Overview

Dataset statistics

Number of variables7
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.4 KiB
Average record size in memory58.3 B

Variable types

Numeric2
Categorical4
Text1

Dataset

Description샘플 데이터
Author다음소프트
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=57

Alerts

수집소스(SOURCE) has constant value ""Constant

Reproduction

Analysis started2023-12-10 14:54:06.283988
Analysis finished2023-12-10 14:54:07.659333
Duration1.38 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DOC_DATE(DATE)
Real number (ℝ)

Distinct400
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20181741
Minimum20170102
Maximum20191224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:07.806767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170102
5-th percentile20170297
Q120171210
median20180910
Q320190414
95-th percentile20191110
Maximum20191224
Range21122
Interquartile range (IQR)19204

Descriptive statistics

Standard deviation7972.7707
Coefficient of variation (CV)0.00039504871
Kurtosis-1.3853248
Mean20181741
Median Absolute Deviation (MAD)9600.5
Skewness-0.20531236
Sum1.009087 × 1010
Variance63565072
MonotonicityNot monotonic
2023-12-10T23:54:08.031514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190831 4
 
0.8%
20171013 3
 
0.6%
20181119 3
 
0.6%
20180720 3
 
0.6%
20191110 3
 
0.6%
20180211 3
 
0.6%
20170403 3
 
0.6%
20170420 3
 
0.6%
20170510 3
 
0.6%
20180709 3
 
0.6%
Other values (390) 469
93.8%
ValueCountFrequency (%)
20170102 1
0.2%
20170104 1
0.2%
20170108 1
0.2%
20170110 1
0.2%
20170114 1
0.2%
20170115 1
0.2%
20170116 1
0.2%
20170117 2
0.4%
20170119 1
0.2%
20170123 1
0.2%
ValueCountFrequency (%)
20191224 2
0.4%
20191223 1
0.2%
20191222 1
0.2%
20191218 1
0.2%
20191217 1
0.2%
20191215 1
0.2%
20191213 1
0.2%
20191212 2
0.4%
20191211 1
0.2%
20191209 1
0.2%

수집소스(SOURCE)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
블로그
500 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
블로그 500
100.0%

Length

2023-12-10T23:54:08.611886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:08.753608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
블로그 500
100.0%

행정동(DONG_NM)
Text

Distinct208
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:54:09.086357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length3.128
Min length2

Characters and Unicode

Total characters1564
Distinct characters184
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)22.0%

Sample

1st row이태원
2nd row종로3가역
3rd row아차산
4th row국회의사당
5th row홍제
ValueCountFrequency (%)
서울 29
 
5.8%
강남역 14
 
2.8%
이태원 11
 
2.2%
강남 11
 
2.2%
연남동 10
 
2.0%
홍대 9
 
1.8%
한강 9
 
1.8%
망원 8
 
1.6%
롯데월드 8
 
1.6%
신촌 8
 
1.6%
Other values (198) 383
76.6%
2023-12-10T23:54:09.766712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 101
 
6.5%
역 88
 
5.6%
구 71
 
4.5%
남 52
 
3.3%
강 50
 
3.2%
서 48
 
3.1%
대 47
 
3.0%
울 37
 
2.4%
원 33
 
2.1%
수 33
 
2.1%
Other values (174) 1004
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1550
99.1%
Lowercase Letter 10
 
0.6%
Decimal Number 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 101
 
6.5%
역 88
 
5.7%
구 71
 
4.6%
남 52
 
3.4%
강 50
 
3.2%
서 48
 
3.1%
대 47
 
3.0%
울 37
 
2.4%
원 33
 
2.1%
수 33
 
2.1%
Other values (166) 990
63.9%
Lowercase Letter
ValueCountFrequency (%)
n 2
20.0%
c 2
20.0%
g 2
20.0%
v 2
20.0%
k 1
10.0%
d 1
10.0%
Decimal Number
ValueCountFrequency (%)
3 3
75.0%
5 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1550
99.1%
Latin 10
 
0.6%
Common 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 101
 
6.5%
역 88
 
5.7%
구 71
 
4.6%
남 52
 
3.4%
강 50
 
3.2%
서 48
 
3.1%
대 47
 
3.0%
울 37
 
2.4%
원 33
 
2.1%
수 33
 
2.1%
Other values (166) 990
63.9%
Latin
ValueCountFrequency (%)
n 2
20.0%
c 2
20.0%
g 2
20.0%
v 2
20.0%
k 1
10.0%
d 1
10.0%
Common
ValueCountFrequency (%)
3 3
75.0%
5 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1550
99.1%
ASCII 14
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 101
 
6.5%
역 88
 
5.7%
구 71
 
4.6%
남 52
 
3.4%
강 50
 
3.2%
서 48
 
3.1%
대 47
 
3.0%
울 37
 
2.4%
원 33
 
2.1%
수 33
 
2.1%
Other values (166) 990
63.9%
ASCII
ValueCountFrequency (%)
3 3
21.4%
n 2
14.3%
c 2
14.3%
g 2
14.3%
v 2
14.3%
k 1
 
7.1%
5 1
 
7.1%
d 1
 
7.1%

행정구(GU_NM)
Categorical

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
마포구
80 
강남구
74 
종로구
57 
용산구
56 
서울
35 
Other values (20)
198 

Length

Max length4
Median length3
Mean length2.928
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row강남구
3rd row용산구
4th row성동구
5th row강남구

Common Values

ValueCountFrequency (%)
마포구 80
16.0%
강남구 74
14.8%
종로구 57
11.4%
용산구 56
11.2%
서울 35
 
7.0%
중구 32
 
6.4%
서초구 24
 
4.8%
송파구 19
 
3.8%
성동구 17
 
3.4%
영등포구 14
 
2.8%
Other values (15) 92
18.4%

Length

2023-12-10T23:54:09.996577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
마포구 80
16.0%
강남구 74
14.8%
종로구 57
11.4%
용산구 56
11.2%
서울 35
 
7.0%
중구 32
 
6.4%
서초구 24
 
4.8%
송파구 19
 
3.8%
성동구 17
 
3.4%
영등포구 14
 
2.8%
Other values (15) 92
18.4%

세부견인요소(KEYWORD_DETAIL)
Categorical

Distinct28
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
인스타그램
56 
비쥬얼
44 
이색메뉴
33 
존맛탱
32 
인생샷
 
29
Other values (23)
306 

Length

Max length5
Median length4
Mean length3.202
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsns
2nd row식감
3rd row존맛탱
4th row인스타그램
5th row인스타그램

Common Values

ValueCountFrequency (%)
인스타그램 56
 
11.2%
비쥬얼 44
 
8.8%
이색메뉴 33
 
6.6%
존맛탱 32
 
6.4%
인생샷 29
 
5.8%
식감 29
 
5.8%
사진촬영 24
 
4.8%
존맛 24
 
4.8%
핫플레이스 24
 
4.8%
포토존 22
 
4.4%
Other values (18) 183
36.6%

Length

2023-12-10T23:54:10.202837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인스타그램 56
 
11.2%
비쥬얼 44
 
8.8%
이색메뉴 33
 
6.6%
존맛탱 32
 
6.4%
인생샷 29
 
5.8%
식감 29
 
5.8%
사진촬영 24
 
4.8%
존맛 24
 
4.8%
핫플레이스 24
 
4.8%
포토존 22
 
4.4%
Other values (18) 183
36.6%

견인요소(KEYWORD)
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
포토제닉
216 
맛
170 
입소문
114 

Length

Max length4
Median length3
Mean length2.752
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row입소문
2nd row입소문
3rd row맛
4th row입소문
5th row포토제닉

Common Values

ValueCountFrequency (%)
포토제닉 216
43.2%
맛 170
34.0%
입소문 114
22.8%

Length

2023-12-10T23:54:10.404347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:10.580251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포토제닉 216
43.2%
맛 170
34.0%
입소문 114
22.8%

FREQ(FREQ)
Real number (ℝ)

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.36
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:10.732668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0278682
Coefficient of variation (CV)0.75578544
Kurtosis31.259866
Mean1.36
Median Absolute Deviation (MAD)0
Skewness4.7159167
Sum680
Variance1.056513
MonotonicityNot monotonic
2023-12-10T23:54:10.890139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 407
81.4%
2 56
 
11.2%
3 17
 
3.4%
5 8
 
1.6%
6 5
 
1.0%
4 5
 
1.0%
12 1
 
0.2%
8 1
 
0.2%
ValueCountFrequency (%)
1 407
81.4%
2 56
 
11.2%
3 17
 
3.4%
4 5
 
1.0%
5 8
 
1.6%
6 5
 
1.0%
8 1
 
0.2%
12 1
 
0.2%
ValueCountFrequency (%)
12 1
 
0.2%
8 1
 
0.2%
6 5
 
1.0%
5 8
 
1.6%
4 5
 
1.0%
3 17
 
3.4%
2 56
 
11.2%
1 407
81.4%

Interactions

2023-12-10T23:54:07.079721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:06.778923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:07.220136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:06.920803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:54:11.028787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DOC_DATE(DATE)행정구(GU_NM)세부견인요소(KEYWORD_DETAIL)견인요소(KEYWORD)FREQ(FREQ)
DOC_DATE(DATE)1.0000.1210.0000.0410.000
행정구(GU_NM)0.1211.0000.1030.2120.000
세부견인요소(KEYWORD_DETAIL)0.0000.1031.0000.2570.304
견인요소(KEYWORD)0.0410.2120.2571.0000.171
FREQ(FREQ)0.0000.0000.3040.1711.000
2023-12-10T23:54:11.189789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
견인요소(KEYWORD)세부견인요소(KEYWORD_DETAIL)행정구(GU_NM)
견인요소(KEYWORD)1.0000.1310.107
세부견인요소(KEYWORD_DETAIL)0.1311.0000.023
행정구(GU_NM)0.1070.0231.000
2023-12-10T23:54:11.332281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DOC_DATE(DATE)FREQ(FREQ)행정구(GU_NM)세부견인요소(KEYWORD_DETAIL)견인요소(KEYWORD)
DOC_DATE(DATE)1.0000.0030.0630.0000.034
FREQ(FREQ)0.0031.0000.0000.1160.115
행정구(GU_NM)0.0630.0001.0000.0230.107
세부견인요소(KEYWORD_DETAIL)0.0000.1160.0231.0000.131
견인요소(KEYWORD)0.0340.1150.1070.1311.000

Missing values

2023-12-10T23:54:07.427181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:54:07.583443image/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

DOC_DATE(DATE)수집소스(SOURCE)행정동(DONG_NM)행정구(GU_NM)세부견인요소(KEYWORD_DETAIL)견인요소(KEYWORD)FREQ(FREQ)
020170817블로그이태원서울sns입소문1
120181015블로그종로3가역강남구식감입소문1
220190831블로그아차산용산구존맛탱맛1
320170614블로그국회의사당성동구인스타그램입소문1
420180428블로그홍제강남구인스타그램포토제닉1
520180719블로그봉천종로구사진촬영포토제닉1
620180319블로그관악구종로구sns맛2
720191016블로그인사동서울이색메뉴포토제닉1
820180408블로그동대입구용산구셀카포토제닉1
920181027블로그연남동동대문구이색메뉴포토제닉1
DOC_DATE(DATE)수집소스(SOURCE)행정동(DONG_NM)행정구(GU_NM)세부견인요소(KEYWORD_DETAIL)견인요소(KEYWORD)FREQ(FREQ)
49020181119블로그구로동마포구꿀맛포토제닉1
49120170726블로그코엑스송파구식감맛1
49220180725블로그서울마포구포토존입소문1
49320180508블로그길동역강남구이색메뉴맛1
49420170912블로그아차산용산구꿀맛포토제닉1
49520170314블로그상수역종로구비쥬얼맛1
49620180110블로그면목역강남구사진촬영포토제닉1
49720180323블로그문래서울핫플레이스포토제닉1
49820191108블로그상계마포구시그니처맛1
49920181226블로그안국은평구비쥬얼맛1