Overview

Dataset statistics

Number of variables19
Number of observations100
Missing cells119
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.9 KiB
Average record size in memory162.3 B

Variable types

Numeric7
Text4
Categorical7
Unsupported1

Alerts

provd_instt_nm has constant value ""Constant
mgc_nm is highly overall correlated with sttree_stret_begin_la and 7 other fieldsHigh correlation
road_knd is highly overall correlated with sttree_stret_begin_la and 5 other fieldsHigh correlation
mgc_telno is highly overall correlated with sttree_stret_begin_la and 6 other fieldsHigh correlation
data_stnd_ymd is highly overall correlated with skey and 8 other fieldsHigh correlation
provd_instt_code is highly overall correlated with skey and 8 other fieldsHigh correlation
skey is highly overall correlated with sttree_stret_lt and 3 other fieldsHigh correlation
sttree_stret_begin_la is highly overall correlated with sttree_stret_begin_lo and 7 other fieldsHigh correlation
sttree_stret_begin_lo is highly overall correlated with sttree_stret_begin_la and 6 other fieldsHigh correlation
sttree_stret_end_la is highly overall correlated with sttree_stret_begin_la and 7 other fieldsHigh correlation
sttree_stret_end_lo is highly overall correlated with sttree_stret_begin_la and 5 other fieldsHigh correlation
sttree_qy is highly overall correlated with sttree_stret_lt and 1 other fieldsHigh correlation
sttree_stret_lt is highly overall correlated with skey and 2 other fieldsHigh correlation
sttree_knd is highly overall correlated with skey and 2 other fieldsHigh correlation
sttree_qy has 19 (19.0%) missing valuesMissing
plt_year has 100 (100.0%) missing valuesMissing
skey has unique valuesUnique
sttree_stret_end_la has unique valuesUnique
sttree_stret_end_lo has unique valuesUnique
rdsc has unique valuesUnique
plt_year is an unsupported type, check if it needs cleaning or further analysisUnsupported
sttree_stret_lt has 42 (42.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:09:32.042142
Analysis finished2023-12-10 10:09:44.244349
Duration12.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.72
Minimum1
Maximum589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:44.371639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum589
Range588
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation96.184385
Coefficient of variation (CV)1.4203246
Kurtosis24.502379
Mean67.72
Median Absolute Deviation (MAD)25.5
Skewness4.8444731
Sum6772
Variance9251.436
MonotonicityNot monotonic
2023-12-10T19:09:44.617634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
589 1
1.0%
588 1
1.0%
587 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

sttree_stret_nm
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:45.099838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length4.84
Min length3

Characters and Unicode

Total characters484
Distinct characters141
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

Unique96 ?
Unique (%)96.0%

Sample

1st row쌍미천복개로
2nd row사하구 하신중앙로
3rd row거제시장로
4th row톳고개로
5th row황령산순환도로
ValueCountFrequency (%)
수영로 5
 
4.3%
사하구 3
 
2.6%
좌수영로 3
 
2.6%
학감대로 2
 
1.7%
황령대로 2
 
1.7%
강변대로 2
 
1.7%
월드컵로(구 2
 
1.7%
전포대로 2
 
1.7%
179번길 1
 
0.9%
신선로 1
 
0.9%
Other values (92) 92
80.0%
2023-12-10T19:09:45.859319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 78
 
16.1%
길 33
 
6.8%
대 19
 
3.9%
15
 
3.1%
수 14
 
2.9%
영 9
 
1.9%
구 9
 
1.9%
동 8
 
1.7%
번 8
 
1.7%
중 7
 
1.4%
Other values (131) 284
58.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 434
89.7%
Decimal Number 28
 
5.8%
Space Separator 15
 
3.1%
Close Punctuation 3
 
0.6%
Open Punctuation 3
 
0.6%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 78
 
18.0%
길 33
 
7.6%
대 19
 
4.4%
수 14
 
3.2%
영 9
 
2.1%
구 9
 
2.1%
동 8
 
1.8%
번 8
 
1.8%
중 7
 
1.6%
천 7
 
1.6%
Other values (118) 242
55.8%
Decimal Number
ValueCountFrequency (%)
1 6
21.4%
5 5
17.9%
9 4
14.3%
7 3
10.7%
2 3
10.7%
8 2
 
7.1%
0 2
 
7.1%
3 2
 
7.1%
4 1
 
3.6%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 434
89.7%
Common 50
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 78
 
18.0%
길 33
 
7.6%
대 19
 
4.4%
수 14
 
3.2%
영 9
 
2.1%
구 9
 
2.1%
동 8
 
1.8%
번 8
 
1.8%
중 7
 
1.6%
천 7
 
1.6%
Other values (118) 242
55.8%
Common
ValueCountFrequency (%)
15
30.0%
1 6
 
12.0%
5 5
 
10.0%
9 4
 
8.0%
7 3
 
6.0%
2 3
 
6.0%
) 3
 
6.0%
( 3
 
6.0%
8 2
 
4.0%
0 2
 
4.0%
Other values (3) 4
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 434
89.7%
ASCII 50
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 78
 
18.0%
길 33
 
7.6%
대 19
 
4.4%
수 14
 
3.2%
영 9
 
2.1%
구 9
 
2.1%
동 8
 
1.8%
번 8
 
1.8%
중 7
 
1.6%
천 7
 
1.6%
Other values (118) 242
55.8%
ASCII
ValueCountFrequency (%)
15
30.0%
1 6
 
12.0%
5 5
 
10.0%
9 4
 
8.0%
7 3
 
6.0%
2 3
 
6.0%
) 3
 
6.0%
( 3
 
6.0%
8 2
 
4.0%
0 2
 
4.0%
Other values (3) 4
 
8.0%

sttree_stret_begin_la
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.131732
Minimum35.073978
Maximum35.199356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:46.155395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.073978
5-th percentile35.079659
Q135.093105
median35.119079
Q335.177116
95-th percentile35.19165
Maximum35.199356
Range0.125378
Interquartile range (IQR)0.08401125

Descriptive statistics

Standard deviation0.040605548
Coefficient of variation (CV)0.0011558083
Kurtosis-1.4295764
Mean35.131732
Median Absolute Deviation (MAD)0.030863
Skewness0.25038837
Sum3513.1732
Variance0.0016488106
MonotonicityNot monotonic
2023-12-10T19:09:46.766563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.137436 2
 
2.0%
35.107516 2
 
2.0%
35.189207 1
 
1.0%
35.130056 1
 
1.0%
35.114866 1
 
1.0%
35.112982 1
 
1.0%
35.082638 1
 
1.0%
35.091971 1
 
1.0%
35.113358 1
 
1.0%
35.094718 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
35.073978 1
1.0%
35.074083 1
1.0%
35.074461 1
1.0%
35.075141 1
1.0%
35.075718 1
1.0%
35.079866 1
1.0%
35.080278 1
1.0%
35.080821 1
1.0%
35.081515 1
1.0%
35.082638 1
1.0%
ValueCountFrequency (%)
35.199356 1
1.0%
35.199061 1
1.0%
35.195958 1
1.0%
35.195378 1
1.0%
35.191751 1
1.0%
35.191645 1
1.0%
35.191262 1
1.0%
35.191039 1
1.0%
35.191027 1
1.0%
35.189207 1
1.0%

sttree_stret_begin_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.90483
Minimum128.5749
Maximum129.12847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:47.056314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.5749
5-th percentile128.58199
Q1128.59214
median129.06612
Q3129.08794
95-th percentile129.11359
Maximum129.12847
Range0.55357
Interquartile range (IQR)0.495799

Descriptive statistics

Standard deviation0.23544696
Coefficient of variation (CV)0.0018265178
Kurtosis-1.6253358
Mean128.90483
Median Absolute Deviation (MAD)0.0489215
Skewness-0.58603926
Sum12890.483
Variance0.055435272
MonotonicityNot monotonic
2023-12-10T19:09:47.545116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.099952 2
 
2.0%
129.112011 2
 
2.0%
129.087465 1
 
1.0%
129.095412 1
 
1.0%
128.591694 1
 
1.0%
128.590448 1
 
1.0%
128.592002 1
 
1.0%
128.592726 1
 
1.0%
128.590036 1
 
1.0%
128.583611 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
128.574904 1
1.0%
128.581167 1
1.0%
128.581452 1
1.0%
128.581891 1
1.0%
128.581961 1
1.0%
128.581995 1
1.0%
128.582127 1
1.0%
128.582733 1
1.0%
128.583611 1
1.0%
128.583673 1
1.0%
ValueCountFrequency (%)
129.128474 1
1.0%
129.128105 1
1.0%
129.124851 1
1.0%
129.124286 1
1.0%
129.116667 1
1.0%
129.113426 1
1.0%
129.112011 2
2.0%
129.111385 1
1.0%
129.111211 1
1.0%
129.111131 1
1.0%

sttree_stret_end_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.131154
Minimum35.071262
Maximum35.199428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:47.935923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.071262
5-th percentile35.07395
Q135.09301
median35.120598
Q335.175251
95-th percentile35.193392
Maximum35.199428
Range0.128166
Interquartile range (IQR)0.08224125

Descriptive statistics

Standard deviation0.041847498
Coefficient of variation (CV)0.0011911791
Kurtosis-1.4596813
Mean35.131154
Median Absolute Deviation (MAD)0.036206
Skewness0.20921375
Sum3513.1154
Variance0.0017512131
MonotonicityNot monotonic
2023-12-10T19:09:48.348757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.187104 1
 
1.0%
35.136849 1
 
1.0%
35.071262 1
 
1.0%
35.112621 1
 
1.0%
35.082225 1
 
1.0%
35.083695 1
 
1.0%
35.110633 1
 
1.0%
35.094344 1
 
1.0%
35.136205 1
 
1.0%
35.121292 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
35.071262 1
1.0%
35.071269 1
1.0%
35.072761 1
1.0%
35.073148 1
1.0%
35.073405 1
1.0%
35.073979 1
1.0%
35.079033 1
1.0%
35.080963 1
1.0%
35.081481 1
1.0%
35.082225 1
1.0%
ValueCountFrequency (%)
35.199428 1
1.0%
35.196598 1
1.0%
35.196304 1
1.0%
35.195581 1
1.0%
35.194114 1
1.0%
35.193354 1
1.0%
35.193087 1
1.0%
35.192935 1
1.0%
35.192874 1
1.0%
35.190524 1
1.0%

sttree_stret_end_lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.89788
Minimum128.57395
Maximum129.131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:48.667189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.57395
5-th percentile128.58189
Q1128.59219
median129.06522
Q3129.09924
95-th percentile129.11564
Maximum129.131
Range0.557043
Interquartile range (IQR)0.507052

Descriptive statistics

Standard deviation0.24059976
Coefficient of variation (CV)0.0018665921
Kurtosis-1.7287579
Mean128.89788
Median Absolute Deviation (MAD)0.0509425
Skewness-0.49672137
Sum12889.788
Variance0.057888244
MonotonicityNot monotonic
2023-12-10T19:09:48.994151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.089883 1
 
1.0%
129.057301 1
 
1.0%
128.574242 1
 
1.0%
128.590093 1
 
1.0%
128.591755 1
 
1.0%
128.591702 1
 
1.0%
129.001362 1
 
1.0%
128.591831 1
 
1.0%
129.110586 1
 
1.0%
129.098621 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
128.573954 1
1.0%
128.574242 1
1.0%
128.580016 1
1.0%
128.580541 1
1.0%
128.580823 1
1.0%
128.581945 1
1.0%
128.582386 1
1.0%
128.582656 1
1.0%
128.583001 1
1.0%
128.583384 1
1.0%
ValueCountFrequency (%)
129.130997 1
1.0%
129.130159 1
1.0%
129.128288 1
1.0%
129.119567 1
1.0%
129.116737 1
1.0%
129.115585 1
1.0%
129.115298 1
1.0%
129.114936 1
1.0%
129.113075 1
1.0%
129.112339 1
1.0%

sttree_knd
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
왕벚나무
15 
은행나무
14 
느티나무
11 
이팝나무
7 
먼나무
6 
Other values (37)
47 

Length

Max length29
Median length25
Mean length7.08
Min length3

Unique

Unique33 ?
Unique (%)33.0%

Sample

1st row느티나무
2nd row왕벚나무+은행나무+양버즘나무+팽나무+후박나무+가시나무
3rd row왕벚나무
4th row왕벚나무
5th row왕벚나무

Common Values

ValueCountFrequency (%)
왕벚나무 15
15.0%
은행나무 14
14.0%
느티나무 11
 
11.0%
이팝나무 7
 
7.0%
먼나무 6
 
6.0%
은행나무+느티나무 5
 
5.0%
왕벚나무+은행나무 4
 
4.0%
은행나무+이팝나무 3
 
3.0%
왕벚나무, 느티나무 2
 
2.0%
느티나무+은행나무 1
 
1.0%
Other values (32) 32
32.0%

Length

2023-12-10T19:09:49.449845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
왕벚나무 18
17.1%
은행나무 14
13.3%
느티나무 13
 
12.4%
이팝나무 7
 
6.7%
먼나무 6
 
5.7%
은행나무+느티나무 5
 
4.8%
왕벚나무+은행나무 4
 
3.8%
은행나무+이팝나무 3
 
2.9%
은행나무+중국단풍 1
 
1.0%
6주 1
 
1.0%
Other values (33) 33
31.4%

sttree_qy
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)88.9%
Missing19
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean222.51852
Minimum13
Maximum1163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:49.885014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile18
Q135
median105
Q3341
95-th percentile665
Maximum1163
Range1150
Interquartile range (IQR)306

Descriptive statistics

Standard deviation262.09336
Coefficient of variation (CV)1.1778496
Kurtosis2.5397247
Mean222.51852
Median Absolute Deviation (MAD)80
Skewness1.6932573
Sum18024
Variance68692.928
MonotonicityNot monotonic
2023-12-10T19:09:50.130872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 3
 
3.0%
65 2
 
2.0%
13 2
 
2.0%
20 2
 
2.0%
26 2
 
2.0%
25 2
 
2.0%
21 2
 
2.0%
18 2
 
2.0%
28 1
 
1.0%
623 1
 
1.0%
Other values (62) 62
62.0%
(Missing) 19
 
19.0%
ValueCountFrequency (%)
13 2
2.0%
15 1
1.0%
17 1
1.0%
18 2
2.0%
20 2
2.0%
21 2
2.0%
22 1
1.0%
23 1
1.0%
25 2
2.0%
26 2
2.0%
ValueCountFrequency (%)
1163 1
1.0%
1016 1
1.0%
965 1
1.0%
939 1
1.0%
665 1
1.0%
661 1
1.0%
623 1
1.0%
616 1
1.0%
554 1
1.0%
544 1
1.0%

sttree_stret_lt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.9
Minimum0
Maximum2160
Zeros42
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:50.360637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32.25
95-th percentile246.7
Maximum2160
Range2160
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation266.92894
Coefficient of variation (CV)4.5319006
Kurtosis42.575051
Mean58.9
Median Absolute Deviation (MAD)1
Skewness6.1817109
Sum5890
Variance71251.061
MonotonicityNot monotonic
2023-12-10T19:09:50.566835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 42
42.0%
2 19
19.0%
1 14
 
14.0%
3 5
 
5.0%
5 4
 
4.0%
4 3
 
3.0%
8 3
 
3.0%
100 2
 
2.0%
465 1
 
1.0%
2160 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
0 42
42.0%
1 14
 
14.0%
2 19
19.0%
3 5
 
5.0%
4 3
 
3.0%
5 4
 
4.0%
6 1
 
1.0%
8 3
 
3.0%
100 2
 
2.0%
200 1
 
1.0%
ValueCountFrequency (%)
2160 1
 
1.0%
1130 1
 
1.0%
992 1
 
1.0%
465 1
 
1.0%
374 1
 
1.0%
240 1
 
1.0%
200 1
 
1.0%
100 2
2.0%
8 3
3.0%
6 1
 
1.0%

plt_year
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

sttree_stret_intrcn
Text

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:51.062871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length37.5
Mean length27.29
Min length5

Characters and Unicode

Total characters2729
Distinct characters198
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)50.0%

Sample

1st row녹음수인 느티나무가 식재되어 있어, 여름철 녹음을 제공
2nd row사하구의 주요 교통요충지인 하단오거리와 신평·장림일반산업단지를 지나는 도로로 왕벚나무와 은행나무가 주요수종으로 조성되어 있음
3rd row벚나무가 양옆으로 식재되어있어 봄철 아름다운 벚꽃길을 제공
4th row벚나무가 터널식으로 조성되어있어 아름다운 벚꽃길을 볼 수 있음
5th row벚나무가 터널식으로 조성되어있어 아름다운 벚꽃길을 볼 수 있음
ValueCountFrequency (%)
있음 59
 
9.4%
식재되어 48
 
7.6%
수 26
 
4.1%
은행나무가 23
 
3.7%
아름다운 20
 
3.2%
느낄 17
 
2.7%
식재되어있어 14
 
2.2%
따라 14
 
2.2%
여름철 14
 
2.2%
느티나무가 13
 
2.1%
Other values (150) 381
60.6%
2023-12-10T19:09:52.163090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
529
 
19.4%
나 124
 
4.5%
무 121
 
4.4%
어 115
 
4.2%
가 101
 
3.7%
있 96
 
3.5%
되 78
 
2.9%
음 77
 
2.8%
식 70
 
2.6%
재 67
 
2.5%
Other values (188) 1351
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2175
79.7%
Space Separator 529
 
19.4%
Other Punctuation 24
 
0.9%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 124
 
5.7%
무 121
 
5.6%
어 115
 
5.3%
가 101
 
4.6%
있 96
 
4.4%
되 78
 
3.6%
음 77
 
3.5%
식 70
 
3.2%
재 67
 
3.1%
을 61
 
2.8%
Other values (184) 1265
58.2%
Other Punctuation
ValueCountFrequency (%)
, 23
95.8%
· 1
 
4.2%
Space Separator
ValueCountFrequency (%)
529
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2175
79.7%
Common 554
 
20.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 124
 
5.7%
무 121
 
5.6%
어 115
 
5.3%
가 101
 
4.6%
있 96
 
4.4%
되 78
 
3.6%
음 77
 
3.5%
식 70
 
3.2%
재 67
 
3.1%
을 61
 
2.8%
Other values (184) 1265
58.2%
Common
ValueCountFrequency (%)
529
95.5%
, 23
 
4.2%
4 1
 
0.2%
· 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2175
79.7%
ASCII 553
 
20.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
529
95.7%
, 23
 
4.2%
4 1
 
0.2%
Hangul
ValueCountFrequency (%)
나 124
 
5.7%
무 121
 
5.6%
어 115
 
5.3%
가 101
 
4.6%
있 96
 
4.4%
되 78
 
3.6%
음 77
 
3.5%
식 70
 
3.2%
재 67
 
3.1%
을 61
 
2.8%
Other values (184) 1265
58.2%
None
ValueCountFrequency (%)
· 1
100.0%

rn
Text

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:52.632364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.76
Min length1

Characters and Unicode

Total characters476
Distinct characters116
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

Unique85 ?
Unique (%)85.0%

Sample

1st row쌍미천로
2nd row-
3rd row거제시장로
4th row톳고개로
5th row황령산로
ValueCountFrequency (%)
수영로 5
 
4.6%
학감대로 3
 
2.8%
월드컵대로 3
 
2.8%
3
 
2.8%
좌수영로 3
 
2.8%
전포대로 2
 
1.9%
황령대로 2
 
1.9%
백양대로700번길 2
 
1.9%
백양대로 2
 
1.9%
모라로 1
 
0.9%
Other values (82) 82
75.9%
2023-12-10T19:09:53.376676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 95
 
20.0%
대 29
 
6.1%
길 21
 
4.4%
번 20
 
4.2%
1 14
 
2.9%
수 12
 
2.5%
영 9
 
1.9%
0 9
 
1.9%
동 8
 
1.7%
5 8
 
1.7%
Other values (106) 251
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 407
85.5%
Decimal Number 58
 
12.2%
Space Separator 8
 
1.7%
Dash Punctuation 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 95
23.3%
대 29
 
7.1%
길 21
 
5.2%
번 20
 
4.9%
수 12
 
2.9%
영 9
 
2.2%
동 8
 
2.0%
장 7
 
1.7%
백 7
 
1.7%
학 6
 
1.5%
Other values (94) 193
47.4%
Decimal Number
ValueCountFrequency (%)
1 14
24.1%
0 9
15.5%
5 8
13.8%
9 6
10.3%
2 5
 
8.6%
7 4
 
6.9%
3 4
 
6.9%
4 3
 
5.2%
8 3
 
5.2%
6 2
 
3.4%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 407
85.5%
Common 69
 
14.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 95
23.3%
대 29
 
7.1%
길 21
 
5.2%
번 20
 
4.9%
수 12
 
2.9%
영 9
 
2.2%
동 8
 
2.0%
장 7
 
1.7%
백 7
 
1.7%
학 6
 
1.5%
Other values (94) 193
47.4%
Common
ValueCountFrequency (%)
1 14
20.3%
0 9
13.0%
5 8
11.6%
8
11.6%
9 6
8.7%
2 5
 
7.2%
7 4
 
5.8%
3 4
 
5.8%
4 3
 
4.3%
8 3
 
4.3%
Other values (2) 5
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 407
85.5%
ASCII 69
 
14.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 95
23.3%
대 29
 
7.1%
길 21
 
5.2%
번 20
 
4.9%
수 12
 
2.9%
영 9
 
2.2%
동 8
 
2.0%
장 7
 
1.7%
백 7
 
1.7%
학 6
 
1.5%
Other values (94) 193
47.4%
ASCII
ValueCountFrequency (%)
1 14
20.3%
0 9
13.0%
5 8
11.6%
8
11.6%
9 6
8.7%
2 5
 
7.2%
7 4
 
5.8%
3 4
 
5.8%
4 3
 
4.3%
8 3
 
4.3%
Other values (2) 5
 
7.2%

road_knd
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
64 
지방도
17 
광역시도
9 
구도
8 
국도
 
2

Length

Max length4
Median length1
Mean length1.71
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방도
2nd row-
3rd row지방도
4th row지방도
5th row지방도

Common Values

ValueCountFrequency (%)
- 64
64.0%
지방도 17
 
17.0%
광역시도 9
 
9.0%
구도 8
 
8.0%
국도 2
 
2.0%

Length

2023-12-10T19:09:53.635442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:53.834668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
64
64.0%
지방도 17
 
17.0%
광역시도 9
 
9.0%
구도 8
 
8.0%
국도 2
 
2.0%

rdsc
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:54.200255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length12.74
Min length7

Characters and Unicode

Total characters1274
Distinct characters222
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row쌍미천로~은광교회
2nd row하단오거리 ~ 65호광장
3rd row거제시장교차로~거제시장입구
4th rowLG아파트~동서그린아파트
5th row물만골~수영구경계
ValueCountFrequency (%)
교차로 8
 
4.7%
입구 6
 
3.5%
4
 
2.4%
앞 3
 
1.8%
구평택지개발 2
 
1.2%
내부도로 2
 
1.2%
산업용품상가 2
 
1.2%
진입로 2
 
1.2%
일원 2
 
1.2%
감전사거리~학장사거리 1
 
0.6%
Other values (138) 138
81.2%
2023-12-10T19:09:54.937460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 94
 
7.4%
70
 
5.5%
교 53
 
4.2%
로 45
 
3.5%
구 41
 
3.2%
차 32
 
2.5%
거 29
 
2.3%
아 27
 
2.1%
파 26
 
2.0%
트 26
 
2.0%
Other values (212) 831
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1072
84.1%
Math Symbol 94
 
7.4%
Space Separator 70
 
5.5%
Uppercase Letter 19
 
1.5%
Decimal Number 16
 
1.3%
Lowercase Letter 2
 
0.2%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 53
 
4.9%
로 45
 
4.2%
구 41
 
3.8%
차 32
 
3.0%
거 29
 
2.7%
아 27
 
2.5%
파 26
 
2.4%
트 26
 
2.4%
대 25
 
2.3%
리 25
 
2.3%
Other values (189) 743
69.3%
Uppercase Letter
ValueCountFrequency (%)
I 4
21.1%
C 3
15.8%
A 2
10.5%
S 2
10.5%
L 2
10.5%
G 2
10.5%
P 1
 
5.3%
T 1
 
5.3%
K 1
 
5.3%
B 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 5
31.2%
4 3
18.8%
2 3
18.8%
0 1
 
6.2%
5 1
 
6.2%
6 1
 
6.2%
8 1
 
6.2%
3 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
g 1
50.0%
Math Symbol
ValueCountFrequency (%)
~ 94
100.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1073
84.2%
Common 180
 
14.1%
Latin 21
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 53
 
4.9%
로 45
 
4.2%
구 41
 
3.8%
차 32
 
3.0%
거 29
 
2.7%
아 27
 
2.5%
파 26
 
2.4%
트 26
 
2.4%
대 25
 
2.3%
리 25
 
2.3%
Other values (190) 744
69.3%
Latin
ValueCountFrequency (%)
I 4
19.0%
C 3
14.3%
A 2
9.5%
S 2
9.5%
L 2
9.5%
G 2
9.5%
P 1
 
4.8%
T 1
 
4.8%
K 1
 
4.8%
s 1
 
4.8%
Other values (2) 2
9.5%
Common
ValueCountFrequency (%)
~ 94
52.2%
70
38.9%
1 5
 
2.8%
4 3
 
1.7%
2 3
 
1.7%
0 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
8 1
 
0.6%
3 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1072
84.1%
ASCII 201
 
15.8%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 94
46.8%
70
34.8%
1 5
 
2.5%
I 4
 
2.0%
4 3
 
1.5%
C 3
 
1.5%
2 3
 
1.5%
A 2
 
1.0%
S 2
 
1.0%
L 2
 
1.0%
Other values (12) 13
 
6.5%
Hangul
ValueCountFrequency (%)
교 53
 
4.9%
로 45
 
4.2%
구 41
 
3.8%
차 32
 
3.0%
거 29
 
2.7%
아 27
 
2.5%
파 26
 
2.4%
트 26
 
2.4%
대 25
 
2.3%
리 25
 
2.3%
Other values (189) 743
69.3%
None
ValueCountFrequency (%)
㈜ 1
100.0%

mgc_telno
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
64 
051-665-4535
27 
051-610-4531
9 

Length

Max length12
Median length1
Mean length4.96
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row051-665-4535
2nd row-
3rd row051-665-4535
4th row051-665-4535
5th row051-665-4535

Common Values

ValueCountFrequency (%)
- 64
64.0%
051-665-4535 27
27.0%
051-610-4531 9
 
9.0%

Length

2023-12-10T19:09:55.189887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:55.376051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
64
64.0%
051-665-4535 27
27.0%
051-610-4531 9
 
9.0%

mgc_nm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
부산광역시 사상구청
42 
부산광역시 연제구청
27 
-
22 
수영구청
9 

Length

Max length10
Median length10
Mean length7.48
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 연제구청
2nd row-
3rd row부산광역시 연제구청
4th row부산광역시 연제구청
5th row부산광역시 연제구청

Common Values

ValueCountFrequency (%)
부산광역시 사상구청 42
42.0%
부산광역시 연제구청 27
27.0%
- 22
22.0%
수영구청 9
 
9.0%

Length

2023-12-10T19:09:55.956634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:56.150440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 69
40.8%
사상구청 42
24.9%
연제구청 27
 
16.0%
22
 
13.0%
수영구청 9
 
5.3%

data_stnd_ymd
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-02-25
42 
2020-12-18
27 
2020-07-13
19 
2020-10-25
9 
2020-12-31
 
3

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-18
2nd row2020-12-31
3rd row2020-12-18
4th row2020-12-18
5th row2020-12-18

Common Values

ValueCountFrequency (%)
2021-02-25 42
42.0%
2020-12-18 27
27.0%
2020-07-13 19
19.0%
2020-10-25 9
 
9.0%
2020-12-31 3
 
3.0%

Length

2023-12-10T19:09:56.373226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:56.578212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-25 42
42.0%
2020-12-18 27
27.0%
2020-07-13 19
19.0%
2020-10-25 9
 
9.0%
2020-12-31 3
 
3.0%

provd_instt_code
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3390000
42 
3370000
27 
3310000
19 
3380000
9 
3340000
 
3

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3370000
2nd row3340000
3rd row3370000
4th row3370000
5th row3370000

Common Values

ValueCountFrequency (%)
3390000 42
42.0%
3370000 27
27.0%
3310000 19
19.0%
3380000 9
 
9.0%
3340000 3
 
3.0%

Length

2023-12-10T19:09:56.906605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:57.105908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3390000 42
42.0%
3370000 27
27.0%
3310000 19
19.0%
3380000 9
 
9.0%
3340000 3
 
3.0%

provd_instt_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 100
100.0%

Length

2023-12-10T19:09:57.355976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:57.539712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

Interactions

2023-12-10T19:09:42.263981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:34.658502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:36.133306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:37.331646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:38.462282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:39.747921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:40.947128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:42.393037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:34.865959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:36.297511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:37.502894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:38.643872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:39.898449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:41.173411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:42.553473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:35.150302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:36.468420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:37.646272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:38.846737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:40.050056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:41.334985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:42.695112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:35.365102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:36.682270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:37.783945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:39.031088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:40.231039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:41.496507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:42.850883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:35.701509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:36.851363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:37.954130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:39.210647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:40.408871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:41.669575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:43.341792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:35.841560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:37.026982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:38.110391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:39.399202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:40.652593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:41.964453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:43.480411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:36.002127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:37.183217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:38.293646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:39.577343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:40.806535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:42.121830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:09:57.669745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeysttree_stret_nmsttree_stret_begin_lasttree_stret_begin_losttree_stret_end_lasttree_stret_end_losttree_kndsttree_qysttree_stret_ltsttree_stret_intrcnrnroad_kndrdscmgc_telnomgc_nmdata_stnd_ymdprovd_instt_code
skey1.0000.9490.4360.5450.4350.5670.9330.4230.0000.9740.9580.4341.0000.7550.4620.7970.797
sttree_stret_nm0.9491.0000.0001.0001.0001.0000.9950.8770.0000.9981.0001.0001.0000.0000.7010.8780.878
sttree_stret_begin_la0.4360.0001.0000.7730.9260.7560.3050.2720.5460.9140.9150.8971.0000.9130.9190.9560.956
sttree_stret_begin_lo0.5451.0000.7731.0000.7640.9880.8930.3650.0000.9450.0000.6701.0000.5280.6310.9100.910
sttree_stret_end_la0.4351.0000.9260.7641.0000.7660.6320.3040.5160.9440.9830.8921.0000.8770.8910.9460.946
sttree_stret_end_lo0.5671.0000.7560.9880.7661.0000.8440.3130.0000.9390.0000.6721.0000.5370.6390.9170.917
sttree_knd0.9330.9950.3050.8930.6320.8441.0000.9380.3721.0000.0000.0001.0000.0000.6560.8480.848
sttree_qy0.4230.8770.2720.3650.3040.3130.9381.0000.0000.9150.0000.1861.0000.0000.3060.3060.306
sttree_stret_lt0.0000.0000.5460.0000.5160.0000.3720.0001.0000.0000.0000.4971.0000.8440.5840.4700.470
sttree_stret_intrcn0.9740.9980.9140.9450.9440.9391.0000.9150.0001.0000.9550.0001.0001.0001.0001.0001.000
rn0.9581.0000.9150.0000.9830.0000.0000.0000.0000.9551.0000.9751.0000.9440.9780.9880.988
road_knd0.4341.0000.8970.6700.8920.6720.0000.1860.4970.0000.9751.0001.0000.9150.8150.9440.944
rdsc1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
mgc_telno0.7550.0000.9130.5280.8770.5370.0000.0000.8441.0000.9440.9151.0001.0001.0001.0001.000
mgc_nm0.4620.7010.9190.6310.8910.6390.6560.3060.5841.0000.9780.8151.0001.0001.0001.0001.000
data_stnd_ymd0.7970.8780.9560.9100.9460.9170.8480.3060.4701.0000.9880.9441.0001.0001.0001.0001.000
provd_instt_code0.7970.8780.9560.9100.9460.9170.8480.3060.4701.0000.9880.9441.0001.0001.0001.0001.000
2023-12-10T19:09:57.966894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
mgc_nmroad_kndmgc_telnosttree_knddata_stnd_ymdprovd_instt_code
mgc_nm1.0000.7750.9950.3000.9950.995
road_knd0.7751.0000.9550.0000.6670.667
mgc_telno0.9950.9551.0000.0000.9900.990
sttree_knd0.3000.0000.0001.0000.4590.459
data_stnd_ymd0.9950.6670.9900.4591.0001.000
provd_instt_code0.9950.6670.9900.4591.0001.000
2023-12-10T19:09:58.263067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeysttree_stret_begin_lasttree_stret_begin_losttree_stret_end_lasttree_stret_end_losttree_qysttree_stret_ltsttree_kndroad_kndmgc_telnomgc_nmdata_stnd_ymdprovd_instt_code
skey1.000-0.3500.028-0.3700.0080.0700.5420.5760.3610.4120.4550.8070.807
sttree_stret_begin_la-0.3501.0000.6840.9360.689-0.0270.1360.0570.5680.8420.7900.6910.691
sttree_stret_begin_lo0.0280.6841.0000.6820.929-0.0170.4430.5150.3080.4640.5570.5880.588
sttree_stret_end_la-0.3700.9360.6821.0000.696-0.0670.1200.2100.5600.7830.7400.6640.664
sttree_stret_end_lo0.0080.6890.9290.6961.000-0.0520.4180.4480.3100.4740.5660.6020.602
sttree_qy0.070-0.027-0.017-0.067-0.0521.0000.5000.5470.1190.0000.1340.1340.134
sttree_stret_lt0.5420.1360.4430.1200.4180.5001.0000.1070.3630.5270.4120.3400.340
sttree_knd0.5760.0570.5150.2100.4480.5470.1071.0000.0000.0000.3000.4590.459
road_knd0.3610.5680.3080.5600.3100.1190.3630.0001.0000.9550.7750.6670.667
mgc_telno0.4120.8420.4640.7830.4740.0000.5270.0000.9551.0000.9950.9900.990
mgc_nm0.4550.7900.5570.7400.5660.1340.4120.3000.7750.9951.0000.9950.995
data_stnd_ymd0.8070.6910.5880.6640.6020.1340.3400.4590.6670.9900.9951.0001.000
provd_instt_code0.8070.6910.5880.6640.6020.1340.3400.4590.6670.9900.9951.0001.000

Missing values

2023-12-10T19:09:43.707754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:09:44.093986image/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

skeysttree_stret_nmsttree_stret_begin_lasttree_stret_begin_losttree_stret_end_lasttree_stret_end_losttree_kndsttree_qysttree_stret_ltplt_yearsttree_stret_intrcnrnroad_kndrdscmgc_telnomgc_nmdata_stnd_ymdprovd_instt_codeprovd_instt_nm
01쌍미천복개로35.189207129.08746535.187104129.089883느티나무640<NA>녹음수인 느티나무가 식재되어 있어, 여름철 녹음을 제공쌍미천로지방도쌍미천로~은광교회051-665-4535부산광역시 연제구청2020-12-183370000-
1587사하구 하신중앙로35.105864128.96789935.079033128.964671왕벚나무+은행나무+양버즘나무+팽나무+후박나무+가시나무5253<NA>사하구의 주요 교통요충지인 하단오거리와 신평·장림일반산업단지를 지나는 도로로 왕벚나무와 은행나무가 주요수종으로 조성되어 있음--하단오거리 ~ 65호광장--2020-12-313340000-
23거제시장로35.181585129.07081835.182099129.070087왕벚나무180<NA>벚나무가 양옆으로 식재되어있어 봄철 아름다운 벚꽃길을 제공거제시장로지방도거제시장교차로~거제시장입구051-665-4535부산광역시 연제구청2020-12-183370000-
34톳고개로35.185476129.10271535.182088129.102641왕벚나무1870<NA>벚나무가 터널식으로 조성되어있어 아름다운 벚꽃길을 볼 수 있음톳고개로지방도LG아파트~동서그린아파트051-665-4535부산광역시 연제구청2020-12-183370000-
45황령산순환도로35.162614129.07977935.160712129.093923왕벚나무6163<NA>벚나무가 터널식으로 조성되어있어 아름다운 벚꽃길을 볼 수 있음황령산로지방도물만골~수영구경계051-665-4535부산광역시 연제구청2020-12-183370000-
56온천천로35.199061129.07952435.190408129.106804왕벚나무+은행나무4472<NA>온천천변을 따라 벚나무가 식재되어있어 봄철 아름다운 벚꽃길을 볼 수 있음온천천로지방도송월타올~안락교051-665-4535부산광역시 연제구청2020-12-183370000-
67종합운동장로35.191027129.06537135.193087129.064795느티나무1100<NA>녹음수인 느티나무가 식재되어 있어, 여름철 녹음을 제공종합운동장로광역시도거성교차로~동래구경계051-665-4535부산광역시 연제구청2020-12-183370000-
7588사하구 구평택지개발 내부도로35.079866128.99009435.081481128.986371이팝나무+먼나무4732<NA>구평택지 내부도로로 이팝나무와 먼나무로 조성되어 있음--구평택지개발 내부도로 ~ 구평택지개발 내부도로--2020-12-313340000-
89여고로35.199356129.07807535.196304129.071615은행나무1131<NA>양옆으로 은행나무가 식재되어있어, 가을철 아름다운 단풍거리가 조성됨여고로광역시도송월타올~동래구경계051-665-4535부산광역시 연제구청2020-12-183370000-
910월드컵로(구 아시아드로)35.186222129.08156435.194114129.066727느티나무+후박나무+단풍나무+먼나무5402<NA>아시아드 상징가로로써 후박나무와 느티나무가 병렬식재되어 있어 아름다운 녹음길을 제공월드컵대로지방도연산교차로~동래구경계051-665-4535부산광역시 연제구청2020-12-183370000-
skeysttree_stret_nmsttree_stret_begin_lasttree_stret_begin_losttree_stret_end_lasttree_stret_end_losttree_kndsttree_qysttree_stret_ltplt_yearsttree_stret_intrcnrnroad_kndrdscmgc_telnomgc_nmdata_stnd_ymdprovd_instt_codeprovd_instt_nm
9091대동로35.080278128.58273335.082611128.585891은행나무+메타세쿼이어+먼나무1840<NA>은행나무와 더불어 메타세쿼이어, 먼나무가 식재되어 있음대동로-청파아파트 앞~엄궁럭키아파트 입구-부산광역시 사상구청2021-02-253390000-
9192민락수변이면도로35.154311129.12428635.155763129.130159왕벚나무, 구실잣밤나무149992<NA>왕벚나무, 구실잣밤나무길민락수변이면도로구도민락수변로이면도로051-610-4531수영구청2020-10-253380000-
9293좌수영로 179번길35.179969129.11666735.179556129.115585이팝나무28100<NA>이팝나무길좌수영로 179번길구도코스트코앞~좌수영로경계051-610-4531수영구청2020-10-253380000-
9394민락본동로35.157167129.12810535.159951129.128288은행나무40374<NA>은행나무길민락본동로구도광남로교차점~광안해변로교차점051-610-4531수영구청2020-10-253380000-
9495민락수변로35.154182129.12847435.160998129.130997왕벚나무, 느티나무2451130<NA>왕벚나무, 느티나무길민락수변로구도수영교~수변공영주차장051-610-4531수영구청2020-10-253380000-
9596수영로 554번길35.154964129.11342635.154311129.116737은행나무18465<NA>은행나무길수영로 554번길구도쌍용예가디오션 이면도로051-610-4531수영구청2020-10-253380000-
9697수영로 581번길35.158053129.11121135.157911129.112202느티나무13200<NA>느티나무길수영로 581번길구도협성엠파이어B단지 일원051-610-4531수영구청2020-10-253380000-
9798수영로 587번길35.158596129.11113135.158408129.112339느티나무15240<NA>느티나무길수영로 587번길구도협성엠파이어A단지 일원051-610-4531수영구청2020-10-253380000-
9899동수영중 앞35.164146129.10655635.165247129.104678왕벚나무32100<NA>왕벚나무길동수영중 앞구도동수영중학교 진입로051-610-4531수영구청2020-10-253380000-
99100좌수영로35.166203129.12485135.182326129.115298왕벚나무, 느티나무5442160<NA>왕벚나무, 느티나무길좌수영로광역시도수영교~연제구경계051-610-4531수영구청2020-10-253380000-