Overview

Dataset statistics

Number of variables14
Number of observations56
Missing cells17
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory121.4 B

Variable types

Text3
Numeric7
Categorical3
DateTime1

Dataset

Description대구광역시 남구 관내 가로수에 대한 데이터로 가로수종류, 수량, 가로수길시작위·경도, 가로수길종료위·경도, 식재연도 등의 항목을 제공합니다.
Author대구광역시 남구
URLhttps://www.data.go.kr/data/15101034/fileData.do

Alerts

관리기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
가로수종류 is highly overall correlated with 가로수길소개High correlation
가로수길소개 is highly overall correlated with 가로수종류High correlation
가로수길시작위도 is highly overall correlated with 가로수길종료위도High correlation
가로수길시작경도 is highly overall correlated with 가로수길종료경도High correlation
가로수길종료위도 is highly overall correlated with 가로수길시작위도High correlation
가로수길종료경도 is highly overall correlated with 가로수길시작경도High correlation
가로수수량 is highly overall correlated with 가로수길길이High correlation
가로수길길이 is highly overall correlated with 가로수수량High correlation
식재연도 has 17 (30.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:14:12.446006
Analysis finished2023-12-12 15:14:18.063134
Duration5.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가로수길명
Text

Distinct45
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size580.0 B
2023-12-13T00:14:18.251751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10
Mean length4.9464286
Min length3

Characters and Unicode

Total characters277
Distinct characters61
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

Unique36 ?
Unique (%)64.3%

Sample

1st row중앙대로
2nd row중앙대로
3rd row대명로
4th row월배로
5th row봉덕로
ValueCountFrequency (%)
앞산순환로 4
 
6.9%
안지랑로 3
 
5.2%
이천로 3
 
5.2%
중앙대로 2
 
3.4%
두류공원로 2
 
3.4%
대봉로 2
 
3.4%
대덕로 2
 
3.4%
현충로 2
 
3.4%
봉덕로 2
 
3.4%
명덕로8길 1
 
1.7%
Other values (35) 35
60.3%
2023-12-13T00:14:18.681116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 55
19.9%
길 25
 
9.0%
대 21
 
7.6%
덕 11
 
4.0%
봉 10
 
3.6%
2 10
 
3.6%
명 10
 
3.6%
1 8
 
2.9%
천 6
 
2.2%
중 5
 
1.8%
Other values (51) 116
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 229
82.7%
Decimal Number 40
 
14.4%
Space Separator 3
 
1.1%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 55
24.0%
길 25
 
10.9%
대 21
 
9.2%
덕 11
 
4.8%
봉 10
 
4.4%
명 10
 
4.4%
천 6
 
2.6%
중 5
 
2.2%
앙 5
 
2.2%
지 5
 
2.2%
Other values (37) 76
33.2%
Decimal Number
ValueCountFrequency (%)
2 10
25.0%
1 8
20.0%
6 5
12.5%
9 4
 
10.0%
3 3
 
7.5%
4 3
 
7.5%
0 2
 
5.0%
8 2
 
5.0%
5 2
 
5.0%
7 1
 
2.5%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 229
82.7%
Common 48
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 55
24.0%
길 25
 
10.9%
대 21
 
9.2%
덕 11
 
4.8%
봉 10
 
4.4%
명 10
 
4.4%
천 6
 
2.6%
중 5
 
2.2%
앙 5
 
2.2%
지 5
 
2.2%
Other values (37) 76
33.2%
Common
ValueCountFrequency (%)
2 10
20.8%
1 8
16.7%
6 5
10.4%
9 4
 
8.3%
3 3
 
6.2%
4 3
 
6.2%
3
 
6.2%
0 2
 
4.2%
8 2
 
4.2%
5 2
 
4.2%
Other values (4) 6
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 229
82.7%
ASCII 48
 
17.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 55
24.0%
길 25
 
10.9%
대 21
 
9.2%
덕 11
 
4.8%
봉 10
 
4.4%
명 10
 
4.4%
천 6
 
2.6%
중 5
 
2.2%
앙 5
 
2.2%
지 5
 
2.2%
Other values (37) 76
33.2%
ASCII
ValueCountFrequency (%)
2 10
20.8%
1 8
16.7%
6 5
10.4%
9 4
 
8.3%
3 3
 
6.2%
4 3
 
6.2%
3
 
6.2%
0 2
 
4.2%
8 2
 
4.2%
5 2
 
4.2%
Other values (4) 6
12.5%

가로수길시작위도
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.843894
Minimum35.832476
Maximum35.859282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T00:14:18.868243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.832476
5-th percentile35.83343
Q135.838873
median35.843029
Q335.848133
95-th percentile35.856287
Maximum35.859282
Range0.0268061
Interquartile range (IQR)0.009260275

Descriptive statistics

Standard deviation0.0072132317
Coefficient of variation (CV)0.00020124018
Kurtosis-0.59089654
Mean35.843894
Median Absolute Deviation (MAD)0.0044855
Skewness0.47713945
Sum2007.2581
Variance5.2030711 × 10-5
MonotonicityNot monotonic
2023-12-13T00:14:19.055108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.841533 2
 
3.6%
35.844665 2
 
3.6%
35.832686 1
 
1.8%
35.840734 1
 
1.8%
35.8389061 1
 
1.8%
35.834724 1
 
1.8%
35.852516 1
 
1.8%
35.84399 1
 
1.8%
35.838774 1
 
1.8%
35.849891 1
 
1.8%
Other values (44) 44
78.6%
ValueCountFrequency (%)
35.8324759 1
1.8%
35.832686 1
1.8%
35.832836 1
1.8%
35.8336276 1
1.8%
35.834724 1
1.8%
35.8347534 1
1.8%
35.8352343 1
1.8%
35.8357409 1
1.8%
35.836244 1
1.8%
35.837222 1
1.8%
ValueCountFrequency (%)
35.859282 1
1.8%
35.8590119 1
1.8%
35.8580032 1
1.8%
35.855715 1
1.8%
35.8551108 1
1.8%
35.8550864 1
1.8%
35.8547741 1
1.8%
35.854742 1
1.8%
35.8531535 1
1.8%
35.852516 1
1.8%

가로수길시작경도
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58728
Minimum128.55593
Maximum128.6074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T00:14:19.244023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.55593
5-th percentile128.56306
Q1128.57631
median128.59051
Q3128.60067
95-th percentile128.60576
Maximum128.6074
Range0.0514749
Interquartile range (IQR)0.0243567

Descriptive statistics

Standard deviation0.014317258
Coefficient of variation (CV)0.00011134272
Kurtosis-0.86212361
Mean128.58728
Median Absolute Deviation (MAD)0.01141735
Skewness-0.35835038
Sum7200.8878
Variance0.00020498387
MonotonicityNot monotonic
2023-12-13T00:14:19.419893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.590705 2
 
3.6%
128.605759 2
 
3.6%
128.596512 1
 
1.8%
128.601105 1
 
1.8%
128.5715081 1
 
1.8%
128.571221 1
 
1.8%
128.583554 1
 
1.8%
128.580761 1
 
1.8%
128.576748 1
 
1.8%
128.595416 1
 
1.8%
Other values (44) 44
78.6%
ValueCountFrequency (%)
128.5559291 1
1.8%
128.557404 1
1.8%
128.558189 1
1.8%
128.5646882 1
1.8%
128.565475 1
1.8%
128.569746 1
1.8%
128.571221 1
1.8%
128.5715081 1
1.8%
128.572254 1
1.8%
128.5726993 1
1.8%
ValueCountFrequency (%)
128.607404 1
1.8%
128.6068576 1
1.8%
128.605759 2
3.6%
128.605634 1
1.8%
128.605237 1
1.8%
128.6051131 1
1.8%
128.604925 1
1.8%
128.604537 1
1.8%
128.6036114 1
1.8%
128.601105 1
1.8%

가로수길종료위도
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.835704
Minimum35.405034
Maximum35.859883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T00:14:19.601808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.405034
5-th percentile35.830468
Q135.835127
median35.843107
Q335.848509
95-th percentile35.85713
Maximum35.859883
Range0.454849
Interquartile range (IQR)0.013381425

Descriptive statistics

Standard deviation0.059217948
Coefficient of variation (CV)0.0016524846
Kurtosis53.56068
Mean35.835704
Median Absolute Deviation (MAD)0.00749815
Skewness-7.2413365
Sum2006.7994
Variance0.0035067653
MonotonicityNot monotonic
2023-12-13T00:14:19.780381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.8568388 2
 
3.6%
35.845657 2
 
3.6%
35.834581 1
 
1.8%
35.840884 1
 
1.8%
35.8350602 1
 
1.8%
35.831575 1
 
1.8%
35.847129 1
 
1.8%
35.8439774 1
 
1.8%
35.830869 1
 
1.8%
35.850146 1
 
1.8%
Other values (44) 44
78.6%
ValueCountFrequency (%)
35.405034 1
1.8%
35.828716 1
1.8%
35.829265 1
1.8%
35.830869 1
1.8%
35.831575 1
1.8%
35.8323212 1
1.8%
35.832325 1
1.8%
35.8324713 1
1.8%
35.8324759 1
1.8%
35.834126 1
1.8%
ValueCountFrequency (%)
35.859883 1
1.8%
35.8590119 1
1.8%
35.8580032 1
1.8%
35.8568388 2
3.6%
35.856154 1
1.8%
35.855131 1
1.8%
35.854946 1
1.8%
35.854625 1
1.8%
35.8546106 1
1.8%
35.8522246 1
1.8%

가로수길종료경도
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58565
Minimum128.55565
Maximum128.60591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T00:14:19.952622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.55565
5-th percentile128.56056
Q1128.5744
median128.59062
Q3128.59818
95-th percentile128.60518
Maximum128.60591
Range0.0502625
Interquartile range (IQR)0.02377875

Descriptive statistics

Standard deviation0.01436891
Coefficient of variation (CV)0.00011174583
Kurtosis-1.0156275
Mean128.58565
Median Absolute Deviation (MAD)0.0119498
Skewness-0.37513631
Sum7200.7965
Variance0.00020646558
MonotonicityNot monotonic
2023-12-13T00:14:20.117740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.590886 2
 
3.6%
128.590795 2
 
3.6%
128.596812 1
 
1.8%
128.602823 1
 
1.8%
128.5712111 1
 
1.8%
128.571267 1
 
1.8%
128.590435 1
 
1.8%
128.5811845 1
 
1.8%
128.573485 1
 
1.8%
128.595285 1
 
1.8%
Other values (44) 44
78.6%
ValueCountFrequency (%)
128.5556505 1
1.8%
128.5580099 1
1.8%
128.5583026 1
1.8%
128.561314 1
1.8%
128.562275 1
1.8%
128.5650151 1
1.8%
128.5668605 1
1.8%
128.569745 1
1.8%
128.5712111 1
1.8%
128.571267 1
1.8%
ValueCountFrequency (%)
128.605913 1
1.8%
128.605586 1
1.8%
128.605567 1
1.8%
128.605051 1
1.8%
128.605041 1
1.8%
128.602823 1
1.8%
128.602483 1
1.8%
128.6008699 1
1.8%
128.6007794 1
1.8%
128.5990671 1
1.8%

가로수종류
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size580.0 B
양버즘나무
14 
이팝나무
12 
은행나무
8 
왕벚나무
3 
은단풍나무
3 
Other values (14)
16 

Length

Max length20
Median length16
Mean length5.625
Min length3

Unique

Unique12 ?
Unique (%)21.4%

Sample

1st row느티나무+은행나무
2nd row이팝나무
3rd row은행나무
4th row은행나무
5th row느티나무

Common Values

ValueCountFrequency (%)
양버즘나무 14
25.0%
이팝나무 12
21.4%
은행나무 8
14.3%
왕벚나무 3
 
5.4%
은단풍나무 3
 
5.4%
느티나무 2
 
3.6%
대왕참나무 2
 
3.6%
중국단풍나무 1
 
1.8%
메타세콰이어 1
 
1.8%
느티나무+소나무+단풍나무 1
 
1.8%
Other values (9) 9
16.1%

Length

2023-12-13T00:14:20.304812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
양버즘나무 14
25.0%
이팝나무 12
21.4%
은행나무 8
14.3%
왕벚나무 3
 
5.4%
은단풍나무 3
 
5.4%
느티나무 2
 
3.6%
대왕참나무 2
 
3.6%
은단풍나무+은행나무+양버즘나무 1
 
1.8%
느티나무+은행나무 1
 
1.8%
은행나무+은단풍나무+이팝나무 1
 
1.8%
Other values (9) 9
16.1%

가로수수량
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.625
Minimum1
Maximum708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T00:14:20.459986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.75
Q116
median75.5
Q3153.75
95-th percentile415
Maximum708
Range707
Interquartile range (IQR)137.75

Descriptive statistics

Standard deviation146.34822
Coefficient of variation (CV)1.2233916
Kurtosis5.2686032
Mean119.625
Median Absolute Deviation (MAD)63
Skewness2.1590099
Sum6699
Variance21417.802
MonotonicityNot monotonic
2023-12-13T00:14:20.637005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
7 4
 
7.1%
30 2
 
3.6%
194 2
 
3.6%
16 2
 
3.6%
1 2
 
3.6%
4 2
 
3.6%
14 2
 
3.6%
309 1
 
1.8%
156 1
 
1.8%
147 1
 
1.8%
Other values (37) 37
66.1%
ValueCountFrequency (%)
1 2
3.6%
3 1
 
1.8%
4 2
3.6%
7 4
7.1%
11 1
 
1.8%
14 2
3.6%
15 1
 
1.8%
16 2
3.6%
19 1
 
1.8%
22 1
 
1.8%
ValueCountFrequency (%)
708 1
1.8%
563 1
1.8%
499 1
1.8%
387 1
1.8%
320 1
1.8%
309 1
1.8%
250 1
1.8%
239 1
1.8%
231 1
1.8%
214 1
1.8%

가로수길길이
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91496429
Minimum0.05
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T00:14:20.770641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.0875
Q10.2
median0.5
Q31.125
95-th percentile3.35
Maximum4.9
Range4.85
Interquartile range (IQR)0.925

Descriptive statistics

Standard deviation1.077341
Coefficient of variation (CV)1.1774679
Kurtosis3.8948525
Mean0.91496429
Median Absolute Deviation (MAD)0.4
Skewness2.0332423
Sum51.238
Variance1.1606637
MonotonicityNot monotonic
2023-12-13T00:14:20.927716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.2 6
 
10.7%
0.4 5
 
8.9%
0.1 5
 
8.9%
1.0 4
 
7.1%
1.2 3
 
5.4%
0.9 3
 
5.4%
0.05 3
 
5.4%
0.5 3
 
5.4%
0.7 3
 
5.4%
1.3 2
 
3.6%
Other values (18) 19
33.9%
ValueCountFrequency (%)
0.05 3
5.4%
0.1 5
8.9%
0.14 1
 
1.8%
0.16 1
 
1.8%
0.2 6
10.7%
0.23 1
 
1.8%
0.238 1
 
1.8%
0.25 1
 
1.8%
0.27 1
 
1.8%
0.28 1
 
1.8%
ValueCountFrequency (%)
4.9 1
 
1.8%
3.8 2
3.6%
3.2 1
 
1.8%
3.1 1
 
1.8%
3.0 1
 
1.8%
2.0 1
 
1.8%
1.8 1
 
1.8%
1.4 1
 
1.8%
1.3 2
3.6%
1.2 3
5.4%

식재연도
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)61.5%
Missing17
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean1994.2564
Minimum1972
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-13T00:14:21.077490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1972
5-th percentile1973
Q11981
median1997
Q32004.5
95-th percentile2019
Maximum2020
Range48
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation14.538005
Coefficient of variation (CV)0.0072899375
Kurtosis-1.0565663
Mean1994.2564
Median Absolute Deviation (MAD)10
Skewness-0.093711071
Sum77776
Variance211.35358
MonotonicityNot monotonic
2023-12-13T00:14:21.553966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1973 5
 
8.9%
1997 4
 
7.1%
2004 3
 
5.4%
1981 3
 
5.4%
1990 2
 
3.6%
2019 2
 
3.6%
2005 2
 
3.6%
1995 2
 
3.6%
2020 1
 
1.8%
1986 1
 
1.8%
Other values (14) 14
25.0%
(Missing) 17
30.4%
ValueCountFrequency (%)
1972 1
 
1.8%
1973 5
8.9%
1974 1
 
1.8%
1977 1
 
1.8%
1981 3
5.4%
1982 1
 
1.8%
1986 1
 
1.8%
1989 1
 
1.8%
1990 2
 
3.6%
1995 2
 
3.6%
ValueCountFrequency (%)
2020 1
 
1.8%
2019 2
3.6%
2013 1
 
1.8%
2011 1
 
1.8%
2008 1
 
1.8%
2007 1
 
1.8%
2006 1
 
1.8%
2005 2
3.6%
2004 3
5.4%
2003 1
 
1.8%

가로수길소개
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Memory size580.0 B
양버즘나무길
13 
이팝나무길
11 
은행나무길
8 
은단풍나무길
3 
왕벚나무길
3 
Other values (15)
18 

Length

Max length24
Median length19
Mean length7.4107143
Min length4

Unique

Unique12 ?
Unique (%)21.4%

Sample

1st row느티나무, 은행나무, 이팝나무길
2nd row느티나무, 은행나무, 이팝나무길
3rd row은행나무길
4th row은행나무길
5th row느티나무, 양버즘나무길

Common Values

ValueCountFrequency (%)
양버즘나무길 13
23.2%
이팝나무길 11
19.6%
은행나무길 8
14.3%
은단풍나무길 3
 
5.4%
왕벚나무길 3
 
5.4%
느티나무, 은행나무, 이팝나무길 2
 
3.6%
느티나무, 양버즘나무길 2
 
3.6%
대왕참나무길 2
 
3.6%
느티나무길 1
 
1.8%
메타세콰이어길 1
 
1.8%
Other values (10) 10
17.9%

Length

2023-12-13T00:14:21.710268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
양버즘나무길 16
21.6%
이팝나무길 14
18.9%
은행나무길 10
13.5%
은행나무 6
 
8.1%
느티나무 5
 
6.8%
은단풍나무길 3
 
4.1%
왕벚나무길 3
 
4.1%
은단풍나무 3
 
4.1%
대왕참나무길 2
 
2.7%
개잎갈나무길 2
 
2.7%
Other values (10) 10
13.5%

도로명
Text

Distinct45
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size580.0 B
2023-12-13T00:14:21.969174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10
Mean length4.9464286
Min length3

Characters and Unicode

Total characters277
Distinct characters61
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

Unique36 ?
Unique (%)64.3%

Sample

1st row중앙대로
2nd row중앙대로
3rd row대명로
4th row월배로
5th row봉덕로
ValueCountFrequency (%)
앞산순환로 4
 
6.9%
안지랑로 3
 
5.2%
이천로 3
 
5.2%
중앙대로 2
 
3.4%
두류공원로 2
 
3.4%
대봉로 2
 
3.4%
대덕로 2
 
3.4%
현충로 2
 
3.4%
봉덕로 2
 
3.4%
명덕로8길 1
 
1.7%
Other values (35) 35
60.3%
2023-12-13T00:14:22.364626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 55
19.9%
길 25
 
9.0%
대 21
 
7.6%
덕 11
 
4.0%
봉 10
 
3.6%
2 10
 
3.6%
명 10
 
3.6%
1 8
 
2.9%
천 6
 
2.2%
중 5
 
1.8%
Other values (51) 116
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 229
82.7%
Decimal Number 40
 
14.4%
Space Separator 3
 
1.1%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 55
24.0%
길 25
 
10.9%
대 21
 
9.2%
덕 11
 
4.8%
봉 10
 
4.4%
명 10
 
4.4%
천 6
 
2.6%
중 5
 
2.2%
앙 5
 
2.2%
지 5
 
2.2%
Other values (37) 76
33.2%
Decimal Number
ValueCountFrequency (%)
2 10
25.0%
1 8
20.0%
6 5
12.5%
9 4
 
10.0%
3 3
 
7.5%
4 3
 
7.5%
0 2
 
5.0%
8 2
 
5.0%
5 2
 
5.0%
7 1
 
2.5%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 229
82.7%
Common 48
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 55
24.0%
길 25
 
10.9%
대 21
 
9.2%
덕 11
 
4.8%
봉 10
 
4.4%
명 10
 
4.4%
천 6
 
2.6%
중 5
 
2.2%
앙 5
 
2.2%
지 5
 
2.2%
Other values (37) 76
33.2%
Common
ValueCountFrequency (%)
2 10
20.8%
1 8
16.7%
6 5
10.4%
9 4
 
8.3%
3 3
 
6.2%
4 3
 
6.2%
3
 
6.2%
0 2
 
4.2%
8 2
 
4.2%
5 2
 
4.2%
Other values (4) 6
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 229
82.7%
ASCII 48
 
17.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 55
24.0%
길 25
 
10.9%
대 21
 
9.2%
덕 11
 
4.8%
봉 10
 
4.4%
명 10
 
4.4%
천 6
 
2.6%
중 5
 
2.2%
앙 5
 
2.2%
지 5
 
2.2%
Other values (37) 76
33.2%
ASCII
ValueCountFrequency (%)
2 10
20.8%
1 8
16.7%
6 5
10.4%
9 4
 
8.3%
3 3
 
6.2%
4 3
 
6.2%
3
 
6.2%
0 2
 
4.2%
8 2
 
4.2%
5 2
 
4.2%
Other values (4) 6
12.5%

도로구간
Text

Distinct53
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size580.0 B
2023-12-13T00:14:22.612048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length22
Mean length12.928571
Min length4

Characters and Unicode

Total characters724
Distinct characters144
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)89.3%

Sample

1st row캠프워커담장~명덕네거리
2nd row캠프워커담장~명덕네거리
3rd row영대네거리~성당네거리
4th row성당네거리~구달성군청앞
5th row신천대로~영대네거리
ValueCountFrequency (%)
캠프워커담장~명덕네거리 2
 
2.5%
앞 2
 
2.5%
대명3동 2
 
2.5%
대봉교~상동교 2
 
2.5%
신천대로~영대네거리 2
 
2.5%
청구위너스 1
 
1.3%
9-1 1
 
1.3%
봉덕로6길 1
 
1.3%
빌라 1
 
1.3%
26 1
 
1.3%
Other values (64) 64
81.0%
2023-12-13T00:14:23.028782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 44
 
6.1%
리 38
 
5.2%
대 35
 
4.8%
거 35
 
4.8%
로 33
 
4.6%
네 33
 
4.6%
24
 
3.3%
덕 19
 
2.6%
길 17
 
2.3%
교 17
 
2.3%
Other values (134) 429
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 584
80.7%
Decimal Number 60
 
8.3%
Math Symbol 44
 
6.1%
Space Separator 24
 
3.3%
Close Punctuation 5
 
0.7%
Open Punctuation 5
 
0.7%
Dash Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 38
 
6.5%
대 35
 
6.0%
거 35
 
6.0%
로 33
 
5.7%
네 33
 
5.7%
덕 19
 
3.3%
길 17
 
2.9%
교 17
 
2.9%
봉 17
 
2.9%
앞 16
 
2.7%
Other values (118) 324
55.5%
Decimal Number
ValueCountFrequency (%)
2 11
18.3%
3 10
16.7%
1 7
11.7%
5 7
11.7%
9 6
10.0%
4 6
10.0%
6 5
8.3%
7 4
 
6.7%
0 3
 
5.0%
8 1
 
1.7%
Math Symbol
ValueCountFrequency (%)
~ 44
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 584
80.7%
Common 140
 
19.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 38
 
6.5%
대 35
 
6.0%
거 35
 
6.0%
로 33
 
5.7%
네 33
 
5.7%
덕 19
 
3.3%
길 17
 
2.9%
교 17
 
2.9%
봉 17
 
2.9%
앞 16
 
2.7%
Other values (118) 324
55.5%
Common
ValueCountFrequency (%)
~ 44
31.4%
24
17.1%
2 11
 
7.9%
3 10
 
7.1%
1 7
 
5.0%
5 7
 
5.0%
9 6
 
4.3%
4 6
 
4.3%
6 5
 
3.6%
) 5
 
3.6%
Other values (6) 15
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 584
80.7%
ASCII 140
 
19.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 44
31.4%
24
17.1%
2 11
 
7.9%
3 10
 
7.1%
1 7
 
5.0%
5 7
 
5.0%
9 6
 
4.3%
4 6
 
4.3%
6 5
 
3.6%
) 5
 
3.6%
Other values (6) 15
 
10.7%
Hangul
ValueCountFrequency (%)
리 38
 
6.5%
대 35
 
6.0%
거 35
 
6.0%
로 33
 
5.7%
네 33
 
5.7%
덕 19
 
3.3%
길 17
 
2.9%
교 17
 
2.9%
봉 17
 
2.9%
앞 16
 
2.7%
Other values (118) 324
55.5%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
대구광역시 남구청
56 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 남구청
2nd row대구광역시 남구청
3rd row대구광역시 남구청
4th row대구광역시 남구청
5th row대구광역시 남구청

Common Values

ValueCountFrequency (%)
대구광역시 남구청 56
100.0%

Length

2023-12-13T00:14:23.196773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:14:23.283959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 56
50.0%
남구청 56
50.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
Minimum2021-12-31 00:00:00
Maximum2021-12-31 00:00:00
2023-12-13T00:14:23.371720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:23.501314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T00:14:17.107670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.185183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.881295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.550831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.093457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.948781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.522103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:17.190644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.278054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.963624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.645683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.468277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.027442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.600339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:17.288083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.370448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.043442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.720130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.541160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.104780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.683708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:17.377983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.466601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.143743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.788452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.629395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.190948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.770996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:17.452962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.550987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.266008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.862034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.703597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.273979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.848547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:17.545787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.658975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.364175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.947305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.787207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.361624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.942272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:17.621967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:13.768220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:14.443594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.023728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:15.863875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:16.438748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:17.025089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:14:23.600385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가로수길명가로수길시작위도가로수길시작경도가로수길종료위도가로수길종료경도가로수종류가로수수량가로수길길이식재연도가로수길소개도로명도로구간
가로수길명1.0000.9440.9511.0000.9310.9120.0000.0000.0000.9631.0000.996
가로수길시작위도0.9441.0000.6920.0000.2260.5470.0000.4180.0000.6900.9441.000
가로수길시작경도0.9510.6921.0000.0000.8600.0000.0000.3530.7050.4340.9511.000
가로수길종료위도1.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0001.000
가로수길종료경도0.9310.2260.8600.0001.0000.6050.6820.6290.0000.7820.9311.000
가로수종류0.9120.5470.0000.0000.6051.0000.0000.0000.5600.9970.9120.000
가로수수량0.0000.0000.0000.0000.6820.0001.0000.9550.4310.0000.0000.000
가로수길길이0.0000.4180.3530.0000.6290.0000.9551.0000.2850.0000.0001.000
식재연도0.0000.0000.7050.0000.0000.5600.4310.2851.0000.5620.0000.493
가로수길소개0.9630.6900.4340.0000.7820.9970.0000.0000.5621.0000.9630.982
도로명1.0000.9440.9511.0000.9310.9120.0000.0000.0000.9631.0000.996
도로구간0.9961.0001.0001.0001.0000.0000.0001.0000.4930.9820.9961.000
2023-12-13T00:14:23.738561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가로수종류가로수길소개
가로수종류1.0000.951
가로수길소개0.9511.000
2023-12-13T00:14:23.832282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가로수길시작위도가로수길시작경도가로수길종료위도가로수길종료경도가로수수량가로수길길이식재연도가로수종류가로수길소개
가로수길시작위도1.0000.3110.7020.406-0.210-0.046-0.1920.1980.243
가로수길시작경도0.3111.0000.0650.7590.1200.0380.2990.1170.209
가로수길종료위도0.7020.0651.0000.248-0.202-0.079-0.1560.0000.000
가로수길종료경도0.4060.7590.2481.000-0.027-0.1640.1860.2670.340
가로수수량-0.2100.120-0.202-0.0271.0000.794-0.2520.0000.000
가로수길길이-0.0460.038-0.079-0.1640.7941.000-0.1420.0000.000
식재연도-0.1920.299-0.1560.186-0.252-0.1421.0000.1870.186
가로수종류0.1980.1170.0000.2670.0000.0000.1871.0000.951
가로수길소개0.2430.2090.0000.3400.0000.0000.1860.9511.000

Missing values

2023-12-13T00:14:17.763776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:14:17.982439image/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

가로수길명가로수길시작위도가로수길시작경도가로수길종료위도가로수길종료경도가로수종류가로수수량가로수길길이식재연도가로수길소개도로명도로구간관리기관명데이터기준일자
0중앙대로35.841533128.59070535.856839128.590886느티나무+은행나무3091.31997느티나무, 은행나무, 이팝나무길중앙대로캠프워커담장~명덕네거리대구광역시 남구청2021-12-31
1중앙대로35.841533128.59070535.856839128.590886이팝나무1481.32013느티나무, 은행나무, 이팝나무길중앙대로캠프워커담장~명덕네거리대구광역시 남구청2021-12-31
2대명로35.845029128.5903435.837637128.55801은행나무5633.21997은행나무길대명로영대네거리~성당네거리대구광역시 남구청2021-12-31
3월배로35.837461128.55740435.834562128.555651은행나무301.21997은행나무길월배로성당네거리~구달성군청앞대구광역시 남구청2021-12-31
4봉덕로35.844665128.60575935.845657128.590795느티나무371.22008느티나무, 양버즘나무길봉덕로신천대로~영대네거리대구광역시 남구청2021-12-31
5봉덕로35.844665128.60575935.845657128.590795양버즘나무1941.21990느티나무, 양버즘나무길봉덕로신천대로~영대네거리대구광역시 남구청2021-12-31
6성당로35.838327128.55818935.859883128.574608양버즘나무2313.11989양버즘나무길성당로성당네거리~내당네거리대구광역시 남구청2021-12-31
7명덕로35.859282128.57602235.854946128.605586양버즘나무1943.01973양버즘나무길명덕로내당네거리~대봉교대구광역시 남구청2021-12-31
8현충로35.839572128.57996935.832476128.578583왕벚나무1460.91972왕벚나무길현충로앞산네거리~현충삼거리대구광역시 남구청2021-12-31
9현충로35.840805128.57974335.856154128.582998양버즘나무3871.81973양버즘나무길현충로앞산네거리~계명네거리대구광역시 남구청2021-12-31
가로수길명가로수길시작위도가로수길시작경도가로수길종료위도가로수길종료경도가로수종류가로수수량가로수길길이식재연도가로수길소개도로명도로구간관리기관명데이터기준일자
46봉덕로19길35.844894128.60081535.846913128.599067이팝나무580.27<NA>이팝나무길봉덕로19길봉덕로19길55~봉덕로93대구광역시 남구청2021-12-31
47봉덕로21길35.844873128.60110535.847063128.600779이팝나무700.252004이팝나무길봉덕로21길봉덕로21길49~봉덕로93대구광역시 남구청2021-12-31
48영선길35.855086128.59124735.854611128.591593개잎갈나무+은행나무110.238<NA>개잎갈나무, 은행나무길영선길영선길6~영선길45대구광역시 남구청2021-12-31
49중앙대로22길35.84293128.59067235.842916128.592084양버즘나무140.1<NA>양버즘나무길중앙대로22길중앙대로110~중앙대로22길 27대구광역시 남구청2021-12-31
50봉덕로20길35.844407128.60103835.842366128.60087이팝나무780.232004이팝나무길봉덕로20길봉덕로20길2~중앙대로22길대구광역시 남구청2021-12-31
51대봉로14길35.843127128.60492535.84307128.605913이팝나무160.12019이팝나무길대봉로14길강변코오롱하늘채 봉덕화성파크드림 사잇길대구광역시 남구청2021-12-31
52삼정2길35.836244128.59520535.837986128.595175대왕참나무220.22019대왕참나무길삼정2길앞산태왕아너스 진입로대구광역시 남구청2021-12-31
53대명로22길35.839252128.56974635.838446128.569745은행나무+개잎갈나무150.1<NA>은행나무, 개잎갈나무길대명로22길홈플러스대구광역시 남구청2021-12-31
54성당로62길35.859012128.5752835.859012128.57528은단풍나무10.1<NA>은단풍나무길성당로62길성당로 62길대구광역시 남구청2021-12-31
55안지랑로 16길35.837222128.57225435.837231128.572837이팝나무70.1<NA>이팝나무길안지랑로 16길청구위너스 빌라 후편대구광역시 남구청2021-12-31