Overview

Dataset statistics

Number of variables5
Number of observations222
Missing cells180
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory43.6 B

Variable types

Numeric3
Text2

Dataset

Description빛가람정보포탈 내 제공중인 나주시 버스 정류소ID 및 정류소위치
Author한전KDN(주)
URLhttps://www.data.go.kr/data/15038343/fileData.do

Alerts

위도 has 90 (40.5%) missing valuesMissing
경도 has 90 (40.5%) missing valuesMissing
정류소ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:32:03.727676
Analysis finished2023-12-12 09:32:05.491539
Duration1.76 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정류소ID
Real number (ℝ)

UNIQUE 

Distinct222
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean697743.42
Minimum47
Maximum7021070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T18:32:05.584728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile1141.5
Q11662.5
median3500
Q34597.5
95-th percentile7011079.5
Maximum7021070
Range7021023
Interquartile range (IQR)2935

Descriptive statistics

Standard deviation2099524.6
Coefficient of variation (CV)3.009021
Kurtosis5.3475221
Mean697743.42
Median Absolute Deviation (MAD)1490
Skewness2.7017421
Sum1.5489904 × 108
Variance4.4080036 × 1012
MonotonicityStrictly increasing
2023-12-12T18:32:05.796154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 1
 
0.5%
4470 1
 
0.5%
4360 1
 
0.5%
4370 1
 
0.5%
4380 1
 
0.5%
4390 1
 
0.5%
4400 1
 
0.5%
4410 1
 
0.5%
4420 1
 
0.5%
4430 1
 
0.5%
Other values (212) 212
95.5%
ValueCountFrequency (%)
47 1
0.5%
48 1
0.5%
811 1
0.5%
817 1
0.5%
1010 1
0.5%
1040 1
0.5%
1050 1
0.5%
1080 1
0.5%
1090 1
0.5%
1100 1
0.5%
ValueCountFrequency (%)
7021070 1
0.5%
7021060 1
0.5%
7021050 1
0.5%
7021040 1
0.5%
7021030 1
0.5%
7021020 1
0.5%
7021010 1
0.5%
7021000 1
0.5%
7011110 1
0.5%
7011100 1
0.5%

정류소명
Text

Distinct123
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T18:32:06.173716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length5.1936937
Min length2

Characters and Unicode

Total characters1153
Distinct characters182
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

Unique24 ?
Unique (%)10.8%

Sample

1st row공항역
2nd row공항역
3rd row광주송정역
4th row광주송정역
5th rowGEP파빌리온
ValueCountFrequency (%)
중흥s클래스센트럴 4
 
1.8%
공항역 2
 
0.9%
서고안마을 2
 
0.9%
금와(정자교)입구 2
 
0.9%
산포면사무소입구 2
 
0.9%
매성(산포농협 2
 
0.9%
산포초교 2
 
0.9%
빛가람병원 2
 
0.9%
신흥 2
 
0.9%
새밭등 2
 
0.9%
Other values (114) 204
90.3%
2023-12-12T18:32:07.066412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 27
 
2.3%
사 26
 
2.3%
원 25
 
2.2%
남 25
 
2.2%
고 24
 
2.1%
산 22
 
1.9%
동 22
 
1.9%
문 21
 
1.8%
전 20
 
1.7%
교 19
 
1.6%
Other values (172) 922
80.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1085
94.1%
Uppercase Letter 28
 
2.4%
Decimal Number 14
 
1.2%
Open Punctuation 11
 
1.0%
Close Punctuation 11
 
1.0%
Space Separator 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 27
 
2.5%
사 26
 
2.4%
원 25
 
2.3%
남 25
 
2.3%
고 24
 
2.2%
산 22
 
2.0%
동 22
 
2.0%
문 21
 
1.9%
전 20
 
1.8%
교 19
 
1.8%
Other values (155) 854
78.7%
Uppercase Letter
ValueCountFrequency (%)
L 8
28.6%
H 8
28.6%
S 6
21.4%
N 1
 
3.6%
D 1
 
3.6%
K 1
 
3.6%
P 1
 
3.6%
E 1
 
3.6%
G 1
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 6
42.9%
2 4
28.6%
6 2
 
14.3%
3 1
 
7.1%
5 1
 
7.1%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1085
94.1%
Common 40
 
3.5%
Latin 28
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 27
 
2.5%
사 26
 
2.4%
원 25
 
2.3%
남 25
 
2.3%
고 24
 
2.2%
산 22
 
2.0%
동 22
 
2.0%
문 21
 
1.9%
전 20
 
1.8%
교 19
 
1.8%
Other values (155) 854
78.7%
Latin
ValueCountFrequency (%)
L 8
28.6%
H 8
28.6%
S 6
21.4%
N 1
 
3.6%
D 1
 
3.6%
K 1
 
3.6%
P 1
 
3.6%
E 1
 
3.6%
G 1
 
3.6%
Common
ValueCountFrequency (%)
( 11
27.5%
) 11
27.5%
1 6
15.0%
4
 
10.0%
2 4
 
10.0%
6 2
 
5.0%
3 1
 
2.5%
5 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1085
94.1%
ASCII 68
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 27
 
2.5%
사 26
 
2.4%
원 25
 
2.3%
남 25
 
2.3%
고 24
 
2.2%
산 22
 
2.0%
동 22
 
2.0%
문 21
 
1.9%
전 20
 
1.8%
교 19
 
1.8%
Other values (155) 854
78.7%
ASCII
ValueCountFrequency (%)
( 11
16.2%
) 11
16.2%
L 8
11.8%
H 8
11.8%
S 6
8.8%
1 6
8.8%
4
 
5.9%
2 4
 
5.9%
6 2
 
2.9%
N 1
 
1.5%
Other values (7) 7
10.3%

위도
Real number (ℝ)

MISSING 

Distinct121
Distinct (%)91.7%
Missing90
Missing (%)40.5%
Infinite0
Infinite (%)0.0%
Mean126.77163
Minimum126.71006
Maximum126.93046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T18:32:07.251453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.71006
5-th percentile126.71237
Q1126.71886
median126.78228
Q3126.79419
95-th percentile126.8673
Maximum126.93046
Range0.2204037
Interquartile range (IQR)0.075330625

Descriptive statistics

Standard deviation0.050839245
Coefficient of variation (CV)0.00040103013
Kurtosis1.5473366
Mean126.77163
Median Absolute Deviation (MAD)0.02215985
Skewness0.97511879
Sum16733.855
Variance0.0025846288
MonotonicityNot monotonic
2023-12-12T18:32:07.388460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.786231 2
 
0.9%
126.7165752 2
 
0.9%
126.7126551 2
 
0.9%
126.7940808 2
 
0.9%
126.7943055 2
 
0.9%
126.787047 2
 
0.9%
126.7215608 2
 
0.9%
126.7955037 2
 
0.9%
126.7863328 2
 
0.9%
126.803639 2
 
0.9%
Other values (111) 112
50.5%
(Missing) 90
40.5%
ValueCountFrequency (%)
126.7100583 1
0.5%
126.7102023 1
0.5%
126.7112393 1
0.5%
126.7116075 1
0.5%
126.7117562 1
0.5%
126.7119066 1
0.5%
126.7120273 1
0.5%
126.7126551 2
0.9%
126.7130638 1
0.5%
126.7131303 1
0.5%
ValueCountFrequency (%)
126.930462 1
0.5%
126.9304597 1
0.5%
126.9222343 1
0.5%
126.922233 1
0.5%
126.9112582 1
0.5%
126.910456 1
0.5%
126.8999653 1
0.5%
126.8405704 1
0.5%
126.8405367 1
0.5%
126.8268571 1
0.5%

경도
Real number (ℝ)

MISSING 

Distinct122
Distinct (%)92.4%
Missing90
Missing (%)40.5%
Infinite0
Infinite (%)0.0%
Mean35.035645
Minimum34.994633
Maximum35.181632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T18:32:07.532595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.994633
5-th percentile35.0077
Q135.020232
median35.027726
Q335.036513
95-th percentile35.144058
Maximum35.181632
Range0.1869987
Interquartile range (IQR)0.016281175

Descriptive statistics

Standard deviation0.03654126
Coefficient of variation (CV)0.0010429738
Kurtosis6.3244066
Mean35.035645
Median Absolute Deviation (MAD)0.00860233
Skewness2.6059746
Sum4624.7052
Variance0.0013352637
MonotonicityNot monotonic
2023-12-12T18:32:07.729748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.02087321 2
 
0.9%
35.0077 2
 
0.9%
35.0408805 2
 
0.9%
35.0370197 2
 
0.9%
35.02399549 2
 
0.9%
35.025575 2
 
0.9%
34.994633 2
 
0.9%
35.0342576 2
 
0.9%
35.0205816 2
 
0.9%
35.02382599 2
 
0.9%
Other values (112) 112
50.5%
(Missing) 90
40.5%
ValueCountFrequency (%)
34.994633 2
0.9%
34.9968108 1
0.5%
34.9974177 1
0.5%
34.999597 1
0.5%
34.9996798 1
0.5%
35.0077 2
0.9%
35.0086884 1
0.5%
35.0086948 1
0.5%
35.00938 1
0.5%
35.0094199 1
0.5%
ValueCountFrequency (%)
35.1816317 1
0.5%
35.1801761 1
0.5%
35.1480602 1
0.5%
35.1477176 1
0.5%
35.1444144 1
0.5%
35.1442341 1
0.5%
35.14410716 1
0.5%
35.14401842 1
0.5%
35.1382692 1
0.5%
35.1382213 1
0.5%

정류소위치
Text

Distinct52
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T18:32:07.982293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.5765766
Min length3

Characters and Unicode

Total characters1238
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

Unique41 ?
Unique (%)18.5%

Sample

1st row공항방향
2nd row혁신도시방향
3rd row송정역길건너
4th row송정역앞
5th row한전앞
ValueCountFrequency (%)
혁신도시방향 68
30.6%
광주시내방향 38
17.1%
나주시내방향 32
14.4%
단지앞 11
 
5.0%
단지길건너 8
 
3.6%
혁신산단방향 6
 
2.7%
신도산단방향 6
 
2.7%
남평읍방향 4
 
1.8%
중흥리조트방향 4
 
1.8%
후문길건너 2
 
0.9%
Other values (42) 43
19.4%
2023-12-12T18:32:08.447824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
방 161
13.0%
향 159
12.8%
시 142
11.5%
신 80
 
6.5%
도 76
 
6.1%
혁 74
 
6.0%
주 72
 
5.8%
내 70
 
5.7%
광 38
 
3.1%
앞 37
 
3.0%
Other values (59) 329
26.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1238
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 161
13.0%
향 159
12.8%
시 142
11.5%
신 80
 
6.5%
도 76
 
6.1%
혁 74
 
6.0%
주 72
 
5.8%
내 70
 
5.7%
광 38
 
3.1%
앞 37
 
3.0%
Other values (59) 329
26.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1238
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 161
13.0%
향 159
12.8%
시 142
11.5%
신 80
 
6.5%
도 76
 
6.1%
혁 74
 
6.0%
주 72
 
5.8%
내 70
 
5.7%
광 38
 
3.1%
앞 37
 
3.0%
Other values (59) 329
26.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1238
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
방 161
13.0%
향 159
12.8%
시 142
11.5%
신 80
 
6.5%
도 76
 
6.1%
혁 74
 
6.0%
주 72
 
5.8%
내 70
 
5.7%
광 38
 
3.1%
앞 37
 
3.0%
Other values (59) 329
26.6%

Interactions

2023-12-12T18:32:04.805735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:04.026289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:04.447843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:04.911995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:04.184683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:04.578185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:05.022361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:04.313478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:04.684113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:32:08.583349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소ID위도경도정류소위치
정류소ID1.000NaNNaN0.991
위도NaN1.0000.7640.000
경도NaN0.7641.0000.000
정류소위치0.9910.0000.0001.000
2023-12-12T18:32:08.714975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소ID위도경도
정류소ID1.0000.0950.169
위도0.0951.0000.338
경도0.1690.3381.000

Missing values

2023-12-12T18:32:05.159861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:32:05.288047image/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.
2023-12-12T18:32:05.420658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

정류소ID정류소명위도경도정류소위치
047공항역126.81053735.144018공항방향
148공항역126.81248535.144107혁신도시방향
2811광주송정역126.79217435.138269송정역길건너
3817광주송정역126.79169235.138221송정역앞
41010GEP파빌리온126.78570935.02495한전앞
51040LH1단지126.77748235.014396단지앞
61050LH3단지126.78193135.01077단지앞
71080LH5단지126.7954535.012725단지앞
81090LH6단지126.79303435.00942단지길건너
91100LH6단지126.79263735.009634단지앞
정류소ID정류소명위도경도정류소위치
2127011100신도산단<NA><NA>신도산단방향
2137011110신도산단<NA><NA>혁신산단방향
2147021000남평터미널<NA><NA>중흥리조트방향
2157021010남평터미널<NA><NA>남평읍방향
2167021020양우내안애<NA><NA>중흥리조트방향
2177021030양우내안애<NA><NA>남평읍방향
2187021040인암마을<NA><NA>중흥리조트방향
2197021050인암마을<NA><NA>남평읍방향
2207021060중흥리조트<NA><NA>중흥리조트방향
2217021070중흥리조트<NA><NA>남평읍방향