Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells46
Missing cells (%)11.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory115.4 B

Variable types

Categorical5
Numeric4
Text2
Unsupported1
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://www.bigdata-region.kr/#/dataset/ccf9e317-02d1-42d5-aaa9-b447269444ff

Alerts

정책일간결제일자 has constant value ""Constant
결제상품ID has constant value ""Constant
사용여부 has constant value ""Constant
결제금액 has constant value ""Constant
시도명 is highly overall correlated with 가맹점번호 and 4 other fieldsHigh correlation
가맹점업종명 is highly overall correlated with 시도명High correlation
가맹점번호 is highly overall correlated with 시도명High correlation
가맹점우편번호 is highly overall correlated with 시도명High correlation
위도 is highly overall correlated with 시도명High correlation
경도 is highly overall correlated with 시도명High correlation
시군구명 has 8 (26.7%) missing valuesMissing
읍면동명 has 8 (26.7%) missing valuesMissing
결제상품명 has 30 (100.0%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
결제상품명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
위도 has 8 (26.7%) zerosZeros
경도 has 8 (26.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:08:28.067989
Analysis finished2023-12-10 14:08:32.037222
Duration3.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정책일간결제일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020-01-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-01-01
2nd row2020-01-01
3rd row2020-01-01
4th row2020-01-01
5th row2020-01-01

Common Values

ValueCountFrequency (%)
2020-01-01 30
100.0%

Length

2023-12-10T23:08:32.216296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:32.382904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-01-01 30
100.0%

가맹점번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3682238 × 108
Minimum7.0281915 × 108
Maximum7.9656494 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:32.527837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0281915 × 108
5-th percentile7.0281919 × 108
Q17.0308352 × 108
median7.0338485 × 108
Q37.8557172 × 108
95-th percentile7.9408212 × 108
Maximum7.9656494 × 108
Range93745792
Interquartile range (IQR)82488206

Descriptive statistics

Standard deviation41964554
Coefficient of variation (CV)0.056953419
Kurtosis-1.8686997
Mean7.3682238 × 108
Median Absolute Deviation (MAD)565689
Skewness0.46517704
Sum2.2104672 × 1010
Variance1.7610238 × 1015
MonotonicityNot monotonic
2023-12-10T23:08:32.750989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
702819148 1
 
3.3%
703432525 1
 
3.3%
703296236 1
 
3.3%
703251879 1
 
3.3%
703164449 1
 
3.3%
703100682 1
 
3.3%
703077796 1
 
3.3%
785771720 1
 
3.3%
703061420 1
 
3.3%
702980976 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
702819148 1
3.3%
702819171 1
3.3%
702819203 1
3.3%
702893781 1
3.3%
702980976 1
3.3%
703061276 1
3.3%
703061420 1
3.3%
703077796 1
3.3%
703100682 1
3.3%
703164368 1
3.3%
ValueCountFrequency (%)
796564940 1
3.3%
794082175 1
3.3%
794082060 1
3.3%
789750641 1
3.3%
789561246 1
3.3%
789560688 1
3.3%
785771720 1
3.3%
785653021 1
3.3%
785327833 1
3.3%
785324461 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

2023-12-10T23:08:32.976606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:33.132324image/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
8 
의류
4 
보건위생
3 
유통업 영리
2 
학원
2 
Other values (9)
11 

Length

Max length6
Median length5
Mean length4.1333333
Min length2

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row일반휴게음식
2nd row병원
3rd row일반휴게음식
4th row일반휴게음식
5th row가구

Common Values

ValueCountFrequency (%)
일반휴게음식 8
26.7%
의류 4
13.3%
보건위생 3
 
10.0%
유통업 영리 2
 
6.7%
학원 2
 
6.7%
음료식품 2
 
6.7%
건축자재 2
 
6.7%
병원 1
 
3.3%
가구 1
 
3.3%
회원제형태 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:08:33.307277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 8
24.2%
의류 4
12.1%
보건위생 3
 
9.1%
유통업 2
 
6.1%
영리 2
 
6.1%
학원 2
 
6.1%
음료식품 2
 
6.1%
건축자재 2
 
6.1%
병원 1
 
3.0%
가구 1
 
3.0%
Other values (6) 6
18.2%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13810.033
Minimum10250
Maximum18569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:33.514333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10250
5-th percentile10334.4
Q111739
median12938.5
Q316547.25
95-th percentile18482.05
Maximum18569
Range8319
Interquartile range (IQR)4808.25

Descriptive statistics

Standard deviation2741.2747
Coefficient of variation (CV)0.19849877
Kurtosis-1.1873315
Mean13810.033
Median Absolute Deviation (MAD)2052
Skewness0.43982311
Sum414301
Variance7514586.8
MonotonicityNot monotonic
2023-12-10T23:08:33.711814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
17046 1
 
3.3%
16703 1
 
3.3%
18505 1
 
3.3%
15374 1
 
3.3%
12097 1
 
3.3%
11167 1
 
3.3%
13311 1
 
3.3%
13984 1
 
3.3%
10407 1
 
3.3%
17129 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10250 1
3.3%
10275 1
3.3%
10407 1
3.3%
10829 1
3.3%
10858 1
3.3%
10915 1
3.3%
11167 1
3.3%
11733 1
3.3%
11757 1
3.3%
12097 1
3.3%
ValueCountFrequency (%)
18569 1
3.3%
18505 1
3.3%
18454 1
3.3%
17578 1
3.3%
17129 1
3.3%
17046 1
3.3%
16703 1
3.3%
16602 1
3.3%
16383 1
3.3%
15374 1
3.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
22 
<NA>
8 

Length

Max length4
Median length3
Mean length3.2666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row경기도
3rd row<NA>
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 22
73.3%
<NA> 8
 
26.7%

Length

2023-12-10T23:08:33.997465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:34.154900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 22
73.3%
na 8
 
26.7%

시군구명
Text

MISSING 

Distinct15
Distinct (%)68.2%
Missing8
Missing (%)26.7%
Memory size372.0 B
2023-12-10T23:08:34.394537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.4090909
Min length3

Characters and Unicode

Total characters119
Distinct characters34
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

Unique9 ?
Unique (%)40.9%

Sample

1st row용인시 처인구
2nd row의정부시
3rd row파주시
4th row화성시
5th row화성시
ValueCountFrequency (%)
화성시 3
 
8.8%
성남시 3
 
8.8%
수원시 3
 
8.8%
고양시 3
 
8.8%
처인구 2
 
5.9%
의정부시 2
 
5.9%
수정구 2
 
5.9%
권선구 2
 
5.9%
일산동구 2
 
5.9%
용인시 2
 
5.9%
Other values (10) 10
29.4%
2023-12-10T23:08:34.986954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 21
17.6%
12
 
10.1%
구 12
 
10.1%
성 7
 
5.9%
양 6
 
5.0%
수 5
 
4.2%
인 4
 
3.4%
정 4
 
3.4%
원 4
 
3.4%
남 4
 
3.4%
Other values (24) 40
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107
89.9%
Space Separator 12
 
10.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 21
19.6%
구 12
 
11.2%
성 7
 
6.5%
양 6
 
5.6%
수 5
 
4.7%
인 4
 
3.7%
정 4
 
3.7%
원 4
 
3.7%
남 4
 
3.7%
산 3
 
2.8%
Other values (23) 37
34.6%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107
89.9%
Common 12
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 21
19.6%
구 12
 
11.2%
성 7
 
6.5%
양 6
 
5.6%
수 5
 
4.7%
인 4
 
3.7%
정 4
 
3.7%
원 4
 
3.7%
남 4
 
3.7%
산 3
 
2.8%
Other values (23) 37
34.6%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107
89.9%
ASCII 12
 
10.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 21
19.6%
구 12
 
11.2%
성 7
 
6.5%
양 6
 
5.6%
수 5
 
4.7%
인 4
 
3.7%
정 4
 
3.7%
원 4
 
3.7%
남 4
 
3.7%
산 3
 
2.8%
Other values (23) 37
34.6%
ASCII
ValueCountFrequency (%)
12
100.0%

읍면동명
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing8
Missing (%)26.7%
Memory size372.0 B
2023-12-10T23:08:35.392362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters36
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 (%)100.0%

Sample

1st row역북동
2nd row신곡동
3rd row탄현면
4th row우정읍
5th row반송동
ValueCountFrequency (%)
금오동 1
 
4.5%
탄현면 1
 
4.5%
양평읍 1
 
4.5%
원곡동 1
 
4.5%
별내동 1
 
4.5%
가산면 1
 
4.5%
태평동 1
 
4.5%
마두동 1
 
4.5%
이동읍 1
 
4.5%
금곡동 1
 
4.5%
Other values (12) 12
54.5%
2023-12-10T23:08:36.112350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 18
27.3%
곡 4
 
6.1%
읍 3
 
4.5%
산 3
 
4.5%
금 2
 
3.0%
면 2
 
3.0%
정 2
 
3.0%
평 2
 
3.0%
고 2
 
3.0%
양 2
 
3.0%
Other values (26) 26
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 18
27.3%
곡 4
 
6.1%
읍 3
 
4.5%
산 3
 
4.5%
금 2
 
3.0%
면 2
 
3.0%
정 2
 
3.0%
평 2
 
3.0%
고 2
 
3.0%
양 2
 
3.0%
Other values (26) 26
39.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 18
27.3%
곡 4
 
6.1%
읍 3
 
4.5%
산 3
 
4.5%
금 2
 
3.0%
면 2
 
3.0%
정 2
 
3.0%
평 2
 
3.0%
고 2
 
3.0%
양 2
 
3.0%
Other values (26) 26
39.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 18
27.3%
곡 4
 
6.1%
읍 3
 
4.5%
산 3
 
4.5%
금 2
 
3.0%
면 2
 
3.0%
정 2
 
3.0%
평 2
 
3.0%
고 2
 
3.0%
양 2
 
3.0%
Other values (26) 26
39.4%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.454033
Minimum0
Maximum37.813
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:36.412938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.25075
median37.257
Q337.62675
95-th percentile37.7806
Maximum37.813
Range37.813
Interquartile range (IQR)28.376

Descriptive statistics

Standard deviation16.839829
Coefficient of variation (CV)0.61338268
Kurtosis-0.82426899
Mean27.454033
Median Absolute Deviation (MAD)0.409
Skewness-1.1110672
Sum823.621
Variance283.57983
MonotonicityNot monotonic
2023-12-10T23:08:36.669860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 8
26.7%
37.666 2
 
6.7%
37.238 1
 
3.3%
37.509 1
 
3.3%
37.167 1
 
3.3%
37.33 1
 
3.3%
37.813 1
 
3.3%
37.44 1
 
3.3%
37.192 1
 
3.3%
37.274 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
0.0 8
26.7%
37.003 1
 
3.3%
37.07 1
 
3.3%
37.167 1
 
3.3%
37.192 1
 
3.3%
37.203 1
 
3.3%
37.238 1
 
3.3%
37.255 1
 
3.3%
37.259 1
 
3.3%
37.274 1
 
3.3%
ValueCountFrequency (%)
37.813 1
3.3%
37.804 1
3.3%
37.752 1
3.3%
37.727 1
3.3%
37.71 1
3.3%
37.7 1
3.3%
37.666 2
6.7%
37.509 1
3.3%
37.472 1
3.3%
37.44 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.164733
Minimum0
Maximum127.515
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:08:36.894807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.6795
median126.962
Q3127.11375
95-th percentile127.23525
Maximum127.515
Range127.515
Interquartile range (IQR)95.43425

Descriptive statistics

Standard deviation57.141095
Coefficient of variation (CV)0.61333396
Kurtosis-0.82388303
Mean93.164733
Median Absolute Deviation (MAD)0.1795
Skewness-1.1116349
Sum2794.942
Variance3265.1048
MonotonicityNot monotonic
2023-12-10T23:08:37.105903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 8
26.7%
127.19 1
 
3.3%
127.074 1
 
3.3%
127.107 1
 
3.3%
126.797 1
 
3.3%
127.116 1
 
3.3%
127.179 1
 
3.3%
127.128 1
 
3.3%
126.784 1
 
3.3%
127.205 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0.0 8
26.7%
126.718 1
 
3.3%
126.784 1
 
3.3%
126.79 1
 
3.3%
126.797 1
 
3.3%
126.81 1
 
3.3%
126.901 1
 
3.3%
126.936 1
 
3.3%
126.988 1
 
3.3%
127.053 1
 
3.3%
ValueCountFrequency (%)
127.515 1
3.3%
127.26 1
3.3%
127.205 1
3.3%
127.19 1
3.3%
127.179 1
3.3%
127.143 1
3.3%
127.128 1
3.3%
127.116 1
3.3%
127.107 1
3.3%
127.106 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%
2023-12-10T23:08:37.297400image/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

2023-12-10T23:08:37.498207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:08:37.667528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T23:08:30.755068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:28.839718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.445487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.178998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.923492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.047731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.705294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.321758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:31.061340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.170409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.829503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.449095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:31.205136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:29.304276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.028550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:08:30.607057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:08:37.760846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점업종명가맹점우편번호시군구명읍면동명위도경도
가맹점번호1.0000.0000.0000.9141.0000.2870.287
가맹점업종명0.0001.0000.0000.7071.0000.2790.279
가맹점우편번호0.0000.0001.0000.9671.0000.6330.633
시군구명0.9140.7070.9671.0001.000NaNNaN
읍면동명1.0001.0001.0001.0001.000NaNNaN
위도0.2870.2790.633NaNNaN1.0000.990
경도0.2870.2790.633NaNNaN0.9901.000
2023-12-10T23:08:37.940977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명가맹점업종명
시도명1.0001.000
가맹점업종명1.0001.000
2023-12-10T23:08:38.080986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호가맹점우편번호위도경도가맹점업종명시도명
가맹점번호1.000-0.129-0.045-0.2790.0001.000
가맹점우편번호-0.1291.000-0.3850.3830.0001.000
위도-0.045-0.3851.0000.4780.1111.000
경도-0.2790.3830.4781.0000.1111.000
가맹점업종명0.0000.0000.1110.1111.0001.000
시도명1.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T23:08:31.398168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:08:31.684177image/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.
2023-12-10T23:08:31.918986image/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
02020-01-01702819148999999999999999일반휴게음식17046경기도용인시 처인구역북동37.238127.19<NA>N0
12020-01-01794082060999999999999999병원11733경기도의정부시신곡동37.727127.053<NA>N0
22020-01-01796564940999999999999999일반휴게음식12417<NA><NA><NA>0.00.0<NA>N0
32020-01-01703337163999999999999999일반휴게음식10858경기도파주시탄현면37.804126.718<NA>N0
42020-01-01702819171999999999999999가구18569경기도화성시우정읍37.07126.81<NA>N0
52020-01-01703061276999999999999999의류18454경기도화성시반송동37.203127.072<NA>N0
62020-01-01703164368999999999999999일반휴게음식13640경기도성남시 수정구창곡동37.472127.143<NA>N0
72020-01-01794082175999999999999999회원제형태12458<NA><NA><NA>0.00.0<NA>N0
82020-01-01768401507999999999999999보건위생10275경기도고양시 덕양구고양동37.7126.901<NA>N0
92020-01-01785324461999999999999999유통업 영리17578경기도안성시옥산동37.003127.26<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202020-01-01702819203999999999999999용역 서비스10915<NA><NA><NA>0.00.0<NA>N0
212020-01-01702893781999999999999999유통업 영리10829<NA><NA><NA>0.00.0<NA>N0
222020-01-01702980976999999999999999일반휴게음식17129경기도용인시 처인구이동읍37.192127.205<NA>N0
232020-01-01703061420999999999999999건축자재10407경기도고양시 일산동구마두동37.666126.784<NA>N0
242020-01-01785771720999999999999999보건위생13984<NA><NA><NA>0.00.0<NA>N0
252020-01-01703077796999999999999999의류13311경기도성남시 수정구태평동37.44127.128<NA>N0
262020-01-01703100682999999999999999음료식품11167경기도포천시가산면37.813127.179<NA>N0
272020-01-01703164449999999999999999학원12097경기도남양주시별내동37.666127.116<NA>N0
282020-01-01703251879999999999999999건강식품15374경기도안산시 단원구원곡동37.33126.797<NA>N0
292020-01-01703296236999999999999999일반휴게음식18505경기도화성시산척동37.167127.107<NA>N0