Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.1 KiB
Average record size in memory134.3 B

Variable types

Text2
Numeric9
Categorical4

Dataset

Description샘플 데이터
Author오픈메이트
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=12

Alerts

기준_년월_코드(STDR_YM) has constant value ""Constant
대학가_여부(UNVTW_AT) is highly imbalanced (50.0%)Imbalance
엑스좌표(XCNTS) has unique valuesUnique
배후지_유형_중분류_코드(H_MLSFC_CD) has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:49:09.140661
Analysis finished2023-12-10 14:49:19.547162
Duration10.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T23:49:19.728479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.7
Min length4

Characters and Unicode

Total characters570
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

Unique82 ?
Unique (%)82.0%

Sample

1st row3*6*7*
2nd row8*2*
3rd row1*9*4
4th row2*3*1
5th row1*6*5*
ValueCountFrequency (%)
1*5*9 3
 
3.0%
2*0*6 2
 
2.0%
1*1*7 2
 
2.0%
2*7*6 2
 
2.0%
2*7*1 2
 
2.0%
3*3*6 2
 
2.0%
1*6*5 2
 
2.0%
2*3*5 2
 
2.0%
2*7*5 2
 
2.0%
2*9*5 2
 
2.0%
Other values (78) 79
79.0%
2023-12-10T23:49:20.102582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 271
47.5%
2 60
 
10.5%
1 48
 
8.4%
3 37
 
6.5%
9 26
 
4.6%
6 24
 
4.2%
5 24
 
4.2%
4 23
 
4.0%
7 22
 
3.9%
8 19
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 299
52.5%
Other Punctuation 271
47.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 60
20.1%
1 48
16.1%
3 37
12.4%
9 26
8.7%
6 24
 
8.0%
5 24
 
8.0%
4 23
 
7.7%
7 22
 
7.4%
8 19
 
6.4%
0 16
 
5.4%
Other Punctuation
ValueCountFrequency (%)
* 271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 271
47.5%
2 60
 
10.5%
1 48
 
8.4%
3 37
 
6.5%
9 26
 
4.6%
6 24
 
4.2%
5 24
 
4.2%
4 23
 
4.0%
7 22
 
3.9%
8 19
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 271
47.5%
2 60
 
10.5%
1 48
 
8.4%
3 37
 
6.5%
9 26
 
4.6%
6 24
 
4.2%
5 24
 
4.2%
4 23
 
4.0%
7 22
 
3.9%
8 19
 
3.3%
Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T23:49:20.359028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.65
Min length7

Characters and Unicode

Total characters865
Distinct characters66
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row목*동*N*5*
2nd row등*3*-*-*6
3rd row신*3*-*-*6
4th row역*동*S*1*
5th row망*2*-*-*2*
ValueCountFrequency (%)
대*2*-*-*7 2
 
2.0%
송*동*s*6 2
 
2.0%
대*1 1
 
1.0%
상*4*-*-*6 1
 
1.0%
목*동*n*5 1
 
1.0%
암*1*-*-*1 1
 
1.0%
화*1*-*-*4 1
 
1.0%
창*동*e*2 1
 
1.0%
오*2*-*-*1 1
 
1.0%
신*림 1
 
1.0%
Other values (88) 88
88.0%
2023-12-10T23:49:20.719800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 403
46.6%
- 146
 
16.9%
1 43
 
5.0%
2 33
 
3.8%
27
 
3.1%
3 20
 
2.3%
5 14
 
1.6%
6 13
 
1.5%
4 11
 
1.3%
11
 
1.3%
Other values (56) 144
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 403
46.6%
Decimal Number 156
 
18.0%
Dash Punctuation 146
 
16.9%
Other Letter 133
 
15.4%
Uppercase Letter 27
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
27
20.3%
11
 
8.3%
6
 
4.5%
5
 
3.8%
5
 
3.8%
4
 
3.0%
4
 
3.0%
4
 
3.0%
4
 
3.0%
3
 
2.3%
Other values (40) 60
45.1%
Decimal Number
ValueCountFrequency (%)
1 43
27.6%
2 33
21.2%
3 20
12.8%
5 14
 
9.0%
6 13
 
8.3%
4 11
 
7.1%
7 9
 
5.8%
8 5
 
3.2%
0 4
 
2.6%
9 4
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
S 10
37.0%
N 7
25.9%
E 6
22.2%
W 4
 
14.8%
Other Punctuation
ValueCountFrequency (%)
* 403
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 705
81.5%
Hangul 133
 
15.4%
Latin 27
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
27
20.3%
11
 
8.3%
6
 
4.5%
5
 
3.8%
5
 
3.8%
4
 
3.0%
4
 
3.0%
4
 
3.0%
4
 
3.0%
3
 
2.3%
Other values (40) 60
45.1%
Common
ValueCountFrequency (%)
* 403
57.2%
- 146
 
20.7%
1 43
 
6.1%
2 33
 
4.7%
3 20
 
2.8%
5 14
 
2.0%
6 13
 
1.8%
4 11
 
1.6%
7 9
 
1.3%
8 5
 
0.7%
Other values (2) 8
 
1.1%
Latin
ValueCountFrequency (%)
S 10
37.0%
N 7
25.9%
E 6
22.2%
W 4
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
84.6%
Hangul 133
 
15.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 403
55.1%
- 146
 
19.9%
1 43
 
5.9%
2 33
 
4.5%
3 20
 
2.7%
5 14
 
1.9%
6 13
 
1.8%
4 11
 
1.5%
S 10
 
1.4%
7 9
 
1.2%
Other values (6) 30
 
4.1%
Hangul
ValueCountFrequency (%)
27
20.3%
11
 
8.3%
6
 
4.5%
5
 
3.8%
5
 
3.8%
4
 
3.0%
4
 
3.0%
4
 
3.0%
4
 
3.0%
3
 
2.3%
Other values (40) 60
45.1%

엑스좌표(XCNTS)
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310897.98
Minimum296939
Maximum324990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:20.837375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum296939
5-th percentile298937.85
Q1304809
median311214
Q3317124.5
95-th percentile321995.8
Maximum324990
Range28051
Interquartile range (IQR)12315.5

Descriptive statistics

Standard deviation7109.175
Coefficient of variation (CV)0.022866585
Kurtosis-0.99726157
Mean310897.98
Median Absolute Deviation (MAD)6091
Skewness-0.003326009
Sum31089798
Variance50540369
MonotonicityNot monotonic
2023-12-10T23:49:20.955574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303367 1
 
1.0%
317665 1
 
1.0%
307378 1
 
1.0%
315744 1
 
1.0%
303846 1
 
1.0%
314029 1
 
1.0%
303270 1
 
1.0%
309220 1
 
1.0%
324057 1
 
1.0%
317772 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
296939 1
1.0%
297795 1
1.0%
297812 1
1.0%
297976 1
1.0%
298631 1
1.0%
298954 1
1.0%
300383 1
1.0%
300795 1
1.0%
302281 1
1.0%
302377 1
1.0%
ValueCountFrequency (%)
324990 1
1.0%
324191 1
1.0%
324057 1
1.0%
323029 1
1.0%
322695 1
1.0%
321959 1
1.0%
321506 1
1.0%
320824 1
1.0%
320553 1
1.0%
320190 1
1.0%

와이좌표(YDNTS)
Real number (ℝ)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean550601.17
Minimum539075
Maximum564233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:21.076318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum539075
5-th percentile541750
Q1545418.75
median550402.5
Q3555480.5
95-th percentile560253.4
Maximum564233
Range25158
Interquartile range (IQR)10061.75

Descriptive statistics

Standard deviation6089.9494
Coefficient of variation (CV)0.011060546
Kurtosis-0.85776704
Mean550601.17
Median Absolute Deviation (MAD)5037
Skewness0.12987193
Sum55060117
Variance37087484
MonotonicityNot monotonic
2023-12-10T23:49:21.468360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551978 2
 
2.0%
547892 1
 
1.0%
559501 1
 
1.0%
549339 1
 
1.0%
555723 1
 
1.0%
546652 1
 
1.0%
564233 1
 
1.0%
553300 1
 
1.0%
543803 1
 
1.0%
555459 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
539075 1
1.0%
539581 1
1.0%
540797 1
1.0%
540960 1
1.0%
541009 1
1.0%
541789 1
1.0%
542162 1
1.0%
542278 1
1.0%
542316 1
1.0%
542458 1
1.0%
ValueCountFrequency (%)
564233 1
1.0%
562888 1
1.0%
562253 1
1.0%
561938 1
1.0%
560603 1
1.0%
560235 1
1.0%
559708 1
1.0%
559501 1
1.0%
559249 1
1.0%
559115 1
1.0%
Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11464024
Minimum11110540
Maximum11740685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:21.584792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110540
5-th percentile11200578
Q111320624
median11500528
Q311620588
95-th percentile11740584
Maximum11740685
Range630145
Interquartile range (IQR)299964

Descriptive statistics

Standard deviation178376.57
Coefficient of variation (CV)0.015559681
Kurtosis-1.0913378
Mean11464024
Median Absolute Deviation (MAD)149952.5
Skewness-0.12593415
Sum1.1464024 × 109
Variance3.18182 × 1010
MonotonicityNot monotonic
2023-12-10T23:49:21.714589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11530780 2
 
2.0%
11260565 2
 
2.0%
11260580 2
 
2.0%
11320690 2
 
2.0%
11260655 2
 
2.0%
11740660 2
 
2.0%
11740685 2
 
2.0%
11350580 2
 
2.0%
11410585 2
 
2.0%
11380690 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
11110540 1
1.0%
11110580 1
1.0%
11140550 1
1.0%
11170630 1
1.0%
11200550 1
1.0%
11200580 1
1.0%
11200720 1
1.0%
11200790 1
1.0%
11215810 1
1.0%
11215847 1
1.0%
ValueCountFrequency (%)
11740685 2
2.0%
11740660 2
2.0%
11740650 1
1.0%
11740580 1
1.0%
11740560 1
1.0%
11740520 1
1.0%
11710641 1
1.0%
11710632 1
1.0%
11710631 1
1.0%
11710620 1
1.0%
Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11434.9
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:21.851190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11138.5
Q111282.5
median11425
Q311590
95-th percentile11711.5
Maximum11740
Range630
Interquartile range (IQR)307.5

Descriptive statistics

Standard deviation192.09633
Coefficient of variation (CV)0.016799126
Kurtosis-1.2230704
Mean11434.9
Median Absolute Deviation (MAD)165
Skewness-0.002144066
Sum1143490
Variance36901
MonotonicityNot monotonic
2023-12-10T23:49:21.973044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11470 7
 
7.0%
11560 7
 
7.0%
11710 7
 
7.0%
11410 6
 
6.0%
11200 6
 
6.0%
11680 5
 
5.0%
11110 5
 
5.0%
11230 5
 
5.0%
11740 5
 
5.0%
11590 4
 
4.0%
Other values (15) 43
43.0%
ValueCountFrequency (%)
11110 5
5.0%
11140 2
 
2.0%
11170 2
 
2.0%
11200 6
6.0%
11215 3
3.0%
11230 5
5.0%
11260 2
 
2.0%
11290 4
4.0%
11305 3
3.0%
11320 4
4.0%
ValueCountFrequency (%)
11740 5
5.0%
11710 7
7.0%
11680 5
5.0%
11650 3
3.0%
11620 4
4.0%
11590 4
4.0%
11560 7
7.0%
11545 2
 
2.0%
11530 1
 
1.0%
11500 3
3.0%
Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.47
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:22.074063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.75
median4
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.96875428
Coefficient of variation (CV)0.27917991
Kurtosis-0.17714899
Mean3.47
Median Absolute Deviation (MAD)0
Skewness-0.7979587
Sum347
Variance0.93848485
MonotonicityNot monotonic
2023-12-10T23:49:22.167253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 67
67.0%
2 23
 
23.0%
3 5
 
5.0%
5 2
 
2.0%
1 2
 
2.0%
6 1
 
1.0%
ValueCountFrequency (%)
1 2
 
2.0%
2 23
 
23.0%
3 5
 
5.0%
4 67
67.0%
5 2
 
2.0%
6 1
 
1.0%
ValueCountFrequency (%)
6 1
 
1.0%
5 2
 
2.0%
4 67
67.0%
3 5
 
5.0%
2 23
 
23.0%
1 2
 
2.0%
Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370.02
Minimum101
Maximum601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:22.263019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile202
Q1401
median401
Q3403
95-th percentile601
Maximum601
Range500
Interquartile range (IQR)2

Descriptive statistics

Standard deviation107.87319
Coefficient of variation (CV)0.29153341
Kurtosis0.53500747
Mean370.02
Median Absolute Deviation (MAD)2
Skewness-0.31447987
Sum37002
Variance11636.626
MonotonicityNot monotonic
2023-12-10T23:49:22.362841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
401 33
33.0%
403 26
26.0%
203 12
 
12.0%
601 7
 
7.0%
202 6
 
6.0%
402 5
 
5.0%
404 4
 
4.0%
101 2
 
2.0%
302 2
 
2.0%
201 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
101 2
 
2.0%
201 1
 
1.0%
202 6
 
6.0%
203 12
 
12.0%
301 1
 
1.0%
302 2
 
2.0%
401 33
33.0%
402 5
 
5.0%
403 26
26.0%
404 4
 
4.0%
ValueCountFrequency (%)
601 7
 
7.0%
502 1
 
1.0%
404 4
 
4.0%
403 26
26.0%
402 5
 
5.0%
401 33
33.0%
302 2
 
2.0%
301 1
 
1.0%
203 12
 
12.0%
202 6
 
6.0%
Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35621.05
Minimum20102
Maximum60102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:22.464075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20102
5-th percentile20201
Q120301.75
median40101
Q340301
95-th percentile50886
Maximum60102
Range40000
Interquartile range (IQR)19999.25

Descriptive statistics

Standard deviation10647.4
Coefficient of variation (CV)0.29890753
Kurtosis-0.23581252
Mean35621.05
Median Absolute Deviation (MAD)200
Skewness-0.10886397
Sum3562105
Variance1.1336713 × 108
MonotonicityNot monotonic
2023-12-10T23:49:22.577787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
40101 29
29.0%
40301 29
29.0%
20301 17
17.0%
20201 6
 
6.0%
40401 4
 
4.0%
60101 4
 
4.0%
30201 2
 
2.0%
30101 2
 
2.0%
20102 2
 
2.0%
20302 2
 
2.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
20102 2
 
2.0%
20201 6
 
6.0%
20301 17
17.0%
20302 2
 
2.0%
30101 2
 
2.0%
30201 2
 
2.0%
40101 29
29.0%
40301 29
29.0%
40401 4
 
4.0%
50201 1
 
1.0%
ValueCountFrequency (%)
60102 1
 
1.0%
60101 4
 
4.0%
50401 1
 
1.0%
50201 1
 
1.0%
40401 4
 
4.0%
40301 29
29.0%
40101 29
29.0%
30201 2
 
2.0%
30101 2
 
2.0%
20302 2
 
2.0%
Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
79 
1
21 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 79
79.0%
1 21
 
21.0%

Length

2023-12-10T23:49:22.697709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:22.791637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 79
79.0%
1 21
 
21.0%

대학가_여부(UNVTW_AT)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
89 
1
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 89
89.0%
1 11
 
11.0%

Length

2023-12-10T23:49:22.880918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:22.968992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 89
89.0%
1 11
 
11.0%
Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4
75 
2
18 
3
 
4
5
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 75
75.0%
2 18
 
18.0%
3 4
 
4.0%
5 3
 
3.0%

Length

2023-12-10T23:49:23.053702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:23.134655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 75
75.0%
2 18
 
18.0%
3 4
 
4.0%
5 3
 
3.0%
Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345.12
Minimum0
Maximum501
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:23.217138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201
Q1204
median402
Q3403
95-th percentile403
Maximum501
Range501
Interquartile range (IQR)199

Descriptive statistics

Standard deviation102.80729
Coefficient of variation (CV)0.29788853
Kurtosis1.0723863
Mean345.12
Median Absolute Deviation (MAD)1
Skewness-1.3576942
Sum34512
Variance10569.339
MonotonicityNot monotonic
2023-12-10T23:49:23.313592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
403 27
27.0%
402 27
27.0%
401 16
16.0%
201 10
 
10.0%
204 8
 
8.0%
202 3
 
3.0%
203 2
 
2.0%
0 2
 
2.0%
501 2
 
2.0%
301 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
0 2
 
2.0%
102 1
 
1.0%
201 10
 
10.0%
202 3
 
3.0%
203 2
 
2.0%
204 8
 
8.0%
301 1
 
1.0%
302 1
 
1.0%
401 16
16.0%
402 27
27.0%
ValueCountFrequency (%)
501 2
 
2.0%
403 27
27.0%
402 27
27.0%
401 16
16.0%
302 1
 
1.0%
301 1
 
1.0%
204 8
 
8.0%
203 2
 
2.0%
202 3
 
3.0%
201 10
 
10.0%
Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34813.04
Minimum10201
Maximum50101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:49:23.408949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10201
5-th percentile20101
Q127676
median40101
Q340201
95-th percentile40301
Maximum50101
Range39900
Interquartile range (IQR)12525

Descriptive statistics

Standard deviation9116.4347
Coefficient of variation (CV)0.26186839
Kurtosis-0.59268239
Mean34813.04
Median Absolute Deviation (MAD)200
Skewness-0.97188595
Sum3481304
Variance83109381
MonotonicityNot monotonic
2023-12-10T23:49:23.525239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
40201 24
24.0%
40101 23
23.0%
40301 21
21.0%
20401 11
11.0%
20101 7
 
7.0%
20202 4
 
4.0%
30201 4
 
4.0%
20301 2
 
2.0%
50101 2
 
2.0%
30101 1
 
1.0%
ValueCountFrequency (%)
10201 1
 
1.0%
20101 7
 
7.0%
20202 4
 
4.0%
20301 2
 
2.0%
20401 11
11.0%
30101 1
 
1.0%
30201 4
 
4.0%
40101 23
23.0%
40201 24
24.0%
40301 21
21.0%
ValueCountFrequency (%)
50101 2
 
2.0%
40301 21
21.0%
40201 24
24.0%
40101 23
23.0%
30201 4
 
4.0%
30101 1
 
1.0%
20401 11
11.0%
20301 2
 
2.0%
20202 4
 
4.0%
20101 7
 
7.0%

기준_년월_코드(STDR_YM)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2016
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 100
100.0%

Length

2023-12-10T23:49:23.656044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:49:23.739739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 100
100.0%

Interactions

2023-12-10T23:49:18.443410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:11.681413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.498691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.229014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.995707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.873575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.671188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:16.913096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.716179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.534893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:11.814023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.582900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.316893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.086334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.944865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.773487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.016105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.808848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.612912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:11.899776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.655963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.399082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.170027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.023890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.863649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.108327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.896788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.713487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:11.987508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.739477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.487351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.264837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.103406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.960137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.202217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.977402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.796708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.079492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.825102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.569765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.370397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.203529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:16.346325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.292112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.057947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.880618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.157791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.899896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.655372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.463250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.298499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:16.453576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.371616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.131728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.981164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.246621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.970621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.734016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.548649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.386286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:16.558727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.452269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.212128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:19.087859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.332699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.056480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.824395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.657124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.477182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:16.670042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.538361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.291044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:19.163927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:12.412730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.135908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:13.903455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:14.749855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:15.566945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:16.799169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:17.616163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:49:18.362255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:49:23.798671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
블록_코드(BLCK_CD)블록_명(BLCK_NM)엑스좌표(XCNTS)와이좌표(YDNTS)행정동코드(ADSTRD_CD)시군구코드(SIGNGU_CD)블록_유형_대분류_코드(LCLAS_CD)블록_유형_중분류_코드(MLSFC_CD)블록_유형_소분류_코드(SCLAS_CD)역세권_여부(RASTSP_AT)대학가_여부(UNVTW_AT)배후지_유형_대분류_코드(H_LCLAS_CD)배후지_유형_중분류_코드(H_MLSFC_CD)배후지_유형_소분류_코드(H_SCLAS_CD)
블록_코드(BLCK_CD)1.0000.9960.6100.0000.7230.4360.9590.0000.9531.0000.0000.7850.9840.000
블록_명(BLCK_NM)0.9961.0000.9380.5170.0000.9140.6020.9960.9591.0001.0000.0000.9600.000
엑스좌표(XCNTS)0.6100.9381.0000.1640.3950.3380.2000.2130.3170.0000.0890.0000.0000.210
와이좌표(YDNTS)0.0000.5170.1641.0000.0000.2650.0000.0000.0920.3960.2050.3340.0000.000
행정동코드(ADSTRD_CD)0.7230.0000.3950.0001.0000.0000.0000.2930.0000.0000.0170.0420.0000.000
시군구코드(SIGNGU_CD)0.4360.9140.3380.2650.0001.0000.3380.0000.0000.1820.0000.0000.1840.000
블록_유형_대분류_코드(LCLAS_CD)0.9590.6020.2000.0000.0000.3381.0000.0000.0000.0000.2490.0000.8010.502
블록_유형_중분류_코드(MLSFC_CD)0.0000.9960.2130.0000.2930.0000.0001.0000.4790.2020.0000.0000.0000.000
블록_유형_소분류_코드(SCLAS_CD)0.9530.9590.3170.0920.0000.0000.0000.4791.0000.1610.3480.0000.0000.000
역세권_여부(RASTSP_AT)1.0001.0000.0000.3960.0000.1820.0000.2020.1611.0000.0000.0000.0000.000
대학가_여부(UNVTW_AT)0.0001.0000.0890.2050.0170.0000.2490.0000.3480.0001.0000.0000.0000.000
배후지_유형_대분류_코드(H_LCLAS_CD)0.7850.0000.0000.3340.0420.0000.0000.0000.0000.0000.0001.0000.2090.247
배후지_유형_중분류_코드(H_MLSFC_CD)0.9840.9600.0000.0000.0000.1840.8010.0000.0000.0000.0000.2091.0000.000
배후지_유형_소분류_코드(H_SCLAS_CD)0.0000.0000.2100.0000.0000.0000.5020.0000.0000.0000.0000.2470.0001.000
2023-12-10T23:49:23.940046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대학가_여부(UNVTW_AT)역세권_여부(RASTSP_AT)배후지_유형_대분류_코드(H_LCLAS_CD)
대학가_여부(UNVTW_AT)1.0000.0000.000
역세권_여부(RASTSP_AT)0.0001.0000.000
배후지_유형_대분류_코드(H_LCLAS_CD)0.0000.0001.000
2023-12-10T23:49:24.027286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
엑스좌표(XCNTS)와이좌표(YDNTS)행정동코드(ADSTRD_CD)시군구코드(SIGNGU_CD)블록_유형_대분류_코드(LCLAS_CD)블록_유형_중분류_코드(MLSFC_CD)블록_유형_소분류_코드(SCLAS_CD)배후지_유형_중분류_코드(H_MLSFC_CD)배후지_유형_소분류_코드(H_SCLAS_CD)역세권_여부(RASTSP_AT)대학가_여부(UNVTW_AT)배후지_유형_대분류_코드(H_LCLAS_CD)
엑스좌표(XCNTS)1.000-0.1110.054-0.111-0.0000.069-0.0510.0350.0840.0200.0000.000
와이좌표(YDNTS)-0.1111.000-0.0180.2740.0630.0070.117-0.076-0.0060.2950.0570.170
행정동코드(ADSTRD_CD)0.054-0.0181.0000.0080.0080.045-0.089-0.022-0.0170.0000.2020.041
시군구코드(SIGNGU_CD)-0.1110.2740.0081.0000.0600.020-0.027-0.1300.2710.0630.0000.000
블록_유형_대분류_코드(LCLAS_CD)-0.0000.0630.0080.0601.000-0.0730.0300.0340.0730.0000.1740.000
블록_유형_중분류_코드(MLSFC_CD)0.0690.0070.0450.020-0.0731.000-0.0600.084-0.0250.1920.0000.000
블록_유형_소분류_코드(SCLAS_CD)-0.0510.117-0.089-0.0270.030-0.0601.000-0.0470.0710.0830.2080.000
배후지_유형_중분류_코드(H_MLSFC_CD)0.035-0.076-0.022-0.1300.0340.084-0.0471.000-0.1910.0000.0000.132
배후지_유형_소분류_코드(H_SCLAS_CD)0.084-0.006-0.0170.2710.073-0.0250.071-0.1911.0000.0000.0000.317
역세권_여부(RASTSP_AT)0.0200.2950.0000.0630.0000.1920.0830.0000.0001.0000.0000.000
대학가_여부(UNVTW_AT)0.0000.0570.2020.0000.1740.0000.2080.0000.0000.0001.0000.000
배후지_유형_대분류_코드(H_LCLAS_CD)0.0000.1700.0410.0000.0000.0000.0000.1320.3170.0000.0001.000

Missing values

2023-12-10T23:49:19.284268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:49:19.465695image/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

블록_코드(BLCK_CD)블록_명(BLCK_NM)엑스좌표(XCNTS)와이좌표(YDNTS)행정동코드(ADSTRD_CD)시군구코드(SIGNGU_CD)블록_유형_대분류_코드(LCLAS_CD)블록_유형_중분류_코드(MLSFC_CD)블록_유형_소분류_코드(SCLAS_CD)역세권_여부(RASTSP_AT)대학가_여부(UNVTW_AT)배후지_유형_대분류_코드(H_LCLAS_CD)배후지_유형_중분류_코드(H_MLSFC_CD)배후지_유형_소분류_코드(H_SCLAS_CD)기준_년월_코드(STDR_YM)
03*6*7*목*동*N*5*3033675478921138069011215520340101004403204012016
18*2*등*3*-*-*62989545519781153078011200540340401114204401012016
21*9*4신*3*-*-*62977955521481153056011650440340301014403401012016
32*3*1역*동*S*1*2979765518121147065011350220360102004301401012016
41*6*5*망*2*-*-*2*3127505425071159053011305440340101104403403012016
52*7*5*신*6*-*-*93124975496041147063011620420140101004403403012016
64*8*0*대*2*-*-*73184045555451114055011320440140301004402401012016
71*7*7보*동*S*1*3022815588551111058011305220340301004402403012016
81*8*4*가*2*-*-*13003835520761120079011650460130201004402402012016
91*7*2*수*동*S*2*3156665589191150053511710440140301004204403012016
블록_코드(BLCK_CD)블록_명(BLCK_NM)엑스좌표(XCNTS)와이좌표(YDNTS)행정동코드(ADSTRD_CD)시군구코드(SIGNGU_CD)블록_유형_대분류_코드(LCLAS_CD)블록_유형_중분류_코드(MLSFC_CD)블록_유형_소분류_코드(SCLAS_CD)역세권_여부(RASTSP_AT)대학가_여부(UNVTW_AT)배후지_유형_대분류_코드(H_LCLAS_CD)배후지_유형_중분류_코드(H_MLSFC_CD)배후지_유형_소분류_코드(H_SCLAS_CD)기준_년월_코드(STDR_YM)
902*5*6신*동*S*3*3046305432941120072011230440340101003501102012016
914*3*2*양*2*-*-*93128375497601126056511620440140301004402403012016
921*0*7신*6*-*-*73112835445351165066011410420220301104204402012016
931*5*9*목*동*W*1*29693954216211380580114404202203010040401012016
941*4*0*개*2*-*-*3174665532171156067011290410120102004401402012016
953*8*2*전*1*-*-*63174465438131135066511215440120201104402402012016
962*7*6*신*5*-*-*3089795546101135057011740440340101004401202022016
973*6*0*인*동*E*1*53075335498371126065511320240140101004403201012016
981*8*2장*1*-*-*03026315579201165062011470420340301104402201012016
992*2*1*혜*동*S*3*3173695464701159060511680440340101014402402012016