Overview

Dataset statistics

Number of variables15
Number of observations358
Missing cells491
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.5 KiB
Average record size in memory124.4 B

Variable types

Numeric3
Text4
Categorical8

Dataset

Description하천기본계획코드,일련번호,기준점번호,하천기본계획 사업명,수립년도,표고,좌우안,주소,시도명,시군구명,읍면동명,리명,매설일자,매설자,비고
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15786/S/1/datasetView.do

Alerts

수립년도 is highly overall correlated with 일련번호 and 7 other fieldsHigh correlation
매설일자 is highly overall correlated with 하천기본계획코드 and 7 other fieldsHigh correlation
하천기본계획 사업명 is highly overall correlated with 하천기본계획코드 and 8 other fieldsHigh correlation
비고 is highly overall correlated with 하천기본계획코드 and 6 other fieldsHigh correlation
매설자 is highly overall correlated with 하천기본계획코드 and 8 other fieldsHigh correlation
하천기본계획코드 is highly overall correlated with 하천기본계획 사업명 and 5 other fieldsHigh correlation
일련번호 is highly overall correlated with 표고 and 8 other fieldsHigh correlation
표고 is highly overall correlated with 일련번호High correlation
좌우안 is highly overall correlated with 일련번호 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 하천기본계획코드 and 6 other fieldsHigh correlation
시군구명 is highly overall correlated with 하천기본계획코드 and 8 other fieldsHigh correlation
비고 is highly imbalanced (58.4%)Imbalance
읍면동명 has 139 (38.8%) missing valuesMissing
리명 has 352 (98.3%) missing valuesMissing
일련번호 has unique valuesUnique
기준점번호 has unique valuesUnique

Reproduction

Analysis started2024-05-10 23:05:33.885090
Analysis finished2024-05-10 23:05:40.006522
Duration6.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천기본계획코드
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.057736 × 1012
Minimum1.0000102 × 1012
Maximum1.5003212 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-05-10T23:05:40.311647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000102 × 1012
5-th percentile1.0000102 × 1012
Q11.0050902 × 1012
median1.0050902 × 1012
Q31.0253502 × 1012
95-th percentile1.5003212 × 1012
Maximum1.5003212 × 1012
Range5.00311 × 1011
Interquartile range (IQR)2.026 × 1010

Descriptive statistics

Standard deviation1.4394217 × 1011
Coefficient of variation (CV)0.13608516
Kurtosis5.6442579
Mean1.057736 × 1012
Median Absolute Deviation (MAD)5.0799992 × 109
Skewness2.748528
Sum3.7866949 × 1014
Variance2.0719349 × 1022
MonotonicityNot monotonic
2024-05-10T23:05:40.709038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1005090201210 106
29.6%
1000010202010 78
21.8%
1500321201210 34
 
9.5%
1005380201510 21
 
5.9%
1025500201510 12
 
3.4%
1025540201510 11
 
3.1%
1025350201410 11
 
3.1%
1025060201510 10
 
2.8%
1024930201510 10
 
2.8%
1024910201510 9
 
2.5%
Other values (13) 56
15.6%
ValueCountFrequency (%)
1000010202010 78
21.8%
1005090201210 106
29.6%
1005380201510 21
 
5.9%
1024880201510 5
 
1.4%
1024900201210 5
 
1.4%
1024910201510 9
 
2.5%
1024920201510 2
 
0.6%
1024930201510 10
 
2.8%
1025050201510 6
 
1.7%
1025060201510 10
 
2.8%
ValueCountFrequency (%)
1500321201210 34
9.5%
1025560201510 5
 
1.4%
1025550201510 5
 
1.4%
1025540201512 7
 
2.0%
1025540201510 11
 
3.1%
1025530201510 4
 
1.1%
1025500201510 12
 
3.4%
1025490201510 3
 
0.8%
1025370201410 2
 
0.6%
1025360201410 5
 
1.4%

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.5
Minimum1
Maximum358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-05-10T23:05:41.014440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.85
Q190.25
median179.5
Q3268.75
95-th percentile340.15
Maximum358
Range357
Interquartile range (IQR)178.5

Descriptive statistics

Standard deviation103.48994
Coefficient of variation (CV)0.5765456
Kurtosis-1.2
Mean179.5
Median Absolute Deviation (MAD)89.5
Skewness0
Sum64261
Variance10710.167
MonotonicityNot monotonic
2024-05-10T23:05:41.362078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 1
 
0.3%
202 1
 
0.3%
135 1
 
0.3%
136 1
 
0.3%
137 1
 
0.3%
138 1
 
0.3%
139 1
 
0.3%
140 1
 
0.3%
141 1
 
0.3%
142 1
 
0.3%
Other values (348) 348
97.2%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
358 1
0.3%
357 1
0.3%
356 1
0.3%
355 1
0.3%
354 1
0.3%
353 1
0.3%
352 1
0.3%
351 1
0.3%
350 1
0.3%
349 1
0.3%

기준점번호
Text

UNIQUE 

Distinct358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-05-10T23:05:42.106713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.2094972
Min length1

Characters and Unicode

Total characters1865
Distinct characters59
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique358 ?
Unique (%)100.0%

Sample

1st rowNO.59
2nd rowNO.03
3rd rowNO.60
4th rowNO.61
5th rowNO.62
ValueCountFrequency (%)
no.59 1
 
0.3%
k001 1
 
0.3%
02 1
 
0.3%
06 1
 
0.3%
07 1
 
0.3%
08 1
 
0.3%
cp.3 1
 
0.3%
cp.4 1
 
0.3%
cp01 1
 
0.3%
cp02 1
 
0.3%
Other values (348) 348
97.2%
2024-05-10T23:05:43.038753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 280
 
15.0%
T 116
 
6.2%
. 107
 
5.7%
N 94
 
5.0%
1 90
 
4.8%
O 89
 
4.8%
천 74
 
4.0%
2 67
 
3.6%
3 63
 
3.4%
5 62
 
3.3%
Other values (49) 823
44.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 827
44.3%
Uppercase Letter 533
28.6%
Other Letter 222
 
11.9%
Other Punctuation 112
 
6.0%
Math Symbol 61
 
3.3%
Close Punctuation 55
 
2.9%
Open Punctuation 55
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 74
33.3%
제 11
 
5.0%
홍 11
 
5.0%
탄 10
 
4.5%
양 10
 
4.5%
재 10
 
4.5%
성 9
 
4.1%
내 9
 
4.1%
림 7
 
3.2%
도 7
 
3.2%
Other values (16) 64
28.8%
Uppercase Letter
ValueCountFrequency (%)
T 116
21.8%
N 94
17.6%
O 89
16.7%
Y 37
 
6.9%
D 28
 
5.3%
A 27
 
5.1%
G 25
 
4.7%
R 22
 
4.1%
S 18
 
3.4%
C 14
 
2.6%
Other values (8) 63
11.8%
Decimal Number
ValueCountFrequency (%)
0 280
33.9%
1 90
 
10.9%
2 67
 
8.1%
3 63
 
7.6%
5 62
 
7.5%
4 61
 
7.4%
6 55
 
6.7%
7 50
 
6.0%
8 50
 
6.0%
9 49
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 107
95.5%
# 5
 
4.5%
Math Symbol
ValueCountFrequency (%)
+ 61
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1110
59.5%
Latin 533
28.6%
Hangul 222
 
11.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 74
33.3%
제 11
 
5.0%
홍 11
 
5.0%
탄 10
 
4.5%
양 10
 
4.5%
재 10
 
4.5%
성 9
 
4.1%
내 9
 
4.1%
림 7
 
3.2%
도 7
 
3.2%
Other values (16) 64
28.8%
Latin
ValueCountFrequency (%)
T 116
21.8%
N 94
17.6%
O 89
16.7%
Y 37
 
6.9%
D 28
 
5.3%
A 27
 
5.1%
G 25
 
4.7%
R 22
 
4.1%
S 18
 
3.4%
C 14
 
2.6%
Other values (8) 63
11.8%
Common
ValueCountFrequency (%)
0 280
25.2%
. 107
 
9.6%
1 90
 
8.1%
2 67
 
6.0%
3 63
 
5.7%
5 62
 
5.6%
4 61
 
5.5%
+ 61
 
5.5%
6 55
 
5.0%
) 55
 
5.0%
Other values (5) 209
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1643
88.1%
Hangul 222
 
11.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 280
17.0%
T 116
 
7.1%
. 107
 
6.5%
N 94
 
5.7%
1 90
 
5.5%
O 89
 
5.4%
2 67
 
4.1%
3 63
 
3.8%
5 62
 
3.8%
4 61
 
3.7%
Other values (23) 614
37.4%
Hangul
ValueCountFrequency (%)
천 74
33.3%
제 11
 
5.0%
홍 11
 
5.0%
탄 10
 
4.5%
양 10
 
4.5%
재 10
 
4.5%
성 9
 
4.1%
내 9
 
4.1%
림 7
 
3.2%
도 7
 
3.2%
Other values (16) 64
28.8%

하천기본계획 사업명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
중랑천권역(서울특별시) 하천기본계획(변경)
106 
한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역
78 
탄천 등 10개 하천기본계획
55 
안양천권역 하천기본계획
40 
아라천 하천기본계획
34 
Other values (3)
45 

Length

Max length36
Median length23
Mean length21.047486
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역
2nd row한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역
3rd row한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역
4th row한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역
5th row한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역

Common Values

ValueCountFrequency (%)
중랑천권역(서울특별시) 하천기본계획(변경) 106
29.6%
한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역 78
21.8%
탄천 등 10개 하천기본계획 55
15.4%
안양천권역 하천기본계획 40
 
11.2%
아라천 하천기본계획 34
 
9.5%
안양천 하천기본계획(변경) 21
 
5.9%
홍제천 등 4개 하천기본계획 19
 
5.3%
망월천하천기본계획(변경) 5
 
1.4%

Length

2024-05-10T23:05:43.325413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:05:43.565422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하천기본계획 148
14.6%
하천기본계획(변경 127
12.5%
중랑천권역(서울특별시 106
10.4%
한강(팔당댐~하구)하천기본계획 78
7.7%
및 78
7.7%
하천시설관리대장작성(보완 78
7.7%
용역 78
7.7%
등 74
7.3%
탄천 55
 
5.4%
10개 55
 
5.4%
Other values (6) 138
13.6%

수립년도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2012
145 
2015
116 
2020
78 
2014
19 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2012 145
40.5%
2015 116
32.4%
2020 78
21.8%
2014 19
 
5.3%

Length

2024-05-10T23:05:43.874272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:05:44.205574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 145
40.5%
2015 116
32.4%
2020 78
21.8%
2014 19
 
5.3%

표고
Real number (ℝ)

HIGH CORRELATION 

Distinct356
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.974277
Minimum4.467
Maximum583.938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-05-10T23:05:44.574112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.467
5-th percentile7.03645
Q113.455
median20.8955
Q357.86275
95-th percentile197.6862
Maximum583.938
Range579.471
Interquartile range (IQR)44.40775

Descriptive statistics

Standard deviation81.920899
Coefficient of variation (CV)1.5761816
Kurtosis17.705605
Mean51.974277
Median Absolute Deviation (MAD)10.7275
Skewness3.864716
Sum18606.791
Variance6711.0336
MonotonicityNot monotonic
2024-05-10T23:05:44.922935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.186 2
 
0.6%
14.052 2
 
0.6%
13.964 1
 
0.3%
7.912 1
 
0.3%
5.974 1
 
0.3%
7.055 1
 
0.3%
12.977 1
 
0.3%
17.704 1
 
0.3%
6.4 1
 
0.3%
9.253 1
 
0.3%
Other values (346) 346
96.6%
ValueCountFrequency (%)
4.467 1
0.3%
5.158 1
0.3%
5.322 1
0.3%
5.389 1
0.3%
5.47 1
0.3%
5.919 1
0.3%
5.967 1
0.3%
5.974 1
0.3%
6.133 1
0.3%
6.174 1
0.3%
ValueCountFrequency (%)
583.938 1
0.3%
563.181 1
0.3%
505.816 1
0.3%
481.41 1
0.3%
466.122 1
0.3%
456.767 1
0.3%
454.496 1
0.3%
280.277 1
0.3%
278.385 1
0.3%
276.405 1
0.3%

좌우안
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
미지정
218 
우안
71 
좌안
69 

Length

Max length3
Median length3
Mean length2.6089385
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우안
2nd row우안
3rd row좌안
4th row우안
5th row좌안

Common Values

ValueCountFrequency (%)
미지정 218
60.9%
우안 71
 
19.8%
좌안 69
 
19.3%

Length

2024-05-10T23:05:45.175877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:05:45.364136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미지정 218
60.9%
우안 71
 
19.8%
좌안 69
 
19.3%

주소
Text

Distinct118
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-05-10T23:05:45.863006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length8.6703911
Min length3

Characters and Unicode

Total characters3104
Distinct characters131
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

Unique67 ?
Unique (%)18.7%

Sample

1st row서울특별시 광진구 광장동
2nd row경기도 파주시 신촌동
3rd row서울특별시 강동구 천호동
4th row서울특별시 광진구 광장동
5th row서울특별시 강동구 암사동
ValueCountFrequency (%)
서울시 100
 
15.1%
경기도 46
 
6.9%
서울특별시 38
 
5.7%
김포시 24
 
3.6%
인천시 21
 
3.2%
서구 18
 
2.7%
서초구 17
 
2.6%
송파구 17
 
2.6%
고촌읍 16
 
2.4%
강남구 13
 
2.0%
Other values (137) 353
53.2%
2024-05-10T23:05:46.832029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1074
34.6%
시 224
 
7.2%
서 202
 
6.5%
동 188
 
6.1%
구 163
 
5.3%
울 138
 
4.4%
도 54
 
1.7%
경 54
 
1.7%
기 46
 
1.5%
포 44
 
1.4%
Other values (121) 917
29.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2029
65.4%
Space Separator 1074
34.6%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 224
 
11.0%
서 202
 
10.0%
동 188
 
9.3%
구 163
 
8.0%
울 138
 
6.8%
도 54
 
2.7%
경 54
 
2.7%
기 46
 
2.3%
포 44
 
2.2%
특 38
 
1.9%
Other values (119) 878
43.3%
Space Separator
ValueCountFrequency (%)
1074
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2029
65.4%
Common 1075
34.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 224
 
11.0%
서 202
 
10.0%
동 188
 
9.3%
구 163
 
8.0%
울 138
 
6.8%
도 54
 
2.7%
경 54
 
2.7%
기 46
 
2.3%
포 44
 
2.2%
특 38
 
1.9%
Other values (119) 878
43.3%
Common
ValueCountFrequency (%)
1074
99.9%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2029
65.4%
ASCII 1075
34.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1074
99.9%
2 1
 
0.1%
Hangul
ValueCountFrequency (%)
시 224
 
11.0%
서 202
 
10.0%
동 188
 
9.3%
구 163
 
8.0%
울 138
 
6.8%
도 54
 
2.7%
경 54
 
2.7%
기 46
 
2.3%
포 44
 
2.2%
특 38
 
1.9%
Other values (119) 878
43.3%

시도명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
139 
서울시
100 
경기도
46 
서울특별시
38 
인천시
21 
Other values (2)
14 

Length

Max length5
Median length3
Mean length3.6005587
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row경기도
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
<NA> 139
38.8%
서울시 100
27.9%
경기도 46
 
12.8%
서울특별시 38
 
10.6%
인천시 21
 
5.9%
고양시 12
 
3.4%
성남시 2
 
0.6%

Length

2024-05-10T23:05:47.230445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:05:47.531971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 139
38.8%
서울시 100
27.9%
경기도 46
 
12.8%
서울특별시 38
 
10.6%
인천시 21
 
5.9%
고양시 12
 
3.4%
성남시 2
 
0.6%

시군구명
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
139 
김포시
24 
서구
18 
서초구
17 
송파구
17 
Other values (28)
143 

Length

Max length4
Median length3
Mean length3.3435754
Min length2

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row광진구
2nd row파주시
3rd row강동구
4th row광진구
5th row강동구

Common Values

ValueCountFrequency (%)
<NA> 139
38.8%
김포시 24
 
6.7%
서구 18
 
5.0%
서초구 17
 
4.7%
송파구 17
 
4.7%
강남구 13
 
3.6%
하남시 12
 
3.4%
관악 11
 
3.1%
강동구 10
 
2.8%
마포구 9
 
2.5%
Other values (23) 88
24.6%

Length

2024-05-10T23:05:47.903111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 139
38.8%
김포시 24
 
6.7%
서구 18
 
5.0%
서초구 17
 
4.7%
송파구 17
 
4.7%
강남구 13
 
3.6%
하남시 12
 
3.4%
관악 11
 
3.1%
강동구 10
 
2.8%
마포구 9
 
2.5%
Other values (23) 88
24.6%

읍면동명
Text

MISSING 

Distinct107
Distinct (%)48.9%
Missing139
Missing (%)38.8%
Memory size2.9 KiB
2024-05-10T23:05:48.481699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9223744
Min length1

Characters and Unicode

Total characters640
Distinct characters113
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

Unique55 ?
Unique (%)25.1%

Sample

1st row광장동
2nd row신촌동
3rd row천호동
4th row광장동
5th row암사동
ValueCountFrequency (%)
고촌읍 16
 
7.3%
신림 8
 
3.7%
경서동 8
 
3.7%
양재동 5
 
2.3%
검암동 5
 
2.3%
신림동 5
 
2.3%
여의도동 4
 
1.8%
성산동 4
 
1.8%
강일동 4
 
1.8%
방이동 4
 
1.8%
Other values (97) 156
71.2%
2024-05-10T23:05:49.604949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 168
26.2%
신 28
 
4.4%
촌 23
 
3.6%
읍 20
 
3.1%
고 19
 
3.0%
림 16
 
2.5%
천 14
 
2.2%
양 13
 
2.0%
암 12
 
1.9%
성 10
 
1.6%
Other values (103) 317
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 639
99.8%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 168
26.3%
신 28
 
4.4%
촌 23
 
3.6%
읍 20
 
3.1%
고 19
 
3.0%
림 16
 
2.5%
천 14
 
2.2%
양 13
 
2.0%
암 12
 
1.9%
성 10
 
1.6%
Other values (102) 316
49.5%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 639
99.8%
Common 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 168
26.3%
신 28
 
4.4%
촌 23
 
3.6%
읍 20
 
3.1%
고 19
 
3.0%
림 16
 
2.5%
천 14
 
2.2%
양 13
 
2.0%
암 12
 
1.9%
성 10
 
1.6%
Other values (102) 316
49.5%
Common
ValueCountFrequency (%)
2 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 639
99.8%
ASCII 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 168
26.3%
신 28
 
4.4%
촌 23
 
3.6%
읍 20
 
3.1%
고 19
 
3.0%
림 16
 
2.5%
천 14
 
2.2%
양 13
 
2.0%
암 12
 
1.9%
성 10
 
1.6%
Other values (102) 316
49.5%
ASCII
ValueCountFrequency (%)
2 1
100.0%

리명
Text

MISSING 

Distinct3
Distinct (%)50.0%
Missing352
Missing (%)98.3%
Memory size2.9 KiB
2024-05-10T23:05:49.913084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.6666667
Min length2

Characters and Unicode

Total characters16
Distinct characters6
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

Unique1 ?
Unique (%)16.7%

Sample

1st row전호리
2nd row전호리
3rd row태리
4th row태리
5th row신곡리
ValueCountFrequency (%)
전호리 3
50.0%
태리 2
33.3%
신곡리 1
 
16.7%
2024-05-10T23:05:50.922923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 6
37.5%
전 3
18.8%
호 3
18.8%
태 2
 
12.5%
신 1
 
6.2%
곡 1
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 6
37.5%
전 3
18.8%
호 3
18.8%
태 2
 
12.5%
신 1
 
6.2%
곡 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 6
37.5%
전 3
18.8%
호 3
18.8%
태 2
 
12.5%
신 1
 
6.2%
곡 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 6
37.5%
전 3
18.8%
호 3
18.8%
태 2
 
12.5%
신 1
 
6.2%
곡 1
 
6.2%

매설일자
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
222 
2009-02-14
 
21
2013-05-15
 
15
2013-03-14
 
12
2009-06-10
 
11
Other values (16)
77 

Length

Max length10
Median length4
Mean length6.2793296
Min length4

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row2013-03-19
2nd row2013-03-13
3rd row2013-03-22
4th row2013-03-22
5th row2013-03-22

Common Values

ValueCountFrequency (%)
<NA> 222
62.0%
2009-02-14 21
 
5.9%
2013-05-15 15
 
4.2%
2013-03-14 12
 
3.4%
2009-06-10 11
 
3.1%
2013-03-12 8
 
2.2%
2013-03-23 7
 
2.0%
2013-03-20 7
 
2.0%
2013-03-18 7
 
2.0%
2013-03-22 7
 
2.0%
Other values (11) 41
 
11.5%

Length

2024-05-10T23:05:51.283085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 222
62.0%
2009-02-14 21
 
5.9%
2013-05-15 15
 
4.2%
2013-03-14 12
 
3.4%
2009-06-10 11
 
3.1%
2013-03-12 8
 
2.2%
2013-03-18 7
 
2.0%
2013-03-22 7
 
2.0%
2013-03-19 7
 
2.0%
2013-03-20 7
 
2.0%
Other values (11) 41
 
11.5%

매설자
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
212 
서울지방국토관리청
78 
국토해양부 서울지방국토관리청
34 
㈜ 이산
28 
한국토지주택공사
 
5

Length

Max length15
Median length4
Mean length6.1927374
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 212
59.2%
서울지방국토관리청 78
 
21.8%
국토해양부 서울지방국토관리청 34
 
9.5%
㈜ 이산 28
 
7.8%
한국토지주택공사 5
 
1.4%
서울특별시 1
 
0.3%

Length

2024-05-10T23:05:51.664900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:05:52.043646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 212
50.5%
서울지방국토관리청 112
26.7%
국토해양부 34
 
8.1%
㈜ 28
 
6.7%
이산 28
 
6.7%
한국토지주택공사 5
 
1.2%
서울특별시 1
 
0.2%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
264 
아라천
34 
양재천
 
10
탄천
 
10
성내천
 
9
Other values (10)
31 

Length

Max length5
Median length4
Mean length3.7486034
Min length2

Unique

Unique5 ?
Unique (%)1.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 264
73.7%
아라천 34
 
9.5%
양재천 10
 
2.8%
탄천 10
 
2.8%
성내천 9
 
2.5%
도림2지류 7
 
2.0%
여의천 6
 
1.7%
세곡천 6
 
1.7%
고덕천 5
 
1.4%
감이천 2
 
0.6%
Other values (5) 5
 
1.4%

Length

2024-05-10T23:05:52.496772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 264
73.7%
아라천 34
 
9.5%
양재천 10
 
2.8%
탄천 10
 
2.8%
성내천 9
 
2.5%
도림2지류 7
 
2.0%
여의천 6
 
1.7%
세곡천 6
 
1.7%
고덕천 5
 
1.4%
감이천 2
 
0.6%
Other values (5) 5
 
1.4%

Interactions

2024-05-10T23:05:38.000436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:05:36.024571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:05:37.040777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:05:38.260745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:05:36.289481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:05:37.431359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:05:38.490259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:05:36.575263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:05:37.732527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:05:52.799360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천기본계획코드일련번호하천기본계획 사업명수립년도표고좌우안시도명시군구명리명매설일자매설자비고
하천기본계획코드1.0000.6621.0000.2140.2460.1090.9871.000NaN1.0001.0001.000
일련번호0.6621.0000.8860.8940.4220.6940.7740.900NaN0.9050.7940.830
하천기본계획 사업명1.0000.8861.0001.0000.6050.7350.8980.953NaN1.0001.0001.000
수립년도0.2140.8941.0001.0000.5840.5590.8340.979NaN1.0001.0001.000
표고0.2460.4220.6050.5841.0000.3370.0000.701NaN0.1610.0590.000
좌우안0.1090.6940.7350.5590.3371.0000.7430.841NaN0.9350.7200.885
시도명0.9870.7740.8980.8340.0000.7431.0000.991NaN0.9520.9210.734
시군구명1.0000.9000.9530.9790.7010.8410.9911.000NaN0.9380.8940.866
리명NaNNaNNaNNaNNaNNaNNaNNaN1.0000.310NaNNaN
매설일자1.0000.9051.0001.0000.1610.9350.9520.9380.3101.0001.0000.507
매설자1.0000.7941.0001.0000.0590.7200.9210.894NaN1.0001.0001.000
비고1.0000.8301.0001.0000.0000.8850.7340.866NaN0.5071.0001.000
2024-05-10T23:05:53.221763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수립년도매설일자하천기본계획 사업명비고시도명좌우안시군구명매설자
수립년도1.0000.9340.9940.9330.6890.5650.7790.996
매설일자0.9341.0000.9370.3640.6860.7880.5860.932
하천기본계획 사업명0.9940.9371.0000.9380.5480.6270.7501.000
비고0.9330.3640.9381.0000.4860.7330.5540.944
시도명0.6890.6860.5480.4861.0000.4180.8890.612
좌우안0.5650.7880.6270.7330.4181.0000.6020.705
시군구명0.7790.5860.7500.5540.8890.6021.0000.632
매설자0.9960.9321.0000.9440.6120.7050.6321.000
2024-05-10T23:05:53.578176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천기본계획코드일련번호표고하천기본계획 사업명수립년도좌우안시도명시군구명매설일자매설자비고
하천기본계획코드1.0000.260-0.0720.9920.3820.2490.7980.7900.9300.9900.933
일련번호0.2601.0000.5970.6940.7640.5450.5150.5830.6500.6670.588
표고-0.0720.5971.0000.2400.2910.2260.0000.4410.1140.0410.000
하천기본계획 사업명0.9920.6940.2401.0000.9940.6270.5480.7500.9371.0000.938
수립년도0.3820.7640.2910.9941.0000.5650.6890.7790.9340.9960.933
좌우안0.2490.5450.2260.6270.5651.0000.4180.6020.7880.7050.733
시도명0.7980.5150.0000.5480.6890.4181.0000.8890.6860.6120.486
시군구명0.7900.5830.4410.7500.7790.6020.8891.0000.5860.6320.554
매설일자0.9300.6500.1140.9370.9340.7880.6860.5861.0000.9320.364
매설자0.9900.6670.0411.0000.9960.7050.6120.6320.9321.0000.944
비고0.9330.5880.0000.9380.9330.7330.4860.5540.3640.9441.000

Missing values

2024-05-10T23:05:38.870773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:05:39.500917image/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.
2024-05-10T23:05:39.776025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

하천기본계획코드일련번호기준점번호하천기본계획 사업명수립년도표고좌우안주소시도명시군구명읍면동명리명매설일자매설자비고
0100001020201056NO.59한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202013.964우안서울특별시 광진구 광장동서울특별시광진구광장동<NA>2013-03-19서울지방국토관리청<NA>
11000010202010358NO.03한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202010.577우안경기도 파주시 신촌동경기도파주시신촌동<NA>2013-03-13서울지방국토관리청<NA>
2100001020201057NO.60한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202012.719좌안서울특별시 강동구 천호동서울특별시강동구천호동<NA>2013-03-22서울지방국토관리청<NA>
3100001020201058NO.61한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역20208.802우안서울특별시 광진구 광장동서울특별시광진구광장동<NA>2013-03-22서울지방국토관리청<NA>
4100001020201059NO.62한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202011.103좌안서울특별시 강동구 암사동서울특별시강동구암사동<NA>2013-03-22서울지방국토관리청<NA>
5100001020201060NO.63한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202020.464우안경기도 구리시 아천동경기도구리시아천동<NA>2013-03-19서울지방국토관리청<NA>
6100001020201061NO.64한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202025.478좌안서울특별시 강동구 암사동서울특별시강동구암사동<NA>2013-03-23서울지방국토관리청<NA>
7100001020201062NO.65한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202022.559우안경기도 구리시 토평동경기도구리시토평동<NA>2013-03-19서울지방국토관리청<NA>
8100001020201063NO.66한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202022.114좌안서울특별시 강동구 강일동서울특별시강동구강일동<NA>2013-03-23서울지방국토관리청<NA>
9100001020201064NO.67한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역202015.253우안경기도 남양주시 수석동경기도남양주시수석동<NA>2013-03-19서울지방국토관리청<NA>
하천기본계획코드일련번호기준점번호하천기본계획 사업명수립년도표고좌우안주소시도명시군구명읍면동명리명매설일자매설자비고
3481005090201210349T76중랑천권역(서울특별시) 하천기본계획(변경)2012563.181미지정<NA><NA><NA><NA><NA><NA><NA>
3491005090201210350T76A중랑천권역(서울특별시) 하천기본계획(변경)2012583.938미지정<NA><NA><NA><NA><NA><NA><NA>
3501005090201210351T77중랑천권역(서울특별시) 하천기본계획(변경)2012130.405미지정<NA><NA><NA><NA><NA><NA><NA>
3511005090201210352T78중랑천권역(서울특별시) 하천기본계획(변경)201247.875미지정<NA><NA><NA><NA><NA><NA><NA>
3521005090201210353T79중랑천권역(서울특별시) 하천기본계획(변경)201254.969미지정<NA><NA><NA><NA><NA><NA><NA>
3531005090201210354T80중랑천권역(서울특별시) 하천기본계획(변경)201292.224미지정<NA><NA><NA><NA><NA><NA><NA>
354100509020121077T82중랑천권역(서울특별시) 하천기본계획(변경)201290.334미지정<NA><NA><NA><NA><NA><NA><NA>
355100509020121076T81A중랑천권역(서울특별시) 하천기본계획(변경)201265.721미지정<NA><NA><NA><NA><NA><NA><NA>
356100509020121075T81중랑천권역(서울특별시) 하천기본계획(변경)201265.416미지정<NA><NA><NA><NA><NA><NA><NA>
357100509020121095T100중랑천권역(서울특별시) 하천기본계획(변경)2012263.353미지정<NA><NA><NA><NA><NA><NA><NA>