Overview

Dataset statistics

Number of variables5
Number of observations928
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.0 KiB
Average record size in memory44.1 B

Variable types

Numeric4
Categorical1

Dataset

DescriptionBC카드 제공한 데이터의 통계 자료로 월별, 시군별, 가맹점수, 휴업가맹점수, 폐업가맹점수를 확인할 수 있습니다. 본 자료는 상업적 이용금지 및 재배포할 수 없습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2155

Alerts

가맹점수 is highly overall correlated with 휴업가맹점수 and 2 other fieldsHigh correlation
휴업가맹점수 is highly overall correlated with 가맹점수 and 2 other fieldsHigh correlation
폐업가맹점수 is highly overall correlated with 가맹점수 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 가맹점수 and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-01-09 20:12:10.729961
Analysis finished2024-01-09 20:12:13.465044
Duration2.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월
Real number (ℝ)

Distinct58
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202099.43
Minimum201901
Maximum202310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2024-01-10T05:12:13.553262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201901
5-th percentile201903
Q1202003
median202105.5
Q3202208
95-th percentile202308
Maximum202310
Range409
Interquartile range (IQR)205

Descriptive statistics

Standard deviation138.64913
Coefficient of variation (CV)0.00068604413
Kurtosis-1.2609616
Mean202099.43
Median Absolute Deviation (MAD)102.5
Skewness0.041595556
Sum1.8754827 × 108
Variance19223.581
MonotonicityIncreasing
2024-01-10T05:12:13.761587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201901 16
 
1.7%
202209 16
 
1.7%
202109 16
 
1.7%
202110 16
 
1.7%
202111 16
 
1.7%
202112 16
 
1.7%
202201 16
 
1.7%
202202 16
 
1.7%
202203 16
 
1.7%
202204 16
 
1.7%
Other values (48) 768
82.8%
ValueCountFrequency (%)
201901 16
1.7%
201902 16
1.7%
201903 16
1.7%
201904 16
1.7%
201905 16
1.7%
201906 16
1.7%
201907 16
1.7%
201908 16
1.7%
201909 16
1.7%
201910 16
1.7%
ValueCountFrequency (%)
202310 16
1.7%
202309 16
1.7%
202308 16
1.7%
202307 16
1.7%
202306 16
1.7%
202305 16
1.7%
202304 16
1.7%
202303 16
1.7%
202302 16
1.7%
202301 16
1.7%

시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
계룡시
 
58
공주시
 
58
금산군
 
58
논산시
 
58
당진시
 
58
Other values (11)
638 

Length

Max length7
Median length3
Mean length3.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row계룡시
2nd row공주시
3rd row금산군
4th row논산시
5th row당진시

Common Values

ValueCountFrequency (%)
계룡시 58
 
6.2%
공주시 58
 
6.2%
금산군 58
 
6.2%
논산시 58
 
6.2%
당진시 58
 
6.2%
보령시 58
 
6.2%
부여군 58
 
6.2%
서산시 58
 
6.2%
서천군 58
 
6.2%
아산시 58
 
6.2%
Other values (6) 348
37.5%

Length

2024-01-10T05:12:13.937011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
천안시 116
 
11.1%
계룡시 58
 
5.6%
아산시 58
 
5.6%
태안군 58
 
5.6%
청양군 58
 
5.6%
서북구 58
 
5.6%
동남구 58
 
5.6%
예산군 58
 
5.6%
서천군 58
 
5.6%
공주시 58
 
5.6%
Other values (7) 406
38.9%

가맹점수
Real number (ℝ)

HIGH CORRELATION 

Distinct883
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8462.4978
Minimum1564
Maximum25198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2024-01-10T05:12:14.508499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1564
5-th percentile1786.7
Q13748.25
median7062
Q310731
95-th percentile21646.35
Maximum25198
Range23634
Interquartile range (IQR)6982.75

Descriptive statistics

Standard deviation5984.2555
Coefficient of variation (CV)0.70715002
Kurtosis0.3501653
Mean8462.4978
Median Absolute Deviation (MAD)3379
Skewness1.1112871
Sum7853198
Variance35811314
MonotonicityNot monotonic
2024-01-10T05:12:14.671578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5374 3
 
0.3%
1859 3
 
0.3%
1916 2
 
0.2%
16917 2
 
0.2%
1608 2
 
0.2%
1846 2
 
0.2%
2018 2
 
0.2%
3909 2
 
0.2%
1825 2
 
0.2%
7808 2
 
0.2%
Other values (873) 906
97.6%
ValueCountFrequency (%)
1564 1
0.1%
1565 1
0.1%
1575 1
0.1%
1584 1
0.1%
1598 1
0.1%
1600 1
0.1%
1602 1
0.1%
1608 2
0.2%
1615 1
0.1%
1618 1
0.1%
ValueCountFrequency (%)
25198 1
0.1%
25137 1
0.1%
25073 1
0.1%
25006 1
0.1%
24931 1
0.1%
24842 1
0.1%
24836 1
0.1%
24814 1
0.1%
24778 1
0.1%
24673 1
0.1%

휴업가맹점수
Real number (ℝ)

HIGH CORRELATION 

Distinct795
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2099.0679
Minimum356
Maximum6625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2024-01-10T05:12:14.842549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum356
5-th percentile454.35
Q11044.75
median1720.5
Q32406.75
95-th percentile5125.55
Maximum6625
Range6269
Interquartile range (IQR)1362

Descriptive statistics

Standard deviation1472.9059
Coefficient of variation (CV)0.70169522
Kurtosis0.54807672
Mean2099.0679
Median Absolute Deviation (MAD)685.5
Skewness1.1761109
Sum1947935
Variance2169451.8
MonotonicityNot monotonic
2024-01-10T05:12:15.015117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
458 3
 
0.3%
454 3
 
0.3%
2203 3
 
0.3%
1994 3
 
0.3%
1164 3
 
0.3%
1181 3
 
0.3%
449 3
 
0.3%
1688 3
 
0.3%
2034 3
 
0.3%
543 3
 
0.3%
Other values (785) 898
96.8%
ValueCountFrequency (%)
356 2
0.2%
360 1
0.1%
361 2
0.2%
363 1
0.1%
364 1
0.1%
367 1
0.1%
368 1
0.1%
369 2
0.2%
371 1
0.1%
373 1
0.1%
ValueCountFrequency (%)
6625 1
0.1%
6619 1
0.1%
6534 1
0.1%
6479 1
0.1%
6399 1
0.1%
6341 1
0.1%
6332 1
0.1%
6303 1
0.1%
6297 1
0.1%
6287 1
0.1%

폐업가맹점수
Real number (ℝ)

HIGH CORRELATION 

Distinct231
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.863147
Minimum1
Maximum1049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 KiB
2024-01-10T05:12:15.213718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q126
median53
Q399
95-th percentile243.65
Maximum1049
Range1048
Interquartile range (IQR)73

Descriptive statistics

Standard deviation86.491119
Coefficient of variation (CV)1.0695987
Kurtosis25.689938
Mean80.863147
Median Absolute Deviation (MAD)30
Skewness3.6348072
Sum75041
Variance7480.7137
MonotonicityNot monotonic
2024-01-10T05:12:15.383880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 20
 
2.2%
22 19
 
2.0%
19 18
 
1.9%
24 16
 
1.7%
26 15
 
1.6%
31 14
 
1.5%
21 13
 
1.4%
20 13
 
1.4%
42 13
 
1.4%
30 13
 
1.4%
Other values (221) 774
83.4%
ValueCountFrequency (%)
1 1
 
0.1%
3 1
 
0.1%
5 5
0.5%
6 3
 
0.3%
7 3
 
0.3%
8 7
0.8%
9 7
0.8%
10 12
1.3%
11 5
0.5%
12 7
0.8%
ValueCountFrequency (%)
1049 1
0.1%
810 1
0.1%
642 1
0.1%
587 1
0.1%
481 1
0.1%
449 1
0.1%
422 1
0.1%
372 1
0.1%
342 1
0.1%
318 1
0.1%

Interactions

2024-01-10T05:12:12.776699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:10.993444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:11.652134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:12.230065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:12.912206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:11.208255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:11.806252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:12.396303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:13.017874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:11.356602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:11.916250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:12.519317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:13.126086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:11.504571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:12.041535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:12.656797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:12:15.501541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월시군구명가맹점수휴업가맹점수폐업가맹점수
기준연월1.0000.0000.3200.3820.000
시군구명0.0001.0000.9340.8970.700
가맹점수0.3200.9341.0000.9060.710
휴업가맹점수0.3820.8970.9061.0000.776
폐업가맹점수0.0000.7000.7100.7761.000
2024-01-10T05:12:15.638250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월가맹점수휴업가맹점수폐업가맹점수시군구명
기준연월1.0000.1060.161-0.0400.000
가맹점수0.1061.0000.9940.9000.733
휴업가맹점수0.1610.9941.0000.8820.658
폐업가맹점수-0.0400.9000.8821.0000.378
시군구명0.0000.7330.6580.3781.000

Missing values

2024-01-10T05:12:13.279105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:12:13.411632image/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

기준연월시군구명가맹점수휴업가맹점수폐업가맹점수
0201901계룡시174039357
1201901공주시68241628151
2201901금산군3735923100
3201901논산시76901841222
4201901당진시93781955269
5201901보령시70981547153
6201901부여군3289767101
7201901서산시100722012239
8201901서천군316967271
9201901아산시162393680481
기준연월시군구명가맹점수휴업가맹점수폐업가맹점수
918202310부여군3680101023
919202310서산시11708258085
920202310서천군364790322
921202310아산시192624632165
922202310예산군5620138342
923202310천안시 동남구179204658140
924202310천안시 서북구245865806270
925202310청양군187048315
926202310태안군6040157642
927202310홍성군7133176750