Overview

Dataset statistics

Number of variables18
Number of observations61
Missing cells337
Missing cells (%)30.7%
Duplicate rows1
Duplicate rows (%)1.6%
Total size in memory9.7 KiB
Average record size in memory163.2 B

Variable types

Text1
Numeric7
Unsupported1
Categorical9

Dataset

Description부산광역시 동래구 가로수 현황에 대한 데이터로 노선명, 은행나무, 왕벚나무, 플라타너스, 히말라야시다, 느티나무, 메타세콰이어, 가시나무, 곰솔, 후박나무, 가중나무, 단풍나무, 향나무, 먼나무, 이팝나무, 회화나무, 튤립나무, 가로수연장(킬로미터)에 대한 항목을 제공합니다.
Author부산광역시 동래구
URLhttps://www.data.go.kr/data/3079676/fileData.do

Alerts

Dataset has 1 (1.6%) duplicate rowsDuplicates
메타세콰이어 is highly overall correlated with 느티나무 and 2 other fieldsHigh correlation
후박나무 is highly overall correlated with 은행나무 and 4 other fieldsHigh correlation
히말라야시다 is highly overall correlated with 은행나무 and 7 other fieldsHigh correlation
은행나무 is highly overall correlated with 왕벚나무 and 5 other fieldsHigh correlation
왕벚나무 is highly overall correlated with 은행나무 and 7 other fieldsHigh correlation
느티나무 is highly overall correlated with 왕벚나무 and 6 other fieldsHigh correlation
가시나무 is highly overall correlated with 은행나무 and 7 other fieldsHigh correlation
먼나무 is highly overall correlated with 은행나무 and 6 other fieldsHigh correlation
이팝나무 is highly overall correlated with 왕벚나무 and 4 other fieldsHigh correlation
가로수연장(km) is highly overall correlated with 왕벚나무 and 6 other fieldsHigh correlation
단풍나무 is highly overall correlated with 느티나무 and 1 other fieldsHigh correlation
향나무 is highly overall correlated with 은행나무 and 3 other fieldsHigh correlation
히말라야시다 is highly imbalanced (73.9%)Imbalance
메타세콰이어 is highly imbalanced (82.0%)Imbalance
곰솔 is highly imbalanced (87.9%)Imbalance
후박나무 is highly imbalanced (82.0%)Imbalance
가중나무 is highly imbalanced (87.9%)Imbalance
단풍나무 is highly imbalanced (84.8%)Imbalance
향나무 is highly imbalanced (84.8%)Imbalance
회화나무 is highly imbalanced (87.9%)Imbalance
튤립나무 is highly imbalanced (87.9%)Imbalance
은행나무 has 42 (68.9%) missing valuesMissing
왕벚나무 has 51 (83.6%) missing valuesMissing
플라타너스 has 61 (100.0%) missing valuesMissing
느티나무 has 45 (73.8%) missing valuesMissing
가시나무 has 54 (88.5%) missing valuesMissing
먼나무 has 51 (83.6%) missing valuesMissing
이팝나무 has 33 (54.1%) missing valuesMissing
플라타너스 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 23:35:43.599535
Analysis finished2023-12-12 23:35:50.324107
Duration6.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선명
Text

Distinct60
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-13T08:35:50.542544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length17
Mean length6.7868852
Min length3

Characters and Unicode

Total characters414
Distinct characters78
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)96.7%

Sample

1st row중앙대로
2nd row충렬대로
3rd row충렬대로107번길
4th row충렬대로238번길
5th row충렬대로350번길
ValueCountFrequency (%)
명장로67번길 2
 
3.2%
명륜로129번길 1
 
1.6%
체육공원로 1
 
1.6%
동래로 1
 
1.6%
수안로 1
 
1.6%
낙민로 1
 
1.6%
안락로 1
 
1.6%
연안로 1
 
1.6%
안연로 1
 
1.6%
안남로 1
 
1.6%
Other values (51) 51
82.3%
2023-12-13T08:35:50.983913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 67
 
16.2%
번 29
 
7.0%
길 29
 
7.0%
1 19
 
4.6%
대 15
 
3.6%
2 14
 
3.4%
장 11
 
2.7%
3 11
 
2.7%
충 9
 
2.2%
5 9
 
2.2%
Other values (68) 201
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 301
72.7%
Decimal Number 95
 
22.9%
Dash Punctuation 6
 
1.4%
Close Punctuation 5
 
1.2%
Open Punctuation 5
 
1.2%
Other Punctuation 1
 
0.2%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 67
22.3%
번 29
 
9.6%
길 29
 
9.6%
대 15
 
5.0%
장 11
 
3.7%
충 9
 
3.0%
렬 9
 
3.0%
아 8
 
2.7%
천 8
 
2.7%
중 8
 
2.7%
Other values (53) 108
35.9%
Decimal Number
ValueCountFrequency (%)
1 19
20.0%
2 14
14.7%
3 11
11.6%
5 9
9.5%
4 9
9.5%
0 8
8.4%
7 7
 
7.4%
8 6
 
6.3%
9 6
 
6.3%
6 6
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 301
72.7%
Common 113
 
27.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 67
22.3%
번 29
 
9.6%
길 29
 
9.6%
대 15
 
5.0%
장 11
 
3.7%
충 9
 
3.0%
렬 9
 
3.0%
아 8
 
2.7%
천 8
 
2.7%
중 8
 
2.7%
Other values (53) 108
35.9%
Common
ValueCountFrequency (%)
1 19
16.8%
2 14
12.4%
3 11
9.7%
5 9
8.0%
4 9
8.0%
0 8
7.1%
7 7
 
6.2%
8 6
 
5.3%
- 6
 
5.3%
9 6
 
5.3%
Other values (5) 18
15.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 301
72.7%
ASCII 113
 
27.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 67
22.3%
번 29
 
9.6%
길 29
 
9.6%
대 15
 
5.0%
장 11
 
3.7%
충 9
 
3.0%
렬 9
 
3.0%
아 8
 
2.7%
천 8
 
2.7%
중 8
 
2.7%
Other values (53) 108
35.9%
ASCII
ValueCountFrequency (%)
1 19
16.8%
2 14
12.4%
3 11
9.7%
5 9
8.0%
4 9
8.0%
0 8
7.1%
7 7
 
6.2%
8 6
 
5.3%
- 6
 
5.3%
9 6
 
5.3%
Other values (5) 18
15.9%

은행나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)89.5%
Missing42
Missing (%)68.9%
Infinite0
Infinite (%)0.0%
Mean103.63158
Minimum1
Maximum432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:35:51.132660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.1
Q121.5
median45
Q3117
95-th percentile323.1
Maximum432
Range431
Interquartile range (IQR)95.5

Descriptive statistics

Standard deviation122.78763
Coefficient of variation (CV)1.1848476
Kurtosis1.7127287
Mean103.63158
Median Absolute Deviation (MAD)35
Skewness1.5813895
Sum1969
Variance15076.801
MonotonicityNot monotonic
2023-12-13T08:35:51.249171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
23 2
 
3.3%
40 2
 
3.3%
225 1
 
1.6%
1 1
 
1.6%
14 1
 
1.6%
98 1
 
1.6%
87 1
 
1.6%
10 1
 
1.6%
293 1
 
1.6%
432 1
 
1.6%
Other values (7) 7
 
11.5%
(Missing) 42
68.9%
ValueCountFrequency (%)
1 1
1.6%
10 1
1.6%
12 1
1.6%
14 1
1.6%
20 1
1.6%
23 2
3.3%
40 2
3.3%
45 1
1.6%
70 1
1.6%
87 1
1.6%
ValueCountFrequency (%)
432 1
1.6%
311 1
1.6%
293 1
1.6%
225 1
1.6%
136 1
1.6%
98 1
1.6%
89 1
1.6%
87 1
1.6%
70 1
1.6%
45 1
1.6%

왕벚나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing51
Missing (%)83.6%
Infinite0
Infinite (%)0.0%
Mean104.2
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:35:51.377930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.8
Q129.5
median67.5
Q3166.75
95-th percentile259.5
Maximum300
Range298
Interquartile range (IQR)137.25

Descriptive statistics

Standard deviation99.258137
Coefficient of variation (CV)0.95257329
Kurtosis-0.11173638
Mean104.2
Median Absolute Deviation (MAD)63.5
Skewness0.89085365
Sum1042
Variance9852.1778
MonotonicityNot monotonic
2023-12-13T08:35:51.516850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
145 1
 
1.6%
210 1
 
1.6%
174 1
 
1.6%
2 1
 
1.6%
300 1
 
1.6%
68 1
 
1.6%
46 1
 
1.6%
67 1
 
1.6%
6 1
 
1.6%
24 1
 
1.6%
(Missing) 51
83.6%
ValueCountFrequency (%)
2 1
1.6%
6 1
1.6%
24 1
1.6%
46 1
1.6%
67 1
1.6%
68 1
1.6%
145 1
1.6%
174 1
1.6%
210 1
1.6%
300 1
1.6%
ValueCountFrequency (%)
300 1
1.6%
210 1
1.6%
174 1
1.6%
145 1
1.6%
68 1
1.6%
67 1
1.6%
46 1
1.6%
24 1
1.6%
6 1
1.6%
2 1
1.6%

플라타너스
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing61
Missing (%)100.0%
Memory size681.0 B

히말라야시다
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
56 
1
 
3
4
 
1
34
 
1

Length

Max length4
Median length4
Mean length3.7704918
Min length1

Unique

Unique2 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 56
91.8%
1 3
 
4.9%
4 1
 
1.6%
34 1
 
1.6%

Length

2023-12-13T08:35:51.646819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:51.778722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 56
91.8%
1 3
 
4.9%
4 1
 
1.6%
34 1
 
1.6%

느티나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)81.2%
Missing45
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean31.6875
Minimum1
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:35:51.864857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median9.5
Q343
95-th percentile104
Maximum179
Range178
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation46.899494
Coefficient of variation (CV)1.4800629
Kurtosis6.2478729
Mean31.6875
Median Absolute Deviation (MAD)8.5
Skewness2.3410129
Sum507
Variance2199.5625
MonotonicityNot monotonic
2023-12-13T08:35:52.007631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 3
 
4.9%
6 2
 
3.3%
5 1
 
1.6%
3 1
 
1.6%
58 1
 
1.6%
15 1
 
1.6%
179 1
 
1.6%
67 1
 
1.6%
79 1
 
1.6%
13 1
 
1.6%
Other values (3) 3
 
4.9%
(Missing) 45
73.8%
ValueCountFrequency (%)
1 3
4.9%
3 1
 
1.6%
4 1
 
1.6%
5 1
 
1.6%
6 2
3.3%
13 1
 
1.6%
15 1
 
1.6%
31 1
 
1.6%
38 1
 
1.6%
58 1
 
1.6%
ValueCountFrequency (%)
179 1
1.6%
79 1
1.6%
67 1
1.6%
58 1
1.6%
38 1
1.6%
31 1
1.6%
15 1
1.6%
13 1
1.6%
6 2
3.3%
5 1
1.6%

메타세콰이어
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
58 
2
 
1
54
 
1
7
 
1

Length

Max length4
Median length4
Mean length3.8688525
Min length1

Unique

Unique3 ?
Unique (%)4.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 58
95.1%
2 1
 
1.6%
54 1
 
1.6%
7 1
 
1.6%

Length

2023-12-13T08:35:52.129719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:52.255458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 58
95.1%
2 1
 
1.6%
54 1
 
1.6%
7 1
 
1.6%

가시나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)85.7%
Missing54
Missing (%)88.5%
Infinite0
Infinite (%)0.0%
Mean26.571429
Minimum6
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:35:52.357447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.2
Q110
median12
Q319.5
95-th percentile82.9
Maximum109
Range103
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation36.723549
Coefficient of variation (CV)1.382069
Kurtosis6.5435972
Mean26.571429
Median Absolute Deviation (MAD)5
Skewness2.5355641
Sum186
Variance1348.619
MonotonicityNot monotonic
2023-12-13T08:35:52.451183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10 2
 
3.3%
22 1
 
1.6%
17 1
 
1.6%
6 1
 
1.6%
109 1
 
1.6%
12 1
 
1.6%
(Missing) 54
88.5%
ValueCountFrequency (%)
6 1
1.6%
10 2
3.3%
12 1
1.6%
17 1
1.6%
22 1
1.6%
109 1
1.6%
ValueCountFrequency (%)
109 1
1.6%
22 1
1.6%
17 1
1.6%
12 1
1.6%
10 2
3.3%
6 1
1.6%

곰솔
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
60 
67
 
1

Length

Max length4
Median length4
Mean length3.9672131
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 60
98.4%
67 1
 
1.6%

Length

2023-12-13T08:35:52.581706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:52.704690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 60
98.4%
67 1
 
1.6%

후박나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
58 
222
 
1
1
 
1
5
 
1

Length

Max length4
Median length4
Mean length3.8852459
Min length1

Unique

Unique3 ?
Unique (%)4.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 58
95.1%
222 1
 
1.6%
1 1
 
1.6%
5 1
 
1.6%

Length

2023-12-13T08:35:52.841404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:52.977978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 58
95.1%
222 1
 
1.6%
1 1
 
1.6%
5 1
 
1.6%

가중나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
60 
7
 
1

Length

Max length4
Median length4
Mean length3.9508197
Min length1

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row7
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 60
98.4%
7 1
 
1.6%

Length

2023-12-13T08:35:53.094957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:53.218985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 60
98.4%
7 1
 
1.6%

단풍나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
59 
17
 
1
48
 
1

Length

Max length4
Median length4
Mean length3.9344262
Min length2

Unique

Unique2 ?
Unique (%)3.3%

Sample

1st row<NA>
2nd row17
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 59
96.7%
17 1
 
1.6%
48 1
 
1.6%

Length

2023-12-13T08:35:53.328361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:53.426346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 59
96.7%
17 1
 
1.6%
48 1
 
1.6%

향나무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
59 
10
 
1
6
 
1

Length

Max length4
Median length4
Mean length3.9180328
Min length1

Unique

Unique2 ?
Unique (%)3.3%

Sample

1st row10
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 59
96.7%
10 1
 
1.6%
6 1
 
1.6%

Length

2023-12-13T08:35:53.536803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:53.644753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 59
96.7%
10 1
 
1.6%
6 1
 
1.6%

먼나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)90.0%
Missing51
Missing (%)83.6%
Infinite0
Infinite (%)0.0%
Mean20.5
Minimum5
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:35:53.729023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.45
Q18.25
median9.5
Q312.5
95-th percentile69.6
Maximum93
Range88
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation27.496464
Coefficient of variation (CV)1.3412909
Kurtosis6.3475902
Mean20.5
Median Absolute Deviation (MAD)2.5
Skewness2.5086158
Sum205
Variance756.05556
MonotonicityNot monotonic
2023-12-13T08:35:53.817729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9 2
 
3.3%
6 1
 
1.6%
8 1
 
1.6%
11 1
 
1.6%
93 1
 
1.6%
41 1
 
1.6%
13 1
 
1.6%
5 1
 
1.6%
10 1
 
1.6%
(Missing) 51
83.6%
ValueCountFrequency (%)
5 1
1.6%
6 1
1.6%
8 1
1.6%
9 2
3.3%
10 1
1.6%
11 1
1.6%
13 1
1.6%
41 1
1.6%
93 1
1.6%
ValueCountFrequency (%)
93 1
1.6%
41 1
1.6%
13 1
1.6%
11 1
1.6%
10 1
1.6%
9 2
3.3%
8 1
1.6%
6 1
1.6%
5 1
1.6%

이팝나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)82.1%
Missing33
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean52.535714
Minimum4
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:35:54.231860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.7
Q112.25
median31.5
Q355
95-th percentile180.95
Maximum223
Range219
Interquartile range (IQR)42.75

Descriptive statistics

Standard deviation59.67969
Coefficient of variation (CV)1.1359832
Kurtosis2.1178852
Mean52.535714
Median Absolute Deviation (MAD)22
Skewness1.7024653
Sum1471
Variance3561.6653
MonotonicityNot monotonic
2023-12-13T08:35:54.354563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 4
 
6.6%
48 2
 
3.3%
13 2
 
3.3%
223 1
 
1.6%
18 1
 
1.6%
42 1
 
1.6%
40 1
 
1.6%
27 1
 
1.6%
82 1
 
1.6%
113 1
 
1.6%
Other values (13) 13
 
21.3%
(Missing) 33
54.1%
ValueCountFrequency (%)
4 1
 
1.6%
6 1
 
1.6%
8 4
6.6%
10 1
 
1.6%
13 2
3.3%
15 1
 
1.6%
18 1
 
1.6%
20 1
 
1.6%
27 1
 
1.6%
29 1
 
1.6%
ValueCountFrequency (%)
223 1
1.6%
196 1
1.6%
153 1
1.6%
147 1
1.6%
113 1
1.6%
82 1
1.6%
58 1
1.6%
54 1
1.6%
48 2
3.3%
46 1
1.6%

회화나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
60 
47
 
1

Length

Max length4
Median length4
Mean length3.9672131
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 60
98.4%
47 1
 
1.6%

Length

2023-12-13T08:35:54.494403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:54.612773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 60
98.4%
47 1
 
1.6%

튤립나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
60 
62
 
1

Length

Max length4
Median length4
Mean length3.9672131
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 60
98.4%
62 1
 
1.6%

Length

2023-12-13T08:35:54.766367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:35:54.885362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 60
98.4%
62 1
 
1.6%

가로수연장(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81278689
Minimum0.05
Maximum5.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-13T08:35:55.000886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.07
Q10.16
median0.3
Q30.8
95-th percentile2.95
Maximum5.5
Range5.45
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation1.2028745
Coefficient of variation (CV)1.4799384
Kurtosis6.459338
Mean0.81278689
Median Absolute Deviation (MAD)0.2
Skewness2.5334808
Sum49.58
Variance1.4469071
MonotonicityNot monotonic
2023-12-13T08:35:55.130791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.2 4
 
6.6%
0.15 4
 
6.6%
0.35 3
 
4.9%
0.3 3
 
4.9%
0.1 3
 
4.9%
0.6 3
 
4.9%
0.09 2
 
3.3%
0.25 2
 
3.3%
1.5 2
 
3.3%
0.05 2
 
3.3%
Other values (30) 33
54.1%
ValueCountFrequency (%)
0.05 2
3.3%
0.06 1
 
1.6%
0.07 1
 
1.6%
0.08 1
 
1.6%
0.09 2
3.3%
0.1 3
4.9%
0.12 1
 
1.6%
0.15 4
6.6%
0.16 1
 
1.6%
0.18 1
 
1.6%
ValueCountFrequency (%)
5.5 1
1.6%
5.0 1
1.6%
4.8 1
1.6%
2.95 1
1.6%
2.5 1
1.6%
2.4 1
1.6%
2.3 1
1.6%
2.0 1
1.6%
1.8 1
1.6%
1.5 2
3.3%

Interactions

2023-12-13T08:35:48.943372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:44.636769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.257691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.877007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.487162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:47.410739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:48.120043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:49.022464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:44.703841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.358843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.953599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.568955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:47.524328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:48.324375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:49.108166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:44.793965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.456744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.035250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.654963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:47.643764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:48.456735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:49.220294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:44.879567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.550176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.130507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.732904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:47.751896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:48.554668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:49.317247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:44.960476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.624704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.223293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.814353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:47.856584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:48.631209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:49.411974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.057274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.704420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.313534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.925214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:47.934349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:48.729062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:49.497008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.152792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:45.802706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:46.398771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:47.311215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:48.039011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:48.844962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:35:55.232230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선명은행나무왕벚나무히말라야시다느티나무메타세콰이어가시나무후박나무단풍나무향나무먼나무이팝나무가로수연장(km)
노선명1.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0001.0001.000
은행나무1.0001.0001.0001.0000.323NaN1.0000.000NaN0.0000.0000.9260.612
왕벚나무1.0001.0001.0000.000NaNNaN0.000NaNNaN0.0001.000NaN0.869
히말라야시다1.0001.0000.0001.0001.000NaNNaN0.000NaNNaNNaN0.0000.416
느티나무1.0000.323NaN1.0001.0000.0001.000NaNNaNNaN0.000NaN0.000
메타세콰이어1.000NaNNaNNaN0.0001.000NaNNaNNaNNaNNaNNaN1.000
가시나무1.0001.0000.000NaN1.000NaN1.000NaNNaNNaNNaN0.0000.672
후박나무1.0000.000NaN0.000NaNNaNNaN1.000NaNNaNNaNNaN1.000
단풍나무0.000NaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaN0.000
향나무0.0000.0000.000NaNNaNNaNNaNNaNNaN1.000NaNNaNNaN
먼나무1.0000.0001.000NaN0.000NaNNaNNaNNaNNaN1.000NaN0.729
이팝나무1.0000.926NaN0.000NaNNaN0.000NaNNaNNaNNaN1.0000.831
가로수연장(km)1.0000.6120.8690.4160.0001.0000.6721.0000.000NaN0.7290.8311.000
2023-12-13T08:35:55.387375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
메타세콰이어후박나무곰솔히말라야시다가중나무회화나무튤립나무단풍나무향나무
메타세콰이어1.000NaNNaN1.000NaNNaNNaNNaNNaN
후박나무NaN1.000NaN1.000NaNNaNNaNNaNNaN
곰솔NaNNaN1.000NaNNaNNaNNaNNaNNaN
히말라야시다1.0001.000NaN1.000NaNNaNNaNNaNNaN
가중나무NaNNaNNaNNaN1.000NaNNaNNaNNaN
회화나무NaNNaNNaNNaNNaN1.000NaNNaNNaN
튤립나무NaNNaNNaNNaNNaNNaN1.000NaNNaN
단풍나무NaNNaNNaNNaNNaNNaNNaN1.000NaN
향나무NaNNaNNaNNaNNaNNaNNaNNaN1.000
2023-12-13T08:35:55.556435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
은행나무왕벚나무느티나무가시나무먼나무이팝나무가로수연장(km)히말라야시다메타세콰이어곰솔후박나무가중나무단풍나무향나무회화나무튤립나무
은행나무1.000-1.000-0.2880.5000.5000.4620.3771.000NaNNaN1.000NaNNaN1.000NaNNaN
왕벚나무-1.0001.0000.800-1.000-0.5001.0000.7941.000NaNNaNNaNNaNNaN1.0000.0000.000
느티나무-0.2880.8001.000-0.500-0.7381.0000.0241.0001.0000.000NaNNaN1.000NaN0.000NaN
가시나무0.500-1.000-0.5001.000-1.0001.0000.6491.000NaNNaN1.0000.000NaNNaN0.0000.000
먼나무0.500-0.500-0.738-1.0001.000NaN-0.2231.000NaNNaN1.000NaN0.0001.0000.0000.000
이팝나무0.4621.0001.0001.000NaN1.0000.6951.0000.000NaNNaN0.000NaNNaNNaN0.000
가로수연장(km)0.3770.7940.0240.649-0.2230.6951.0000.0001.000NaN1.000NaN1.0001.000NaNNaN
히말라야시다1.0001.0001.0001.0001.0001.0000.0001.0001.000NaN1.0000.000NaNNaN0.000NaN
메타세콰이어NaNNaN1.000NaNNaN0.0001.0001.0001.0000.000NaN0.000NaN0.0000.0000.000
곰솔NaNNaN0.000NaNNaNNaNNaNNaN0.0001.000NaN0.0000.000NaN0.0000.000
후박나무1.000NaNNaN1.0001.000NaN1.0001.000NaNNaN1.0000.0000.000NaN0.0000.000
가중나무NaNNaNNaN0.000NaN0.000NaN0.0000.0000.0000.0001.0000.000NaN0.0000.000
단풍나무NaNNaN1.000NaN0.000NaN1.000NaNNaN0.0000.0000.0001.0000.0000.0000.000
향나무1.0001.000NaNNaN1.000NaN1.000NaN0.000NaNNaNNaN0.0001.0000.0000.000
회화나무NaN0.0000.0000.0000.000NaNNaN0.0000.0000.0000.0000.0000.0000.0001.0000.000
튤립나무NaN0.000NaN0.0000.0000.000NaNNaN0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T08:35:49.650289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:35:49.878932image/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.
2023-12-13T08:35:50.113039image/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

노선명은행나무왕벚나무플라타너스히말라야시다느티나무메타세콰이어가시나무곰솔후박나무가중나무단풍나무향나무먼나무이팝나무회화나무튤립나무가로수연장(km)
0중앙대로293145<NA><NA>5<NA><NA><NA><NA>7<NA>106<NA><NA><NA>5.5
1충렬대로432<NA><NA><NA>3<NA>22<NA><NA><NA>17<NA><NA>58<NA><NA>4.8
2충렬대로107번길70<NA><NA><NA>6<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0.3
3충렬대로238번길12<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0.2
4충렬대로350번길<NA><NA><NA><NA><NA><NA>17<NA><NA><NA><NA><NA><NA><NA><NA><NA>0.15
5충렬대로410번길<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8<NA><NA><NA>0.1
6아시아드대로<NA><NA><NA>158210<NA>222<NA><NA><NA>9<NA><NA><NA>1.8
7아시아드대로146번길<NA><NA><NA><NA>1<NA><NA><NA><NA><NA><NA><NA>11<NA><NA><NA>0.06
8아시아드대로228번길20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0.2
9아시아드대로208번길<NA><NA><NA><NA><NA><NA>6<NA><NA><NA><NA><NA><NA><NA><NA><NA>0.05
노선명은행나무왕벚나무플라타너스히말라야시다느티나무메타세콰이어가시나무곰솔후박나무가중나무단풍나무향나무먼나무이팝나무회화나무튤립나무가로수연장(km)
51명장로67번길<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8<NA><NA>0.09
52우장춘로9번길, 충렬대로75번길(중로3-224)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>113<NA><NA>0.6
53금정마을로(중로1-142)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>82<NA><NA>0.42
54우장춘로59번길(중로3-223)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>27<NA><NA>0.26
55중앙대로1381번길(중로1-141)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>40<NA><NA>0.19
56우장춘로18번길(중로2-206)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>42<NA><NA>0.24
57차밭골로<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>9<NA><NA><NA>0.1
58온천천로531번길<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>10<NA><NA><NA>0.15
59충렬대로75번길<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>18<NA><NA>0.24
60명장로67번길<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8<NA><NA>0.09

Duplicate rows

Most frequently occurring

노선명은행나무왕벚나무히말라야시다느티나무메타세콰이어가시나무곰솔후박나무가중나무단풍나무향나무먼나무이팝나무회화나무튤립나무가로수연장(km)# duplicates
0명장로67번길<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8<NA><NA>0.092