Overview

Dataset statistics

Number of variables7
Number of observations424
Missing cells14
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.2 KiB
Average record size in memory63.3 B

Variable types

Numeric7

Dataset

Description샘플 데이터
Author신한은행
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=317

Alerts

인프라지수(INFRASTRUCTURE) has 14 (3.3%) missing valuesMissing
행정동코드(ADSTRD_CD) has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:58:03.463946
Analysis finished2023-12-10 14:58:14.049554
Duration10.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자(DATE)
Real number (ℝ)

Distinct23
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201859.56
Minimum201802
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-10T23:58:14.159577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201802
5-th percentile201803
Q1201808
median201901
Q3201907
95-th percentile201912
Maximum201912
Range110
Interquartile range (IQR)99

Descriptive statistics

Standard deviation49.827994
Coefficient of variation (CV)0.00024684486
Kurtosis-1.9821763
Mean201859.56
Median Absolute Deviation (MAD)11
Skewness-0.10187178
Sum85588452
Variance2482.8289
MonotonicityNot monotonic
2023-12-10T23:58:14.402718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
201808 24
 
5.7%
201904 23
 
5.4%
201906 23
 
5.4%
201912 23
 
5.4%
201911 22
 
5.2%
201903 22
 
5.2%
201810 21
 
5.0%
201807 21
 
5.0%
201909 19
 
4.5%
201811 19
 
4.5%
Other values (13) 207
48.8%
ValueCountFrequency (%)
201802 10
2.4%
201803 18
4.2%
201804 18
4.2%
201805 18
4.2%
201806 18
4.2%
201807 21
5.0%
201808 24
5.7%
201809 18
4.2%
201810 21
5.0%
201811 19
4.5%
ValueCountFrequency (%)
201912 23
5.4%
201911 22
5.2%
201910 13
3.1%
201909 19
4.5%
201908 19
4.5%
201907 15
3.5%
201906 23
5.4%
201905 17
4.0%
201904 23
5.4%
201903 22
5.2%

행정동코드(ADSTRD_CD)
Real number (ℝ)

UNIQUE 

Distinct424
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11433195
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-10T23:58:14.761805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140582
Q111260649
median11440620
Q311598141
95-th percentile11710678
Maximum11740700
Range630185
Interquartile range (IQR)337492.5

Descriptive statistics

Standard deviation191894.28
Coefficient of variation (CV)0.016783959
Kurtosis-1.2660373
Mean11433195
Median Absolute Deviation (MAD)179942.5
Skewness-0.011417148
Sum4.8476747 × 109
Variance3.6823416 × 1010
MonotonicityNot monotonic
2023-12-10T23:58:15.227149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11260575 1
 
0.2%
11260620 1
 
0.2%
11380520 1
 
0.2%
11140580 1
 
0.2%
11230740 1
 
0.2%
11260680 1
 
0.2%
11560670 1
 
0.2%
11680590 1
 
0.2%
11320512 1
 
0.2%
11110530 1
 
0.2%
Other values (414) 414
97.6%
ValueCountFrequency (%)
11110515 1
0.2%
11110530 1
0.2%
11110540 1
0.2%
11110550 1
0.2%
11110560 1
0.2%
11110570 1
0.2%
11110580 1
0.2%
11110600 1
0.2%
11110615 1
0.2%
11110630 1
0.2%
ValueCountFrequency (%)
11740700 1
0.2%
11740690 1
0.2%
11740685 1
0.2%
11740660 1
0.2%
11740650 1
0.2%
11740640 1
0.2%
11740620 1
0.2%
11740610 1
0.2%
11740600 1
0.2%
11740590 1
0.2%

매출지수(SALES)
Real number (ℝ)

Distinct376
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.473962
Minimum5.55
Maximum56.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-10T23:58:15.529142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.55
5-th percentile8.7805
Q112.5075
median16.13
Q321.855
95-th percentile37.0625
Maximum56.78
Range51.23
Interquartile range (IQR)9.3475

Descriptive statistics

Standard deviation8.9350113
Coefficient of variation (CV)0.4836543
Kurtosis3.5628704
Mean18.473962
Median Absolute Deviation (MAD)4.135
Skewness1.7113941
Sum7832.96
Variance79.834428
MonotonicityNot monotonic
2023-12-10T23:58:15.818317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.57 4
 
0.9%
18.77 3
 
0.7%
11.34 3
 
0.7%
13.67 3
 
0.7%
11.3 2
 
0.5%
15.15 2
 
0.5%
10.51 2
 
0.5%
17.44 2
 
0.5%
16.57 2
 
0.5%
23.01 2
 
0.5%
Other values (366) 399
94.1%
ValueCountFrequency (%)
5.55 1
0.2%
6.3 1
0.2%
6.63 1
0.2%
6.79 1
0.2%
6.91 1
0.2%
7.05 1
0.2%
7.22 1
0.2%
7.27 1
0.2%
7.32 1
0.2%
7.83 1
0.2%
ValueCountFrequency (%)
56.78 1
0.2%
55.89 1
0.2%
55.74 1
0.2%
54.09 1
0.2%
51.59 1
0.2%
51.07 1
0.2%
50.41 1
0.2%
49.76 1
0.2%
48.72 1
0.2%
43.07 1
0.2%

인프라지수(INFRASTRUCTURE)
Real number (ℝ)

MISSING 

Distinct368
Distinct (%)89.8%
Missing14
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean18.627317
Minimum4.1
Maximum87.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-10T23:58:16.093193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile7.66
Q111.4825
median15.355
Q320.2725
95-th percentile45.1375
Maximum87.47
Range83.37
Interquartile range (IQR)8.79

Descriptive statistics

Standard deviation12.23684
Coefficient of variation (CV)0.65692984
Kurtosis7.4423654
Mean18.627317
Median Absolute Deviation (MAD)4.165
Skewness2.4763925
Sum7637.2
Variance149.74026
MonotonicityNot monotonic
2023-12-10T23:58:16.371027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.49 4
 
0.9%
7.66 3
 
0.7%
15.84 3
 
0.7%
13.9 3
 
0.7%
11.63 3
 
0.7%
16.88 3
 
0.7%
11.16 2
 
0.5%
11.37 2
 
0.5%
14.76 2
 
0.5%
17.16 2
 
0.5%
Other values (358) 383
90.3%
(Missing) 14
 
3.3%
ValueCountFrequency (%)
4.1 1
0.2%
4.96 1
0.2%
5.29 1
0.2%
5.44 1
0.2%
5.86 1
0.2%
6.33 1
0.2%
6.35 1
0.2%
6.36 1
0.2%
6.46 1
0.2%
6.59 1
0.2%
ValueCountFrequency (%)
87.47 1
0.2%
81.77 1
0.2%
72.4 1
0.2%
72.19 1
0.2%
70.09 1
0.2%
63.41 1
0.2%
61.31 1
0.2%
60.27 1
0.2%
60.0 1
0.2%
58.12 1
0.2%

가맹점지수(STORE)
Real number (ℝ)

Distinct392
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.173538
Minimum4.88
Maximum53.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-10T23:58:16.639259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.88
5-th percentile10.272
Q115.8575
median19.875
Q325.545
95-th percentile34.514
Maximum53.62
Range48.74
Interquartile range (IQR)9.6875

Descriptive statistics

Standard deviation7.7415806
Coefficient of variation (CV)0.36562528
Kurtosis1.4474743
Mean21.173538
Median Absolute Deviation (MAD)4.775
Skewness0.84224209
Sum8977.58
Variance59.93207
MonotonicityNot monotonic
2023-12-10T23:58:16.987379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 3
 
0.7%
19.24 3
 
0.7%
19.87 3
 
0.7%
17.86 3
 
0.7%
18.78 2
 
0.5%
21.83 2
 
0.5%
20.47 2
 
0.5%
17.76 2
 
0.5%
19.32 2
 
0.5%
12.78 2
 
0.5%
Other values (382) 400
94.3%
ValueCountFrequency (%)
4.88 1
0.2%
5.2 1
0.2%
5.45 1
0.2%
5.8 1
0.2%
6.6 2
0.5%
6.92 1
0.2%
7.59 1
0.2%
7.79 1
0.2%
8.56 1
0.2%
8.74 1
0.2%
ValueCountFrequency (%)
53.62 1
0.2%
50.22 1
0.2%
50.01 1
0.2%
48.38 1
0.2%
46.77 1
0.2%
44.2 1
0.2%
43.58 1
0.2%
40.99 1
0.2%
39.8 1
0.2%
39.38 1
0.2%

인구지수(POPULATION)
Real number (ℝ)

Distinct386
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.621627
Minimum3.34
Maximum57.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-10T23:58:17.322645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.34
5-th percentile11.956
Q117.3
median20.35
Q325.1175
95-th percentile35.705
Maximum57.99
Range54.65
Interquartile range (IQR)7.8175

Descriptive statistics

Standard deviation7.51359
Coefficient of variation (CV)0.34750344
Kurtosis3.001321
Mean21.621627
Median Absolute Deviation (MAD)3.785
Skewness1.2370877
Sum9167.57
Variance56.454034
MonotonicityNot monotonic
2023-12-10T23:58:17.596241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.98 3
 
0.7%
21.91 3
 
0.7%
29.66 2
 
0.5%
18.08 2
 
0.5%
24.24 2
 
0.5%
12.74 2
 
0.5%
27.11 2
 
0.5%
20.77 2
 
0.5%
19.79 2
 
0.5%
16.25 2
 
0.5%
Other values (376) 402
94.8%
ValueCountFrequency (%)
3.34 1
0.2%
3.91 1
0.2%
4.35 1
0.2%
5.92 1
0.2%
6.16 1
0.2%
7.59 1
0.2%
8.98 1
0.2%
9.28 1
0.2%
9.64 1
0.2%
10.17 1
0.2%
ValueCountFrequency (%)
57.99 1
0.2%
53.17 1
0.2%
48.7 1
0.2%
47.57 1
0.2%
47.33 1
0.2%
46.6 1
0.2%
45.47 1
0.2%
45.26 1
0.2%
44.35 1
0.2%
43.51 1
0.2%

금융지수(DEPOSIT)
Real number (ℝ)

Distinct394
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.620472
Minimum34.39
Maximum89.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-10T23:58:17.883999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.39
5-th percentile42.1215
Q147.3225
median52.57
Q359.735
95-th percentile75.5785
Maximum89.05
Range54.66
Interquartile range (IQR)12.4125

Descriptive statistics

Standard deviation10.397603
Coefficient of variation (CV)0.19036092
Kurtosis1.103676
Mean54.620472
Median Absolute Deviation (MAD)5.92
Skewness1.0828768
Sum23159.08
Variance108.11016
MonotonicityNot monotonic
2023-12-10T23:58:18.167807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.41 3
 
0.7%
43.99 2
 
0.5%
48.61 2
 
0.5%
44.72 2
 
0.5%
42.13 2
 
0.5%
51.84 2
 
0.5%
52.63 2
 
0.5%
45.91 2
 
0.5%
55.77 2
 
0.5%
53.97 2
 
0.5%
Other values (384) 403
95.0%
ValueCountFrequency (%)
34.39 1
0.2%
36.19 1
0.2%
36.21 1
0.2%
37.05 1
0.2%
37.29 1
0.2%
38.85 1
0.2%
39.76 1
0.2%
40.05 1
0.2%
40.36 1
0.2%
40.71 1
0.2%
ValueCountFrequency (%)
89.05 1
0.2%
87.91 1
0.2%
87.08 1
0.2%
87.03 1
0.2%
85.87 1
0.2%
85.64 1
0.2%
85.44 1
0.2%
84.78 1
0.2%
84.01 1
0.2%
83.71 1
0.2%

Interactions

2023-12-10T23:58:12.291725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:03.917067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.196054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.736471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:08.012261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:09.324586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:10.605359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:12.481592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.088887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.395613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.932572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:08.177991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:09.502351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:11.217350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:12.684397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.306701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.603905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:07.136721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:08.370957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:09.683140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:11.414759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:12.937520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.481794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.799440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:07.312704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:08.571311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:09.859889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:11.565388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.101494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.653203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.037766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:07.484807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:08.740553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:10.016330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:11.729027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.274577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:04.829408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.333134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:07.661616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:08.936040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:10.212564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:11.954806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:13.466995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:05.006654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:06.543794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:07.824935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:09.140077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:10.423592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:12.140356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:18.368265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자(DATE)행정동코드(ADSTRD_CD)매출지수(SALES)인프라지수(INFRASTRUCTURE)가맹점지수(STORE)인구지수(POPULATION)금융지수(DEPOSIT)
일자(DATE)1.0000.1100.0000.0000.0000.0000.183
행정동코드(ADSTRD_CD)0.1101.0000.1480.0000.0000.0000.104
매출지수(SALES)0.0000.1481.0000.1720.1660.1270.000
인프라지수(INFRASTRUCTURE)0.0000.0000.1721.0000.5310.0000.000
가맹점지수(STORE)0.0000.0000.1660.5311.0000.0000.000
인구지수(POPULATION)0.0000.0000.1270.0000.0001.0000.290
금융지수(DEPOSIT)0.1830.1040.0000.0000.0000.2901.000
2023-12-10T23:58:18.601619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자(DATE)행정동코드(ADSTRD_CD)매출지수(SALES)인프라지수(INFRASTRUCTURE)가맹점지수(STORE)인구지수(POPULATION)금융지수(DEPOSIT)
일자(DATE)1.000-0.0530.023-0.021-0.0390.0280.058
행정동코드(ADSTRD_CD)-0.0531.000-0.063-0.0200.002-0.1210.032
매출지수(SALES)0.023-0.0631.000-0.046-0.047-0.0280.056
인프라지수(INFRASTRUCTURE)-0.021-0.020-0.0461.0000.093-0.021-0.006
가맹점지수(STORE)-0.0390.002-0.0470.0931.000-0.025-0.014
인구지수(POPULATION)0.028-0.121-0.028-0.021-0.0251.000-0.015
금융지수(DEPOSIT)0.0580.0320.056-0.006-0.014-0.0151.000

Missing values

2023-12-10T23:58:13.714947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:58:13.950264image/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

일자(DATE)행정동코드(ADSTRD_CD)매출지수(SALES)인프라지수(INFRASTRUCTURE)가맹점지수(STORE)인구지수(POPULATION)금융지수(DEPOSIT)
0201907112605759.1111.4723.1924.4960.92
12019051138057029.3310.8123.9718.9153.73
22019111132068118.7712.9527.5453.1762.88
32018101130561022.3211.3935.6325.4787.91
42018081138060014.4644.6717.6912.4753.18
52019111135069532.4722.6110.0221.1848.75
62018101150053516.3513.828.0820.5947.02
72018061114068012.0910.114.0443.5147.12
82018091154561013.4918.8718.9520.0756.4
92018041114052025.91<NA>8.5623.059.28
일자(DATE)행정동코드(ADSTRD_CD)매출지수(SALES)인프라지수(INFRASTRUCTURE)가맹점지수(STORE)인구지수(POPULATION)금융지수(DEPOSIT)
4142019091121584710.8915.8423.8433.2250.51
4152019041141061550.4114.5728.547.5940.71
4162019021156072010.368.2632.613.0566.79
4172019041156070031.4712.6616.9718.0850.48
4182018111138059016.2438.2229.6235.7247.84
4192019121144058536.913.1929.7827.9878.25
4202019011156063030.7517.6411.3819.6246.24
4212019061154569023.788.3310.2617.1869.05
4222019071144073021.716.5919.9810.3245.03
4232018061111064024.347.3829.1622.5172.18