Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory507.8 KiB
Average record size in memory52.0 B

Variable types

Numeric4
Text1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12912/S/1/datasetView.do

Alerts

NODE_ID(노드ID) is highly overall correlated with STTN_NO(정류소ID) and 1 other fieldsHigh correlation
STTN_NO(정류소ID) is highly overall correlated with NODE_ID(노드ID) and 1 other fieldsHigh correlation
CRDNT_Y(Y좌표) is highly overall correlated with NODE_ID(노드ID) and 1 other fieldsHigh correlation
NODE_ID(노드ID) has unique valuesUnique
STTN_NO(정류소ID) has unique valuesUnique

Reproduction

Analysis started2024-04-29 16:28:37.388058
Analysis finished2024-04-29 16:28:40.972988
Duration3.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

NODE_ID(노드ID)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1310799 × 108
Minimum1 × 108
Maximum1.2900019 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T01:28:41.058535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100028 × 108
Q11.0790014 × 108
median1.1300047 × 108
Q31.1900014 × 108
95-th percentile1.2300022 × 108
Maximum1.2900019 × 108
Range29000185
Interquartile range (IQR)11099994

Descriptive statistics

Standard deviation6872993.7
Coefficient of variation (CV)0.06076488
Kurtosis-1.1042772
Mean1.1310799 × 108
Median Absolute Deviation (MAD)5899727
Skewness-0.14394119
Sum1.1310799 × 1012
Variance4.7238042 × 1013
MonotonicityNot monotonic
2024-04-30T01:28:41.190777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114000157 1
 
< 0.1%
121900217 1
 
< 0.1%
107900298 1
 
< 0.1%
124900077 1
 
< 0.1%
108000364 1
 
< 0.1%
116900273 1
 
< 0.1%
122000214 1
 
< 0.1%
116900006 1
 
< 0.1%
108000094 1
 
< 0.1%
107900004 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
100000001 1
< 0.1%
100000002 1
< 0.1%
100000003 1
< 0.1%
100000004 1
< 0.1%
100000005 1
< 0.1%
100000006 1
< 0.1%
100000007 1
< 0.1%
100000008 1
< 0.1%
100000010 1
< 0.1%
100000011 1
< 0.1%
ValueCountFrequency (%)
129000186 1
< 0.1%
124900124 1
< 0.1%
124900123 1
< 0.1%
124900122 1
< 0.1%
124900120 1
< 0.1%
124900119 1
< 0.1%
124900118 1
< 0.1%
124900116 1
< 0.1%
124900113 1
< 0.1%
124900112 1
< 0.1%

STTN_NM(정류소명칭)
Text

Distinct6436
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-30T01:28:41.436219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length7.4161
Min length2

Characters and Unicode

Total characters74161
Distinct characters651
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3762 ?
Unique (%)37.6%

Sample

1st row서울남부지방법원.서울남부지방검찰청
2nd row방배역.방배서리풀이편한세상
3rd row동성주택
4th row거성푸르뫼아파트
5th row동아아파트
ValueCountFrequency (%)
벽산아파트 11
 
0.1%
현대아파트 11
 
0.1%
국민은행 11
 
0.1%
경남아파트 11
 
0.1%
새마을금고 10
 
0.1%
성원아파트 10
 
0.1%
가산디지털단지역 10
 
0.1%
우성아파트 9
 
0.1%
신대방역 9
 
0.1%
북서울꿈의숲 9
 
0.1%
Other values (6434) 9914
99.0%
2024-04-30T01:28:41.787649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2278
 
3.1%
아 2098
 
2.8%
파 2037
 
2.7%
트 2033
 
2.7%
. 1904
 
2.6%
교 1712
 
2.3%
구 1442
 
1.9%
역 1425
 
1.9%
대 1246
 
1.7%
지 1233
 
1.7%
Other values (641) 56753
76.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69197
93.3%
Decimal Number 2274
 
3.1%
Other Punctuation 1914
 
2.6%
Uppercase Letter 651
 
0.9%
Open Punctuation 39
 
0.1%
Close Punctuation 39
 
0.1%
Lowercase Letter 22
 
< 0.1%
Space Separator 15
 
< 0.1%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2278
 
3.3%
아 2098
 
3.0%
파 2037
 
2.9%
트 2033
 
2.9%
교 1712
 
2.5%
구 1442
 
2.1%
역 1425
 
2.1%
대 1246
 
1.8%
지 1233
 
1.8%
학 1198
 
1.7%
Other values (599) 52495
75.9%
Uppercase Letter
ValueCountFrequency (%)
T 99
15.2%
K 83
12.7%
A 64
9.8%
S 56
8.6%
C 54
8.3%
P 50
7.7%
G 39
 
6.0%
B 38
 
5.8%
M 29
 
4.5%
L 28
 
4.3%
Other values (12) 111
17.1%
Decimal Number
ValueCountFrequency (%)
1 693
30.5%
2 446
19.6%
3 323
14.2%
4 181
 
8.0%
5 149
 
6.6%
0 138
 
6.1%
7 109
 
4.8%
6 99
 
4.4%
9 86
 
3.8%
8 50
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 1904
99.5%
& 6
 
0.3%
· 4
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 18
81.8%
k 2
 
9.1%
t 2
 
9.1%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69197
93.3%
Common 4291
 
5.8%
Latin 673
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2278
 
3.3%
아 2098
 
3.0%
파 2037
 
2.9%
트 2033
 
2.9%
교 1712
 
2.5%
구 1442
 
2.1%
역 1425
 
2.1%
대 1246
 
1.8%
지 1233
 
1.8%
학 1198
 
1.7%
Other values (599) 52495
75.9%
Latin
ValueCountFrequency (%)
T 99
14.7%
K 83
12.3%
A 64
9.5%
S 56
8.3%
C 54
8.0%
P 50
 
7.4%
G 39
 
5.8%
B 38
 
5.6%
M 29
 
4.3%
L 28
 
4.2%
Other values (15) 133
19.8%
Common
ValueCountFrequency (%)
. 1904
44.4%
1 693
 
16.2%
2 446
 
10.4%
3 323
 
7.5%
4 181
 
4.2%
5 149
 
3.5%
0 138
 
3.2%
7 109
 
2.5%
6 99
 
2.3%
9 86
 
2.0%
Other values (7) 163
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69197
93.3%
ASCII 4960
 
6.7%
None 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2278
 
3.3%
아 2098
 
3.0%
파 2037
 
2.9%
트 2033
 
2.9%
교 1712
 
2.5%
구 1442
 
2.1%
역 1425
 
2.1%
대 1246
 
1.8%
지 1233
 
1.8%
학 1198
 
1.7%
Other values (599) 52495
75.9%
ASCII
ValueCountFrequency (%)
. 1904
38.4%
1 693
 
14.0%
2 446
 
9.0%
3 323
 
6.5%
4 181
 
3.6%
5 149
 
3.0%
0 138
 
2.8%
7 109
 
2.2%
6 99
 
2.0%
T 99
 
2.0%
Other values (31) 819
16.5%
None
ValueCountFrequency (%)
· 4
100.0%

STTN_NO(정류소ID)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14197.906
Minimum1001
Maximum25990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T01:28:41.904004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2521.95
Q18755.5
median14355.5
Q320228.5
95-th percentile24306.05
Maximum25990
Range24989
Interquartile range (IQR)11473

Descriptive statistics

Standard deviation6883.9191
Coefficient of variation (CV)0.48485453
Kurtosis-1.0987394
Mean14197.906
Median Absolute Deviation (MAD)5807.5
Skewness-0.13831109
Sum1.4197906 × 108
Variance47388342
MonotonicityNot monotonic
2024-04-30T01:28:42.043868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15260 1
 
< 0.1%
22694 1
 
< 0.1%
8568 1
 
< 0.1%
25574 1
 
< 0.1%
9304 1
 
< 0.1%
17695 1
 
< 0.1%
23318 1
 
< 0.1%
17682 1
 
< 0.1%
9182 1
 
< 0.1%
8474 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
1011 1
< 0.1%
ValueCountFrequency (%)
25990 1
< 0.1%
25989 1
< 0.1%
25988 1
< 0.1%
25784 1
< 0.1%
25782 1
< 0.1%
25781 1
< 0.1%
25752 1
< 0.1%
25749 1
< 0.1%
25746 1
< 0.1%
25740 1
< 0.1%

CRDNT_X(X좌표)
Real number (ℝ)

Distinct9979
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98484
Minimum126.79835
Maximum127.18027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T01:28:42.169865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.79835
5-th percentile126.8438
Q1126.9168
median126.9931
Q3127.04987
95-th percentile127.12252
Maximum127.18027
Range0.38191232
Interquartile range (IQR)0.13307738

Descriptive statistics

Standard deviation0.084557844
Coefficient of variation (CV)0.00066588931
Kurtosis-0.86908477
Mean126.98484
Median Absolute Deviation (MAD)0.067004976
Skewness-0.045195181
Sum1269848.4
Variance0.007150029
MonotonicityNot monotonic
2024-04-30T01:28:42.281846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0324578678 3
 
< 0.1%
126.8992453681 2
 
< 0.1%
126.9375749794 2
 
< 0.1%
126.948037 2
 
< 0.1%
126.9478121337 2
 
< 0.1%
126.9275473964 2
 
< 0.1%
126.9760324524 2
 
< 0.1%
126.8995348053 2
 
< 0.1%
127.056404146 2
 
< 0.1%
126.840232155 2
 
< 0.1%
Other values (9969) 9979
99.8%
ValueCountFrequency (%)
126.7983534326 1
< 0.1%
126.7984749196 1
< 0.1%
126.798649 1
< 0.1%
126.7986847129 1
< 0.1%
126.798773 1
< 0.1%
126.799863985 1
< 0.1%
126.8000835715 1
< 0.1%
126.8013423185 1
< 0.1%
126.8017961457 1
< 0.1%
126.8019653455 1
< 0.1%
ValueCountFrequency (%)
127.1802657501 1
< 0.1%
127.1800898791 1
< 0.1%
127.1795399999 1
< 0.1%
127.1795016106 1
< 0.1%
127.1783352627 1
< 0.1%
127.1780401265 1
< 0.1%
127.1779976114 1
< 0.1%
127.1779314283 1
< 0.1%
127.1776973726 1
< 0.1%
127.1774135685 1
< 0.1%

CRDNT_Y(Y좌표)
Real number (ℝ)

HIGH CORRELATION 

Distinct9978
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.551092
Minimum37.43078
Maximum37.781594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T01:28:42.396539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.43078
5-th percentile37.471178
Q137.502786
median37.54995
Q337.592236
95-th percentile37.648054
Maximum37.781594
Range0.35081385
Interquartile range (IQR)0.089449235

Descriptive statistics

Standard deviation0.055445342
Coefficient of variation (CV)0.0014765307
Kurtosis-0.78801501
Mean37.551092
Median Absolute Deviation (MAD)0.045286304
Skewness0.25694316
Sum375510.92
Variance0.003074186
MonotonicityNot monotonic
2024-04-30T01:28:42.520598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6255057955 3
 
< 0.1%
37.5661890393 2
 
< 0.1%
37.4941827082 2
 
< 0.1%
37.5614344732 2
 
< 0.1%
37.490902 2
 
< 0.1%
37.5560249673 2
 
< 0.1%
37.5781517573 2
 
< 0.1%
37.5498210288 2
 
< 0.1%
37.520832794 2
 
< 0.1%
37.477212 2
 
< 0.1%
Other values (9968) 9979
99.8%
ValueCountFrequency (%)
37.430779662 1
< 0.1%
37.4337190645 1
< 0.1%
37.4346702667 1
< 0.1%
37.434793586 1
< 0.1%
37.4348444186 1
< 0.1%
37.4349898625 1
< 0.1%
37.4350042396 1
< 0.1%
37.4355268028 1
< 0.1%
37.436857042 1
< 0.1%
37.437324573 1
< 0.1%
ValueCountFrequency (%)
37.7815935083 1
< 0.1%
37.690199 1
< 0.1%
37.6899469943 1
< 0.1%
37.6893523043 1
< 0.1%
37.6891947508 1
< 0.1%
37.689128 1
< 0.1%
37.6890060442 1
< 0.1%
37.6887853785 1
< 0.1%
37.6879849018 1
< 0.1%
37.6879546953 1
< 0.1%

Interactions

2024-04-30T01:28:40.467117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:39.332875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:39.732949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:40.078600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:40.571527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:39.474151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:39.828812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:40.176497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:40.659348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:39.550996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:39.904257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:40.266804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:40.742594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:39.635377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:39.990878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:28:40.364917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:28:42.615101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NODE_ID(노드ID)STTN_NO(정류소ID)CRDNT_X(X좌표)CRDNT_Y(Y좌표)
NODE_ID(노드ID)1.0000.9860.9010.735
STTN_NO(정류소ID)0.9861.0000.9040.734
CRDNT_X(X좌표)0.9010.9041.0000.437
CRDNT_Y(Y좌표)0.7350.7340.4371.000
2024-04-30T01:28:42.701780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NODE_ID(노드ID)STTN_NO(정류소ID)CRDNT_X(X좌표)CRDNT_Y(Y좌표)
NODE_ID(노드ID)1.0000.998-0.095-0.676
STTN_NO(정류소ID)0.9981.000-0.096-0.676
CRDNT_X(X좌표)-0.095-0.0961.0000.247
CRDNT_Y(Y좌표)-0.676-0.6760.2471.000

Missing values

2024-04-30T01:28:40.844382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:28:40.924948image/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

NODE_ID(노드ID)STTN_NM(정류소명칭)STTN_NO(정류소ID)CRDNT_X(X좌표)CRDNT_Y(Y좌표)
5977114000157서울남부지방법원.서울남부지방검찰청15260126.86579637.521801
9206121000153방배역.방배서리풀이편한세상22229126.9964137.483396
3183108900122동성주택9550127.03220837.623052
6958116000287거성푸르뫼아파트17306126.84367637.500611
1901105900022동아아파트6515127.04548537.574201
995103000074천주교성수동성당앞4173127.04691237.53908
3422109000035도봉보건소10118127.04005737.657774
3218108900103경남아파트9588127.03396237.616613
2087106000127동서그랜드맨션7222127.07819637.58618
1074103000162뚝섬서울숲4264127.03753537.543533
NODE_ID(노드ID)STTN_NM(정류소명칭)STTN_NO(정류소ID)CRDNT_X(X좌표)CRDNT_Y(Y좌표)
2440107000105석관중고등학교앞8195127.06408337.609297
5368113000080서강대학교14171126.9374337.551849
1573104900018장신대앞5581127.10519837.547619
4376111000057806의무경찰대.우남아파트12145126.90636637.617937
975103000054금남시장앞.백범학원터4153127.02188537.548256
4333111000929동명여고.천주교불광동성당12020126.92389237.616191
3202108900167롯데백화점9570127.03087937.614569
3140108900152빨래골9504127.00995437.627385
2729107900081풍림106동8584127.02201537.597573
1702105000058장안동현대아파트앞6144127.06841937.579746