Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.7 KiB
Average record size in memory83.3 B

Variable types

Categorical5
Text2
Numeric3

Dataset

Description샘플 데이터
Author신한카드
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=55

Reproduction

Analysis started2023-12-10 14:56:26.549850
Analysis finished2023-12-10 14:56:30.239518
Duration3.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고객주소시군구(CUSTM_GU_NM)
Categorical

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
관악구
41 
마포구
38 
양천구
37 
강서구
 
28
성북구
 
27
Other values (20)
329 

Length

Max length4
Median length3
Mean length3.096
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영등포구
2nd row금천구
3rd row용산구
4th row성북구
5th row서대문구

Common Values

ValueCountFrequency (%)
관악구 41
 
8.2%
마포구 38
 
7.6%
양천구 37
 
7.4%
강서구 28
 
5.6%
성북구 27
 
5.4%
서초구 25
 
5.0%
동작구 22
 
4.4%
송파구 22
 
4.4%
은평구 21
 
4.2%
강동구 21
 
4.2%
Other values (15) 218
43.6%

Length

2023-12-10T23:56:30.395929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
관악구 41
 
8.2%
마포구 38
 
7.6%
양천구 37
 
7.4%
강서구 28
 
5.6%
성북구 27
 
5.4%
서초구 25
 
5.0%
동작구 22
 
4.4%
송파구 22
 
4.4%
은평구 21
 
4.2%
강동구 21
 
4.2%
Other values (15) 218
43.6%

고객주소행정동(CUSTM_DONG_NM)
Text

Distinct279
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:56:30.964620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.778
Min length2

Characters and Unicode

Total characters1889
Distinct characters172
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

Unique150 ?
Unique (%)30.0%

Sample

1st row남현동
2nd row사당3동
3rd row삼선동
4th row상암동
5th row고척2동
ValueCountFrequency (%)
공덕동 7
 
1.4%
인헌동 6
 
1.2%
신정3동 5
 
1.0%
우장산동 5
 
1.0%
대방동 5
 
1.0%
장안1동 5
 
1.0%
금호2.3가동 4
 
0.8%
길음1동 4
 
0.8%
양재1동 4
 
0.8%
신림동 4
 
0.8%
Other values (269) 451
90.2%
2023-12-10T23:56:31.766995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 502
26.6%
1 136
 
7.2%
2 92
 
4.9%
3 55
 
2.9%
신 40
 
2.1%
방 30
 
1.6%
화 23
 
1.2%
양 22
 
1.2%
가 22
 
1.2%
산 21
 
1.1%
Other values (162) 946
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1550
82.1%
Decimal Number 329
 
17.4%
Other Punctuation 10
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 502
32.4%
신 40
 
2.6%
방 30
 
1.9%
화 23
 
1.5%
양 22
 
1.4%
가 22
 
1.4%
산 21
 
1.4%
정 19
 
1.2%
장 19
 
1.2%
상 19
 
1.2%
Other values (151) 833
53.7%
Decimal Number
ValueCountFrequency (%)
1 136
41.3%
2 92
28.0%
3 55
16.7%
4 15
 
4.6%
7 10
 
3.0%
5 10
 
3.0%
6 6
 
1.8%
8 3
 
0.9%
9 1
 
0.3%
0 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1550
82.1%
Common 339
 
17.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 502
32.4%
신 40
 
2.6%
방 30
 
1.9%
화 23
 
1.5%
양 22
 
1.4%
가 22
 
1.4%
산 21
 
1.4%
정 19
 
1.2%
장 19
 
1.2%
상 19
 
1.2%
Other values (151) 833
53.7%
Common
ValueCountFrequency (%)
1 136
40.1%
2 92
27.1%
3 55
16.2%
4 15
 
4.4%
7 10
 
2.9%
5 10
 
2.9%
. 10
 
2.9%
6 6
 
1.8%
8 3
 
0.9%
9 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1550
82.1%
ASCII 339
 
17.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 502
32.4%
신 40
 
2.6%
방 30
 
1.9%
화 23
 
1.5%
양 22
 
1.4%
가 22
 
1.4%
산 21
 
1.4%
정 19
 
1.2%
장 19
 
1.2%
상 19
 
1.2%
Other values (151) 833
53.7%
ASCII
ValueCountFrequency (%)
1 136
40.1%
2 92
27.1%
3 55
16.2%
4 15
 
4.4%
7 10
 
2.9%
5 10
 
2.9%
. 10
 
2.9%
6 6
 
1.8%
8 3
 
0.9%
9 1
 
0.3%

가맹점주소시군구(STORE_GU_NM)
Categorical

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
종로구
57 
마포구
48 
강남구
44 
용산구
35 
서초구
33 
Other values (20)
283 

Length

Max length4
Median length3
Mean length3.084
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중구
2nd row중랑구
3rd row종로구
4th row송파구
5th row광진구

Common Values

ValueCountFrequency (%)
종로구 57
 
11.4%
마포구 48
 
9.6%
강남구 44
 
8.8%
용산구 35
 
7.0%
서초구 33
 
6.6%
영등포구 26
 
5.2%
서대문구 26
 
5.2%
중구 25
 
5.0%
관악구 22
 
4.4%
광진구 20
 
4.0%
Other values (15) 164
32.8%

Length

2023-12-10T23:56:32.027305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 57
 
11.4%
마포구 48
 
9.6%
강남구 44
 
8.8%
용산구 35
 
7.0%
서초구 33
 
6.6%
영등포구 26
 
5.2%
서대문구 26
 
5.2%
중구 25
 
5.0%
관악구 22
 
4.4%
광진구 20
 
4.0%
Other values (15) 164
32.8%

가맹점주소행정동(STORE_DONG_NM)
Text

Distinct188
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:56:32.503733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length4.072
Min length2

Characters and Unicode

Total characters2036
Distinct characters152
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

Unique91 ?
Unique (%)18.2%

Sample

1st row아현동
2nd row중곡4동
3rd row방배2동
4th row상봉2동
5th row잠실6동
ValueCountFrequency (%)
종로1.2.3.4가동 28
 
5.6%
신촌동 22
 
4.4%
서교동 19
 
3.8%
역삼1동 15
 
3.0%
반포4동 9
 
1.8%
삼성1동 8
 
1.6%
화양동 8
 
1.6%
혜화동 8
 
1.6%
회현동 7
 
1.4%
여의동 7
 
1.4%
Other values (178) 369
73.8%
2023-12-10T23:56:33.209840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 505
24.8%
1 123
 
6.0%
2 98
 
4.8%
. 90
 
4.4%
3 57
 
2.8%
가 57
 
2.8%
4 52
 
2.6%
로 50
 
2.5%
신 43
 
2.1%
서 40
 
2.0%
Other values (142) 921
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1595
78.3%
Decimal Number 351
 
17.2%
Other Punctuation 90
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 505
31.7%
가 57
 
3.6%
로 50
 
3.1%
신 43
 
2.7%
서 40
 
2.5%
종 35
 
2.2%
삼 34
 
2.1%
성 29
 
1.8%
화 25
 
1.6%
촌 24
 
1.5%
Other values (133) 753
47.2%
Decimal Number
ValueCountFrequency (%)
1 123
35.0%
2 98
27.9%
3 57
16.2%
4 52
14.8%
5 10
 
2.8%
6 9
 
2.6%
8 1
 
0.3%
7 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1595
78.3%
Common 441
 
21.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 505
31.7%
가 57
 
3.6%
로 50
 
3.1%
신 43
 
2.7%
서 40
 
2.5%
종 35
 
2.2%
삼 34
 
2.1%
성 29
 
1.8%
화 25
 
1.6%
촌 24
 
1.5%
Other values (133) 753
47.2%
Common
ValueCountFrequency (%)
1 123
27.9%
2 98
22.2%
. 90
20.4%
3 57
12.9%
4 52
11.8%
5 10
 
2.3%
6 9
 
2.0%
8 1
 
0.2%
7 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1595
78.3%
ASCII 441
 
21.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 505
31.7%
가 57
 
3.6%
로 50
 
3.1%
신 43
 
2.7%
서 40
 
2.5%
종 35
 
2.2%
삼 34
 
2.1%
성 29
 
1.8%
화 25
 
1.6%
촌 24
 
1.5%
Other values (133) 753
47.2%
ASCII
ValueCountFrequency (%)
1 123
27.9%
2 98
22.2%
. 90
20.4%
3 57
12.9%
4 52
11.8%
5 10
 
2.3%
6 9
 
2.0%
8 1
 
0.2%
7 1
 
0.2%

업종(UPJONG_NM)
Categorical

Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
단품요리 전문
169 
가정식
116 
커피
36 
햄버거
25 
중식
21 
Other values (16)
133 

Length

Max length9
Median length8
Mean length4.336
Min length1

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row중식
2nd row단품요리 전문
3rd row커피
4th row가정식
5th row분식

Common Values

ValueCountFrequency (%)
단품요리 전문 169
33.8%
가정식 116
23.2%
커피 36
 
7.2%
햄버거 25
 
5.0%
중식 21
 
4.2%
분식 21
 
4.2%
양식 21
 
4.2%
베이커리 21
 
4.2%
일식 17
 
3.4%
피자 12
 
2.4%
Other values (11) 41
 
8.2%

Length

2023-12-10T23:56:33.557243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단품요리 169
25.2%
전문 169
25.2%
가정식 116
17.3%
커피 36
 
5.4%
햄버거 25
 
3.7%
중식 21
 
3.1%
분식 21
 
3.1%
양식 21
 
3.1%
베이커리 21
 
3.1%
일식 17
 
2.5%
Other values (13) 55
 
8.2%

일별(DATE)
Real number (ℝ)

Distinct404
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20181178
Minimum20170106
Maximum20191229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:33.833057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170106
5-th percentile20170221
Q120171112
median20180722
Q320190419
95-th percentile20191118
Maximum20191229
Range21123
Interquartile range (IQR)19307.25

Descriptive statistics

Standard deviation8019.5008
Coefficient of variation (CV)0.00039737526
Kurtosis-1.427018
Mean20181178
Median Absolute Deviation (MAD)9684
Skewness-0.092888424
Sum1.0090589 × 1010
Variance64312393
MonotonicityNot monotonic
2023-12-10T23:56:34.107070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191013 3
 
0.6%
20170107 3
 
0.6%
20180605 3
 
0.6%
20191213 3
 
0.6%
20180530 3
 
0.6%
20170910 3
 
0.6%
20191225 3
 
0.6%
20181212 3
 
0.6%
20180512 3
 
0.6%
20170725 3
 
0.6%
Other values (394) 470
94.0%
ValueCountFrequency (%)
20170106 1
 
0.2%
20170107 3
0.6%
20170114 1
 
0.2%
20170122 1
 
0.2%
20170123 2
0.4%
20170124 2
0.4%
20170126 2
0.4%
20170127 1
 
0.2%
20170203 1
 
0.2%
20170205 1
 
0.2%
ValueCountFrequency (%)
20191229 1
 
0.2%
20191228 1
 
0.2%
20191227 1
 
0.2%
20191225 3
0.6%
20191224 2
0.4%
20191218 1
 
0.2%
20191215 1
 
0.2%
20191214 2
0.4%
20191213 3
0.6%
20191210 2
0.4%

성별(GENDER)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
F
266 
M
234 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 266
53.2%
M 234
46.8%

Length

2023-12-10T23:56:34.367290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:34.548178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 266
53.2%
m 234
46.8%

연령대별(AGE_GR)
Categorical

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
20대후
138 
30대초
86 
30대후
47 
40대후
44 
20대초
42 
Other values (6)
143 

Length

Max length5
Median length4
Mean length4.016
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20대후
2nd row50대후
3rd row50대초
4th row40대후
5th row50대후

Common Values

ValueCountFrequency (%)
20대후 138
27.6%
30대초 86
17.2%
30대후 47
 
9.4%
40대후 44
 
8.8%
20대초 42
 
8.4%
50대후 39
 
7.8%
50대초 35
 
7.0%
40대초 35
 
7.0%
60대초 17
 
3.4%
60대후 9
 
1.8%

Length

2023-12-10T23:56:34.768526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20대후 138
27.6%
30대초 86
17.2%
30대후 47
 
9.4%
40대후 44
 
8.8%
20대초 42
 
8.4%
50대후 39
 
7.8%
50대초 35
 
7.0%
40대초 35
 
7.0%
60대초 17
 
3.4%
60대후 9
 
1.8%

카드이용금액(USE_AMT)
Real number (ℝ)

Distinct472
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2375028.1
Minimum9780
Maximum65729664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:35.011310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9780
5-th percentile135702
Q1494431
median1065582
Q32704680
95-th percentile8039401.2
Maximum65729664
Range65719884
Interquartile range (IQR)2210249

Descriptive statistics

Standard deviation4244127.1
Coefficient of variation (CV)1.7869798
Kurtosis104.06192
Mean2375028.1
Median Absolute Deviation (MAD)742924.5
Skewness8.1122011
Sum1.187514 × 109
Variance1.8012615 × 1013
MonotonicityNot monotonic
2023-12-10T23:56:35.290977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362160.0 3
 
0.6%
545400.0 3
 
0.6%
58200.0 2
 
0.4%
273240.0 2
 
0.4%
8181000.0 2
 
0.4%
4363200.0 2
 
0.4%
1071360.0 2
 
0.4%
1359360.0 2
 
0.4%
993300.0 2
 
0.4%
5999400.0 2
 
0.4%
Other values (462) 478
95.6%
ValueCountFrequency (%)
9780.0 1
0.2%
15296.0 1
0.2%
21150.0 1
0.2%
21242.0 1
0.2%
27170.0 1
0.2%
41990.0 1
0.2%
44370.0 1
0.2%
47424.0 1
0.2%
49350.0 1
0.2%
49900.0 1
0.2%
ValueCountFrequency (%)
65729664.0 1
0.2%
28389888.0 1
0.2%
23652720.0 1
0.2%
20219760.0 1
0.2%
17784000.0 1
0.2%
16005600.0 1
0.2%
15649920.0 1
0.2%
15543900.0 1
0.2%
14362200.0 1
0.2%
14054040.0 1
0.2%

카드이용건수(USE_CNT)
Real number (ℝ)

Distinct255
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.21412
Minimum4.72
Maximum362.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:35.567887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.72
5-th percentile14.817
Q160.53
median126.89
Q3177.84
95-th percentile181.8
Maximum362.88
Range358.16
Interquartile range (IQR)117.31

Descriptive statistics

Standard deviation65.924466
Coefficient of variation (CV)0.54839204
Kurtosis-0.032986033
Mean120.21412
Median Absolute Deviation (MAD)51.67
Skewness0.10634379
Sum60107.06
Variance4346.0352
MonotonicityNot monotonic
2023-12-10T23:56:35.855066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181.8 29
 
5.8%
177.84 25
 
5.0%
180.36 15
 
3.0%
170.28 12
 
2.4%
181.08 12
 
2.4%
167.04 11
 
2.2%
176.76 10
 
2.0%
182.52 10
 
2.0%
178.56 9
 
1.8%
169.92 8
 
1.6%
Other values (245) 359
71.8%
ValueCountFrequency (%)
4.72 1
0.2%
4.92 1
0.2%
4.94 1
0.2%
4.96 1
0.2%
4.99 1
0.2%
5.04 1
0.2%
5.05 1
0.2%
9.82 1
0.2%
9.92 1
0.2%
10.02 1
0.2%
ValueCountFrequency (%)
362.88 1
0.2%
355.68 1
0.2%
354.24 1
0.2%
351.36 1
0.2%
340.56 1
0.2%
281.68 1
0.2%
276.65 1
0.2%
227.25 1
0.2%
227.04 1
0.2%
200.4 1
0.2%

Interactions

2023-12-10T23:56:28.837122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:27.711669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:28.256281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:29.014576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:27.882911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:28.472940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:29.549379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:28.072173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:28.654790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:56:36.058485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객주소시군구(CUSTM_GU_NM)가맹점주소시군구(STORE_GU_NM)업종(UPJONG_NM)일별(DATE)성별(GENDER)연령대별(AGE_GR)카드이용금액(USE_AMT)카드이용건수(USE_CNT)
고객주소시군구(CUSTM_GU_NM)1.0000.0570.1620.0000.0000.1420.1020.131
가맹점주소시군구(STORE_GU_NM)0.0571.0000.1840.1430.0880.0430.0000.363
업종(UPJONG_NM)0.1620.1841.0000.0000.0000.0000.0000.000
일별(DATE)0.0000.1430.0001.0000.1260.0000.0000.059
성별(GENDER)0.0000.0880.0000.1261.0000.0530.0000.000
연령대별(AGE_GR)0.1420.0430.0000.0000.0531.0000.0000.067
카드이용금액(USE_AMT)0.1020.0000.0000.0000.0000.0001.0000.306
카드이용건수(USE_CNT)0.1310.3630.0000.0590.0000.0670.3061.000
2023-12-10T23:56:36.292501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객주소시군구(CUSTM_GU_NM)연령대별(AGE_GR)업종(UPJONG_NM)성별(GENDER)가맹점주소시군구(STORE_GU_NM)
고객주소시군구(CUSTM_GU_NM)1.0000.0480.0430.0000.006
연령대별(AGE_GR)0.0481.0000.0000.0490.011
업종(UPJONG_NM)0.0430.0001.0000.0000.049
성별(GENDER)0.0000.0490.0001.0000.073
가맹점주소시군구(STORE_GU_NM)0.0060.0110.0490.0731.000
2023-12-10T23:56:36.501440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일별(DATE)카드이용금액(USE_AMT)카드이용건수(USE_CNT)고객주소시군구(CUSTM_GU_NM)가맹점주소시군구(STORE_GU_NM)업종(UPJONG_NM)성별(GENDER)연령대별(AGE_GR)
일별(DATE)1.0000.009-0.0220.0000.0720.0000.0810.000
카드이용금액(USE_AMT)0.0091.000-0.0730.0450.0000.0000.0000.000
카드이용건수(USE_CNT)-0.022-0.0731.0000.0480.1450.0000.0000.030
고객주소시군구(CUSTM_GU_NM)0.0000.0450.0481.0000.0060.0430.0000.048
가맹점주소시군구(STORE_GU_NM)0.0720.0000.1450.0061.0000.0490.0730.011
업종(UPJONG_NM)0.0000.0000.0000.0430.0491.0000.0000.000
성별(GENDER)0.0810.0000.0000.0000.0730.0001.0000.049
연령대별(AGE_GR)0.0000.0000.0300.0480.0110.0000.0491.000

Missing values

2023-12-10T23:56:29.822130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:56:30.123599image/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

고객주소시군구(CUSTM_GU_NM)고객주소행정동(CUSTM_DONG_NM)가맹점주소시군구(STORE_GU_NM)가맹점주소행정동(STORE_DONG_NM)업종(UPJONG_NM)일별(DATE)성별(GENDER)연령대별(AGE_GR)카드이용금액(USE_AMT)카드이용건수(USE_CNT)
0영등포구남현동중구아현동중식20170313F20대후169920.0114.72
1금천구사당3동중랑구중곡4동단품요리 전문20181209M50대후1307800.0156.09
2용산구삼선동종로구방배2동커피20190604F50대초496860.0172.08
3성북구상암동송파구상봉2동가정식20170506F40대후8184960.039.68
4서대문구고척2동광진구잠실6동분식20171010M50대후353500.083.81
5동작구면목7동구로구창2동단품요리 전문20171109F20대후3741660.028.2
6관악구진관동마포구부암동가정식20180713M60대초4148280.039.52
7양천구서원동중랑구중화2동단품요리 전문20170505M20대후4044096.0143.26
8은평구이화동종로구정릉2동단품요리 전문20171121F30대후250560.078.72
9마포구잠실6동강남구여의동양식20180328M30대후449100.0182.52
고객주소시군구(CUSTM_GU_NM)고객주소행정동(CUSTM_DONG_NM)가맹점주소시군구(STORE_GU_NM)가맹점주소행정동(STORE_DONG_NM)업종(UPJONG_NM)일별(DATE)성별(GENDER)연령대별(AGE_GR)카드이용금액(USE_AMT)카드이용건수(USE_CNT)
490성북구개봉3동노원구상계1동커피20190201M30대초1503360.0174.6
491도봉구목3동영등포구노량진1동단품요리 전문20191014F30대초3150000.014.19
492중랑구방배3동광진구발산1동동남아/인도음식20170212M30대후1166784.070.56
493강남구방학3동중랑구방이2동일식20180104F20대후1054690.059.52
494관악구월곡2동광진구신사동커피20180316F40대초1892880.0169.2
495동작구신대방1동강동구압구정동구내식당/푸드코트20191207F20대초273240.0181.8
496성북구청량리동구로구목1동분식20170516M20대초1818000.0181.8
497강서구필동성동구창4동햄버거20190118M20대초802445.0181.8
498강북구장안1동마포구이문1동가정식20190912F20대후5251320.077.6
499송파구화양동도봉구신촌동단품요리 전문20171129F50대초651456.0227.04