Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells34
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory115.4 B

Variable types

DateTime1
Numeric4
Categorical4
Text2
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/8e150b73-cdc5-4c7a-a4f5-7313d98bec03

Alerts

정책일간결제일자 has constant value ""Constant
결제상품ID has constant value ""Constant
사용여부 has constant value ""Constant
결제금액 has constant value ""Constant
가맹점우편번호 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 가맹점우편번호 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 가맹점우편번호 and 2 other fieldsHigh correlation
시도명 is highly imbalanced (64.7%)Imbalance
시군구명 has 2 (6.7%) missing valuesMissing
읍면동명 has 2 (6.7%) missing valuesMissing
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
위도 has 2 (6.7%) zerosZeros
경도 has 2 (6.7%) zerosZeros

Reproduction

Analysis started2024-03-13 11:51:44.379673
Analysis finished2024-03-13 11:51:47.593725
Duration3.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책일간결제일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-12-01 00:00:00
Maximum2023-12-01 00:00:00
2024-03-13T20:51:47.709791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:47.888793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

가맹점번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0663752 × 108
Minimum7.000005 × 108
Maximum7.9933217 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:51:48.014710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000005 × 108
5-th percentile7.0000308 × 108
Q17.0000994 × 108
median7.0001676 × 108
Q37.0002632 × 108
95-th percentile7.5464883 × 108
Maximum7.9933217 × 108
Range99331673
Interquartile range (IQR)16381.25

Descriptive statistics

Standard deviation25197182
Coefficient of variation (CV)0.03565786
Kurtosis12.206628
Mean7.0663752 × 108
Median Absolute Deviation (MAD)9345.5
Skewness3.6599978
Sum2.1199126 × 1010
Variance6.3489796 × 1014
MonotonicityNot monotonic
2024-03-13T20:51:48.168839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700000498 1
 
3.3%
700016498 1
 
3.3%
700036031 1
 
3.3%
700030717 1
 
3.3%
700029032 1
 
3.3%
700027942 1
 
3.3%
700027439 1
 
3.3%
700026548 1
 
3.3%
700025655 1
 
3.3%
700024781 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000498 1
3.3%
700002638 1
3.3%
700003616 1
3.3%
700004011 1
3.3%
700004775 1
3.3%
700005007 1
3.3%
700005244 1
3.3%
700009863 1
3.3%
700010185 1
3.3%
700010217 1
3.3%
ValueCountFrequency (%)
799332171 1
3.3%
799332025 1
3.3%
700036031 1
3.3%
700030717 1
3.3%
700029032 1
3.3%
700027942 1
3.3%
700027439 1
3.3%
700026548 1
3.3%
700025655 1
3.3%
700024781 1
3.3%

결제상품ID
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
999999999999999
30 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row999999999999999
2nd row999999999999999
3rd row999999999999999
4th row999999999999999
5th row999999999999999

Common Values

ValueCountFrequency (%)
999999999999999 30
100.0%

Length

2024-03-13T20:51:48.319068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:51:48.445251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반/휴게 음식
7 
미용/위생
6 
의류
2 
레저/스포츠 서비스
2 
전자제품
2 
Other values (9)
11 

Length

Max length10
Median length8
Mean length5.3
Min length2

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row약국
2nd row미용/위생
3rd row의류
4th row레저/스포츠 서비스
5th row일반/휴게 음식

Common Values

ValueCountFrequency (%)
일반/휴게 음식 7
23.3%
미용/위생 6
20.0%
의류 2
 
6.7%
레저/스포츠 서비스 2
 
6.7%
전자제품 2
 
6.7%
학원 2
 
6.7%
용역서비스 2
 
6.7%
약국 1
 
3.3%
신변잡화 1
 
3.3%
숙박업 1
 
3.3%
Other values (4) 4
13.3%

Length

2024-03-13T20:51:48.554102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반/휴게 7
17.9%
음식 7
17.9%
미용/위생 6
15.4%
의류 2
 
5.1%
레저/스포츠 2
 
5.1%
서비스 2
 
5.1%
전자제품 2
 
5.1%
학원 2
 
5.1%
용역서비스 2
 
5.1%
약국 1
 
2.6%
Other values (6) 6
15.4%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14317.9
Minimum10038
Maximum18477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:51:48.710412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10038
5-th percentile10087.55
Q111747.5
median14658
Q316465.25
95-th percentile18463.45
Maximum18477
Range8439
Interquartile range (IQR)4717.75

Descriptive statistics

Standard deviation2974.5277
Coefficient of variation (CV)0.20774888
Kurtosis-1.2684543
Mean14317.9
Median Absolute Deviation (MAD)2838
Skewness-0.036690027
Sum429537
Variance8847815.3
MonotonicityNot monotonic
2024-03-13T20:51:48.856558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
14948 2
 
6.7%
13599 1
 
3.3%
10322 1
 
3.3%
18394 1
 
3.3%
16509 1
 
3.3%
10262 1
 
3.3%
10480 1
 
3.3%
15471 1
 
3.3%
15360 1
 
3.3%
18477 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
10038 1
3.3%
10079 1
3.3%
10098 1
3.3%
10262 1
3.3%
10322 1
3.3%
10480 1
3.3%
10855 1
3.3%
11695 1
3.3%
11905 1
3.3%
12449 1
3.3%
ValueCountFrequency (%)
18477 1
3.3%
18472 1
3.3%
18453 1
3.3%
18434 1
3.3%
18394 1
3.3%
18297 1
3.3%
17581 1
3.3%
16509 1
3.3%
16334 1
3.3%
15471 1
3.3%

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
28 
NONE
 
2

Length

Max length4
Median length3
Mean length3.0666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 28
93.3%
NONE 2
 
6.7%

Length

2024-03-13T20:51:49.011722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:51:49.146996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 28
93.3%
none 2
 
6.7%

시군구명
Text

MISSING 

Distinct17
Distinct (%)60.7%
Missing2
Missing (%)6.7%
Memory size372.0 B
2024-03-13T20:51:49.309330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.6071429
Min length3

Characters and Unicode

Total characters129
Distinct characters35
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

Unique11 ?
Unique (%)39.3%

Sample

1st row성남시 분당구
2nd row고양시 일산동구
3rd row김포시
4th row수원시 장안구
5th row화성시
ValueCountFrequency (%)
화성시 5
12.8%
안산시 3
 
7.7%
단원구 3
 
7.7%
고양시 3
 
7.7%
김포시 3
 
7.7%
덕양구 2
 
5.1%
시흥시 2
 
5.1%
부천시 2
 
5.1%
수원시 2
 
5.1%
성남시 2
 
5.1%
Other values (12) 12
30.8%
2024-03-13T20:51:49.699084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
22.5%
구 12
 
9.3%
11
 
8.5%
성 8
 
6.2%
안 7
 
5.4%
원 6
 
4.7%
양 6
 
4.7%
화 5
 
3.9%
산 4
 
3.1%
김 3
 
2.3%
Other values (25) 38
29.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
91.5%
Space Separator 11
 
8.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
24.6%
구 12
 
10.2%
성 8
 
6.8%
안 7
 
5.9%
원 6
 
5.1%
양 6
 
5.1%
화 5
 
4.2%
산 4
 
3.4%
김 3
 
2.5%
포 3
 
2.5%
Other values (24) 35
29.7%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118
91.5%
Common 11
 
8.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
24.6%
구 12
 
10.2%
성 8
 
6.8%
안 7
 
5.9%
원 6
 
5.1%
양 6
 
5.1%
화 5
 
4.2%
산 4
 
3.4%
김 3
 
2.5%
포 3
 
2.5%
Other values (24) 35
29.7%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118
91.5%
ASCII 11
 
8.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
24.6%
구 12
 
10.2%
성 8
 
6.8%
안 7
 
5.9%
원 6
 
5.1%
양 6
 
5.1%
화 5
 
4.2%
산 4
 
3.4%
김 3
 
2.5%
포 3
 
2.5%
Other values (24) 35
29.7%
ASCII
ValueCountFrequency (%)
11
100.0%

읍면동명
Text

MISSING 

Distinct25
Distinct (%)89.3%
Missing2
Missing (%)6.7%
Memory size372.0 B
2024-03-13T20:51:49.951960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9642857
Min length2

Characters and Unicode

Total characters83
Distinct characters42
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

Unique22 ?
Unique (%)78.6%

Sample

1st row수내동
2nd row식사동
3rd row북변동
4th row정자동
5th row영천동
ValueCountFrequency (%)
고잔동 2
 
7.1%
반송동 2
 
7.1%
신천동 2
 
7.1%
식사동 1
 
3.6%
수내동 1
 
3.6%
청평면 1
 
3.6%
관산동 1
 
3.6%
성사동 1
 
3.6%
청계동 1
 
3.6%
금광동 1
 
3.6%
Other values (15) 15
53.6%
2024-03-13T20:51:50.316299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 25
30.1%
사 3
 
3.6%
신 3
 
3.6%
천 3
 
3.6%
송 3
 
3.6%
면 3
 
3.6%
잔 2
 
2.4%
광 2
 
2.4%
고 2
 
2.4%
금 2
 
2.4%
Other values (32) 35
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
30.1%
사 3
 
3.6%
신 3
 
3.6%
천 3
 
3.6%
송 3
 
3.6%
면 3
 
3.6%
잔 2
 
2.4%
광 2
 
2.4%
고 2
 
2.4%
금 2
 
2.4%
Other values (32) 35
42.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
30.1%
사 3
 
3.6%
신 3
 
3.6%
천 3
 
3.6%
송 3
 
3.6%
면 3
 
3.6%
잔 2
 
2.4%
광 2
 
2.4%
고 2
 
2.4%
금 2
 
2.4%
Other values (32) 35
42.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 25
30.1%
사 3
 
3.6%
신 3
 
3.6%
천 3
 
3.6%
송 3
 
3.6%
면 3
 
3.6%
잔 2
 
2.4%
광 2
 
2.4%
고 2
 
2.4%
금 2
 
2.4%
Other values (32) 35
42.2%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9431
Minimum0
Maximum37.765
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:51:50.461298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.6509
Q137.253
median37.421
Q337.63525
95-th percentile37.7371
Maximum37.765
Range37.765
Interquartile range (IQR)0.38225

Descriptive statistics

Standard deviation9.5005836
Coefficient of variation (CV)0.27188726
Kurtosis12.193789
Mean34.9431
Median Absolute Deviation (MAD)0.2095
Skewness-3.6573382
Sum1048.293
Variance90.261089
MonotonicityNot monotonic
2024-03-13T20:51:50.630478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
37.765 2
 
6.7%
0.0 2
 
6.7%
37.373 1
 
3.3%
37.638 1
 
3.3%
37.286 1
 
3.3%
37.703 1
 
3.3%
37.642 1
 
3.3%
37.311 1
 
3.3%
37.437 1
 
3.3%
37.317 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0 2
6.7%
37.002 1
3.3%
37.201 1
3.3%
37.204 1
3.3%
37.207 1
3.3%
37.208 1
3.3%
37.242 1
3.3%
37.286 1
3.3%
37.296 1
3.3%
37.311 1
3.3%
ValueCountFrequency (%)
37.765 2
6.7%
37.703 1
3.3%
37.681 1
3.3%
37.664 1
3.3%
37.646 1
3.3%
37.642 1
3.3%
37.638 1
3.3%
37.627 1
3.3%
37.507 1
3.3%
37.479 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.47307
Minimum0
Maximum127.45
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:51:50.775264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56.9664
Q1126.78075
median126.851
Q3127.07075
95-th percentile127.22525
Maximum127.45
Range127.45
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation32.205111
Coefficient of variation (CV)0.27183487
Kurtosis12.205569
Mean118.47307
Median Absolute Deviation (MAD)0.1425
Skewness-3.6597781
Sum3554.192
Variance1037.1692
MonotonicityNot monotonic
2024-03-13T20:51:50.930202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
127.119 1
 
3.3%
126.815 1
 
3.3%
127.054 1
 
3.3%
126.854 1
 
3.3%
126.85 1
 
3.3%
126.827 1
 
3.3%
126.78 1
 
3.3%
126.835 1
 
3.3%
127.1 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
126.592 1
3.3%
126.673 1
3.3%
126.708 1
3.3%
126.77 1
3.3%
126.775 1
3.3%
126.78 1
3.3%
126.783 1
3.3%
126.802 1
3.3%
126.813 1
3.3%
ValueCountFrequency (%)
127.45 1
3.3%
127.268 1
3.3%
127.173 1
3.3%
127.138 1
3.3%
127.119 1
3.3%
127.108 1
3.3%
127.1 1
3.3%
127.073 1
3.3%
127.064 1
3.3%
127.054 1
3.3%

결제상품명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
30 
ValueCountFrequency (%)
False 30
100.0%
2024-03-13T20:51:51.061198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
100.0%

Length

2024-03-13T20:51:51.185119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:51:51.310773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2024-03-13T20:51:46.146648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:44.770665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:45.198617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:45.706887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:46.283252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:44.868202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:45.318349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:45.829908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:46.388112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:44.957256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:45.454384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:45.918083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:46.556506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:45.095332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:45.598565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:46.030120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:51:51.400624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도
가맹점번호1.0000.0000.0000.0000.7731.0000.0000.000
가맹점업종명0.0001.0000.7890.0000.7870.9340.0000.000
가맹점우편번호0.0000.7891.0000.7541.0001.0000.7540.754
시도명0.0000.0000.7541.000NaNNaN0.9060.906
시군구명0.7730.7871.000NaN1.0001.000NaNNaN
읍면동명1.0000.9341.000NaN1.0001.000NaNNaN
위도0.0000.0000.7540.906NaNNaN1.0000.906
경도0.0000.0000.7540.906NaNNaN0.9061.000
2024-03-13T20:51:51.538836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명가맹점업종명
시도명1.0000.000
가맹점업종명0.0001.000
2024-03-13T20:51:51.635678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명시도명
가맹점번호1.000-0.0230.035-0.2170.0000.000
가맹점우편번호-0.0231.000-0.8180.3710.4290.502
위도0.035-0.8181.000-0.1730.0000.721
경도-0.2170.371-0.1731.0000.0000.721
가맹점업종명0.0000.4290.0000.0001.0000.000
시도명0.0000.5020.7210.7210.0001.000

Missing values

2024-03-13T20:51:47.108015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:51:47.346564image/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.
2024-03-13T20:51:47.503531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02023-12-01700000498999999999999999약국13599경기도성남시 분당구수내동37.373127.119<NA>N0
12023-12-01799332025999999999999999미용/위생10322경기도고양시 일산동구식사동37.681126.815<NA>N0
22023-12-01700002638999999999999999의류10098경기도김포시북변동37.627126.708<NA>N0
32023-12-01700003616999999999999999레저/스포츠 서비스16334경기도수원시 장안구정자동37.296126.993<NA>N0
42023-12-01700004011999999999999999일반/휴게 음식18472경기도화성시영천동37.204127.108<NA>N0
52023-12-01700004775999999999999999레저/스포츠 서비스18434경기도화성시반송동37.208127.064<NA>N0
62023-12-01700005007999999999999999미용/위생14285경기도광명시광명동37.479126.852<NA>N0
72023-12-01799332171999999999999999미용/위생14533경기도부천시중동37.507126.775<NA>N0
82023-12-01700005244999999999999999전자제품10855경기도파주시금촌동37.765126.77<NA>N0
92023-12-01700009863999999999999999학원18453경기도화성시반송동37.207127.073<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202023-12-01700022489999999999999999의류11695NONE<NA><NA>0.00.0<NA>N0
212023-12-01700022895999999999999999일반/휴게 음식14948경기도시흥시신천동37.438126.783<NA>N0
222023-12-01700024781999999999999999용역서비스18477경기도화성시청계동37.201127.1<NA>N0
232023-12-01700025655999999999999999일반/휴게 음식15360경기도안산시 단원구고잔동37.317126.835<NA>N0
242023-12-01700026548999999999999999전자제품14948경기도시흥시신천동37.437126.78<NA>N0
252023-12-01700027439999999999999999미용/위생15471경기도안산시 단원구고잔동37.311126.827<NA>N0
262023-12-01700027942999999999999999일반/휴게 음식10480경기도고양시 덕양구성사동37.642126.85<NA>N0
272023-12-01700029032999999999999999문화/취미10262경기도고양시 덕양구관산동37.703126.854<NA>N0
282023-12-01700030717999999999999999음료/식품16509경기도수원시 영통구이의동37.286127.054<NA>N0
292023-12-01700036031999999999999999일반/휴게 음식18394NONE<NA><NA>0.00.0<NA>N0