Overview

Dataset statistics

Number of variables10
Number of observations870
Missing cells870
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.7 KiB
Average record size in memory89.2 B

Variable types

Numeric8
Categorical1
Unsupported1

Dataset

Description년월,기관/유형,전화,방문,이메일,게시판,SNS,이동상담,문자,합계
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15735/S/1/datasetView.do

Alerts

년월 is highly overall correlated with 합계High correlation
전화 is highly overall correlated with 방문 and 2 other fieldsHigh correlation
방문 is highly overall correlated with 전화 and 2 other fieldsHigh correlation
이메일 is highly overall correlated with 전화 and 2 other fieldsHigh correlation
합계 is highly overall correlated with 년월 and 3 other fieldsHigh correlation
문자 has 870 (100.0%) missing valuesMissing
문자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
방문 has 123 (14.1%) zerosZeros
이메일 has 426 (49.0%) zerosZeros
게시판 has 697 (80.1%) zerosZeros
SNS has 851 (97.8%) zerosZeros
이동상담 has 848 (97.5%) zerosZeros
합계 has 158 (18.2%) zerosZeros

Reproduction

Analysis started2024-05-03 21:02:53.181837
Analysis finished2024-05-03 21:03:08.727390
Duration15.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202147.91
Minimum201907
Maximum202404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-05-03T21:03:08.895518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201907
5-th percentile201909
Q1202009
median202111.5
Q3202302
95-th percentile202402
Maximum202404
Range497
Interquartile range (IQR)293

Descriptive statistics

Standard deviation144.18753
Coefficient of variation (CV)0.00071327737
Kurtosis-1.0477929
Mean202147.91
Median Absolute Deviation (MAD)104
Skewness0.0010646508
Sum1.7586868 × 108
Variance20790.044
MonotonicityDecreasing
2024-05-03T21:03:09.177431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202404 15
 
1.7%
202008 15
 
1.7%
202108 15
 
1.7%
202107 15
 
1.7%
202106 15
 
1.7%
202105 15
 
1.7%
202104 15
 
1.7%
202103 15
 
1.7%
202102 15
 
1.7%
202101 15
 
1.7%
Other values (48) 720
82.8%
ValueCountFrequency (%)
201907 15
1.7%
201908 15
1.7%
201909 15
1.7%
201910 15
1.7%
201911 15
1.7%
201912 15
1.7%
202001 15
1.7%
202002 15
1.7%
202003 15
1.7%
202004 15
1.7%
ValueCountFrequency (%)
202404 15
1.7%
202403 15
1.7%
202402 15
1.7%
202401 15
1.7%
202312 15
1.7%
202311 15
1.7%
202310 15
1.7%
202309 15
1.7%
202308 15
1.7%
202307 15
1.7%

기관/유형
Categorical

Distinct15
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
교육
 
58
교통
 
58
금융
 
58
기타
 
58
노무
 
58
Other values (10)
580 

Length

Max length6
Median length2
Mean length3.2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육
2nd row교통
3rd row금융
4th row기타
5th row노무

Common Values

ValueCountFrequency (%)
교육 58
 
6.7%
교통 58
 
6.7%
금융 58
 
6.7%
기타 58
 
6.7%
노무 58
 
6.7%
문화/관광 58
 
6.7%
보건/복지 58
 
6.7%
비즈니스 58
 
6.7%
안전 58
 
6.7%
여성/가족 58
 
6.7%
Other values (5) 290
33.3%

Length

2024-05-03T21:03:09.564988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육 58
 
6.2%
교통 58
 
6.2%
금융 58
 
6.2%
기타 58
 
6.2%
노무 58
 
6.2%
문화/관광 58
 
6.2%
보건/복지 58
 
6.2%
비즈니스 58
 
6.2%
안전 58
 
6.2%
여성/가족 58
 
6.2%
Other values (6) 348
37.5%

전화
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.87126
Minimum0
Maximum3957
Zeros6
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-05-03T21:03:10.030977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q113
median36
Q3221.75
95-th percentile1374.35
Maximum3957
Range3957
Interquartile range (IQR)208.75

Descriptive statistics

Standard deviation464.71326
Coefficient of variation (CV)2.1329718
Kurtosis15.952084
Mean217.87126
Median Absolute Deviation (MAD)31
Skewness3.7632533
Sum189548
Variance215958.42
MonotonicityNot monotonic
2024-05-03T21:03:10.490982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 23
 
2.6%
9 23
 
2.6%
5 23
 
2.6%
4 20
 
2.3%
8 19
 
2.2%
6 19
 
2.2%
15 18
 
2.1%
2 16
 
1.8%
11 16
 
1.8%
3 16
 
1.8%
Other values (324) 677
77.8%
ValueCountFrequency (%)
0 6
 
0.7%
1 12
1.4%
2 16
1.8%
3 16
1.8%
4 20
2.3%
5 23
2.6%
6 19
2.2%
7 23
2.6%
8 19
2.2%
9 23
2.6%
ValueCountFrequency (%)
3957 1
0.1%
3382 1
0.1%
2731 1
0.1%
2694 1
0.1%
2537 1
0.1%
2402 1
0.1%
2317 1
0.1%
2287 2
0.2%
2259 1
0.1%
2253 1
0.1%

방문
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.367816
Minimum0
Maximum753
Zeros123
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-05-03T21:03:10.888611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q331
95-th percentile210.1
Maximum753
Range753
Interquartile range (IQR)30

Descriptive statistics

Standard deviation91.384469
Coefficient of variation (CV)2.3818001
Kurtosis20.740304
Mean38.367816
Median Absolute Deviation (MAD)6
Skewness4.229627
Sum33380
Variance8351.1212
MonotonicityNot monotonic
2024-05-03T21:03:11.331397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 123
 
14.1%
1 98
 
11.3%
2 78
 
9.0%
3 56
 
6.4%
5 38
 
4.4%
4 36
 
4.1%
7 35
 
4.0%
6 26
 
3.0%
9 16
 
1.8%
11 15
 
1.7%
Other values (148) 349
40.1%
ValueCountFrequency (%)
0 123
14.1%
1 98
11.3%
2 78
9.0%
3 56
6.4%
4 36
 
4.1%
5 38
 
4.4%
6 26
 
3.0%
7 35
 
4.0%
8 14
 
1.6%
9 16
 
1.8%
ValueCountFrequency (%)
753 1
0.1%
730 1
0.1%
613 1
0.1%
611 1
0.1%
584 1
0.1%
573 1
0.1%
551 1
0.1%
524 1
0.1%
516 1
0.1%
508 1
0.1%

이메일
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5149425
Minimum0
Maximum587
Zeros426
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-05-03T21:03:11.817410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile41
Maximum587
Range587
Interquartile range (IQR)3

Descriptive statistics

Standard deviation40.617462
Coefficient of variation (CV)4.268808
Kurtosis100.94976
Mean9.5149425
Median Absolute Deviation (MAD)1
Skewness8.8792676
Sum8278
Variance1649.7783
MonotonicityNot monotonic
2024-05-03T21:03:12.260109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 426
49.0%
1 142
 
16.3%
2 49
 
5.6%
3 40
 
4.6%
4 34
 
3.9%
5 29
 
3.3%
7 16
 
1.8%
6 12
 
1.4%
9 9
 
1.0%
8 8
 
0.9%
Other values (67) 105
 
12.1%
ValueCountFrequency (%)
0 426
49.0%
1 142
 
16.3%
2 49
 
5.6%
3 40
 
4.6%
4 34
 
3.9%
5 29
 
3.3%
6 12
 
1.4%
7 16
 
1.8%
8 8
 
0.9%
9 9
 
1.0%
ValueCountFrequency (%)
587 1
0.1%
565 1
0.1%
357 1
0.1%
279 1
0.1%
269 1
0.1%
245 1
0.1%
244 1
0.1%
241 1
0.1%
179 1
0.1%
160 1
0.1%

게시판
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0758621
Minimum0
Maximum177
Zeros697
Zeros (%)80.1%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-05-03T21:03:12.698713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum177
Range177
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.985417
Coefficient of variation (CV)4.8719403
Kurtosis62.305898
Mean3.0758621
Median Absolute Deviation (MAD)0
Skewness7.4193796
Sum2676
Variance224.56271
MonotonicityNot monotonic
2024-05-03T21:03:13.150033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 697
80.1%
1 48
 
5.5%
2 22
 
2.5%
3 15
 
1.7%
4 13
 
1.5%
10 8
 
0.9%
9 6
 
0.7%
7 6
 
0.7%
6 6
 
0.7%
13 4
 
0.5%
Other values (33) 45
 
5.2%
ValueCountFrequency (%)
0 697
80.1%
1 48
 
5.5%
2 22
 
2.5%
3 15
 
1.7%
4 13
 
1.5%
5 4
 
0.5%
6 6
 
0.7%
7 6
 
0.7%
9 6
 
0.7%
10 8
 
0.9%
ValueCountFrequency (%)
177 1
0.1%
153 1
0.1%
147 1
0.1%
134 1
0.1%
128 1
0.1%
96 1
0.1%
91 1
0.1%
90 1
0.1%
89 1
0.1%
86 2
0.2%

SNS
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.055172414
Minimum0
Maximum8
Zeros851
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-05-03T21:03:13.536265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45176005
Coefficient of variation (CV)8.1881508
Kurtosis148.04872
Mean0.055172414
Median Absolute Deviation (MAD)0
Skewness11.040038
Sum48
Variance0.20408714
MonotonicityNot monotonic
2024-05-03T21:03:13.944237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 851
97.8%
1 7
 
0.8%
2 4
 
0.5%
3 4
 
0.5%
4 2
 
0.2%
5 1
 
0.1%
8 1
 
0.1%
ValueCountFrequency (%)
0 851
97.8%
1 7
 
0.8%
2 4
 
0.5%
3 4
 
0.5%
4 2
 
0.2%
5 1
 
0.1%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
5 1
 
0.1%
4 2
 
0.2%
3 4
 
0.5%
2 4
 
0.5%
1 7
 
0.8%
0 851
97.8%

이동상담
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6436782
Minimum0
Maximum333
Zeros848
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-05-03T21:03:14.318259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum333
Range333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.490365
Coefficient of variation (CV)11.249383
Kurtosis208.69106
Mean1.6436782
Median Absolute Deviation (MAD)0
Skewness13.755993
Sum1430
Variance341.8936
MonotonicityNot monotonic
2024-05-03T21:03:14.720083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 848
97.5%
1 5
 
0.6%
2 3
 
0.3%
292 1
 
0.1%
4 1
 
0.1%
333 1
 
0.1%
176 1
 
0.1%
147 1
 
0.1%
10 1
 
0.1%
3 1
 
0.1%
Other values (7) 7
 
0.8%
ValueCountFrequency (%)
0 848
97.5%
1 5
 
0.6%
2 3
 
0.3%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
10 1
 
0.1%
16 1
 
0.1%
61 1
 
0.1%
ValueCountFrequency (%)
333 1
0.1%
292 1
0.1%
176 1
0.1%
155 1
0.1%
147 1
0.1%
120 1
0.1%
90 1
0.1%
61 1
0.1%
16 1
0.1%
10 1
0.1%

문자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing870
Missing (%)100.0%
Memory size7.8 KiB

합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct334
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.90115
Minimum0
Maximum4450
Zeros158
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-05-03T21:03:15.158981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.25
median30
Q3265.75
95-th percentile1633.85
Maximum4450
Range4450
Interquartile range (IQR)258.5

Descriptive statistics

Standard deviation579.12075
Coefficient of variation (CV)2.2455144
Kurtosis14.962402
Mean257.90115
Median Absolute Deviation (MAD)30
Skewness3.7130708
Sum224374
Variance335380.84
MonotonicityNot monotonic
2024-05-03T21:03:15.569480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 158
 
18.2%
20 21
 
2.4%
9 16
 
1.8%
5 15
 
1.7%
12 14
 
1.6%
10 14
 
1.6%
24 14
 
1.6%
15 13
 
1.5%
7 13
 
1.5%
11 12
 
1.4%
Other values (324) 580
66.7%
ValueCountFrequency (%)
0 158
18.2%
1 7
 
0.8%
2 4
 
0.5%
3 5
 
0.6%
4 4
 
0.5%
5 15
 
1.7%
6 12
 
1.4%
7 13
 
1.5%
8 9
 
1.0%
9 16
 
1.8%
ValueCountFrequency (%)
4450 1
0.1%
3988 1
0.1%
3630 1
0.1%
3420 1
0.1%
3393 1
0.1%
3232 1
0.1%
2916 1
0.1%
2915 1
0.1%
2913 1
0.1%
2847 1
0.1%

Interactions

2024-05-03T21:03:06.591616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:54.031696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:55.844687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:57.484208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:59.352386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:00.915083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:02.609742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:04.404054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:06.759900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:54.219307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:56.033998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:57.675979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:59.547946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:01.097861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:02.797360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:04.606348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:06.971637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:54.603602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:56.269364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:57.880743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:59.769494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:01.339846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:02.998248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:04.949559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:07.152652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:54.851545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:56.470636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:58.068766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:59.956636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:01.564903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:03.190419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:05.228174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:07.324264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:55.073667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:56.659949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:58.252625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:00.173110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:01.750671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:03.377308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:05.511874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:07.505459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:55.271203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:56.858545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:58.471177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:00.364254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:01.940489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:03.834187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:05.788362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:07.757334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:55.499653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:57.060946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:58.818049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:00.566376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:02.138022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:04.034354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:06.074431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:08.024705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:55.678575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:57.255898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:02:59.110818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:00.745987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:02.345568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:04.231400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T21:03:06.341853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T21:03:16.028835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월기관/유형전화방문이메일게시판SNS이동상담합계
년월1.0000.0000.1790.1470.1600.1800.2210.1250.249
기관/유형0.0001.0000.6730.6510.3740.2970.1180.1140.689
전화0.1790.6731.0000.7530.7170.8170.0000.2690.934
방문0.1470.6510.7531.0000.5860.7300.1150.5420.918
이메일0.1600.3740.7170.5861.0000.5580.0000.0000.658
게시판0.1800.2970.8170.7300.5581.0000.0000.0000.759
SNS0.2210.1180.0000.1150.0000.0001.0000.0000.000
이동상담0.1250.1140.2690.5420.0000.0000.0001.0000.404
합계0.2490.6890.9340.9180.6580.7590.0000.4041.000
2024-05-03T21:03:16.247971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월전화방문이메일게시판SNS이동상담합계기관/유형
년월1.000-0.244-0.123-0.267-0.4700.197-0.246-0.5190.000
전화-0.2441.0000.8760.7100.3760.0200.1410.8300.349
방문-0.1230.8761.0000.6670.2410.0610.1820.6910.305
이메일-0.2670.7100.6671.0000.311-0.0080.1010.7100.178
게시판-0.4700.3760.2410.3111.000-0.0740.0510.4310.125
SNS0.1970.0200.061-0.008-0.0741.000-0.024-0.2070.053
이동상담-0.2460.1410.1820.1010.051-0.0241.0000.1660.048
합계-0.5190.8300.6910.7100.431-0.2070.1661.0000.336
기관/유형0.0000.3490.3050.1780.1250.0530.0480.3361.000

Missing values

2024-05-03T21:03:08.336214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T21:03:08.616170image/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

년월기관/유형전화방문이메일게시판SNS이동상담문자합계
0202404교육23110000<NA>0
1202404교통710000<NA>0
2202404금융440000<NA>0
3202404기타4360000<NA>0
4202404노무116280000<NA>0
5202404문화/관광110000<NA>0
6202404보건/복지42160000<NA>0
7202404비즈니스30100000<NA>0
8202404안전700000<NA>0
9202404여성/가족1030000<NA>0
년월기관/유형전화방문이메일게시판SNS이동상담문자합계
860201907문화/관광1710000<NA>18
861201907보건/복지348644000<NA>416
862201907비즈니스1131000<NA>15
863201907안전49221000<NA>72
864201907여성/가족2240000<NA>26
865201907전년도 총계222150491200<NA>2818
866201907정보통신740000<NA>11
867201907주거3471000<NA>42
868201907출입국4551844000<NA>643
869201907행정3705222100<NA>445