Overview

Dataset statistics

Number of variables50
Number of observations695
Missing cells11051
Missing cells (%)31.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory300.8 KiB
Average record size in memory443.2 B

Variable types

Numeric24
Text4
Categorical20
DateTime2

Alerts

last_load_dttm has constant value ""Constant
acer_buergerianum is highly imbalanced (80.6%)Imbalance
celtis_sinensis is highly imbalanced (97.3%)Imbalance
firmiana_simplex is highly imbalanced (71.1%)Imbalance
pin_oak is highly imbalanced (70.8%)Imbalance
persimmon is highly imbalanced (70.8%)Imbalance
chinese_quince is highly imbalanced (70.8%)Imbalance
goldenrain_tree is highly imbalanced (76.0%)Imbalance
cinnamon_tree is highly imbalanced (70.8%)Imbalance
ailanthus_altissima is highly imbalanced (70.8%)Imbalance
amur_cork_tree is highly imbalanced (70.8%)Imbalance
babylon_willow is highly imbalanced (55.2%)Imbalance
three_flowered_maple is highly imbalanced (70.8%)Imbalance
japanese_elm is highly imbalanced (55.2%)Imbalance
jujube is highly imbalanced (55.2%)Imbalance
torulosa is highly imbalanced (80.5%)Imbalance
neolitsea_sericea is highly imbalanced (76.0%)Imbalance
taxus_cuspidata is highly imbalanced (70.8%)Imbalance
sweet_viburnum is highly imbalanced (55.2%)Imbalance
instt_code is highly imbalanced (83.2%)Imbalance
plant_distance has 191 (27.5%) missing valuesMissing
prunus_yedoensis has 357 (51.4%) missing valuesMissing
ginkgo has 351 (50.5%) missing valuesMissing
sawleaf_zelkova has 419 (60.3%) missing valuesMissing
platanus_orientalis has 614 (88.3%) missing valuesMissing
platanus has 543 (78.1%) missing valuesMissing
chinese_fringe_tree has 463 (66.6%) missing valuesMissing
sophora_japonica has 620 (89.2%) missing valuesMissing
metasequoia has 546 (78.6%) missing valuesMissing
horse_chestnut has 557 (80.1%) missing valuesMissing
tulipifera has 545 (78.4%) missing valuesMissing
acer_palmatum has 623 (89.6%) missing valuesMissing
cornus_kousa has 629 (90.5%) missing valuesMissing
silver_magnolia has 510 (73.4%) missing valuesMissing
kurogane_holly has 499 (71.8%) missing valuesMissing
pinus_thunbergii has 606 (87.2%) missing valuesMissing
myrsinaleaf_oak has 541 (77.8%) missing valuesMissing
castanopsis_sieboldii has 621 (89.4%) missing valuesMissing
cedrus_deodara has 620 (89.2%) missing valuesMissing
camphor_tree has 561 (80.7%) missing valuesMissing
etc_tree has 626 (90.1%) missing valuesMissing
skey has unique valuesUnique
total has 32 (4.6%) zerosZeros
prunus_yedoensis has 73 (10.5%) zerosZeros
ginkgo has 88 (12.7%) zerosZeros
sawleaf_zelkova has 81 (11.7%) zerosZeros
platanus_orientalis has 65 (9.4%) zerosZeros
platanus has 117 (16.8%) zerosZeros
chinese_fringe_tree has 112 (16.1%) zerosZeros
sophora_japonica has 57 (8.2%) zerosZeros
metasequoia has 122 (17.6%) zerosZeros
horse_chestnut has 124 (17.8%) zerosZeros
tulipifera has 126 (18.1%) zerosZeros
acer_palmatum has 65 (9.4%) zerosZeros
cornus_kousa has 61 (8.8%) zerosZeros
silver_magnolia has 117 (16.8%) zerosZeros
kurogane_holly has 118 (17.0%) zerosZeros
pinus_thunbergii has 64 (9.2%) zerosZeros
myrsinaleaf_oak has 127 (18.3%) zerosZeros
castanopsis_sieboldii has 65 (9.4%) zerosZeros
cedrus_deodara has 62 (8.9%) zerosZeros
camphor_tree has 127 (18.3%) zerosZeros
etc_tree has 64 (9.2%) zerosZeros

Reproduction

Analysis started2024-04-16 05:52:01.990067
Analysis finished2024-04-16 05:52:02.912939
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5232.7007
Minimum4277
Maximum5673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:02.968174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4277
5-th percentile4847.7
Q15044.5
median5283
Q35456.5
95-th percentile5638.3
Maximum5673
Range1396
Interquartile range (IQR)412

Descriptive statistics

Standard deviation306.62062
Coefficient of variation (CV)0.058597011
Kurtosis1.519105
Mean5232.7007
Median Absolute Deviation (MAD)215
Skewness-0.99886587
Sum3636727
Variance94016.204
MonotonicityNot monotonic
2024-04-16T14:52:03.082299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5432 1
 
0.1%
5444 1
 
0.1%
5436 1
 
0.1%
5437 1
 
0.1%
5438 1
 
0.1%
5439 1
 
0.1%
5440 1
 
0.1%
5441 1
 
0.1%
5442 1
 
0.1%
5443 1
 
0.1%
Other values (685) 685
98.6%
ValueCountFrequency (%)
4277 1
0.1%
4278 1
0.1%
4279 1
0.1%
4280 1
0.1%
4281 1
0.1%
4282 1
0.1%
4283 1
0.1%
4284 1
0.1%
4285 1
0.1%
4286 1
0.1%
ValueCountFrequency (%)
5673 1
0.1%
5672 1
0.1%
5671 1
0.1%
5670 1
0.1%
5669 1
0.1%
5668 1
0.1%
5667 1
0.1%
5666 1
0.1%
5665 1
0.1%
5664 1
0.1%

loc_nm
Text

Distinct637
Distinct (%)91.8%
Missing1
Missing (%)0.1%
Memory size5.6 KiB
2024-04-16T14:52:03.357989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length14.70317
Min length3

Characters and Unicode

Total characters10204
Distinct characters269
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique592 ?
Unique (%)85.3%

Sample

1st row부산광역시 부산진구 전포대로171번길
2nd row부산광역시 기장군 기장대로
3rd row부산광역시 기장군 정관로
4th row부산광역시 기장군 장곡길
5th row부산광역시 기장군 대변로
ValueCountFrequency (%)
부산광역시 594
29.3%
해운대구 67
 
3.3%
부산진구 65
 
3.2%
기장군 60
 
3.0%
연제구 58
 
2.9%
동래구 49
 
2.4%
사하구 49
 
2.4%
북구 48
 
2.4%
사상구 43
 
2.1%
영도구 32
 
1.6%
Other values (627) 963
47.5%
2024-04-16T14:52:03.777827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1429
 
14.0%
산 742
 
7.3%
로 685
 
6.7%
부 666
 
6.5%
시 621
 
6.1%
광 611
 
6.0%
역 594
 
5.8%
구 576
 
5.6%
대 231
 
2.3%
길 227
 
2.2%
Other values (259) 3822
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8053
78.9%
Space Separator 1429
 
14.0%
Decimal Number 532
 
5.2%
Close Punctuation 74
 
0.7%
Open Punctuation 74
 
0.7%
Other Punctuation 24
 
0.2%
Math Symbol 8
 
0.1%
Uppercase Letter 8
 
0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 742
 
9.2%
로 685
 
8.5%
부 666
 
8.3%
시 621
 
7.7%
광 611
 
7.6%
역 594
 
7.4%
구 576
 
7.2%
대 231
 
2.9%
길 227
 
2.8%
번 168
 
2.1%
Other values (233) 2932
36.4%
Decimal Number
ValueCountFrequency (%)
1 103
19.4%
3 74
13.9%
2 71
13.3%
4 53
10.0%
0 51
9.6%
5 41
 
7.7%
7 38
 
7.1%
9 38
 
7.1%
6 37
 
7.0%
8 26
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
C 2
25.0%
G 1
12.5%
L 1
12.5%
A 1
12.5%
P 1
12.5%
E 1
12.5%
I 1
12.5%
Other Punctuation
ValueCountFrequency (%)
. 15
62.5%
, 8
33.3%
? 1
 
4.2%
Math Symbol
ValueCountFrequency (%)
~ 6
75.0%
+ 2
 
25.0%
Space Separator
ValueCountFrequency (%)
1429
100.0%
Close Punctuation
ValueCountFrequency (%)
) 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 74
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8053
78.9%
Common 2143
 
21.0%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 742
 
9.2%
로 685
 
8.5%
부 666
 
8.3%
시 621
 
7.7%
광 611
 
7.6%
역 594
 
7.4%
구 576
 
7.2%
대 231
 
2.9%
길 227
 
2.8%
번 168
 
2.1%
Other values (233) 2932
36.4%
Common
ValueCountFrequency (%)
1429
66.7%
1 103
 
4.8%
) 74
 
3.5%
( 74
 
3.5%
3 74
 
3.5%
2 71
 
3.3%
4 53
 
2.5%
0 51
 
2.4%
5 41
 
1.9%
7 38
 
1.8%
Other values (9) 135
 
6.3%
Latin
ValueCountFrequency (%)
C 2
25.0%
G 1
12.5%
L 1
12.5%
A 1
12.5%
P 1
12.5%
E 1
12.5%
I 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8053
78.9%
ASCII 2151
 
21.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1429
66.4%
1 103
 
4.8%
) 74
 
3.4%
( 74
 
3.4%
3 74
 
3.4%
2 71
 
3.3%
4 53
 
2.5%
0 51
 
2.4%
5 41
 
1.9%
7 38
 
1.8%
Other values (16) 143
 
6.6%
Hangul
ValueCountFrequency (%)
산 742
 
9.2%
로 685
 
8.5%
부 666
 
8.3%
시 621
 
7.7%
광 611
 
7.6%
역 594
 
7.4%
구 576
 
7.2%
대 231
 
2.9%
길 227
 
2.8%
번 168
 
2.1%
Other values (233) 2932
36.4%

lat
Real number (ℝ)

Distinct646
Distinct (%)93.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean35.207021
Minimum34.752398
Maximum37.860478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:03.908478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.752398
5-th percentile35.080565
Q135.10577
median35.155227
Q335.199061
95-th percentile35.322676
Maximum37.860478
Range3.1080802
Interquartile range (IQR)0.09329075

Descriptive statistics

Standard deviation0.31786685
Coefficient of variation (CV)0.0090285074
Kurtosis40.123361
Mean35.207021
Median Absolute Deviation (MAD)0.0454995
Skewness6.2636791
Sum24433.673
Variance0.10103934
MonotonicityNot monotonic
2024-04-16T14:52:04.035836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.0892077866 7
 
1.0%
35.0867584467 5
 
0.7%
35.208078 4
 
0.6%
35.0748792013 3
 
0.4%
35.191714 3
 
0.4%
35.095758 3
 
0.4%
35.15545937 3
 
0.4%
35.1623 3
 
0.4%
35.124695 2
 
0.3%
35.197626 2
 
0.3%
Other values (636) 659
94.8%
ValueCountFrequency (%)
34.7523980835 1
0.1%
34.9049888397 1
0.1%
35.0069101984 1
0.1%
35.0220637302 1
0.1%
35.022465875 1
0.1%
35.049232 1
0.1%
35.05039 1
0.1%
35.053596 1
0.1%
35.053981 1
0.1%
35.056847 1
0.1%
ValueCountFrequency (%)
37.86047829 1
0.1%
37.4442297073 1
0.1%
37.423498931 1
0.1%
37.4192664864 1
0.1%
37.3667570698 1
0.1%
37.3413319625 1
0.1%
37.3400154596 1
0.1%
37.3268107895 1
0.1%
37.3155148245 1
0.1%
37.3057994893 1
0.1%

lng
Real number (ℝ)

Distinct647
Distinct (%)93.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean128.99435
Minimum126.35848
Maximum129.27168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:04.146782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.35848
5-th percentile128.85258
Q1129.00506
median129.06154
Q3129.08981
95-th percentile129.18308
Maximum129.27168
Range2.9132061
Interquartile range (IQR)0.084749815

Descriptive statistics

Standard deviation0.34756751
Coefficient of variation (CV)0.0026944398
Kurtosis33.022051
Mean128.99435
Median Absolute Deviation (MAD)0.041350637
Skewness-5.665096
Sum89522.079
Variance0.12080318
MonotonicityNot monotonic
2024-04-16T14:52:04.271612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0686159146 7
 
1.0%
129.0729496246 5
 
0.7%
129.069547 4
 
0.6%
128.9913329 3
 
0.4%
128.9859387 3
 
0.4%
129.0506702167 3
 
0.4%
129.153866 3
 
0.4%
129.100261 3
 
0.4%
129.103867 2
 
0.3%
129.072894 2
 
0.3%
Other values (637) 659
94.8%
ValueCountFrequency (%)
126.3584789252 1
0.1%
126.449637021 1
0.1%
126.654566009 1
0.1%
126.6574921236 1
0.1%
126.7122578917 1
0.1%
126.7917367522 1
0.1%
126.7976397897 1
0.1%
126.820081832 1
0.1%
126.8623186142 1
0.1%
126.9423850116 1
0.1%
ValueCountFrequency (%)
129.271685 1
0.1%
129.269173 1
0.1%
129.258504 1
0.1%
129.258446 1
0.1%
129.255467 1
0.1%
129.253591 1
0.1%
129.249412 1
0.1%
129.243941 1
0.1%
129.243469 1
0.1%
129.242121 1
0.1%

sec_timepoint
Text

Distinct583
Distinct (%)84.0%
Missing1
Missing (%)0.1%
Memory size5.6 KiB
2024-04-16T14:52:04.510731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length6.0172911
Min length3

Characters and Unicode

Total characters4176
Distinct characters349
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique501 ?
Unique (%)72.2%

Sample

1st row글로벌연구센터
2nd row송정1호교
3rd row달음교입구
4th row좌천마을
5th row청강사거리
ValueCountFrequency (%)
내 15
 
1.8%
아파트 9
 
1.1%
연산교차로 8
 
1.0%
앞 8
 
1.0%
삼거리 6
 
0.7%
충렬대로 5
 
0.6%
입구 5
 
0.6%
백양대로 5
 
0.6%
국제물류 5
 
0.6%
봉래교차로 4
 
0.5%
Other values (631) 768
91.6%
2024-04-16T14:52:04.879455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
208
 
5.0%
로 192
 
4.6%
교 124
 
3.0%
대 94
 
2.3%
리 91
 
2.2%
동 90
 
2.2%
거 84
 
2.0%
아 77
 
1.8%
차 72
 
1.7%
산 71
 
1.7%
Other values (339) 3073
73.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3641
87.2%
Decimal Number 230
 
5.5%
Space Separator 208
 
5.0%
Uppercase Letter 64
 
1.5%
Dash Punctuation 13
 
0.3%
Open Punctuation 8
 
0.2%
Close Punctuation 8
 
0.2%
Other Punctuation 2
 
< 0.1%
Other Symbol 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 192
 
5.3%
교 124
 
3.4%
대 94
 
2.6%
리 91
 
2.5%
동 90
 
2.5%
거 84
 
2.3%
아 77
 
2.1%
차 72
 
2.0%
산 71
 
2.0%
구 70
 
1.9%
Other values (307) 2676
73.5%
Uppercase Letter
ValueCountFrequency (%)
K 9
14.1%
S 9
14.1%
I 8
12.5%
C 7
10.9%
E 7
10.9%
L 5
7.8%
G 4
6.2%
W 4
6.2%
V 4
6.2%
N 2
 
3.1%
Other values (5) 5
7.8%
Decimal Number
ValueCountFrequency (%)
1 65
28.3%
2 37
16.1%
5 24
 
10.4%
0 19
 
8.3%
3 18
 
7.8%
4 17
 
7.4%
6 17
 
7.4%
7 13
 
5.7%
9 12
 
5.2%
8 8
 
3.5%
Space Separator
ValueCountFrequency (%)
208
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3642
87.2%
Common 470
 
11.3%
Latin 64
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 192
 
5.3%
교 124
 
3.4%
대 94
 
2.6%
리 91
 
2.5%
동 90
 
2.5%
거 84
 
2.3%
아 77
 
2.1%
차 72
 
2.0%
산 71
 
1.9%
구 70
 
1.9%
Other values (308) 2677
73.5%
Common
ValueCountFrequency (%)
208
44.3%
1 65
 
13.8%
2 37
 
7.9%
5 24
 
5.1%
0 19
 
4.0%
3 18
 
3.8%
4 17
 
3.6%
6 17
 
3.6%
7 13
 
2.8%
- 13
 
2.8%
Other values (6) 39
 
8.3%
Latin
ValueCountFrequency (%)
K 9
14.1%
S 9
14.1%
I 8
12.5%
C 7
10.9%
E 7
10.9%
L 5
7.8%
G 4
6.2%
W 4
6.2%
V 4
6.2%
N 2
 
3.1%
Other values (5) 5
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3641
87.2%
ASCII 534
 
12.8%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
208
39.0%
1 65
 
12.2%
2 37
 
6.9%
5 24
 
4.5%
0 19
 
3.6%
3 18
 
3.4%
4 17
 
3.2%
6 17
 
3.2%
7 13
 
2.4%
- 13
 
2.4%
Other values (21) 103
19.3%
Hangul
ValueCountFrequency (%)
로 192
 
5.3%
교 124
 
3.4%
대 94
 
2.6%
리 91
 
2.5%
동 90
 
2.5%
거 84
 
2.3%
아 77
 
2.1%
차 72
 
2.0%
산 71
 
2.0%
구 70
 
1.9%
Other values (307) 2676
73.5%
None
ValueCountFrequency (%)
㈜ 1
100.0%

sec_endpoint
Text

Distinct599
Distinct (%)86.6%
Missing3
Missing (%)0.4%
Memory size5.6 KiB
2024-04-16T14:52:05.123895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length6.1950867
Min length2

Characters and Unicode

Total characters4287
Distinct characters377
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique532 ?
Unique (%)76.9%

Sample

1st row글로벌연구센터
2nd row기장체육관
3rd roweg1차
4th row문중마을
5th row무양교차로
ValueCountFrequency (%)
입구 18
 
2.1%
내 15
 
1.7%
경계 13
 
1.5%
아파트 12
 
1.4%
앞 10
 
1.2%
수영구경계 8
 
0.9%
동래구경계 7
 
0.8%
국제물류 7
 
0.8%
수영 4
 
0.5%
기장군 4
 
0.5%
Other values (653) 767
88.7%
2024-04-16T14:52:05.486701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
232
 
5.4%
로 128
 
3.0%
교 117
 
2.7%
구 107
 
2.5%
동 104
 
2.4%
아 93
 
2.2%
트 89
 
2.1%
파 85
 
2.0%
대 80
 
1.9%
경 68
 
1.6%
Other values (367) 3184
74.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3705
86.4%
Decimal Number 237
 
5.5%
Space Separator 232
 
5.4%
Uppercase Letter 67
 
1.6%
Dash Punctuation 14
 
0.3%
Open Punctuation 10
 
0.2%
Close Punctuation 10
 
0.2%
Other Punctuation 6
 
0.1%
Math Symbol 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 128
 
3.5%
교 117
 
3.2%
구 107
 
2.9%
동 104
 
2.8%
아 93
 
2.5%
트 89
 
2.4%
파 85
 
2.3%
대 80
 
2.2%
경 68
 
1.8%
산 61
 
1.6%
Other values (333) 2773
74.8%
Uppercase Letter
ValueCountFrequency (%)
I 12
17.9%
C 10
14.9%
S 9
13.4%
E 6
9.0%
K 6
9.0%
L 5
7.5%
W 5
7.5%
V 4
 
6.0%
H 3
 
4.5%
G 3
 
4.5%
Other values (3) 4
 
6.0%
Decimal Number
ValueCountFrequency (%)
1 58
24.5%
2 43
18.1%
3 27
11.4%
6 21
 
8.9%
5 20
 
8.4%
4 17
 
7.2%
0 16
 
6.8%
9 13
 
5.5%
8 11
 
4.6%
7 11
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 5
83.3%
; 1
 
16.7%
Lowercase Letter
ValueCountFrequency (%)
g 1
50.0%
e 1
50.0%
Space Separator
ValueCountFrequency (%)
232
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3706
86.4%
Common 511
 
11.9%
Latin 70
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 128
 
3.5%
교 117
 
3.2%
구 107
 
2.9%
동 104
 
2.8%
아 93
 
2.5%
트 89
 
2.4%
파 85
 
2.3%
대 80
 
2.2%
경 68
 
1.8%
산 61
 
1.6%
Other values (334) 2774
74.9%
Common
ValueCountFrequency (%)
232
45.4%
1 58
 
11.4%
2 43
 
8.4%
3 27
 
5.3%
6 21
 
4.1%
5 20
 
3.9%
4 17
 
3.3%
0 16
 
3.1%
- 14
 
2.7%
9 13
 
2.5%
Other values (7) 50
 
9.8%
Latin
ValueCountFrequency (%)
I 12
17.1%
C 10
14.3%
S 9
12.9%
E 6
8.6%
K 6
8.6%
L 5
7.1%
W 5
7.1%
V 4
 
5.7%
H 3
 
4.3%
G 3
 
4.3%
Other values (6) 7
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3705
86.4%
ASCII 580
 
13.5%
Number Forms 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
232
40.0%
1 58
 
10.0%
2 43
 
7.4%
3 27
 
4.7%
6 21
 
3.6%
5 20
 
3.4%
4 17
 
2.9%
0 16
 
2.8%
- 14
 
2.4%
9 13
 
2.2%
Other values (22) 119
20.5%
Hangul
ValueCountFrequency (%)
로 128
 
3.5%
교 117
 
3.2%
구 107
 
2.9%
동 104
 
2.8%
아 93
 
2.5%
트 89
 
2.4%
파 85
 
2.3%
대 80
 
2.2%
경 68
 
1.8%
산 61
 
1.6%
Other values (333) 2773
74.8%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

plant_distance
Text

MISSING 

Distinct281
Distinct (%)55.8%
Missing191
Missing (%)27.5%
Memory size5.6 KiB
2024-04-16T14:52:05.815942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.4603175
Min length1

Characters and Unicode

Total characters1744
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)36.1%

Sample

1st row430
2nd row20
3rd row900
4th row400
5th row433
ValueCountFrequency (%)
200 14
 
2.8%
100 13
 
2.6%
500 11
 
2.2%
300 9
 
1.8%
0.4 8
 
1.6%
900 8
 
1.6%
0.2 7
 
1.4%
350 6
 
1.2%
800 6
 
1.2%
280 5
 
1.0%
Other values (271) 417
82.7%
2024-04-16T14:52:06.298296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 577
33.1%
1 184
 
10.6%
2 173
 
9.9%
. 128
 
7.3%
3 125
 
7.2%
5 115
 
6.6%
4 98
 
5.6%
6 93
 
5.3%
8 85
 
4.9%
9 69
 
4.0%
Other values (2) 97
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1586
90.9%
Other Punctuation 158
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 577
36.4%
1 184
 
11.6%
2 173
 
10.9%
3 125
 
7.9%
5 115
 
7.3%
4 98
 
6.2%
6 93
 
5.9%
8 85
 
5.4%
9 69
 
4.4%
7 67
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 128
81.0%
, 30
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1744
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 577
33.1%
1 184
 
10.6%
2 173
 
9.9%
. 128
 
7.3%
3 125
 
7.2%
5 115
 
6.6%
4 98
 
5.6%
6 93
 
5.3%
8 85
 
4.9%
9 69
 
4.0%
Other values (2) 97
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 577
33.1%
1 184
 
10.6%
2 173
 
9.9%
. 128
 
7.3%
3 125
 
7.2%
5 115
 
6.6%
4 98
 
5.6%
6 93
 
5.3%
8 85
 
4.9%
9 69
 
4.0%
Other values (2) 97
 
5.6%

total
Real number (ℝ)

ZEROS 

Distinct338
Distinct (%)48.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean243.11527
Minimum0
Maximum8608
Zeros32
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:06.443076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q131.25
median93.5
Q3245
95-th percentile941.15
Maximum8608
Range8608
Interquartile range (IQR)213.75

Descriptive statistics

Standard deviation505.64943
Coefficient of variation (CV)2.0798752
Kurtosis114.92425
Mean243.11527
Median Absolute Deviation (MAD)75.5
Skewness8.413516
Sum168722
Variance255681.35
MonotonicityNot monotonic
2024-04-16T14:52:06.563722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
4.6%
20 12
 
1.7%
34 9
 
1.3%
13 8
 
1.2%
14 8
 
1.2%
25 8
 
1.2%
16 7
 
1.0%
18 7
 
1.0%
63 7
 
1.0%
62 7
 
1.0%
Other values (328) 589
84.7%
ValueCountFrequency (%)
0 32
4.6%
2 2
 
0.3%
3 3
 
0.4%
4 3
 
0.4%
5 3
 
0.4%
6 3
 
0.4%
7 4
 
0.6%
8 4
 
0.6%
9 3
 
0.4%
10 3
 
0.4%
ValueCountFrequency (%)
8608 1
0.1%
3805 1
0.1%
2977 1
0.1%
2664 1
0.1%
2390 1
0.1%
2376 1
0.1%
2353 1
0.1%
2240 1
0.1%
2205 1
0.1%
2002 1
0.1%

prunus_yedoensis
Real number (ℝ)

MISSING  ZEROS 

Distinct171
Distinct (%)50.6%
Missing357
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean157
Minimum0
Maximum2916
Zeros73
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:06.696709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median46
Q3151
95-th percentile584.95
Maximum2916
Range2916
Interquartile range (IQR)145

Descriptive statistics

Standard deviation329.68636
Coefficient of variation (CV)2.0999131
Kurtosis29.640066
Mean157
Median Absolute Deviation (MAD)46
Skewness4.8532705
Sum53066
Variance108693.1
MonotonicityNot monotonic
2024-04-16T14:52:06.825667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
 
10.5%
62 6
 
0.9%
20 6
 
0.9%
13 6
 
0.9%
28 5
 
0.7%
16 5
 
0.7%
142 4
 
0.6%
25 4
 
0.6%
45 4
 
0.6%
3 3
 
0.4%
Other values (161) 222
31.9%
(Missing) 357
51.4%
ValueCountFrequency (%)
0 73
10.5%
1 1
 
0.1%
2 2
 
0.3%
3 3
 
0.4%
4 3
 
0.4%
5 2
 
0.3%
6 2
 
0.3%
7 3
 
0.4%
8 2
 
0.3%
9 1
 
0.1%
ValueCountFrequency (%)
2916 1
0.1%
2410 1
0.1%
2376 1
0.1%
1788 1
0.1%
1704 1
0.1%
1485 1
0.1%
1223 1
0.1%
1129 1
0.1%
906 1
0.1%
899 1
0.1%

ginkgo
Real number (ℝ)

MISSING  ZEROS 

Distinct159
Distinct (%)46.2%
Missing351
Missing (%)50.5%
Infinite0
Infinite (%)0.0%
Mean106.8314
Minimum0
Maximum1270
Zeros88
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:06.959969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median41
Q3118
95-th percentile469.4
Maximum1270
Range1270
Interquartile range (IQR)118

Descriptive statistics

Standard deviation175.2538
Coefficient of variation (CV)1.640471
Kurtosis11.029392
Mean106.8314
Median Absolute Deviation (MAD)41
Skewness2.9681428
Sum36750
Variance30713.896
MonotonicityNot monotonic
2024-04-16T14:52:07.101943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88
 
12.7%
41 7
 
1.0%
66 7
 
1.0%
14 7
 
1.0%
21 7
 
1.0%
221 4
 
0.6%
65 4
 
0.6%
2 4
 
0.6%
20 3
 
0.4%
24 3
 
0.4%
Other values (149) 210
30.2%
(Missing) 351
50.5%
ValueCountFrequency (%)
0 88
12.7%
1 2
 
0.3%
2 4
 
0.6%
3 1
 
0.1%
4 2
 
0.3%
5 1
 
0.1%
6 1
 
0.1%
7 3
 
0.4%
8 1
 
0.1%
9 3
 
0.4%
ValueCountFrequency (%)
1270 1
0.1%
1000 1
0.1%
899 1
0.1%
894 1
0.1%
891 1
0.1%
698 1
0.1%
678 1
0.1%
658 1
0.1%
643 1
0.1%
597 1
0.1%

sawleaf_zelkova
Real number (ℝ)

MISSING  ZEROS 

Distinct118
Distinct (%)42.8%
Missing419
Missing (%)60.3%
Infinite0
Infinite (%)0.0%
Mean89.728261
Minimum0
Maximum1527
Zeros81
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:07.233802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22.5
Q390.5
95-th percentile452.75
Maximum1527
Range1527
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation170.22454
Coefficient of variation (CV)1.8971118
Kurtosis21.284106
Mean89.728261
Median Absolute Deviation (MAD)22.5
Skewness3.8025225
Sum24765
Variance28976.395
MonotonicityNot monotonic
2024-04-16T14:52:07.385800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
11.7%
1 6
 
0.9%
13 6
 
0.9%
16 6
 
0.9%
20 4
 
0.6%
27 4
 
0.6%
31 4
 
0.6%
17 4
 
0.6%
23 4
 
0.6%
272 3
 
0.4%
Other values (108) 154
 
22.2%
(Missing) 419
60.3%
ValueCountFrequency (%)
0 81
11.7%
1 6
 
0.9%
2 1
 
0.1%
3 3
 
0.4%
4 2
 
0.3%
5 3
 
0.4%
6 3
 
0.4%
7 3
 
0.4%
8 3
 
0.4%
9 2
 
0.3%
ValueCountFrequency (%)
1527 1
0.1%
837 1
0.1%
789 1
0.1%
733 1
0.1%
580 1
0.1%
568 1
0.1%
559 2
0.3%
551 1
0.1%
542 1
0.1%
519 1
0.1%

platanus_orientalis
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)19.8%
Missing614
Missing (%)88.3%
Infinite0
Infinite (%)0.0%
Mean19.814815
Minimum0
Maximum463
Zeros65
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:07.515572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile112
Maximum463
Range463
Interquartile range (IQR)0

Descriptive statistics

Standard deviation62.269999
Coefficient of variation (CV)3.1425981
Kurtosis32.991982
Mean19.814815
Median Absolute Deviation (MAD)0
Skewness5.1936219
Sum1605
Variance3877.5528
MonotonicityNot monotonic
2024-04-16T14:52:07.626815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 65
 
9.4%
54 2
 
0.3%
463 1
 
0.1%
122 1
 
0.1%
65 1
 
0.1%
70 1
 
0.1%
99 1
 
0.1%
100 1
 
0.1%
53 1
 
0.1%
8 1
 
0.1%
Other values (6) 6
 
0.9%
(Missing) 614
88.3%
ValueCountFrequency (%)
0 65
9.4%
3 1
 
0.1%
8 1
 
0.1%
30 1
 
0.1%
33 1
 
0.1%
53 1
 
0.1%
54 2
 
0.3%
65 1
 
0.1%
70 1
 
0.1%
99 1
 
0.1%
ValueCountFrequency (%)
463 1
0.1%
209 1
0.1%
130 1
0.1%
122 1
0.1%
112 1
0.1%
100 1
0.1%
99 1
0.1%
70 1
0.1%
65 1
0.1%
54 2
0.3%

platanus
Real number (ℝ)

MISSING  ZEROS 

Distinct32
Distinct (%)21.1%
Missing543
Missing (%)78.1%
Infinite0
Infinite (%)0.0%
Mean40.717105
Minimum0
Maximum3019
Zeros117
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:07.728706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile115.35
Maximum3019
Range3019
Interquartile range (IQR)0

Descriptive statistics

Standard deviation258.54851
Coefficient of variation (CV)6.3498744
Kurtosis118.92089
Mean40.717105
Median Absolute Deviation (MAD)0
Skewness10.49472
Sum6189
Variance66847.33
MonotonicityNot monotonic
2024-04-16T14:52:07.850142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 117
 
16.8%
27 2
 
0.3%
7 2
 
0.3%
1 2
 
0.3%
2 2
 
0.3%
3 1
 
0.1%
3019 1
 
0.1%
186 1
 
0.1%
79 1
 
0.1%
11 1
 
0.1%
Other values (22) 22
 
3.2%
(Missing) 543
78.1%
ValueCountFrequency (%)
0 117
16.8%
1 2
 
0.3%
2 2
 
0.3%
3 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 2
 
0.3%
8 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
ValueCountFrequency (%)
3019 1
0.1%
803 1
0.1%
564 1
0.1%
328 1
0.1%
186 1
0.1%
179 1
0.1%
172 1
0.1%
128 1
0.1%
105 1
0.1%
95 1
0.1%

chinese_fringe_tree
Real number (ℝ)

MISSING  ZEROS 

Distinct85
Distinct (%)36.6%
Missing463
Missing (%)66.6%
Infinite0
Infinite (%)0.0%
Mean68.293103
Minimum0
Maximum1686
Zeros112
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:07.988277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q358.5
95-th percentile291.25
Maximum1686
Range1686
Interquartile range (IQR)58.5

Descriptive statistics

Standard deviation173.57803
Coefficient of variation (CV)2.5416627
Kurtosis39.125266
Mean68.293103
Median Absolute Deviation (MAD)5
Skewness5.4395309
Sum15844
Variance30129.334
MonotonicityNot monotonic
2024-04-16T14:52:08.449332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
 
16.1%
17 4
 
0.6%
60 4
 
0.6%
8 4
 
0.6%
5 3
 
0.4%
18 3
 
0.4%
48 3
 
0.4%
34 3
 
0.4%
83 3
 
0.4%
20 3
 
0.4%
Other values (75) 90
 
12.9%
(Missing) 463
66.6%
ValueCountFrequency (%)
0 112
16.1%
1 2
 
0.3%
4 1
 
0.1%
5 3
 
0.4%
6 1
 
0.1%
8 4
 
0.6%
9 2
 
0.3%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
ValueCountFrequency (%)
1686 1
0.1%
1086 1
0.1%
765 1
0.1%
679 1
0.1%
637 1
0.1%
574 1
0.1%
513 2
0.3%
481 1
0.1%
454 1
0.1%
329 1
0.1%

sophora_japonica
Real number (ℝ)

MISSING  ZEROS 

Distinct18
Distinct (%)24.0%
Missing620
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean56
Minimum0
Maximum1135
Zeros57
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:08.577227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile365.2
Maximum1135
Range1135
Interquartile range (IQR)0

Descriptive statistics

Standard deviation189.61725
Coefficient of variation (CV)3.3860224
Kurtosis19.677006
Mean56
Median Absolute Deviation (MAD)0
Skewness4.3155975
Sum4200
Variance35954.703
MonotonicityNot monotonic
2024-04-16T14:52:08.685409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 57
 
8.2%
7 2
 
0.3%
25 1
 
0.1%
32 1
 
0.1%
9 1
 
0.1%
12 1
 
0.1%
85 1
 
0.1%
1135 1
 
0.1%
47 1
 
0.1%
560 1
 
0.1%
Other values (8) 8
 
1.2%
(Missing) 620
89.2%
ValueCountFrequency (%)
0 57
8.2%
7 2
 
0.3%
9 1
 
0.1%
12 1
 
0.1%
19 1
 
0.1%
25 1
 
0.1%
32 1
 
0.1%
47 1
 
0.1%
61 1
 
0.1%
66 1
 
0.1%
ValueCountFrequency (%)
1135 1
0.1%
919 1
0.1%
560 1
0.1%
459 1
0.1%
325 1
0.1%
316 1
0.1%
116 1
0.1%
85 1
0.1%
66 1
0.1%
61 1
0.1%

metasequoia
Real number (ℝ)

MISSING  ZEROS 

Distinct27
Distinct (%)18.1%
Missing546
Missing (%)78.6%
Infinite0
Infinite (%)0.0%
Mean23.973154
Minimum0
Maximum416
Zeros122
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:08.805488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile178
Maximum416
Range416
Interquartile range (IQR)0

Descriptive statistics

Standard deviation73.54838
Coefficient of variation (CV)3.0679475
Kurtosis15.069159
Mean23.973154
Median Absolute Deviation (MAD)0
Skewness3.8139617
Sum3572
Variance5409.3641
MonotonicityNot monotonic
2024-04-16T14:52:08.930062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 122
 
17.6%
45 2
 
0.3%
13 1
 
0.1%
83 1
 
0.1%
163 1
 
0.1%
404 1
 
0.1%
129 1
 
0.1%
394 1
 
0.1%
275 1
 
0.1%
416 1
 
0.1%
Other values (17) 17
 
2.4%
(Missing) 546
78.6%
ValueCountFrequency (%)
0 122
17.6%
6 1
 
0.1%
7 1
 
0.1%
13 1
 
0.1%
16 1
 
0.1%
33 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
42 1
 
0.1%
45 2
 
0.3%
ValueCountFrequency (%)
416 1
0.1%
404 1
0.1%
394 1
0.1%
275 1
0.1%
256 1
0.1%
243 1
0.1%
220 1
0.1%
188 1
0.1%
163 1
0.1%
134 1
0.1%

horse_chestnut
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)10.9%
Missing557
Missing (%)80.1%
Infinite0
Infinite (%)0.0%
Mean15.630435
Minimum0
Maximum900
Zeros124
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:09.064153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile40.4
Maximum900
Range900
Interquartile range (IQR)0

Descriptive statistics

Standard deviation87.588543
Coefficient of variation (CV)5.6037177
Kurtosis78.383561
Mean15.630435
Median Absolute Deviation (MAD)0
Skewness8.2933461
Sum2157
Variance7671.7529
MonotonicityNot monotonic
2024-04-16T14:52:09.175902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 124
 
17.8%
3 1
 
0.1%
54 1
 
0.1%
13 1
 
0.1%
10 1
 
0.1%
179 1
 
0.1%
900 1
 
0.1%
194 1
 
0.1%
103 1
 
0.1%
274 1
 
0.1%
Other values (5) 5
 
0.7%
(Missing) 557
80.1%
ValueCountFrequency (%)
0 124
17.8%
3 1
 
0.1%
4 1
 
0.1%
10 1
 
0.1%
13 1
 
0.1%
22 1
 
0.1%
26 1
 
0.1%
38 1
 
0.1%
54 1
 
0.1%
103 1
 
0.1%
ValueCountFrequency (%)
900 1
0.1%
337 1
0.1%
274 1
0.1%
194 1
0.1%
179 1
0.1%
103 1
0.1%
54 1
0.1%
38 1
0.1%
26 1
0.1%
22 1
0.1%

acer_buergerianum
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
628 
0
63 
25
 
1
35
 
1
112
 
1

Length

Max length4
Median length4
Mean length3.7208633
Min length1

Unique

Unique4 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 628
90.4%
0 63
 
9.1%
25 1
 
0.1%
35 1
 
0.1%
112 1
 
0.1%
2051 1
 
0.1%

Length

2024-04-16T14:52:09.293790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:09.436919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 628
90.4%
0 63
 
9.1%
25 1
 
0.1%
35 1
 
0.1%
112 1
 
0.1%
2051 1
 
0.1%

celtis_sinensis
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
691 
28
 
1
47
 
1
78
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.9870504
Min length1

Unique

Unique4 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 691
99.4%
28 1
 
0.1%
47 1
 
0.1%
78 1
 
0.1%
3 1
 
0.1%

Length

2024-04-16T14:52:09.637918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:09.760928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 691
99.4%
28 1
 
0.1%
47 1
 
0.1%
78 1
 
0.1%
3 1
 
0.1%

tulipifera
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)14.0%
Missing545
Missing (%)78.4%
Infinite0
Infinite (%)0.0%
Mean9.0666667
Minimum0
Maximum167
Zeros126
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:09.847365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile57.05
Maximum167
Range167
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.852243
Coefficient of variation (CV)3.1822326
Kurtosis15.987396
Mean9.0666667
Median Absolute Deviation (MAD)0
Skewness3.9195636
Sum1360
Variance832.4519
MonotonicityNot monotonic
2024-04-16T14:52:09.973917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 126
 
18.1%
92 2
 
0.3%
36 2
 
0.3%
45 2
 
0.3%
167 2
 
0.3%
62 1
 
0.1%
10 1
 
0.1%
24 1
 
0.1%
124 1
 
0.1%
13 1
 
0.1%
Other values (11) 11
 
1.6%
(Missing) 545
78.4%
ValueCountFrequency (%)
0 126
18.1%
1 1
 
0.1%
3 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
13 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
24 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
167 2
0.3%
140 1
0.1%
124 1
0.1%
98 1
0.1%
92 2
0.3%
62 1
0.1%
51 1
0.1%
47 1
0.1%
45 2
0.3%
40 1
0.1%

acer_palmatum
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)8.3%
Missing623
Missing (%)89.6%
Infinite0
Infinite (%)0.0%
Mean5.5277778
Minimum0
Maximum181
Zeros65
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:10.094154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile28
Maximum181
Range181
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.836871
Coefficient of variation (CV)4.3121977
Kurtosis42.890913
Mean5.5277778
Median Absolute Deviation (MAD)0
Skewness6.1731875
Sum398
Variance568.1964
MonotonicityNot monotonic
2024-04-16T14:52:10.184201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 65
 
9.4%
28 3
 
0.4%
48 1
 
0.1%
70 1
 
0.1%
15 1
 
0.1%
181 1
 
0.1%
(Missing) 623
89.6%
ValueCountFrequency (%)
0 65
9.4%
15 1
 
0.1%
28 3
 
0.4%
48 1
 
0.1%
70 1
 
0.1%
181 1
 
0.1%
ValueCountFrequency (%)
181 1
 
0.1%
70 1
 
0.1%
48 1
 
0.1%
28 3
 
0.4%
15 1
 
0.1%
0 65
9.4%

firmiana_simplex
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
564 
0
127 
5
 
1
3
 
1
1
 
1

Length

Max length4
Median length4
Mean length3.4374101
Min length1

Unique

Unique4 ?
Unique (%)0.6%

Sample

1st row0
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 564
81.2%
0 127
 
18.3%
5 1
 
0.1%
3 1
 
0.1%
1 1
 
0.1%
107 1
 
0.1%

Length

2024-04-16T14:52:10.301527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:10.396633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 564
81.2%
0 127
 
18.3%
5 1
 
0.1%
3 1
 
0.1%
1 1
 
0.1%
107 1
 
0.1%

pin_oak
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
629 
0
65 
53
 
1

Length

Max length4
Median length4
Mean length3.7165468
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 629
90.5%
0 65
 
9.4%
53 1
 
0.1%

Length

2024-04-16T14:52:10.600411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:10.696267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 629
90.5%
0 65
 
9.4%
53 1
 
0.1%

persimmon
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
629 
0
65 
14
 
1

Length

Max length4
Median length4
Mean length3.7165468
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 629
90.5%
0 65
 
9.4%
14 1
 
0.1%

Length

2024-04-16T14:52:10.805194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:10.911406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 629
90.5%
0 65
 
9.4%
14 1
 
0.1%

cornus_kousa
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)9.1%
Missing629
Missing (%)90.5%
Infinite0
Infinite (%)0.0%
Mean12.545455
Minimum0
Maximum446
Zeros61
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:10.990054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile49.25
Maximum446
Range446
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.902743
Coefficient of variation (CV)4.8545664
Kurtosis41.768339
Mean12.545455
Median Absolute Deviation (MAD)0
Skewness6.221691
Sum828
Variance3709.1441
MonotonicityNot monotonic
2024-04-16T14:52:11.070876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 61
 
8.8%
446 1
 
0.1%
202 1
 
0.1%
85 1
 
0.1%
44 1
 
0.1%
51 1
 
0.1%
(Missing) 629
90.5%
ValueCountFrequency (%)
0 61
8.8%
44 1
 
0.1%
51 1
 
0.1%
85 1
 
0.1%
202 1
 
0.1%
446 1
 
0.1%
ValueCountFrequency (%)
446 1
 
0.1%
202 1
 
0.1%
85 1
 
0.1%
51 1
 
0.1%
44 1
 
0.1%
0 61
8.8%

chinese_quince
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
629 
0
65 
27
 
1

Length

Max length4
Median length4
Mean length3.7165468
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 629
90.5%
0 65
 
9.4%
27 1
 
0.1%

Length

2024-04-16T14:52:11.184932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:11.275478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 629
90.5%
0 65
 
9.4%
27 1
 
0.1%

goldenrain_tree
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
628 
0
65 
9
 
1
30
 
1

Length

Max length4
Median length4
Mean length3.7122302
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 628
90.4%
0 65
 
9.4%
9 1
 
0.1%
30 1
 
0.1%

Length

2024-04-16T14:52:11.367280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:11.459989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 628
90.4%
0 65
 
9.4%
9 1
 
0.1%
30 1
 
0.1%

cinnamon_tree
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
629 
0
65 
14
 
1

Length

Max length4
Median length4
Mean length3.7165468
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 629
90.5%
0 65
 
9.4%
14 1
 
0.1%

Length

2024-04-16T14:52:11.581505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:11.691895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 629
90.5%
0 65
 
9.4%
14 1
 
0.1%

ailanthus_altissima
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
629 
0
65 
7
 
1

Length

Max length4
Median length4
Mean length3.7151079
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 629
90.5%
0 65
 
9.4%
7 1
 
0.1%

Length

2024-04-16T14:52:11.796096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:11.906198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 629
90.5%
0 65
 
9.4%
7 1
 
0.1%

amur_cork_tree
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
629 
0
65 
7
 
1

Length

Max length4
Median length4
Mean length3.7151079
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 629
90.5%
0 65
 
9.4%
7 1
 
0.1%

Length

2024-04-16T14:52:12.040035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:12.139407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 629
90.5%
0 65
 
9.4%
7 1
 
0.1%

babylon_willow
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
630 
0
65 

Length

Max length4
Median length4
Mean length3.7194245
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 630
90.6%
0 65
 
9.4%

Length

2024-04-16T14:52:12.253525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:12.363229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 630
90.6%
0 65
 
9.4%

three_flowered_maple
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
629 
0
65 
4
 
1

Length

Max length4
Median length4
Mean length3.7151079
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 629
90.5%
0 65
 
9.4%
4 1
 
0.1%

Length

2024-04-16T14:52:12.476987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:12.585356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 629
90.5%
0 65
 
9.4%
4 1
 
0.1%

japanese_elm
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
630 
0
65 

Length

Max length4
Median length4
Mean length3.7194245
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 630
90.6%
0 65
 
9.4%

Length

2024-04-16T14:52:12.710246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:12.821775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 630
90.6%
0 65
 
9.4%

jujube
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
630 
0
65 

Length

Max length4
Median length4
Mean length3.7194245
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 630
90.6%
0 65
 
9.4%

Length

2024-04-16T14:52:12.921902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:13.008196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 630
90.6%
0 65
 
9.4%

silver_magnolia
Real number (ℝ)

MISSING  ZEROS 

Distinct49
Distinct (%)26.5%
Missing510
Missing (%)73.4%
Infinite0
Infinite (%)0.0%
Mean30.016216
Minimum0
Maximum817
Zeros117
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:13.101656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile134.2
Maximum817
Range817
Interquartile range (IQR)13

Descriptive statistics

Standard deviation91.862402
Coefficient of variation (CV)3.0604258
Kurtosis36.394421
Mean30.016216
Median Absolute Deviation (MAD)0
Skewness5.4819431
Sum5553
Variance8438.7008
MonotonicityNot monotonic
2024-04-16T14:52:13.248899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 117
 
16.8%
5 3
 
0.4%
13 3
 
0.4%
3 3
 
0.4%
14 3
 
0.4%
60 3
 
0.4%
135 2
 
0.3%
92 2
 
0.3%
58 2
 
0.3%
4 2
 
0.3%
Other values (39) 45
 
6.5%
(Missing) 510
73.4%
ValueCountFrequency (%)
0 117
16.8%
1 1
 
0.1%
2 2
 
0.3%
3 3
 
0.4%
4 2
 
0.3%
5 3
 
0.4%
6 1
 
0.1%
7 2
 
0.3%
9 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
817 1
0.1%
507 1
0.1%
460 1
0.1%
384 1
0.1%
358 1
0.1%
235 1
0.1%
191 1
0.1%
158 1
0.1%
135 2
0.3%
131 1
0.1%

kurogane_holly
Real number (ℝ)

MISSING  ZEROS 

Distinct57
Distinct (%)29.1%
Missing499
Missing (%)71.8%
Infinite0
Infinite (%)0.0%
Mean31.933673
Minimum0
Maximum563
Zeros118
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:13.381022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319.5
95-th percentile194.25
Maximum563
Range563
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation81.022951
Coefficient of variation (CV)2.5372262
Kurtosis18.134836
Mean31.933673
Median Absolute Deviation (MAD)0
Skewness3.9002371
Sum6259
Variance6564.7187
MonotonicityNot monotonic
2024-04-16T14:52:13.502528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
17.0%
2 5
 
0.7%
26 4
 
0.6%
1 3
 
0.4%
14 3
 
0.4%
13 3
 
0.4%
31 3
 
0.4%
75 2
 
0.3%
16 2
 
0.3%
11 2
 
0.3%
Other values (47) 51
 
7.3%
(Missing) 499
71.8%
ValueCountFrequency (%)
0 118
17.0%
1 3
 
0.4%
2 5
 
0.7%
3 2
 
0.3%
4 1
 
0.1%
5 1
 
0.1%
6 2
 
0.3%
8 1
 
0.1%
9 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
563 1
0.1%
532 1
0.1%
317 1
0.1%
294 1
0.1%
292 1
0.1%
286 1
0.1%
278 1
0.1%
258 1
0.1%
209 1
0.1%
198 1
0.1%

pinus_thunbergii
Real number (ℝ)

MISSING  ZEROS 

Distinct24
Distinct (%)27.0%
Missing606
Missing (%)87.2%
Infinite0
Infinite (%)0.0%
Mean40.393258
Minimum0
Maximum1062
Zeros64
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:13.613609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile144
Maximum1062
Range1062
Interquartile range (IQR)6

Descriptive statistics

Standard deviation146.80031
Coefficient of variation (CV)3.6342776
Kurtosis31.873045
Mean40.393258
Median Absolute Deviation (MAD)0
Skewness5.3936347
Sum3595
Variance21550.332
MonotonicityNot monotonic
2024-04-16T14:52:13.725984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 64
 
9.2%
21 2
 
0.3%
7 2
 
0.3%
77 1
 
0.1%
3 1
 
0.1%
29 1
 
0.1%
51 1
 
0.1%
156 1
 
0.1%
73 1
 
0.1%
457 1
 
0.1%
Other values (14) 14
 
2.0%
(Missing) 606
87.2%
ValueCountFrequency (%)
0 64
9.2%
1 1
 
0.1%
3 1
 
0.1%
6 1
 
0.1%
7 2
 
0.3%
15 1
 
0.1%
21 2
 
0.3%
29 1
 
0.1%
37 1
 
0.1%
42 1
 
0.1%
ValueCountFrequency (%)
1062 1
0.1%
717 1
0.1%
457 1
0.1%
317 1
0.1%
156 1
0.1%
126 1
0.1%
115 1
0.1%
95 1
0.1%
77 1
0.1%
73 1
0.1%

myrsinaleaf_oak
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)16.9%
Missing541
Missing (%)77.8%
Infinite0
Infinite (%)0.0%
Mean19.168831
Minimum0
Maximum512
Zeros127
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:13.824619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile91.5
Maximum512
Range512
Interquartile range (IQR)0

Descriptive statistics

Standard deviation71.999165
Coefficient of variation (CV)3.756054
Kurtosis23.353303
Mean19.168831
Median Absolute Deviation (MAD)0
Skewness4.717306
Sum2952
Variance5183.8798
MonotonicityNot monotonic
2024-04-16T14:52:14.190694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 127
 
18.3%
12 2
 
0.3%
27 2
 
0.3%
59 1
 
0.1%
8 1
 
0.1%
42 1
 
0.1%
21 1
 
0.1%
260 1
 
0.1%
5 1
 
0.1%
202 1
 
0.1%
Other values (16) 16
 
2.3%
(Missing) 541
77.8%
ValueCountFrequency (%)
0 127
18.3%
1 1
 
0.1%
5 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
11 1
 
0.1%
12 2
 
0.3%
17 1
 
0.1%
20 1
 
0.1%
21 1
 
0.1%
ValueCountFrequency (%)
512 1
0.1%
372 1
0.1%
361 1
0.1%
315 1
0.1%
271 1
0.1%
260 1
0.1%
202 1
0.1%
111 1
0.1%
81 1
0.1%
73 1
0.1%

castanopsis_sieboldii
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)13.5%
Missing621
Missing (%)89.4%
Infinite0
Infinite (%)0.0%
Mean2.7837838
Minimum0
Maximum50
Zeros65
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:14.297175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile19.9
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.5361896
Coefficient of variation (CV)3.4256215
Kurtosis14.641976
Mean2.7837838
Median Absolute Deviation (MAD)0
Skewness3.8539377
Sum206
Variance90.938912
MonotonicityNot monotonic
2024-04-16T14:52:14.421112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 65
 
9.4%
45 1
 
0.1%
14 1
 
0.1%
36 1
 
0.1%
29 1
 
0.1%
15 1
 
0.1%
50 1
 
0.1%
5 1
 
0.1%
10 1
 
0.1%
2 1
 
0.1%
(Missing) 621
89.4%
ValueCountFrequency (%)
0 65
9.4%
2 1
 
0.1%
5 1
 
0.1%
10 1
 
0.1%
14 1
 
0.1%
15 1
 
0.1%
29 1
 
0.1%
36 1
 
0.1%
45 1
 
0.1%
50 1
 
0.1%
ValueCountFrequency (%)
50 1
 
0.1%
45 1
 
0.1%
36 1
 
0.1%
29 1
 
0.1%
15 1
 
0.1%
14 1
 
0.1%
10 1
 
0.1%
5 1
 
0.1%
2 1
 
0.1%
0 65
9.4%

cedrus_deodara
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)12.0%
Missing620
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean4.4533333
Minimum0
Maximum237
Zeros62
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:14.512740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.8
Maximum237
Range237
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.680266
Coefficient of variation (CV)6.2156286
Kurtosis69.900521
Mean4.4533333
Median Absolute Deviation (MAD)0
Skewness8.2496297
Sum334
Variance766.19712
MonotonicityNot monotonic
2024-04-16T14:52:14.597396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 62
 
8.9%
1 6
 
0.9%
34 1
 
0.1%
10 1
 
0.1%
7 1
 
0.1%
3 1
 
0.1%
16 1
 
0.1%
237 1
 
0.1%
21 1
 
0.1%
(Missing) 620
89.2%
ValueCountFrequency (%)
0 62
8.9%
1 6
 
0.9%
3 1
 
0.1%
7 1
 
0.1%
10 1
 
0.1%
16 1
 
0.1%
21 1
 
0.1%
34 1
 
0.1%
237 1
 
0.1%
ValueCountFrequency (%)
237 1
 
0.1%
34 1
 
0.1%
21 1
 
0.1%
16 1
 
0.1%
10 1
 
0.1%
7 1
 
0.1%
3 1
 
0.1%
1 6
 
0.9%
0 62
8.9%

camphor_tree
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)5.2%
Missing561
Missing (%)80.7%
Infinite0
Infinite (%)0.0%
Mean1.1044776
Minimum0
Maximum48
Zeros127
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:14.704689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.8
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.7245923
Coefficient of variation (CV)5.1830768
Kurtosis43.101585
Mean1.1044776
Median Absolute Deviation (MAD)0
Skewness6.3015263
Sum148
Variance32.770957
MonotonicityNot monotonic
2024-04-16T14:52:14.796739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 127
 
18.3%
8 2
 
0.3%
26 1
 
0.1%
48 1
 
0.1%
10 1
 
0.1%
33 1
 
0.1%
15 1
 
0.1%
(Missing) 561
80.7%
ValueCountFrequency (%)
0 127
18.3%
8 2
 
0.3%
10 1
 
0.1%
15 1
 
0.1%
26 1
 
0.1%
33 1
 
0.1%
48 1
 
0.1%
ValueCountFrequency (%)
48 1
 
0.1%
33 1
 
0.1%
26 1
 
0.1%
15 1
 
0.1%
10 1
 
0.1%
8 2
 
0.3%
0 127
18.3%

torulosa
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
627 
0
64 
10
 
1
6
 
1
5
 
1

Length

Max length4
Median length4
Mean length3.7107914
Min length1

Unique

Unique4 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 627
90.2%
0 64
 
9.2%
10 1
 
0.1%
6 1
 
0.1%
5 1
 
0.1%
110 1
 
0.1%

Length

2024-04-16T14:52:14.902090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:14.996139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 627
90.2%
0 64
 
9.2%
10 1
 
0.1%
6 1
 
0.1%
5 1
 
0.1%
110 1
 
0.1%

neolitsea_sericea
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
628 
0
65 
12
 
1
5
 
1

Length

Max length4
Median length4
Mean length3.7122302
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 628
90.4%
0 65
 
9.4%
12 1
 
0.1%
5 1
 
0.1%

Length

2024-04-16T14:52:15.100641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:15.191302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 628
90.4%
0 65
 
9.4%
12 1
 
0.1%
5 1
 
0.1%

taxus_cuspidata
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
629 
0
65 
3
 
1

Length

Max length4
Median length4
Mean length3.7151079
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 629
90.5%
0 65
 
9.4%
3 1
 
0.1%

Length

2024-04-16T14:52:15.289168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:15.377331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 629
90.5%
0 65
 
9.4%
3 1
 
0.1%

sweet_viburnum
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
630 
0
65 

Length

Max length4
Median length4
Mean length3.7194245
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 630
90.6%
0 65
 
9.4%

Length

2024-04-16T14:52:15.475664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:15.574576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 630
90.6%
0 65
 
9.4%

etc_tree
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)8.7%
Missing626
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean5
Minimum0
Maximum146
Zeros64
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-04-16T14:52:15.655931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30.4
Maximum146
Range146
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.076738
Coefficient of variation (CV)4.4153476
Kurtosis27.125505
Mean5
Median Absolute Deviation (MAD)0
Skewness5.015889
Sum345
Variance487.38235
MonotonicityNot monotonic
2024-04-16T14:52:15.760774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 64
 
9.2%
81 1
 
0.1%
50 1
 
0.1%
146 1
 
0.1%
67 1
 
0.1%
1 1
 
0.1%
(Missing) 626
90.1%
ValueCountFrequency (%)
0 64
9.2%
1 1
 
0.1%
50 1
 
0.1%
67 1
 
0.1%
81 1
 
0.1%
146 1
 
0.1%
ValueCountFrequency (%)
146 1
 
0.1%
81 1
 
0.1%
67 1
 
0.1%
50 1
 
0.1%
1 1
 
0.1%
0 64
9.2%

gugun
Categorical

Distinct17
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
부산광역시 해운대구
68 
부산진구
65 
부산광역시 강서구
65 
부산광역시 기장군
60 
부산광역시 연제구
58 
Other values (12)
379 

Length

Max length10
Median length9
Mean length8.418705
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row부산진구
2nd row부산광역시 기장군
3rd row부산광역시 기장군
4th row부산광역시 기장군
5th row부산광역시 기장군

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 68
9.8%
부산진구 65
9.4%
부산광역시 강서구 65
9.4%
부산광역시 기장군 60
 
8.6%
부산광역시 연제구 58
 
8.3%
부산광역시 사하구 49
 
7.1%
부산광역시 동래구 49
 
7.1%
부산광역시 북구 48
 
6.9%
부산광역시 사상구 43
 
6.2%
부산광역시 금정구 34
 
4.9%
Other values (7) 156
22.4%

Length

2024-04-16T14:52:15.904513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 629
47.5%
해운대구 68
 
5.1%
부산진구 65
 
4.9%
강서구 65
 
4.9%
기장군 60
 
4.5%
연제구 58
 
4.4%
사하구 49
 
3.7%
동래구 49
 
3.7%
북구 48
 
3.6%
사상구 43
 
3.2%
Other values (8) 190
 
14.4%

reference_date
Date

Distinct4
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Memory size5.6 KiB
Minimum2020-08-26 00:00:00
Maximum2021-01-21 00:00:00
2024-04-16T14:52:16.010491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T14:52:16.118028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

instt_code
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
665 
3370000
 
29
3280000
 
1

Length

Max length7
Median length4
Mean length4.1294964
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 665
95.7%
3370000 29
 
4.2%
3280000 1
 
0.1%

Length

2024-04-16T14:52:16.230675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T14:52:16.322968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 665
95.7%
3370000 29
 
4.2%
3280000 1
 
0.1%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2021-05-01 05:34:03
Maximum2021-05-01 05:34:03
2024-04-16T14:52:16.407481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T14:52:16.512574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Sample

skeyloc_nmlatlngsec_timepointsec_endpointplant_distancetotalprunus_yedoensisginkgosawleaf_zelkovaplatanus_orientalisplatanuschinese_fringe_treesophora_japonicametasequoiahorse_chestnutacer_buergerianumceltis_sinensistulipiferaacer_palmatumfirmiana_simplexpin_oakpersimmoncornus_kousachinese_quincegoldenrain_treecinnamon_treeailanthus_altissimaamur_cork_treebabylon_willowthree_flowered_maplejapanese_elmjujubesilver_magnoliakurogane_hollypinus_thunbergiimyrsinaleaf_oakcastanopsis_sieboldiicedrus_deodaracamphor_treetorulosaneolitsea_sericeataxus_cuspidatasweet_viburnumetc_treegugunreference_dateinstt_codelast_load_dttm
05432부산광역시 부산진구 전포대로171번길35.151377129.063887글로벌연구센터글로벌연구센터<NA>161600<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>0<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
15100부산광역시 기장군 기장대로35.197558129.205996송정1호교기장체육관<NA>12031129<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>74<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 기장군2020-12-31<NA>2021-05-01 05:34:03
25101부산광역시 기장군 정관로35.323242129.196571달음교입구eg1차<NA>1086<NA><NA><NA><NA><NA>1086<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 기장군2020-12-31<NA>2021-05-01 05:34:03
35102부산광역시 기장군 장곡길35.310137129.241097좌천마을문중마을<NA>206206<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 기장군2020-12-31<NA>2021-05-01 05:34:03
45103부산광역시 기장군 대변로35.235843129.217788청강사거리무양교차로<NA>212212<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 기장군2020-12-31<NA>2021-05-01 05:34:03
55104부산광역시 기장군 기장해안로35.182464129.208422송정2호교연화리<NA>2002510321272<NA><NA>513<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>115271<NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 기장군2020-12-31<NA>2021-05-01 05:34:03
65105부산광역시 기장군 철마로35.283522129.125725신리마을금정경계<NA>26719933<NA><NA><NA><NA><NA>35<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 기장군2020-12-31<NA>2021-05-01 05:34:03
75106부산광역시 기장군 곰내길35.294346129.168449웅천마을정관곰내제<NA>510510<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 기장군2020-12-31<NA>2021-05-01 05:34:03
85127부산광역시 서구 동대로35.119169129.017961브라운스톤 앞교차로동아대학병원앞4305026<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 서구2020-12-31<NA>2021-05-01 05:34:03
95200부산광역시 영도구 절영로35.074879129.05067대교사거리부산은행205<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>5<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 영도구2020-12-31<NA>2021-05-01 05:34:03
skeyloc_nmlatlngsec_timepointsec_endpointplant_distancetotalprunus_yedoensisginkgosawleaf_zelkovaplatanus_orientalisplatanuschinese_fringe_treesophora_japonicametasequoiahorse_chestnutacer_buergerianumceltis_sinensistulipiferaacer_palmatumfirmiana_simplexpin_oakpersimmoncornus_kousachinese_quincegoldenrain_treecinnamon_treeailanthus_altissimaamur_cork_treebabylon_willowthree_flowered_maplejapanese_elmjujubesilver_magnoliakurogane_hollypinus_thunbergiimyrsinaleaf_oakcastanopsis_sieboldiicedrus_deodaracamphor_treetorulosaneolitsea_sericeataxus_cuspidatasweet_viburnumetc_treegugunreference_dateinstt_codelast_load_dttm
6855382부산광역시 부산진구 신천대로 165번길35.155665129.050021신천대로 165번길 43신천대로 165번길 3<NA>464600<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>0<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6865383부산광역시 부산진구 동천로35.155117129.06235동천로140중앙대로 666번길 50<NA>002210<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>0<NA><NA>15<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6875384부산광역시 부산진구 새싹로35.169171129.050335새싹로 287-1새싹로1<NA>1919935<NA>089<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>20<NA>0<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6885385부산광역시 부산진구 시민공원로 19번길35.165743129.051273새싹로 87시민공원로19번길 22-5<NA>1313140<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>0<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6895386부산광역시 부산진구 백양대로35.159292129.03276진양교차로사상구경계<NA>334310<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>42<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6905387부산광역시 부산진구 자유평화로35.141372129.061997평화도매시장시민장례식장<NA>00240<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>0<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6915388부산광역시 부산진구 가야벽산아파트길35.146863129.027936가야벽산아파트내(122동인근)가야벽산아파트내(112동인근)<NA>15115100<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>0<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6925389부산광역시 부산진구 냉정로35.151042129.024271가야반도보라진사로19<NA>25251640<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>0<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6935390부산광역시 부산진구 대학로 45번길35.151672129.036503대학로45번길 4엄광로 233<NA>00720<NA>00<NA>00<NA><NA>0<NA>0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>00<NA>0<NA><NA>0<NA><NA><NA><NA><NA>부산진구2020-12-31<NA>2021-05-01 05:34:03
6945567부산광역시 사하구 원양로35.07888129.005098삼천사거리서구경계990173<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3170<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 사하구2020-12-31<NA>2021-05-01 05:34:03