Overview

Dataset statistics

Number of variables13
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory120.6 B

Variable types

Text1
Numeric12

Dataset

Description인천교통공사에서 운영 중인 인천지하철 1호선, 2호선, 7호선(인천구간+부천구간)의 2022년 7월 1일부터 2023년 6월 30일까지 역별 일별 이용이원 현황자료입니다. (호선, 통행일자, 역명, 구분, 이용인원)
URLhttps://www.data.go.kr/data/15004329/fileData.do

Alerts

1월 is highly overall correlated with 2월 and 10 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
4월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
5월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
7월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
8월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
10월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
11월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
12월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
역사명 has unique valuesUnique
1월 has unique valuesUnique
2월 has unique valuesUnique
3월 has unique valuesUnique
4월 has unique valuesUnique
5월 has unique valuesUnique
6월 has unique valuesUnique
7월 has unique valuesUnique
8월 has unique valuesUnique
9월 has unique valuesUnique
10월 has unique valuesUnique
11월 has unique valuesUnique
12월 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:44:32.182658
Analysis finished2023-12-12 07:44:48.585942
Duration16.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역사명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T16:44:48.714147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.5862069
Min length2

Characters and Unicode

Total characters104
Distinct characters69
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

Unique29 ?
Unique (%)100.0%

Sample

1st row계양
2nd row귤현
3rd row박촌
4th row임학
5th row계산
ValueCountFrequency (%)
계양 1
 
3.4%
예술회관 1
 
3.4%
센트럴파크 1
 
3.4%
인천대입구 1
 
3.4%
지식정보단지 1
 
3.4%
테크노파크 1
 
3.4%
캠퍼스타운 1
 
3.4%
동막 1
 
3.4%
동춘 1
 
3.4%
원인재 1
 
3.4%
Other values (19) 19
65.5%
2023-12-12T16:44:49.068639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
4.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
2
 
1.9%
Other values (59) 70
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
4.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
2
 
1.9%
Other values (59) 70
67.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5
 
4.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
2
 
1.9%
Other values (59) 70
67.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
5
 
4.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
2
 
1.9%
Other values (59) 70
67.3%

1월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266878.69
Minimum13174
Maximum700022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:49.182525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13174
5-th percentile42288.2
Q1118371
median231616
Q3405872
95-th percentile620275
Maximum700022
Range686848
Interquartile range (IQR)287501

Descriptive statistics

Standard deviation189565.68
Coefficient of variation (CV)0.71030654
Kurtosis-0.23189537
Mean266878.69
Median Absolute Deviation (MAD)141275
Skewness0.72149888
Sum7739482
Variance3.5935147 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:49.316740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
138696 1
 
3.4%
40363 1
 
3.4%
13174 1
 
3.4%
59987 1
 
3.4%
75399 1
 
3.4%
118371 1
 
3.4%
90341 1
 
3.4%
150210 1
 
3.4%
231616 1
 
3.4%
405872 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
13174 1
3.4%
40363 1
3.4%
45176 1
3.4%
59987 1
3.4%
75399 1
3.4%
90341 1
3.4%
102992 1
3.4%
118371 1
3.4%
138696 1
3.4%
142918 1
3.4%
ValueCountFrequency (%)
700022 1
3.4%
679677 1
3.4%
531172 1
3.4%
478195 1
3.4%
473803 1
3.4%
467257 1
3.4%
409221 1
3.4%
405872 1
3.4%
383321 1
3.4%
323959 1
3.4%

2월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255673.07
Minimum13120
Maximum672332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:49.455817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13120
5-th percentile39670.2
Q1115936
median224472
Q3389378
95-th percentile590675
Maximum672332
Range659212
Interquartile range (IQR)273442

Descriptive statistics

Standard deviation181050.39
Coefficient of variation (CV)0.70813244
Kurtosis-0.23249641
Mean255673.07
Median Absolute Deviation (MAD)134772
Skewness0.71493444
Sum7414519
Variance3.2779245 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:49.563369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
132686 1
 
3.4%
38163 1
 
3.4%
13120 1
 
3.4%
57144 1
 
3.4%
77506 1
 
3.4%
115936 1
 
3.4%
90238 1
 
3.4%
146542 1
 
3.4%
224472 1
 
3.4%
389378 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
13120 1
3.4%
38163 1
3.4%
41931 1
3.4%
57144 1
3.4%
77506 1
3.4%
89700 1
3.4%
90238 1
3.4%
115936 1
3.4%
132686 1
3.4%
139255 1
3.4%
ValueCountFrequency (%)
672332 1
3.4%
646139 1
3.4%
507479 1
3.4%
459449 1
3.4%
450647 1
3.4%
446551 1
3.4%
391590 1
3.4%
389378 1
3.4%
362556 1
3.4%
311379 1
3.4%

3월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313014.76
Minimum19851
Maximum779471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:49.673669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19851
5-th percentile55640
Q1168386
median278324
Q3437974
95-th percentile739570
Maximum779471
Range759620
Interquartile range (IQR)269588

Descriptive statistics

Standard deviation210904.81
Coefficient of variation (CV)0.67378552
Kurtosis-0.11548011
Mean313014.76
Median Absolute Deviation (MAD)151482
Skewness0.76285704
Sum9077428
Variance4.448084 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:49.786353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
176741 1
 
3.4%
46962 1
 
3.4%
19851 1
 
3.4%
68657 1
 
3.4%
216638 1
 
3.4%
156022 1
 
3.4%
105657 1
 
3.4%
200653 1
 
3.4%
258112 1
 
3.4%
437974 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
19851 1
3.4%
46962 1
3.4%
68657 1
3.4%
87373 1
3.4%
105657 1
3.4%
107852 1
3.4%
156022 1
3.4%
168386 1
3.4%
175103 1
3.4%
176741 1
3.4%
ValueCountFrequency (%)
779471 1
3.4%
777482 1
3.4%
682702 1
3.4%
538491 1
3.4%
529762 1
3.4%
520420 1
3.4%
467888 1
3.4%
437974 1
3.4%
429806 1
3.4%
384463 1
3.4%

4월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309327.72
Minimum20736
Maximum777147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:49.918619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20736
5-th percentile58029.6
Q1170265
median276047
Q3426682
95-th percentile710529.6
Maximum777147
Range756411
Interquartile range (IQR)256417

Descriptive statistics

Standard deviation204963.56
Coefficient of variation (CV)0.66260972
Kurtosis-0.14762408
Mean309327.72
Median Absolute Deviation (MAD)148711
Skewness0.7541974
Sum8970504
Variance4.2010059 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:50.039663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
181216 1
 
3.4%
47430 1
 
3.4%
20736 1
 
3.4%
73929 1
 
3.4%
221369 1
 
3.4%
154763 1
 
3.4%
114022 1
 
3.4%
189416 1
 
3.4%
256806 1
 
3.4%
424758 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
20736 1
3.4%
47430 1
3.4%
73929 1
3.4%
91828 1
3.4%
109663 1
3.4%
114022 1
3.4%
154763 1
3.4%
170265 1
3.4%
175436 1
3.4%
181216 1
3.4%
ValueCountFrequency (%)
777147 1
3.4%
732928 1
3.4%
676932 1
3.4%
538236 1
3.4%
528456 1
3.4%
503437 1
3.4%
461019 1
3.4%
426682 1
3.4%
424758 1
3.4%
378283 1
3.4%

5월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312980.66
Minimum19122
Maximum800913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:50.171888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19122
5-th percentile63582.2
Q1160812
median269232
Q3435813
95-th percentile740648.8
Maximum800913
Range781791
Interquartile range (IQR)275001

Descriptive statistics

Standard deviation210112.56
Coefficient of variation (CV)0.67132763
Kurtosis0.10309786
Mean312980.66
Median Absolute Deviation (MAD)155858
Skewness0.85784725
Sum9076439
Variance4.4147288 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:50.315976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
186124 1
 
3.4%
51681 1
 
3.4%
19122 1
 
3.4%
81434 1
 
3.4%
214404 1
 
3.4%
152917 1
 
3.4%
112699 1
 
3.4%
198423 1
 
3.4%
252274 1
 
3.4%
435813 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
19122 1
3.4%
51681 1
3.4%
81434 1
3.4%
111581 1
3.4%
112699 1
3.4%
113374 1
3.4%
152917 1
3.4%
160812 1
3.4%
174852 1
3.4%
186124 1
3.4%
ValueCountFrequency (%)
800913 1
3.4%
781526 1
3.4%
679333 1
3.4%
536551 1
3.4%
523184 1
3.4%
520125 1
3.4%
450016 1
3.4%
435813 1
3.4%
429184 1
3.4%
377457 1
3.4%

6월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286444.45
Minimum21744
Maximum716998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:50.445804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21744
5-th percentile62153.6
Q1153907
median249461
Q3396247
95-th percentile668456.6
Maximum716998
Range695254
Interquartile range (IQR)242340

Descriptive statistics

Standard deviation190816.15
Coefficient of variation (CV)0.66615412
Kurtosis-0.072008202
Mean286444.45
Median Absolute Deviation (MAD)138677
Skewness0.81680851
Sum8306889
Variance3.6410803 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:50.619096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
168722 1
 
3.4%
48930 1
 
3.4%
21744 1
 
3.4%
81989 1
 
3.4%
162354 1
 
3.4%
137762 1
 
3.4%
110784 1
 
3.4%
171681 1
 
3.4%
235149 1
 
3.4%
396247 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
21744 1
3.4%
48930 1
3.4%
81989 1
3.4%
100005 1
3.4%
102861 1
3.4%
110784 1
3.4%
137762 1
3.4%
153907 1
3.4%
162354 1
3.4%
166048 1
3.4%
ValueCountFrequency (%)
716998 1
3.4%
712413 1
3.4%
602522 1
3.4%
497496 1
3.4%
497228 1
3.4%
483906 1
3.4%
417704 1
3.4%
396247 1
3.4%
393408 1
3.4%
347287 1
3.4%

7월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290113.52
Minimum38657
Maximum722620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:50.774659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38657
5-th percentile58499
Q1136447
median250234
Q3429032
95-th percentile657026.2
Maximum722620
Range683963
Interquartile range (IQR)292585

Descriptive statistics

Standard deviation194212.1
Coefficient of variation (CV)0.66943485
Kurtosis-0.28721067
Mean290113.52
Median Absolute Deviation (MAD)143411
Skewness0.75878998
Sum8413292
Variance3.771834 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:50.928678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
163476 1
 
3.4%
48769 1
 
3.4%
38657 1
 
3.4%
93991 1
 
3.4%
104686 1
 
3.4%
136447 1
 
3.4%
124280 1
 
3.4%
172408 1
 
3.4%
250234 1
 
3.4%
429032 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
38657 1
3.4%
48769 1
3.4%
73094 1
3.4%
93991 1
3.4%
104686 1
3.4%
106823 1
3.4%
124280 1
3.4%
136447 1
3.4%
162154 1
3.4%
163476 1
3.4%
ValueCountFrequency (%)
722620 1
3.4%
715187 1
3.4%
569785 1
3.4%
522611 1
3.4%
516940 1
3.4%
513519 1
3.4%
431298 1
3.4%
429032 1
3.4%
398631 1
3.4%
337632 1
3.4%

8월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271504.34
Minimum33711
Maximum719067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:51.151761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33711
5-th percentile61779
Q1124040
median224424
Q3388688
95-th percentile614636
Maximum719067
Range685356
Interquartile range (IQR)264648

Descriptive statistics

Standard deviation184760
Coefficient of variation (CV)0.68050478
Kurtosis0.016850224
Mean271504.34
Median Absolute Deviation (MAD)124953
Skewness0.8604099
Sum7873626
Variance3.4136259 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:51.306991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
156186 1
 
3.4%
46531 1
 
3.4%
33711 1
 
3.4%
95426 1
 
3.4%
84651 1
 
3.4%
124040 1
 
3.4%
110428 1
 
3.4%
162854 1
 
3.4%
224424 1
 
3.4%
397879 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
33711 1
3.4%
46531 1
3.4%
84651 1
3.4%
89209 1
3.4%
95426 1
3.4%
99471 1
3.4%
110428 1
3.4%
124040 1
3.4%
146560 1
3.4%
156186 1
3.4%
ValueCountFrequency (%)
719067 1
3.4%
671718 1
3.4%
529013 1
3.4%
491152 1
3.4%
483294 1
3.4%
474344 1
3.4%
397879 1
3.4%
388688 1
3.4%
373986 1
3.4%
322090 1
3.4%

9월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300356.66
Minimum27519
Maximum745986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:51.473288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27519
5-th percentile73597.2
Q1166155
median254166
Q3406364
95-th percentile686168.4
Maximum745986
Range718467
Interquartile range (IQR)240209

Descriptive statistics

Standard deviation189726.63
Coefficient of variation (CV)0.63167113
Kurtosis0.20806193
Mean300356.66
Median Absolute Deviation (MAD)108636
Skewness0.89424041
Sum8710343
Variance3.5996193 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:51.600183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
178965 1
 
3.4%
50776 1
 
3.4%
27519 1
 
3.4%
107829 1
 
3.4%
205892 1
 
3.4%
145530 1
 
3.4%
126266 1
 
3.4%
186375 1
 
3.4%
234254 1
 
3.4%
406364 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
27519 1
3.4%
50776 1
3.4%
107829 1
3.4%
122834 1
3.4%
126266 1
3.4%
145530 1
3.4%
153337 1
3.4%
166155 1
3.4%
178965 1
3.4%
181439 1
3.4%
ValueCountFrequency (%)
745986 1
3.4%
729466 1
3.4%
621222 1
3.4%
510756 1
3.4%
502170 1
3.4%
495154 1
3.4%
415073 1
3.4%
406364 1
3.4%
392213 1
3.4%
345818 1
3.4%

10월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314646.97
Minimum34017
Maximum772357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:51.769631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34017
5-th percentile77138.6
Q1165411
median273372
Q3426241
95-th percentile722763.2
Maximum772357
Range738340
Interquartile range (IQR)260830

Descriptive statistics

Standard deviation200923.54
Coefficient of variation (CV)0.63856819
Kurtosis0.011439188
Mean314646.97
Median Absolute Deviation (MAD)134898
Skewness0.85382189
Sum9124762
Variance4.037027 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:51.940673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
183765 1
 
3.4%
55025 1
 
3.4%
34017 1
 
3.4%
138474 1
 
3.4%
227715 1
 
3.4%
159860 1
 
3.4%
125551 1
 
3.4%
200001 1
 
3.4%
252926 1
 
3.4%
426241 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
34017 1
3.4%
55025 1
3.4%
110309 1
3.4%
125551 1
3.4%
134440 1
3.4%
138474 1
3.4%
159860 1
3.4%
165411 1
3.4%
173601 1
3.4%
183765 1
3.4%
ValueCountFrequency (%)
772357 1
3.4%
760564 1
3.4%
666062 1
3.4%
539977 1
3.4%
536933 1
3.4%
507523 1
3.4%
454568 1
3.4%
426241 1
3.4%
423679 1
3.4%
372048 1
3.4%

11월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292537.48
Minimum19719
Maximum733285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:52.417820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19719
5-th percentile50055.2
Q1153040
median254379
Q3403226
95-th percentile681467.4
Maximum733285
Range713566
Interquartile range (IQR)250186

Descriptive statistics

Standard deviation196202.32
Coefficient of variation (CV)0.67069122
Kurtosis-0.13274846
Mean292537.48
Median Absolute Deviation (MAD)138766
Skewness0.77122827
Sum8483587
Variance3.8495351 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:52.570804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
161751 1
 
3.4%
50843 1
 
3.4%
19719 1
 
3.4%
92954 1
 
3.4%
214608 1
 
3.4%
149944 1
 
3.4%
115613 1
 
3.4%
185818 1
 
3.4%
238218 1
 
3.4%
403226 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
19719 1
3.4%
49530 1
3.4%
50843 1
3.4%
92954 1
3.4%
101969 1
3.4%
115613 1
3.4%
149944 1
3.4%
153040 1
3.4%
161751 1
3.4%
166285 1
3.4%
ValueCountFrequency (%)
733285 1
3.4%
717451 1
3.4%
627492 1
3.4%
510158 1
3.4%
504405 1
3.4%
501173 1
3.4%
429145 1
3.4%
403226 1
3.4%
400381 1
3.4%
356090 1
3.4%

12월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302819.21
Minimum20615
Maximum764455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T16:44:52.717820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20615
5-th percentile49448.8
Q1152639
median259972
Q3435645
95-th percentile704039.2
Maximum764455
Range743840
Interquartile range (IQR)283006

Descriptive statistics

Standard deviation207222.65
Coefficient of variation (CV)0.68431145
Kurtosis-0.21095077
Mean302819.21
Median Absolute Deviation (MAD)138530
Skewness0.75533762
Sum8781757
Variance4.2941226 × 1010
MonotonicityNot monotonic
2023-12-12T16:44:52.858319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
157278 1
 
3.4%
51037 1
 
3.4%
20615 1
 
3.4%
88254 1
 
3.4%
171335 1
 
3.4%
150094 1
 
3.4%
121442 1
 
3.4%
181461 1
 
3.4%
249710 1
 
3.4%
435645 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
20615 1
3.4%
48390 1
3.4%
51037 1
3.4%
88254 1
3.4%
105300 1
3.4%
121442 1
3.4%
150094 1
3.4%
152639 1
3.4%
157278 1
3.4%
171335 1
3.4%
ValueCountFrequency (%)
764455 1
3.4%
759608 1
3.4%
620686 1
3.4%
538391 1
3.4%
536678 1
3.4%
533717 1
3.4%
452731 1
3.4%
435645 1
3.4%
417389 1
3.4%
359808 1
3.4%

Interactions

2023-12-12T16:44:46.846622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:32.591365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.970821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.251059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.345947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:37.789029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.103807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:40.651129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.791609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.084785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.098239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:45.118489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:46.979929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:32.714216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:34.084491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.324551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.465092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:37.912544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.201004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:40.735059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.896177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.166430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.182559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:45.199037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:47.107323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:32.801037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:34.176552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.397139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.596450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.024105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.295214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:40.830688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.004334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.245291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.276699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:45.284930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:47.233035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:32.900264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:34.269959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.479161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.726871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.128229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.409377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:40.934725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.109126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.334934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.372580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:45.409465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:47.359917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.017097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:34.353804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.569262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.873885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.228544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.531132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.030801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.203361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.417836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.452576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:45.527832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:47.490821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.131669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:34.443671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.667721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.995167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.338277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.648225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.135860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.327623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.518155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.536338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:45.973772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:47.599079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.247637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:34.525723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.809445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:37.109480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.445394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.752218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.245317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.449093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.603078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.611703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:46.124119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:47.713574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.359633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:34.606880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.888139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:37.222703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.562119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.861357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.336341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.561538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.683405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.689408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:46.233647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:47.832166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.463183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:34.688066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.973483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:37.340732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.668237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:39.961473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.418928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.674574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.759945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.772168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:46.370945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:47.933345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.578769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.025460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.060432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:37.442053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.768852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:40.057818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.528472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.788731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.841029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.868137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:46.488105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:48.049924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.716174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.103628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.163134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:37.569581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.883869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:40.154082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.611627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.884887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:43.934672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.953590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:46.627329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:48.148867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:33.818472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:35.176485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:36.249341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:37.677938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:38.993788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:40.261990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:41.698967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:42.988009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:44.013705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:45.033629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:44:46.736228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:44:52.980806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역사명1월2월3월4월5월6월7월8월9월10월11월12월
역사명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1월1.0001.0000.9980.9970.9970.9900.9950.9930.9820.9690.9870.9960.998
2월1.0000.9981.0000.9960.9960.9850.9920.9840.9810.9710.9810.9950.998
3월1.0000.9970.9961.0001.0000.9970.9990.9860.9750.9810.9940.9981.000
4월1.0000.9970.9961.0001.0000.9970.9990.9860.9750.9810.9940.9981.000
5월1.0000.9900.9850.9970.9971.0000.9990.9790.9730.9900.9990.9900.994
6월1.0000.9950.9920.9990.9990.9991.0000.9830.9710.9820.9960.9960.998
7월1.0000.9930.9840.9860.9860.9790.9831.0000.9800.9450.9750.9840.987
8월1.0000.9820.9810.9750.9750.9730.9710.9801.0000.9680.9690.9710.976
9월1.0000.9690.9710.9810.9810.9900.9820.9450.9681.0000.9960.9820.976
10월1.0000.9870.9810.9940.9940.9990.9960.9750.9690.9961.0000.9940.992
11월1.0000.9960.9950.9980.9980.9900.9960.9840.9710.9820.9941.0000.999
12월1.0000.9980.9981.0001.0000.9940.9980.9870.9760.9760.9920.9991.000
2023-12-12T16:44:53.157658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월
1월1.0001.0000.9750.9740.9720.9870.9970.9950.9470.9680.9760.990
2월1.0001.0000.9750.9750.9720.9880.9970.9960.9470.9680.9770.990
3월0.9750.9751.0000.9980.9980.9940.9770.9670.9760.9940.9970.992
4월0.9740.9750.9981.0000.9970.9950.9790.9680.9760.9940.9970.992
5월0.9720.9720.9980.9971.0000.9920.9740.9660.9850.9960.9930.989
6월0.9870.9880.9940.9950.9921.0000.9920.9860.9710.9900.9920.995
7월0.9970.9970.9770.9790.9740.9921.0000.9970.9510.9720.9800.990
8월0.9950.9960.9670.9680.9660.9860.9971.0000.9470.9650.9690.984
9월0.9470.9470.9760.9760.9850.9710.9510.9471.0000.9780.9660.961
10월0.9680.9680.9940.9940.9960.9900.9720.9650.9781.0000.9930.987
11월0.9760.9770.9970.9970.9930.9920.9800.9690.9660.9931.0000.995
12월0.9900.9900.9920.9920.9890.9950.9900.9840.9610.9870.9951.000

Missing values

2023-12-12T16:44:48.307723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:44:48.516862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

역사명1월2월3월4월5월6월7월8월9월10월11월12월
0계양138696132686176741181216186124168722163476156186178965183765161751157278
1귤현403633816346962474305168148930487694653150776550255084351037
2박촌171196162175195458197832198069184572190481174460181439192599179701190447
3임학383321362556429806426682429184393408398631373986392213423679400381417389
4계산531172507479682702676932679333602522569785529013621222666062627492620686
5경인교대입구323959311379384463378283377457347287337632322090345818372048356090359808
6작전679677646139779471777147781526716998722620671718729466772357733285759608
7갈산409221391590467888461019450016417704431298388688415073454568429145452731
8부평구청298806285201335445339003336702313516321617296522314099340445322192338487
9부평시장473803450647538491538236536551497496522611483294510756536933510158536678
역사명1월2월3월4월5월6월7월8월9월10월11월12월
19신연수229123219508278324276047269232249461246414223754254166273372254379259972
20원인재142918139255168386170265160812153907162154146560153337165411153040152639
21동춘405872389378437974424758435813396247429032397879406364426241403226435645
22동막231616224472258112256806252274235149250234224424234254252926238218249710
23캠퍼스타운150210146542200653189416198423171681172408162854186375200001185818181461
24테크노파크9034190238105657114022112699110784124280110428126266125551115613121442
25지식정보단지118371115936156022154763152917137762136447124040145530159860149944150094
26인천대입구753997750621663822136921440416235410468684651205892227715214608171335
27센트럴파크59987571446865773929814348198993991954261078291384749295488254
28국제업무지구131741312019851207361912221744386573371127519340171971920615