Overview

Dataset statistics

Number of variables7
Number of observations103
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory60.3 B

Variable types

Numeric3
Text2
Categorical2

Dataset

Description도봉구 관내 제설시설 현황 데이터
Author서울특별시 도봉구
URLhttps://www.data.go.kr/data/15068818/fileData.do

Alerts

규격 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
행정동 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 규격 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
규격 is highly imbalanced (92.1%)Imbalance
연번 has unique valuesUnique
관리번호 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:13:53.662741
Analysis finished2023-12-12 16:13:55.052533
Duration1.39 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52
Minimum1
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T01:13:55.195302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.1
Q126.5
median52
Q377.5
95-th percentile97.9
Maximum103
Range102
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.877528
Coefficient of variation (CV)0.57456784
Kurtosis-1.2
Mean52
Median Absolute Deviation (MAD)26
Skewness0
Sum5356
Variance892.66667
MonotonicityStrictly increasing
2023-12-13T01:13:55.362927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
2 1
 
1.0%
77 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
Other values (93) 93
90.3%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
103 1
1.0%
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

관리번호
Text

UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-13T01:13:55.671648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.038835
Min length4

Characters and Unicode

Total characters416
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)100.0%

Sample

1st row도봉01
2nd row도봉02
3rd row도봉03
4th row도봉04
5th row도봉05
ValueCountFrequency (%)
도봉01 1
 
1.0%
도봉79 1
 
1.0%
도봉76 1
 
1.0%
도봉75 1
 
1.0%
도봉74 1
 
1.0%
도봉73 1
 
1.0%
도봉72 1
 
1.0%
도봉71 1
 
1.0%
도봉70 1
 
1.0%
도봉69 1
 
1.0%
Other values (93) 93
90.3%
2023-12-13T01:13:56.163587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 103
24.8%
봉 103
24.8%
1 25
 
6.0%
0 23
 
5.5%
2 21
 
5.0%
3 21
 
5.0%
6 20
 
4.8%
4 20
 
4.8%
5 20
 
4.8%
7 20
 
4.8%
Other values (2) 40
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
50.5%
Other Letter 206
49.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25
11.9%
0 23
11.0%
2 21
10.0%
3 21
10.0%
6 20
9.5%
4 20
9.5%
5 20
9.5%
7 20
9.5%
8 20
9.5%
9 20
9.5%
Other Letter
ValueCountFrequency (%)
도 103
50.0%
봉 103
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
50.5%
Hangul 206
49.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25
11.9%
0 23
11.0%
2 21
10.0%
3 21
10.0%
6 20
9.5%
4 20
9.5%
5 20
9.5%
7 20
9.5%
8 20
9.5%
9 20
9.5%
Hangul
ValueCountFrequency (%)
도 103
50.0%
봉 103
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
50.5%
Hangul 206
49.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 103
50.0%
봉 103
50.0%
ASCII
ValueCountFrequency (%)
1 25
11.9%
0 23
11.0%
2 21
10.0%
3 21
10.0%
6 20
9.5%
4 20
9.5%
5 20
9.5%
7 20
9.5%
8 20
9.5%
9 20
9.5%

규격
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
소
102 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0291262
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
소 102
99.0%
<NA> 1
 
1.0%

Length

2023-12-13T01:13:56.324175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:13:56.433907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소 102
99.0%
na 1
 
1.0%

주소
Text

Distinct101
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-13T01:13:56.697855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length33
Mean length22.475728
Min length17

Characters and Unicode

Total characters2315
Distinct characters109
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)97.1%

Sample

1st row서울특별시 도봉구 도봉1동 287-16
2nd row서울특별시 도봉구 도봉1동 411-6
3rd row서울특별시 도봉구 도봉1동 450-0
4th row서울특별시 도봉구 도봉1동 530-13
5th row서울특별시 도봉구 도봉로169길 43
ValueCountFrequency (%)
서울특별시 103
21.9%
도봉구 103
21.9%
노해로 13
 
2.8%
도봉로 13
 
2.8%
우이천로 9
 
1.9%
시루봉로 7
 
1.5%
도봉1동 6
 
1.3%
앞 5
 
1.1%
도봉로121길 4
 
0.8%
마들로 4
 
0.8%
Other values (164) 204
43.3%
2023-12-13T01:13:57.183253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
375
16.2%
봉 154
 
6.7%
도 143
 
6.2%
시 116
 
5.0%
1 113
 
4.9%
구 109
 
4.7%
서 103
 
4.4%
특 103
 
4.4%
별 103
 
4.4%
울 103
 
4.4%
Other values (99) 893
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1377
59.5%
Decimal Number 485
 
21.0%
Space Separator 375
 
16.2%
Dash Punctuation 40
 
1.7%
Close Punctuation 16
 
0.7%
Open Punctuation 16
 
0.7%
Uppercase Letter 5
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
봉 154
11.2%
도 143
10.4%
시 116
8.4%
구 109
7.9%
서 103
 
7.5%
특 103
 
7.5%
별 103
 
7.5%
울 103
 
7.5%
로 95
 
6.9%
길 78
 
5.7%
Other values (82) 270
19.6%
Decimal Number
ValueCountFrequency (%)
1 113
23.3%
2 66
13.6%
5 51
10.5%
3 46
9.5%
0 44
 
9.1%
6 39
 
8.0%
4 38
 
7.8%
9 32
 
6.6%
8 31
 
6.4%
7 25
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
A 3
60.0%
B 2
40.0%
Space Separator
ValueCountFrequency (%)
375
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1377
59.5%
Common 933
40.3%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
봉 154
11.2%
도 143
10.4%
시 116
8.4%
구 109
7.9%
서 103
 
7.5%
특 103
 
7.5%
별 103
 
7.5%
울 103
 
7.5%
로 95
 
6.9%
길 78
 
5.7%
Other values (82) 270
19.6%
Common
ValueCountFrequency (%)
375
40.2%
1 113
 
12.1%
2 66
 
7.1%
5 51
 
5.5%
3 46
 
4.9%
0 44
 
4.7%
- 40
 
4.3%
6 39
 
4.2%
4 38
 
4.1%
9 32
 
3.4%
Other values (5) 89
 
9.5%
Latin
ValueCountFrequency (%)
A 3
60.0%
B 2
40.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1377
59.5%
ASCII 938
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
375
40.0%
1 113
 
12.0%
2 66
 
7.0%
5 51
 
5.4%
3 46
 
4.9%
0 44
 
4.7%
- 40
 
4.3%
6 39
 
4.2%
4 38
 
4.1%
9 32
 
3.4%
Other values (7) 94
 
10.0%
Hangul
ValueCountFrequency (%)
봉 154
11.2%
도 143
10.4%
시 116
8.4%
구 109
7.9%
서 103
 
7.5%
특 103
 
7.5%
별 103
 
7.5%
울 103
 
7.5%
로 95
 
6.9%
길 78
 
5.7%
Other values (82) 270
19.6%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203035.57
Minimum201484.35
Maximum204813.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T01:13:57.640451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201484.35
5-th percentile201725.63
Q1202520.45
median202988.54
Q3203654.32
95-th percentile204167.33
Maximum204813.83
Range3329.475
Interquartile range (IQR)1133.871

Descriptive statistics

Standard deviation751.36996
Coefficient of variation (CV)0.0037006814
Kurtosis-0.49465811
Mean203035.57
Median Absolute Deviation (MAD)565.075
Skewness0.017182725
Sum20912664
Variance564556.81
MonotonicityNot monotonic
2023-12-13T01:13:57.808553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
203724.222 1
 
1.0%
203283.949 1
 
1.0%
202884.583 1
 
1.0%
202475.552 1
 
1.0%
202344.72 1
 
1.0%
202747.161 1
 
1.0%
202926.091 1
 
1.0%
202842.832 1
 
1.0%
202617.059 1
 
1.0%
202673.397 1
 
1.0%
Other values (93) 93
90.3%
ValueCountFrequency (%)
201484.35 1
1.0%
201532.075 1
1.0%
201579.45 1
1.0%
201603.775 1
1.0%
201688.1625 1
1.0%
201703.5 1
1.0%
201924.8 1
1.0%
201926.975 1
1.0%
201933.915 1
1.0%
201934.107 1
1.0%
ValueCountFrequency (%)
204813.825 1
1.0%
204647.825 1
1.0%
204646.4625 1
1.0%
204234.38 1
1.0%
204221.0 1
1.0%
204168.971 1
1.0%
204152.56 1
1.0%
204097.1 1
1.0%
204095.26 1
1.0%
204050.28 1
1.0%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean561927.58
Minimum559194
Maximum565481.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T01:13:57.956376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum559194
5-th percentile559672.97
Q1561322.27
median561572.25
Q3562693.87
95-th percentile564218.52
Maximum565481.98
Range6287.984
Interquartile range (IQR)1371.596

Descriptive statistics

Standard deviation1356.831
Coefficient of variation (CV)0.0024146011
Kurtosis0.19779459
Mean561927.58
Median Absolute Deviation (MAD)697.45
Skewness0.51366742
Sum57878541
Variance1840990.2
MonotonicityNot monotonic
2023-12-13T01:13:58.098289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
565481.984 1
 
1.0%
565170.41 1
 
1.0%
561338.876 1
 
1.0%
561752.402 1
 
1.0%
562078.119 1
 
1.0%
561325.436 1
 
1.0%
561343.551 1
 
1.0%
561418.707 1
 
1.0%
561312.143 1
 
1.0%
561319.109 1
 
1.0%
Other values (93) 93
90.3%
ValueCountFrequency (%)
559194.0 1
1.0%
559481.7 1
1.0%
559533.235 1
1.0%
559535.0 1
1.0%
559625.169 1
1.0%
559667.376 1
1.0%
559723.35 1
1.0%
559885.882 1
1.0%
560003.266 1
1.0%
560088.491 1
1.0%
ValueCountFrequency (%)
565481.984 1
1.0%
565354.06 1
1.0%
565321.32 1
1.0%
565170.41 1
1.0%
564435.701 1
1.0%
564222.544 1
1.0%
564182.307 1
1.0%
564017.948 1
1.0%
563931.95 1
1.0%
563931.25 1
1.0%

행정동
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size956.0 B
쌍문2동
15 
쌍문1동
14 
쌍문4동
11 
도봉2동
10 
창3동
10 
Other values (8)
43 

Length

Max length4
Median length4
Mean length3.7475728
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row도봉1동
2nd row도봉1동
3rd row도봉1동
4th row도봉1동
5th row도봉1동

Common Values

ValueCountFrequency (%)
쌍문2동 15
14.6%
쌍문1동 14
13.6%
쌍문4동 11
10.7%
도봉2동 10
9.7%
창3동 10
9.7%
방학3동 9
8.7%
도봉1동 7
6.8%
창5동 7
6.8%
방학2동 6
 
5.8%
쌍문3동 5
 
4.9%
Other values (3) 9
8.7%

Length

2023-12-13T01:13:58.251265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
쌍문2동 15
14.6%
쌍문1동 14
13.6%
쌍문4동 11
10.7%
도봉2동 10
9.7%
창3동 10
9.7%
방학3동 9
8.7%
도봉1동 7
6.8%
창5동 7
6.8%
방학2동 6
 
5.8%
쌍문3동 5
 
4.9%
Other values (3) 9
8.7%

Interactions

2023-12-13T01:13:54.538101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:13:53.935368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:13:54.209673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:13:54.631796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:13:54.022537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:13:54.304757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:13:54.722324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:13:54.114143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:13:54.425974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:13:58.332554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도행정동
연번1.0000.8740.9030.938
위도0.8741.0000.8340.890
경도0.9030.8341.0000.875
행정동0.9380.8900.8751.000
2023-12-13T01:13:58.444692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규격행정동
규격1.0001.000
행정동1.0001.000
2023-12-13T01:13:58.532284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도규격행정동
연번1.0000.219-0.8031.0000.757
위도0.2191.000-0.0311.0000.640
경도-0.803-0.0311.0001.0000.609
규격1.0001.0001.0001.0001.000
행정동0.7570.6400.6091.0001.000

Missing values

2023-12-13T01:13:54.868192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:13:54.993212image/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

연번관리번호규격주소위도경도행정동
01도봉01소서울특별시 도봉구 도봉1동 287-16203724.222565481.984도봉1동
12도봉02소서울특별시 도봉구 도봉1동 411-6203283.949565170.41도봉1동
23도봉03소서울특별시 도봉구 도봉1동 450-0203462.013563689.533도봉1동
34도봉04소서울특별시 도봉구 도봉1동 530-13202523.467564182.307도봉1동
45도봉05소서울특별시 도봉구 도봉로169길 43203835.84564435.701도봉1동
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9798도봉98소서울특별시 도봉구 노해로 265203317.365561329.342창5동
9899도봉99소서울특별시 도봉구 도봉로 126길9203375.378561472.233창5동
99100도봉100소서울특별시 도봉구 도봉로 602-10203645.633562002.68창5동
100101도봉101소서울특별시 도봉구 도봉로 602-13203665.41562051.402창5동
101102도봉102소서울특별시 도봉구 도봉로 134길 28203670.384561953.988창5동
102103도봉103소서울특별시 도봉구 도봉로 642-16 (플로렌스빌 앞)203817.838562454.804창5동