Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells500
Missing cells (%)38.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.0 KiB
Average record size in memory112.3 B

Variable types

Text1
Categorical5
Numeric2
Unsupported5

Alerts

wrdn_efc_yy has constant value ""Constant
prtc_efc_yy has constant value ""Constant
aq_dt is highly overall correlated with satr_efc_yy and 1 other fieldsHigh correlation
satr_efc_yy is highly overall correlated with aq_dt and 2 other fieldsHigh correlation
qf_grade_nm is highly overall correlated with aq_dt and 2 other fieldsHigh correlation
qf_itm_nm is highly overall correlated with satr_efc_yy and 1 other fieldsHigh correlation
cour_nm has 100 (100.0%) missing valuesMissing
wrdn_tot_grde has 100 (100.0%) missing valuesMissing
prtc_tot_grde has 100 (100.0%) missing valuesMissing
orst_tot_grde has 100 (100.0%) missing valuesMissing
satr_atnd_ptm has 100 (100.0%) missing valuesMissing
cour_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
wrdn_tot_grde is an unsupported type, check if it needs cleaning or further analysisUnsupported
prtc_tot_grde is an unsupported type, check if it needs cleaning or further analysisUnsupported
orst_tot_grde is an unsupported type, check if it needs cleaning or further analysisUnsupported
satr_atnd_ptm is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:06:05.692821
Analysis finished2023-12-10 10:06:07.470328
Duration1.78 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

usr_no
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:06:07.810714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters12
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

Unique98 ?
Unique (%)98.0%

Sample

1st rowC000000001
2nd rowP000217185
3rd rowC000000009
4th rowC000000011
5th rowC000000012
ValueCountFrequency (%)
c000000026 2
 
2.0%
c000000088 1
 
1.0%
c000000085 1
 
1.0%
c000000084 1
 
1.0%
c000000083 1
 
1.0%
c000000082 1
 
1.0%
c000000081 1
 
1.0%
c000000080 1
 
1.0%
c000000079 1
 
1.0%
c000000078 1
 
1.0%
Other values (89) 89
89.0%
2023-12-10T19:06:08.622702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 697
69.7%
C 97
 
9.7%
1 34
 
3.4%
2 24
 
2.4%
7 23
 
2.3%
6 22
 
2.2%
5 22
 
2.2%
9 21
 
2.1%
8 21
 
2.1%
3 18
 
1.8%
Other values (2) 21
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 900
90.0%
Uppercase Letter 100
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 697
77.4%
1 34
 
3.8%
2 24
 
2.7%
7 23
 
2.6%
6 22
 
2.4%
5 22
 
2.4%
9 21
 
2.3%
8 21
 
2.3%
3 18
 
2.0%
4 18
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
C 97
97.0%
P 3
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900
90.0%
Latin 100
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 697
77.4%
1 34
 
3.8%
2 24
 
2.7%
7 23
 
2.6%
6 22
 
2.4%
5 22
 
2.4%
9 21
 
2.3%
8 21
 
2.3%
3 18
 
2.0%
4 18
 
2.0%
Latin
ValueCountFrequency (%)
C 97
97.0%
P 3
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 697
69.7%
C 97
 
9.7%
1 34
 
3.4%
2 24
 
2.4%
7 23
 
2.3%
6 22
 
2.2%
5 22
 
2.2%
9 21
 
2.1%
8 21
 
2.1%
3 18
 
1.8%
Other values (2) 21
 
2.1%

qf_grade_nm
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2급 생활스포츠지도사
78 
2급 전문스포츠지도사
21 
1급 전문스포츠지도사
 
1

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2급 생활스포츠지도사
2nd row2급 생활스포츠지도사
3rd row2급 생활스포츠지도사
4th row2급 생활스포츠지도사
5th row2급 생활스포츠지도사

Common Values

ValueCountFrequency (%)
2급 생활스포츠지도사 78
78.0%
2급 전문스포츠지도사 21
 
21.0%
1급 전문스포츠지도사 1
 
1.0%

Length

2023-12-10T19:06:08.894565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:09.098933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2급 99
49.5%
생활스포츠지도사 78
39.0%
전문스포츠지도사 22
 
11.0%
1급 1
 
0.5%

qf_itm_nm
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
활기도
21 
활법
20 
검도
8 
승마
7 
요가
5 
Other values (20)
39 

Length

Max length11
Median length2
Mean length2.66
Min length2

Unique

Unique7 ?
Unique (%)7.0%

Sample

1st row수영
2nd row태권도
3rd row골프
4th row우슈
5th row활기도

Common Values

ValueCountFrequency (%)
활기도 21
21.0%
활법 20
20.0%
검도 8
 
8.0%
승마 7
 
7.0%
요가 5
 
5.0%
골프 4
 
4.0%
테니스 4
 
4.0%
보디빌딩(구.육체미) 3
 
3.0%
태권도 3
 
3.0%
볼링 2
 
2.0%
Other values (15) 23
23.0%

Length

2023-12-10T19:06:09.298920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
활기도 21
21.0%
활법 20
20.0%
검도 8
 
8.0%
승마 7
 
7.0%
요가 5
 
5.0%
골프 4
 
4.0%
테니스 4
 
4.0%
보디빌딩(구.육체미 3
 
3.0%
태권도 3
 
3.0%
수영 2
 
2.0%
Other values (15) 23
23.0%

aq_dt
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19908427
Minimum19760226
Maximum20070823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:09.487478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19760226
5-th percentile19810873
Q119901001
median19910925
Q319930115
95-th percentile20030830
Maximum20070823
Range310597
Interquartile range (IQR)29114

Descriptive statistics

Standard deviation56851.158
Coefficient of variation (CV)0.0028556329
Kurtosis1.3715681
Mean19908427
Median Absolute Deviation (MAD)19190
Skewness0.15464968
Sum1.9908427 × 109
Variance3.2320542 × 109
MonotonicityNot monotonic
2023-12-10T19:06:09.702368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
19901001 16
16.0%
19911231 13
13.0%
19910925 12
 
12.0%
19930115 5
 
5.0%
19891226 4
 
4.0%
19930920 4
 
4.0%
19821108 4
 
4.0%
19920810 4
 
4.0%
19940120 3
 
3.0%
19910415 3
 
3.0%
Other values (24) 32
32.0%
ValueCountFrequency (%)
19760226 1
 
1.0%
19770311 1
 
1.0%
19780306 1
 
1.0%
19800320 1
 
1.0%
19810305 1
 
1.0%
19810903 1
 
1.0%
19820319 1
 
1.0%
19821108 4
4.0%
19840406 2
2.0%
19841214 2
2.0%
ValueCountFrequency (%)
20070823 1
 
1.0%
20060830 1
 
1.0%
20040211 1
 
1.0%
20030830 3
3.0%
20020822 1
 
1.0%
19991022 1
 
1.0%
19980122 1
 
1.0%
19970825 2
2.0%
19960830 1
 
1.0%
19960112 1
 
1.0%

wrdn_efc_yy
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
100
100.0%

Length

2023-12-10T19:06:10.061104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:10.259685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

cour_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

wrdn_tot_grde
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

prtc_efc_yy
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
100
100.0%

Length

2023-12-10T19:06:10.435394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:10.596761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

prtc_tot_grde
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

orst_tot_grde
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

satr_efc_yy
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1990.74
Minimum1976
Maximum2007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:10.796232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1981
Q11990
median1991
Q31993
95-th percentile2003
Maximum2007
Range31
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.6829001
Coefficient of variation (CV)0.0028546672
Kurtosis1.3403393
Mean1990.74
Median Absolute Deviation (MAD)2
Skewness0.12622594
Sum199074
Variance32.295354
MonotonicityNot monotonic
2023-12-10T19:06:10.987243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1991 28
28.0%
1990 16
16.0%
1993 10
 
10.0%
1982 5
 
5.0%
1989 5
 
5.0%
1992 4
 
4.0%
1984 4
 
4.0%
2003 4
 
4.0%
1994 3
 
3.0%
1985 3
 
3.0%
Other values (14) 18
18.0%
ValueCountFrequency (%)
1976 1
 
1.0%
1977 1
 
1.0%
1978 1
 
1.0%
1979 1
 
1.0%
1981 2
 
2.0%
1982 5
5.0%
1984 4
4.0%
1985 3
3.0%
1988 1
 
1.0%
1989 5
5.0%
ValueCountFrequency (%)
2007 1
 
1.0%
2006 1
 
1.0%
2003 4
4.0%
2002 1
 
1.0%
1999 1
 
1.0%
1998 1
 
1.0%
1997 2
2.0%
1996 2
2.0%
1995 2
2.0%
1994 3
3.0%

satr_atnd_ptm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

add1
Categorical

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
44 
경기
14 
부산
7 
대전
5 
제주
5 
Other values (9)
25 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row서울
2nd row전남
3rd row대구
4th row경기
5th row대전

Common Values

ValueCountFrequency (%)
서울 44
44.0%
경기 14
 
14.0%
부산 7
 
7.0%
대전 5
 
5.0%
제주 5
 
5.0%
광주 5
 
5.0%
대구 4
 
4.0%
전남 3
 
3.0%
강원 3
 
3.0%
인천 3
 
3.0%
Other values (4) 7
 
7.0%

Length

2023-12-10T19:06:11.263603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 44
44.0%
경기 14
 
14.0%
부산 7
 
7.0%
대전 5
 
5.0%
제주 5
 
5.0%
광주 5
 
5.0%
대구 4
 
4.0%
전남 3
 
3.0%
강원 3
 
3.0%
인천 3
 
3.0%
Other values (4) 7
 
7.0%

Interactions

2023-12-10T19:06:06.610489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.241242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.777142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.446727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:06:11.416495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
usr_noqf_grade_nmqf_itm_nmaq_dtsatr_efc_yyadd1
usr_no1.0000.7531.0000.0000.0000.994
qf_grade_nm0.7531.0000.9850.7650.8250.000
qf_itm_nm1.0000.9851.0000.8740.8930.000
aq_dt0.0000.7650.8741.0000.9990.000
satr_efc_yy0.0000.8250.8930.9991.0000.000
add10.9940.0000.0000.0000.0001.000
2023-12-10T19:06:11.627566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
add1qf_grade_nmqf_itm_nm
add11.0000.0000.000
qf_grade_nm0.0001.0000.843
qf_itm_nm0.0000.8431.000
2023-12-10T19:06:11.779437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
aq_dtsatr_efc_yyqf_grade_nmqf_itm_nmadd1
aq_dt1.0000.9900.6210.4910.000
satr_efc_yy0.9901.0000.6720.5170.000
qf_grade_nm0.6210.6721.0000.8430.000
qf_itm_nm0.4910.5170.8431.0000.000
add10.0000.0000.0000.0001.000

Missing values

2023-12-10T19:06:07.011323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:06:07.351742image/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

usr_noqf_grade_nmqf_itm_nmaq_dtwrdn_efc_yycour_nmwrdn_tot_grdeprtc_efc_yyprtc_tot_grdeorst_tot_grdesatr_efc_yysatr_atnd_ptmadd1
0C0000000012급 생활스포츠지도사수영20020822<NA><NA><NA><NA>2002<NA>서울
1P0002171852급 생활스포츠지도사태권도20040211<NA><NA><NA><NA>2003<NA>전남
2C0000000092급 생활스포츠지도사골프19940120<NA><NA><NA><NA>1994<NA>대구
3C0000000112급 생활스포츠지도사우슈19901001<NA><NA><NA><NA>1990<NA>경기
4C0000000122급 생활스포츠지도사활기도19911231<NA><NA><NA><NA>1991<NA>대전
5C0000000152급 생활스포츠지도사검도19891226<NA><NA><NA><NA>1989<NA>제주
6C0000000192급 생활스포츠지도사탁구20070823<NA><NA><NA><NA>2007<NA>광주
7P0002172672급 생활스포츠지도사테니스20030830<NA><NA><NA><NA>2003<NA>충남
8C0000000212급 전문스포츠지도사볼링19930914<NA><NA><NA><NA>1993<NA>서울
9C0000000222급 생활스포츠지도사골프19970825<NA><NA><NA><NA>1997<NA>서울
usr_noqf_grade_nmqf_itm_nmaq_dtwrdn_efc_yycour_nmwrdn_tot_grdeprtc_efc_yyprtc_tot_grdeorst_tot_grdesatr_efc_yysatr_atnd_ptmadd1
90C0000001022급 생활스포츠지도사활기도19891226<NA><NA><NA><NA>1989<NA>제주
91C0000001032급 전문스포츠지도사볼링19840406<NA><NA><NA><NA>1984<NA>서울
92C0000001042급 전문스포츠지도사승마19840406<NA><NA><NA><NA>1984<NA>전남
93C0000001052급 전문스포츠지도사야구19810305<NA><NA><NA><NA>1981<NA>서울
94C0000001062급 생활스포츠지도사활기도19901001<NA><NA><NA><NA>1990<NA>서울
95C0000001072급 생활스포츠지도사테니스19960112<NA><NA><NA><NA>1996<NA>대구
96C0000001082급 전문스포츠지도사야구19880318<NA><NA><NA><NA>1988<NA>경기
97C0000001092급 생활스포츠지도사활기도19911231<NA><NA><NA><NA>1991<NA>제주
98C0000001102급 생활스포츠지도사보디빌딩(구.육체미)19901001<NA><NA><NA><NA>1990<NA>경기
99C0000001112급 전문스포츠지도사사격19841214<NA><NA><NA><NA>1984<NA>서울