Overview

Dataset statistics

Number of variables11
Number of observations6643
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory635.9 KiB
Average record size in memory98.0 B

Variable types

Numeric9
Categorical2

Dataset

Description인덱스,시군구,등록일자,서신,인터넷,120 다산콜센터,해피콜,전화,방문,진정건의,기타
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-17394/S/1/datasetView.do

Alerts

인덱스 is highly overall correlated with 등록일자 and 2 other fieldsHigh correlation
등록일자 is highly overall correlated with 인덱스 and 2 other fieldsHigh correlation
서신 is highly overall correlated with 시군구High correlation
인터넷 is highly overall correlated with 시군구 and 1 other fieldsHigh correlation
120 다산콜센터 is highly overall correlated with 인덱스 and 2 other fieldsHigh correlation
전화 is highly overall correlated with 인덱스 and 2 other fieldsHigh correlation
기타 is highly overall correlated with 시군구High correlation
시군구 is highly overall correlated with 서신 and 4 other fieldsHigh correlation
진정건의 is highly overall correlated with 인터넷High correlation
진정건의 is highly imbalanced (89.7%)Imbalance
서신 is highly skewed (γ1 = 42.2794221)Skewed
인터넷 is highly skewed (γ1 = 42.01321211)Skewed
120 다산콜센터 is highly skewed (γ1 = 36.23280739)Skewed
해피콜 is highly skewed (γ1 = 23.56767779)Skewed
전화 is highly skewed (γ1 = 45.63039026)Skewed
방문 is highly skewed (γ1 = 24.22866591)Skewed
기타 is highly skewed (γ1 = 41.45867012)Skewed
인덱스 has unique valuesUnique
서신 has 6603 (99.4%) zerosZeros
인터넷 has 4205 (63.3%) zerosZeros
120 다산콜센터 has 546 (8.2%) zerosZeros
해피콜 has 6392 (96.2%) zerosZeros
전화 has 3578 (53.9%) zerosZeros
방문 has 6498 (97.8%) zerosZeros
기타 has 5828 (87.7%) zerosZeros

Reproduction

Analysis started2024-05-18 02:15:59.900263
Analysis finished2024-05-18 02:16:32.988426
Duration33.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

인덱스
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6643
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3322
Minimum1
Maximum6643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:33.234946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile333.1
Q11661.5
median3322
Q34982.5
95-th percentile6310.9
Maximum6643
Range6642
Interquartile range (IQR)3321

Descriptive statistics

Standard deviation1917.8133
Coefficient of variation (CV)0.57730682
Kurtosis-1.2
Mean3322
Median Absolute Deviation (MAD)1661
Skewness0
Sum22068046
Variance3678007.7
MonotonicityNot monotonic
2024-05-18T11:16:33.679646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6581 1
 
< 0.1%
2352 1
 
< 0.1%
1994 1
 
< 0.1%
2375 1
 
< 0.1%
2324 1
 
< 0.1%
2680 1
 
< 0.1%
2667 1
 
< 0.1%
1723 1
 
< 0.1%
2009 1
 
< 0.1%
2681 1
 
< 0.1%
Other values (6633) 6633
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
6643 1
< 0.1%
6642 1
< 0.1%
6641 1
< 0.1%
6640 1
< 0.1%
6639 1
< 0.1%
6638 1
< 0.1%
6637 1
< 0.1%
6636 1
< 0.1%
6635 1
< 0.1%
6634 1
< 0.1%

시군구
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size52.0 KiB
송파구
2858 
중랑구
1244 
동대문구
925 
강동구
650 
강북구
362 
Other values (12)
604 

Length

Max length4
Median length3
Mean length3.142255
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
송파구 2858
43.0%
중랑구 1244
18.7%
동대문구 925
 
13.9%
강동구 650
 
9.8%
강북구 362
 
5.4%
양천구 276
 
4.2%
관악구 209
 
3.1%
성동구 45
 
0.7%
성북구 38
 
0.6%
서대문구 20
 
0.3%
Other values (7) 16
 
0.2%

Length

2024-05-18T11:16:34.140406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 2858
43.0%
중랑구 1244
18.7%
동대문구 925
 
13.9%
강동구 650
 
9.8%
강북구 362
 
5.4%
양천구 276
 
4.2%
관악구 209
 
3.1%
성동구 45
 
0.7%
성북구 38
 
0.6%
서대문구 20
 
0.3%
Other values (7) 16
 
0.2%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct3878
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20156957
Minimum20111123
Maximum20230619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:34.500744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20111123
5-th percentile20120328
Q120130202
median20140623
Q320181204
95-th percentile20220721
Maximum20230619
Range119496
Interquartile range (IQR)51002.5

Descriptive statistics

Standard deviation34477.041
Coefficient of variation (CV)0.0017104289
Kurtosis-0.95615879
Mean20156957
Median Absolute Deviation (MAD)20091
Skewness0.63218558
Sum1.3390266 × 1011
Variance1.1886664 × 109
MonotonicityNot monotonic
2024-05-18T11:16:35.113096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120404 9
 
0.1%
20120405 9
 
0.1%
20120406 9
 
0.1%
20120409 9
 
0.1%
20140108 8
 
0.1%
20120416 8
 
0.1%
20120402 8
 
0.1%
20140103 8
 
0.1%
20140102 8
 
0.1%
20120403 8
 
0.1%
Other values (3868) 6559
98.7%
ValueCountFrequency (%)
20111123 2
 
< 0.1%
20120102 5
0.1%
20120103 5
0.1%
20120104 4
0.1%
20120105 4
0.1%
20120106 5
0.1%
20120107 3
< 0.1%
20120108 1
 
< 0.1%
20120109 6
0.1%
20120110 5
0.1%
ValueCountFrequency (%)
20230619 1
< 0.1%
20230617 1
< 0.1%
20230616 1
< 0.1%
20230615 1
< 0.1%
20230614 1
< 0.1%
20230613 1
< 0.1%
20230612 1
< 0.1%
20230611 1
< 0.1%
20230610 1
< 0.1%
20230609 1
< 0.1%

서신
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0093331326
Minimum0
Maximum11
Zeros6603
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:35.496623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18985935
Coefficient of variation (CV)20.342511
Kurtosis2180.5985
Mean0.0093331326
Median Absolute Deviation (MAD)0
Skewness42.279422
Sum62
Variance0.036046574
MonotonicityNot monotonic
2024-05-18T11:16:35.729561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 6603
99.4%
1 34
 
0.5%
2 3
 
< 0.1%
11 1
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 6603
99.4%
1 34
 
0.5%
2 3
 
< 0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
8 1
 
< 0.1%
3 1
 
< 0.1%
2 3
 
< 0.1%
1 34
 
0.5%
0 6603
99.4%

인터넷
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct24
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0630739
Minimum0
Maximum609
Zeros4205
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:35.941520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum609
Range609
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.003917
Coefficient of variation (CV)11.291705
Kurtosis1909.8547
Mean1.0630739
Median Absolute Deviation (MAD)0
Skewness42.013212
Sum7062
Variance144.09403
MonotonicityNot monotonic
2024-05-18T11:16:36.195629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 4205
63.3%
1 1230
 
18.5%
2 623
 
9.4%
3 284
 
4.3%
4 138
 
2.1%
5 59
 
0.9%
6 39
 
0.6%
7 19
 
0.3%
8 13
 
0.2%
9 9
 
0.1%
Other values (14) 24
 
0.4%
ValueCountFrequency (%)
0 4205
63.3%
1 1230
 
18.5%
2 623
 
9.4%
3 284
 
4.3%
4 138
 
2.1%
5 59
 
0.9%
6 39
 
0.6%
7 19
 
0.3%
8 13
 
0.2%
9 9
 
0.1%
ValueCountFrequency (%)
609 1
< 0.1%
567 1
< 0.1%
366 1
< 0.1%
241 2
< 0.1%
65 1
< 0.1%
35 1
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
15 2
< 0.1%
14 1
< 0.1%

120 다산콜센터
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct47
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8940238
Minimum0
Maximum674
Zeros546
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:36.713734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q39
95-th percentile19
Maximum674
Range674
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.325536
Coefficient of variation (CV)1.9329113
Kurtosis1725.6533
Mean6.8940238
Median Absolute Deviation (MAD)3
Skewness36.232807
Sum45797
Variance177.56992
MonotonicityNot monotonic
2024-05-18T11:16:36.983760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
3 675
10.2%
4 661
10.0%
2 633
9.5%
5 613
9.2%
0 546
 
8.2%
6 485
 
7.3%
1 466
 
7.0%
7 424
 
6.4%
8 339
 
5.1%
10 273
 
4.1%
Other values (37) 1528
23.0%
ValueCountFrequency (%)
0 546
8.2%
1 466
7.0%
2 633
9.5%
3 675
10.2%
4 661
10.0%
5 613
9.2%
6 485
7.3%
7 424
6.4%
8 339
5.1%
9 268
 
4.0%
ValueCountFrequency (%)
674 1
< 0.1%
640 1
< 0.1%
225 1
< 0.1%
131 1
< 0.1%
122 1
< 0.1%
105 1
< 0.1%
100 1
< 0.1%
95 1
< 0.1%
89 1
< 0.1%
51 1
< 0.1%

해피콜
Real number (ℝ)

SKEWED  ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.055095589
Minimum0
Maximum20
Zeros6392
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:37.320034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39490793
Coefficient of variation (CV)7.1676869
Kurtosis1016.6159
Mean0.055095589
Median Absolute Deviation (MAD)0
Skewness23.567678
Sum366
Variance0.15595228
MonotonicityNot monotonic
2024-05-18T11:16:37.661967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 6392
96.2%
1 192
 
2.9%
2 34
 
0.5%
3 15
 
0.2%
4 6
 
0.1%
6 2
 
< 0.1%
20 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 6392
96.2%
1 192
 
2.9%
2 34
 
0.5%
3 15
 
0.2%
4 6
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
4 6
 
0.1%
3 15
 
0.2%
2 34
 
0.5%
1 192
 
2.9%
0 6392
96.2%

전화
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct31
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5300316
Minimum0
Maximum542
Zeros3578
Zeros (%)53.9%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:38.054294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum542
Range542
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.6783
Coefficient of variation (CV)6.3255556
Kurtosis2365.2964
Mean1.5300316
Median Absolute Deviation (MAD)0
Skewness45.63039
Sum10164
Variance93.669491
MonotonicityNot monotonic
2024-05-18T11:16:38.334409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 3578
53.9%
1 952
 
14.3%
2 785
 
11.8%
3 541
 
8.1%
4 343
 
5.2%
5 192
 
2.9%
6 98
 
1.5%
7 65
 
1.0%
8 26
 
0.4%
9 20
 
0.3%
Other values (21) 43
 
0.6%
ValueCountFrequency (%)
0 3578
53.9%
1 952
 
14.3%
2 785
 
11.8%
3 541
 
8.1%
4 343
 
5.2%
5 192
 
2.9%
6 98
 
1.5%
7 65
 
1.0%
8 26
 
0.4%
9 20
 
0.3%
ValueCountFrequency (%)
542 1
< 0.1%
478 1
< 0.1%
150 1
< 0.1%
127 1
< 0.1%
106 1
< 0.1%
103 1
< 0.1%
74 1
< 0.1%
64 1
< 0.1%
54 1
< 0.1%
47 2
< 0.1%

방문
Real number (ℝ)

SKEWED  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02739726
Minimum0
Maximum12
Zeros6498
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:38.541115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24754552
Coefficient of variation (CV)9.0354116
Kurtosis959.11301
Mean0.02739726
Median Absolute Deviation (MAD)0
Skewness24.228666
Sum182
Variance0.061278786
MonotonicityNot monotonic
2024-05-18T11:16:38.930675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6498
97.8%
1 128
 
1.9%
2 12
 
0.2%
3 2
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
0 6498
97.8%
1 128
 
1.9%
2 12
 
0.2%
3 2
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
3 2
 
< 0.1%
2 12
 
0.2%
1 128
 
1.9%
0 6498
97.8%

진정건의
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.0 KiB
0
6428 
1
 
166
2
 
34
3
 
14
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6428
96.8%
1 166
 
2.5%
2 34
 
0.5%
3 14
 
0.2%
5 1
 
< 0.1%

Length

2024-05-18T11:16:39.243926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:16:39.439863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6428
96.8%
1 166
 
2.5%
2 34
 
0.5%
3 14
 
0.2%
5 1
 
< 0.1%

기타
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct50
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6051483
Minimum0
Maximum999
Zeros5828
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size58.5 KiB
2024-05-18T11:16:39.687619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.010865
Coefficient of variation (CV)12.466677
Kurtosis1943.302
Mean1.6051483
Median Absolute Deviation (MAD)0
Skewness41.45867
Sum10663
Variance400.4347
MonotonicityNot monotonic
2024-05-18T11:16:40.135190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5828
87.7%
1 274
 
4.1%
2 56
 
0.8%
5 33
 
0.5%
3 33
 
0.5%
6 26
 
0.4%
8 26
 
0.4%
10 25
 
0.4%
7 25
 
0.4%
4 25
 
0.4%
Other values (40) 292
 
4.4%
ValueCountFrequency (%)
0 5828
87.7%
1 274
 
4.1%
2 56
 
0.8%
3 33
 
0.5%
4 25
 
0.4%
5 33
 
0.5%
6 26
 
0.4%
7 25
 
0.4%
8 26
 
0.4%
9 24
 
0.4%
ValueCountFrequency (%)
999 2
< 0.1%
454 1
 
< 0.1%
412 1
 
< 0.1%
384 1
 
< 0.1%
48 2
< 0.1%
45 2
< 0.1%
44 2
< 0.1%
43 2
< 0.1%
42 3
< 0.1%
41 4
0.1%

Interactions

2024-05-18T11:16:29.859719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:04.438860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:07.427818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:10.603504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:14.048779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:18.018221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:21.439084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:24.676260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:27.375532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:30.098168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:04.729862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:07.730175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:10.960577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:14.524279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:18.512275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:21.749264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:25.058686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:27.668480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:30.325003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:05.070267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:08.025304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:11.375170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:15.018784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:18.813818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:22.116244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:25.367965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:27.946196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:30.583931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:05.409359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:08.388934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:11.726660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:15.364051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:19.123011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:22.554188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:25.624664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:28.212796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:30.853082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:05.698314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:08.852851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:12.096133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:15.739059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:19.550623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:22.848610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:25.891639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:28.494643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:31.157006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:06.125139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:09.181161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:12.525748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:16.107186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:19.857239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:23.248096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:26.175733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:28.791928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:31.431442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:06.433461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:09.572520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:12.944275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:16.523306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:20.205826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:23.669127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:26.557580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:29.085102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:31.685702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:06.715940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:09.940836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:13.354973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:16.918332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:20.595540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:24.045872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:26.818337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:29.340038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:31.920922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:07.000121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:10.302661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:13.757515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:17.300891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:21.135711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:24.334171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:27.111694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:16:29.601575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T11:16:40.370806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인덱스시군구등록일자서신인터넷120 다산콜센터해피콜전화방문진정건의기타
인덱스1.0000.6350.9300.0320.0420.0620.0690.0360.0000.1810.034
시군구0.6351.0000.6960.8540.8830.9330.3930.8330.5430.5620.897
등록일자0.9300.6961.0000.0470.0800.0830.0690.0690.0210.2100.068
서신0.0320.8540.0471.0000.9660.4250.7280.8840.8170.8590.789
인터넷0.0420.8830.0800.9661.0000.6470.7280.9640.8130.8600.938
120 다산콜센터0.0620.9330.0830.4250.6471.0000.0000.7170.2710.0930.895
해피콜0.0690.3930.0690.7280.7280.0001.0000.4360.4820.7330.345
전화0.0360.8330.0690.8840.9640.7170.4361.0000.5780.5840.823
방문0.0000.5430.0210.8170.8130.2710.4820.5781.0000.6390.845
진정건의0.1810.5620.2100.8590.8600.0930.7330.5840.6391.0000.479
기타0.0340.8970.0680.7890.9380.8950.3450.8230.8450.4791.000
2024-05-18T11:16:40.665156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구진정건의
시군구1.0000.331
진정건의0.3311.000
2024-05-18T11:16:40.926798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인덱스등록일자서신인터넷120 다산콜센터해피콜전화방문기타시군구진정건의
인덱스1.0000.9560.0260.2340.501-0.106-0.546-0.0750.0880.3070.076
등록일자0.9561.0000.0150.2580.521-0.097-0.547-0.0810.1070.3580.089
서신0.0260.0151.0000.0320.0430.0150.0170.0830.0820.6550.500
인터넷0.2340.2580.0321.0000.3640.027-0.0590.0080.0460.7060.500
120 다산콜센터0.5010.5210.0430.3641.000-0.000-0.191-0.0170.0530.8200.076
해피콜-0.106-0.0970.0150.027-0.0001.0000.1560.0680.0040.1920.334
전화-0.546-0.5470.017-0.059-0.1910.1561.0000.108-0.0080.6200.252
방문-0.075-0.0810.0830.008-0.0170.0680.1081.0000.0240.2920.499
기타0.0880.1070.0820.0460.0530.004-0.0080.0241.0000.7450.409
시군구0.3070.3580.6550.7060.8200.1920.6200.2920.7451.0000.331
진정건의0.0760.0890.5000.5000.0760.3340.2520.4990.4090.3311.000

Missing values

2024-05-18T11:16:32.309000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T11:16:32.826432image/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

인덱스시군구등록일자서신인터넷120 다산콜센터해피콜전화방문진정건의기타
06581송파구2023050600500000
15374송파구2017033101500000
26566송파구2023031901900000
36485송파구2023050500800000
46379송파구2022121700600000
56429송파구20221216021700000
66596송파구2022111201800000
76370송파구20221110032300000
86369송파구20221109011900000
96537송파구20221108002400000
인덱스시군구등록일자서신인터넷120 다산콜센터해피콜전화방문진정건의기타
6633635강북구2012010300003000
66341256중랑구2012010301002000
6635944성북구2012010301202005
6636328강동구2012010200200000
6637319강북구2012010201002000
6638627중랑구2012010201413000
66391264성북구2012010200704003
664098양천구2012010200403000
6641597송파구2011112300200000
66421중랑구2011112300704000