Overview

Dataset statistics

Number of variables8
Number of observations307
Missing cells105
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.8 KiB
Average record size in memory69.4 B

Variable types

Categorical3
Numeric5

Dataset

Description대전광역시 자치구별 신용카드(KB국민카드) 매출액 현황 입니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15064213/fileData.do

Alerts

3월(억원) is highly overall correlated with 4월(억원) and 3 other fieldsHigh correlation
4월(억원) is highly overall correlated with 3월(억원) and 3 other fieldsHigh correlation
5월(억원) is highly overall correlated with 3월(억원) and 3 other fieldsHigh correlation
6월(억원) is highly overall correlated with 3월(억원) and 3 other fieldsHigh correlation
7월(억원) is highly overall correlated with 3월(억원) and 3 other fieldsHigh correlation
3월(억원) has 25 (8.1%) missing valuesMissing
4월(억원) has 21 (6.8%) missing valuesMissing
5월(억원) has 15 (4.9%) missing valuesMissing
6월(억원) has 20 (6.5%) missing valuesMissing
7월(억원) has 24 (7.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 05:39:07.224881
Analysis finished2023-12-12 05:39:10.563036
Duration3.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2019년
155 
2020년
152 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019년
2nd row2019년
3rd row2019년
4th row2019년
5th row2019년

Common Values

ValueCountFrequency (%)
2019년 155
50.5%
2020년 152
49.5%

Length

2023-12-12T14:39:10.626275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:39:10.721937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019년 155
50.5%
2020년 152
49.5%

구
Categorical

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
서구
67 
동구
62 
중구
61 
유성구
60 
대덕구
57 

Length

Max length3
Median length2
Mean length2.3811075
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 67
21.8%
동구 62
20.2%
중구 61
19.9%
유성구 60
19.5%
대덕구 57
18.6%

Length

2023-12-12T14:39:10.847271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:39:10.965686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 67
21.8%
동구 62
20.2%
중구 61
19.9%
유성구 60
19.5%
대덕구 57
18.6%

업종
Categorical

Distinct38
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
건축관련업
 
10
유흥
 
10
용역서비스
 
10
문화/취미
 
10
숙박
 
10
Other values (33)
257 

Length

Max length8
Median length7
Mean length4.2345277
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row건축관련업
2nd row관광여행
3rd row기타유통
4th row기타제조/도매
5th row레저/스포츠

Common Values

ValueCountFrequency (%)
건축관련업 10
 
3.3%
유흥 10
 
3.3%
용역서비스 10
 
3.3%
문화/취미 10
 
3.3%
숙박 10
 
3.3%
신변잡화 10
 
3.3%
유아교육기관 10
 
3.3%
기타제조/도매 10
 
3.3%
기타유통 10
 
3.3%
소매 10
 
3.3%
Other values (28) 207
67.4%

Length

2023-12-12T14:39:11.106362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건축관련업 10
 
3.3%
음식료품 10
 
3.3%
의류 10
 
3.3%
휴게음식점 10
 
3.3%
학원 10
 
3.3%
학습자재 10
 
3.3%
유흥 10
 
3.3%
주유 10
 
3.3%
주방용품 10
 
3.3%
전자제품 10
 
3.3%
Other values (28) 207
67.4%

3월(억원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct199
Distinct (%)70.6%
Missing25
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean43.888652
Minimum0
Maximum854.8
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T14:39:11.250374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q11.95
median9.35
Q332.85
95-th percentile183.48
Maximum854.8
Range854.8
Interquartile range (IQR)30.9

Descriptive statistics

Standard deviation103.70633
Coefficient of variation (CV)2.3629418
Kurtosis26.984733
Mean43.888652
Median Absolute Deviation (MAD)8.95
Skewness4.7497502
Sum12376.6
Variance10755.003
MonotonicityNot monotonic
2023-12-12T14:39:11.405656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 16
 
5.2%
0.2 11
 
3.6%
0.3 6
 
2.0%
0.4 5
 
1.6%
0.6 5
 
1.6%
1.0 4
 
1.3%
2.5 3
 
1.0%
2.3 3
 
1.0%
20.9 3
 
1.0%
6.4 3
 
1.0%
Other values (189) 223
72.6%
(Missing) 25
 
8.1%
ValueCountFrequency (%)
0.0 2
 
0.7%
0.1 16
5.2%
0.2 11
3.6%
0.3 6
 
2.0%
0.4 5
 
1.6%
0.5 2
 
0.7%
0.6 5
 
1.6%
0.7 2
 
0.7%
1.0 4
 
1.3%
1.1 2
 
0.7%
ValueCountFrequency (%)
854.8 1
0.3%
764.9 1
0.3%
586.4 1
0.3%
523.5 1
0.3%
494.4 1
0.3%
489.0 1
0.3%
344.4 1
0.3%
343.7 1
0.3%
330.4 1
0.3%
257.3 1
0.3%

4월(억원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct195
Distinct (%)68.2%
Missing21
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean45.282867
Minimum0
Maximum798
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T14:39:11.669078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q12.1
median9.45
Q332
95-th percentile197.525
Maximum798
Range798
Interquartile range (IQR)29.9

Descriptive statistics

Standard deviation106.55351
Coefficient of variation (CV)2.3530645
Kurtosis24.409472
Mean45.282867
Median Absolute Deviation (MAD)8.95
Skewness4.6093109
Sum12950.9
Variance11353.65
MonotonicityNot monotonic
2023-12-12T14:39:12.121425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 15
 
4.9%
0.2 8
 
2.6%
0.4 7
 
2.3%
0.5 6
 
2.0%
0.7 6
 
2.0%
0.3 5
 
1.6%
0.6 5
 
1.6%
1.8 4
 
1.3%
2.1 4
 
1.3%
8.0 3
 
1.0%
Other values (185) 223
72.6%
(Missing) 21
 
6.8%
ValueCountFrequency (%)
0.0 1
 
0.3%
0.1 15
4.9%
0.2 8
2.6%
0.3 5
 
1.6%
0.4 7
2.3%
0.5 6
 
2.0%
0.6 5
 
1.6%
0.7 6
 
2.0%
0.9 1
 
0.3%
1.0 1
 
0.3%
ValueCountFrequency (%)
798.0 1
0.3%
735.5 1
0.3%
691.9 1
0.3%
644.5 1
0.3%
533.1 1
0.3%
510.8 1
0.3%
343.4 1
0.3%
325.2 1
0.3%
323.9 1
0.3%
305.6 1
0.3%

5월(억원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct203
Distinct (%)69.5%
Missing15
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean50.344178
Minimum0
Maximum866.8
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T14:39:12.644186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q12.3
median10.85
Q339.15
95-th percentile224.265
Maximum866.8
Range866.8
Interquartile range (IQR)36.85

Descriptive statistics

Standard deviation118.38022
Coefficient of variation (CV)2.3514182
Kurtosis25.669304
Mean50.344178
Median Absolute Deviation (MAD)10.35
Skewness4.7160418
Sum14700.5
Variance14013.876
MonotonicityNot monotonic
2023-12-12T14:39:13.010687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 12
 
3.9%
0.2 8
 
2.6%
0.6 6
 
2.0%
0.5 5
 
1.6%
0.3 5
 
1.6%
9.5 4
 
1.3%
2.9 4
 
1.3%
0.4 4
 
1.3%
1.4 4
 
1.3%
9.9 4
 
1.3%
Other values (193) 236
76.9%
(Missing) 15
 
4.9%
ValueCountFrequency (%)
0.0 1
 
0.3%
0.1 12
3.9%
0.2 8
2.6%
0.3 5
1.6%
0.4 4
 
1.3%
0.5 5
1.6%
0.6 6
2.0%
0.7 1
 
0.3%
0.8 2
 
0.7%
0.9 1
 
0.3%
ValueCountFrequency (%)
866.8 1
0.3%
852.4 1
0.3%
783.5 1
0.3%
780.4 1
0.3%
586.0 1
0.3%
508.0 1
0.3%
372.3 1
0.3%
371.2 1
0.3%
359.0 1
0.3%
345.2 1
0.3%

6월(억원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct198
Distinct (%)69.0%
Missing20
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean45.029268
Minimum0.1
Maximum823.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T14:39:13.190213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.13
Q12.15
median9.4
Q333.25
95-th percentile198.53
Maximum823.6
Range823.5
Interquartile range (IQR)31.1

Descriptive statistics

Standard deviation105.90428
Coefficient of variation (CV)2.3518988
Kurtosis25.097223
Mean45.029268
Median Absolute Deviation (MAD)9
Skewness4.6478676
Sum12923.4
Variance11215.717
MonotonicityNot monotonic
2023-12-12T14:39:13.380932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 15
 
4.9%
0.2 8
 
2.6%
0.4 8
 
2.6%
0.3 8
 
2.6%
1.2 5
 
1.6%
2.8 4
 
1.3%
0.8 4
 
1.3%
0.5 3
 
1.0%
3.0 3
 
1.0%
1.7 3
 
1.0%
Other values (188) 226
73.6%
(Missing) 20
 
6.5%
ValueCountFrequency (%)
0.1 15
4.9%
0.2 8
2.6%
0.3 8
2.6%
0.4 8
2.6%
0.5 3
 
1.0%
0.6 1
 
0.3%
0.7 2
 
0.7%
0.8 4
 
1.3%
0.9 3
 
1.0%
1.0 2
 
0.7%
ValueCountFrequency (%)
823.6 1
0.3%
734.5 1
0.3%
662.3 1
0.3%
647.9 1
0.3%
524.5 1
0.3%
476.1 1
0.3%
340.8 1
0.3%
339.7 1
0.3%
328.9 1
0.3%
291.8 1
0.3%

7월(억원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct194
Distinct (%)68.6%
Missing24
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean43.738163
Minimum0
Maximum825.2
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T14:39:13.583127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q11.9
median9
Q330.7
95-th percentile187.04
Maximum825.2
Range825.2
Interquartile range (IQR)28.8

Descriptive statistics

Standard deviation104.06833
Coefficient of variation (CV)2.3793484
Kurtosis26.005023
Mean43.738163
Median Absolute Deviation (MAD)8.6
Skewness4.7160732
Sum12377.9
Variance10830.216
MonotonicityNot monotonic
2023-12-12T14:39:13.789624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 15
 
4.9%
0.2 12
 
3.9%
0.3 7
 
2.3%
0.5 6
 
2.0%
1.0 5
 
1.6%
4.3 4
 
1.3%
2.2 4
 
1.3%
8.9 4
 
1.3%
0.9 4
 
1.3%
6.2 3
 
1.0%
Other values (184) 219
71.3%
(Missing) 24
 
7.8%
ValueCountFrequency (%)
0.0 1
 
0.3%
0.1 15
4.9%
0.2 12
3.9%
0.3 7
2.3%
0.4 3
 
1.0%
0.5 6
 
2.0%
0.7 3
 
1.0%
0.8 1
 
0.3%
0.9 4
 
1.3%
1.0 5
 
1.6%
ValueCountFrequency (%)
825.2 1
0.3%
752.6 1
0.3%
603.0 1
0.3%
592.5 1
0.3%
548.9 1
0.3%
468.0 1
0.3%
347.3 1
0.3%
337.3 1
0.3%
299.5 1
0.3%
266.3 1
0.3%

Interactions

2023-12-12T14:39:09.769584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:07.595752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:08.054266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:08.641865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.264174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.853444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:07.688078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:08.156425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:08.768502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.359703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.951408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:07.787560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:08.271548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:08.899921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.476841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:10.045050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:07.885760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:08.384893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.024517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.571456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:10.128147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:07.969149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:08.506198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.156964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:39:09.675852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:39:14.272474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도구업종3월(억원)4월(억원)5월(억원)6월(억원)7월(억원)
기준년도1.0000.0000.0000.0000.0000.0000.0000.094
구0.0001.0000.0000.0000.0980.0760.0000.000
업종0.0000.0001.0000.6060.7170.6800.5460.603
3월(억원)0.0000.0000.6061.0000.9550.9370.9440.986
4월(억원)0.0000.0980.7170.9551.0000.9880.9530.965
5월(억원)0.0000.0760.6800.9370.9881.0000.9610.959
6월(억원)0.0000.0000.5460.9440.9530.9611.0000.980
7월(억원)0.0940.0000.6030.9860.9650.9590.9801.000
2023-12-12T14:39:14.406371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도구업종
기준년도1.0000.0000.000
구0.0001.0000.000
업종0.0000.0001.000
2023-12-12T14:39:14.514112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
3월(억원)4월(억원)5월(억원)6월(억원)7월(억원)기준년도구업종
3월(억원)1.0000.9890.9760.9750.9830.0000.0000.235
4월(억원)0.9891.0000.9820.9820.9810.0000.0590.348
5월(억원)0.9760.9821.0000.9810.9760.0000.0450.322
6월(억원)0.9750.9820.9811.0000.9920.0000.0000.211
7월(억원)0.9830.9810.9760.9921.0000.0920.0000.252
기준년도0.0000.0000.0000.0000.0921.0000.0000.000
구0.0000.0590.0450.0000.0000.0001.0000.000
업종0.2350.3480.3220.2110.2520.0000.0001.000

Missing values

2023-12-12T14:39:10.242946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:39:10.367154image/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.
2023-12-12T14:39:10.494519image/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

기준년도구업종3월(억원)4월(억원)5월(억원)6월(억원)7월(억원)
02019년동구건축관련업5.65.66.65.66.4
12019년동구관광여행0.10.10.10.10.1
22019년동구기타유통0.30.40.50.20.2
32019년동구기타제조/도매1.00.40.80.90.7
42019년동구레저/스포츠15.715.817.013.611.5
52019년동구문화/취미24.511.313.411.99.9
62019년동구미용30.027.328.927.827.4
72019년동구사무기기0.60.50.50.40.4
82019년동구소매184.2177.5187.9182.9176.7
92019년동구수리서비스0.10.10.10.10.2
기준년도구업종3월(억원)4월(억원)5월(억원)6월(억원)7월(억원)
2972020년대덕구의류2.44.16.24.12.3
2982020년대덕구일반음식점163.0189.8229.6190.2182.0
2992020년대덕구자동차정비/유지65.271.182.388.380.2
3002020년대덕구전자제품8.19.88.98.99.8
3012020년대덕구주방용품0.60.92.21.61.5
3022020년대덕구주유68.066.964.478.876.6
3032020년대덕구직물/침구류0.30.21.80.80.3
3042020년대덕구학습자재1.11.41.41.21.0
3052020년대덕구학원1.72.64.72.91.9
3062020년대덕구휴게음식점25.728.430.828.925.5