Overview

Dataset statistics

Number of variables11
Number of observations7328
Missing cells7328
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory694.3 KiB
Average record size in memory97.0 B

Variable types

Numeric7
Categorical2
Text1
Unsupported1

Dataset

Description년월,대분류,중분류,전화,방문,이메일,게시판,SNS,이동상담,문자,합계
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15739/S/1/datasetView.do

Alerts

전화 is highly overall correlated with 방문 and 1 other fieldsHigh correlation
방문 is highly overall correlated with 전화 and 1 other fieldsHigh correlation
합계 is highly overall correlated with 전화 and 1 other fieldsHigh correlation
SNS is highly imbalanced (98.7%)Imbalance
문자 has 7328 (100.0%) missing valuesMissing
전화 is highly skewed (γ1 = 22.41413426)Skewed
방문 is highly skewed (γ1 = 24.56290837)Skewed
이메일 is highly skewed (γ1 = 40.18779888)Skewed
게시판 is highly skewed (γ1 = 25.32681799)Skewed
이동상담 is highly skewed (γ1 = 42.30299396)Skewed
문자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전화 has 2539 (34.6%) zerosZeros
방문 has 4731 (64.6%) zerosZeros
이메일 has 6560 (89.5%) zerosZeros
게시판 has 6960 (95.0%) zerosZeros
이동상담 has 7278 (99.3%) zerosZeros
합계 has 3070 (41.9%) zerosZeros

Reproduction

Analysis started2024-05-11 05:53:31.950266
Analysis finished2024-05-11 05:53:42.950458
Duration11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월
Real number (ℝ)

Distinct58
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202148.89
Minimum201907
Maximum202404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-05-11T14:53:43.097193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201907
5-th percentile201909
Q1202009
median202112
Q3202302
95-th percentile202402
Maximum202404
Range497
Interquartile range (IQR)293

Descriptive statistics

Standard deviation143.80339
Coefficient of variation (CV)0.00071137361
Kurtosis-1.0428683
Mean202148.89
Median Absolute Deviation (MAD)104
Skewness-0.0054034382
Sum1.4813471 × 109
Variance20679.414
MonotonicityDecreasing
2024-05-11T14:53:43.395776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202404 127
 
1.7%
202106 127
 
1.7%
202203 127
 
1.7%
202202 127
 
1.7%
202201 127
 
1.7%
202112 127
 
1.7%
202403 127
 
1.7%
202110 127
 
1.7%
202109 127
 
1.7%
202108 127
 
1.7%
Other values (48) 6058
82.7%
ValueCountFrequency (%)
201907 124
1.7%
201908 124
1.7%
201909 124
1.7%
201910 124
1.7%
201911 124
1.7%
201912 124
1.7%
202001 124
1.7%
202002 124
1.7%
202003 124
1.7%
202004 124
1.7%
ValueCountFrequency (%)
202404 127
1.7%
202403 127
1.7%
202402 127
1.7%
202401 127
1.7%
202312 127
1.7%
202311 127
1.7%
202310 127
1.7%
202309 127
1.7%
202308 127
1.7%
202307 127
1.7%

대분류
Categorical

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
비즈니스
1044 
노무
870 
행정
870 
보건/복지
716 
출입국
696 
Other values (9)
3132 

Length

Max length5
Median length2
Mean length3.0687773
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비즈니스 1044
14.2%
노무 870
11.9%
행정 870
11.9%
보건/복지 716
9.8%
출입국 696
9.5%
안전 406
 
5.5%
정보통신 406
 
5.5%
주거 406
 
5.5%
교육 348
 
4.7%
기타 348
 
4.7%
Other values (4) 1218
16.6%

Length

2024-05-11T14:53:43.665525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
비즈니스 1044
14.2%
노무 870
11.9%
행정 870
11.9%
보건/복지 716
9.8%
출입국 696
9.5%
안전 406
 
5.5%
정보통신 406
 
5.5%
주거 406
 
5.5%
교육 348
 
4.7%
기타 348
 
4.7%
Other values (4) 1218
16.6%

중분류
Text

Distinct101
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
2024-05-11T14:53:44.167024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.1614356
Min length2

Characters and Unicode

Total characters30495
Distinct characters168
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육기관
2nd row기타
3rd row보육시설
4th row불편/개선건의
5th row일반교육
ValueCountFrequency (%)
기타 812
 
10.7%
불편/개선건의 812
 
10.7%
및 116
 
1.5%
심리상담 104
 
1.4%
창업교육 58
 
0.8%
임신/출산 58
 
0.8%
지식재산권 58
 
0.8%
가정폭력 58
 
0.8%
부동산매매 58
 
0.8%
공과금 58
 
0.8%
Other values (94) 5414
71.2%
2024-05-11T14:53:44.896899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1624
 
5.3%
사 1160
 
3.8%
기 1044
 
3.4%
건 1044
 
3.4%
불 870
 
2.9%
편 870
 
2.9%
선 870
 
2.9%
의 870
 
2.9%
타 812
 
2.7%
개 812
 
2.7%
Other values (158) 20519
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28303
92.8%
Other Punctuation 1624
 
5.3%
Uppercase Letter 290
 
1.0%
Space Separator 278
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 1160
 
4.1%
기 1044
 
3.7%
건 1044
 
3.7%
불 870
 
3.1%
편 870
 
3.1%
선 870
 
3.1%
의 870
 
3.1%
타 812
 
2.9%
개 812
 
2.9%
관 580
 
2.0%
Other values (151) 19371
68.4%
Uppercase Letter
ValueCountFrequency (%)
V 58
20.0%
T 58
20.0%
I 58
20.0%
D 58
20.0%
F 58
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 1624
100.0%
Space Separator
ValueCountFrequency (%)
278
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28303
92.8%
Common 1902
 
6.2%
Latin 290
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 1160
 
4.1%
기 1044
 
3.7%
건 1044
 
3.7%
불 870
 
3.1%
편 870
 
3.1%
선 870
 
3.1%
의 870
 
3.1%
타 812
 
2.9%
개 812
 
2.9%
관 580
 
2.0%
Other values (151) 19371
68.4%
Latin
ValueCountFrequency (%)
V 58
20.0%
T 58
20.0%
I 58
20.0%
D 58
20.0%
F 58
20.0%
Common
ValueCountFrequency (%)
/ 1624
85.4%
278
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28303
92.8%
ASCII 2192
 
7.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 1624
74.1%
278
 
12.7%
V 58
 
2.6%
T 58
 
2.6%
I 58
 
2.6%
D 58
 
2.6%
F 58
 
2.6%
Hangul
ValueCountFrequency (%)
사 1160
 
4.1%
기 1044
 
3.7%
건 1044
 
3.7%
불 870
 
3.1%
편 870
 
3.1%
선 870
 
3.1%
의 870
 
3.1%
타 812
 
2.9%
개 812
 
2.9%
관 580
 
2.0%
Other values (151) 19371
68.4%

전화
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct174
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.933133
Minimum0
Maximum1646
Zeros2539
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-05-11T14:53:45.159494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312
95-th percentile60
Maximum1646
Range1646
Interquartile range (IQR)12

Descriptive statistics

Standard deviation39.915892
Coefficient of variation (CV)3.0863281
Kurtosis819.54788
Mean12.933133
Median Absolute Deviation (MAD)2
Skewness22.414134
Sum94774
Variance1593.2785
MonotonicityNot monotonic
2024-05-11T14:53:45.410830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2539
34.6%
1 885
 
12.1%
2 534
 
7.3%
3 357
 
4.9%
4 249
 
3.4%
5 207
 
2.8%
6 161
 
2.2%
7 141
 
1.9%
8 124
 
1.7%
9 104
 
1.4%
Other values (164) 2027
27.7%
ValueCountFrequency (%)
0 2539
34.6%
1 885
 
12.1%
2 534
 
7.3%
3 357
 
4.9%
4 249
 
3.4%
5 207
 
2.8%
6 161
 
2.2%
7 141
 
1.9%
8 124
 
1.7%
9 104
 
1.4%
ValueCountFrequency (%)
1646 1
< 0.1%
1632 1
< 0.1%
1033 1
< 0.1%
546 1
< 0.1%
412 1
< 0.1%
343 1
< 0.1%
341 1
< 0.1%
314 1
< 0.1%
307 1
< 0.1%
302 1
< 0.1%

방문
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct66
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2775655
Minimum0
Maximum458
Zeros4731
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-05-11T14:53:45.769586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11
Maximum458
Range458
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.326533
Coefficient of variation (CV)4.9730878
Kurtosis809.18477
Mean2.2775655
Median Absolute Deviation (MAD)0
Skewness24.562908
Sum16690
Variance128.29036
MonotonicityNot monotonic
2024-05-11T14:53:46.044949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4731
64.6%
1 895
 
12.2%
2 419
 
5.7%
3 280
 
3.8%
4 160
 
2.2%
5 135
 
1.8%
6 101
 
1.4%
7 72
 
1.0%
9 58
 
0.8%
8 57
 
0.8%
Other values (56) 420
 
5.7%
ValueCountFrequency (%)
0 4731
64.6%
1 895
 
12.2%
2 419
 
5.7%
3 280
 
3.8%
4 160
 
2.2%
5 135
 
1.8%
6 101
 
1.4%
7 72
 
1.0%
8 57
 
0.8%
9 58
 
0.8%
ValueCountFrequency (%)
458 1
< 0.1%
390 1
< 0.1%
371 1
< 0.1%
313 1
< 0.1%
219 1
< 0.1%
181 1
< 0.1%
158 1
< 0.1%
112 1
< 0.1%
91 1
< 0.1%
88 1
< 0.1%

이메일
Real number (ℝ)

SKEWED  ZEROS 

Distinct39
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56481987
Minimum0
Maximum565
Zeros6560
Zeros (%)89.5%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-05-11T14:53:46.269538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum565
Range565
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.3057875
Coefficient of variation (CV)16.475673
Kurtosis2066.2451
Mean0.56481987
Median Absolute Deviation (MAD)0
Skewness40.187799
Sum4139
Variance86.597681
MonotonicityNot monotonic
2024-05-11T14:53:46.572294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 6560
89.5%
1 496
 
6.8%
2 129
 
1.8%
3 35
 
0.5%
4 25
 
0.3%
5 21
 
0.3%
6 8
 
0.1%
7 6
 
0.1%
8 6
 
0.1%
11 5
 
0.1%
Other values (29) 37
 
0.5%
ValueCountFrequency (%)
0 6560
89.5%
1 496
 
6.8%
2 129
 
1.8%
3 35
 
0.5%
4 25
 
0.3%
5 21
 
0.3%
6 8
 
0.1%
7 6
 
0.1%
8 6
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
565 1
< 0.1%
245 1
< 0.1%
243 1
< 0.1%
240 1
< 0.1%
155 1
< 0.1%
132 1
< 0.1%
129 1
< 0.1%
121 1
< 0.1%
120 1
< 0.1%
89 1
< 0.1%

게시판
Real number (ℝ)

SKEWED  ZEROS 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18258734
Minimum0
Maximum73
Zeros6960
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-05-11T14:53:46.771147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum73
Range73
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8334389
Coefficient of variation (CV)10.041435
Kurtosis810.58031
Mean0.18258734
Median Absolute Deviation (MAD)0
Skewness25.326818
Sum1338
Variance3.3614983
MonotonicityNot monotonic
2024-05-11T14:53:46.990061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 6960
95.0%
1 187
 
2.6%
2 69
 
0.9%
3 26
 
0.4%
6 22
 
0.3%
4 13
 
0.2%
5 11
 
0.2%
8 7
 
0.1%
9 6
 
0.1%
12 5
 
0.1%
Other values (15) 22
 
0.3%
ValueCountFrequency (%)
0 6960
95.0%
1 187
 
2.6%
2 69
 
0.9%
3 26
 
0.4%
4 13
 
0.2%
5 11
 
0.2%
6 22
 
0.3%
7 5
 
0.1%
8 7
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
73 1
< 0.1%
64 1
< 0.1%
60 1
< 0.1%
47 1
< 0.1%
37 1
< 0.1%
36 1
< 0.1%
27 1
< 0.1%
25 1
< 0.1%
20 1
< 0.1%
18 1
< 0.1%

SNS
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
0
7311 
1
 
12
2
 
4
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 7311
99.8%
1 12
 
0.2%
2 4
 
0.1%
5 1
 
< 0.1%

Length

2024-05-11T14:53:47.229639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:47.422267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7311
99.8%
1 12
 
0.2%
2 4
 
0.1%
5 1
 
< 0.1%

이동상담
Real number (ℝ)

SKEWED  ZEROS 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.097570961
Minimum0
Maximum175
Zeros7278
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-05-11T14:53:47.586324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum175
Range175
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4261344
Coefficient of variation (CV)35.114284
Kurtosis1857.9785
Mean0.097570961
Median Absolute Deviation (MAD)0
Skewness42.302994
Sum715
Variance11.738397
MonotonicityNot monotonic
2024-05-11T14:53:47.818201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 7278
99.3%
1 30
 
0.4%
2 7
 
0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
8 1
 
< 0.1%
49 1
 
< 0.1%
16 1
 
< 0.1%
7 1
 
< 0.1%
143 1
 
< 0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
0 7278
99.3%
1 30
 
0.4%
2 7
 
0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
16 1
 
< 0.1%
49 1
 
< 0.1%
ValueCountFrequency (%)
175 1
< 0.1%
143 1
< 0.1%
133 1
< 0.1%
120 1
< 0.1%
49 1
< 0.1%
16 1
< 0.1%
8 1
< 0.1%
7 1
< 0.1%
6 1
< 0.1%
4 2
< 0.1%

문자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7328
Missing (%)100.0%
Memory size64.5 KiB

합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct213
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.584607
Minimum0
Maximum2208
Zeros3070
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-05-11T14:53:48.074128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q313
95-th percentile73
Maximum2208
Range2208
Interquartile range (IQR)13

Descriptive statistics

Standard deviation51.925004
Coefficient of variation (CV)3.3318135
Kurtosis661.9573
Mean15.584607
Median Absolute Deviation (MAD)1
Skewness19.742672
Sum114204
Variance2696.206
MonotonicityNot monotonic
2024-05-11T14:53:48.317391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3070
41.9%
1 697
 
9.5%
2 433
 
5.9%
3 292
 
4.0%
4 208
 
2.8%
5 170
 
2.3%
6 120
 
1.6%
7 110
 
1.5%
8 99
 
1.4%
9 82
 
1.1%
Other values (203) 2047
27.9%
ValueCountFrequency (%)
0 3070
41.9%
1 697
 
9.5%
2 433
 
5.9%
3 292
 
4.0%
4 208
 
2.8%
5 170
 
2.3%
6 120
 
1.6%
7 110
 
1.5%
8 99
 
1.4%
9 82
 
1.1%
ValueCountFrequency (%)
2208 1
< 0.1%
1766 1
< 0.1%
1065 1
< 0.1%
874 1
< 0.1%
672 1
< 0.1%
649 1
< 0.1%
647 1
< 0.1%
625 1
< 0.1%
581 1
< 0.1%
506 1
< 0.1%

Interactions

2024-05-11T14:53:41.228855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:33.852781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:34.987414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:35.986783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:37.072499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:38.205057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:39.865611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:41.414623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:33.992305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:35.125282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:36.135355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:37.235199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:38.366106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:40.055097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:41.576108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:34.149352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:35.282192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:36.287613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:37.415885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:38.907304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:40.227427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:41.724980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:34.337227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:35.408353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:36.426840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:37.566263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:39.145324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:40.392138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:41.954793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:34.502765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:35.558531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:36.590190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:37.699079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:39.322899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:40.564761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:42.162009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:34.669835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:35.698465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:36.772077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:37.865755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:39.518495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:40.746307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:42.315892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:34.854446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:35.866262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:36.928229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:38.045487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:39.704082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:40.969569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:53:48.453241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월대분류전화방문이메일게시판SNS이동상담합계
년월1.0000.0000.0400.0390.0220.0720.0910.0290.039
대분류0.0001.0000.0950.0830.1650.0980.0570.0000.112
전화0.0400.0951.0000.6870.2920.6140.0000.0000.889
방문0.0390.0830.6871.0000.0000.8350.0000.0000.814
이메일0.0220.1650.2920.0001.0000.3710.0000.0000.517
게시판0.0720.0980.6140.8350.3711.0000.0000.0000.653
SNS0.0910.0570.0000.0000.0000.0001.0000.0000.000
이동상담0.0290.0000.0000.0000.0000.0000.0001.0000.233
합계0.0390.1120.8890.8140.5170.6530.0000.2331.000
2024-05-11T14:53:48.958682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류SNS
대분류1.0000.032
SNS0.0321.000
2024-05-11T14:53:49.109808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월전화방문이메일게시판이동상담합계대분류SNS
년월1.000-0.183-0.097-0.115-0.221-0.127-0.3860.0000.055
전화-0.1831.0000.6760.3470.2580.1060.8750.0460.000
방문-0.0970.6761.0000.3230.1690.1100.6300.0360.000
이메일-0.1150.3470.3231.0000.1200.0520.3870.0860.000
게시판-0.2210.2580.1690.1201.0000.0030.2850.0410.000
이동상담-0.1270.1060.1100.0520.0031.0000.1150.0000.000
합계-0.3860.8750.6300.3870.2850.1151.0000.0410.000
대분류0.0000.0460.0360.0860.0410.0000.0411.0000.032
SNS0.0550.0000.0000.0000.0000.0000.0000.0321.000

Missing values

2024-05-11T14:53:42.523037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:53:42.827901image/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

년월대분류중분류전화방문이메일게시판SNS이동상담문자합계
0202404교육교육기관430000<NA>0
1202404교육기타700000<NA>0
2202404교육보육시설000000<NA>0
3202404교육불편/개선건의000000<NA>0
4202404교육일반교육650000<NA>0
5202404교육한국어교육630000<NA>0
6202404교통기타400000<NA>0
7202404교통대중교통000000<NA>0
8202404교통불편/개선건의000000<NA>0
9202404교통유실물000000<NA>0
년월대분류중분류전화방문이메일게시판SNS이동상담문자합계
7318201907행정쓰레기처리110000<NA>2
7319201907행정여권410000<NA>5
7320201907행정운전면허168315100<NA>205
7321201907행정인감000000<NA>0
7322201907행정일반법률3650000<NA>41
7323201907행정자동차관련811000<NA>10
7324201907행정출생 및 사망802000<NA>10
7325201907행정행정기관9870000<NA>105
7326201907행정행정소송300000<NA>3
7327201907행정혼인관계6313000<NA>22