Overview

Dataset statistics

Number of variables17
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory143.7 B

Variable types

Categorical12
Boolean1
Text3
Numeric1

Dataset

Description지정된 상품 및 서비스들을 통해 산출한 전국 소비자물가지수를, 2020년을 기준으로 하여(2020=100) 월별로 나타낸 데이터
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=3110

Alerts

통계표ID has constant value ""Constant
기관코드 has constant value ""Constant
항목영문명 has constant value ""Constant
수록주기 has constant value ""Constant
항목명 has constant value ""Constant
항목 ID has constant value ""Constant
단위명 has constant value ""Constant
단위영문명 has constant value ""Constant
분류 영문명1 has constant value ""Constant
통계표명 has constant value ""Constant
분류명1 has constant value ""Constant
수록시점 is highly overall correlated with 수집날짜High correlation
수집날짜 is highly overall correlated with 수록시점High correlation

Reproduction

Analysis started2024-01-09 20:38:15.255633
Analysis finished2024-01-09 20:38:15.915488
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계표ID
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
DT_1J20003
36 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDT_1J20003
2nd rowDT_1J20003
3rd rowDT_1J20003
4th rowDT_1J20003
5th rowDT_1J20003

Common Values

ValueCountFrequency (%)
DT_1J20003 36
100.0%

Length

2024-01-10T05:38:15.971904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:16.060492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
dt_1j20003 36
100.0%

기관코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
101
36 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row101
2nd row101
3rd row101
4th row101
5th row101

Common Values

ValueCountFrequency (%)
101 36
100.0%

Length

2024-01-10T05:38:16.147891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:16.240607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
101 36
100.0%

항목영문명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
CPI
36 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
CPI 36
100.0%

Length

2024-01-10T05:38:16.616391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:16.702343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cpi 36
100.0%

수록주기
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
M
36 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 36
100.0%

Length

2024-01-10T05:38:16.789352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:16.879643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 36
100.0%

항목명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
소비자물가지수(총지수)
36 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소비자물가지수(총지수)
2nd row소비자물가지수(총지수)
3rd row소비자물가지수(총지수)
4th row소비자물가지수(총지수)
5th row소비자물가지수(총지수)

Common Values

ValueCountFrequency (%)
소비자물가지수(총지수) 36
100.0%

Length

2024-01-10T05:38:16.977328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:17.089217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소비자물가지수(총지수 36
100.0%

항목 ID
Boolean

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size168.0 B
True
36 
ValueCountFrequency (%)
True 36
100.0%
2024-01-10T05:38:17.173640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

단위명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
2020＝100
36 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020＝100
2nd row2020＝100
3rd row2020＝100
4th row2020＝100
5th row2020＝100

Common Values

ValueCountFrequency (%)
2020＝100 36
100.0%

Length

2024-01-10T05:38:17.268462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:17.369218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020＝100 36
100.0%

분류값 ID1
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-01-10T05:38:17.511868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters108
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowT10
2nd rowT11
3rd rowT12
4th rowT13
5th rowT14
ValueCountFrequency (%)
t10 2
 
5.6%
t11 2
 
5.6%
t90 2
 
5.6%
t81 2
 
5.6%
t71 2
 
5.6%
t61 2
 
5.6%
t51 2
 
5.6%
t41 2
 
5.6%
t31 2
 
5.6%
t21 2
 
5.6%
Other values (8) 16
44.4%
2024-01-10T05:38:17.775132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 36
33.3%
1 34
31.5%
6 6
 
5.6%
0 4
 
3.7%
2 4
 
3.7%
3 4
 
3.7%
4 4
 
3.7%
5 4
 
3.7%
7 4
 
3.7%
8 4
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
66.7%
Uppercase Letter 36
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 34
47.2%
6 6
 
8.3%
0 4
 
5.6%
2 4
 
5.6%
3 4
 
5.6%
4 4
 
5.6%
5 4
 
5.6%
7 4
 
5.6%
8 4
 
5.6%
9 4
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
T 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
66.7%
Latin 36
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 34
47.2%
6 6
 
8.3%
0 4
 
5.6%
2 4
 
5.6%
3 4
 
5.6%
4 4
 
5.6%
5 4
 
5.6%
7 4
 
5.6%
8 4
 
5.6%
9 4
 
5.6%
Latin
ValueCountFrequency (%)
T 36
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 36
33.3%
1 34
31.5%
6 6
 
5.6%
0 4
 
3.7%
2 4
 
3.7%
3 4
 
3.7%
4 4
 
3.7%
5 4
 
3.7%
7 4
 
3.7%
8 4
 
3.7%

단위영문명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
2020＝100
36 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020＝100
2nd row2020＝100
3rd row2020＝100
4th row2020＝100
5th row2020＝100

Common Values

ValueCountFrequency (%)
2020＝100 36
100.0%

Length

2024-01-10T05:38:17.903884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:17.991461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020＝100 36
100.0%

수치값
Real number (ℝ)

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.28556
Minimum112.16
Maximum114.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-01-10T05:38:18.081230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum112.16
5-th percentile112.365
Q1112.7625
median113.295
Q3113.6425
95-th percentile114.3025
Maximum114.64
Range2.48
Interquartile range (IQR)0.88

Descriptive statistics

Standard deviation0.64005109
Coefficient of variation (CV)0.0056498914
Kurtosis-0.62205988
Mean113.28556
Median Absolute Deviation (MAD)0.475
Skewness0.25265439
Sum4078.28
Variance0.4096654
MonotonicityNot monotonic
2024-01-10T05:38:18.189402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
112.73 2
 
5.6%
113.01 2
 
5.6%
112.54 1
 
2.8%
112.65 1
 
2.8%
112.79 1
 
2.8%
113.14 1
 
2.8%
112.2 1
 
2.8%
112.49 1
 
2.8%
112.42 1
 
2.8%
113.37 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
112.16 1
2.8%
112.2 1
2.8%
112.42 1
2.8%
112.49 1
2.8%
112.54 1
2.8%
112.65 1
2.8%
112.73 2
5.6%
112.74 1
2.8%
112.77 1
2.8%
112.79 1
2.8%
ValueCountFrequency (%)
114.64 1
2.8%
114.46 1
2.8%
114.25 1
2.8%
114.24 1
2.8%
114.17 1
2.8%
114.11 1
2.8%
113.86 1
2.8%
113.74 1
2.8%
113.71 1
2.8%
113.62 1
2.8%

분류 영문명1
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
by City
36 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowby City
2nd rowby City
3rd rowby City
4th rowby City
5th rowby City

Common Values

ValueCountFrequency (%)
by City 36
100.0%

Length

2024-01-10T05:38:18.299295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:18.381871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
by 36
50.0%
city 36
50.0%

분류값 명1
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-01-10T05:38:18.530234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.5
Min length2

Characters and Unicode

Total characters162
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row전국
2nd row서울특별시
3rd row부산광역시
4th row대구광역시
5th row인천광역시
ValueCountFrequency (%)
전국 2
 
5.6%
서울특별시 2
 
5.6%
경상남도 2
 
5.6%
경상북도 2
 
5.6%
전라남도 2
 
5.6%
전라북도 2
 
5.6%
충청남도 2
 
5.6%
충청북도 2
 
5.6%
강원도 2
 
5.6%
경기도 2
 
5.6%
Other values (8) 16
44.4%
2024-01-10T05:38:18.822605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 18
 
11.1%
시 16
 
9.9%
광 14
 
8.6%
역 12
 
7.4%
전 8
 
4.9%
경 6
 
3.7%
특 6
 
3.7%
별 6
 
3.7%
남 6
 
3.7%
북 6
 
3.7%
Other values (22) 64
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 162
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 18
 
11.1%
시 16
 
9.9%
광 14
 
8.6%
역 12
 
7.4%
전 8
 
4.9%
경 6
 
3.7%
특 6
 
3.7%
별 6
 
3.7%
남 6
 
3.7%
북 6
 
3.7%
Other values (22) 64
39.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 162
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 18
 
11.1%
시 16
 
9.9%
광 14
 
8.6%
역 12
 
7.4%
전 8
 
4.9%
경 6
 
3.7%
특 6
 
3.7%
별 6
 
3.7%
남 6
 
3.7%
북 6
 
3.7%
Other values (22) 64
39.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 162
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 18
 
11.1%
시 16
 
9.9%
광 14
 
8.6%
역 12
 
7.4%
전 8
 
4.9%
경 6
 
3.7%
특 6
 
3.7%
별 6
 
3.7%
남 6
 
3.7%
북 6
 
3.7%
Other values (22) 64
39.5%

수록시점
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
202310
18 
202311
18 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202310
2nd row202310
3rd row202310
4th row202310
5th row202310

Common Values

ValueCountFrequency (%)
202310 18
50.0%
202311 18
50.0%

Length

2024-01-10T05:38:18.938743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:19.027625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202310 18
50.0%
202311 18
50.0%

분류값 영문명1
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-01-10T05:38:19.178575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length9.7222222
Min length5

Characters and Unicode

Total characters350
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowAll cities
2nd rowSeoul
3rd rowBusan
4th rowDaegu
5th rowIncheon
ValueCountFrequency (%)
all 2
 
5.3%
gyeonggi-do 2
 
5.3%
gyeongsangnam-do 2
 
5.3%
gyeongsangbuk-do 2
 
5.3%
jeollanam-do 2
 
5.3%
jeollabuk-do 2
 
5.3%
chungcheongnam-do 2
 
5.3%
chungcheongbuk-do 2
 
5.3%
gangwon-do 2
 
5.3%
sejong 2
 
5.3%
Other values (9) 18
47.4%
2024-01-10T05:38:19.465067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 42
12.0%
n 42
12.0%
e 30
 
8.6%
g 28
 
8.0%
a 26
 
7.4%
u 20
 
5.7%
d 18
 
5.1%
- 18
 
5.1%
l 16
 
4.6%
s 10
 
2.9%
Other values (20) 100
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 294
84.0%
Uppercase Letter 36
 
10.3%
Dash Punctuation 18
 
5.1%
Space Separator 2
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 42
14.3%
n 42
14.3%
e 30
10.2%
g 28
9.5%
a 26
8.8%
u 20
 
6.8%
d 18
 
6.1%
l 16
 
5.4%
s 10
 
3.4%
h 10
 
3.4%
Other values (9) 52
17.7%
Uppercase Letter
ValueCountFrequency (%)
G 10
27.8%
J 6
16.7%
D 4
 
11.1%
S 4
 
11.1%
C 4
 
11.1%
I 2
 
5.6%
U 2
 
5.6%
B 2
 
5.6%
A 2
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 330
94.3%
Common 20
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 42
12.7%
n 42
12.7%
e 30
 
9.1%
g 28
 
8.5%
a 26
 
7.9%
u 20
 
6.1%
d 18
 
5.5%
l 16
 
4.8%
s 10
 
3.0%
G 10
 
3.0%
Other values (18) 88
26.7%
Common
ValueCountFrequency (%)
- 18
90.0%
2
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 42
12.0%
n 42
12.0%
e 30
 
8.6%
g 28
 
8.0%
a 26
 
7.4%
u 20
 
5.7%
d 18
 
5.1%
- 18
 
5.1%
l 16
 
4.6%
s 10
 
2.9%
Other values (20) 100
28.6%

통계표명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
소비자물가지수(2020＝100)
36 

Length

Max length17
Median length17
Mean length17
Min length17

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소비자물가지수(2020＝100)
2nd row소비자물가지수(2020＝100)
3rd row소비자물가지수(2020＝100)
4th row소비자물가지수(2020＝100)
5th row소비자물가지수(2020＝100)

Common Values

ValueCountFrequency (%)
소비자물가지수(2020＝100) 36
100.0%

Length

2024-01-10T05:38:19.585596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:19.671231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소비자물가지수(2020＝100 36
100.0%

분류명1
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
시도별
36 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시도별 36
100.0%

Length

2024-01-10T05:38:19.767069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:19.875424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시도별 36
100.0%

수집날짜
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
20231109
18 
20231209
18 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20231109
2nd row20231109
3rd row20231109
4th row20231109
5th row20231109

Common Values

ValueCountFrequency (%)
20231109 18
50.0%
20231209 18
50.0%

Length

2024-01-10T05:38:19.985748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:38:20.099463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20231109 18
50.0%
20231209 18
50.0%

Interactions

2024-01-10T05:38:15.569470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:38:20.172902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류값 ID1수치값분류값 명1수록시점분류값 영문명1수집날짜
분류값 ID11.0000.0001.0000.0001.0000.000
수치값0.0001.0000.0000.7090.0000.709
분류값 명11.0000.0001.0000.0001.0000.000
수록시점0.0000.7090.0001.0000.0000.996
분류값 영문명11.0000.0001.0000.0001.0000.000
수집날짜0.0000.7090.0000.9960.0001.000
2024-01-10T05:38:20.285219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수록시점수집날짜
수록시점1.0000.943
수집날짜0.9431.000
2024-01-10T05:38:20.371724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수치값수록시점수집날짜
수치값1.0000.4790.479
수록시점0.4791.0000.943
수집날짜0.4790.9431.000

Missing values

2024-01-10T05:38:15.675279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:38:15.847006image/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

통계표ID기관코드항목영문명수록주기항목명항목 ID단위명분류값 ID1단위영문명수치값분류 영문명1분류값 명1수록시점분류값 영문명1통계표명분류명1수집날짜
0DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T102020＝100113.37by City전국202310All cities소비자물가지수(2020＝100)시도별20231109
1DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T112020＝100112.73by City서울특별시202310Seoul소비자물가지수(2020＝100)시도별20231109
2DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T122020＝100113.22by City부산광역시202310Busan소비자물가지수(2020＝100)시도별20231109
3DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T132020＝100113.48by City대구광역시202310Daegu소비자물가지수(2020＝100)시도별20231109
4DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T142020＝100113.86by City인천광역시202310Incheon소비자물가지수(2020＝100)시도별20231109
5DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T152020＝100113.62by City광주광역시202310Gwangju소비자물가지수(2020＝100)시도별20231109
6DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T162020＝100112.93by City대전광역시202310Daejeon소비자물가지수(2020＝100)시도별20231109
7DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T172020＝100113.51by City울산광역시202310Ulsan소비자물가지수(2020＝100)시도별20231109
8DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T182020＝100113.01by City세종특별자치시202310Sejong소비자물가지수(2020＝100)시도별20231109
9DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T212020＝100113.08by City경기도202310Gyeonggi-do소비자물가지수(2020＝100)시도별20231109
통계표ID기관코드항목영문명수록주기항목명항목 ID단위명분류값 ID1단위영문명수치값분류 영문명1분류값 명1수록시점분류값 영문명1통계표명분류명1수집날짜
26DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T182020＝100112.42by City세종특별자치시202311Sejong소비자물가지수(2020＝100)시도별20231209
27DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T212020＝100112.54by City경기도202311Gyeonggi-do소비자물가지수(2020＝100)시도별20231209
28DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T312020＝100114.17by City강원도202311Gangwon-do소비자물가지수(2020＝100)시도별20231209
29DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T412020＝100113.55by City충청북도202311Chungcheongbuk-do소비자물가지수(2020＝100)시도별20231209
30DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T512020＝100113.58by City충청남도202311Chungcheongnam-do소비자물가지수(2020＝100)시도별20231209
31DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T612020＝100112.73by City전라북도202311Jeollabuk-do소비자물가지수(2020＝100)시도별20231209
32DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T712020＝100113.47by City전라남도202311Jeollanam-do소비자물가지수(2020＝100)시도별20231209
33DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T812020＝100113.52by City경상북도202311Gyeongsangbuk-do소비자물가지수(2020＝100)시도별20231209
34DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T902020＝100113.0by City경상남도202311Gyeongsangnam-do소비자물가지수(2020＝100)시도별20231209
35DT_1J20003101CPIM소비자물가지수(총지수)T2020＝100T962020＝100112.77by City제주특별자치도202311Jeju-do소비자물가지수(2020＝100)시도별20231209