Overview

Dataset statistics

Number of variables7
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.4 KiB
Average record size in memory60.3 B

Variable types

Numeric3
Text1
Categorical3

Dataset

Description샘플 데이터
Author헹정자치부
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=33

Reproduction

Analysis started2024-01-14 06:51:00.012801
Analysis finished2024-01-14 06:51:03.643945
Duration3.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월(STD_YM)
Real number (ℝ)

Distinct111
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201642.17
Minimum201201
Maximum202106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:51:03.734080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201201
5-th percentile201207.95
Q1201407
median201701
Q3201902.25
95-th percentile202011.05
Maximum202106
Range905
Interquartile range (IQR)495.25

Descriptive statistics

Standard deviation273.56289
Coefficient of variation (CV)0.001356675
Kurtosis-1.2095075
Mean201642.17
Median Absolute Deviation (MAD)207
Skewness-0.064269711
Sum1.0082109 × 108
Variance74836.656
MonotonicityNot monotonic
2024-01-14T15:51:03.902109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201801 10
 
2.0%
202006 10
 
2.0%
201407 10
 
2.0%
201211 9
 
1.8%
201509 9
 
1.8%
202011 9
 
1.8%
201701 9
 
1.8%
201709 8
 
1.6%
201901 8
 
1.6%
201301 8
 
1.6%
Other values (101) 410
82.0%
ValueCountFrequency (%)
201201 5
1.0%
201202 4
0.8%
201203 3
0.6%
201204 4
0.8%
201205 3
0.6%
201206 3
0.6%
201207 3
0.6%
201208 2
 
0.4%
201209 6
1.2%
201210 5
1.0%
ValueCountFrequency (%)
202106 6
1.2%
202105 3
 
0.6%
202104 3
 
0.6%
202102 1
 
0.2%
202101 8
1.6%
202012 4
0.8%
202011 9
1.8%
202010 4
0.8%
202009 2
 
0.4%
202008 2
 
0.4%

행정동코드(ADMI_CD)
Real number (ℝ)

Distinct291
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11433454
Minimum11110540
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:51:04.065912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110540
5-th percentile11140635
Q111290562
median11440630
Q311590620
95-th percentile11710611
Maximum11740700
Range630160
Interquartile range (IQR)300057.5

Descriptive statistics

Standard deviation184101.69
Coefficient of variation (CV)0.016102019
Kurtosis-1.1876207
Mean11433454
Median Absolute Deviation (MAD)150040
Skewness-0.019650485
Sum5.7167272 × 109
Variance3.3893434 × 1010
MonotonicityNot monotonic
2024-01-14T15:51:04.255231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11410620 6
 
1.2%
11470520 5
 
1.0%
11500640 5
 
1.0%
11410700 4
 
0.8%
11350600 4
 
0.8%
11650651 4
 
0.8%
11290685 4
 
0.8%
11230710 4
 
0.8%
11560610 4
 
0.8%
11215760 4
 
0.8%
Other values (281) 456
91.2%
ValueCountFrequency (%)
11110540 1
 
0.2%
11110560 1
 
0.2%
11110570 1
 
0.2%
11110580 4
0.8%
11110615 2
0.4%
11110630 4
0.8%
11110640 1
 
0.2%
11110650 3
0.6%
11110690 1
 
0.2%
11110700 1
 
0.2%
ValueCountFrequency (%)
11740700 1
 
0.2%
11740690 1
 
0.2%
11740685 1
 
0.2%
11740660 1
 
0.2%
11740640 1
 
0.2%
11740610 1
 
0.2%
11740590 3
0.6%
11740570 1
 
0.2%
11740560 1
 
0.2%
11740550 1
 
0.2%

행정동명(ADMI_NM)
Text

Distinct295
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-01-14T15:51:04.537669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.81
Min length2

Characters and Unicode

Total characters1905
Distinct characters165
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)31.6%

Sample

1st row번1동
2nd row용산2가동
3rd row등촌3동
4th row대림1동
5th row삼청동
ValueCountFrequency (%)
창신1동 6
 
1.2%
반포1동 5
 
1.0%
발산1동 5
 
1.0%
화곡본동 4
 
0.8%
동선동 4
 
0.8%
공항동 4
 
0.8%
신정1동 4
 
0.8%
하계2동 4
 
0.8%
홍제1동 4
 
0.8%
삼양동 4
 
0.8%
Other values (285) 456
91.2%
2024-01-14T15:51:04.939908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 506
26.6%
1 128
 
6.7%
2 115
 
6.0%
신 58
 
3.0%
3 56
 
2.9%
가 34
 
1.8%
화 30
 
1.6%
정 25
 
1.3%
산 24
 
1.3%
4 22
 
1.2%
Other values (155) 907
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1543
81.0%
Decimal Number 349
 
18.3%
Other Punctuation 13
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 506
32.8%
신 58
 
3.8%
가 34
 
2.2%
화 30
 
1.9%
정 25
 
1.6%
산 24
 
1.6%
방 22
 
1.4%
창 22
 
1.4%
곡 21
 
1.4%
계 18
 
1.2%
Other values (146) 783
50.7%
Decimal Number
ValueCountFrequency (%)
1 128
36.7%
2 115
33.0%
3 56
16.0%
4 22
 
6.3%
6 12
 
3.4%
5 8
 
2.3%
7 5
 
1.4%
8 3
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1543
81.0%
Common 362
 
19.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 506
32.8%
신 58
 
3.8%
가 34
 
2.2%
화 30
 
1.9%
정 25
 
1.6%
산 24
 
1.6%
방 22
 
1.4%
창 22
 
1.4%
곡 21
 
1.4%
계 18
 
1.2%
Other values (146) 783
50.7%
Common
ValueCountFrequency (%)
1 128
35.4%
2 115
31.8%
3 56
15.5%
4 22
 
6.1%
. 13
 
3.6%
6 12
 
3.3%
5 8
 
2.2%
7 5
 
1.4%
8 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1543
81.0%
ASCII 362
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 506
32.8%
신 58
 
3.8%
가 34
 
2.2%
화 30
 
1.9%
정 25
 
1.6%
산 24
 
1.6%
방 22
 
1.4%
창 22
 
1.4%
곡 21
 
1.4%
계 18
 
1.2%
Other values (146) 783
50.7%
ASCII
ValueCountFrequency (%)
1 128
35.4%
2 115
31.8%
3 56
15.5%
4 22
 
6.1%
. 13
 
3.6%
6 12
 
3.3%
5 8
 
2.2%
7 5
 
1.4%
8 3
 
0.8%

성별코드(SEXDSTN_CD)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
257 
2
243 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 257
51.4%
2 243
48.6%

Length

2024-01-14T15:51:05.056292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:51:05.137871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 257
51.4%
2 243
48.6%

연령대코드(AGRDE_CD)
Categorical

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2529
 
31
1014
 
28
8084
 
28
0509
 
28
6064
 
28
Other values (17)
357 

Length

Max length4
Median length4
Mean length3.984
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1014
2nd row7074
3rd row3034
4th row5559
5th row8084

Common Values

ValueCountFrequency (%)
2529 31
 
6.2%
1014 28
 
5.6%
8084 28
 
5.6%
0509 28
 
5.6%
6064 28
 
5.6%
0004 27
 
5.4%
6569 27
 
5.4%
7579 27
 
5.4%
9094 26
 
5.2%
2024 25
 
5.0%
Other values (12) 225
45.0%

Length

2024-01-14T15:51:05.232636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2529 31
 
6.2%
1014 28
 
5.6%
8084 28
 
5.6%
0509 28
 
5.6%
6064 28
 
5.6%
0004 27
 
5.4%
6569 27
 
5.4%
7579 27
 
5.4%
9094 26
 
5.2%
2024 25
 
5.0%
Other values (12) 225
45.0%

거주인구_수(RSPOP_CNT)
Real number (ℝ)

Distinct384
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599.08
Minimum0
Maximum2491
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:51:05.551790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q1195
median514
Q3898.75
95-th percentile1582.15
Maximum2491
Range2491
Interquartile range (IQR)703.75

Descriptive statistics

Standard deviation496.91384
Coefficient of variation (CV)0.82946158
Kurtosis0.4255144
Mean599.08
Median Absolute Deviation (MAD)351.5
Skewness0.88762014
Sum299540
Variance246923.37
MonotonicityNot monotonic
2024-01-14T15:51:06.062564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 7
 
1.4%
17 7
 
1.4%
2 6
 
1.2%
7 6
 
1.2%
12 5
 
1.0%
9 4
 
0.8%
13 3
 
0.6%
479 3
 
0.6%
11 3
 
0.6%
5 3
 
0.6%
Other values (374) 453
90.6%
ValueCountFrequency (%)
0 1
 
0.2%
1 3
0.6%
2 6
1.2%
3 3
0.6%
4 2
 
0.4%
5 3
0.6%
6 3
0.6%
7 6
1.2%
8 7
1.4%
9 4
0.8%
ValueCountFrequency (%)
2491 1
0.2%
2338 1
0.2%
2212 1
0.2%
2186 1
0.2%
2098 1
0.2%
1931 1
0.2%
1838 1
0.2%
1833 1
0.2%
1781 1
0.2%
1764 1
0.2%

시군구명(CTY_NM)
Categorical

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
송파구
44 
성북구
 
31
강남구
 
25
도봉구
 
25
양천구
 
24
Other values (20)
351 

Length

Max length4
Median length3
Mean length3.066
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금천구
2nd row광진구
3rd row동대문구
4th row광진구
5th row송파구

Common Values

ValueCountFrequency (%)
송파구 44
 
8.8%
성북구 31
 
6.2%
강남구 25
 
5.0%
도봉구 25
 
5.0%
양천구 24
 
4.8%
종로구 22
 
4.4%
은평구 22
 
4.4%
영등포구 21
 
4.2%
중구 21
 
4.2%
강서구 20
 
4.0%
Other values (15) 245
49.0%

Length

2024-01-14T15:51:06.508480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 44
 
8.8%
성북구 31
 
6.2%
강남구 25
 
5.0%
도봉구 25
 
5.0%
양천구 24
 
4.8%
종로구 22
 
4.4%
은평구 22
 
4.4%
영등포구 21
 
4.2%
중구 21
 
4.2%
강서구 20
 
4.0%
Other values (15) 245
49.0%

Interactions

2024-01-14T15:51:03.087939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:51:02.333721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:51:02.734747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:51:03.190634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:51:02.499015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:51:02.835976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:51:03.309134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:51:02.623923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:51:02.957467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:51:06.635206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동코드(ADMI_CD)성별코드(SEXDSTN_CD)연령대코드(AGRDE_CD)거주인구_수(RSPOP_CNT)시군구명(CTY_NM)
기준년월(STD_YM)1.0000.0000.0550.2900.1740.127
행정동코드(ADMI_CD)0.0001.0000.0000.1670.0000.216
성별코드(SEXDSTN_CD)0.0550.0001.0000.0930.0180.000
연령대코드(AGRDE_CD)0.2900.1670.0931.0000.0000.000
거주인구_수(RSPOP_CNT)0.1740.0000.0180.0001.0000.067
시군구명(CTY_NM)0.1270.2160.0000.0000.0671.000
2024-01-14T15:51:06.785853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드(SEXDSTN_CD)연령대코드(AGRDE_CD)시군구명(CTY_NM)
성별코드(SEXDSTN_CD)1.0000.0720.000
연령대코드(AGRDE_CD)0.0721.0000.000
시군구명(CTY_NM)0.0000.0001.000
2024-01-14T15:51:06.911707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동코드(ADMI_CD)거주인구_수(RSPOP_CNT)성별코드(SEXDSTN_CD)연령대코드(AGRDE_CD)시군구명(CTY_NM)
기준년월(STD_YM)1.0000.0200.0790.0410.1080.040
행정동코드(ADMI_CD)0.0201.0000.0510.0000.0600.072
거주인구_수(RSPOP_CNT)0.0790.0511.0000.0120.0000.021
성별코드(SEXDSTN_CD)0.0410.0000.0121.0000.0720.000
연령대코드(AGRDE_CD)0.1080.0600.0000.0721.0000.000
시군구명(CTY_NM)0.0400.0720.0210.0000.0001.000

Missing values

2024-01-14T15:51:03.445409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:51:03.581006image/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

기준년월(STD_YM)행정동코드(ADMI_CD)행정동명(ADMI_NM)성별코드(SEXDSTN_CD)연령대코드(AGRDE_CD)거주인구_수(RSPOP_CNT)시군구명(CTY_NM)
020200511350640번1동1101424금천구
120120411230730용산2가동1707417광진구
220210511260690등촌3동23034674동대문구
320120911500591대림1동25559103광진구
420210611350560삼청동280841456송파구
520161111215750신월3동20004529강서구
620190811470620대림2동2959917동작구
720171211680656일원2동225291050관악구
820140611170690쌍문2동19999515마포구
920170211680656구산동16569549서초구
기준년월(STD_YM)행정동코드(ADMI_CD)행정동명(ADMI_NM)성별코드(SEXDSTN_CD)연령대코드(AGRDE_CD)거주인구_수(RSPOP_CNT)시군구명(CTY_NM)
49020121211410620방이1동2707483종로구
49120141011560690불광1동1151931노원구
49220210111710531상계3.4동27074154강북구
49320201011560540보문동26064670노원구
49420170711530560이화동17579796서대문구
49520120411215840우장산동26064751송파구
49620121011500535충현동165695마포구
49720200411545610답십리2동2I008성동구
49820180311410555암사1동25054804동작구
49920161011290590응암1동14044314종로구