Overview

Dataset statistics

Number of variables9
Number of observations23
Missing cells23
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory84.7 B

Variable types

Numeric4
Text2
Categorical3

Dataset

Description화성시 버스정보안내단말기 설치현황에 대한 데이터로 연번. 읍면동, 버스정류장수, 쉘터형, 독립형 버스정류장 수에 대한 데이터를 포함하고 있습니다.
Author경기도 화성시
URLhttps://www.data.go.kr/data/15041970/fileData.do

Alerts

버스정류장수 is highly overall correlated with 쉘터형High correlation
쉘터형 is highly overall correlated with 버스정류장수 and 4 other fieldsHigh correlation
독립형 is highly overall correlated with 쉘터형 and 1 other fieldsHigh correlation
전자종이형 is highly overall correlated with 쉘터형High correlation
교통약자 is highly overall correlated with 쉘터형 and 1 other fieldsHigh correlation
확인필요 is highly overall correlated with 쉘터형 and 2 other fieldsHigh correlation
전자종이형 is highly imbalanced (57.4%)Imbalance
버스정류장수 has 2 (8.7%) missing valuesMissing
비고 has 21 (91.3%) missing valuesMissing
연번 has unique valuesUnique
읍면동 has unique valuesUnique
쉘터형 has 1 (4.3%) zerosZeros
독립형 has 1 (4.3%) zerosZeros

Reproduction

Analysis started2024-05-04 08:01:26.757592
Analysis finished2024-05-04 08:01:33.823427
Duration7.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-05-04T08:01:34.048344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q16.5
median12
Q317.5
95-th percentile21.9
Maximum23
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.56519417
Kurtosis-1.2
Mean12
Median Absolute Deviation (MAD)6
Skewness0
Sum276
Variance46
MonotonicityStrictly increasing
2024-05-04T08:01:34.532317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
23 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 1
 
4.3%
19 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 1
4.3%
5 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
ValueCountFrequency (%)
23 1
4.3%
22 1
4.3%
21 1
4.3%
20 1
4.3%
19 1
4.3%
18 1
4.3%
17 1
4.3%
16 1
4.3%
15 1
4.3%
14 1
4.3%

읍면동
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-05-04T08:01:35.235599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.3913043
Min length2

Characters and Unicode

Total characters78
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row정남면
2nd row진안동
3rd row병점1,2동
4th row반월동
5th row기배동
ValueCountFrequency (%)
정남면 1
 
4.3%
마도면 1
 
4.3%
수원 1
 
4.3%
양감면 1
 
4.3%
장안면 1
 
4.3%
팔탄면 1
 
4.3%
우정읍 1
 
4.3%
향남읍 1
 
4.3%
새솔동 1
 
4.3%
서신면 1
 
4.3%
Other values (13) 13
56.5%
2024-05-04T08:01:36.805670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 10
 
12.8%
면 9
 
11.5%
읍 4
 
5.1%
산 3
 
3.8%
남 3
 
3.8%
탄 3
 
3.8%
, 3
 
3.8%
정 2
 
2.6%
양 2
 
2.6%
봉 2
 
2.6%
Other values (33) 37
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67
85.9%
Decimal Number 7
 
9.0%
Other Punctuation 3
 
3.8%
Math Symbol 1
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10
 
14.9%
면 9
 
13.4%
읍 4
 
6.0%
산 3
 
4.5%
남 3
 
4.5%
탄 3
 
4.5%
정 2
 
3.0%
양 2
 
3.0%
봉 2
 
3.0%
송 2
 
3.0%
Other values (26) 27
40.3%
Decimal Number
ValueCountFrequency (%)
2 2
28.6%
1 2
28.6%
8 1
14.3%
4 1
14.3%
3 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67
85.9%
Common 11
 
14.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10
 
14.9%
면 9
 
13.4%
읍 4
 
6.0%
산 3
 
4.5%
남 3
 
4.5%
탄 3
 
4.5%
정 2
 
3.0%
양 2
 
3.0%
봉 2
 
3.0%
송 2
 
3.0%
Other values (26) 27
40.3%
Common
ValueCountFrequency (%)
, 3
27.3%
2 2
18.2%
1 2
18.2%
8 1
 
9.1%
~ 1
 
9.1%
4 1
 
9.1%
3 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67
85.9%
ASCII 11
 
14.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 10
 
14.9%
면 9
 
13.4%
읍 4
 
6.0%
산 3
 
4.5%
남 3
 
4.5%
탄 3
 
4.5%
정 2
 
3.0%
양 2
 
3.0%
봉 2
 
3.0%
송 2
 
3.0%
Other values (26) 27
40.3%
ASCII
ValueCountFrequency (%)
, 3
27.3%
2 2
18.2%
1 2
18.2%
8 1
 
9.1%
~ 1
 
9.1%
4 1
 
9.1%
3 1
 
9.1%

버스정류장수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean144.38095
Minimum27
Maximum333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-05-04T08:01:37.528132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile29
Q180
median125
Q3179
95-th percentile297
Maximum333
Range306
Interquartile range (IQR)99

Descriptive statistics

Standard deviation88.179633
Coefficient of variation (CV)0.61074284
Kurtosis-0.39060572
Mean144.38095
Median Absolute Deviation (MAD)53
Skewness0.63269435
Sum3032
Variance7775.6476
MonotonicityNot monotonic
2024-05-04T08:01:38.151211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
67 1
 
4.3%
118 1
 
4.3%
166 1
 
4.3%
176 1
 
4.3%
197 1
 
4.3%
297 1
 
4.3%
27 1
 
4.3%
179 1
 
4.3%
142 1
 
4.3%
116 1
 
4.3%
Other values (11) 11
47.8%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
27 1
4.3%
29 1
4.3%
40 1
4.3%
67 1
4.3%
73 1
4.3%
80 1
4.3%
81 1
4.3%
85 1
4.3%
116 1
4.3%
118 1
4.3%
ValueCountFrequency (%)
333 1
4.3%
297 1
4.3%
263 1
4.3%
260 1
4.3%
197 1
4.3%
179 1
4.3%
178 1
4.3%
176 1
4.3%
166 1
4.3%
142 1
4.3%

쉘터형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.478261
Minimum0
Maximum250
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-05-04T08:01:38.712631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q136.5
median50
Q382
95-th percentile156.5
Maximum250
Range250
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation58.492322
Coefficient of variation (CV)0.8668321
Kurtosis3.1819864
Mean67.478261
Median Absolute Deviation (MAD)25
Skewness1.6412432
Sum1552
Variance3421.3518
MonotonicityNot monotonic
2024-05-04T08:01:39.085921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
50 2
 
8.7%
80 1
 
4.3%
45 1
 
4.3%
1 1
 
4.3%
0 1
 
4.3%
48 1
 
4.3%
59 1
 
4.3%
61 1
 
4.3%
84 1
 
4.3%
143 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
0 1
4.3%
1 1
4.3%
9 1
4.3%
22 1
4.3%
25 1
4.3%
35 1
4.3%
38 1
4.3%
39 1
4.3%
45 1
4.3%
48 1
4.3%
ValueCountFrequency (%)
250 1
4.3%
158 1
4.3%
143 1
4.3%
139 1
4.3%
107 1
4.3%
84 1
4.3%
80 1
4.3%
61 1
4.3%
59 1
4.3%
56 1
4.3%

독립형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.130435
Minimum0
Maximum24
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-05-04T08:01:39.524215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median10
Q315
95-th percentile22.8
Maximum24
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.6056459
Coefficient of variation (CV)0.75077191
Kurtosis-1.0538642
Mean10.130435
Median Absolute Deviation (MAD)7
Skewness0.34523572
Sum233
Variance57.84585
MonotonicityNot monotonic
2024-05-04T08:01:39.960393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3
13.0%
6 2
 
8.7%
3 2
 
8.7%
15 2
 
8.7%
13 2
 
8.7%
0 1
 
4.3%
11 1
 
4.3%
7 1
 
4.3%
21 1
 
4.3%
2 1
 
4.3%
Other values (7) 7
30.4%
ValueCountFrequency (%)
0 1
 
4.3%
1 3
13.0%
2 1
 
4.3%
3 2
8.7%
6 2
8.7%
7 1
 
4.3%
8 1
 
4.3%
10 1
 
4.3%
11 1
 
4.3%
12 1
 
4.3%
ValueCountFrequency (%)
24 1
4.3%
23 1
4.3%
21 1
4.3%
20 1
4.3%
18 1
4.3%
15 2
8.7%
13 2
8.7%
12 1
4.3%
11 1
4.3%
10 1
4.3%

전자종이형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
21 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21
91.3%
1 2
 
8.7%

Length

2024-05-04T08:01:40.355102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:01:40.724183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
91.3%
1 2
 
8.7%

교통약자
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
17 
4
3 
2
 
1
11
 
1
8
 
1

Length

Max length2
Median length1
Mean length1.0434783
Min length1

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17
73.9%
4 3
 
13.0%
2 1
 
4.3%
11 1
 
4.3%
8 1
 
4.3%

Length

2024-05-04T08:01:41.198699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:01:41.558592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17
73.9%
4 3
 
13.0%
2 1
 
4.3%
11 1
 
4.3%
8 1
 
4.3%

확인필요
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
15 
1
5 
2
2 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row0
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 15
65.2%
1 5
 
21.7%
2 2
 
8.7%
4 1
 
4.3%

Length

2024-05-04T08:01:41.930900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:01:42.288895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15
65.2%
1 5
 
21.7%
2 2
 
8.7%
4 1
 
4.3%

비고
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing21
Missing (%)91.3%
Memory size316.0 B
2024-05-04T08:01:42.700249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5.5
Mean length5.5
Min length4

Characters and Unicode

Total characters11
Distinct characters11
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

Unique2 ?
Unique (%)100.0%

Sample

1st row국립축산과학원
2nd row서동탄역
ValueCountFrequency (%)
국립축산과학원 1
50.0%
서동탄역 1
50.0%
2024-05-04T08:01:43.600326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 1
9.1%
립 1
9.1%
축 1
9.1%
산 1
9.1%
과 1
9.1%
학 1
9.1%
원 1
9.1%
서 1
9.1%
동 1
9.1%
탄 1
9.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 1
9.1%
립 1
9.1%
축 1
9.1%
산 1
9.1%
과 1
9.1%
학 1
9.1%
원 1
9.1%
서 1
9.1%
동 1
9.1%
탄 1
9.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 1
9.1%
립 1
9.1%
축 1
9.1%
산 1
9.1%
과 1
9.1%
학 1
9.1%
원 1
9.1%
서 1
9.1%
동 1
9.1%
탄 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 1
9.1%
립 1
9.1%
축 1
9.1%
산 1
9.1%
과 1
9.1%
학 1
9.1%
원 1
9.1%
서 1
9.1%
동 1
9.1%
탄 1
9.1%

Interactions

2024-05-04T08:01:31.169530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:27.421896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:28.445559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:29.772551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:31.478865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:27.634323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:28.785664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:30.040754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:31.827919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:27.907144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:29.126081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:30.518619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:32.321225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:28.170166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:29.417300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:30.819299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T08:01:43.881467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동버스정류장수쉘터형독립형전자종이형교통약자확인필요비고
연번1.0001.0000.6750.7400.7210.0000.5180.000NaN
읍면동1.0001.0001.0001.0001.0001.0001.0001.0000.000
버스정류장수0.6751.0001.0000.8400.1920.3280.7640.375NaN
쉘터형0.7401.0000.8401.0000.7570.9350.8050.922NaN
독립형0.7211.0000.1920.7571.0000.0000.7100.791NaN
전자종이형0.0001.0000.3280.9350.0001.0000.0000.462NaN
교통약자0.5181.0000.7640.8050.7100.0001.0000.599NaN
확인필요0.0001.0000.3750.9220.7910.4620.5991.000NaN
비고NaN0.000NaNNaNNaNNaNNaNNaN1.000
2024-05-04T08:01:44.249566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교통약자전자종이형확인필요
교통약자1.0000.0000.506
전자종이형0.0001.0000.287
확인필요0.5060.2871.000
2024-05-04T08:01:44.540536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번버스정류장수쉘터형독립형전자종이형교통약자확인필요
연번1.0000.347-0.190-0.3560.0000.0360.000
버스정류장수0.3471.0000.8590.4930.2230.4830.150
쉘터형-0.1900.8591.0000.7270.6540.5940.557
독립형-0.3560.4930.7271.0000.0000.3680.510
전자종이형0.0000.2230.6540.0001.0000.0000.287
교통약자0.0360.4830.5940.3680.0001.0000.506
확인필요0.0000.1500.5570.5100.2870.5061.000

Missing values

2024-05-04T08:01:32.867982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T08:01:33.396294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-04T08:01:33.691429image/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

연번읍면동버스정류장수쉘터형독립형전자종이형교통약자확인필요비고
01정남면125806100<NA>
12진안동67508000<NA>
23병점1,2동735015022<NA>
34반월동402213001<NA>
45기배동2991000<NA>
56화산동815620000<NA>
67동탄1,2,3동178139130110<NA>
78동탄4~8동33325024081<NA>
89봉담읍26015823142<NA>
910남양읍26310718044<NA>
연번읍면동버스정류장수쉘터형독립형전자종이형교통약자확인필요비고
1314송산면142451000<NA>
1415서신면179533000<NA>
1516새솔동27352000<NA>
1617향남읍29714321041<NA>
1718우정읍197847000<NA>
1819팔탄면1766115001<NA>
1920장안면1665911000<NA>
2021양감면118483001<NA>
2122수원<NA>01000국립축산과학원
2223오산<NA>10000서동탄역