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

Alerts

업종(UPJONG_NM) is highly imbalanced (65.7%)Imbalance

Reproduction

Analysis started2023-12-10 14:56:15.182085
Analysis finished2023-12-10 14:56:18.333536
Duration3.15 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
강남구
38 
송파구
36 
관악구
34 
노원구
 
30
은평구
 
26
Other values (20)
336 

Length

Max length4
Median length3
Mean length3.09
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금천구
2nd row양천구
3rd row구로구
4th row송파구
5th row동작구

Common Values

ValueCountFrequency (%)
강남구 38
 
7.6%
송파구 36
 
7.2%
관악구 34
 
6.8%
노원구 30
 
6.0%
은평구 26
 
5.2%
강서구 25
 
5.0%
광진구 24
 
4.8%
동작구 24
 
4.8%
용산구 23
 
4.6%
서초구 21
 
4.2%
Other values (15) 219
43.8%

Length

2023-12-10T23:56:18.459486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 38
 
7.6%
송파구 36
 
7.2%
관악구 34
 
6.8%
노원구 30
 
6.0%
은평구 26
 
5.2%
강서구 25
 
5.0%
광진구 24
 
4.8%
동작구 24
 
4.8%
용산구 23
 
4.6%
서초구 21
 
4.2%
Other values (15) 219
43.8%

고객주소행정동(CUSTM_DONG_NM)
Text

Distinct271
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:56:18.876770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.742
Min length2

Characters and Unicode

Total characters1871
Distinct characters168
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

Unique120 ?
Unique (%)24.0%

Sample

1st row독산3동
2nd row신정7동
3rd row상일동
4th row군자동
5th row광장동
ValueCountFrequency (%)
잠원동 6
 
1.2%
연희동 6
 
1.2%
신도림동 5
 
1.0%
신정1동 5
 
1.0%
대흥동 5
 
1.0%
방학3동 5
 
1.0%
상계1동 5
 
1.0%
난곡동 5
 
1.0%
가산동 5
 
1.0%
군자동 4
 
0.8%
Other values (261) 449
89.8%
2023-12-10T23:56:19.609855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 504
26.9%
1 114
 
6.1%
2 109
 
5.8%
3 45
 
2.4%
신 34
 
1.8%
계 27
 
1.4%
상 25
 
1.3%
가 24
 
1.3%
4 24
 
1.3%
방 23
 
1.2%
Other values (158) 942
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1543
82.5%
Decimal Number 323
 
17.3%
Other Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 504
32.7%
신 34
 
2.2%
계 27
 
1.7%
상 25
 
1.6%
가 24
 
1.6%
방 23
 
1.5%
산 21
 
1.4%
대 21
 
1.4%
화 21
 
1.4%
원 21
 
1.4%
Other values (147) 822
53.3%
Decimal Number
ValueCountFrequency (%)
1 114
35.3%
2 109
33.7%
3 45
 
13.9%
4 24
 
7.4%
7 9
 
2.8%
5 9
 
2.8%
6 6
 
1.9%
8 5
 
1.5%
0 1
 
0.3%
9 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1543
82.5%
Common 328
 
17.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 504
32.7%
신 34
 
2.2%
계 27
 
1.7%
상 25
 
1.6%
가 24
 
1.6%
방 23
 
1.5%
산 21
 
1.4%
대 21
 
1.4%
화 21
 
1.4%
원 21
 
1.4%
Other values (147) 822
53.3%
Common
ValueCountFrequency (%)
1 114
34.8%
2 109
33.2%
3 45
 
13.7%
4 24
 
7.3%
7 9
 
2.7%
5 9
 
2.7%
6 6
 
1.8%
8 5
 
1.5%
. 5
 
1.5%
0 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1543
82.5%
ASCII 328
 
17.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 504
32.7%
신 34
 
2.2%
계 27
 
1.7%
상 25
 
1.6%
가 24
 
1.6%
방 23
 
1.5%
산 21
 
1.4%
대 21
 
1.4%
화 21
 
1.4%
원 21
 
1.4%
Other values (147) 822
53.3%
ASCII
ValueCountFrequency (%)
1 114
34.8%
2 109
33.2%
3 45
 
13.7%
4 24
 
7.3%
7 9
 
2.7%
5 9
 
2.7%
6 6
 
1.8%
8 5
 
1.5%
. 5
 
1.5%
0 1
 
0.3%

가맹점주소시군구(STORE_GU_NM)
Categorical

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
용산구
45 
강남구
44 
광진구
39 
영등포구
34 
중구
 
29
Other values (20)
309 

Length

Max length4
Median length3
Mean length3.062
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중랑구
2nd row서초구
3rd row강서구
4th row마포구
5th row노원구

Common Values

ValueCountFrequency (%)
용산구 45
 
9.0%
강남구 44
 
8.8%
광진구 39
 
7.8%
영등포구 34
 
6.8%
중구 29
 
5.8%
마포구 26
 
5.2%
종로구 25
 
5.0%
강서구 24
 
4.8%
송파구 22
 
4.4%
강동구 22
 
4.4%
Other values (15) 190
38.0%

Length

2023-12-10T23:56:19.936182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용산구 45
 
9.0%
강남구 44
 
8.8%
광진구 39
 
7.8%
영등포구 34
 
6.8%
중구 29
 
5.8%
마포구 26
 
5.2%
종로구 25
 
5.0%
강서구 24
 
4.8%
송파구 22
 
4.4%
강동구 22
 
4.4%
Other values (15) 190
38.0%

가맹점주소행정동(STORE_DONG_NM)
Text

Distinct97
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:56:20.329687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.766
Min length2

Characters and Unicode

Total characters1883
Distinct characters107
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

Unique33 ?
Unique (%)6.6%

Sample

1st row여의동
2nd row문정2동
3rd row영등포동
4th row잠실6동
5th row사직동
ValueCountFrequency (%)
한강로동 35
 
7.0%
잠실6동 30
 
6.0%
영등포동 23
 
4.6%
행당1동 20
 
4.0%
여의동 18
 
3.6%
자양3동 17
 
3.4%
서교동 17
 
3.4%
신촌동 15
 
3.0%
목1동 14
 
2.8%
압구정동 13
 
2.6%
Other values (87) 298
59.6%
2023-12-10T23:56:20.964982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 510
27.1%
1 101
 
5.4%
2 66
 
3.5%
3 58
 
3.1%
로 47
 
2.5%
서 39
 
2.1%
한 36
 
1.9%
강 35
 
1.9%
등 34
 
1.8%
포 33
 
1.8%
Other values (97) 924
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1573
83.5%
Decimal Number 286
 
15.2%
Other Punctuation 24
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 510
32.4%
로 47
 
3.0%
서 39
 
2.5%
한 36
 
2.3%
강 35
 
2.2%
등 34
 
2.2%
포 33
 
2.1%
잠 33
 
2.1%
실 32
 
2.0%
구 29
 
1.8%
Other values (89) 745
47.4%
Decimal Number
ValueCountFrequency (%)
1 101
35.3%
2 66
23.1%
3 58
20.3%
6 31
 
10.8%
4 19
 
6.6%
5 10
 
3.5%
7 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1573
83.5%
Common 310
 
16.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 510
32.4%
로 47
 
3.0%
서 39
 
2.5%
한 36
 
2.3%
강 35
 
2.2%
등 34
 
2.2%
포 33
 
2.1%
잠 33
 
2.1%
실 32
 
2.0%
구 29
 
1.8%
Other values (89) 745
47.4%
Common
ValueCountFrequency (%)
1 101
32.6%
2 66
21.3%
3 58
18.7%
6 31
 
10.0%
. 24
 
7.7%
4 19
 
6.1%
5 10
 
3.2%
7 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1573
83.5%
ASCII 310
 
16.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 510
32.4%
로 47
 
3.0%
서 39
 
2.5%
한 36
 
2.3%
강 35
 
2.2%
등 34
 
2.2%
포 33
 
2.1%
잠 33
 
2.1%
실 32
 
2.0%
구 29
 
1.8%
Other values (89) 745
47.4%
ASCII
ValueCountFrequency (%)
1 101
32.6%
2 66
21.3%
3 58
18.7%
6 31
 
10.0%
. 24
 
7.7%
4 19
 
6.1%
5 10
 
3.2%
7 1
 
0.3%

업종(UPJONG_NM)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
영화관
468 
박물관/전시관
 
32

Length

Max length7
Median length3
Mean length3.256
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영화관
2nd row영화관
3rd row영화관
4th row영화관
5th row박물관/전시관

Common Values

ValueCountFrequency (%)
영화관 468
93.6%
박물관/전시관 32
 
6.4%

Length

2023-12-10T23:56:21.223982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:21.407700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영화관 468
93.6%
박물관/전시관 32
 
6.4%

일별(DATE)
Real number (ℝ)

Distinct401
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20180736
Minimum20170103
Maximum20191225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:21.640779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170103
5-th percentile20170313
Q120171007
median20180706
Q320190408
95-th percentile20191019
Maximum20191225
Range21122
Interquartile range (IQR)19401.25

Descriptive statistics

Standard deviation8041.2425
Coefficient of variation (CV)0.0003984613
Kurtosis-1.4499023
Mean20180736
Median Absolute Deviation (MAD)9701
Skewness-0.0072641791
Sum1.0090368 × 1010
Variance64661581
MonotonicityNot monotonic
2023-12-10T23:56:21.927510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170205 4
 
0.8%
20181129 3
 
0.6%
20181225 3
 
0.6%
20170708 3
 
0.6%
20180706 3
 
0.6%
20180816 3
 
0.6%
20170728 3
 
0.6%
20170731 3
 
0.6%
20170808 3
 
0.6%
20180720 3
 
0.6%
Other values (391) 469
93.8%
ValueCountFrequency (%)
20170103 1
0.2%
20170105 1
0.2%
20170107 1
0.2%
20170108 1
0.2%
20170110 1
0.2%
20170112 1
0.2%
20170113 1
0.2%
20170114 1
0.2%
20170122 1
0.2%
20170128 1
0.2%
ValueCountFrequency (%)
20191225 1
0.2%
20191223 1
0.2%
20191222 1
0.2%
20191219 1
0.2%
20191217 1
0.2%
20191215 1
0.2%
20191212 1
0.2%
20191211 1
0.2%
20191208 1
0.2%
20191205 1
0.2%

성별(GENDER)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
F
265 
M
235 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 265
53.0%
M 235
47.0%

Length

2023-12-10T23:56:22.144919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:22.320736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 265
53.0%
m 235
47.0%

연령대별(AGE_GR)
Categorical

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
20대후
105 
20대초
83 
30대후
68 
30대초
66 
40대후
59 
Other values (6)
119 

Length

Max length5
Median length4
Mean length4.006
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20대초
2nd row30대초
3rd row40대후
4th row20대초
5th row20대초

Common Values

ValueCountFrequency (%)
20대후 105
21.0%
20대초 83
16.6%
30대후 68
13.6%
30대초 66
13.2%
40대후 59
11.8%
40대초 49
9.8%
50대초 30
 
6.0%
50대후 18
 
3.6%
60대초 13
 
2.6%
60대후 6
 
1.2%

Length

2023-12-10T23:56:22.504491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20대후 105
21.0%
20대초 83
16.6%
30대후 68
13.6%
30대초 66
13.2%
40대후 59
11.8%
40대초 49
9.8%
50대초 30
 
6.0%
50대후 18
 
3.6%
60대초 13
 
2.6%
60대후 6
 
1.2%

카드이용금액(USE_AMT)
Real number (ℝ)

Distinct387
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52308.933
Minimum2575
Maximum438555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:22.718528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2575
5-th percentile9920
Q124700
median43222.5
Q369160
95-th percentile119040
Maximum438555
Range435980
Interquartile range (IQR)44460

Descriptive statistics

Standard deviation42609.241
Coefficient of variation (CV)0.81456911
Kurtosis16.34992
Mean52308.933
Median Absolute Deviation (MAD)20902.5
Skewness2.8701169
Sum26154466
Variance1.8155474 × 109
MonotonicityNot monotonic
2023-12-10T23:56:22.983117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24700.0 6
 
1.2%
11022.0 4
 
0.8%
88920.0 4
 
0.8%
69160.0 4
 
0.8%
10060.0 4
 
0.8%
50700.0 4
 
0.8%
12350.0 4
 
0.8%
54340.0 4
 
0.8%
28680.0 3
 
0.6%
27170.0 3
 
0.6%
Other values (377) 460
92.0%
ValueCountFrequency (%)
2575.0 1
 
0.2%
4730.0 1
 
0.2%
4780.0 1
 
0.2%
4850.0 1
 
0.2%
4910.0 1
 
0.2%
4920.0 2
0.4%
5010.0 1
 
0.2%
5050.0 1
 
0.2%
5070.0 3
0.6%
7080.0 1
 
0.2%
ValueCountFrequency (%)
438555.0 1
0.2%
254140.0 1
0.2%
252000.0 1
0.2%
246170.0 1
0.2%
227273.0 1
0.2%
200099.0 1
0.2%
197340.0 1
0.2%
189200.0 1
0.2%
179945.0 1
0.2%
176050.0 1
0.2%

카드이용건수(USE_CNT)
Real number (ℝ)

Distinct53
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.96442
Minimum4.64
Maximum20.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:23.197458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.64
5-th percentile4.697
Q14.89
median4.96
Q35.07
95-th percentile10.1
Maximum20.2
Range15.56
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation2.3749429
Coefficient of variation (CV)0.39818505
Kurtosis6.7971737
Mean5.96442
Median Absolute Deviation (MAD)0.11
Skewness2.4655698
Sum2982.21
Variance5.6403538
MonotonicityNot monotonic
2023-12-10T23:56:23.444054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.94 53
 
10.6%
5.07 35
 
7.0%
5.03 28
 
5.6%
4.64 25
 
5.0%
4.78 23
 
4.6%
4.73 22
 
4.4%
4.91 21
 
4.2%
5.01 20
 
4.0%
4.72 19
 
3.8%
4.96 19
 
3.8%
Other values (43) 235
47.0%
ValueCountFrequency (%)
4.64 25
5.0%
4.7 11
2.2%
4.72 19
3.8%
4.73 22
4.4%
4.78 23
4.6%
4.85 7
 
1.4%
4.88 12
2.4%
4.89 14
2.8%
4.91 21
4.2%
4.92 7
 
1.4%
ValueCountFrequency (%)
20.2 1
 
0.2%
19.64 1
 
0.2%
15.45 1
 
0.2%
14.88 2
0.4%
14.82 1
 
0.2%
14.64 1
 
0.2%
14.55 1
 
0.2%
14.19 3
0.6%
13.92 2
0.4%
10.3 2
0.4%

Interactions

2023-12-10T23:56:16.986523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:16.229036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:16.591192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:17.147681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:16.347043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:16.712164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:17.285008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:16.460026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:16.835985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:56:23.618498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객주소시군구(CUSTM_GU_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)
고객주소시군구(CUSTM_GU_NM)1.0000.0000.0000.0000.0000.0000.0000.0000.000
가맹점주소시군구(STORE_GU_NM)0.0001.0000.2460.0000.0000.0170.1750.0000.251
가맹점주소행정동(STORE_DONG_NM)0.0000.2461.0000.0000.2030.0900.0000.0000.000
업종(UPJONG_NM)0.0000.0000.0001.0000.0490.0000.0000.0000.149
일별(DATE)0.0000.0000.2030.0491.0000.0910.0000.0180.000
성별(GENDER)0.0000.0170.0900.0000.0911.0000.0500.0000.057
연령대별(AGE_GR)0.0000.1750.0000.0000.0000.0501.0000.0810.000
카드이용금액(USE_AMT)0.0000.0000.0000.0000.0180.0000.0811.0000.000
카드이용건수(USE_CNT)0.0000.2510.0000.1490.0000.0570.0000.0001.000
2023-12-10T23:56:23.859430image/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)1.0000.0000.0000.0000.000
연령대별(AGE_GR)0.0001.0000.0000.0470.060
업종(UPJONG_NM)0.0000.0001.0000.0000.000
성별(GENDER)0.0000.0470.0001.0000.011
가맹점주소시군구(STORE_GU_NM)0.0000.0600.0000.0111.000
2023-12-10T23:56:24.052063image/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)1.0000.077-0.0340.0000.0000.0310.0620.011
카드이용금액(USE_AMT)0.0771.000-0.0150.0000.0000.0000.0000.039
카드이용건수(USE_CNT)-0.034-0.0151.0000.0000.1120.1060.0410.000
고객주소시군구(CUSTM_GU_NM)0.0000.0000.0001.0000.0000.0000.0000.000
가맹점주소시군구(STORE_GU_NM)0.0000.0000.1120.0001.0000.0000.0110.060
업종(UPJONG_NM)0.0310.0000.1060.0000.0001.0000.0000.000
성별(GENDER)0.0620.0000.0410.0000.0110.0001.0000.047
연령대별(AGE_GR)0.0110.0390.0000.0000.0600.0000.0471.000

Missing values

2023-12-10T23:56:17.525176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:56:18.214386image/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)
0금천구독산3동중랑구여의동영화관20180609F20대초86450.05.05
1양천구신정7동서초구문정2동영화관20190112F30대초19760.05.07
2구로구상일동강서구영등포동영화관20191123F40대후55330.05.01
3송파구군자동마포구잠실6동영화관20180214F20대초114075.09.78
4동작구광장동노원구사직동박물관/전시관20181116F20대초83470.05.01
5서초구장지동은평구번3동영화관20170429F20대후58968.04.94
6강남구가양1동강서구행당1동영화관20190604M50대초58000.05.03
7동대문구서림동강동구잠실6동영화관20170808M40대후14970.04.94
8노원구종암동광진구서초1동영화관20180929M20대후39520.04.72
9은평구연남동강남구자양4동영화관20170930M20대후66088.65.03
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)
490중랑구성산1동중구대치1동영화관20170328F20대후80160.05.01
491강동구강일동서초구신촌동영화관20190104M20대초27005.05.07
492은평구대방동중랑구수유3동영화관20170721F40대초110660.04.7
493동대문구연희동강남구목1동영화관20180314F20대초50700.04.93
494동대문구잠실4동강남구사직동영화관20180206M20대초119040.09.92
495용산구구로5동마포구혜화동영화관20190721M50대초22950.04.78
496관악구중계1동중구등촌1동영화관20170425F30대초49400.05.04
497양천구성내1동마포구잠실6동영화관20170813M20대후56650.04.94
498마포구개포1동관악구목5동영화관20180609M20대초246170.04.88
499은평구대치2동강북구자양3동영화관20180318M20대초66220.05.15