Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells27
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory114.4 B

Variable types

DateTime1
Numeric6
Categorical2
Text3
Boolean1

Dataset

Description샘플 데이터
Author코나아이㈜
URLhttps://bigdata-region.kr/#/dataset/5fde3ad8-e729-4286-91bf-f125edb00c52

Alerts

정책일간결제일자 has constant value ""Constant
결제상품ID is highly overall correlated with 결제금액 and 1 other fieldsHigh correlation
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
결제금액 is highly overall correlated with 결제상품ID and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 가맹점우편번호 and 2 other fieldsHigh correlation
사용여부 is highly overall correlated with 결제상품ID and 1 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 23 (76.7%) missing valuesMissing
가맹점번호 has unique valuesUnique
가맹점우편번호 has unique valuesUnique
위도 has 2 (6.7%) zerosZeros
경도 has 2 (6.7%) zerosZeros
결제금액 has 23 (76.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:00:29.983785
Analysis finished2023-12-10 14:00:37.497965
Duration7.51 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
Minimum2021-07-01 00:00:00
Maximum2021-07-01 00:00:00
2023-12-10T23:00:37.570393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:37.791767image/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%
Mean7.0001159 × 108
Minimum7.000008 × 108
Maximum7.0002322 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:37.990235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.000008 × 108
5-th percentile7.0000195 × 108
Q17.0000585 × 108
median7.0001324 × 108
Q37.0001612 × 108
95-th percentile7.0002015 × 108
Maximum7.0002322 × 108
Range22412
Interquartile range (IQR)10266

Descriptive statistics

Standard deviation6110.4846
Coefficient of variation (CV)8.7291191 × 10-6
Kurtosis-0.97993014
Mean7.0001159 × 108
Median Absolute Deviation (MAD)4767.5
Skewness-0.091729874
Sum2.1000348 × 1010
Variance37338022
MonotonicityNot monotonic
2023-12-10T23:00:38.179073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
700001681 1
 
3.3%
700013638 1
 
3.3%
700023216 1
 
3.3%
700020487 1
 
3.3%
700019736 1
 
3.3%
700017752 1
 
3.3%
700019316 1
 
3.3%
700017353 1
 
3.3%
700016480 1
 
3.3%
700016238 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
700000804 1
3.3%
700001681 1
3.3%
700002272 1
3.3%
700004075 1
3.3%
700004947 1
3.3%
700005536 1
3.3%
700005739 1
3.3%
700005788 1
3.3%
700006053 1
3.3%
700007677 1
3.3%
ValueCountFrequency (%)
700023216 1
3.3%
700020487 1
3.3%
700019736 1
3.3%
700019316 1
3.3%
700017752 1
3.3%
700017353 1
3.3%
700016480 1
3.3%
700016238 1
3.3%
700015767 1
3.3%
700014797 1
3.3%

결제상품ID
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6669933 × 1014
Minimum1.4000002 × 1011
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:38.415156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4000002 × 1011
5-th percentile1.4000003 × 1011
Q11 × 1015
median1 × 1015
Q31 × 1015
95-th percentile1 × 1015
Maximum1 × 1015
Range9.9986 × 1014
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3012284 × 1014
Coefficient of variation (CV)0.56100589
Kurtosis-0.25732032
Mean7.6669933 × 1014
Median Absolute Deviation (MAD)0
Skewness-1.3283381
Sum2.300098 × 1016
Variance1.8500566 × 1029
MonotonicityNot monotonic
2023-12-10T23:00:38.595931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
999999999999999 23
76.7%
140000018000 1
 
3.3%
140000026000 1
 
3.3%
140000046000 1
 
3.3%
140000034000 1
 
3.3%
140000059000 1
 
3.3%
140000092000 1
 
3.3%
140000065000 1
 
3.3%
ValueCountFrequency (%)
140000018000 1
 
3.3%
140000026000 1
 
3.3%
140000034000 1
 
3.3%
140000046000 1
 
3.3%
140000059000 1
 
3.3%
140000065000 1
 
3.3%
140000092000 1
 
3.3%
999999999999999 23
76.7%
ValueCountFrequency (%)
999999999999999 23
76.7%
140000092000 1
 
3.3%
140000065000 1
 
3.3%
140000059000 1
 
3.3%
140000046000 1
 
3.3%
140000034000 1
 
3.3%
140000026000 1
 
3.3%
140000018000 1
 
3.3%

가맹점업종명
Categorical

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
일반휴게음식
9 
보건위생
3 
레저업소
3 
레져용품
3 
자동차정비 유지
2 
Other values (8)
10 

Length

Max length8
Median length6
Mean length4.9333333
Min length2

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row자동차정비 유지
2nd row일반휴게음식
3rd row일반휴게음식
4th row자동차판매
5th row의류

Common Values

ValueCountFrequency (%)
일반휴게음식 9
30.0%
보건위생 3
 
10.0%
레저업소 3
 
10.0%
레져용품 3
 
10.0%
자동차정비 유지 2
 
6.7%
음료식품 2
 
6.7%
유통업 영리 2
 
6.7%
자동차판매 1
 
3.3%
의류 1
 
3.3%
문화.취미 1
 
3.3%
Other values (3) 3
 
10.0%

Length

2023-12-10T23:00:38.809736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반휴게음식 9
26.5%
보건위생 3
 
8.8%
레저업소 3
 
8.8%
레져용품 3
 
8.8%
자동차정비 2
 
5.9%
유지 2
 
5.9%
음료식품 2
 
5.9%
유통업 2
 
5.9%
영리 2
 
5.9%
자동차판매 1
 
2.9%
Other values (5) 5
14.7%

가맹점우편번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14776
Minimum10038
Maximum18442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:38.990912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10038
5-th percentile10314.8
Q112995.25
median14620
Q317040.5
95-th percentile18393.05
Maximum18442
Range8404
Interquartile range (IQR)4045.25

Descriptive statistics

Standard deviation2723.028
Coefficient of variation (CV)0.18428722
Kurtosis-1.1175095
Mean14776
Median Absolute Deviation (MAD)2409
Skewness-0.27156159
Sum443280
Variance7414881.4
MonotonicityNot monotonic
2023-12-10T23:00:39.162486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10205 1
 
3.3%
12911 1
 
3.3%
14714 1
 
3.3%
16898 1
 
3.3%
18434 1
 
3.3%
17814 1
 
3.3%
16827 1
 
3.3%
15015 1
 
3.3%
14102 1
 
3.3%
17006 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10038 1
3.3%
10205 1
3.3%
10449 1
3.3%
10915 1
3.3%
11184 1
3.3%
11678 1
3.3%
11740 1
3.3%
12911 1
3.3%
13248 1
3.3%
13929 1
3.3%
ValueCountFrequency (%)
18442 1
3.3%
18434 1
3.3%
18343 1
3.3%
18255 1
3.3%
18136 1
3.3%
17814 1
3.3%
17085 1
3.3%
17052 1
3.3%
17006 1
3.3%
16898 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 rowNONE

Common Values

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

Length

2023-12-10T23:00:39.330556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:39.477200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 28
93.3%
none 2
 
6.7%

시군구명
Text

MISSING 

Distinct20
Distinct (%)71.4%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T23:00:39.698079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.6785714
Min length3

Characters and Unicode

Total characters131
Distinct characters38
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

Unique16 ?
Unique (%)57.1%

Sample

1st row고양시 일산서구
2nd row고양시 일산동구
3rd row부천시
4th row광명시
5th row화성시
ValueCountFrequency (%)
용인시 5
 
12.8%
화성시 4
 
10.3%
기흥구 3
 
7.7%
부천시 3
 
7.7%
안양시 3
 
7.7%
고양시 2
 
5.1%
동안구 2
 
5.1%
의정부시 1
 
2.6%
일산서구 1
 
2.6%
하남시 1
 
2.6%
Other values (14) 14
35.9%
2023-12-10T23:00:40.195967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
22.1%
11
 
8.4%
구 11
 
8.4%
인 6
 
4.6%
안 6
 
4.6%
용 5
 
3.8%
양 5
 
3.8%
성 5
 
3.8%
화 4
 
3.1%
천 4
 
3.1%
Other values (28) 45
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 120
91.6%
Space Separator 11
 
8.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
24.2%
구 11
 
9.2%
인 6
 
5.0%
안 6
 
5.0%
용 5
 
4.2%
양 5
 
4.2%
성 5
 
4.2%
화 4
 
3.3%
천 4
 
3.3%
부 4
 
3.3%
Other values (27) 41
34.2%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 120
91.6%
Common 11
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
24.2%
구 11
 
9.2%
인 6
 
5.0%
안 6
 
5.0%
용 5
 
4.2%
양 5
 
4.2%
성 5
 
4.2%
화 4
 
3.3%
천 4
 
3.3%
부 4
 
3.3%
Other values (27) 41
34.2%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 120
91.6%
ASCII 11
 
8.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
24.2%
구 11
 
9.2%
인 6
 
5.0%
안 6
 
5.0%
용 5
 
4.2%
양 5
 
4.2%
성 5
 
4.2%
화 4
 
3.3%
천 4
 
3.3%
부 4
 
3.3%
Other values (27) 41
34.2%
ASCII
ValueCountFrequency (%)
11
100.0%

읍면동명
Text

MISSING 

Distinct26
Distinct (%)92.9%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-10T23:00:40.515688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9642857
Min length2

Characters and Unicode

Total characters83
Distinct characters46
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

Unique24 ?
Unique (%)85.7%

Sample

1st row가좌동
2nd row백석동
3rd row작동
4th row하안동
5th row반송동
ValueCountFrequency (%)
관양동 2
 
7.1%
반송동 2
 
7.1%
가좌동 1
 
3.6%
망월동 1
 
3.6%
보정동 1
 
3.6%
포승읍 1
 
3.6%
동천동 1
 
3.6%
정왕동 1
 
3.6%
중동 1
 
3.6%
안양동 1
 
3.6%
Other values (16) 16
57.1%
2023-12-10T23:00:41.120649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
31.3%
양 4
 
4.8%
안 3
 
3.6%
관 2
 
2.4%
반 2
 
2.4%
송 2
 
2.4%
가 2
 
2.4%
중 2
 
2.4%
읍 2
 
2.4%
정 2
 
2.4%
Other values (36) 36
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
31.3%
양 4
 
4.8%
안 3
 
3.6%
관 2
 
2.4%
반 2
 
2.4%
송 2
 
2.4%
가 2
 
2.4%
중 2
 
2.4%
읍 2
 
2.4%
정 2
 
2.4%
Other values (36) 36
43.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
31.3%
양 4
 
4.8%
안 3
 
3.6%
관 2
 
2.4%
반 2
 
2.4%
송 2
 
2.4%
가 2
 
2.4%
중 2
 
2.4%
읍 2
 
2.4%
정 2
 
2.4%
Other values (36) 36
43.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
31.3%
양 4
 
4.8%
안 3
 
3.6%
관 2
 
2.4%
반 2
 
2.4%
송 2
 
2.4%
가 2
 
2.4%
중 2
 
2.4%
읍 2
 
2.4%
정 2
 
2.4%
Other values (36) 36
43.4%

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.919
Minimum0
Maximum37.85
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:41.360226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.6428
Q137.22525
median37.368
Q337.51
95-th percentile37.7492
Maximum37.85
Range37.85
Interquartile range (IQR)0.28475

Descriptive statistics

Standard deviation9.4942015
Coefficient of variation (CV)0.27189214
Kurtosis12.192697
Mean34.919
Median Absolute Deviation (MAD)0.144
Skewness-3.6571077
Sum1047.57
Variance90.139862
MonotonicityNot monotonic
2023-12-10T23:00:41.560091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
37.7 1
 
3.3%
37.664 1
 
3.3%
37.481 1
 
3.3%
37.32 1
 
3.3%
37.208 1
 
3.3%
36.984 1
 
3.3%
37.338 1
 
3.3%
37.346 1
 
3.3%
37.404 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
36.984 1
3.3%
37.15 1
3.3%
37.193 1
3.3%
37.208 1
3.3%
37.221 1
3.3%
37.223 1
3.3%
37.232 1
3.3%
37.235 1
3.3%
37.277 1
3.3%
ValueCountFrequency (%)
37.85 1
3.3%
37.751 1
3.3%
37.747 1
3.3%
37.7 1
3.3%
37.664 1
3.3%
37.639 1
3.3%
37.572 1
3.3%
37.511 1
3.3%
37.507 1
3.3%
37.481 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.49193
Minimum0
Maximum127.202
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:41.742857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56.9664
Q1126.78225
median126.9535
Q3127.094
95-th percentile127.1803
Maximum127.202
Range127.202
Interquartile range (IQR)0.31175

Descriptive statistics

Standard deviation32.210098
Coefficient of variation (CV)0.27183368
Kurtosis12.205837
Mean118.49193
Median Absolute Deviation (MAD)0.1565
Skewness-3.6598338
Sum3554.758
Variance1037.4904
MonotonicityNot monotonic
2023-12-10T23:00:41.978954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
126.726 1
 
3.3%
126.592 1
 
3.3%
126.779 1
 
3.3%
127.111 1
 
3.3%
127.063 1
 
3.3%
126.878 1
 
3.3%
127.101 1
 
3.3%
126.688 1
 
3.3%
126.956 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
126.592 1
3.3%
126.688 1
3.3%
126.726 1
3.3%
126.767 1
3.3%
126.779 1
3.3%
126.781 1
3.3%
126.786 1
3.3%
126.815 1
3.3%
126.838 1
3.3%
ValueCountFrequency (%)
127.202 1
3.3%
127.192 1
3.3%
127.166 1
3.3%
127.154 1
3.3%
127.152 1
3.3%
127.111 1
3.3%
127.109 1
3.3%
127.101 1
3.3%
127.073 1
3.3%
127.068 1
3.3%

결제상품명
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing23
Missing (%)76.7%
Memory size372.0 B
2023-12-10T23:00:42.318478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length8.1428571
Min length6

Characters and Unicode

Total characters57
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st row고양페이카드
2nd row의왕사랑상품권
3rd row용인와이페이
4th row안양사랑페이
5th row평택사랑카드(통합)
ValueCountFrequency (%)
고양페이카드 1
14.3%
의왕사랑상품권 1
14.3%
용인와이페이 1
14.3%
안양사랑페이 1
14.3%
평택사랑카드(통합 1
14.3%
행복화성지역화폐(통합 1
14.3%
용인와이페이(통합 1
14.3%
2023-12-10T23:00:43.216455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 6
 
10.5%
페 4
 
7.0%
( 3
 
5.3%
합 3
 
5.3%
) 3
 
5.3%
사 3
 
5.3%
랑 3
 
5.3%
통 3
 
5.3%
용 2
 
3.5%
양 2
 
3.5%
Other values (20) 25
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51
89.5%
Open Punctuation 3
 
5.3%
Close Punctuation 3
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 6
 
11.8%
페 4
 
7.8%
합 3
 
5.9%
사 3
 
5.9%
랑 3
 
5.9%
통 3
 
5.9%
용 2
 
3.9%
양 2
 
3.9%
인 2
 
3.9%
와 2
 
3.9%
Other values (18) 21
41.2%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51
89.5%
Common 6
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 6
 
11.8%
페 4
 
7.8%
합 3
 
5.9%
사 3
 
5.9%
랑 3
 
5.9%
통 3
 
5.9%
용 2
 
3.9%
양 2
 
3.9%
인 2
 
3.9%
와 2
 
3.9%
Other values (18) 21
41.2%
Common
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51
89.5%
ASCII 6
 
10.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 6
 
11.8%
페 4
 
7.8%
합 3
 
5.9%
사 3
 
5.9%
랑 3
 
5.9%
통 3
 
5.9%
용 2
 
3.9%
양 2
 
3.9%
인 2
 
3.9%
와 2
 
3.9%
Other values (18) 21
41.2%
ASCII
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

사용여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
23 
True
7 
ValueCountFrequency (%)
False 23
76.7%
True 7
 
23.3%
2023-12-10T23:00:43.520045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

결제금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11138.667
Minimum0
Maximum84000
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:43.671377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73154
Maximum84000
Range84000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24584.025
Coefficient of variation (CV)2.2070887
Kurtosis3.7770809
Mean11138.667
Median Absolute Deviation (MAD)0
Skewness2.1943757
Sum334160
Variance6.0437428 × 108
MonotonicityNot monotonic
2023-12-10T23:00:43.899860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 23
76.7%
39000 1
 
3.3%
34900 1
 
3.3%
84000 1
 
3.3%
62990 1
 
3.3%
81470 1
 
3.3%
2800 1
 
3.3%
29000 1
 
3.3%
ValueCountFrequency (%)
0 23
76.7%
2800 1
 
3.3%
29000 1
 
3.3%
34900 1
 
3.3%
39000 1
 
3.3%
62990 1
 
3.3%
81470 1
 
3.3%
84000 1
 
3.3%
ValueCountFrequency (%)
84000 1
 
3.3%
81470 1
 
3.3%
62990 1
 
3.3%
39000 1
 
3.3%
34900 1
 
3.3%
29000 1
 
3.3%
2800 1
 
3.3%
0 23
76.7%

Interactions

2023-12-10T23:00:35.876143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:30.910705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:31.917599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:32.888497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:33.644159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:34.972712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:36.050417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:31.116531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:32.052734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:33.001080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:33.773389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:35.117821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:36.188099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:31.260362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:32.297868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:33.150013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:33.917560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:35.313010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:36.341770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:31.437823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:32.464952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:33.266595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:34.429705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:35.447458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:36.536974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:31.576144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:32.621541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:33.411927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:34.690347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:35.593660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:36.726528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:31.720195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:32.759588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:33.533778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:34.834205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:35.727889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:00:44.087069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
가맹점번호1.0000.2920.5810.6270.2130.6190.9160.2130.2131.0000.2920.000
결제상품ID0.2921.0000.4880.0000.0000.0000.0000.0000.000NaN0.9850.741
가맹점업종명0.5810.4881.0000.5150.6720.8290.9180.6720.6721.0000.3440.380
가맹점우편번호0.6270.0000.5151.0001.0001.0001.0001.0001.0001.0000.0000.399
시도명0.2130.0000.6721.0001.000NaNNaN0.9060.906NaN0.0000.000
시군구명0.6190.0000.8291.000NaN1.0001.000NaNNaN1.0000.0000.000
읍면동명0.9160.0000.9181.000NaN1.0001.000NaNNaN1.0000.0000.000
위도0.2130.0000.6721.0000.906NaNNaN1.0000.906NaN0.0000.000
경도0.2130.0000.6721.0000.906NaNNaN0.9061.000NaN0.0000.000
결제상품명1.000NaN1.0001.000NaN1.0001.000NaNNaN1.000NaN1.000
사용여부0.2920.9850.3440.0000.0000.0000.0000.0000.000NaN1.0000.757
결제금액0.0000.7410.3800.3990.0000.0000.0000.0000.0001.0000.7571.000
2023-12-10T23:00:44.356667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부시도명가맹점업종명
사용여부1.0000.0000.223
시도명0.0001.0000.487
가맹점업종명0.2230.4871.000
2023-12-10T23:00:44.561400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가맹점번호결제상품ID가맹점우편번호위도경도결제금액가맹점업종명시도명사용여부
가맹점번호1.000-0.1890.227-0.0720.2860.2630.2470.1440.000
결제상품ID-0.1891.000-0.1460.150-0.110-0.9850.2230.0000.903
가맹점우편번호0.227-0.1461.000-0.7890.3360.1910.2050.8450.000
위도-0.0720.150-0.7891.000-0.072-0.1930.4870.7210.000
경도0.286-0.1100.336-0.0721.0000.1370.4870.7210.000
결제금액0.263-0.9850.191-0.1930.1371.0000.1290.0000.841
가맹점업종명0.2470.2230.2050.4870.4870.1291.0000.4870.223
시도명0.1440.0000.8450.7210.7210.0000.4871.0000.000
사용여부0.0000.9030.0000.0000.0000.8410.2230.0001.000

Missing values

2023-12-10T23:00:36.912996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:00:37.210106image/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.
2023-12-10T23:00:37.408988image/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
02021-07-01700001681999999999999999자동차정비 유지10205경기도고양시 일산서구가좌동37.7126.726<NA>N0
12021-07-01700000804140000018000일반휴게음식10449경기도고양시 일산동구백석동37.639126.786고양페이카드Y39000
22021-07-01700002272999999999999999일반휴게음식14477경기도부천시작동37.511126.815<NA>N0
32021-07-01700004947999999999999999자동차판매14303경기도광명시하안동37.458126.876<NA>N0
42021-07-01700004075999999999999999의류15382NONE<NA><NA>0.00.0<NA>N0
52021-07-01700005536999999999999999보건위생18442경기도화성시반송동37.193127.073<NA>N0
62021-07-01700005739140000026000일반휴게음식16086경기도의왕시삼동37.321126.951의왕사랑상품권Y34900
72021-07-01700005788999999999999999레저업소17052경기도용인시 처인구김량장동37.235127.202<NA>N0
82021-07-01700006053999999999999999보건위생11740NONE<NA><NA>0.00.0<NA>N0
92021-07-01700007677999999999999999일반휴게음식18136경기도오산시오산동37.15127.068<NA>N0
정책일간결제일자가맹점번호결제상품ID가맹점업종명가맹점우편번호시도명시군구명읍면동명위도경도결제상품명사용여부결제금액
202021-07-01700014797999999999999999레져용품17085경기도용인시 기흥구공세동37.232127.109<NA>N0
212021-07-01700015767999999999999999신변잡화14086경기도안양시 만안구안양동37.39126.936<NA>N0
222021-07-01700016238140000046000학원17006경기도용인시 기흥구중동37.277127.152용인와이페이Y84000
232021-07-01700016480140000034000유통업 영리14102경기도안양시 동안구관양동37.404126.956안양사랑페이Y62990
242021-07-01700017353999999999999999일반휴게음식15015경기도시흥시정왕동37.346126.688<NA>N0
252021-07-01700019316999999999999999음료식품16827경기도용인시 수지구동천동37.338127.101<NA>N0
262021-07-01700017752140000059000유통업 영리17814경기도평택시포승읍36.984126.878평택사랑카드(통합)Y81470
272021-07-01700019736140000092000레저업소18434경기도화성시반송동37.208127.063행복화성지역화폐(통합)Y2800
282021-07-01700020487140000065000보건위생16898경기도용인시 기흥구보정동37.32127.111용인와이페이(통합)Y29000
292021-07-01700023216999999999999999레저업소14714경기도부천시심곡본동37.481126.779<NA>N0