Overview

Dataset statistics

Number of variables12
Number of observations3175
Missing cells3177
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory313.3 KiB
Average record size in memory101.0 B

Variable types

Text2
Numeric4
Categorical2
Boolean2
Unsupported1
DateTime1

Dataset

Description국토지리정보원에서 생성하는 국토통계지도(인구, 건물, 토지, 주택) 원천데이터 파일의 이력(총건수, 등록건수, 원천자료 기준년월, 설명, 등록일자 등)을 제공합니다.
Author국토교통부 국토지리정보원
URLhttps://www.data.go.kr/data/15122744/fileData.do

Alerts

전체 테이블 여부 has constant value ""Constant
변경자 has constant value ""Constant
취합이력일련번호 is highly overall correlated with 원천자료기준년월High correlation
총건수 is highly overall correlated with 등록건수 and 1 other fieldsHigh correlation
등록건수 is highly overall correlated with 총건수 and 1 other fieldsHigh correlation
원천자료기준년월 is highly overall correlated with 취합이력일련번호High correlation
연계여부 is highly overall correlated with 총건수 and 1 other fieldsHigh correlation
연계여부 is highly imbalanced (98.0%)Imbalance
설명 has 3175 (100.0%) missing valuesMissing
설명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 20:16:18.875010
Analysis finished2023-12-12 20:16:21.779765
Duration2.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

테이블아이디
Text

Distinct195
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
2023-12-13T05:16:21.943490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length24.03622
Min length15

Characters and Unicode

Total characters76315
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.3%

Sample

1st rowTN_ADRES_201710
2nd rowTN_ADRES_201710
3rd rowAPMM_NV_JIGA_MNG_201707
4th rowAPMM_NV_JIGA_MNG_201707
5th rowTN_POPLTN_201710
ValueCountFrequency (%)
apmm_nv_jiga_mng_201707 35
 
1.1%
apmm_hp_prc_mng_201706 35
 
1.1%
tn_popltn_202204 17
 
0.5%
tn_popltn_202104 17
 
0.5%
apmm_nv_jiga_mng_201701 17
 
0.5%
abpm_bldg_obj_info_202102 17
 
0.5%
abpd_bldg_dong_info_202102 17
 
0.5%
abpm_bldg_obj_info_202103 17
 
0.5%
abpd_bldg_dong_info_202103 17
 
0.5%
apmm_hp_prc_mng_202006 17
 
0.5%
Other values (185) 2969
93.5%
2023-12-13T05:16:22.361548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 12082
15.8%
0 6503
 
8.5%
2 5866
 
7.7%
B 5795
 
7.6%
O 4877
 
6.4%
N 4849
 
6.4%
D 4704
 
6.2%
G 4299
 
5.6%
P 3896
 
5.1%
1 3259
 
4.3%
Other values (20) 20185
26.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 45183
59.2%
Decimal Number 19050
25.0%
Connector Punctuation 12082
 
15.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 5795
12.8%
O 4877
10.8%
N 4849
10.7%
D 4704
10.4%
G 4299
9.5%
P 3896
8.6%
A 3208
7.1%
M 2803
6.2%
I 2592
5.7%
L 2559
5.7%
Other values (9) 5601
12.4%
Decimal Number
ValueCountFrequency (%)
0 6503
34.1%
2 5866
30.8%
1 3259
17.1%
8 735
 
3.9%
9 697
 
3.7%
7 533
 
2.8%
3 496
 
2.6%
6 428
 
2.2%
4 328
 
1.7%
5 205
 
1.1%
Connector Punctuation
ValueCountFrequency (%)
_ 12082
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45183
59.2%
Common 31132
40.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 5795
12.8%
O 4877
10.8%
N 4849
10.7%
D 4704
10.4%
G 4299
9.5%
P 3896
8.6%
A 3208
7.1%
M 2803
6.2%
I 2592
5.7%
L 2559
5.7%
Other values (9) 5601
12.4%
Common
ValueCountFrequency (%)
_ 12082
38.8%
0 6503
20.9%
2 5866
18.8%
1 3259
 
10.5%
8 735
 
2.4%
9 697
 
2.2%
7 533
 
1.7%
3 496
 
1.6%
6 428
 
1.4%
4 328
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 12082
15.8%
0 6503
 
8.5%
2 5866
 
7.7%
B 5795
 
7.6%
O 4877
 
6.4%
N 4849
 
6.4%
D 4704
 
6.2%
G 4299
 
5.6%
P 3896
 
5.1%
1 3259
 
4.3%
Other values (20) 20185
26.4%

취합이력일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct2678
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0199737 × 109
Minimum2.01407 × 109
Maximum2.02401 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.0 KiB
2023-12-13T05:16:22.514214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.01407 × 109
5-th percentile2.01706 × 109
Q12.01811 × 109
median2.02005 × 109
Q32.02112 × 109
95-th percentile2.02303 × 109
Maximum2.02401 × 109
Range9940000
Interquartile range (IQR)3009989

Descriptive statistics

Standard deviation1848686.5
Coefficient of variation (CV)0.00091520327
Kurtosis-0.84082228
Mean2.0199737 × 109
Median Absolute Deviation (MAD)1930022
Skewness-0.10363476
Sum6.4134164 × 1012
Variance3.4176418 × 1012
MonotonicityNot monotonic
2023-12-13T05:16:22.737665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020010010 3
 
0.1%
2019010001 3
 
0.1%
2018030016 3
 
0.1%
2018030017 3
 
0.1%
2018010014 3
 
0.1%
2018010013 3
 
0.1%
2018010012 3
 
0.1%
2018010011 3
 
0.1%
2018010010 3
 
0.1%
2018010009 3
 
0.1%
Other values (2668) 3145
99.1%
ValueCountFrequency (%)
2014070001 3
0.1%
2015100001 1
 
< 0.1%
2016010001 2
0.1%
2016010002 2
0.1%
2016010003 2
0.1%
2016010004 2
0.1%
2016010005 2
0.1%
2016010006 2
0.1%
2016010007 2
0.1%
2016010008 2
0.1%
ValueCountFrequency (%)
2024010001 2
0.1%
2023100001 1
< 0.1%
2023080002 1
< 0.1%
2023080001 1
< 0.1%
2023070034 1
< 0.1%
2023070033 1
< 0.1%
2023070032 1
< 0.1%
2023070031 1
< 0.1%
2023070030 1
< 0.1%
2023070029 1
< 0.1%

데이터구분
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
AAAAA00002
2318 
AAAAA00004
274 
AAAAA00005
274 
AAAAA00001
241 
AAAAA00003
 
68

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAAAAA00003
2nd rowAAAAA00003
3rd rowAAAAA00004
4th rowAAAAA00004
5th rowAAAAA00001

Common Values

ValueCountFrequency (%)
AAAAA00002 2318
73.0%
AAAAA00004 274
 
8.6%
AAAAA00005 274
 
8.6%
AAAAA00001 241
 
7.6%
AAAAA00003 68
 
2.1%

Length

2023-12-13T05:16:22.876815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:16:23.009193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
aaaaa00002 2318
73.0%
aaaaa00004 274
 
8.6%
aaaaa00005 274
 
8.6%
aaaaa00001 241
 
7.6%
aaaaa00003 68
 
2.1%

전체 테이블 여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
True
3175 
ValueCountFrequency (%)
True 3175
100.0%
2023-12-13T05:16:23.129676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

연계여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
True
3169 
False
 
6
ValueCountFrequency (%)
True 3169
99.8%
False 6
 
0.2%
2023-12-13T05:16:23.256875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

총건수
Real number (ℝ)

HIGH CORRELATION 

Distinct3113
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1150224.2
Minimum-224178
Maximum51457616
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.1%
Memory size28.0 KiB
2023-12-13T05:16:23.406864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-224178
5-th percentile37894.1
Q1265106
median619595
Q31248468
95-th percentile4163657.9
Maximum51457616
Range51681794
Interquartile range (IQR)983362

Descriptive statistics

Standard deviation2038490.8
Coefficient of variation (CV)1.7722552
Kurtosis240.40052
Mean1150224.2
Median Absolute Deviation (MAD)415978
Skewness11.449971
Sum3.6519619 × 109
Variance4.1554449 × 1012
MonotonicityNot monotonic
2023-12-13T05:16:23.607680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
630373 4
 
0.1%
144623 4
 
0.1%
205836 3
 
0.1%
264985 2
 
0.1%
4659860 2
 
0.1%
549851 2
 
0.1%
278385 2
 
0.1%
14976 2
 
0.1%
91438 2
 
0.1%
89670 2
 
0.1%
Other values (3103) 3150
99.2%
ValueCountFrequency (%)
-224178 1
< 0.1%
-16174 1
< 0.1%
282 1
< 0.1%
283 1
< 0.1%
302 1
< 0.1%
342 1
< 0.1%
429 1
< 0.1%
442 1
< 0.1%
472 1
< 0.1%
490 1
< 0.1%
ValueCountFrequency (%)
51457616 1
< 0.1%
51202280 1
< 0.1%
22373490 1
< 0.1%
13604344 1
< 0.1%
13575729 1
< 0.1%
13573839 1
< 0.1%
13545459 1
< 0.1%
13475336 1
< 0.1%
13395833 1
< 0.1%
13292943 1
< 0.1%

등록건수
Real number (ℝ)

HIGH CORRELATION 

Distinct3113
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1150224.2
Minimum-224178
Maximum51457616
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.1%
Memory size28.0 KiB
2023-12-13T05:16:23.817311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-224178
5-th percentile37894.1
Q1265106
median619595
Q31248468
95-th percentile4163657.9
Maximum51457616
Range51681794
Interquartile range (IQR)983362

Descriptive statistics

Standard deviation2038490.8
Coefficient of variation (CV)1.7722552
Kurtosis240.40052
Mean1150224.2
Median Absolute Deviation (MAD)415978
Skewness11.449971
Sum3.6519619 × 109
Variance4.1554449 × 1012
MonotonicityNot monotonic
2023-12-13T05:16:23.997549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
630373 4
 
0.1%
144623 4
 
0.1%
205836 3
 
0.1%
264985 2
 
0.1%
4659860 2
 
0.1%
549851 2
 
0.1%
278385 2
 
0.1%
14976 2
 
0.1%
91438 2
 
0.1%
89670 2
 
0.1%
Other values (3103) 3150
99.2%
ValueCountFrequency (%)
-224178 1
< 0.1%
-16174 1
< 0.1%
282 1
< 0.1%
283 1
< 0.1%
302 1
< 0.1%
342 1
< 0.1%
429 1
< 0.1%
442 1
< 0.1%
472 1
< 0.1%
490 1
< 0.1%
ValueCountFrequency (%)
51457616 1
< 0.1%
51202280 1
< 0.1%
22373490 1
< 0.1%
13604344 1
< 0.1%
13575729 1
< 0.1%
13573839 1
< 0.1%
13545459 1
< 0.1%
13475336 1
< 0.1%
13395833 1
< 0.1%
13292943 1
< 0.1%

변경자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
SYSTEM
3175 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
SYSTEM 3175
100.0%

Length

2023-12-13T05:16:24.155478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:16:24.284783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
system 3175
100.0%

원천자료기준년월
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201997.36
Minimum201407
Maximum202401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.0 KiB
2023-12-13T05:16:24.422732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201407
5-th percentile201706
Q1201811
median202005
Q3202112
95-th percentile202303
Maximum202401
Range994
Interquartile range (IQR)301

Descriptive statistics

Standard deviation184.86912
Coefficient of variation (CV)0.00091520559
Kurtosis-0.84084398
Mean201997.36
Median Absolute Deviation (MAD)193
Skewness-0.10362964
Sum6.4134163 × 108
Variance34176.59
MonotonicityNot monotonic
2023-12-13T05:16:24.580129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201910 68
 
2.1%
201803 68
 
2.1%
201801 68
 
2.1%
202010 68
 
2.1%
201901 68
 
2.1%
202001 68
 
2.1%
201810 68
 
2.1%
202101 68
 
2.1%
202201 68
 
2.1%
202006 51
 
1.6%
Other values (73) 2512
79.1%
ValueCountFrequency (%)
201407 3
 
0.1%
201510 1
 
< 0.1%
201601 34
1.1%
201606 17
0.5%
201607 17
0.5%
201608 2
 
0.1%
201610 17
0.5%
201701 34
1.1%
201704 17
0.5%
201706 35
1.1%
ValueCountFrequency (%)
202401 2
 
0.1%
202310 1
 
< 0.1%
202308 2
 
0.1%
202307 34
1.1%
202306 34
1.1%
202305 34
1.1%
202304 51
1.6%
202303 34
1.1%
202302 34
1.1%
202301 34
1.1%

파일명
Text

Distinct3172
Distinct (%)> 99.9%
Missing2
Missing (%)0.1%
Memory size24.9 KiB
2023-12-13T05:16:24.850710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length50
Mean length49.054838
Min length31

Characters and Unicode

Total characters155651
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3171 ?
Unique (%)99.9%

Sample

1st rowAAAAA00003\201710\build_gyeongbuk.txt
2nd rowAAAAA00003\201710\build_jeonbuk.txt
3rd rowAAAAA00004\201707\APMM_NV_JIGA_MNG_11_2017_01.zip
4th rowAAAAA00004\201707\APMM_NV_JIGA_MNG_27_2017_01.zip
5th rowAAAAA00001\201710\kukto_jiryingu_11.lst.gz
ValueCountFrequency (%)
aaaaa00002\201407\tn_bild_2014_gid.csv 2
 
0.1%
aaaaa00003\201710\build_gyeongbuk.txt 1
 
< 0.1%
aaaaa00004/202007/apmm_nv_jiga_mng_2020_46.zip 1
 
< 0.1%
aaaaa00004/202007/apmm_nv_jiga_mng_2020_42.zip 1
 
< 0.1%
aaaaa00004/202007/apmm_nv_jiga_mng_2020_45.zip 1
 
< 0.1%
aaaaa00004/202007/apmm_nv_jiga_mng_2020_31.zip 1
 
< 0.1%
aaaaa00004/202007/apmm_nv_jiga_mng_2020_27.zip 1
 
< 0.1%
aaaaa00004/202007/apmm_nv_jiga_mng_2020_28.zip 1
 
< 0.1%
aaaaa00004/202007/apmm_nv_jiga_mng_2020_44.zip 1
 
< 0.1%
aaaaa00004/202007/apmm_nv_jiga_mng_2020_43.zip 1
 
< 0.1%
Other values (3162) 3162
99.7%
2023-12-13T05:16:25.310869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25346
16.3%
A 18999
 
12.2%
_ 15236
 
9.8%
2 14475
 
9.3%
1 7122
 
4.6%
B 5787
 
3.7%
D 4633
 
3.0%
O 4629
 
3.0%
N 4295
 
2.8%
G 4294
 
2.8%
Other values (47) 50835
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 58593
37.6%
Decimal Number 58205
37.4%
Connector Punctuation 15236
 
9.8%
Lowercase Letter 13859
 
8.9%
Other Punctuation 9758
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3382
24.4%
z 3099
22.4%
p 2862
20.7%
t 633
 
4.6%
u 594
 
4.3%
g 524
 
3.8%
k 490
 
3.5%
n 301
 
2.2%
l 300
 
2.2%
o 296
 
2.1%
Other values (15) 1378
9.9%
Uppercase Letter
ValueCountFrequency (%)
A 18999
32.4%
B 5787
 
9.9%
D 4633
 
7.9%
O 4629
 
7.9%
N 4295
 
7.3%
G 4294
 
7.3%
P 3408
 
5.8%
M 2795
 
4.8%
I 2593
 
4.4%
L 2319
 
4.0%
Other values (8) 4841
 
8.3%
Decimal Number
ValueCountFrequency (%)
0 25346
43.5%
2 14475
24.9%
1 7122
 
12.2%
4 2444
 
4.2%
8 1779
 
3.1%
3 1775
 
3.0%
9 1525
 
2.6%
6 1352
 
2.3%
7 1339
 
2.3%
5 1048
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 4216
43.2%
. 3412
35.0%
\ 2130
21.8%
Connector Punctuation
ValueCountFrequency (%)
_ 15236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 83199
53.5%
Latin 72452
46.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 18999
26.2%
B 5787
 
8.0%
D 4633
 
6.4%
O 4629
 
6.4%
N 4295
 
5.9%
G 4294
 
5.9%
P 3408
 
4.7%
i 3382
 
4.7%
z 3099
 
4.3%
p 2862
 
4.0%
Other values (33) 17064
23.6%
Common
ValueCountFrequency (%)
0 25346
30.5%
_ 15236
18.3%
2 14475
17.4%
1 7122
 
8.6%
/ 4216
 
5.1%
. 3412
 
4.1%
4 2444
 
2.9%
\ 2130
 
2.6%
8 1779
 
2.1%
3 1775
 
2.1%
Other values (4) 5264
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25346
16.3%
A 18999
 
12.2%
_ 15236
 
9.8%
2 14475
 
9.3%
1 7122
 
4.6%
B 5787
 
3.7%
D 4633
 
3.0%
O 4629
 
3.0%
N 4295
 
2.8%
G 4294
 
2.8%
Other values (47) 50835
32.7%

설명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3175
Missing (%)100.0%
Memory size28.0 KiB

등록 일자
Date

Distinct87
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
Minimum2018-04-26 00:00:00
Maximum2023-09-06 00:00:00
2023-12-13T05:16:25.460109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:25.596742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T05:16:20.914357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:19.711338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:20.089843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:20.474525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:21.014349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:19.820899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:20.186899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:20.581524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:21.160523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:19.909326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:20.281433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:20.729089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:21.298547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:19.999032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:20.379004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:16:20.818246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:16:25.693241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취합이력일련번호데이터구분연계여부총건수등록건수원천자료기준년월등록 일자
취합이력일련번호1.0000.3890.1080.1500.1501.0000.996
데이터구분0.3891.0000.0270.3240.3240.3900.973
연계여부0.1080.0271.0000.6460.6460.1080.189
총건수0.1500.3240.6461.0001.0000.1500.239
등록건수0.1500.3240.6461.0001.0000.1500.239
원천자료기준년월1.0000.3900.1080.1500.1501.0000.996
등록 일자0.9960.9730.1890.2390.2390.9961.000
2023-12-13T05:16:25.797079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연계여부데이터구분
연계여부1.0000.034
데이터구분0.0341.000
2023-12-13T05:16:25.875606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취합이력일련번호총건수등록건수원천자료기준년월데이터구분연계여부
취합이력일련번호1.0000.0310.0311.0000.2370.253
총건수0.0311.0001.0000.0360.1260.707
등록건수0.0311.0001.0000.0360.1260.707
원천자료기준년월1.0000.0360.0361.0000.2370.253
데이터구분0.2370.1260.1260.2371.0000.034
연계여부0.2530.7070.7070.2530.0341.000

Missing values

2023-12-13T05:16:21.484905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:16:21.694099image/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

테이블아이디취합이력일련번호데이터구분전체 테이블 여부연계여부총건수등록건수변경자원천자료기준년월파일명설명등록 일자
0TN_ADRES_2017102017100008AAAAA00003YY14209271420927SYSTEM201710AAAAA00003\201710\build_gyeongbuk.txt<NA>2018-04-26
1TN_ADRES_2017102017100013AAAAA00003YY821342821342SYSTEM201710AAAAA00003\201710\build_jeonbuk.txt<NA>2018-04-26
2APMM_NV_JIGA_MNG_2017072017070001AAAAA00004YY927501927501SYSTEM201707AAAAA00004\201707\APMM_NV_JIGA_MNG_11_2017_01.zip<NA>2018-05-13
3APMM_NV_JIGA_MNG_2017072017070005AAAAA00004YY444376444376SYSTEM201707AAAAA00004\201707\APMM_NV_JIGA_MNG_27_2017_01.zip<NA>2018-05-13
4TN_POPLTN_2017102017100001AAAAA00001YY98854759885475SYSTEM201710AAAAA00001\201710\kukto_jiryingu_11.lst.gz<NA>2018-04-26
5TN_POPLTN_2017102017100006AAAAA00001YY15062111506211SYSTEM201710AAAAA00001\201710\kukto_jiryingu_30.lst.gz<NA>2018-04-26
6TN_POPLTN_2017102017100011AAAAA00001YY15914121591412SYSTEM201710AAAAA00001\201710\kukto_jiryingu_43.lst.gz<NA>2018-04-26
7APMM_HP_PRC_MNG_2017012017010001AAAAA00005YY345798345798SYSTEM201701AAAAA00005\201701\APMM_HP_PRC_MNG_11_2017_01.zip<NA>2018-04-26
8APMM_HP_PRC_MNG_2017012017010006AAAAA00005YY8481484814SYSTEM201701AAAAA00005\201701\APMM_HP_PRC_MNG_30_2017_01.zip<NA>2018-04-26
9APMM_HP_PRC_MNG_2017012017010011AAAAA00005YY217958217958SYSTEM201701AAAAA00005\201701\APMM_HP_PRC_MNG_43_2017_01.zip<NA>2018-04-26
테이블아이디취합이력일련번호데이터구분전체 테이블 여부연계여부총건수등록건수변경자원천자료기준년월파일명설명등록 일자
3165ABPD_BLDG_DONG_INFO_2023072023070030AAAAA00002YY729484729484SYSTEM202307AAAAA00002\202307\ABPD_BLDG_DONG_INFO_46_202307.zip<NA>2023-08-29
3166ABPD_BLDG_DONG_INFO_2023072023070031AAAAA00002YY893665893665SYSTEM202307AAAAA00002\202307\ABPD_BLDG_DONG_INFO_47_202307.zip<NA>2023-08-29
3167ABPD_BLDG_DONG_INFO_2023072023070032AAAAA00002YY795712795712SYSTEM202307AAAAA00002\202307\ABPD_BLDG_DONG_INFO_48_202307.zip<NA>2023-08-29
3168ABPD_BLDG_DONG_INFO_2023072023070033AAAAA00002YY209167209167SYSTEM202307AAAAA00002\202307\ABPD_BLDG_DONG_INFO_50_202307.zip<NA>2023-08-29
3169ABPD_BLDG_DONG_INFO_2023072023070034AAAAA00002YY462284462284SYSTEM202307AAAAA00002\202307\ABPD_BLDG_DONG_INFO_51_202307.zip<NA>2023-08-29
3170ABPM_BLDG_OBJ_INFO_2023082023080001AAAAA00002YY205842205842SYSTEM202308AAAAA00002\202308\ABPM_BLDG_OBJ_INFO_36_202307.zip<NA>2023-09-05
3171ABPD_BLDG_DONG_INFO_2023082023080002AAAAA00002YY3814838148SYSTEM202308AAAAA00002\202308\ABPD_BLDG_DONG_INFO_36_202307.zip<NA>2023-09-05
3172TN_POPLTN_2023102023100001AAAAA00001YY385809385809SYSTEM202310AAAAA00001\202310\kukto_jiryingu_36.lst.gz<NA>2023-09-05
3173APMM_NV_JIGA_MNG_2024012024010001AAAAA00004YY37123712SYSTEM202401AAAAA00004\202401\APMM_NV_JIGA_MNG_36_202207.zip<NA>2023-09-05
3174APMM_HP_PRC_MNG_2024012024010001AAAAA00005YY1541715417SYSTEM202401AAAAA00005\202401\APMM_HP_PRC_MNG_36_202201.zip<NA>2023-09-06