Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

Numeric2
Text1
DateTime1

Dataset

Description공정거래위원회의 소비자 민원학습에 대한 데이터로, 소비자 민원상담에 대한 전화상담 건수별 통화시간 이력에 대한 데이터입니다.
Author공정거래위원회
URLhttps://www.data.go.kr/data/15098345/fileData.do

Alerts

통화접수일련번호(CALL_RCPT_SEQ) has unique valuesUnique
통화시간(CALL_TIME) has 2945 (29.4%) zerosZeros

Reproduction

Analysis started2024-04-21 10:19:01.564244
Analysis finished2024-04-21 10:19:03.138585
Duration1.57 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통화접수일련번호(CALL_RCPT_SEQ)
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14962510
Minimum14865083
Maximum15087727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T19:19:03.271061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14865083
5-th percentile14875621
Q114914186
median14962225
Q315010987
95-th percentile15049492
Maximum15087727
Range222644
Interquartile range (IQR)96800.75

Descriptive statistics

Standard deviation56051.464
Coefficient of variation (CV)0.003746127
Kurtosis-1.1382258
Mean14962510
Median Absolute Deviation (MAD)48421
Skewness0.044518169
Sum1.496251 × 1011
Variance3.1417666 × 109
MonotonicityNot monotonic
2024-04-21T19:19:03.511401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15022048 1
 
< 0.1%
15018131 1
 
< 0.1%
14931683 1
 
< 0.1%
14933234 1
 
< 0.1%
14988411 1
 
< 0.1%
14995570 1
 
< 0.1%
14998133 1
 
< 0.1%
14970800 1
 
< 0.1%
15034866 1
 
< 0.1%
14879501 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
14865083 1
< 0.1%
14865196 1
< 0.1%
14865253 1
< 0.1%
14865262 1
< 0.1%
14865282 1
< 0.1%
14865303 1
< 0.1%
14865381 1
< 0.1%
14865422 1
< 0.1%
14865427 1
< 0.1%
14865441 1
< 0.1%
ValueCountFrequency (%)
15087727 1
< 0.1%
15087202 1
< 0.1%
15086988 1
< 0.1%
15086691 1
< 0.1%
15086673 1
< 0.1%
15086334 1
< 0.1%
15086287 1
< 0.1%
15085713 1
< 0.1%
15085648 1
< 0.1%
15085103 1
< 0.1%

사건번호(ACCIDENT_NO)
Text

Distinct9299
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T19:19:04.234554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters120000
Distinct characters11
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

Unique8672 ?
Unique (%)86.7%

Sample

1st row2020-0711609
2nd row2020-0658608
3rd row2020-0684196
4th row2020-0723790
5th row2020-0673028
ValueCountFrequency (%)
2020-0651921 5
 
< 0.1%
2020-0684150 4
 
< 0.1%
2020-0634726 4
 
< 0.1%
2020-0618676 4
 
< 0.1%
2020-0707692 4
 
< 0.1%
2020-0659936 4
 
< 0.1%
2020-0651013 4
 
< 0.1%
2020-0719181 4
 
< 0.1%
2020-0703492 4
 
< 0.1%
2020-0647201 4
 
< 0.1%
Other values (9289) 9959
99.6%
2024-04-21T19:19:05.157247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34926
29.1%
2 25267
21.1%
6 12367
 
10.3%
- 10000
 
8.3%
7 7452
 
6.2%
3 5112
 
4.3%
5 5098
 
4.2%
4 4985
 
4.2%
1 4964
 
4.1%
9 4922
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110000
91.7%
Dash Punctuation 10000
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34926
31.8%
2 25267
23.0%
6 12367
 
11.2%
7 7452
 
6.8%
3 5112
 
4.6%
5 5098
 
4.6%
4 4985
 
4.5%
1 4964
 
4.5%
9 4922
 
4.5%
8 4907
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34926
29.1%
2 25267
21.1%
6 12367
 
10.3%
- 10000
 
8.3%
7 7452
 
6.2%
3 5112
 
4.3%
5 5098
 
4.2%
4 4985
 
4.2%
1 4964
 
4.1%
9 4922
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34926
29.1%
2 25267
21.1%
6 12367
 
10.3%
- 10000
 
8.3%
7 7452
 
6.2%
3 5112
 
4.3%
5 5098
 
4.2%
4 4985
 
4.2%
1 4964
 
4.1%
9 4922
 
4.1%

접수일자(RCPT_YMD)
Date

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-09-08 00:00:00
Maximum2020-12-28 00:00:00
2024-04-21T19:19:05.403144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:19:05.639965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

통화시간(CALL_TIME)
Real number (ℝ)

ZEROS 

Distinct1008
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.4499
Minimum0
Maximum16217
Zeros2945
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T19:19:05.876304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median137
Q3328
95-th percentile683
Maximum16217
Range16217
Interquartile range (IQR)328

Descriptive statistics

Standard deviation317.99796
Coefficient of variation (CV)1.4828543
Kurtosis694.62569
Mean214.4499
Median Absolute Deviation (MAD)137
Skewness16.11739
Sum2144499
Variance101122.7
MonotonicityNot monotonic
2024-04-21T19:19:06.132502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2945
29.4%
64 32
 
0.3%
98 29
 
0.3%
129 27
 
0.3%
52 27
 
0.3%
62 27
 
0.3%
119 25
 
0.2%
42 24
 
0.2%
72 24
 
0.2%
68 24
 
0.2%
Other values (998) 6816
68.2%
ValueCountFrequency (%)
0 2945
29.4%
1 9
 
0.1%
2 6
 
0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 4
 
< 0.1%
9 7
 
0.1%
ValueCountFrequency (%)
16217 1
< 0.1%
8421 1
< 0.1%
4363 1
< 0.1%
3975 1
< 0.1%
3901 1
< 0.1%
2773 1
< 0.1%
2667 1
< 0.1%
2333 1
< 0.1%
2284 1
< 0.1%
2283 1
< 0.1%

Interactions

2024-04-21T19:19:02.512109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:19:02.187843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:19:02.678703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:19:02.334090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T19:19:06.299670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통화접수일련번호(CALL_RCPT_SEQ)접수일자(RCPT_YMD)통화시간(CALL_TIME)
통화접수일련번호(CALL_RCPT_SEQ)1.0000.9710.091
접수일자(RCPT_YMD)0.9711.0000.224
통화시간(CALL_TIME)0.0910.2241.000
2024-04-21T19:19:06.449100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통화접수일련번호(CALL_RCPT_SEQ)통화시간(CALL_TIME)
통화접수일련번호(CALL_RCPT_SEQ)1.0000.002
통화시간(CALL_TIME)0.0021.000

Missing values

2024-04-21T19:19:02.908558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T19:19:03.065012image/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

통화접수일련번호(CALL_RCPT_SEQ)사건번호(ACCIDENT_NO)접수일자(RCPT_YMD)통화시간(CALL_TIME)
86766150220482020-07116092020-12-09185
49984149631482020-06586082020-11-160
52095149719012020-06841962020-11-26579
87797150433572020-07237902020-12-16254
44352149563322020-06730282020-11-2381
21995149141162020-06510792020-11-11413
14219148946832020-06398202020-11-05163
54189149702772020-06830312020-11-260
74514150134152020-07066992020-12-080
7651148782902020-06309842020-11-02203
통화접수일련번호(CALL_RCPT_SEQ)사건번호(ACCIDENT_NO)접수일자(RCPT_YMD)통화시간(CALL_TIME)
34678149325532020-06614472020-11-160
746148662282020-06243362020-10-280
61279149825722020-06836972020-11-260
7864148778102020-06303192020-11-020
94210150477682020-07262232020-12-16505
28982149238042020-06558872020-11-130
4414148784042020-06310522020-11-02248
95747150513922020-07282082020-12-17252
99652150857132020-07458092020-12-2890
80724150518562020-07284752020-12-170