Overview

Dataset statistics

Number of variables11
Number of observations1118
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory100.6 KiB
Average record size in memory92.1 B

Variable types

Text5
Numeric4
Categorical2

Dataset

Description환경공간정보서비스에서 제공하는 지역별 자연환경보전 지역정보(토지피복시계열변화량, DMZ토지피복변화율, 녹지녹피면적비율, 자연환경보전지역, 환경부자연환경보전지역)
Author환경부
URLhttps://www.data.go.kr/data/15124212/fileData.do

Alerts

피복 is highly imbalanced (73.6%)Imbalance

Reproduction

Analysis started2023-12-12 00:06:54.335373
Analysis finished2023-12-12 00:06:57.377514
Duration3.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역명
Text

Distinct243
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-12-12T09:06:57.745687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.3220036
Min length2

Characters and Unicode

Total characters3714
Distinct characters148
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row종로구
3rd row중구
4th row용산구
5th row성동구
ValueCountFrequency (%)
중구 24
 
2.1%
남구 24
 
2.1%
동구 24
 
2.1%
북구 20
 
1.8%
서구 20
 
1.8%
고성군 13
 
1.2%
인제군 9
 
0.8%
양구군 9
 
0.8%
화천군 9
 
0.8%
철원군 9
 
0.8%
Other values (233) 957
85.6%
2023-12-12T09:06:58.347528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 433
 
11.7%
시 409
 
11.0%
군 370
 
10.0%
주 109
 
2.9%
천 106
 
2.9%
산 100
 
2.7%
양 93
 
2.5%
성 89
 
2.4%
동 80
 
2.2%
남 80
 
2.2%
Other values (138) 1845
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3699
99.6%
Uppercase Letter 15
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 433
 
11.7%
시 409
 
11.1%
군 370
 
10.0%
주 109
 
2.9%
천 106
 
2.9%
산 100
 
2.7%
양 93
 
2.5%
성 89
 
2.4%
동 80
 
2.2%
남 80
 
2.2%
Other values (135) 1830
49.5%
Uppercase Letter
ValueCountFrequency (%)
Z 5
33.3%
D 5
33.3%
M 5
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3699
99.6%
Latin 15
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 433
 
11.7%
시 409
 
11.1%
군 370
 
10.0%
주 109
 
2.9%
천 106
 
2.9%
산 100
 
2.7%
양 93
 
2.5%
성 89
 
2.4%
동 80
 
2.2%
남 80
 
2.2%
Other values (135) 1830
49.5%
Latin
ValueCountFrequency (%)
Z 5
33.3%
D 5
33.3%
M 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3699
99.6%
ASCII 15
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 433
 
11.7%
시 409
 
11.1%
군 370
 
10.0%
주 109
 
2.9%
천 106
 
2.9%
산 100
 
2.7%
양 93
 
2.5%
성 89
 
2.4%
동 80
 
2.2%
남 80
 
2.2%
Other values (135) 1830
49.5%
ASCII
ValueCountFrequency (%)
Z 5
33.3%
D 5
33.3%
M 5
33.3%

행정구역코드
Real number (ℝ)

Distinct268
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27721.636
Minimum0
Maximum39020
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-12-12T09:06:58.545709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q123010
median31380
Q335852.5
95-th percentile38113.15
Maximum39020
Range39020
Interquartile range (IQR)12842.5

Descriptive statistics

Standard deviation10954.676
Coefficient of variation (CV)0.39516701
Kurtosis0.70809672
Mean27721.636
Median Absolute Deviation (MAD)5355
Skewness-1.3167345
Sum30992789
Variance1.2000492 × 108
MonotonicityNot monotonic
2023-12-12T09:06:58.735707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31200 9
 
0.8%
32360 9
 
0.8%
32370 9
 
0.8%
32380 9
 
0.8%
32390 9
 
0.8%
32400 9
 
0.8%
31 9
 
0.8%
32 9
 
0.8%
31350 9
 
0.8%
0 5
 
0.4%
Other values (258) 1032
92.3%
ValueCountFrequency (%)
0 5
0.4%
11 4
0.4%
21 4
0.4%
22 4
0.4%
23 4
0.4%
24 4
0.4%
25 4
0.4%
26 4
0.4%
29 4
0.4%
31 9
0.8%
ValueCountFrequency (%)
39020 4
0.4%
39010 4
0.4%
38400 4
0.4%
38390 4
0.4%
38380 4
0.4%
38370 4
0.4%
38360 4
0.4%
38350 4
0.4%
38340 4
0.4%
38330 4
0.4%

피복
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
일반지역
1068 
접경지역
 
50

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반지역
2nd row일반지역
3rd row일반지역
4th row일반지역
5th row일반지역

Common Values

ValueCountFrequency (%)
일반지역 1068
95.5%
접경지역 50
 
4.5%

Length

2023-12-12T09:06:58.924855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:06:59.035283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반지역 1068
95.5%
접경지역 50
 
4.5%

기간
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
80-90년대
277 
90-00년대
277 
80-10년대
277 
00-10년대
277 
80-00년대
 
10

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80-90년대
2nd row80-90년대
3rd row80-90년대
4th row80-90년대
5th row80-90년대

Common Values

ValueCountFrequency (%)
80-90년대 277
24.8%
90-00년대 277
24.8%
80-10년대 277
24.8%
00-10년대 277
24.8%
80-00년대 10
 
0.9%

Length

2023-12-12T09:06:59.160050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:06:59.302828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
80-90년대 277
24.8%
90-00년대 277
24.8%
80-10년대 277
24.8%
00-10년대 277
24.8%
80-00년대 10
 
0.9%

시가지 변화량
Real number (ℝ)

Distinct1073
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.706247
Minimum-4.242
Maximum798.405
Zeros0
Zeros (%)0.0%
Negative104
Negative (%)9.3%
Memory size10.0 KiB
2023-12-12T09:06:59.467638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.242
5-th percentile-0.23305
Q11.18625
median4.068
Q310.417
95-th percentile41.83295
Maximum798.405
Range802.647
Interquartile range (IQR)9.23075

Descriptive statistics

Standard deviation39.019727
Coefficient of variation (CV)3.070909
Kurtosis168.90903
Mean12.706247
Median Absolute Deviation (MAD)3.6805
Skewness10.766748
Sum14205.584
Variance1522.5391
MonotonicityNot monotonic
2023-12-12T09:06:59.650769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.503 3
 
0.3%
1.832 2
 
0.2%
0.023 2
 
0.2%
2.514 2
 
0.2%
4.418 2
 
0.2%
-0.973 2
 
0.2%
2.097 2
 
0.2%
2.366 2
 
0.2%
0.124 2
 
0.2%
1.745 2
 
0.2%
Other values (1063) 1097
98.1%
ValueCountFrequency (%)
-4.242 1
0.1%
-2.621 1
0.1%
-2.567 1
0.1%
-2.394 1
0.1%
-2.388 1
0.1%
-2.095 1
0.1%
-1.986 1
0.1%
-1.814 1
0.1%
-1.728 1
0.1%
-1.654 1
0.1%
ValueCountFrequency (%)
798.405 1
0.1%
338.198 1
0.1%
324.04 1
0.1%
315.892 1
0.1%
315.302 1
0.1%
300.299 1
0.1%
286.856 1
0.1%
282.983 1
0.1%
208.504 1
0.1%
200.12 1
0.1%

농경지 변화량
Text

Distinct1012
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-12-12T09:07:00.060691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.0778175
Min length3

Characters and Unicode

Total characters6795
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique983 ?
Unique (%)87.9%

Sample

1st row-29.368
2nd row -
3rd row0.001
4th row -
5th row -
ValueCountFrequency (%)
69
 
6.2%
0.001 10
 
0.9%
0.005 5
 
0.4%
0.002 5
 
0.4%
0.003 3
 
0.3%
2.462 3
 
0.3%
6.847 2
 
0.2%
0.161 2
 
0.2%
8.753 2
 
0.2%
5.137 2
 
0.2%
Other values (991) 1015
90.8%
2023-12-12T09:07:00.646012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1049
15.4%
- 976
14.4%
1 705
10.4%
2 533
7.8%
0 512
7.5%
3 471
6.9%
4 436
6.4%
7 426
6.3%
6 394
 
5.8%
8 388
 
5.7%
Other values (4) 905
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4631
68.2%
Other Punctuation 1050
 
15.5%
Dash Punctuation 976
 
14.4%
Space Separator 138
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 705
15.2%
2 533
11.5%
0 512
11.1%
3 471
10.2%
4 436
9.4%
7 426
9.2%
6 394
8.5%
8 388
8.4%
5 387
8.4%
9 379
8.2%
Other Punctuation
ValueCountFrequency (%)
. 1049
99.9%
, 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 976
100.0%
Space Separator
ValueCountFrequency (%)
138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6795
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1049
15.4%
- 976
14.4%
1 705
10.4%
2 533
7.8%
0 512
7.5%
3 471
6.9%
4 436
6.4%
7 426
6.3%
6 394
 
5.8%
8 388
 
5.7%
Other values (4) 905
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1049
15.4%
- 976
14.4%
1 705
10.4%
2 533
7.8%
0 512
7.5%
3 471
6.9%
4 436
6.4%
7 426
6.3%
6 394
 
5.8%
8 388
 
5.7%
Other values (4) 905
13.3%

산림 변화량
Real number (ℝ)

Distinct1086
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6665714
Minimum-386.635
Maximum832.961
Zeros0
Zeros (%)0.0%
Negative599
Negative (%)53.6%
Memory size10.0 KiB
2023-12-12T09:07:00.871378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-386.635
5-th percentile-25.3339
Q1-3.2115
median-0.214
Q36.7515
95-th percentile44.8961
Maximum832.961
Range1219.596
Interquartile range (IQR)9.963

Descriptive statistics

Standard deviation54.964243
Coefficient of variation (CV)9.6997354
Kurtosis101.77147
Mean5.6665714
Median Absolute Deviation (MAD)4.1755
Skewness7.1893156
Sum6335.2268
Variance3021.068
MonotonicityNot monotonic
2023-12-12T09:07:01.054167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.817 3
 
0.3%
0.001 3
 
0.3%
0.365 3
 
0.3%
-1.742 2
 
0.2%
-4.199 2
 
0.2%
-0.356 2
 
0.2%
0.174 2
 
0.2%
-10.915 2
 
0.2%
1.096 2
 
0.2%
-0.834 2
 
0.2%
Other values (1076) 1095
97.9%
ValueCountFrequency (%)
-386.635 1
0.1%
-327.147 1
0.1%
-220.119 1
0.1%
-218.865 1
0.1%
-197.654 1
0.1%
-169.914 1
0.1%
-167.77 1
0.1%
-146.253 1
0.1%
-133.556 1
0.1%
-109.554 1
0.1%
ValueCountFrequency (%)
832.961 1
0.1%
770.072 1
0.1%
578.178 1
0.1%
428.485 1
0.1%
419.027 1
0.1%
355.462 1
0.1%
303.386 1
0.1%
284.905 1
0.1%
272.092 1
0.1%
191.239 1
0.1%

초지 변화량
Real number (ℝ)

Distinct1062
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.5364256
Minimum-466.439
Maximum342.123
Zeros0
Zeros (%)0.0%
Negative750
Negative (%)67.1%
Memory size10.0 KiB
2023-12-12T09:07:01.192206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-466.439
5-th percentile-25.7865
Q1-3.94325
median-0.636
Q30.654
95-th percentile14.5518
Maximum342.123
Range808.562
Interquartile range (IQR)4.59725

Descriptive statistics

Standard deviation27.292968
Coefficient of variation (CV)-7.7176708
Kurtosis110.54015
Mean-3.5364256
Median Absolute Deviation (MAD)2.192
Skewness-4.4568571
Sum-3953.7238
Variance744.90612
MonotonicityNot monotonic
2023-12-12T09:07:01.329399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.372 3
 
0.3%
-0.293 3
 
0.3%
-0.181 3
 
0.3%
-6.527 2
 
0.2%
-0.633 2
 
0.2%
-12.701 2
 
0.2%
-0.247 2
 
0.2%
-0.176 2
 
0.2%
-0.214 2
 
0.2%
-0.229 2
 
0.2%
Other values (1052) 1095
97.9%
ValueCountFrequency (%)
-466.439 1
0.1%
-259.817 1
0.1%
-184.554 1
0.1%
-165.364 1
0.1%
-144.226 1
0.1%
-141.81 1
0.1%
-140.022 1
0.1%
-135.053 1
0.1%
-132.903 1
0.1%
-131.67 1
0.1%
ValueCountFrequency (%)
342.123 1
0.1%
159.231 1
0.1%
81.176 1
0.1%
64.0323 1
0.1%
62.296 1
0.1%
61.89 1
0.1%
59.991 1
0.1%
58.475 1
0.1%
53.754 1
0.1%
53.493 1
0.1%

습지 변화량
Text

Distinct721
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-12-12T09:07:01.795256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.234347
Min length1

Characters and Unicode

Total characters5852
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique605 ?
Unique (%)54.1%

Sample

1st row-0.553
2nd row-0.002
3rd row -
4th row-0.053
5th row-0.038
ValueCountFrequency (%)
100
 
8.9%
0.001 38
 
3.4%
0.002 27
 
2.4%
0.005 25
 
2.2%
0.003 20
 
1.8%
0.01 11
 
1.0%
0.008 11
 
1.0%
0.016 9
 
0.8%
0.023 9
 
0.8%
0.02 8
 
0.7%
Other values (649) 860
76.9%
2023-12-12T09:07:02.372422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1287
22.0%
. 1010
17.3%
- 582
9.9%
1 539
9.2%
2 378
 
6.5%
3 354
 
6.0%
4 289
 
4.9%
5 287
 
4.9%
8 244
 
4.2%
6 238
 
4.1%
Other values (3) 644
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4060
69.4%
Other Punctuation 1010
 
17.3%
Dash Punctuation 582
 
9.9%
Space Separator 200
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1287
31.7%
1 539
13.3%
2 378
 
9.3%
3 354
 
8.7%
4 289
 
7.1%
5 287
 
7.1%
8 244
 
6.0%
6 238
 
5.9%
9 222
 
5.5%
7 222
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 1010
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 582
100.0%
Space Separator
ValueCountFrequency (%)
200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5852
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1287
22.0%
. 1010
17.3%
- 582
9.9%
1 539
9.2%
2 378
 
6.5%
3 354
 
6.0%
4 289
 
4.9%
5 287
 
4.9%
8 244
 
4.2%
6 238
 
4.1%
Other values (3) 644
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1287
22.0%
. 1010
17.3%
- 582
9.9%
1 539
9.2%
2 378
 
6.5%
3 354
 
6.0%
4 289
 
4.9%
5 287
 
4.9%
8 244
 
4.2%
6 238
 
4.1%
Other values (3) 644
11.0%

나지 변화량
Text

Distinct1016
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-12-12T09:07:02.765708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.5017889
Min length2

Characters and Unicode

Total characters6151
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique921 ?
Unique (%)82.4%

Sample

1st row10.511
2nd row0.727
3rd row0.21
4th row0.568
5th row0.575
ValueCountFrequency (%)
0.014 5
 
0.4%
0.033 4
 
0.4%
0.111 4
 
0.4%
0.347 3
 
0.3%
0.401 3
 
0.3%
0.86 3
 
0.3%
0.351 3
 
0.3%
0.356 3
 
0.3%
0.036 3
 
0.3%
0.018 3
 
0.3%
Other values (949) 1084
97.0%
2023-12-12T09:07:03.405866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1116
18.1%
0 799
13.0%
1 633
10.3%
- 563
9.2%
2 489
7.9%
3 469
7.6%
4 377
 
6.1%
5 370
 
6.0%
7 354
 
5.8%
6 347
 
5.6%
Other values (3) 634
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4470
72.7%
Other Punctuation 1116
 
18.1%
Dash Punctuation 563
 
9.2%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 799
17.9%
1 633
14.2%
2 489
10.9%
3 469
10.5%
4 377
8.4%
5 370
8.3%
7 354
7.9%
6 347
7.8%
9 317
 
7.1%
8 315
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 1116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 563
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6151
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1116
18.1%
0 799
13.0%
1 633
10.3%
- 563
9.2%
2 489
7.9%
3 469
7.6%
4 377
 
6.1%
5 370
 
6.0%
7 354
 
5.8%
6 347
 
5.6%
Other values (3) 634
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1116
18.1%
0 799
13.0%
1 633
10.3%
- 563
9.2%
2 489
7.9%
3 469
7.6%
4 377
 
6.1%
5 370
 
6.0%
7 354
 
5.8%
6 347
 
5.6%
Other values (3) 634
10.3%

수역 변화량
Text

Distinct932
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-12-12T09:07:03.792494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.4203936
Min length1

Characters and Unicode

Total characters6060
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique831 ?
Unique (%)74.3%

Sample

1st row-0.784
2nd row-0.005
3rd row-0.003
4th row-0.139
5th row-0.021
ValueCountFrequency (%)
24
 
2.1%
0.005 17
 
1.5%
0.022 11
 
1.0%
0.014 8
 
0.7%
0.01 7
 
0.6%
0.002 7
 
0.6%
0.006 6
 
0.5%
0.003 6
 
0.5%
0.032 6
 
0.5%
0.013 5
 
0.4%
Other values (837) 1021
91.3%
2023-12-12T09:07:04.645778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1107
18.3%
. 1093
18.0%
1 597
9.9%
- 520
8.6%
2 470
7.8%
3 387
 
6.4%
5 363
 
6.0%
4 340
 
5.6%
6 317
 
5.2%
7 310
 
5.1%
Other values (3) 556
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4399
72.6%
Other Punctuation 1093
 
18.0%
Dash Punctuation 520
 
8.6%
Space Separator 48
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1107
25.2%
1 597
13.6%
2 470
10.7%
3 387
 
8.8%
5 363
 
8.3%
4 340
 
7.7%
6 317
 
7.2%
7 310
 
7.0%
9 255
 
5.8%
8 253
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 1093
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 520
100.0%
Space Separator
ValueCountFrequency (%)
48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1107
18.3%
. 1093
18.0%
1 597
9.9%
- 520
8.6%
2 470
7.8%
3 387
 
6.4%
5 363
 
6.0%
4 340
 
5.6%
6 317
 
5.2%
7 310
 
5.1%
Other values (3) 556
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1107
18.3%
. 1093
18.0%
1 597
9.9%
- 520
8.6%
2 470
7.8%
3 387
 
6.4%
5 363
 
6.0%
4 340
 
5.6%
6 317
 
5.2%
7 310
 
5.1%
Other values (3) 556
9.2%

Interactions

2023-12-12T09:06:56.494296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:54.923577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:55.423118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:55.955535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:56.633800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:55.052396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:55.564446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:56.106826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:56.751158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:55.182448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:55.689450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:56.246946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:56.864960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:55.296164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:55.816770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:06:56.374879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:07:04.774622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드피복기간시가지 변화량산림 변화량초지 변화량
행정구역코드1.0000.2540.0000.3880.3460.488
피복0.2541.0000.3570.0000.0590.128
기간0.0000.3571.0000.0320.1230.153
시가지 변화량0.3880.0000.0321.0000.8520.438
산림 변화량0.3460.0590.1230.8521.0000.658
초지 변화량0.4880.1280.1530.4380.6581.000
2023-12-12T09:07:04.875099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기간피복
기간1.0000.435
피복0.4351.000
2023-12-12T09:07:04.959894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드시가지 변화량산림 변화량초지 변화량피복기간
행정구역코드1.0000.0320.141-0.0950.2710.000
시가지 변화량0.0321.000-0.194-0.1020.0000.021
산림 변화량0.141-0.1941.000-0.3770.0590.069
초지 변화량-0.095-0.102-0.3771.0000.1330.097
피복0.2710.0000.0590.1331.0000.435
기간0.0000.0210.0690.0970.4351.000

Missing values

2023-12-12T09:06:57.067917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:06:57.283359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

행정구역명행정구역코드피복기간시가지 변화량농경지 변화량산림 변화량초지 변화량습지 변화량나지 변화량수역 변화량
0서울특별시11일반지역80-90년대-0.374-29.36812.0438.524-0.55310.511-0.784
1종로구11010일반지역80-90년대-0.729-1.112-1.103-0.0020.727-0.005
2중구11020일반지역80-90년대-0.4960.0010.0230.265-0.21-0.003
3용산구11030일반지역80-90년대-1.611--0.3481.583-0.0530.568-0.139
4성동구11040일반지역80-90년대-0.261--0.16-0.095-0.0380.575-0.021
5광진구11050일반지역80-90년대-0.753-0.4280.360.4420.0220.365-0.006
6동대문구11060일반지역80-90년대-0.228--0.180.017-0.0040.3620.032
7중랑구11070일반지역80-90년대0.869-1.713-0.3340.8480.0010.365-0.035
8성북구11080일반지역80-90년대0.4380.0060.188-1.161-0.0030.544-0.013
9강북구11090일반지역80-90년대0.447-0.1840.365-1.077-0.0010.45-
행정구역명행정구역코드피복기간시가지 변화량농경지 변화량산림 변화량초지 변화량습지 변화량나지 변화량수역 변화량
1108DMZ0접경지역00-10년대19.1034-15.04899.0189-26.6733-1.10341.724412.9789
1109경기도31접경지역00-10년대14.4963-6.84-14.7510.3132-6.61861.086312.3138
1110연천군31350접경지역00-10년대3.51542.3328-10.44992.4327-0.01351.04311.1394
1111파주시31200접경지역00-10년대10.9809-9.1728-4.3011-2.1195-6.60510.043211.1744
1112강원도32접경지역00-10년대4.6071-8.208923.7699-26.98655.51520.63810.6651
1113고성군32400접경지역00-10년대0.0522-0.7902-1.0602-0.16111.39230.06030.5067
1114양구군32380접경지역00-10년대-0.0315-0.6480.18720.08730.1080.25110.0459
1115인제군32390접경지역00-10년대-0.41581.00980.3915-1.10790.1053-0.06030.0774
1116철원군32360접경지역00-10년대4.9104-6.747322.5558-25.8123.85830.56520.6696
1117화천군32370접경지역00-10년대0.0918-1.03321.69560.00720.0513-0.1782-0.6345