KD_MD자녀 성장(막내 중고생)7.1
39202012MRST_KD_CL자녀 성인(막내 대학)21.8
40202012MRST_KD_MR자녀 독립(막내 결혼)5.5