Overview

Dataset statistics

Number of variables16
Number of observations4026
Missing cells1808
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory534.8 KiB
Average record size in memory136.0 B

Variable types

Numeric8
Categorical4
Text4

Dataset

Description인천광역시 주요 시설현황 데이터 (행정기관,교육,보육, 환경, 안전, 문화,관광,체육, 복지,건강, 일자리,경제 분야)의 데이터셋 25종 시설정보 입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15076595&srcSe=7661IVAWM27C61E190

Alerts

분야 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
시군구 is highly overall correlated with 우편번호 and 2 other fieldsHigh correlation
유형 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
시도 is highly overall correlated with 우편번호 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 필드코드 and 3 other fieldsHigh correlation
필드코드 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
타입코드 is highly overall correlated with 유형High correlation
분류코드 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
분류 is highly overall correlated with 유형High correlation
우편번호 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 우편번호 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 시도 and 1 other fieldsHigh correlation
시도 is highly imbalanced (99.5%)Imbalance
전화번호 has 128 (3.2%) missing valuesMissing
정보 has 1652 (41.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 07:46:45.697989
Analysis finished2024-01-28 07:46:53.808410
Duration8.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct4026
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.5
Minimum1
Maximum4026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.5 KiB
2024-01-28T16:46:53.881062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile202.25
Q11007.25
median2013.5
Q33019.75
95-th percentile3824.75
Maximum4026
Range4025
Interquartile range (IQR)2012.5

Descriptive statistics

Standard deviation1162.3504
Coefficient of variation (CV)0.57727858
Kurtosis-1.2
Mean2013.5
Median Absolute Deviation (MAD)1006.5
Skewness0
Sum8106351
Variance1351058.5
MonotonicityStrictly increasing
2024-01-28T16:46:54.001577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2690 1
 
< 0.1%
2677 1
 
< 0.1%
2678 1
 
< 0.1%
2679 1
 
< 0.1%
2680 1
 
< 0.1%
2681 1
 
< 0.1%
2682 1
 
< 0.1%
2683 1
 
< 0.1%
2684 1
 
< 0.1%
Other values (4016) 4016
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4026 1
< 0.1%
4025 1
< 0.1%
4024 1
< 0.1%
4023 1
< 0.1%
4022 1
< 0.1%
4021 1
< 0.1%
4020 1
< 0.1%
4019 1
< 0.1%
4018 1
< 0.1%
4017 1
< 0.1%

분야
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.6 KiB
복지·건강
1950 
문화관광·체육
855 
행정기관
409 
교육·복지
404 
환경
242 
Other values (2)
 
166

Length

Max length7
Median length5
Mean length5.1105315
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
복지·건강 1950
48.4%
문화관광·체육 855
21.2%
행정기관 409
 
10.2%
교육·복지 404
 
10.0%
환경 242
 
6.0%
일자리/경제 92
 
2.3%
안전 74
 
1.8%

Length

2024-01-28T16:46:54.136664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T16:46:54.231126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
복지·건강 1950
48.4%
문화관광·체육 855
21.2%
행정기관 409
 
10.2%
교육·복지 404
 
10.0%
환경 242
 
6.0%
일자리/경제 92
 
2.3%
안전 74
 
1.8%

유형
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size31.6 KiB
약국
1143 
체육시설
482 
유치원
404 
노인복지시설
401 
도시숲
228 
Other values (43)
1368 

Length

Max length9
Median length7
Mean length3.7970691
Min length2

Unique

Unique14 ?
Unique (%)0.3%

Sample

1st row시청
2nd row군구청
3rd row군구청
4th row군구청
5th row군구청

Common Values

ValueCountFrequency (%)
약국 1143
28.4%
체육시설 482
12.0%
유치원 404
 
10.0%
노인복지시설 401
 
10.0%
도시숲 228
 
5.7%
착한가격업소 212
 
5.3%
병원 195
 
4.8%
문화재 161
 
4.0%
주민자치센터 154
 
3.8%
무인민원발급 150
 
3.7%
Other values (38) 496
12.3%

Length

2024-01-28T16:46:54.340564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
약국 1143
28.4%
체육시설 482
12.0%
유치원 404
 
10.0%
노인복지시설 401
 
10.0%
도시숲 228
 
5.7%
착한가격업소 212
 
5.3%
병원 195
 
4.8%
문화재 161
 
4.0%
주민자치센터 154
 
3.8%
무인민원발급 150
 
3.7%
Other values (38) 496
12.3%

필드코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6840537
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.5 KiB
2024-01-28T16:46:54.427277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8066899
Coefficient of variation (CV)0.38571075
Kurtosis-0.50873523
Mean4.6840537
Median Absolute Deviation (MAD)1
Skewness-1.0043624
Sum18858
Variance3.2641283
MonotonicityIncreasing
2024-01-28T16:46:54.504116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1950
48.4%
5 855
21.2%
1 409
 
10.2%
2 404
 
10.0%
3 242
 
6.0%
7 92
 
2.3%
4 74
 
1.8%
ValueCountFrequency (%)
1 409
 
10.2%
2 404
 
10.0%
3 242
 
6.0%
4 74
 
1.8%
5 855
21.2%
6 1950
48.4%
7 92
 
2.3%
ValueCountFrequency (%)
7 92
 
2.3%
6 1950
48.4%
5 855
21.2%
4 74
 
1.8%
3 242
 
6.0%
2 404
 
10.0%
1 409
 
10.2%

타입코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2965723
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.5 KiB
2024-01-28T16:46:54.603047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q39
95-th percentile11
Maximum29
Range28
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6611743
Coefficient of variation (CV)0.58145513
Kurtosis-0.14882757
Mean6.2965723
Median Absolute Deviation (MAD)3
Skewness0.10591685
Sum25350
Variance13.404197
MonotonicityNot monotonic
2024-01-28T16:46:54.709625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8 1150
28.6%
2 642
15.9%
10 498
12.4%
1 461
11.5%
11 408
 
10.1%
4 262
 
6.5%
6 204
 
5.1%
3 184
 
4.6%
9 89
 
2.2%
5 49
 
1.2%
Other values (16) 79
 
2.0%
ValueCountFrequency (%)
1 461
11.5%
2 642
15.9%
3 184
 
4.6%
4 262
 
6.5%
5 49
 
1.2%
6 204
 
5.1%
7 13
 
0.3%
8 1150
28.6%
9 89
 
2.2%
10 498
12.4%
ValueCountFrequency (%)
29 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 2
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
18 2
 
< 0.1%
17 5
0.1%

분류코드
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4690.3502
Minimum1001
Maximum7005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.5 KiB
2024-01-28T16:46:54.836179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1006
Q13002
median6001
Q36008
95-th percentile6011
Maximum7005
Range6004
Interquartile range (IQR)3006

Descriptive statistics

Standard deviation1807.9523
Coefficient of variation (CV)0.3854621
Kurtosis-0.51109011
Mean4690.3502
Median Absolute Deviation (MAD)990
Skewness-1.0044415
Sum18883350
Variance3268691.3
MonotonicityIncreasing
2024-01-28T16:46:54.966918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
6008 1143
28.4%
5010 482
12.0%
2002 404
 
10.0%
6001 401
 
10.0%
3002 228
 
5.7%
5011 212
 
5.3%
6011 195
 
4.8%
5004 161
 
4.0%
1003 154
 
3.8%
1006 150
 
3.7%
Other values (36) 496
12.3%
ValueCountFrequency (%)
1001 1
 
< 0.1%
1002 10
 
0.2%
1003 154
3.8%
1004 26
 
0.6%
1005 1
 
< 0.1%
1006 150
3.7%
1007 13
 
0.3%
1008 7
 
0.2%
1009 2
 
< 0.1%
1010 6
 
0.1%
ValueCountFrequency (%)
7005 17
 
0.4%
7004 75
 
1.9%
6012 28
 
0.7%
6011 195
 
4.8%
6010 10
 
0.2%
6009 87
 
2.2%
6008 1143
28.4%
6006 52
 
1.3%
6005 31
 
0.8%
6003 3
 
0.1%

시도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.6 KiB
인천광역시
4023 
서울특별시
 
1
세종특별자치시
 
1
경기도
 
1

Length

Max length7
Median length5
Mean length5
Min length3

Unique

Unique3 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 4023
99.9%
서울특별시 1
 
< 0.1%
세종특별자치시 1
 
< 0.1%
경기도 1
 
< 0.1%

Length

2024-01-28T16:46:55.081342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T16:46:55.173529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 4023
99.9%
서울특별시 1
 
< 0.1%
세종특별자치시 1
 
< 0.1%
경기도 1
 
< 0.1%

시군구
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size31.6 KiB
서구
698 
남동구
686 
미추홀구
581 
부평구
531 
강화군
388 
Other values (10)
1142 

Length

Max length35
Median length3
Mean length2.8817685
Min length2

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row남동구
2nd row중구
3rd row동구
4th row미추홀구
5th row연수구

Common Values

ValueCountFrequency (%)
서구 698
17.3%
남동구 686
17.0%
미추홀구 581
14.4%
부평구 531
13.2%
강화군 388
9.6%
계양구 386
9.6%
연수구 330
8.2%
중구 242
 
6.0%
동구 136
 
3.4%
옹진군 29
 
0.7%
Other values (5) 19
 
0.5%

Length

2024-01-28T16:46:55.270872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서구 698
17.3%
남동구 686
17.0%
미추홀구 581
14.4%
부평구 531
13.2%
강화군 388
9.6%
계양구 386
9.6%
연수구 330
8.2%
중구 242
 
6.0%
동구 136
 
3.4%
옹진군 29
 
0.7%
Other values (11) 26
 
0.6%
Distinct3591
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size31.6 KiB
2024-01-28T16:46:55.539378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length25
Mean length7.6785892
Min length1

Characters and Unicode

Total characters30914
Distinct characters638
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3349 ?
Unique (%)83.2%

Sample

1st row인천광역시청
2nd row중구청
3rd row동구청 민원실
4th row미추홀구청
5th row연수구청
ValueCountFrequency (%)
행정복지센터 151
 
3.0%
강화군야외운동기구 83
 
1.6%
게이트볼장 56
 
1.1%
안전센터 47
 
0.9%
농구장 35
 
0.7%
야외운동기구 32
 
0.6%
배드민턴장 21
 
0.4%
족구장 16
 
0.3%
강화 14
 
0.3%
의료법인 14
 
0.3%
Other values (3804) 4597
90.7%
2024-01-28T16:46:56.282367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1364
 
4.4%
1230
 
4.0%
1155
 
3.7%
1060
 
3.4%
741
 
2.4%
623
 
2.0%
490
 
1.6%
483
 
1.6%
479
 
1.5%
445
 
1.4%
Other values (628) 22844
73.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28866
93.4%
Space Separator 1060
 
3.4%
Decimal Number 543
 
1.8%
Dash Punctuation 132
 
0.4%
Close Punctuation 69
 
0.2%
Open Punctuation 64
 
0.2%
Uppercase Letter 60
 
0.2%
Connector Punctuation 37
 
0.1%
Lowercase Letter 36
 
0.1%
Control 23
 
0.1%
Other values (4) 24
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1364
 
4.7%
1230
 
4.3%
1155
 
4.0%
741
 
2.6%
623
 
2.2%
490
 
1.7%
483
 
1.7%
479
 
1.7%
445
 
1.5%
443
 
1.5%
Other values (568) 21413
74.2%
Uppercase Letter
ValueCountFrequency (%)
W 5
 
8.3%
H 5
 
8.3%
C 4
 
6.7%
N 4
 
6.7%
M 4
 
6.7%
S 4
 
6.7%
Y 4
 
6.7%
B 3
 
5.0%
R 3
 
5.0%
P 3
 
5.0%
Other values (12) 21
35.0%
Lowercase Letter
ValueCountFrequency (%)
e 8
22.2%
l 5
13.9%
o 4
11.1%
h 4
11.1%
t 2
 
5.6%
i 2
 
5.6%
p 2
 
5.6%
b 2
 
5.6%
g 1
 
2.8%
a 1
 
2.8%
Other values (5) 5
13.9%
Decimal Number
ValueCountFrequency (%)
1 219
40.3%
2 122
22.5%
3 54
 
9.9%
9 50
 
9.2%
4 30
 
5.5%
5 28
 
5.2%
6 22
 
4.1%
0 7
 
1.3%
8 7
 
1.3%
7 4
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 9
47.4%
, 5
26.3%
& 4
21.1%
/ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
1060
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 132
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 37
100.0%
Control
ValueCountFrequency (%)
23
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28866
93.4%
Common 1951
 
6.3%
Latin 97
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1364
 
4.7%
1230
 
4.3%
1155
 
4.0%
741
 
2.6%
623
 
2.2%
490
 
1.7%
483
 
1.7%
479
 
1.7%
445
 
1.5%
443
 
1.5%
Other values (568) 21413
74.2%
Latin
ValueCountFrequency (%)
e 8
 
8.2%
l 5
 
5.2%
W 5
 
5.2%
H 5
 
5.2%
o 4
 
4.1%
C 4
 
4.1%
N 4
 
4.1%
M 4
 
4.1%
S 4
 
4.1%
h 4
 
4.1%
Other values (28) 50
51.5%
Common
ValueCountFrequency (%)
1060
54.3%
1 219
 
11.2%
- 132
 
6.8%
2 122
 
6.3%
) 69
 
3.5%
( 64
 
3.3%
3 54
 
2.8%
9 50
 
2.6%
_ 37
 
1.9%
4 30
 
1.5%
Other values (12) 114
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28863
93.4%
ASCII 2046
 
6.6%
Compat Jamo 3
 
< 0.1%
Number Forms 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1364
 
4.7%
1230
 
4.3%
1155
 
4.0%
741
 
2.6%
623
 
2.2%
490
 
1.7%
483
 
1.7%
479
 
1.7%
445
 
1.5%
443
 
1.5%
Other values (567) 21410
74.2%
ASCII
ValueCountFrequency (%)
1060
51.8%
1 219
 
10.7%
- 132
 
6.5%
2 122
 
6.0%
) 69
 
3.4%
( 64
 
3.1%
3 54
 
2.6%
9 50
 
2.4%
_ 37
 
1.8%
4 30
 
1.5%
Other values (48) 209
 
10.2%
Compat Jamo
ValueCountFrequency (%)
3
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

분류
Real number (ℝ)

HIGH CORRELATION 

Distinct155
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean953.43691
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.5 KiB
2024-01-28T16:46:56.435915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation201.7175
Coefficient of variation (CV)0.21156879
Kurtosis15.831657
Mean953.43691
Median Absolute Deviation (MAD)0
Skewness-4.214994
Sum3838537
Variance40689.948
MonotonicityNot monotonic
2024-01-28T16:46:56.558217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999 3830
95.1%
1 4
 
0.1%
3 4
 
0.1%
4 4
 
0.1%
5 4
 
0.1%
2 4
 
0.1%
9 3
 
0.1%
11 3
 
0.1%
10 3
 
0.1%
8 3
 
0.1%
Other values (145) 164
 
4.1%
ValueCountFrequency (%)
1 4
0.1%
2 4
0.1%
3 4
0.1%
4 4
0.1%
5 4
0.1%
6 3
0.1%
7 3
0.1%
8 3
0.1%
9 3
0.1%
10 3
0.1%
ValueCountFrequency (%)
999 3830
95.1%
154 1
 
< 0.1%
153 1
 
< 0.1%
152 1
 
< 0.1%
151 1
 
< 0.1%
150 1
 
< 0.1%
149 1
 
< 0.1%
148 1
 
< 0.1%
147 1
 
< 0.1%
146 1
 
< 0.1%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct965
Distinct (%)24.1%
Missing28
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean22134.221
Minimum14602
Maximum41794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.5 KiB
2024-01-28T16:46:56.677063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14602
5-th percentile21066.85
Q121521
median22127.5
Q322631
95-th percentile23037
Maximum41794
Range27192
Interquartile range (IQR)1110

Descriptive statistics

Standard deviation1480.6762
Coefficient of variation (CV)0.066895339
Kurtosis130.84614
Mean22134.221
Median Absolute Deviation (MAD)577.5
Skewness10.361109
Sum88492616
Variance2192402.1
MonotonicityNot monotonic
2024-01-28T16:46:56.792932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22711 33
 
0.8%
23050 29
 
0.7%
21344 27
 
0.7%
21679 23
 
0.6%
21534 21
 
0.5%
21079 19
 
0.5%
21565 18
 
0.4%
22736 18
 
0.4%
21511 17
 
0.4%
21591 17
 
0.4%
Other values (955) 3776
93.8%
(Missing) 28
 
0.7%
ValueCountFrequency (%)
14602 1
 
< 0.1%
21002 4
0.1%
21004 1
 
< 0.1%
21005 2
 
< 0.1%
21006 3
0.1%
21007 6
0.1%
21008 3
0.1%
21009 1
 
< 0.1%
21010 1
 
< 0.1%
21011 2
 
< 0.1%
ValueCountFrequency (%)
41794 1
 
< 0.1%
41789 1
 
< 0.1%
41784 1
 
< 0.1%
41783 4
0.1%
41780 2
 
< 0.1%
40684 2
 
< 0.1%
40522 1
 
< 0.1%
40423 2
 
< 0.1%
40301 5
0.1%
40005 1
 
< 0.1%

주소
Text

Distinct3871
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size31.6 KiB
2024-01-28T16:46:57.124858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length81
Median length53
Mean length26.984352
Min length14

Characters and Unicode

Total characters108639
Distinct characters523
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3761 ?
Unique (%)93.4%

Sample

1st row인천광역시 남동구 정각로 29 (구월동 1138)
2nd row인천광역시 중구 신포로27번길 80 (관동1가)
3rd row인천광역시 동구 금곡로 67
4th row인천광역시 미추홀구 독정이로 95
5th row인천광역시 연수구 원인재로 115
ValueCountFrequency (%)
인천광역시 4026
 
18.7%
서구 701
 
3.3%
남동구 686
 
3.2%
미추홀구 582
 
2.7%
부평구 532
 
2.5%
강화군 388
 
1.8%
계양구 386
 
1.8%
연수구 330
 
1.5%
1층 293
 
1.4%
중구 243
 
1.1%
Other values (4679) 13391
62.1%
2024-01-28T16:46:57.608810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17579
 
16.2%
4436
 
4.1%
4190
 
3.9%
4093
 
3.8%
4059
 
3.7%
1 4056
 
3.7%
4049
 
3.7%
3971
 
3.7%
3911
 
3.6%
3538
 
3.3%
Other values (513) 54757
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64938
59.8%
Decimal Number 18000
 
16.6%
Space Separator 17579
 
16.2%
Close Punctuation 2481
 
2.3%
Open Punctuation 2481
 
2.3%
Other Punctuation 1911
 
1.8%
Dash Punctuation 989
 
0.9%
Math Symbol 127
 
0.1%
Uppercase Letter 94
 
0.1%
Control 36
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4436
 
6.8%
4190
 
6.5%
4093
 
6.3%
4059
 
6.3%
4049
 
6.2%
3971
 
6.1%
3911
 
6.0%
3538
 
5.4%
1297
 
2.0%
1184
 
1.8%
Other values (464) 30210
46.5%
Uppercase Letter
ValueCountFrequency (%)
A 23
24.5%
B 14
14.9%
C 11
11.7%
M 6
 
6.4%
Y 5
 
5.3%
S 5
 
5.3%
E 4
 
4.3%
L 3
 
3.2%
K 3
 
3.2%
T 2
 
2.1%
Other values (13) 18
19.1%
Decimal Number
ValueCountFrequency (%)
1 4056
22.5%
2 2364
13.1%
3 1909
10.6%
4 1735
9.6%
0 1605
 
8.9%
5 1433
 
8.0%
6 1351
 
7.5%
7 1285
 
7.1%
8 1186
 
6.6%
9 1076
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 1900
99.4%
/ 5
 
0.3%
. 3
 
0.2%
· 2
 
0.1%
@ 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
k 1
33.3%
i 1
33.3%
n 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 2480
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2480
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
17579
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 989
100.0%
Math Symbol
ValueCountFrequency (%)
~ 127
100.0%
Control
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64935
59.8%
Common 43604
40.1%
Latin 97
 
0.1%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4436
 
6.8%
4190
 
6.5%
4093
 
6.3%
4059
 
6.3%
4049
 
6.2%
3971
 
6.1%
3911
 
6.0%
3538
 
5.4%
1297
 
2.0%
1184
 
1.8%
Other values (461) 30207
46.5%
Latin
ValueCountFrequency (%)
A 23
23.7%
B 14
14.4%
C 11
11.3%
M 6
 
6.2%
Y 5
 
5.2%
S 5
 
5.2%
E 4
 
4.1%
L 3
 
3.1%
K 3
 
3.1%
T 2
 
2.1%
Other values (16) 21
21.6%
Common
ValueCountFrequency (%)
17579
40.3%
1 4056
 
9.3%
) 2480
 
5.7%
( 2480
 
5.7%
2 2364
 
5.4%
3 1909
 
4.4%
, 1900
 
4.4%
4 1735
 
4.0%
0 1605
 
3.7%
5 1433
 
3.3%
Other values (13) 6063
 
13.9%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64935
59.8%
ASCII 43699
40.2%
CJK 3
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17579
40.2%
1 4056
 
9.3%
) 2480
 
5.7%
( 2480
 
5.7%
2 2364
 
5.4%
3 1909
 
4.4%
, 1900
 
4.3%
4 1735
 
4.0%
0 1605
 
3.7%
5 1433
 
3.3%
Other values (38) 6158
 
14.1%
Hangul
ValueCountFrequency (%)
4436
 
6.8%
4190
 
6.5%
4093
 
6.3%
4059
 
6.3%
4049
 
6.2%
3971
 
6.1%
3911
 
6.0%
3538
 
5.4%
1297
 
2.0%
1184
 
1.8%
Other values (461) 30207
46.5%
None
ValueCountFrequency (%)
· 2
100.0%
CJK
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

전화번호
Text

MISSING 

Distinct3126
Distinct (%)80.2%
Missing128
Missing (%)3.2%
Memory size31.6 KiB
2024-01-28T16:46:57.849474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.982812
Min length4

Characters and Unicode

Total characters46709
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3025 ?
Unique (%)77.6%

Sample

1st row032-120
2nd row032-760-7114
3rd row032-770-6114
4th row032-887-1011
5th row032-749-7114
ValueCountFrequency (%)
032-569-7980 194
 
5.0%
032-930-7026 180
 
4.6%
032-880-4492 71
 
1.8%
032-453-2433 31
 
0.8%
032-560-4694 30
 
0.8%
032-450-5204 22
 
0.6%
032-509-6446 21
 
0.5%
032-760-7093 20
 
0.5%
032-880-4217 16
 
0.4%
032-749-7552 16
 
0.4%
Other values (3116) 3297
84.6%
2024-01-28T16:46:58.237267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7758
16.6%
0 7360
15.8%
2 6367
13.6%
3 6170
13.2%
5 3473
7.4%
7 2913
 
6.2%
4 2827
 
6.1%
8 2790
 
6.0%
6 2725
 
5.8%
9 2237
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38951
83.4%
Dash Punctuation 7758
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7360
18.9%
2 6367
16.3%
3 6170
15.8%
5 3473
8.9%
7 2913
 
7.5%
4 2827
 
7.3%
8 2790
 
7.2%
6 2725
 
7.0%
9 2237
 
5.7%
1 2089
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 7758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 7758
16.6%
0 7360
15.8%
2 6367
13.6%
3 6170
13.2%
5 3473
7.4%
7 2913
 
6.2%
4 2827
 
6.1%
8 2790
 
6.0%
6 2725
 
5.8%
9 2237
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7758
16.6%
0 7360
15.8%
2 6367
13.6%
3 6170
13.2%
5 3473
7.4%
7 2913
 
6.2%
4 2827
 
6.1%
8 2790
 
6.0%
6 2725
 
5.8%
9 2237
 
4.8%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct3305
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.65454
Minimum124.66047
Maximum127.26312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.5 KiB
2024-01-28T16:46:58.368287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.66047
5-th percentile126.43837
Q1126.6478
median126.67931
Q3126.72002
95-th percentile126.73915
Maximum127.26312
Range2.60265
Interquartile range (IQR)0.072219025

Descriptive statistics

Standard deviation0.13572421
Coefficient of variation (CV)0.0010716095
Kurtosis105.73977
Mean126.65454
Median Absolute Deviation (MAD)0.0362525
Skewness-8.1191072
Sum509911.18
Variance0.018421061
MonotonicityNot monotonic
2024-01-28T16:46:58.487154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6529675 12
 
0.3%
126.4851892 12
 
0.3%
126.7143465 10
 
0.2%
126.4491058 9
 
0.2%
126.6593509 9
 
0.2%
126.6652319 8
 
0.2%
126.6697938 7
 
0.2%
126.6553651 7
 
0.2%
126.6783386 6
 
0.1%
126.7357715 6
 
0.1%
Other values (3295) 3940
97.9%
ValueCountFrequency (%)
124.6604701 1
< 0.1%
124.678433 1
< 0.1%
124.6964221 1
< 0.1%
124.7104337 1
< 0.1%
124.7122786 1
< 0.1%
124.7175346 1
< 0.1%
124.718031 1
< 0.1%
124.718502 1
< 0.1%
124.7206279 1
< 0.1%
124.7207802 1
< 0.1%
ValueCountFrequency (%)
127.2631201 1
< 0.1%
126.9197646 1
< 0.1%
126.7804418 1
< 0.1%
126.7775651 1
< 0.1%
126.7752481 1
< 0.1%
126.7731404 1
< 0.1%
126.772528 1
< 0.1%
126.7705477 1
< 0.1%
126.7696197 1
< 0.1%
126.7694649 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct3417
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.50743
Minimum36.494206
Maximum37.974224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.5 KiB
2024-01-28T16:46:58.606580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.494206
5-th percentile37.404162
Q137.452421
median37.484606
Q337.538009
95-th percentile37.733179
Maximum37.974224
Range1.4800179
Interquartile range (IQR)0.085587545

Descriptive statistics

Standard deviation0.091474921
Coefficient of variation (CV)0.002438848
Kurtosis6.2478087
Mean37.50743
Median Absolute Deviation (MAD)0.039096115
Skewness1.0834856
Sum151004.91
Variance0.0083676612
MonotonicityNot monotonic
2024-01-28T16:46:58.746999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.63227602 12
 
0.3%
37.42066925 11
 
0.3%
37.46824505 10
 
0.2%
37.4575366 9
 
0.2%
37.41677806 9
 
0.2%
37.55690657 8
 
0.2%
37.54855129 7
 
0.2%
37.55419249 7
 
0.2%
37.53839755 6
 
0.1%
37.47321214 6
 
0.1%
Other values (3407) 3941
97.9%
ValueCountFrequency (%)
36.49420643 1
< 0.1%
37.2269771 1
< 0.1%
37.22766981 1
< 0.1%
37.23905343 1
< 0.1%
37.23964984 1
< 0.1%
37.25378665 1
< 0.1%
37.2538808 1
< 0.1%
37.25406406 1
< 0.1%
37.25570102 1
< 0.1%
37.25601787 1
< 0.1%
ValueCountFrequency (%)
37.97422437 1
< 0.1%
37.97320126 1
< 0.1%
37.97134568 1
< 0.1%
37.96748093 1
< 0.1%
37.96745067 1
< 0.1%
37.96516528 1
< 0.1%
37.96425471 1
< 0.1%
37.95898576 1
< 0.1%
37.83310065 1
< 0.1%
37.82534085 1
< 0.1%

정보
Text

MISSING 

Distinct1981
Distinct (%)83.4%
Missing1652
Missing (%)41.0%
Memory size31.6 KiB
2024-01-28T16:46:58.948798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length200
Median length168
Mean length68.391744
Min length1

Characters and Unicode

Total characters162362
Distinct characters108
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1878 ?
Unique (%)79.1%

Sample

1st rowhttps://www.incheon.go.kr
2nd rowhttps://www.icjg.go.kr
3rd rowhttps://www.icdonggu.go.kr/
4th rowhttps://www.michuhol.go.kr/
5th rowhttps://www.yeonsu.go.kr/
ValueCountFrequency (%)
http://www.seo.incheon.kr/open_content/main/part/park/facility_outside.jsp 108
 
4.5%
https://www.ganghwa.go.kr/open_content/main/part/culture/exercise.jsp 91
 
3.8%
http://www.seo.incheon.kr/open_content/main/part/park/facility_badminton.jsp 25
 
1.1%
https://www.icjg.go.kr/comm/getfilesrvcid=beffat&upperno=1790&filety=attach&fileno=1 19
 
0.8%
https://www.yeonsu.go.kr/welfare/life/youth/physical.asp 14
 
0.6%
https://www.icbp.go.kr/main/bbs/bbsmsgfiledown.dobcd=officially&msg_seq=17957&fileno=1 8
 
0.3%
http://www.seo.incheon.kr/open_content/main/locinfo/footvolleyball.doorder=view&pgno=1 8
 
0.3%
http://www.seo.incheon.kr/open_content/main/locinfo/gateball.doact=list&order=view 8
 
0.3%
https://www.icbp.go.kr/main/life/physical/facilities_bupyeong.jsp 6
 
0.3%
https://www.icdonggu.go.kr/open_content/main/part/life/facility.jsp 6
 
0.3%
Other values (1974) 2087
87.7%
2024-01-28T16:46:59.297741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 10879
 
6.7%
% 10844
 
6.7%
t 8173
 
5.0%
. 7300
 
4.5%
e 6995
 
4.3%
o 6071
 
3.7%
a 5678
 
3.5%
p 5302
 
3.3%
r 5033
 
3.1%
c 4959
 
3.1%
Other values (98) 91128
56.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82521
50.8%
Other Punctuation 33577
20.7%
Decimal Number 29467
 
18.1%
Uppercase Letter 13672
 
8.4%
Math Symbol 1548
 
1.0%
Connector Punctuation 1235
 
0.8%
Dash Punctuation 276
 
0.2%
Other Letter 52
 
< 0.1%
Space Separator 6
 
< 0.1%
Open Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
9.6%
4
 
7.7%
4
 
7.7%
3
 
5.8%
3
 
5.8%
3
 
5.8%
3
 
5.8%
3
 
5.8%
2
 
3.8%
1
 
1.9%
Other values (21) 21
40.4%
Lowercase Letter
ValueCountFrequency (%)
t 8173
 
9.9%
e 6995
 
8.5%
o 6071
 
7.4%
a 5678
 
6.9%
p 5302
 
6.4%
r 5033
 
6.1%
c 4959
 
6.0%
n 4913
 
6.0%
h 4667
 
5.7%
w 4395
 
5.3%
Other values (16) 26335
31.9%
Uppercase Letter
ValueCountFrequency (%)
E 3305
24.2%
B 2904
21.2%
C 2662
19.5%
A 1838
13.4%
D 1099
 
8.0%
F 686
 
5.0%
P 178
 
1.3%
T 176
 
1.3%
I 153
 
1.1%
V 124
 
0.9%
Other values (16) 547
 
4.0%
Decimal Number
ValueCountFrequency (%)
0 4362
14.8%
9 4018
13.6%
1 3497
11.9%
8 3494
11.9%
2 3047
10.3%
4 2918
9.9%
5 2748
9.3%
3 2008
6.8%
6 1739
 
5.9%
7 1636
 
5.6%
Other Punctuation
ValueCountFrequency (%)
/ 10879
32.4%
% 10844
32.3%
. 7300
21.7%
: 2375
 
7.1%
, 1472
 
4.4%
& 701
 
2.1%
# 5
 
< 0.1%
@ 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 1439
93.0%
+ 109
 
7.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1235
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96193
59.2%
Common 66117
40.7%
Hangul 52
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 8173
 
8.5%
e 6995
 
7.3%
o 6071
 
6.3%
a 5678
 
5.9%
p 5302
 
5.5%
r 5033
 
5.2%
c 4959
 
5.2%
n 4913
 
5.1%
h 4667
 
4.9%
w 4395
 
4.6%
Other values (42) 40007
41.6%
Hangul
ValueCountFrequency (%)
5
 
9.6%
4
 
7.7%
4
 
7.7%
3
 
5.8%
3
 
5.8%
3
 
5.8%
3
 
5.8%
3
 
5.8%
2
 
3.8%
1
 
1.9%
Other values (21) 21
40.4%
Common
ValueCountFrequency (%)
/ 10879
16.5%
% 10844
16.4%
. 7300
11.0%
0 4362
 
6.6%
9 4018
 
6.1%
1 3497
 
5.3%
8 3494
 
5.3%
2 3047
 
4.6%
4 2918
 
4.4%
5 2748
 
4.2%
Other values (15) 13010
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162310
> 99.9%
Hangul 51
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 10879
 
6.7%
% 10844
 
6.7%
t 8173
 
5.0%
. 7300
 
4.5%
e 6995
 
4.3%
o 6071
 
3.7%
a 5678
 
3.5%
p 5302
 
3.3%
r 5033
 
3.1%
c 4959
 
3.1%
Other values (67) 91076
56.1%
Hangul
ValueCountFrequency (%)
5
 
9.8%
4
 
7.8%
4
 
7.8%
3
 
5.9%
3
 
5.9%
3
 
5.9%
3
 
5.9%
3
 
5.9%
2
 
3.9%
1
 
2.0%
Other values (20) 20
39.2%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

Interactions

2024-01-28T16:46:52.718160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:47.325710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.009099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.675744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.317484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.334284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:51.060351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.001638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.813200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:47.409872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.089560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.758179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.405885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.421427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:51.152356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.115531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.916157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:47.491822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.173461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.841946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.511221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.514714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:51.236950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.222308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:53.013447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:47.577586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.258339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.916926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.611501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.601994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:51.351685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.314541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:53.104595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:47.673965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.337517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.996816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.688900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.704325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:51.516753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.397633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:53.187410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:47.766275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.413779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.071858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.765835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.792752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:51.687918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.492373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:53.270452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:47.845234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.506923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.151370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.845733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.868718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:51.822568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.562565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:53.360001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:47.921653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:48.594503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:49.227185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.231685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:50.959590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:51.909747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:46:52.635131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T16:46:59.374086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번분야유형필드코드타입코드분류코드시도시군구분류우편번호경도위도
연번1.0000.9200.9760.9200.8720.9200.0000.6000.6320.3990.2970.350
분야0.9201.0001.0001.0000.7261.0000.0390.5260.5830.3150.2330.400
유형0.9761.0001.0001.0001.0001.0000.4990.6160.9250.5640.3850.446
필드코드0.9201.0001.0001.0000.7261.0000.0390.5260.5830.3150.2330.400
타입코드0.8720.7261.0000.7261.0000.7260.0000.2740.2970.2380.1630.210
분류코드0.9201.0001.0001.0000.7261.0000.0390.5260.5820.3150.2330.400
시도0.0000.0390.4990.0390.0000.0391.0001.0000.0830.0000.8890.708
시군구0.6000.5260.6160.5260.2740.5261.0001.0000.3090.7450.7840.834
분류0.6320.5830.9250.5830.2970.5820.0830.3091.0000.3120.2150.204
우편번호0.3990.3150.5640.3150.2380.3150.0000.7450.3121.0000.5240.553
경도0.2970.2330.3850.2330.1630.2330.8890.7840.2150.5241.0000.830
위도0.3500.4000.4460.4000.2100.4000.7080.8340.2040.5530.8301.000
2024-01-28T16:46:59.480731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분야시군구유형시도
분야1.0000.2210.9950.027
시군구0.2211.0000.2250.999
유형0.9950.2251.0000.257
시도0.0270.9990.2571.000
2024-01-28T16:46:59.567529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번필드코드타입코드분류코드분류우편번호경도위도분야유형시도시군구
연번1.0000.9350.4060.9860.372-0.1050.110-0.0860.7960.8210.0000.292
필드코드0.9351.0000.1970.9480.376-0.1370.141-0.0451.0000.9950.0270.221
타입코드0.4060.1971.0000.4120.1580.093-0.0600.0870.4800.9950.0000.114
분류코드0.9860.9480.4121.0000.377-0.1460.151-0.0721.0000.9950.0270.221
분류0.3720.3760.1580.3771.000-0.0170.0240.0300.4740.7270.0780.180
우편번호-0.105-0.1370.093-0.146-0.0171.000-0.8120.2790.1830.3320.5770.747
경도0.1100.141-0.0600.1510.024-0.8121.000-0.1920.1270.1460.5800.413
위도-0.086-0.0450.087-0.0720.0300.279-0.1921.0000.1490.1940.5760.607
분야0.7961.0000.4801.0000.4740.1830.1270.1491.0000.9950.0270.221
유형0.8210.9950.9950.9950.7270.3320.1460.1940.9951.0000.2570.225
시도0.0000.0270.0000.0270.0780.5770.5800.5760.0270.2571.0000.999
시군구0.2920.2210.1140.2210.1800.7470.4130.6070.2210.2250.9991.000

Missing values

2024-01-28T16:46:53.481201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T16:46:53.643907image/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.
2024-01-28T16:46:53.755541image/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

연번분야유형필드코드타입코드분류코드시도시군구시설명분류우편번호주소전화번호경도위도정보
01행정기관시청111001인천광역시남동구인천광역시청121554인천광역시 남동구 정각로 29 (구월동 1138)032-120126.70621237.456133https://www.incheon.go.kr
12행정기관군구청121002인천광역시중구중구청222315인천광역시 중구 신포로27번길 80 (관동1가)032-760-7114126.62161237.473917https://www.icjg.go.kr
23행정기관군구청121002인천광역시동구동구청 민원실322556인천광역시 동구 금곡로 67032-770-6114126.64290637.473407https://www.icdonggu.go.kr/
34행정기관군구청121002인천광역시미추홀구미추홀구청422169인천광역시 미추홀구 독정이로 95032-887-1011126.65033937.463206https://www.michuhol.go.kr/
45행정기관군구청121002인천광역시연수구연수구청521967인천광역시 연수구 원인재로 115032-749-7114126.67833937.409495https://www.yeonsu.go.kr/
56행정기관군구청121002인천광역시남동구남동구청621589인천광역시 남동구 소래로 633032-466-3811126.73067837.447389https://www.namdong.go.kr/
67행정기관군구청121002인천광역시부평구부평구청721354인천광역시 부평구 부평대로 168032-504-2114126.72133337.506674https://www.icbp.go.kr/
78행정기관군구청121002인천광역시계양구계양구청821067인천광역시 계양구 계산새로 88032-551-5701126.7377637.53744https://www.gyeyang.go.kr/
89행정기관군구청121002인천광역시서구서구청922726인천광역시 서구 서곶로 307032-562-5301126.6766337.545119https://www.seo.incheon.kr/
910행정기관군구청121002인천광역시강화군강화군청1023031인천광역시 강화군 강화읍 강화대로 394032-930-3114126.48786137.746117https://www.ganghwa.go.kr/
연번분야유형필드코드타입코드분류코드시도시군구시설명분류우편번호주소전화번호경도위도정보
40164017일자리/경제산업단지757005인천광역시서구인천서부일반산업단지99922744인천광역시 서구 경서동 일원032-561-6571126.63956737.551719https://blog.naver.com/jon-idea/220997847575
40174018일자리/경제산업단지757005인천광역시서구검단일반산업단지99922666인천광역시 서구 오류동 일원032-578-5200126.60925737.592495http://www.gdinco.kr/
40184019일자리/경제산업단지757005인천광역시부평구한국수출산업부평국가산업단지99921314인천광역시 부평구 청천동 일원070-8895-7471126.71625437.51974https://blog.naver.com/jon-idea/220997861843
40194020일자리/경제산업단지757005인천광역시계양구서운산업단지99921072인천광역시 계양구 서운동 96-19번지 일원032-450-5522126.75672237.535066https://blog.naver.com/jon-idea/220997853399
40204021일자리/경제산업단지757005인천광역시서구청라IHP도시첨단산업단지999<NA>인천광역시 서구 청라동 일원(인천경제자유구역 청라국제도시 내)032-453-7933126.64218137.523783https://www.industryland.or.kr/web/il/ILCplxQry.jspq_danji_cd=228130
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