Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory49.3 B

Variable types

Numeric4
Text1

Dataset

Description자치구_코드,자치구_명,엑스좌표_값,와이좌표_값,영역_면적
Author서울신용보증재단
URLhttps://data.seoul.go.kr/dataList/OA-22161/S/1/datasetView.do

Alerts

자치구_코드 is highly overall correlated with 와이좌표_값High correlation
와이좌표_값 is highly overall correlated with 자치구_코드High correlation
자치구_코드 has unique valuesUnique
자치구_명 has unique valuesUnique
엑스좌표_값 has unique valuesUnique
와이좌표_값 has unique valuesUnique
영역_면적 has unique valuesUnique

Reproduction

Analysis started2024-05-04 05:47:54.944108
Analysis finished2024-05-04 05:48:01.380858
Duration6.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구_코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11416.6
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-04T05:48:01.589926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11146
Q111260
median11410
Q311560
95-th percentile11704
Maximum11740
Range630
Interquartile range (IQR)300

Descriptive statistics

Standard deviation190.18478
Coefficient of variation (CV)0.016658618
Kurtosis-1.1960489
Mean11416.6
Median Absolute Deviation (MAD)150
Skewness0.083150093
Sum285415
Variance36170.25
MonotonicityStrictly increasing
2024-05-04T05:48:01.987138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11110 1
 
4.0%
11140 1
 
4.0%
11740 1
 
4.0%
11710 1
 
4.0%
11680 1
 
4.0%
11650 1
 
4.0%
11620 1
 
4.0%
11590 1
 
4.0%
11560 1
 
4.0%
11545 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
11110 1
4.0%
11140 1
4.0%
11170 1
4.0%
11200 1
4.0%
11215 1
4.0%
11230 1
4.0%
11260 1
4.0%
11290 1
4.0%
11305 1
4.0%
11320 1
4.0%
ValueCountFrequency (%)
11740 1
4.0%
11710 1
4.0%
11680 1
4.0%
11650 1
4.0%
11620 1
4.0%
11590 1
4.0%
11560 1
4.0%
11545 1
4.0%
11530 1
4.0%
11500 1
4.0%

자치구_명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-04T05:48:02.467807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.08
Min length2

Characters and Unicode

Total characters77
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
종로구 1
 
4.0%
마포구 1
 
4.0%
송파구 1
 
4.0%
강남구 1
 
4.0%
서초구 1
 
4.0%
관악구 1
 
4.0%
동작구 1
 
4.0%
영등포구 1
 
4.0%
금천구 1
 
4.0%
구로구 1
 
4.0%
Other values (15) 15
60.0%
2024-05-04T05:48:03.491492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

엑스좌표_값
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199069.84
Minimum184345
Maximum212991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-04T05:48:03.938860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184345
5-th percentile187230.8
Q1193557
median199639
Q3204847
95-th percentile209795.8
Maximum212991
Range28646
Interquartile range (IQR)11290

Descriptive statistics

Standard deviation7641.9959
Coefficient of variation (CV)0.038388517
Kurtosis-0.7688697
Mean199069.84
Median Absolute Deviation (MAD)5930
Skewness-0.14923797
Sum4976746
Variance58400102
MonotonicityNot monotonic
2024-05-04T05:48:04.502766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
197997 1
 
4.0%
199639 1
 
4.0%
212991 1
 
4.0%
210194 1
 
4.0%
205569 1
 
4.0%
202764 1
 
4.0%
195164 1
 
4.0%
195725 1
 
4.0%
192060 1
 
4.0%
191226 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
184345 1
4.0%
187215 1
4.0%
187294 1
4.0%
191226 1
4.0%
191895 1
4.0%
192060 1
4.0%
193557 1
4.0%
194617 1
4.0%
195164 1
4.0%
195725 1
4.0%
ValueCountFrequency (%)
212991 1
4.0%
210194 1
4.0%
208203 1
4.0%
207577 1
4.0%
206623 1
4.0%
205569 1
4.0%
204847 1
4.0%
203628 1
4.0%
202857 1
4.0%
202764 1
4.0%

와이좌표_값
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450485.96
Minimum440133
Maximum463273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-04T05:48:04.945657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum440133
5-th percentile441016.2
Q1445134
median450168
Q3455039
95-th percentile461231.8
Maximum463273
Range23140
Interquartile range (IQR)9905

Descriptive statistics

Standard deviation6421.1714
Coefficient of variation (CV)0.014253877
Kurtosis-0.63580263
Mean450485.96
Median Absolute Deviation (MAD)5034
Skewness0.2549726
Sum11262149
Variance41231442
MonotonicityNot monotonic
2024-05-04T05:48:05.327021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
455039 1
 
4.0%
451178 1
 
4.0%
450114 1
 
4.0%
445134 1
 
4.0%
444135 1
 
4.0%
441541 1
 
4.0%
440885 1
 
4.0%
444381 1
 
4.0%
446984 1
 
4.0%
440133 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
440133 1
4.0%
440885 1
4.0%
441541 1
4.0%
443893 1
4.0%
444135 1
4.0%
444381 1
4.0%
445134 1
4.0%
446984 1
4.0%
447260 1
4.0%
447985 1
4.0%
ValueCountFrequency (%)
463273 1
4.0%
461433 1
4.0%
460427 1
4.0%
457737 1
4.0%
456236 1
4.0%
455365 1
4.0%
455039 1
4.0%
453608 1
4.0%
453140 1
4.0%
451315 1
4.0%

영역_면적
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24230187
Minimum9990317
Maximum46920555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-04T05:48:05.695190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9990317
5-th percentile13272338
Q117462673
median23638414
Q329578708
95-th percentile41063151
Maximum46920555
Range36930238
Interquartile range (IQR)12116035

Descriptive statistics

Standard deviation9305149.8
Coefficient of variation (CV)0.38403129
Kurtosis0.26880245
Mean24230187
Median Absolute Deviation (MAD)6157258
Skewness0.87603016
Sum6.0575467 × 108
Variance8.6585814 × 1013
MonotonicityNot monotonic
2024-05-04T05:48:06.095253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
23990643 1
 
4.0%
9990317 1
 
4.0%
24557023 1
 
4.0%
33853024 1
 
4.0%
39488793 1
 
4.0%
46920555 1
 
4.0%
29578708 1
 
4.0%
16382215 1
 
4.0%
24523722 1
 
4.0%
13021079 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
9990317 1
4.0%
13021079 1
4.0%
14277373 1
4.0%
16382215 1
4.0%
16817016 1
4.0%
17049029 1
4.0%
17462673 1
4.0%
17687798 1
4.0%
18537446 1
4.0%
20097922 1
4.0%
ValueCountFrequency (%)
46920555 1
4.0%
41456740 1
4.0%
39488793 1
4.0%
35589181 1
4.0%
33853024 1
4.0%
29795672 1
4.0%
29578708 1
4.0%
24578799 1
4.0%
24557023 1
4.0%
24523722 1
4.0%

Interactions

2024-05-04T05:47:59.102408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:55.293400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:56.442481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:57.900128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:59.527140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:55.647729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:56.794855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:58.145782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:59.884962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:55.922066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:57.176221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:58.378531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:48:00.182697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:56.169980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:57.528947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:47:58.704085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T05:48:06.400684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구_코드자치구_명엑스좌표_값와이좌표_값영역_면적
자치구_코드1.0001.0000.5320.8920.195
자치구_명1.0001.0001.0001.0001.000
엑스좌표_값0.5321.0001.0000.0000.227
와이좌표_값0.8921.0000.0001.0000.000
영역_면적0.1951.0000.2270.0001.000
2024-05-04T05:48:06.758163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구_코드엑스좌표_값와이좌표_값영역_면적
자치구_코드1.000-0.081-0.6010.460
엑스좌표_값-0.0811.0000.2270.100
와이좌표_값-0.6010.2271.0000.025
영역_면적0.4600.1000.0251.000

Missing values

2024-05-04T05:48:00.871115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T05:48:01.269071image/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

자치구_코드자치구_명엑스좌표_값와이좌표_값영역_면적
011110종로구19799745503923990643
111140중구1996394511789990317
211170용산구19822444798521892688
311200성동구20362845016816817016
411215광진구20757744969317049029
511230동대문구20484745360814277373
611260중랑구20820345536518537446
711290성북구20154745623624578799
811305강북구20098846042723638414
911320도봉구20285746327320705585
자치구_코드자치구_명엑스좌표_값와이좌표_값영역_면적
1511500강서구18434545131541456740
1611530구로구18729444389320097922
1711545금천구19122644013313021079
1811560영등포구19206044698424523722
1911590동작구19572544438116382215
2011620관악구19516444088529578708
2111650서초구20276444154146920555
2211680강남구20556944413539488793
2311710송파구21019444513433853024
2411740강동구21299145011424557023