Overview

Dataset statistics

Number of variables16
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.3 KiB
Average record size in memory140.7 B

Variable types

Categorical2
Text2
Numeric12

Alerts

11680720 is highly overall correlated with 15395 and 4 other fieldsHigh correlation
15395 is highly overall correlated with 11680720 and 4 other fieldsHigh correlation
519799 is highly overall correlated with 267620.1High correlation
267620.1 is highly overall correlated with 519799High correlation
319849 is highly overall correlated with 11680720 and 4 other fieldsHigh correlation
542536 is highly overall correlated with 11680720 and 4 other fieldsHigh correlation
127.0915339 is highly overall correlated with 11680720 and 4 other fieldsHigh correlation
37.4814905 is highly overall correlated with 11680720 and 4 other fieldsHigh correlation
서울 is highly imbalanced (61.4%)Imbalance
1 has 5 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-10 06:42:56.629428
Analysis finished2023-12-10 06:43:23.435189
Duration26.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

서울
Categorical

IMBALANCE 

Distinct14
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울
156 
광주
 
9
김포
 
9
부천
 
4
성남
 
4
Other values (9)
17 

Length

Max length3
Median length2
Mean length2.0100503
Min length2

Unique

Unique3 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
서울 156
78.4%
광주 9
 
4.5%
김포 9
 
4.5%
부천 4
 
2.0%
성남 4
 
2.0%
용인 3
 
1.5%
안양 3
 
1.5%
남양주 2
 
1.0%
파주 2
 
1.0%
양주 2
 
1.0%
Other values (4) 5
 
2.5%

Length

2023-12-10T15:43:23.540209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 156
78.4%
광주 9
 
4.5%
김포 9
 
4.5%
부천 4
 
2.0%
성남 4
 
2.0%
용인 3
 
1.5%
안양 3
 
1.5%
남양주 2
 
1.0%
파주 2
 
1.0%
양주 2
 
1.0%
Other values (4) 5
 
2.5%

강남03
Text

Distinct142
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:43:24.044340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length3.7286432
Min length1

Characters and Unicode

Total characters742
Distinct characters34
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)51.8%

Sample

1st row강북02
2nd row605
3rd row4412
4th row652
5th row6712
ValueCountFrequency (%)
109 5
 
2.5%
6647 4
 
2.0%
652 4
 
2.0%
6712 4
 
2.0%
140 4
 
2.0%
461 3
 
1.5%
145 3
 
1.5%
500-2 3
 
1.5%
472 3
 
1.5%
강서03 3
 
1.5%
Other values (132) 163
81.9%
2023-12-10T15:43:24.751069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 124
16.7%
0 114
15.4%
4 83
11.2%
6 82
11.1%
2 71
9.6%
3 54
7.3%
5 43
 
5.8%
7 36
 
4.9%
강 29
 
3.9%
9 18
 
2.4%
Other values (24) 88
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 639
86.1%
Other Letter 68
 
9.2%
Uppercase Letter 24
 
3.2%
Dash Punctuation 9
 
1.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 29
42.6%
남 12
17.6%
북 9
 
13.2%
서 8
 
11.8%
노 2
 
2.9%
원 2
 
2.9%
스 1
 
1.5%
버 1
 
1.5%
춤 1
 
1.5%
도 1
 
1.5%
Other values (2) 2
 
2.9%
Decimal Number
ValueCountFrequency (%)
1 124
19.4%
0 114
17.8%
4 83
13.0%
6 82
12.8%
2 71
11.1%
3 54
8.5%
5 43
 
6.7%
7 36
 
5.6%
9 18
 
2.8%
8 14
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
N 7
29.2%
G 4
16.7%
M 2
 
8.3%
U 2
 
8.3%
O 2
 
8.3%
T 2
 
8.3%
A 2
 
8.3%
R 2
 
8.3%
B 1
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 650
87.6%
Hangul 68
 
9.2%
Latin 24
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 124
19.1%
0 114
17.5%
4 83
12.8%
6 82
12.6%
2 71
10.9%
3 54
8.3%
5 43
 
6.6%
7 36
 
5.5%
9 18
 
2.8%
8 14
 
2.2%
Other values (3) 11
 
1.7%
Hangul
ValueCountFrequency (%)
강 29
42.6%
남 12
17.6%
북 9
 
13.2%
서 8
 
11.8%
노 2
 
2.9%
원 2
 
2.9%
스 1
 
1.5%
버 1
 
1.5%
춤 1
 
1.5%
도 1
 
1.5%
Other values (2) 2
 
2.9%
Latin
ValueCountFrequency (%)
N 7
29.2%
G 4
16.7%
M 2
 
8.3%
U 2
 
8.3%
O 2
 
8.3%
T 2
 
8.3%
A 2
 
8.3%
R 2
 
8.3%
B 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 674
90.8%
Hangul 68
 
9.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 124
18.4%
0 114
16.9%
4 83
12.3%
6 82
12.2%
2 71
10.5%
3 54
8.0%
5 43
 
6.4%
7 36
 
5.3%
9 18
 
2.7%
8 14
 
2.1%
Other values (12) 35
 
5.2%
Hangul
ValueCountFrequency (%)
강 29
42.6%
남 12
17.6%
북 9
 
13.2%
서 8
 
11.8%
노 2
 
2.9%
원 2
 
2.9%
스 1
 
1.5%
버 1
 
1.5%
춤 1
 
1.5%
도 1
 
1.5%
Other values (2) 2
 
2.9%

11680720
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11541621
Minimum11305534
Maximum11680750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:24.959010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11305534
5-th percentile11305554
Q111500510
median11500640
Q311680640
95-th percentile11680722
Maximum11680750
Range375216
Interquartile range (IQR)180130

Descriptive statistics

Standard deviation148270.79
Coefficient of variation (CV)0.012846617
Kurtosis-1.1991225
Mean11541621
Median Absolute Deviation (MAD)180000
Skewness-0.5211046
Sum2.2967827 × 109
Variance2.1984227 × 1010
MonotonicityNot monotonic
2023-12-10T15:43:25.165426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11680700 18
 
9.0%
11680640 12
 
6.0%
11500603 9
 
4.5%
11305595 8
 
4.0%
11500620 8
 
4.0%
11500510 7
 
3.5%
11680565 7
 
3.5%
11680521 7
 
3.5%
11680750 7
 
3.5%
11500640 7
 
3.5%
Other values (42) 109
54.8%
ValueCountFrequency (%)
11305534 5
2.5%
11305535 2
 
1.0%
11305545 3
 
1.5%
11305555 3
 
1.5%
11305575 2
 
1.0%
11305595 8
4.0%
11305603 2
 
1.0%
11305608 1
 
0.5%
11305615 3
 
1.5%
11305625 3
 
1.5%
ValueCountFrequency (%)
11680750 7
 
3.5%
11680740 3
 
1.5%
11680720 6
 
3.0%
11680700 18
9.0%
11680690 1
 
0.5%
11680670 4
 
2.0%
11680660 3
 
1.5%
11680656 1
 
0.5%
11680655 2
 
1.0%
11680650 1
 
0.5%

267620
Real number (ℝ)

Distinct164
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152836.92
Minimum11245
Maximum365536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:25.391267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11245
5-th percentile14640.1
Q117016.5
median151845
Q3221582
95-th percentile355452.5
Maximum365536
Range354291
Interquartile range (IQR)204565.5

Descriptive statistics

Standard deviation119691.03
Coefficient of variation (CV)0.78312905
Kurtosis-1.4068274
Mean152836.92
Median Absolute Deviation (MAD)123660
Skewness0.10806931
Sum30414547
Variance1.4325943 × 1010
MonotonicityNot monotonic
2023-12-10T15:43:25.605960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16560 5
 
2.5%
283012 3
 
1.5%
218961 3
 
1.5%
358243 3
 
1.5%
23055 3
 
1.5%
28185 3
 
1.5%
221582 2
 
1.0%
277651 2
 
1.0%
13787 2
 
1.0%
151757 2
 
1.0%
Other values (154) 171
85.9%
ValueCountFrequency (%)
11245 2
1.0%
13778 1
0.5%
13781 1
0.5%
13787 2
1.0%
14432 2
1.0%
14512 1
0.5%
14569 1
0.5%
14648 1
0.5%
14713 1
0.5%
14720 1
0.5%
ValueCountFrequency (%)
365536 1
 
0.5%
365443 1
 
0.5%
364689 2
1.0%
358243 3
1.5%
355918 2
1.0%
355628 1
 
0.5%
355433 1
 
0.5%
354477 1
 
0.5%
350525 1
 
0.5%
349823 2
1.0%

1506
Real number (ℝ)

Distinct142
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45375.724
Minimum500
Maximum2805932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:25.816171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile557.9
Q1996.5
median1403
Q31596
95-th percentile13065.1
Maximum2805932
Range2805432
Interquartile range (IQR)599.5

Descriptive statistics

Standard deviation342296.6
Coefficient of variation (CV)7.5436063
Kurtosis62.928952
Mean45375.724
Median Absolute Deviation (MAD)375
Skewness8.017861
Sum9029769
Variance1.1716696 × 1011
MonotonicityNot monotonic
2023-12-10T15:43:26.079154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1403 5
 
2.5%
1481 4
 
2.0%
1219 4
 
2.0%
1228 4
 
2.0%
868 4
 
2.0%
1211 3
 
1.5%
507 3
 
1.5%
1441 3
 
1.5%
874 3
 
1.5%
10017 3
 
1.5%
Other values (132) 163
81.9%
ValueCountFrequency (%)
500 1
 
0.5%
507 3
1.5%
510 1
 
0.5%
517 1
 
0.5%
523 2
1.0%
524 1
 
0.5%
557 1
 
0.5%
558 1
 
0.5%
567 2
1.0%
571 2
1.0%
ValueCountFrequency (%)
2805932 1
0.5%
2805931 1
0.5%
2802996 1
0.5%
18591 1
0.5%
18522 1
0.5%
17573 1
0.5%
17503 1
0.5%
17047 2
1.0%
13120 1
0.5%
13059 1
0.5%

15395
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11179.95
Minimum4287
Maximum15528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:26.315921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4287
5-th percentile4416.4
Q19452
median10276
Q315079
95-th percentile15521
Maximum15528
Range11241
Interquartile range (IQR)5627

Descriptive statistics

Standard deviation4190.935
Coefficient of variation (CV)0.37486171
Kurtosis-1.2244489
Mean11179.95
Median Absolute Deviation (MAD)4789
Skewness-0.48811031
Sum2224810
Variance17563936
MonotonicityNot monotonic
2023-12-10T15:43:26.554240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10145 6
 
3.0%
15524 5
 
2.5%
15065 4
 
2.0%
15079 4
 
2.0%
9986 4
 
2.0%
15521 4
 
2.0%
4746 3
 
1.5%
14590 3
 
1.5%
15519 3
 
1.5%
9990 3
 
1.5%
Other values (131) 160
80.4%
ValueCountFrequency (%)
4287 2
1.0%
4306 1
0.5%
4319 1
0.5%
4320 1
0.5%
4351 1
0.5%
4353 1
0.5%
4369 2
1.0%
4411 1
0.5%
4417 1
0.5%
4421 1
0.5%
ValueCountFrequency (%)
15528 2
 
1.0%
15524 5
2.5%
15522 1
 
0.5%
15521 4
2.0%
15520 1
 
0.5%
15519 3
1.5%
15516 2
 
1.0%
15515 1
 
0.5%
15512 1
 
0.5%
15510 1
 
0.5%

519799
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228131.95
Minimum11245
Maximum518516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:26.783660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11245
5-th percentile15027.1
Q131734
median220441
Q3358243
95-th percentile500514.7
Maximum518516
Range507271
Interquartile range (IQR)326509

Descriptive statistics

Standard deviation156587.5
Coefficient of variation (CV)0.68639004
Kurtosis-1.1428902
Mean228131.95
Median Absolute Deviation (MAD)184385
Skewness0.016611523
Sum45398259
Variance2.4519646 × 1010
MonotonicityNot monotonic
2023-12-10T15:43:26.995385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
508825 6
 
3.0%
358243 3
 
1.5%
218961 3
 
1.5%
23055 3
 
1.5%
28185 3
 
1.5%
412914 3
 
1.5%
221582 2
 
1.0%
151757 2
 
1.0%
277651 2
 
1.0%
413210 2
 
1.0%
Other values (155) 170
85.4%
ValueCountFrequency (%)
11245 2
1.0%
13778 1
0.5%
13781 1
0.5%
14432 2
1.0%
14569 1
0.5%
14720 1
0.5%
14826 1
0.5%
14839 1
0.5%
15048 1
0.5%
15145 1
0.5%
ValueCountFrequency (%)
518516 2
 
1.0%
509249 1
 
0.5%
508825 6
3.0%
502249 1
 
0.5%
500322 1
 
0.5%
422551 2
 
1.0%
422428 1
 
0.5%
422303 1
 
0.5%
422262 1
 
0.5%
422213 1
 
0.5%

267620.1
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221816.15
Minimum11245
Maximum502249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:27.232491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11245
5-th percentile15027.1
Q131209.5
median220331
Q3357080.5
95-th percentile421817
Maximum502249
Range491004
Interquartile range (IQR)325871

Descriptive statistics

Standard deviation149954.32
Coefficient of variation (CV)0.67602975
Kurtosis-1.282527
Mean221816.15
Median Absolute Deviation (MAD)184275
Skewness-0.096144368
Sum44141414
Variance2.2486297 × 1010
MonotonicityNot monotonic
2023-12-10T15:43:27.566009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
413592 6
 
3.0%
358243 3
 
1.5%
218961 3
 
1.5%
23055 3
 
1.5%
28185 3
 
1.5%
412914 3
 
1.5%
221582 2
 
1.0%
151757 2
 
1.0%
277651 2
 
1.0%
413210 2
 
1.0%
Other values (155) 170
85.4%
ValueCountFrequency (%)
11245 2
1.0%
13778 1
0.5%
13781 1
0.5%
14432 2
1.0%
14569 1
0.5%
14720 1
0.5%
14826 1
0.5%
14839 1
0.5%
15048 1
0.5%
15145 1
0.5%
ValueCountFrequency (%)
502249 1
0.5%
500322 1
0.5%
422551 2
1.0%
422428 1
0.5%
422303 1
0.5%
422262 1
0.5%
422213 1
0.5%
421898 2
1.0%
421808 1
0.5%
421722 2
1.0%

1
Real number (ℝ)

ZEROS 

Distinct84
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.582915
Minimum0
Maximum137
Zeros5
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:27.841707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q115
median36
Q359
95-th percentile93
Maximum137
Range137
Interquartile range (IQR)44

Descriptive statistics

Standard deviation29.692905
Coefficient of variation (CV)0.73166023
Kurtosis-0.10255948
Mean40.582915
Median Absolute Deviation (MAD)22
Skewness0.71946939
Sum8076
Variance881.6686
MonotonicityNot monotonic
2023-12-10T15:43:28.098304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 7
 
3.5%
10 6
 
3.0%
15 6
 
3.0%
13 6
 
3.0%
72 5
 
2.5%
0 5
 
2.5%
29 4
 
2.0%
59 4
 
2.0%
37 4
 
2.0%
12 4
 
2.0%
Other values (74) 148
74.4%
ValueCountFrequency (%)
0 5
2.5%
2 2
 
1.0%
3 2
 
1.0%
4 4
2.0%
6 2
 
1.0%
7 4
2.0%
8 7
3.5%
9 3
1.5%
10 6
3.0%
11 1
 
0.5%
ValueCountFrequency (%)
137 1
 
0.5%
126 1
 
0.5%
119 1
 
0.5%
111 1
 
0.5%
110 1
 
0.5%
106 1
 
0.5%
98 1
 
0.5%
97 1
 
0.5%
96 1
 
0.5%
93 3
1.5%

강남데시앙포레상가앞
Text

Distinct165
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:43:28.542911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length7.5829146
Min length2

Characters and Unicode

Total characters1509
Distinct characters249
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)70.4%

Sample

1st row송암교회.화계사거리
2nd row강서구청사거리.서울디지털대학교
3rd row강남구청역
4th row발산역3번출구
5th row서울식물원.식물문화센터
ValueCountFrequency (%)
수유3동우체국 4
 
2.0%
세곡푸르지오 3
 
1.5%
신사역 3
 
1.5%
논현역 3
 
1.5%
88jc 3
 
1.5%
래미안아파트.파이낸셜뉴스 3
 
1.5%
쟁골마을 3
 
1.5%
수서역 3
 
1.5%
고속철도수서역 2
 
1.0%
개화역광역환승센터 2
 
1.0%
Other values (155) 170
85.4%
2023-12-10T15:43:29.187902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 53
 
3.5%
. 43
 
2.8%
교 42
 
2.8%
파 40
 
2.7%
아 35
 
2.3%
동 31
 
2.1%
구 30
 
2.0%
트 30
 
2.0%
학 28
 
1.9%
사 27
 
1.8%
Other values (239) 1150
76.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1399
92.7%
Decimal Number 44
 
2.9%
Other Punctuation 43
 
2.8%
Uppercase Letter 16
 
1.1%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 53
 
3.8%
교 42
 
3.0%
파 40
 
2.9%
아 35
 
2.5%
동 31
 
2.2%
구 30
 
2.1%
트 30
 
2.1%
학 28
 
2.0%
사 27
 
1.9%
수 25
 
1.8%
Other values (220) 1058
75.6%
Decimal Number
ValueCountFrequency (%)
1 11
25.0%
2 9
20.5%
3 8
18.2%
8 6
13.6%
4 5
11.4%
7 3
 
6.8%
9 1
 
2.3%
6 1
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
C 5
31.2%
J 4
25.0%
K 3
18.8%
T 1
 
6.2%
G 1
 
6.2%
L 1
 
6.2%
S 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1399
92.7%
Common 94
 
6.2%
Latin 16
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 53
 
3.8%
교 42
 
3.0%
파 40
 
2.9%
아 35
 
2.5%
동 31
 
2.2%
구 30
 
2.1%
트 30
 
2.1%
학 28
 
2.0%
사 27
 
1.9%
수 25
 
1.8%
Other values (220) 1058
75.6%
Common
ValueCountFrequency (%)
. 43
45.7%
1 11
 
11.7%
2 9
 
9.6%
3 8
 
8.5%
8 6
 
6.4%
4 5
 
5.3%
( 3
 
3.2%
) 3
 
3.2%
7 3
 
3.2%
9 1
 
1.1%
Other values (2) 2
 
2.1%
Latin
ValueCountFrequency (%)
C 5
31.2%
J 4
25.0%
K 3
18.8%
T 1
 
6.2%
G 1
 
6.2%
L 1
 
6.2%
S 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1399
92.7%
ASCII 110
 
7.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 53
 
3.8%
교 42
 
3.0%
파 40
 
2.9%
아 35
 
2.5%
동 31
 
2.2%
구 30
 
2.1%
트 30
 
2.1%
학 28
 
2.0%
사 27
 
1.9%
수 25
 
1.8%
Other values (220) 1058
75.6%
ASCII
ValueCountFrequency (%)
. 43
39.1%
1 11
 
10.0%
2 9
 
8.2%
3 8
 
7.3%
8 6
 
5.5%
4 5
 
4.5%
C 5
 
4.5%
J 4
 
3.6%
( 3
 
2.7%
) 3
 
2.7%
Other values (9) 13
 
11.8%

319849
Real number (ℝ)

HIGH CORRELATION 

Distinct172
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310345.93
Minimum294039
Maximum322243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:29.444014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294039
5-th percentile295120.5
Q1299302.5
median314188
Q3316724.5
95-th percentile320990
Maximum322243
Range28204
Interquartile range (IQR)17422

Descriptive statistics

Standard deviation9165.1789
Coefficient of variation (CV)0.029532138
Kurtosis-1.1962978
Mean310345.93
Median Absolute Deviation (MAD)3304
Skewness-0.66618051
Sum61758840
Variance84000504
MonotonicityNot monotonic
2023-12-10T15:43:29.679339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320990 3
 
1.5%
313880 3
 
1.5%
313617 3
 
1.5%
314469 3
 
1.5%
314468 3
 
1.5%
317045 2
 
1.0%
314367 2
 
1.0%
321166 2
 
1.0%
314345 2
 
1.0%
319246 2
 
1.0%
Other values (162) 174
87.4%
ValueCountFrequency (%)
294039 2
1.0%
294072 1
0.5%
294386 1
0.5%
294411 1
0.5%
294454 1
0.5%
294536 1
0.5%
294569 1
0.5%
294763 1
0.5%
294954 1
0.5%
295139 1
0.5%
ValueCountFrequency (%)
322243 1
 
0.5%
321456 1
 
0.5%
321343 1
 
0.5%
321204 1
 
0.5%
321187 2
1.0%
321166 2
1.0%
321034 1
 
0.5%
320990 3
1.5%
320930 1
 
0.5%
320849 1
 
0.5%

542536
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean549736.34
Minimum540695
Maximum562626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:29.961323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum540695
5-th percentile541447.7
Q1544071.5
median548997
Q3553473.5
95-th percentile560267
Maximum562626
Range21931
Interquartile range (IQR)9402

Descriptive statistics

Standard deviation6267.8405
Coefficient of variation (CV)0.011401539
Kurtosis-1.0689189
Mean549736.34
Median Absolute Deviation (MAD)4842
Skewness0.40553176
Sum1.0939753 × 108
Variance39285825
MonotonicityNot monotonic
2023-12-10T15:43:30.257764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
543736 3
 
1.5%
545351 3
 
1.5%
560267 3
 
1.5%
546077 3
 
1.5%
542877 2
 
1.0%
541004 2
 
1.0%
543772 2
 
1.0%
544639 2
 
1.0%
554209 2
 
1.0%
541735 2
 
1.0%
Other values (160) 175
87.9%
ValueCountFrequency (%)
540695 1
0.5%
540703 2
1.0%
540717 1
0.5%
540723 1
0.5%
540778 1
0.5%
540915 1
0.5%
540917 1
0.5%
541004 2
1.0%
541497 1
0.5%
541735 2
1.0%
ValueCountFrequency (%)
562626 1
 
0.5%
561910 1
 
0.5%
561468 1
 
0.5%
560761 1
 
0.5%
560728 1
 
0.5%
560587 1
 
0.5%
560356 1
 
0.5%
560323 1
 
0.5%
560267 3
1.5%
560217 1
 
0.5%

86024
Real number (ℝ)

Distinct173
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161309.82
Minimum80200
Maximum5003593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:30.547166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80200
5-th percentile82512.6
Q1101352
median106420
Q3109083
95-th percentile193529.4
Maximum5003593
Range4923393
Interquartile range (IQR)7731

Descriptive statistics

Standard deviation489754.96
Coefficient of variation (CV)3.0361138
Kurtosis96.451298
Mean161309.82
Median Absolute Deviation (MAD)4971
Skewness9.8604994
Sum32100654
Variance2.3985992 × 1011
MonotonicityNot monotonic
2023-12-10T15:43:30.863115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106332 3
 
1.5%
105923 3
 
1.5%
105884 3
 
1.5%
116252 3
 
1.5%
109083 2
 
1.0%
206285 2
 
1.0%
160678 2
 
1.0%
108577 2
 
1.0%
82166 2
 
1.0%
105833 2
 
1.0%
Other values (163) 175
87.9%
ValueCountFrequency (%)
80200 1
0.5%
80326 1
0.5%
80477 1
0.5%
80821 1
0.5%
81286 1
0.5%
82108 1
0.5%
82110 1
0.5%
82166 2
1.0%
82221 1
0.5%
82545 2
1.0%
ValueCountFrequency (%)
5003593 1
0.5%
5003590 1
0.5%
206285 2
1.0%
206274 1
0.5%
197105 1
0.5%
195628 1
0.5%
195625 1
0.5%
194851 1
0.5%
193974 1
0.5%
193480 1
0.5%

127.0915339
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98319
Minimum126.79816
Maximum127.11877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:31.130969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.79816
5-th percentile126.81067
Q1126.8582
median127.02615
Q3127.05589
95-th percentile127.10442
Maximum127.11877
Range0.3206133
Interquartile range (IQR)0.1976958

Descriptive statistics

Standard deviation0.10395961
Coefficient of variation (CV)0.00081868793
Kurtosis-1.1961624
Mean126.98319
Median Absolute Deviation (MAD)0.0387539
Skewness-0.65992833
Sum25269.655
Variance0.0108076
MonotonicityNot monotonic
2023-12-10T15:43:31.739983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.03057 3
 
1.5%
127.0237209 3
 
1.5%
127.0206612 3
 
1.5%
127.0286293 3
 
1.5%
127.1067469 2
 
1.0%
126.7981581 2
 
1.0%
127.0293335 2
 
1.0%
127.084691 2
 
1.0%
127.0940519 2
 
1.0%
127.0189555 2
 
1.0%
Other values (163) 175
87.9%
ValueCountFrequency (%)
126.7981581 2
1.0%
126.7987841 1
0.5%
126.8021366 1
0.5%
126.8026602 1
0.5%
126.8031104 1
0.5%
126.8040929 1
0.5%
126.8044714 1
0.5%
126.8065763 1
0.5%
126.808768 1
0.5%
126.8108788 1
0.5%
ValueCountFrequency (%)
127.1187714 1
0.5%
127.1097591 1
0.5%
127.1086127 1
0.5%
127.1069646 1
0.5%
127.1067469 2
1.0%
127.10662 2
1.0%
127.1049252 1
0.5%
127.1044212 2
1.0%
127.1043955 1
0.5%
127.1037179 1
0.5%

37.4814905
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.545442
Minimum37.464983
Maximum37.661884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:43:32.002070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.464983
5-th percentile37.471792
Q137.494853
median37.537477
Q337.577497
95-th percentile37.640755
Maximum37.661884
Range0.1969014
Interquartile range (IQR)0.08264435

Descriptive statistics

Standard deviation0.056176711
Coefficient of variation (CV)0.0014962325
Kurtosis-1.0334356
Mean37.545442
Median Absolute Deviation (MAD)0.0417898
Skewness0.43871126
Sum7471.5429
Variance0.0031558229
MonotonicityNot monotonic
2023-12-10T15:43:32.309455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4918194 3
 
1.5%
37.5063151 3
 
1.5%
37.5128315 3
 
1.5%
37.6407553 3
 
1.5%
37.4743888 2
 
1.0%
37.5782397 2
 
1.0%
37.4982428 2
 
1.0%
37.4835372 2
 
1.0%
37.4677055 2
 
1.0%
37.6232854 2
 
1.0%
Other values (163) 175
87.9%
ValueCountFrequency (%)
37.4649831 1
0.5%
37.4650888 2
1.0%
37.4651262 1
0.5%
37.4652188 1
0.5%
37.4657797 1
0.5%
37.4669471 1
0.5%
37.4670902 1
0.5%
37.4677055 2
1.0%
37.4722459 1
0.5%
37.4743888 2
1.0%
ValueCountFrequency (%)
37.6618845 1
 
0.5%
37.6554296 1
 
0.5%
37.6514442 1
 
0.5%
37.6450981 1
 
0.5%
37.6448396 1
 
0.5%
37.6435908 1
 
0.5%
37.6414603 1
 
0.5%
37.6412639 1
 
0.5%
37.6407553 3
1.5%
37.640285 1
 
0.5%

마을
Categorical

Distinct11
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
간선
65 
지선
55 
마을
32 
직행좌석
23 
일반
13 
Other values (6)
11 

Length

Max length4
Median length2
Mean length2.241206
Min length2

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row마을
2nd row간선
3rd row지선
4th row간선
5th row지선

Common Values

ValueCountFrequency (%)
간선 65
32.7%
지선 55
27.6%
마을 32
16.1%
직행좌석 23
 
11.6%
일반 13
 
6.5%
광역 3
 
1.5%
공항 2
 
1.0%
시외 2
 
1.0%
관광 2
 
1.0%
간선급행 1
 
0.5%

Length

2023-12-10T15:43:32.561468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
간선 65
32.7%
지선 55
27.6%
마을 32
16.1%
직행좌석 23
 
11.6%
일반 13
 
6.5%
광역 3
 
1.5%
공항 2
 
1.0%
시외 2
 
1.0%
관광 2
 
1.0%
간선급행 1
 
0.5%

Interactions

2023-12-10T15:43:20.781048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:57.701900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:59.576943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:01.384719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:03.308301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:06.028590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:07.891764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:09.751919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:12.021625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:14.150709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:15.856373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:17.750856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:20.942732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:57.862238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:59.747519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:01.525613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:03.447781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:06.286849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:08.039145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:09.934175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:12.172515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:14.293569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:16.047130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:17.922882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:21.088591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:57.991291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:59.906365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:02.006091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:03.631274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:06.421087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:08.207632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:10.138490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:12.709024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:14.438013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:16.274904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:18.087349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:21.235033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:58.130294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:00.063332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:02.138114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:03.824694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:06.555530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:08.358250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:10.425585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:12.874094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:14.575556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:16.441108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:18.432619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:21.391427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:58.275987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:00.224689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:02.280768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:04.086447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:06.710896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:08.479341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:10.677336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:13.044244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:14.687243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:16.619381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:18.768452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:21.544843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:58.431956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:00.370693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:02.416909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:04.276698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:06.862928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:08.626511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:10.846170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:13.179464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:14.835992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:16.769760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:18.985121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:21.699216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:58.582398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:00.497507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:02.555419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:04.494857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:06.996546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:08.790209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:11.000868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:13.312028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:14.984131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:16.904067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:19.242583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:21.855349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:58.771477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:00.679396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:02.700436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:04.798250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:07.179165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:08.962285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:11.165798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:13.469198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:15.120078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:17.068380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:19.467040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:21.996575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:58.927341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:00.831802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:02.835334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:05.003272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:07.327998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:09.127995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:11.341339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:13.614675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:15.255749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:17.216861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:19.725231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:22.144482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:59.073776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:00.961524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:02.966983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:05.213154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:07.449176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:09.298143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:11.526804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:13.737678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:15.382351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:17.340722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:19.985134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:22.290078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:59.248024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:01.085300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:03.069013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:05.537198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:07.586349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:09.447933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:11.670233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:13.860914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:15.532671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:17.443627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:20.256689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:22.439768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:59.415062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:01.233515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:03.172506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:05.810633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:07.747386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:09.597259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:11.851043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:14.006336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:15.691077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:17.601874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:20.556033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:43:32.745996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울11680720267620150615395519799267620.1131984954253686024127.091533937.4814905마을
서울1.0000.4820.6230.5610.4830.5290.4920.0000.4270.4440.0000.5050.4580.774
116807200.4821.0000.8220.0001.0000.9720.9560.0000.8420.9730.0660.8460.9720.434
2676200.6230.8221.0000.0000.7580.9280.9180.0000.8730.7380.0000.8730.7360.274
15060.5610.0000.0001.0000.1240.0000.0000.0000.0000.3670.0000.0000.3750.276
153950.4831.0000.7580.1241.0000.8020.7760.0000.8580.9610.2390.8620.9520.428
5197990.5290.9720.9280.0000.8021.0000.9930.1640.7270.7380.2280.7320.7440.375
267620.10.4920.9560.9180.0000.7760.9931.0000.1020.6740.6980.0000.6810.7000.166
10.0000.0000.0000.0000.0000.1640.1021.0000.0510.2390.0000.1040.1910.293
3198490.4270.8420.8730.0000.8580.7270.6740.0511.0000.7980.1751.0000.7940.290
5425360.4440.9730.7380.3670.9610.7380.6980.2390.7981.0000.1840.7981.0000.371
860240.0000.0660.0000.0000.2390.2280.0000.0000.1750.1841.0000.1760.0000.000
127.09153390.5050.8460.8730.0000.8620.7320.6810.1041.0000.7980.1761.0000.7930.295
37.48149050.4580.9720.7360.3750.9520.7440.7000.1910.7941.0000.0000.7931.0000.354
마을0.7740.4340.2740.2760.4280.3750.1660.2930.2900.3710.0000.2950.3541.000
2023-12-10T15:43:32.981701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
마을서울
마을1.0000.438
서울0.4381.000
2023-12-10T15:43:33.136360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
11680720267620150615395519799267620.1131984954253686024127.091533937.4814905서울마을
116807201.0000.1570.0390.9910.1700.1830.0200.620-0.9120.0550.631-0.9120.3000.295
2676200.1571.000-0.1470.1550.4260.452-0.0340.457-0.1360.1760.458-0.1360.2300.132
15060.039-0.1471.0000.039-0.026-0.0460.012-0.1840.006-0.007-0.1800.0060.4280.259
153950.9910.1550.0391.0000.1980.2180.0240.625-0.9230.0360.637-0.9230.2860.271
5197990.1700.426-0.0260.1981.0000.9740.0200.124-0.1620.0370.125-0.1620.2290.177
267620.10.1830.452-0.0460.2180.9741.000-0.0010.168-0.1750.0390.170-0.1750.2030.071
10.020-0.0340.0120.0240.020-0.0011.0000.044-0.020-0.0170.043-0.0190.0000.127
3198490.6200.457-0.1840.6250.1240.1680.0441.000-0.6570.1591.000-0.6560.2050.145
542536-0.912-0.1360.006-0.923-0.162-0.175-0.020-0.6571.000-0.078-0.6691.0000.1930.168
860240.0550.176-0.0070.0360.0370.039-0.0170.159-0.0781.0000.158-0.0780.0000.000
127.09153390.6310.458-0.1800.6370.1250.1700.0431.000-0.6690.1581.000-0.6680.2060.148
37.4814905-0.912-0.1360.006-0.923-0.162-0.175-0.019-0.6561.000-0.078-0.6681.0000.2010.158
서울0.3000.2300.4280.2860.2290.2030.0000.2050.1930.0000.2060.2011.0000.438
마을0.2950.1320.2590.2710.1770.0710.1270.1450.1680.0000.1480.1580.4381.000

Missing values

2023-12-10T15:43:23.005523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:43:23.307338image/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

서울강남0311680720267620150615395519799267620.11강남데시앙포레상가앞31984954253686024127.091533937.4814905마을
0서울강북02113056602215821538482222158222158219송암교회.화계사거리313432559555195625127.01697637.634243마을
1서울605115005913480001171987234800034800020강서구청사거리.서울디지털대학교298954550954194851126.85415337.555279간선
2서울44121168056524450105714787244502445058강남구청역315416546694106821127.04093737.518557지선
3서울652115005353544771219959735447735447715발산역3번출구297619551514100988126.83896837.560177간선
4서울67121150060316337122899864133934133938서울식물원.식물문화센터2972835524205003590126.83503937.568301지선
5서울강서03115005703556281577981135562835562813곰달래약국29926654831881286126.85804637.531565마을
6서울강북04113055352204411545435122044122044124율곡어린이놀이터314717558853195628127.03161637.628039마을
7서울6721150060521558413631007721558421558456가양역.현대아파트299197551515101366126.85682637.56036간선
8서울6009116806552627951785151652627952627958매봉삼성아파트SK리더스뷰316083543317107230127.04886837.488192공항
9서울42121168053131169105514647311693116917영동119.안전센터315153547137106687127.03791237.522524지선
서울강남0311680720267620150615395519799267620.11강남데시앙포레상가앞31984954253686024127.091533937.4814905마을
189서울N13116806403554335711506535543335543396강남역12번출구314387544511106323127.02955337.498794간선
190서울3011116805453088950714687308893088938현대아파트314526547816106354127.0307437.528584지선
191서울4011168058034982310481480034982334982372봉은사.삼성1파출소앞317045546286107673127.0594137.515029간선
192서울강남06-111680700151857567155284223034223032은곡마을.강남신동아파밀리에32077354069582110127.10217937.464983마을
193김포10021150060316381130089993163811638133양천향교역.휴먼빌아파트297862552498101053126.84158137.569068일반
194서울14511305603219403874459921940321940393초안교315571559171106836127.04125337.630983간선
195김포G600211500641137872805931102904132104132101088JC29594455420991953126.8196337.584268직행좌석
196서울강남05116806602350615081522523506235063현대1차우성아파트316666542644121706127.05553637.482182마을
197서울112011305645221149802479822114922114929인수동장미원313319560761105683127.01555337.645098지선
198서울6632115006111621614991008341506841506853마곡수명산파크7단지296106550302165078126.82201837.549088지선