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/cd98e401-6133-4ccd-bb3b-b3a3f4a891a6

Alerts

일반일간결제일자 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 위도High correlation
위도 is highly overall correlated with 가맹점우편번호 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 가맹점업종명 and 1 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
시도명 is highly overall correlated with 위도 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 started2023-12-10 14:14:42.861449
Analysis finished2023-12-10 14:14:47.270484
Duration4.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일반일간결제일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-04-01 00:00:00
Maximum2023-04-01 00:00:00
2023-12-10T23:14:47.435459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:47.613902image/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%
Mean7.0664904 × 108
Minimum7.000047 × 108
Maximum7.9933477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:47.830000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000047 × 108
5-th percentile7.0000856 × 108
Q17.0001984 × 108
median7.0003053 × 108
Q37.0004256 × 108
95-th percentile7.546545 × 108
Maximum7.9933477 × 108
Range99330072
Interquartile range (IQR)22713.5

Descriptive statistics

Standard deviation25194715
Coefficient of variation (CV)0.035653788
Kurtosis12.206626
Mean7.0664904 × 108
Median Absolute Deviation (MAD)10963
Skewness3.6599973
Sum2.1199471 × 1010
Variance6.3477367 × 1014
MonotonicityNot monotonic
2023-12-10T23:14:48.064469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700004700 1
 
3.3%
700030010 1
 
3.3%
700045865 1
 
3.3%
700045052 1
 
3.3%
700044968 1
 
3.3%
700043647 1
 
3.3%
700043620 1
 
3.3%
700043215 1
 
3.3%
700040575 1
 
3.3%
700037620 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700004700 1
3.3%
700006427 1
3.3%
700011157 1
3.3%
700012666 1
3.3%
700015812 1
3.3%
700017071 1
3.3%
700019316 1
3.3%
700019825 1
3.3%
700019891 1
3.3%
700024199 1
3.3%
ValueCountFrequency (%)
799334772 1
3.3%
799334301 1
3.3%
700045865 1
3.3%
700045052 1
3.3%
700044968 1
3.3%
700043647 1
3.3%
700043620 1
3.3%
700043215 1
3.3%
700040575 1
3.3%
700037620 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

2023-12-10T23:14:48.352155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:48.650886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
999999999999999 30
100.0%

가맹점업종명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
9 
음료식품
3 
유통업 영리
3 
학원
2 
의류
2 
Other values (9)
11 

Length

Max length8
Median length7
Mean length4.8666667
Min length2

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row음료식품
2nd row유통업 영리
3rd row학원
4th row용역 서비스
5th row일반휴게음식

Common Values

ValueCountFrequency (%)
일반휴게음식 9
30.0%
음료식품 3
 
10.0%
유통업 영리 3
 
10.0%
학원 2
 
6.7%
의류 2
 
6.7%
자동차정비 유지 2
 
6.7%
보건위생 2
 
6.7%
용역 서비스 1
 
3.3%
전기제품 1
 
3.3%
사무통신 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:14:48.839847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 9
24.3%
유통업 4
10.8%
영리 3
 
8.1%
음료식품 3
 
8.1%
자동차정비 2
 
5.4%
유지 2
 
5.4%
보건위생 2
 
5.4%
의류 2
 
5.4%
학원 2
 
5.4%
용역 1
 
2.7%
Other values (7) 7
18.9%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14301.367
Minimum10449
Maximum17586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:49.049877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10449
5-th percentile10683.65
Q112380.75
median14495.5
Q316824.25
95-th percentile17030.2
Maximum17586
Range7137
Interquartile range (IQR)4443.5

Descriptive statistics

Standard deviation2326.0748
Coefficient of variation (CV)0.16264703
Kurtosis-1.3815107
Mean14301.367
Median Absolute Deviation (MAD)2326
Skewness-0.19031609
Sum429041
Variance5410623.8
MonotonicityNot monotonic
2023-12-10T23:14:49.287282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
16827 2
 
6.7%
11453 1
 
3.3%
12773 1
 
3.3%
13232 1
 
3.3%
14111 1
 
3.3%
16525 1
 
3.3%
16816 1
 
3.3%
15360 1
 
3.3%
14449 1
 
3.3%
16845 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
10449 1
3.3%
10550 1
3.3%
10847 1
3.3%
11161 1
3.3%
11453 1
3.3%
11757 1
3.3%
11900 1
3.3%
12274 1
3.3%
12701 1
3.3%
12768 1
3.3%
ValueCountFrequency (%)
17586 1
3.3%
17050 1
3.3%
17006 1
3.3%
16996 1
3.3%
16876 1
3.3%
16845 1
3.3%
16827 2
6.7%
16816 1
3.3%
16525 1
3.3%
15532 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

2023-12-10T23:14:49.537727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:49.722039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 28
93.3%
none 2
 
6.7%

시군구명
Text

MISSING 

Distinct19
Distinct (%)67.9%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T23:14:49.970099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.1071429
Min length3

Characters and Unicode

Total characters143
Distinct characters36
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

Unique15 ?
Unique (%)53.6%

Sample

1st row양주시
2nd row광주시
3rd row용인시 수지구
4th row용인시 수지구
5th row시흥시
ValueCountFrequency (%)
용인시 8
19.0%
수지구 5
 
11.9%
광주시 3
 
7.1%
부천시 3
 
7.1%
안산시 2
 
4.8%
기흥구 2
 
4.8%
고양시 2
 
4.8%
성남시 1
 
2.4%
양주시 1
 
2.4%
남양주시 1
 
2.4%
Other values (14) 14
33.3%
2023-12-10T23:14:50.499856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
20.3%
구 15
 
10.5%
14
 
9.8%
인 9
 
6.3%
용 8
 
5.6%
수 6
 
4.2%
주 6
 
4.2%
양 5
 
3.5%
지 5
 
3.5%
광 4
 
2.8%
Other values (26) 42
29.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 129
90.2%
Space Separator 14
 
9.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
22.5%
구 15
 
11.6%
인 9
 
7.0%
용 8
 
6.2%
수 6
 
4.7%
주 6
 
4.7%
양 5
 
3.9%
지 5
 
3.9%
광 4
 
3.1%
부 4
 
3.1%
Other values (25) 38
29.5%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 129
90.2%
Common 14
 
9.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
22.5%
구 15
 
11.6%
인 9
 
7.0%
용 8
 
6.2%
수 6
 
4.7%
주 6
 
4.7%
양 5
 
3.9%
지 5
 
3.9%
광 4
 
3.1%
부 4
 
3.1%
Other values (25) 38
29.5%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 129
90.2%
ASCII 14
 
9.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
22.5%
구 15
 
11.6%
인 9
 
7.0%
용 8
 
6.2%
수 6
 
4.7%
주 6
 
4.7%
양 5
 
3.9%
지 5
 
3.9%
광 4
 
3.1%
부 4
 
3.1%
Other values (25) 38
29.5%
ASCII
ValueCountFrequency (%)
14
100.0%

읍면동명
Text

MISSING 

Distinct25
Distinct (%)89.3%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T23:14:50.841104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0357143
Min length2

Characters and Unicode

Total characters85
Distinct characters43
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 row고암동
2nd row중대동
3rd row죽전동
4th row동천동
5th row월곶동
ValueCountFrequency (%)
동천동 2
 
7.1%
상동 2
 
7.1%
신봉동 2
 
7.1%
철산동 1
 
3.6%
고암동 1
 
3.6%
남한산성면 1
 
3.6%
매탄동 1
 
3.6%
고잔동 1
 
3.6%
삼정동 1
 
3.6%
본오동 1
 
3.6%
Other values (15) 15
53.6%
2023-12-10T23:14:51.402242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
31.8%
상 4
 
4.7%
오 3
 
3.5%
읍 2
 
2.4%
금 2
 
2.4%
고 2
 
2.4%
매 2
 
2.4%
천 2
 
2.4%
삼 2
 
2.4%
산 2
 
2.4%
Other values (33) 37
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
31.8%
상 4
 
4.7%
오 3
 
3.5%
읍 2
 
2.4%
금 2
 
2.4%
고 2
 
2.4%
매 2
 
2.4%
천 2
 
2.4%
삼 2
 
2.4%
산 2
 
2.4%
Other values (33) 37
43.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
31.8%
상 4
 
4.7%
오 3
 
3.5%
읍 2
 
2.4%
금 2
 
2.4%
고 2
 
2.4%
매 2
 
2.4%
천 2
 
2.4%
삼 2
 
2.4%
산 2
 
2.4%
Other values (33) 37
43.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
31.8%
상 4
 
4.7%
오 3
 
3.5%
읍 2
 
2.4%
금 2
 
2.4%
고 2
 
2.4%
매 2
 
2.4%
천 2
 
2.4%
삼 2
 
2.4%
산 2
 
2.4%
Other values (33) 37
43.5%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9691
Minimum0
Maximum37.852
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:51.625609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.7562
Q137.3175
median37.399
Q337.564
95-th percentile37.8096
Maximum37.852
Range37.852
Interquartile range (IQR)0.2465

Descriptive statistics

Standard deviation9.5073095
Coefficient of variation (CV)0.27187744
Kurtosis12.196002
Mean34.9691
Median Absolute Deviation (MAD)0.115
Skewness-3.6577898
Sum1049.073
Variance90.388934
MonotonicityNot monotonic
2023-12-10T23:14:51.845261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
37.833 1
 
3.3%
37.408 1
 
3.3%
37.44 1
 
3.3%
37.272 1
 
3.3%
37.323 1
 
3.3%
37.317 1
 
3.3%
37.522 1
 
3.3%
37.319 1
 
3.3%
37.301 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
37.236 1
3.3%
37.253 1
3.3%
37.272 1
3.3%
37.277 1
3.3%
37.301 1
3.3%
37.317 1
3.3%
37.319 1
3.3%
37.323 1
3.3%
37.331 1
3.3%
ValueCountFrequency (%)
37.852 1
3.3%
37.833 1
3.3%
37.781 1
3.3%
37.752 1
3.3%
37.645 1
3.3%
37.639 1
3.3%
37.637 1
3.3%
37.578 1
3.3%
37.522 1
3.3%
37.507 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.54857
Minimum0
Maximum127.23
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:52.127416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57.02535
Q1126.802
median127.0775
Q3127.15175
95-th percentile127.2172
Maximum127.23
Range127.23
Interquartile range (IQR)0.34975

Descriptive statistics

Standard deviation32.225518
Coefficient of variation (CV)0.27183389
Kurtosis12.205789
Mean118.54857
Median Absolute Deviation (MAD)0.1305
Skewness-3.6598241
Sum3556.457
Variance1038.484
MonotonicityNot monotonic
2023-12-10T23:14:52.416764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
127.075 1
 
3.3%
127.215 1
 
3.3%
127.163 1
 
3.3%
127.05 1
 
3.3%
127.08 1
 
3.3%
126.835 1
 
3.3%
126.768 1
 
3.3%
127.083 1
 
3.3%
126.865 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
126.723 1
3.3%
126.74 1
3.3%
126.753 1
3.3%
126.754 1
3.3%
126.768 1
3.3%
126.791 1
3.3%
126.835 1
3.3%
126.865 1
3.3%
126.87 1
3.3%
ValueCountFrequency (%)
127.23 1
3.3%
127.219 1
3.3%
127.215 1
3.3%
127.201 1
3.3%
127.187 1
3.3%
127.163 1
3.3%
127.162 1
3.3%
127.154 1
3.3%
127.145 1
3.3%
127.127 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%
2023-12-10T23:14:52.602681image/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

2023-12-10T23:14:52.788428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:52.942997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T23:14:45.654245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:43.484008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.280736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.025438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.867772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:43.615428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.535399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.180521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:46.033805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:43.930711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.710239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.340147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:46.183990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.086463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:44.873370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:45.498218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:14:53.394672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도
가맹점번호1.0000.0000.0000.0000.0001.0000.0000.000
가맹점업종명0.0001.0000.1600.8770.0000.0000.8770.877
가맹점우편번호0.0000.1601.0000.0000.9991.0000.0000.000
시도명0.0000.8770.0001.000NaNNaN0.9060.906
시군구명0.0000.0000.999NaN1.0001.000NaNNaN
읍면동명1.0000.0001.000NaN1.0001.000NaNNaN
위도0.0000.8770.0000.906NaNNaN1.0000.906
경도0.0000.8770.0000.906NaNNaN0.9061.000
2023-12-10T23:14:53.605095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명가맹점업종명
시도명1.0000.542
가맹점업종명0.5421.000
2023-12-10T23:14:53.745155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명시도명
가맹점번호1.0000.092-0.3440.0990.0000.000
가맹점우편번호0.0921.000-0.8470.0140.0000.000
위도-0.344-0.8471.0000.0080.5420.721
경도0.0990.0140.0081.0000.5420.721
가맹점업종명0.0000.0000.5420.5421.0000.542
시도명0.0000.0000.7210.7210.5421.000

Missing values

2023-12-10T23:14:46.420436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:14:46.843098image/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.
2023-12-10T23:14:47.104546image/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

일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
02023-04-01700004700999999999999999음료식품11453경기도양주시고암동37.833127.075<NA>N0
12023-04-01799334301999999999999999유통업 영리12768경기도광주시중대동37.408127.215<NA>N0
22023-04-01700006427999999999999999학원16876경기도용인시 수지구죽전동37.331127.127<NA>N0
32023-04-01700011157999999999999999용역 서비스16827경기도용인시 수지구동천동37.335127.099<NA>N0
42023-04-01700012666999999999999999일반휴게음식14966경기도시흥시월곶동37.39126.74<NA>N0
52023-04-01700015812999999999999999전기제품14623경기도부천시상동37.488126.754<NA>N0
62023-04-01700017071999999999999999일반휴게음식17050경기도용인시 처인구김량장동37.236127.201<NA>N0
72023-04-01799334772999999999999999일반휴게음식17006경기도용인시 기흥구중동37.277127.154<NA>N0
82023-04-01700019316999999999999999음료식품16827경기도용인시 수지구동천동37.338127.101<NA>N0
92023-04-01700019825999999999999999의류10449경기도고양시 일산동구백석동37.639126.791<NA>N0
일반일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202023-04-01700034145999999999999999유통업 영리11900경기도구리시갈매동37.637127.121<NA>N0
212023-04-01700034778999999999999999사무통신15532경기도안산시 상록구본오동37.301126.865<NA>N0
222023-04-01700037620999999999999999유통업 비영리17586NONE<NA><NA>0.00.0<NA>N0
232023-04-01700040575999999999999999의원16845경기도용인시 수지구신봉동37.319127.083<NA>N0
242023-04-01700043215999999999999999일반휴게음식14449경기도부천시삼정동37.522126.768<NA>N0
252023-04-01700043620999999999999999숙박업15360경기도안산시 단원구고잔동37.317126.835<NA>N0
262023-04-01700043647999999999999999보건위생16816경기도용인시 수지구신봉동37.323127.08<NA>N0
272023-04-01700044968999999999999999일반휴게음식16525경기도수원시 영통구매탄동37.272127.05<NA>N0
282023-04-01700045052999999999999999의류14111NONE<NA><NA>0.00.0<NA>N0
292023-04-01700045865999999999999999레저업소13232경기도성남시 중원구상대원동37.44127.163<NA>N0