Overview

Dataset statistics

Number of variables11
Number of observations1939
Missing cells5848
Missing cells (%)27.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory181.9 KiB
Average record size in memory96.1 B

Variable types

Numeric7
Text3
Categorical1

Dataset

Description남동구 도로현황(노선번호, 노선명, 기점, 종점, 전체길이, 연장길이, 1차선길이, 2차선길이, 4차선길이, 6차선길이) 개방
URLhttps://www.data.go.kr/data/15067452/fileData.do

Alerts

전체길이 is highly overall correlated with 연장길이 and 4 other fieldsHigh correlation
연장길이 is highly overall correlated with 전체길이 and 4 other fieldsHigh correlation
1차선길이 is highly overall correlated with 전체길이 and 1 other fieldsHigh correlation
2차선길이 is highly overall correlated with 전체길이 and 1 other fieldsHigh correlation
3차선길이 is highly overall correlated with 전체길이 and 1 other fieldsHigh correlation
4차선길이 is highly overall correlated with 전체길이 and 1 other fieldsHigh correlation
6차선길이 is highly imbalanced (99.4%)Imbalance
종점 has 24 (1.2%) missing valuesMissing
1차선길이 has 699 (36.0%) missing valuesMissing
2차선길이 has 1292 (66.6%) missing valuesMissing
3차선길이 has 1917 (98.9%) missing valuesMissing
4차선길이 has 1908 (98.4%) missing valuesMissing

Reproduction

Analysis started2023-12-13 01:01:45.649456
Analysis finished2023-12-13 01:01:49.966489
Duration4.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선번호
Real number (ℝ)

Distinct1935
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2293.8901
Minimum1
Maximum3956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-13T10:01:50.029725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile97.9
Q11264.5
median2438
Q33220.5
95-th percentile3847.1
Maximum3956
Range3955
Interquartile range (IQR)1956

Descriptive statistics

Standard deviation1128.2235
Coefficient of variation (CV)0.49183851
Kurtosis-0.66808366
Mean2293.8901
Median Absolute Deviation (MAD)844
Skewness-0.56887768
Sum4447853
Variance1272888.3
MonotonicityIncreasing
2023-12-13T10:01:50.149315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2531 2
 
0.1%
2097 2
 
0.1%
2738 2
 
0.1%
3843 2
 
0.1%
1 1
 
0.1%
3057 1
 
0.1%
3067 1
 
0.1%
3066 1
 
0.1%
3065 1
 
0.1%
3064 1
 
0.1%
Other values (1925) 1925
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
3956 1
0.1%
3955 1
0.1%
3954 1
0.1%
3953 1
0.1%
3952 1
0.1%
3951 1
0.1%
3950 1
0.1%
3949 1
0.1%
3948 1
0.1%
3947 1
0.1%

노선명
Text

Distinct862
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2023-12-13T10:01:50.427811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.0226921
Min length4

Characters and Unicode

Total characters9739
Distinct characters17
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

Unique594 ?
Unique (%)30.6%

Sample

1st row중1-42
2nd row중3-184
3rd row중1-325
4th row중1-315
5th row중3-69
ValueCountFrequency (%)
소3-1 39
 
2.0%
소3-2 36
 
1.9%
소3-3 32
 
1.6%
소3-5 30
 
1.5%
소3-4 30
 
1.5%
소2-1 28
 
1.4%
소3-6 23
 
1.2%
소2-2 21
 
1.1%
소3-7 21
 
1.1%
소1-3 19
 
1.0%
Other values (853) 1661
85.6%
2023-12-13T10:01:50.807608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1939
19.9%
소 1720
17.7%
2 1348
13.8%
1 1221
12.5%
3 1180
12.1%
4 350
 
3.6%
6 334
 
3.4%
5 331
 
3.4%
7 301
 
3.1%
8 261
 
2.7%
Other values (7) 754
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5825
59.8%
Other Letter 1974
 
20.3%
Dash Punctuation 1939
 
19.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1348
23.1%
1 1221
21.0%
3 1180
20.3%
4 350
 
6.0%
6 334
 
5.7%
5 331
 
5.7%
7 301
 
5.2%
8 261
 
4.5%
9 253
 
4.3%
0 246
 
4.2%
Other Letter
ValueCountFrequency (%)
소 1720
87.1%
중 228
 
11.6%
래 10
 
0.5%
호 8
 
0.4%
선 8
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 1939
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7765
79.7%
Hangul 1974
 
20.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1939
25.0%
2 1348
17.4%
1 1221
15.7%
3 1180
15.2%
4 350
 
4.5%
6 334
 
4.3%
5 331
 
4.3%
7 301
 
3.9%
8 261
 
3.4%
9 253
 
3.3%
Other values (2) 247
 
3.2%
Hangul
ValueCountFrequency (%)
소 1720
87.1%
중 228
 
11.6%
래 10
 
0.5%
호 8
 
0.4%
선 8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7765
79.7%
Hangul 1974
 
20.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1939
25.0%
2 1348
17.4%
1 1221
15.7%
3 1180
15.2%
4 350
 
4.5%
6 334
 
4.3%
5 331
 
4.3%
7 301
 
3.9%
8 261
 
3.4%
9 253
 
3.3%
Other values (2) 247
 
3.2%
Hangul
ValueCountFrequency (%)
소 1720
87.1%
중 228
 
11.6%
래 10
 
0.5%
호 8
 
0.4%
선 8
 
0.4%

기점
Text

Distinct1655
Distinct (%)85.7%
Missing8
Missing (%)0.4%
Memory size15.3 KiB
2023-12-13T10:01:51.033145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length8.5152771
Min length3

Characters and Unicode

Total characters16443
Distinct characters143
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

Unique1498 ?
Unique (%)77.6%

Sample

1st row수인로
2nd row광3-7
3rd row수인로
4th row서창동86답
5th row운연천로
ValueCountFrequency (%)
논현동 101
 
2.9%
구월동 74
 
2.1%
도림동 54
 
1.5%
서창동 42
 
1.2%
수산동 34
 
1.0%
인주대로 32
 
0.9%
1 29
 
0.8%
남동대로 29
 
0.8%
2 28
 
0.8%
호구포로 27
 
0.8%
Other values (1452) 3042
87.1%
2023-12-13T10:01:51.347584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1561
 
9.5%
1 1304
 
7.9%
로 1233
 
7.5%
2 1001
 
6.1%
- 822
 
5.0%
3 785
 
4.8%
길 689
 
4.2%
번 686
 
4.2%
4 656
 
4.0%
5 608
 
3.7%
Other values (133) 7098
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7247
44.1%
Decimal Number 6795
41.3%
Space Separator 1561
 
9.5%
Dash Punctuation 822
 
5.0%
Open Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1233
17.0%
길 689
 
9.5%
번 686
 
9.5%
동 532
 
7.3%
구 255
 
3.5%
소 214
 
3.0%
대 210
 
2.9%
남 196
 
2.7%
월 182
 
2.5%
논 176
 
2.4%
Other values (117) 2874
39.7%
Decimal Number
ValueCountFrequency (%)
1 1304
19.2%
2 1001
14.7%
3 785
11.6%
4 656
9.7%
5 608
8.9%
6 597
8.8%
7 558
8.2%
8 467
 
6.9%
0 411
 
6.0%
9 408
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Space Separator
ValueCountFrequency (%)
1561
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 822
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9192
55.9%
Hangul 7247
44.1%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1233
17.0%
길 689
 
9.5%
번 686
 
9.5%
동 532
 
7.3%
구 255
 
3.5%
소 214
 
3.0%
대 210
 
2.9%
남 196
 
2.7%
월 182
 
2.5%
논 176
 
2.4%
Other values (117) 2874
39.7%
Common
ValueCountFrequency (%)
1561
17.0%
1 1304
14.2%
2 1001
10.9%
- 822
8.9%
3 785
8.5%
4 656
7.1%
5 608
 
6.6%
6 597
 
6.5%
7 558
 
6.1%
8 467
 
5.1%
Other values (4) 833
9.1%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9196
55.9%
Hangul 7247
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1561
17.0%
1 1304
14.2%
2 1001
10.9%
- 822
8.9%
3 785
8.5%
4 656
7.1%
5 608
 
6.6%
6 597
 
6.5%
7 558
 
6.1%
8 467
 
5.1%
Other values (6) 837
9.1%
Hangul
ValueCountFrequency (%)
로 1233
17.0%
길 689
 
9.5%
번 686
 
9.5%
동 532
 
7.3%
구 255
 
3.5%
소 214
 
3.0%
대 210
 
2.9%
남 196
 
2.7%
월 182
 
2.5%
논 176
 
2.4%
Other values (117) 2874
39.7%

종점
Text

MISSING 

Distinct1665
Distinct (%)86.9%
Missing24
Missing (%)1.2%
Memory size15.3 KiB
2023-12-13T10:01:51.565958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length9.0031332
Min length3

Characters and Unicode

Total characters17241
Distinct characters139
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1517 ?
Unique (%)79.2%

Sample

1st row장수동 30-3
2nd row수인로
3rd row운연천로 제2선
4th row운연동195-44임
5th row시흥시계
ValueCountFrequency (%)
논현동 111
 
3.2%
구월동 84
 
2.4%
도림동 56
 
1.6%
서창동 48
 
1.4%
수산동 35
 
1.0%
만수동 31
 
0.9%
간석동 26
 
0.7%
19 21
 
0.6%
3 20
 
0.6%
22 17
 
0.5%
Other values (1535) 3037
87.1%
2023-12-13T10:01:51.893003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1571
 
9.1%
1 1294
 
7.5%
로 1183
 
6.9%
2 1042
 
6.0%
- 892
 
5.2%
3 852
 
4.9%
길 819
 
4.8%
번 816
 
4.7%
4 715
 
4.1%
5 664
 
3.9%
Other values (129) 7393
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7519
43.6%
Decimal Number 7247
42.0%
Space Separator 1571
 
9.1%
Dash Punctuation 892
 
5.2%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1183
15.7%
길 819
 
10.9%
번 816
 
10.9%
동 564
 
7.5%
소 276
 
3.7%
구 240
 
3.2%
논 198
 
2.6%
월 182
 
2.4%
남 174
 
2.3%
서 173
 
2.3%
Other values (115) 2894
38.5%
Decimal Number
ValueCountFrequency (%)
1 1294
17.9%
2 1042
14.4%
3 852
11.8%
4 715
9.9%
5 664
9.2%
6 653
9.0%
7 597
8.2%
8 506
 
7.0%
0 474
 
6.5%
9 450
 
6.2%
Space Separator
ValueCountFrequency (%)
1571
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 892
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9722
56.4%
Hangul 7519
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1183
15.7%
길 819
 
10.9%
번 816
 
10.9%
동 564
 
7.5%
소 276
 
3.7%
구 240
 
3.2%
논 198
 
2.6%
월 182
 
2.4%
남 174
 
2.3%
서 173
 
2.3%
Other values (115) 2894
38.5%
Common
ValueCountFrequency (%)
1571
16.2%
1 1294
13.3%
2 1042
10.7%
- 892
9.2%
3 852
8.8%
4 715
7.4%
5 664
6.8%
6 653
6.7%
7 597
 
6.1%
8 506
 
5.2%
Other values (4) 936
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9722
56.4%
Hangul 7519
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1571
16.2%
1 1294
13.3%
2 1042
10.7%
- 892
9.2%
3 852
8.8%
4 715
7.4%
5 664
6.8%
6 653
6.7%
7 597
 
6.1%
8 506
 
5.2%
Other values (4) 936
9.6%
Hangul
ValueCountFrequency (%)
로 1183
15.7%
길 819
 
10.9%
번 816
 
10.9%
동 564
 
7.5%
소 276
 
3.7%
구 240
 
3.2%
논 198
 
2.6%
월 182
 
2.4%
남 174
 
2.3%
서 173
 
2.3%
Other values (115) 2894
38.5%

전체길이
Real number (ℝ)

HIGH CORRELATION 

Distinct526
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.213
Minimum5
Maximum5646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-13T10:01:51.999331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median125
Q3236
95-th percentile572.2
Maximum5646
Range5641
Interquartile range (IQR)167

Descriptive statistics

Standard deviation327.11641
Coefficient of variation (CV)1.6018393
Kurtosis109.67834
Mean204.213
Median Absolute Deviation (MAD)73
Skewness8.5294619
Sum395969
Variance107005.15
MonotonicityNot monotonic
2023-12-13T10:01:52.115179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 19
 
1.0%
48 19
 
1.0%
135 18
 
0.9%
108 17
 
0.9%
10 17
 
0.9%
106 16
 
0.8%
100 15
 
0.8%
29 15
 
0.8%
36 15
 
0.8%
85 14
 
0.7%
Other values (516) 1774
91.5%
ValueCountFrequency (%)
5 2
 
0.1%
7 8
0.4%
8 5
 
0.3%
9 5
 
0.3%
10 17
0.9%
11 4
 
0.2%
12 3
 
0.2%
13 5
 
0.3%
14 5
 
0.3%
15 4
 
0.2%
ValueCountFrequency (%)
5646 1
0.1%
5598 1
0.1%
4408 1
0.1%
3710 1
0.1%
3625 1
0.1%
2550 1
0.1%
2374 1
0.1%
2287 1
0.1%
2212 1
0.1%
2060 1
0.1%

연장길이
Real number (ℝ)

HIGH CORRELATION 

Distinct526
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.213
Minimum5
Maximum5646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-13T10:01:52.223734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median125
Q3236
95-th percentile572.2
Maximum5646
Range5641
Interquartile range (IQR)167

Descriptive statistics

Standard deviation327.11641
Coefficient of variation (CV)1.6018393
Kurtosis109.67834
Mean204.213
Median Absolute Deviation (MAD)73
Skewness8.5294619
Sum395969
Variance107005.15
MonotonicityNot monotonic
2023-12-13T10:01:52.322834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 19
 
1.0%
48 19
 
1.0%
135 18
 
0.9%
108 17
 
0.9%
10 17
 
0.9%
106 16
 
0.8%
100 15
 
0.8%
29 15
 
0.8%
36 15
 
0.8%
85 14
 
0.7%
Other values (516) 1774
91.5%
ValueCountFrequency (%)
5 2
 
0.1%
7 8
0.4%
8 5
 
0.3%
9 5
 
0.3%
10 17
0.9%
11 4
 
0.2%
12 3
 
0.2%
13 5
 
0.3%
14 5
 
0.3%
15 4
 
0.2%
ValueCountFrequency (%)
5646 1
0.1%
5598 1
0.1%
4408 1
0.1%
3710 1
0.1%
3625 1
0.1%
2550 1
0.1%
2374 1
0.1%
2287 1
0.1%
2212 1
0.1%
2060 1
0.1%

1차선길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct355
Distinct (%)28.6%
Missing699
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean144.72661
Minimum5
Maximum3625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-13T10:01:52.423061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile17
Q151
median104
Q3167
95-th percentile426
Maximum3625
Range3620
Interquartile range (IQR)116

Descriptive statistics

Standard deviation181.19214
Coefficient of variation (CV)1.2519615
Kurtosis117.36745
Mean144.72661
Median Absolute Deviation (MAD)56
Skewness7.7194967
Sum179461
Variance32830.593
MonotonicityNot monotonic
2023-12-13T10:01:52.523593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 16
 
0.8%
108 16
 
0.8%
135 15
 
0.8%
48 15
 
0.8%
86 14
 
0.7%
85 13
 
0.7%
97 13
 
0.7%
29 12
 
0.6%
26 12
 
0.6%
100 11
 
0.6%
Other values (345) 1103
56.9%
(Missing) 699
36.0%
ValueCountFrequency (%)
5 2
 
0.1%
7 8
0.4%
8 5
 
0.3%
9 5
 
0.3%
10 16
0.8%
11 4
 
0.2%
12 3
 
0.2%
13 5
 
0.3%
14 5
 
0.3%
15 3
 
0.2%
ValueCountFrequency (%)
3625 1
0.1%
1415 1
0.1%
1221 1
0.1%
1220 1
0.1%
1205 1
0.1%
1180 1
0.1%
1170 1
0.1%
1146 1
0.1%
960 1
0.1%
921 1
0.1%

2차선길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct360
Distinct (%)55.6%
Missing1292
Missing (%)66.6%
Infinite0
Infinite (%)0.0%
Mean260.42968
Minimum10
Maximum4408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-13T10:01:52.630084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile38
Q1106
median186
Q3308.5
95-th percentile678
Maximum4408
Range4398
Interquartile range (IQR)202.5

Descriptive statistics

Standard deviation300.63684
Coefficient of variation (CV)1.1543878
Kurtosis63.185903
Mean260.42968
Median Absolute Deviation (MAD)91
Skewness6.0230359
Sum168498
Variance90382.509
MonotonicityNot monotonic
2023-12-13T10:01:52.728894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 10
 
0.5%
106 9
 
0.5%
107 6
 
0.3%
105 6
 
0.3%
98 5
 
0.3%
99 5
 
0.3%
149 5
 
0.3%
92 5
 
0.3%
30 5
 
0.3%
109 5
 
0.3%
Other values (350) 586
30.2%
(Missing) 1292
66.6%
ValueCountFrequency (%)
10 1
0.1%
15 1
0.1%
18 1
0.1%
19 1
0.1%
23 1
0.1%
24 1
0.1%
25 2
0.1%
26 1
0.1%
27 1
0.1%
28 2
0.1%
ValueCountFrequency (%)
4408 1
0.1%
2212 1
0.1%
2060 1
0.1%
1867 1
0.1%
1659 1
0.1%
1550 1
0.1%
1500 1
0.1%
1454 1
0.1%
1448 1
0.1%
1249 1
0.1%

3차선길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1917
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean988.90909
Minimum50
Maximum5646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-13T10:01:52.813447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile100.3
Q1175
median400.5
Q3674
95-th percentile5436.8
Maximum5646
Range5596
Interquartile range (IQR)499

Descriptive statistics

Standard deviation1598.6434
Coefficient of variation (CV)1.6165727
Kurtosis5.5540257
Mean988.90909
Median Absolute Deviation (MAD)265
Skewness2.5102777
Sum21756
Variance2555660.8
MonotonicityNot monotonic
2023-12-13T10:01:52.898306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
373 1
 
0.1%
150 1
 
0.1%
50 1
 
0.1%
394 1
 
0.1%
465 1
 
0.1%
699 1
 
0.1%
5646 1
 
0.1%
5598 1
 
0.1%
308 1
 
0.1%
933 1
 
0.1%
Other values (12) 12
 
0.6%
(Missing) 1917
98.9%
ValueCountFrequency (%)
50 1
0.1%
100 1
0.1%
106 1
0.1%
107 1
0.1%
121 1
0.1%
150 1
0.1%
250 1
0.1%
308 1
0.1%
373 1
0.1%
386 1
0.1%
ValueCountFrequency (%)
5646 1
0.1%
5598 1
0.1%
2374 1
0.1%
1770 1
0.1%
933 1
0.1%
699 1
0.1%
599 1
0.1%
504 1
0.1%
465 1
0.1%
416 1
0.1%

4차선길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)100.0%
Missing1908
Missing (%)98.4%
Infinite0
Infinite (%)0.0%
Mean833.58065
Minimum52
Maximum3710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2023-12-13T10:01:52.982732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile108.5
Q1260.5
median572
Q31064.5
95-th percentile2129
Maximum3710
Range3658
Interquartile range (IQR)804

Descriptive statistics

Standard deviation794.23677
Coefficient of variation (CV)0.95280136
Kurtosis4.6868589
Mean833.58065
Median Absolute Deviation (MAD)399
Skewness1.9100297
Sum25841
Variance630812.05
MonotonicityNot monotonic
2023-12-13T10:01:53.072689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
905 1
 
0.1%
1086 1
 
0.1%
3710 1
 
0.1%
320 1
 
0.1%
346 1
 
0.1%
1318 1
 
0.1%
173 1
 
0.1%
1043 1
 
0.1%
52 1
 
0.1%
811 1
 
0.1%
Other values (21) 21
 
1.1%
(Missing) 1908
98.4%
ValueCountFrequency (%)
52 1
0.1%
107 1
0.1%
110 1
0.1%
129 1
0.1%
165 1
0.1%
173 1
0.1%
254 1
0.1%
258 1
0.1%
263 1
0.1%
320 1
0.1%
ValueCountFrequency (%)
3710 1
0.1%
2287 1
0.1%
1971 1
0.1%
1917 1
0.1%
1375 1
0.1%
1360 1
0.1%
1318 1
0.1%
1086 1
0.1%
1043 1
0.1%
1000 1
0.1%

6차선길이
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
<NA>
1938 
413
 
1

Length

Max length4
Median length4
Mean length3.9994843
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1938
99.9%
413 1
 
0.1%

Length

2023-12-13T10:01:53.173714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T10:01:53.247583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1938
99.9%
413 1
 
0.1%

Interactions

2023-12-13T10:01:49.004200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.168950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.677834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.153064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.623933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.148304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.574322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:49.062509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.270938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.750343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.224270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.703606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.215158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.630267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:49.341476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.351509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.822087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.307592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.789049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.276706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.691626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:49.433196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.421602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.893948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.371521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.862953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.334514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.762193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:49.488317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.495353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.967109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.443873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.947177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.397771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.827326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:49.548953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.560064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.027666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.502688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.017109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.460896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.882255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:49.602679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:46.615577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.085328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:47.558050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.082850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.515873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:01:48.939984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T10:01:53.300116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호전체길이연장길이1차선길이2차선길이3차선길이4차선길이
노선번호1.0000.3500.3500.2650.156NaNNaN
전체길이0.3501.0001.0000.8990.9070.9980.947
연장길이0.3501.0001.0000.8990.9070.9980.947
1차선길이0.2650.8990.8991.000NaNNaNNaN
2차선길이0.1560.9070.907NaN1.000NaNNaN
3차선길이NaN0.9980.998NaNNaN1.000NaN
4차선길이NaN0.9470.947NaNNaNNaN1.000
2023-12-13T10:01:53.388337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호전체길이연장길이1차선길이2차선길이3차선길이4차선길이6차선길이
노선번호1.000-0.422-0.422-0.244-0.3040.067-0.001NaN
전체길이-0.4221.0001.0001.0001.0001.0000.985NaN
연장길이-0.4221.0001.0001.0001.0001.0000.985NaN
1차선길이-0.2441.0001.0001.000NaNNaNNaN0.000
2차선길이-0.3041.0001.000NaN1.000NaNNaN0.000
3차선길이0.0671.0001.000NaNNaN1.000NaN0.000
4차선길이-0.0010.9850.985NaNNaNNaN1.0000.000
6차선길이NaNNaNNaN0.0000.0000.0000.0001.000

Missing values

2023-12-13T10:01:49.694034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T10:01:49.804134image/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.
2023-12-13T10:01:49.902212image/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

노선번호노선명기점종점전체길이연장길이1차선길이2차선길이3차선길이4차선길이6차선길이
01중1-42수인로장수동 30-322122212<NA>2212<NA><NA><NA>
12중3-184광3-7수인로400400<NA>400<NA><NA><NA>
23중1-325수인로운연천로 제2선16591659<NA>1659<NA><NA><NA>
34중1-315서창동86답운연동195-44임254254<NA><NA><NA>254<NA>
45중3-69운연천로시흥시계900900900<NA><NA><NA><NA>
56중1-326서창2지구계운연동 462-24905905<NA><NA><NA>905<NA>
67중1-316서창2지구계운연천로 제2선145145<NA>145<NA><NA><NA>
78중3-373서창동 85-11서창동 85-1969696<NA><NA><NA><NA>
89중3-376서창동 84-1서창동 83-11117117117<NA><NA><NA><NA>
910중2-604서창동 83-47서창동 83-67407407<NA><NA>407<NA><NA>
노선번호노선명기점종점전체길이연장길이1차선길이2차선길이3차선길이4차선길이6차선길이
19293947소3-201소2-116소3-202535353<NA><NA><NA><NA>
19303948소3-203소2-110소3-204123123123<NA><NA><NA><NA>
19313949소3-204소2-112소2-114949494<NA><NA><NA><NA>
19323950소3-223대3-13소2-81239239239<NA><NA><NA><NA>
19333951소3-224소3-223소3-225323232<NA><NA><NA><NA>
19343952소3-225대3-13소2-81241241241<NA><NA><NA><NA>
19353953소3-226중2-64소2-225180180180<NA><NA><NA><NA>
19363954소3-227소2-226소2-81110110110<NA><NA><NA><NA>
19373955소3-267소2-25광3-7848484<NA><NA><NA><NA>
19383956소3-53용천로 174-1간석로 105464464464<NA><NA><NA><NA>