Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells152
Missing cells (%)15.2%
Duplicate rows3
Duplicate rows (%)3.0%
Total size in memory8.4 KiB
Average record size in memory86.3 B

Variable types

Categorical2
Text3
Numeric4
Boolean1

Alerts

supli_sle_at has constant value ""Constant
Dataset has 3 (3.0%) duplicate rowsDuplicates
item_nm is highly overall correlated with item_cdHigh correlation
item_cd is highly overall correlated with item_nmHigh correlation
item_cd is highly imbalanced (51.1%)Imbalance
item_nm is highly imbalanced (51.1%)Imbalance
trobl_ty_nm has 85 (85.0%) missing valuesMissing
course_detail_cn has 67 (67.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 10:10:08.953460
Analysis finished2023-12-10 10:10:13.809056
Duration4.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

item_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
78 
2
17 
98
 
3
3
 
2

Length

Max length2
Median length1
Mean length1.03
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row98
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 78
78.0%
2 17
 
17.0%
98 3
 
3.0%
3 2
 
2.0%

Length

2023-12-10T19:10:13.930722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:14.115978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 78
78.0%
2 17
 
17.0%
98 3
 
3.0%
3 2
 
2.0%

item_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
검도
78 
골프
17 
헬스
 
3
기타종목
 
2

Length

Max length4
Median length2
Mean length2.04
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row검도
2nd row헬스
3rd row검도
4th row검도
5th row검도

Common Values

ValueCountFrequency (%)
검도 78
78.0%
골프 17
 
17.0%
헬스 3
 
3.0%
기타종목 2
 
2.0%

Length

2023-12-10T19:10:14.363712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:10:14.562840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
검도 78
78.0%
골프 17
 
17.0%
헬스 3
 
3.0%
기타종목 2
 
2.0%

course_nm
Text

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:10:14.924712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length5.87
Min length2

Characters and Unicode

Total characters587
Distinct characters131
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)42.0%

Sample

1st row검도수업
2nd row온라인헬스케어
3rd row해동검도 및 활쏘기
4th row어울림검도
5th row검도수업
ValueCountFrequency (%)
검도 22
 
16.2%
해동검도 13
 
9.6%
검도교실 11
 
8.1%
검도수업 4
 
2.9%
골프 4
 
2.9%
검도기본기,본국검법 4
 
2.9%
등 4
 
2.9%
장애인 3
 
2.2%
장애인검도교실 3
 
2.2%
검도수련 3
 
2.2%
Other values (60) 65
47.8%
2023-12-10T19:10:15.728205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
검 82
 
14.0%
도 76
 
12.9%
36
 
6.1%
교 20
 
3.4%
실 19
 
3.2%
골 16
 
2.7%
프 16
 
2.7%
기 16
 
2.7%
인 14
 
2.4%
동 14
 
2.4%
Other values (121) 278
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 499
85.0%
Space Separator 36
 
6.1%
Other Punctuation 23
 
3.9%
Lowercase Letter 13
 
2.2%
Decimal Number 7
 
1.2%
Uppercase Letter 5
 
0.9%
Close Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
검 82
 
16.4%
도 76
 
15.2%
교 20
 
4.0%
실 19
 
3.8%
골 16
 
3.2%
프 16
 
3.2%
기 16
 
3.2%
인 14
 
2.8%
동 14
 
2.8%
해 13
 
2.6%
Other values (97) 213
42.7%
Lowercase Letter
ValueCountFrequency (%)
t 4
30.8%
g 2
15.4%
l 2
15.4%
j 1
 
7.7%
h 1
 
7.7%
r 1
 
7.7%
e 1
 
7.7%
a 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 13
56.5%
& 4
 
17.4%
; 4
 
17.4%
. 1
 
4.3%
! 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
5 3
42.9%
4 2
28.6%
3 1
 
14.3%
1 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
B 1
20.0%
O 1
20.0%
N 1
20.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 499
85.0%
Common 70
 
11.9%
Latin 18
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
검 82
 
16.4%
도 76
 
15.2%
교 20
 
4.0%
실 19
 
3.8%
골 16
 
3.2%
프 16
 
3.2%
기 16
 
3.2%
인 14
 
2.8%
동 14
 
2.8%
해 13
 
2.6%
Other values (97) 213
42.7%
Common
ValueCountFrequency (%)
36
51.4%
, 13
 
18.6%
& 4
 
5.7%
; 4
 
5.7%
5 3
 
4.3%
) 2
 
2.9%
( 2
 
2.9%
4 2
 
2.9%
3 1
 
1.4%
1 1
 
1.4%
Other values (2) 2
 
2.9%
Latin
ValueCountFrequency (%)
t 4
22.2%
A 2
11.1%
g 2
11.1%
l 2
11.1%
j 1
 
5.6%
B 1
 
5.6%
h 1
 
5.6%
r 1
 
5.6%
e 1
 
5.6%
a 1
 
5.6%
Other values (2) 2
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 499
85.0%
ASCII 88
 
15.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
검 82
 
16.4%
도 76
 
15.2%
교 20
 
4.0%
실 19
 
3.8%
골 16
 
3.2%
프 16
 
3.2%
기 16
 
3.2%
인 14
 
2.8%
동 14
 
2.8%
해 13
 
2.6%
Other values (97) 213
42.7%
ASCII
ValueCountFrequency (%)
36
40.9%
, 13
 
14.8%
t 4
 
4.5%
& 4
 
4.5%
; 4
 
4.5%
5 3
 
3.4%
) 2
 
2.3%
( 2
 
2.3%
A 2
 
2.3%
g 2
 
2.3%
Other values (14) 16
18.2%

trobl_ty_nm
Text

MISSING 

Distinct10
Distinct (%)66.7%
Missing85
Missing (%)85.0%
Memory size932.0 B
2023-12-10T19:10:15.988987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length5.6
Min length2

Characters and Unicode

Total characters84
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)46.7%

Sample

1st row지적3급
2nd row지적3급
3rd row지체, 농아, 정신
4th row시각, 청각 등
5th row지적장애
ValueCountFrequency (%)
지적장애 4
17.4%
지적 4
17.4%
지체 3
13.0%
지적3급 2
8.7%
지체/발달 2
8.7%
농아 2
8.7%
정신 1
 
4.3%
시각 1
 
4.3%
청각 1
 
4.3%
등 1
 
4.3%
Other values (2) 2
8.7%
2023-12-10T19:10:16.664896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 17
20.2%
적 11
13.1%
, 9
10.7%
8
9.5%
체 6
 
7.1%
장 4
 
4.8%
애 4
 
4.8%
발 3
 
3.6%
달 3
 
3.6%
각 3
 
3.6%
Other values (10) 16
19.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
75.0%
Other Punctuation 11
 
13.1%
Space Separator 8
 
9.5%
Decimal Number 2
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 17
27.0%
적 11
17.5%
체 6
 
9.5%
장 4
 
6.3%
애 4
 
6.3%
발 3
 
4.8%
달 3
 
4.8%
각 3
 
4.8%
급 2
 
3.2%
농 2
 
3.2%
Other values (6) 8
12.7%
Other Punctuation
ValueCountFrequency (%)
, 9
81.8%
/ 2
 
18.2%
Space Separator
ValueCountFrequency (%)
8
100.0%
Decimal Number
ValueCountFrequency (%)
3 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
75.0%
Common 21
 
25.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 17
27.0%
적 11
17.5%
체 6
 
9.5%
장 4
 
6.3%
애 4
 
6.3%
발 3
 
4.8%
달 3
 
4.8%
각 3
 
4.8%
급 2
 
3.2%
농 2
 
3.2%
Other values (6) 8
12.7%
Common
ValueCountFrequency (%)
, 9
42.9%
8
38.1%
/ 2
 
9.5%
3 2
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
75.0%
ASCII 21
 
25.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 17
27.0%
적 11
17.5%
체 6
 
9.5%
장 4
 
6.3%
애 4
 
6.3%
발 3
 
4.8%
달 3
 
4.8%
각 3
 
4.8%
급 2
 
3.2%
농 2
 
3.2%
Other values (6) 8
12.7%
ASCII
ValueCountFrequency (%)
, 9
42.9%
8
38.1%
/ 2
 
9.5%
3 2
 
9.5%

begin_tm
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128540
Minimum10000
Maximum203000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:16.923328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile60000
Q1100000
median140000
Q3155500
95-th percentile190000
Maximum203000
Range193000
Interquartile range (IQR)55500

Descriptive statistics

Standard deviation42523.059
Coefficient of variation (CV)0.33081577
Kurtosis-0.13379065
Mean128540
Median Absolute Deviation (MAD)30000
Skewness-0.52072131
Sum12854000
Variance1.8082105 × 109
MonotonicityNot monotonic
2023-12-10T19:10:17.179863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
100000 13
 
13.0%
150000 10
 
10.0%
110000 9
 
9.0%
140000 7
 
7.0%
60000 7
 
7.0%
130000 5
 
5.0%
190000 5
 
5.0%
160000 4
 
4.0%
143000 4
 
4.0%
193000 3
 
3.0%
Other values (20) 33
33.0%
ValueCountFrequency (%)
10000 1
 
1.0%
20000 1
 
1.0%
30000 1
 
1.0%
43000 1
 
1.0%
60000 7
7.0%
64000 1
 
1.0%
83000 2
 
2.0%
90000 3
 
3.0%
100000 13
13.0%
103000 1
 
1.0%
ValueCountFrequency (%)
203000 1
 
1.0%
193000 3
3.0%
190000 5
5.0%
183000 2
 
2.0%
180000 3
3.0%
173000 2
 
2.0%
170000 3
3.0%
163000 2
 
2.0%
160000 4
4.0%
154000 2
 
2.0%

end_tm
Real number (ℝ)

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174920
Minimum33000
Maximum230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:17.441160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33000
5-th percentile93000
Q1150000
median194000
Q3210000
95-th percentile220000
Maximum230000
Range197000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation43297.346
Coefficient of variation (CV)0.24752656
Kurtosis0.071076774
Mean174920
Median Absolute Deviation (MAD)19000
Skewness-0.96187849
Sum17492000
Variance1.8746602 × 109
MonotonicityNot monotonic
2023-12-10T19:10:17.813618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
210000 12
 
12.0%
203000 11
 
11.0%
200000 9
 
9.0%
220000 7
 
7.0%
150000 6
 
6.0%
160000 5
 
5.0%
120000 5
 
5.0%
180000 5
 
5.0%
110000 5
 
5.0%
213000 3
 
3.0%
Other values (25) 32
32.0%
ValueCountFrequency (%)
33000 1
 
1.0%
70000 1
 
1.0%
83000 1
 
1.0%
90000 1
 
1.0%
93000 2
 
2.0%
110000 5
5.0%
113000 1
 
1.0%
115000 2
 
2.0%
120000 5
5.0%
123000 1
 
1.0%
ValueCountFrequency (%)
230000 1
 
1.0%
225000 2
 
2.0%
221000 1
 
1.0%
220000 7
7.0%
215000 1
 
1.0%
213000 3
 
3.0%
211000 1
 
1.0%
210000 12
12.0%
203000 11
11.0%
202000 1
 
1.0%

posbl_wkday_flag_cd
Real number (ℝ)

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean848690.02
Minimum10
Maximum1111111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:18.111978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q11010100
median1111100
Q31111100
95-th percentile1111110
Maximum1111111
Range1111101
Interquartile range (IQR)101000

Descriptive statistics

Standard deviation437646.11
Coefficient of variation (CV)0.51567251
Kurtosis-0.12032435
Mean848690.02
Median Absolute Deviation (MAD)11
Skewness-1.3483967
Sum84869002
Variance1.9153412 × 1011
MonotonicityNot monotonic
2023-12-10T19:10:18.461653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1111100 41
41.0%
1010100 22
22.0%
10 10
 
10.0%
1111110 8
 
8.0%
101000 6
 
6.0%
1111111 2
 
2.0%
10100 2
 
2.0%
1000 2
 
2.0%
100000 1
 
1.0%
1010000 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
10 10
10.0%
1000 2
 
2.0%
10000 1
 
1.0%
10100 2
 
2.0%
100000 1
 
1.0%
101000 6
 
6.0%
1000100 1
 
1.0%
1010000 1
 
1.0%
1010100 22
22.0%
1011100 1
 
1.0%
ValueCountFrequency (%)
1111111 2
 
2.0%
1111110 8
 
8.0%
1111100 41
41.0%
1111000 1
 
1.0%
1110100 1
 
1.0%
1011100 1
 
1.0%
1010100 22
22.0%
1010000 1
 
1.0%
1000100 1
 
1.0%
101000 6
 
6.0%

course_detail_cn
Text

MISSING 

Distinct28
Distinct (%)84.8%
Missing67
Missing (%)67.0%
Memory size932.0 B
2023-12-10T19:10:19.001384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length239
Median length44
Mean length46.939394
Min length3

Characters and Unicode

Total characters1549
Distinct characters262
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)69.7%

Sample

1st row개인장비지참,검도관내에 비치 [구입가능]
2nd row★ 필 독 ★,장애인스포츠강좌이용권 이용을 위한 '온라인'결제 강좌입니다.,본 온라인 헬스케어 서비스 수강신청 후,반드시 아래번호로 전화 또는 문자로 신청완료를 말씀해 주셔야,수업운영 절차가 진행이 됩니다.,051-746-1152 / 010-4883-1152
3rd row선한영향력가게 정회원 등록으로 수련비 추가금 없음
4th row주 1회, 1시간
5th row주3회
ValueCountFrequency (%)
시간 6
 
2.2%
전화 5
 
1.8%
5
 
1.8%
후 5
 
1.8%
궁금한 4
 
1.4%
상담 4
 
1.4%
주십시오 3
 
1.1%
051-208-3155 3
 
1.1%
바랍니다 3
 
1.1%
수련 3
 
1.1%
Other values (189) 237
85.3%
2023-12-10T19:10:20.017296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
285
 
18.4%
0 55
 
3.6%
, 41
 
2.6%
시 33
 
2.1%
1 31
 
2.0%
. 30
 
1.9%
니 29
 
1.9%
다 28
 
1.8%
5 27
 
1.7%
- 25
 
1.6%
Other values (252) 965
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 955
61.7%
Space Separator 285
 
18.4%
Decimal Number 177
 
11.4%
Other Punctuation 84
 
5.4%
Dash Punctuation 25
 
1.6%
Lowercase Letter 8
 
0.5%
Open Punctuation 4
 
0.3%
Close Punctuation 4
 
0.3%
Math Symbol 3
 
0.2%
Other Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 33
 
3.5%
니 29
 
3.0%
다 28
 
2.9%
이 21
 
2.2%
인 20
 
2.1%
수 20
 
2.1%
용 19
 
2.0%
도 18
 
1.9%
로 17
 
1.8%
주 17
 
1.8%
Other values (222) 733
76.8%
Decimal Number
ValueCountFrequency (%)
0 55
31.1%
1 31
17.5%
5 27
15.3%
3 16
 
9.0%
2 10
 
5.6%
8 10
 
5.6%
9 8
 
4.5%
4 8
 
4.5%
6 7
 
4.0%
7 5
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 41
48.8%
. 30
35.7%
: 5
 
6.0%
/ 4
 
4.8%
; 2
 
2.4%
& 2
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
s 2
25.0%
o 2
25.0%
p 2
25.0%
a 2
25.0%
Open Punctuation
ValueCountFrequency (%)
( 3
75.0%
[ 1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 3
75.0%
] 1
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
O 1
50.0%
N 1
50.0%
Space Separator
ValueCountFrequency (%)
285
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Other Symbol
ValueCountFrequency (%)
★ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 955
61.7%
Common 584
37.7%
Latin 10
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 33
 
3.5%
니 29
 
3.0%
다 28
 
2.9%
이 21
 
2.2%
인 20
 
2.1%
수 20
 
2.1%
용 19
 
2.0%
도 18
 
1.9%
로 17
 
1.8%
주 17
 
1.8%
Other values (222) 733
76.8%
Common
ValueCountFrequency (%)
285
48.8%
0 55
 
9.4%
, 41
 
7.0%
1 31
 
5.3%
. 30
 
5.1%
5 27
 
4.6%
- 25
 
4.3%
3 16
 
2.7%
2 10
 
1.7%
8 10
 
1.7%
Other values (14) 54
 
9.2%
Latin
ValueCountFrequency (%)
s 2
20.0%
o 2
20.0%
p 2
20.0%
a 2
20.0%
O 1
10.0%
N 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 955
61.7%
ASCII 592
38.2%
Misc Symbols 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
285
48.1%
0 55
 
9.3%
, 41
 
6.9%
1 31
 
5.2%
. 30
 
5.1%
5 27
 
4.6%
- 25
 
4.2%
3 16
 
2.7%
2 10
 
1.7%
8 10
 
1.7%
Other values (19) 62
 
10.5%
Hangul
ValueCountFrequency (%)
시 33
 
3.5%
니 29
 
3.0%
다 28
 
2.9%
이 21
 
2.2%
인 20
 
2.1%
수 20
 
2.1%
용 19
 
2.0%
도 18
 
1.9%
로 17
 
1.8%
주 17
 
1.8%
Other values (222) 733
76.8%
Misc Symbols
ValueCountFrequency (%)
★ 2
100.0%

supli_sle_at
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
100 
ValueCountFrequency (%)
False 100
100.0%
2023-12-10T19:10:20.222330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

setle_price
Real number (ℝ)

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95720
Minimum20000
Maximum248000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:10:20.376945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile45950
Q180000
median80000
Q3120000
95-th percentile140500
Maximum248000
Range228000
Interquartile range (IQR)40000

Descriptive statistics

Standard deviation33008.976
Coefficient of variation (CV)0.34484932
Kurtosis4.0464584
Mean95720
Median Absolute Deviation (MAD)10000
Skewness1.012764
Sum9572000
Variance1.0895925 × 109
MonotonicityNot monotonic
2023-12-10T19:10:20.607562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
80000 45
45.0%
130000 14
 
14.0%
120000 10
 
10.0%
100000 10
 
10.0%
20000 3
 
3.0%
70000 3
 
3.0%
160000 2
 
2.0%
110000 2
 
2.0%
248000 1
 
1.0%
35000 1
 
1.0%
Other values (9) 9
 
9.0%
ValueCountFrequency (%)
20000 3
 
3.0%
35000 1
 
1.0%
45000 1
 
1.0%
46000 1
 
1.0%
55000 1
 
1.0%
70000 3
 
3.0%
75500 1
 
1.0%
77500 1
 
1.0%
80000 45
45.0%
90000 1
 
1.0%
ValueCountFrequency (%)
248000 1
 
1.0%
180000 1
 
1.0%
160000 2
 
2.0%
150000 1
 
1.0%
140000 1
 
1.0%
130000 14
14.0%
120000 10
10.0%
110000 2
 
2.0%
100000 10
10.0%
90000 1
 
1.0%

Interactions

2023-12-10T19:10:12.577097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:09.874332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:10.835481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.968032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.732932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:10.039622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.009229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.121624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.913345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:10.232769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.549245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.288598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:13.058482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:10.661795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:11.709530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:10:12.426847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:10:20.782229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
item_cditem_nmcourse_nmtrobl_ty_nmbegin_tmend_tmposbl_wkday_flag_cdcourse_detail_cnsetle_price
item_cd1.0001.0001.0000.7080.3470.2700.0001.0000.221
item_nm1.0001.0001.0000.7080.3470.2700.0001.0000.221
course_nm1.0001.0001.0001.0000.8890.1020.6290.9400.973
trobl_ty_nm0.7080.7081.0001.0000.9010.9420.4991.0000.000
begin_tm0.3470.3470.8890.9011.0000.9010.4621.0000.313
end_tm0.2700.2700.1020.9420.9011.0000.3531.0000.000
posbl_wkday_flag_cd0.0000.0000.6290.4990.4620.3531.0000.0000.129
course_detail_cn1.0001.0000.9401.0001.0001.0000.0001.0001.000
setle_price0.2210.2210.9730.0000.3130.0000.1291.0001.000
2023-12-10T19:10:21.008940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
item_nmitem_cd
item_nm1.0001.000
item_cd1.0001.000
2023-12-10T19:10:21.160506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
begin_tmend_tmposbl_wkday_flag_cdsetle_priceitem_cditem_nm
begin_tm1.0000.405-0.0070.0410.2090.209
end_tm0.4051.0000.416-0.1840.1400.140
posbl_wkday_flag_cd-0.0070.4161.000-0.0750.0000.000
setle_price0.041-0.184-0.0751.0000.3360.336
item_cd0.2090.1400.0000.3361.0001.000
item_nm0.2090.1400.0000.3361.0001.000

Missing values

2023-12-10T19:10:13.259626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:10:13.515561image/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.
2023-12-10T19:10:13.707006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

item_cditem_nmcourse_nmtrobl_ty_nmbegin_tmend_tmposbl_wkday_flag_cdcourse_detail_cnsupli_sle_atsetle_price
01검도검도수업<NA>1600002000001111110개인장비지참,검도관내에 비치 [구입가능]N120000
198헬스온라인헬스케어<NA>900001800001111100★ 필 독 ★,장애인스포츠강좌이용권 이용을 위한 &apos;온라인&apos;결제 강좌입니다.,본 온라인 헬스케어 서비스 수강신청 후,반드시 아래번호로 전화 또는 문자로 신청완료를 말씀해 주셔야,수업운영 절차가 진행이 됩니다.,051-746-1152 / 010-4883-1152N80000
21검도해동검도 및 활쏘기<NA>30000900001111100선한영향력가게 정회원 등록으로 수련비 추가금 없음N80000
31검도어울림검도지적3급1300002100001111100주 1회, 1시간N140000
41검도검도수업지적3급1000002100001111100주3회N130000
51검도장애인검도교실<NA>14000020300010<NA>N80000
61검도꿈꾸는작은화랑(검도교실)<NA>150000160000100000<NA>N20000
798헬스&lt;성인&gt;자유헬스<NA>600002000001111111코로나19로 인해 4부로 나눠 운영중이며 이용시 시설불편 사항이 있기에 담당자화 필히 상담 후 등록 바랍니다.N20000
81검도검도교실<NA>1900002000001010100<NA>N70000
91검도장애인검도교실지체, 농아, 정신1540002200001111100<NA>N120000
item_cditem_nmcourse_nmtrobl_ty_nmbegin_tmend_tmposbl_wkday_flag_cdcourse_detail_cnsupli_sle_atsetle_price
902골프골프<NA>600002100001111110<NA>N70000
912골프파크골프지체/발달1700001800001010100<NA>N100000
922골프골프<NA>600002200001111100<NA>N110000
932골프체형교정 필라테스<NA>1100001150001111110결제전 전화 요망 031-979-9609N80000
942골프홀인원 골프교실지적장애190000210000100040,000/회N160000
952골프골프기초레슨<NA>1300002000001111100<NA>N80000
962골프필드골프지체/발달1700001800001010100<NA>N100000
972골프골프<NA>900001900001111110<NA>N80000
983기타종목당구기초<NA>1140001240001010100강의 선택 수 시간 변경 가능합니다.N80000
993기타종목당구기초<NA>1030001130001111100시간 선택은 변경 가능 합니다.,강의 신청 후 시간 변경 문의주세요.N100000

Duplicate rows

Most frequently occurring

item_cditem_nmcourse_nmtrobl_ty_nmbegin_tmend_tmposbl_wkday_flag_cdcourse_detail_cnsupli_sle_atsetle_price# duplicates
01검도검도<NA>1100002000001010100<NA>N800002
11검도검도수련<NA>1400001500001111100<NA>N800002
21검도해동검도<NA>100000140000101000<NA>N1000002