Overview

Dataset statistics

Number of variables13
Number of observations96
Missing cells336
Missing cells (%)26.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory113.4 B

Variable types

Numeric6
Categorical3
Text4

Dataset

Description인천광역시 행정정보클라우드GIS포털 시스템 내 지하철 정보에 관한 데이터로 2019년도 지하철 역에 대한 내용을 제공하고 있습니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15120612&srcSe=7661IVAWM27C61E190

Alerts

이력필드 is highly overall correlated with 연번 and 7 other fieldsHigh correlation
지하철코드 is highly overall correlated with 배경일련번호 and 3 other fieldsHigh correlation
화면표현_코드 is highly overall correlated with 배경일련번호 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 이력필드High correlation
배경일련번호 is highly overall correlated with Y좌표 and 3 other fieldsHigh correlation
법정동_코드 is highly overall correlated with 행정동_코드 and 3 other fieldsHigh correlation
행정동_코드 is highly overall correlated with 법정동_코드 and 3 other fieldsHigh correlation
X좌표 is highly overall correlated with 법정동_코드 and 3 other fieldsHigh correlation
Y좌표 is highly overall correlated with 배경일련번호 and 6 other fieldsHigh correlation
배경일련번호 has 42 (43.8%) missing valuesMissing
지하철역_이름 has 42 (43.8%) missing valuesMissing
법정동_코드 has 42 (43.8%) missing valuesMissing
법정동_이름 has 42 (43.8%) missing valuesMissing
행정동_코드 has 42 (43.8%) missing valuesMissing
행정동_이름 has 42 (43.8%) missing valuesMissing
X좌표 has 42 (43.8%) missing valuesMissing
Y좌표 has 42 (43.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 16:34:43.328574
Analysis finished2024-01-28 16:34:49.456950
Duration6.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.5
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-01-29T01:34:49.558731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.75
Q124.75
median48.5
Q372.25
95-th percentile91.25
Maximum96
Range95
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation27.856777
Coefficient of variation (CV)0.57436653
Kurtosis-1.2
Mean48.5
Median Absolute Deviation (MAD)24
Skewness0
Sum4656
Variance776
MonotonicityStrictly increasing
2024-01-29T01:34:49.754596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
50 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
Other values (86) 86
89.6%
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 (%)
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%
87 1
1.0%

배경일련번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)100.0%
Missing42
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean1382515
Minimum71038
Maximum4946494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-01-29T01:34:49.928243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71038
5-th percentile190188.9
Q1695799
median1024792.5
Q31664491.5
95-th percentile4946480
Maximum4946494
Range4875456
Interquartile range (IQR)968692.5

Descriptive statistics

Standard deviation1196112.9
Coefficient of variation (CV)0.86517174
Kurtosis3.7295863
Mean1382515
Median Absolute Deviation (MAD)511020
Skewness1.9147373
Sum74655808
Variance1.430686 × 1012
MonotonicityNot monotonic
2024-01-29T01:34:50.100547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
238435 1
 
1.0%
1213964 1
 
1.0%
4946479 1
 
1.0%
4946482 1
 
1.0%
704034 1
 
1.0%
2266402 1
 
1.0%
2201981 1
 
1.0%
2266401 1
 
1.0%
667177 1
 
1.0%
1664494 1
 
1.0%
Other values (44) 44
45.8%
(Missing) 42
43.8%
ValueCountFrequency (%)
71038 1
1.0%
91554 1
1.0%
100589 1
1.0%
238435 1
1.0%
257765 1
1.0%
281318 1
1.0%
320523 1
1.0%
393959 1
1.0%
444188 1
1.0%
487304 1
1.0%
ValueCountFrequency (%)
4946494 1
1.0%
4946489 1
1.0%
4946482 1
1.0%
4946479 1
1.0%
2266402 1
1.0%
2266401 1
1.0%
2266400 1
1.0%
2266399 1
1.0%
2266398 1
1.0%
2249182 1
1.0%

지하철코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
<NA>
42 
160418
29 
160401
11 
160435
11 
160409
 
3

Length

Max length6
Median length6
Mean length5.125
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row160401
2nd row160418
3rd row160401
4th row160401
5th row160401

Common Values

ValueCountFrequency (%)
<NA> 42
43.8%
160418 29
30.2%
160401 11
 
11.5%
160435 11
 
11.5%
160409 3
 
3.1%

Length

2024-01-29T01:34:50.280353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:34:50.425985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 42
43.8%
160418 29
30.2%
160401 11
 
11.5%
160435 11
 
11.5%
160409 3
 
3.1%

화면표현_코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
<NA>
42 
160418
29 
160401
11 
160435
11 
160409
 
3

Length

Max length6
Median length6
Mean length5.125
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row160401
2nd row160418
3rd row160401
4th row160401
5th row160401

Common Values

ValueCountFrequency (%)
<NA> 42
43.8%
160418 29
30.2%
160401 11
 
11.5%
160435 11
 
11.5%
160409 3
 
3.1%

Length

2024-01-29T01:34:50.585095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:34:50.714935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 42
43.8%
160418 29
30.2%
160401 11
 
11.5%
160435 11
 
11.5%
160409 3
 
3.1%

지하철역_풀네임
Text

Distinct91
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size900.0 B
2024-01-29T01:34:51.010097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length7.375
Min length3

Characters and Unicode

Total characters708
Distinct characters183
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)89.6%

Sample

1st row간석역
2nd row부평삼거리역
3rd row동암역
4th row백운역
5th row도원역
ValueCountFrequency (%)
부평구청역(로이문화예술실용전문학교 2
 
2.1%
계양역 2
 
2.1%
주안역 2
 
2.1%
검암역 2
 
2.1%
인천공항1터미널역 2
 
2.1%
서부여성회관역 1
 
1.0%
만수역 1
 
1.0%
인천가좌역 1
 
1.0%
가재울역 1
 
1.0%
주안국가산단역 1
 
1.0%
Other values (81) 81
84.4%
2024-01-29T01:34:51.536116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 97
 
13.7%
) 27
 
3.8%
( 27
 
3.8%
인 24
 
3.4%
천 22
 
3.1%
원 17
 
2.4%
시 12
 
1.7%
학 12
 
1.7%
대 11
 
1.6%
문 9
 
1.3%
Other values (173) 450
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 647
91.4%
Close Punctuation 27
 
3.8%
Open Punctuation 27
 
3.8%
Decimal Number 5
 
0.7%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 97
 
15.0%
인 24
 
3.7%
천 22
 
3.4%
원 17
 
2.6%
시 12
 
1.9%
학 12
 
1.9%
대 11
 
1.7%
문 9
 
1.4%
동 9
 
1.4%
병 9
 
1.4%
Other values (167) 425
65.7%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
2 2
40.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
I 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 647
91.4%
Common 59
 
8.3%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 97
 
15.0%
인 24
 
3.7%
천 22
 
3.4%
원 17
 
2.6%
시 12
 
1.9%
학 12
 
1.9%
대 11
 
1.7%
문 9
 
1.4%
동 9
 
1.4%
병 9
 
1.4%
Other values (167) 425
65.7%
Common
ValueCountFrequency (%)
) 27
45.8%
( 27
45.8%
1 3
 
5.1%
2 2
 
3.4%
Latin
ValueCountFrequency (%)
S 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 647
91.4%
ASCII 61
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 97
 
15.0%
인 24
 
3.7%
천 22
 
3.4%
원 17
 
2.6%
시 12
 
1.9%
학 12
 
1.9%
대 11
 
1.7%
문 9
 
1.4%
동 9
 
1.4%
병 9
 
1.4%
Other values (167) 425
65.7%
ASCII
ValueCountFrequency (%)
) 27
44.3%
( 27
44.3%
1 3
 
4.9%
2 2
 
3.3%
S 1
 
1.6%
I 1
 
1.6%

지하철역_이름
Text

MISSING 

Distinct50
Distinct (%)92.6%
Missing42
Missing (%)43.8%
Memory size900.0 B
2024-01-29T01:34:51.846364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.2222222
Min length3

Characters and Unicode

Total characters228
Distinct characters92
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

Unique46 ?
Unique (%)85.2%

Sample

1st row간석역
2nd row부평삼거리역
3rd row동암역
4th row백운역
5th row도원역
ValueCountFrequency (%)
부평역 2
 
3.7%
인천역 2
 
3.7%
부평구청역 2
 
3.7%
원인재역 2
 
3.7%
캠퍼스타운역 1
 
1.9%
주안역 1
 
1.9%
인천시청역 1
 
1.9%
삼산체육관역 1
 
1.9%
간석오거리역 1
 
1.9%
신포역 1
 
1.9%
Other values (40) 40
74.1%
2024-01-29T01:34:52.309877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 54
23.7%
인 12
 
5.3%
천 8
 
3.5%
부 7
 
3.1%
구 7
 
3.1%
평 6
 
2.6%
동 6
 
2.6%
포 5
 
2.2%
크 4
 
1.8%
도 3
 
1.3%
Other values (82) 116
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 54
23.7%
인 12
 
5.3%
천 8
 
3.5%
부 7
 
3.1%
구 7
 
3.1%
평 6
 
2.6%
동 6
 
2.6%
포 5
 
2.2%
크 4
 
1.8%
도 3
 
1.3%
Other values (82) 116
50.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 54
23.7%
인 12
 
5.3%
천 8
 
3.5%
부 7
 
3.1%
구 7
 
3.1%
평 6
 
2.6%
동 6
 
2.6%
포 5
 
2.2%
크 4
 
1.8%
도 3
 
1.3%
Other values (82) 116
50.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 54
23.7%
인 12
 
5.3%
천 8
 
3.5%
부 7
 
3.1%
구 7
 
3.1%
평 6
 
2.6%
동 6
 
2.6%
포 5
 
2.2%
크 4
 
1.8%
도 3
 
1.3%
Other values (82) 116
50.9%

이력필드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
U
54 
<NA>
42 

Length

Max length4
Median length1
Mean length2.3125
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
U 54
56.2%
<NA> 42
43.8%

Length

2024-01-29T01:34:52.488825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:34:52.614991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
u 54
56.2%
na 42
43.8%

법정동_코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)53.7%
Missing42
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean2.8200347 × 109
Minimum2.8110122 × 109
Maximum2.8245111 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-01-29T01:34:52.735296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8110122 × 109
5-th percentile2.8110139 × 109
Q12.8185102 × 109
median2.8200102 × 109
Q32.8237102 × 109
95-th percentile2.8245106 × 109
Maximum2.8245111 × 109
Range13498900
Interquartile range (IQR)5199975

Descriptive statistics

Standard deviation3635699.5
Coefficient of variation (CV)0.0012892393
Kurtosis0.66560001
Mean2.8200347 × 109
Median Absolute Deviation (MAD)2299750
Skewness-0.75600483
Sum1.5228188 × 1011
Variance1.3218311 × 1013
MonotonicityNot monotonic
2024-01-29T01:34:52.922490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2818510600 6
 
6.2%
2823710100 5
 
5.2%
2820010200 4
 
4.2%
2818510200 3
 
3.1%
2818510300 3
 
3.1%
2820011000 3
 
3.1%
2823710200 2
 
2.1%
2818510500 2
 
2.1%
2817710200 2
 
2.1%
2817710400 2
 
2.1%
Other values (19) 22
22.9%
(Missing) 42
43.8%
ValueCountFrequency (%)
2811012200 1
1.0%
2811013600 1
1.0%
2811013800 1
1.0%
2811013900 1
1.0%
2817710100 1
1.0%
2817710200 2
2.1%
2817710400 2
2.1%
2817710500 1
1.0%
2817710600 1
1.0%
2818510100 1
1.0%
ValueCountFrequency (%)
2824511100 2
2.1%
2824510900 1
1.0%
2824510500 1
1.0%
2824510300 1
1.0%
2824510200 2
2.1%
2823710700 1
1.0%
2823710600 2
2.1%
2823710500 1
1.0%
2823710400 1
1.0%
2823710200 2
2.1%

법정동_이름
Text

MISSING 

Distinct29
Distinct (%)53.7%
Missing42
Missing (%)43.8%
Memory size900.0 B
2024-01-29T01:34:53.212796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.111111
Min length9

Characters and Unicode

Total characters600
Distinct characters59
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

Unique16 ?
Unique (%)29.6%

Sample

1st row인천광역시남동구간석동
2nd row인천광역시남동구간석동
3rd row인천광역시부평구십정동
4th row인천광역시부평구십정동
5th row인천광역시미추홀구숭의동
ValueCountFrequency (%)
인천광역시연수구송도동 6
 
11.1%
인천광역시부평구부평동 5
 
9.3%
인천광역시남동구간석동 4
 
7.4%
인천광역시연수구선학동 3
 
5.6%
인천광역시연수구연수동 3
 
5.6%
인천광역시남동구논현동 3
 
5.6%
인천광역시미추홀구도화동 2
 
3.7%
인천광역시부평구십정동 2
 
3.7%
인천광역시연수구동춘동 2
 
3.7%
인천광역시미추홀구용현동 2
 
3.7%
Other values (19) 22
40.7%
2024-01-29T01:34:53.652147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 65
10.8%
구 55
 
9.2%
천 55
 
9.2%
인 55
 
9.2%
시 54
 
9.0%
역 54
 
9.0%
광 54
 
9.0%
부 18
 
3.0%
수 18
 
3.0%
연 18
 
3.0%
Other values (49) 154
25.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 598
99.7%
Decimal Number 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 65
10.9%
구 55
 
9.2%
천 55
 
9.2%
인 55
 
9.2%
시 54
 
9.0%
역 54
 
9.0%
광 54
 
9.0%
부 18
 
3.0%
수 18
 
3.0%
연 18
 
3.0%
Other values (47) 152
25.4%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 598
99.7%
Common 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 65
10.9%
구 55
 
9.2%
천 55
 
9.2%
인 55
 
9.2%
시 54
 
9.0%
역 54
 
9.0%
광 54
 
9.0%
부 18
 
3.0%
수 18
 
3.0%
연 18
 
3.0%
Other values (47) 152
25.4%
Common
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 598
99.7%
ASCII 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 65
10.9%
구 55
 
9.2%
천 55
 
9.2%
인 55
 
9.2%
시 54
 
9.0%
역 54
 
9.0%
광 54
 
9.0%
부 18
 
3.0%
수 18
 
3.0%
연 18
 
3.0%
Other values (47) 152
25.4%
ASCII
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%

행정동_코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)72.2%
Missing42
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean2.8200896 × 109
Minimum2.811053 × 109
Maximum2.824573 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-01-29T01:34:53.832059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.811053 × 109
5-th percentile2.81106 × 109
Q12.818575 × 109
median2.8200526 × 109
Q32.8237538 × 109
95-th percentile2.824571 × 109
Maximum2.824573 × 109
Range13520000
Interquartile range (IQR)5178750

Descriptive statistics

Standard deviation3635357.4
Coefficient of variation (CV)0.0012890929
Kurtosis0.6803518
Mean2.8200896 × 109
Median Absolute Deviation (MAD)2292550
Skewness-0.7611471
Sum1.5228484 × 1011
Variance1.3215823 × 1013
MonotonicityNot monotonic
2024-01-29T01:34:54.024080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2823751000 3
 
3.1%
2818582000 3
 
3.1%
2818575000 3
 
3.1%
2818583000 3
 
3.1%
2811060000 2
 
2.1%
2824571000 2
 
2.1%
2820071000 2
 
2.1%
2818576200 2
 
2.1%
2817760000 2
 
2.1%
2820053000 2
 
2.1%
Other values (29) 30
31.2%
(Missing) 42
43.8%
ValueCountFrequency (%)
2811053000 1
1.0%
2811058500 1
1.0%
2811060000 2
2.1%
2817752000 1
1.0%
2817755000 1
1.0%
2817757000 1
1.0%
2817760000 2
2.1%
2817762000 1
1.0%
2817770000 1
1.0%
2818564000 1
1.0%
ValueCountFrequency (%)
2824573000 1
1.0%
2824572000 1
1.0%
2824571000 2
2.1%
2824562200 1
1.0%
2824561200 1
1.0%
2824561100 1
1.0%
2823769000 1
1.0%
2823766000 1
1.0%
2823764800 1
1.0%
2823764200 1
1.0%

행정동_이름
Text

MISSING 

Distinct39
Distinct (%)72.2%
Missing42
Missing (%)43.8%
Memory size900.0 B
2024-01-29T01:34:54.281012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length12
Min length10

Characters and Unicode

Total characters648
Distinct characters59
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

Unique28 ?
Unique (%)51.9%

Sample

1st row인천광역시남동구간석4동
2nd row인천광역시남동구간석3동
3rd row인천광역시부평구십정2동
4th row인천광역시부평구부평3동
5th row인천광역시미추홀구숭의1.3동
ValueCountFrequency (%)
인천광역시연수구송도1동 3
 
5.6%
인천광역시연수구송도2동 3
 
5.6%
인천광역시부평구부평1동 3
 
5.6%
인천광역시연수구선학동 3
 
5.6%
인천광역시미추홀구도화1동 2
 
3.7%
인천광역시남동구논현2동 2
 
3.7%
인천광역시남동구간석1동 2
 
3.7%
인천광역시연수구연수2동 2
 
3.7%
인천광역시남동구논현고잔동 2
 
3.7%
인천광역시계양구계양1동 2
 
3.7%
Other values (29) 30
55.6%
2024-01-29T01:34:55.174917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 66
 
10.2%
천 56
 
8.6%
인 55
 
8.5%
구 55
 
8.5%
광 54
 
8.3%
역 54
 
8.3%
시 54
 
8.3%
2 19
 
2.9%
부 19
 
2.9%
연 18
 
2.8%
Other values (49) 198
30.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 602
92.9%
Decimal Number 45
 
6.9%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 66
11.0%
천 56
 
9.3%
인 55
 
9.1%
구 55
 
9.1%
광 54
 
9.0%
역 54
 
9.0%
시 54
 
9.0%
부 19
 
3.2%
연 18
 
3.0%
수 18
 
3.0%
Other values (43) 153
25.4%
Decimal Number
ValueCountFrequency (%)
2 19
42.2%
1 17
37.8%
3 6
 
13.3%
4 2
 
4.4%
5 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 602
92.9%
Common 46
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 66
11.0%
천 56
 
9.3%
인 55
 
9.1%
구 55
 
9.1%
광 54
 
9.0%
역 54
 
9.0%
시 54
 
9.0%
부 19
 
3.2%
연 18
 
3.0%
수 18
 
3.0%
Other values (43) 153
25.4%
Common
ValueCountFrequency (%)
2 19
41.3%
1 17
37.0%
3 6
 
13.0%
4 2
 
4.3%
5 1
 
2.2%
. 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 602
92.9%
ASCII 46
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 66
11.0%
천 56
 
9.3%
인 55
 
9.1%
구 55
 
9.1%
광 54
 
9.0%
역 54
 
9.0%
시 54
 
9.0%
부 19
 
3.2%
연 18
 
3.0%
수 18
 
3.0%
Other values (43) 153
25.4%
ASCII
ValueCountFrequency (%)
2 19
41.3%
1 17
37.0%
3 6
 
13.0%
4 2
 
4.3%
5 1
 
2.2%
. 1
 
2.2%

X좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)100.0%
Missing42
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean928493.12
Minimum921921.9
Maximum933312.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-01-29T01:34:55.347903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum921921.9
5-th percentile922859.62
Q1925558.49
median929163.34
Q3931243.47
95-th percentile932913.97
Maximum933312.21
Range11390.307
Interquartile range (IQR)5684.9775

Descriptive statistics

Standard deviation3333.0878
Coefficient of variation (CV)0.0035897819
Kurtosis-0.98454394
Mean928493.12
Median Absolute Deviation (MAD)2239.964
Skewness-0.45204345
Sum50138628
Variance11109474
MonotonicityNot monotonic
2024-01-29T01:34:55.547188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
931322.02 1
 
1.0%
929954.558 1
 
1.0%
921961.3167 1
 
1.0%
922583.3306 1
 
1.0%
926493.9987 1
 
1.0%
931186.003 1
 
1.0%
928005.9296 1
 
1.0%
929964.3483 1
 
1.0%
933312.2086 1
 
1.0%
925825.6123 1
 
1.0%
Other values (44) 44
45.8%
(Missing) 42
43.8%
ValueCountFrequency (%)
921921.9019 1
1.0%
921961.3167 1
1.0%
922583.3306 1
1.0%
923008.3896 1
1.0%
923305.7839 1
1.0%
923431.2404 1
1.0%
923776.0223 1
1.0%
923817.0665 1
1.0%
924203.0353 1
1.0%
924340.6753 1
1.0%
ValueCountFrequency (%)
933312.2086 1
1.0%
933111.3327 1
1.0%
933009.2027 1
1.0%
932862.6872 1
1.0%
932745.9512 1
1.0%
932570.1524 1
1.0%
932163.256 1
1.0%
932073.3256 1
1.0%
931850.2753 1
1.0%
931452.1101 1
1.0%

Y좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)100.0%
Missing42
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean1940575.3
Minimum1931335.8
Maximum1952727.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-01-29T01:34:55.714223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1931335.8
5-th percentile1932381.8
Q11935335.1
median1941120.8
Q31943725.4
95-th percentile1950127.3
Maximum1952727.6
Range21391.743
Interquartile range (IQR)8390.291

Descriptive statistics

Standard deviation5672.8018
Coefficient of variation (CV)0.0029232577
Kurtosis-0.72781035
Mean1940575.3
Median Absolute Deviation (MAD)4428.5515
Skewness0.23410743
Sum1.0479107 × 108
Variance32180680
MonotonicityNot monotonic
2024-01-29T01:34:55.889955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1949046.043 1
 
1.0%
1941149.455 1
 
1.0%
1942281.663 1
 
1.0%
1941386.429 1
 
1.0%
1941092.075 1
 
1.0%
1933788.129 1
 
1.0%
1935206.67 1
 
1.0%
1933907.238 1
 
1.0%
1950733.701 1
 
1.0%
1932452.925 1
 
1.0%
Other values (44) 44
45.8%
(Missing) 42
43.8%
ValueCountFrequency (%)
1931335.814 1
1.0%
1931802.997 1
1.0%
1932249.568 1
1.0%
1932452.925 1
1.0%
1932986.61 1
1.0%
1933545.587 1
1.0%
1933788.129 1
1.0%
1933815.893 1
1.0%
1933835.345 1
1.0%
1933907.238 1
1.0%
ValueCountFrequency (%)
1952727.557 1
1.0%
1952163.408 1
1.0%
1950733.701 1
1.0%
1949800.72 1
1.0%
1949606.583 1
1.0%
1949046.043 1
1.0%
1948170.815 1
1.0%
1946704.499 1
1.0%
1945747.625 1
1.0%
1945637.677 1
1.0%

Interactions

2024-01-29T01:34:48.140219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:44.221654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:44.857876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:45.512195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:46.723044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:47.410497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:48.259493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:44.319172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:44.963225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:45.647679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:46.836885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:47.521957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:48.372010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:44.424512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:45.068111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:45.764703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:46.961765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:47.638864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:48.482108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:44.542316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:45.188159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:45.886786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:47.072663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:47.783745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:48.594266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:44.664871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:45.298054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:46.014938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:47.198234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:47.905592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:48.695426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:44.764010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:45.411779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:46.136762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:47.305253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:34:48.024689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:34:56.015966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번배경일련번호지하철코드화면표현_코드지하철역_풀네임지하철역_이름법정동_코드법정동_이름행정동_코드행정동_이름X좌표Y좌표
연번1.0000.2740.3130.3130.8580.8480.0000.3960.0000.6170.3850.336
배경일련번호0.2741.0000.7790.7790.9240.5210.5200.9340.5200.9430.5240.689
지하철코드0.3130.7791.0001.0000.0000.0000.4120.8910.4120.9670.0000.743
화면표현_코드0.3130.7791.0001.0000.0000.0000.4120.8910.4120.9670.0000.743
지하철역_풀네임0.8580.9240.0000.0001.0001.0001.0000.9761.0000.9671.0001.000
지하철역_이름0.8480.5210.0000.0001.0001.0001.0000.9711.0000.9961.0001.000
법정동_코드0.0000.5200.4120.4121.0001.0001.0001.0001.0001.0000.8340.916
법정동_이름0.3960.9340.8910.8910.9760.9711.0001.0001.0000.9930.8100.960
행정동_코드0.0000.5200.4120.4121.0001.0001.0001.0001.0001.0000.8340.916
행정동_이름0.6170.9430.9670.9670.9670.9961.0000.9931.0001.0000.8570.990
X좌표0.3850.5240.0000.0001.0001.0000.8340.8100.8340.8571.0000.676
Y좌표0.3360.6890.7430.7431.0001.0000.9160.9600.9160.9900.6761.000
2024-01-29T01:34:56.202085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력필드지하철코드화면표현_코드
이력필드1.0001.0001.000
지하철코드1.0001.0001.000
화면표현_코드1.0001.0001.000
2024-01-29T01:34:56.319954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번배경일련번호법정동_코드행정동_코드X좌표Y좌표지하철코드화면표현_코드이력필드
연번1.0000.316-0.137-0.154-0.050-0.3030.2010.2011.000
배경일련번호0.3161.000-0.305-0.299-0.331-0.5710.6140.6141.000
법정동_코드-0.137-0.3051.0000.9970.8740.5620.3440.3441.000
행정동_코드-0.154-0.2990.9971.0000.8680.5580.3440.3441.000
X좌표-0.050-0.3310.8740.8681.0000.6160.0000.0001.000
Y좌표-0.303-0.5710.5620.5580.6161.0000.5200.5201.000
지하철코드0.2010.6140.3440.3440.0000.5201.0001.0001.000
화면표현_코드0.2010.6140.3440.3440.0000.5201.0001.0001.000
이력필드1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-01-29T01:34:48.829973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:34:49.047813image/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.
2024-01-29T01:34:49.287608image/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

연번배경일련번호지하철코드화면표현_코드지하철역_풀네임지하철역_이름이력필드법정동_코드법정동_이름행정동_코드행정동_이름X좌표Y좌표
01393959160401160401간석역간석역U2820010200인천광역시남동구간석동2820055100인천광역시남동구간석4동928672.46381940915.564
12320523160418160418부평삼거리역부평삼거리역U2820010200인천광역시남동구간석동2820055000인천광역시남동구간석3동930193.2381942409.2
23895360160401160401동암역동암역U2823710200인천광역시부평구십정동2823769000인천광역시부평구십정2동929516.28521941594.143
34257765160401160401백운역백운역U2823710200인천광역시부평구십정동2823753000인천광역시부평구부평3동929907.70461942970.18
4571038160401160401도원역도원역U2817710100인천광역시미추홀구숭의동2817752000인천광역시미추홀구숭의1.3동924203.03531941389.511
56444188160418160418계산역(인천하이병원)계산역U2824510200인천광역시계양구계산동2824561200인천광역시계양구계산2동931850.27531949606.583
674946489160435160435숭의역(인하대병원)숭의역U2817710200인천광역시미추홀구용현동2817755000인천광역시미추홀구용현2동923776.02231940537.466
78858275160401160401인천역(차이나타운)인천역U2811013800인천광역시중구북성동1가2811060000인천광역시중구북성동921921.90191942271.624
89738209160418160418계양역계양역U2824511100인천광역시계양구귤현동2824571000인천광역시계양구계양1동932570.15241952727.557
910927026160418160418임학역임학역U2824510500인천광역시계양구임학동2824572000인천광역시계양구계양2동932745.95121949800.72
연번배경일련번호지하철코드화면표현_코드지하철역_풀네임지하철역_이름이력필드법정동_코드법정동_이름행정동_코드행정동_이름X좌표Y좌표
8687<NA><NA><NA>운서역<NA><NA><NA><NA><NA><NA><NA><NA>
8788<NA><NA><NA>공항화물청사역<NA><NA><NA><NA><NA><NA><NA><NA>
8889<NA><NA><NA>인천공항1터미널역<NA><NA><NA><NA><NA><NA><NA><NA>
8990<NA><NA><NA>인천공항2터미널역<NA><NA><NA><NA><NA><NA><NA><NA>
9091<NA><NA><NA>인천공항1터미널역<NA><NA><NA><NA><NA><NA><NA><NA>
9192<NA><NA><NA>장기주차장역<NA><NA><NA><NA><NA><NA><NA><NA>
9293<NA><NA><NA>합동청사역<NA><NA><NA><NA><NA><NA><NA><NA>
9394<NA><NA><NA>파라다이스시티역<NA><NA><NA><NA><NA><NA><NA><NA>
9495<NA><NA><NA>워터파크역<NA><NA><NA><NA><NA><NA><NA><NA>
9596<NA><NA><NA>용유역<NA><NA><NA><NA><NA><NA><NA><NA>