Overview

Dataset statistics

Number of variables11
Number of observations364
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.0 KiB
Average record size in memory98.4 B

Variable types

DateTime1
Numeric7
Categorical3

Dataset

Description사립학교교직원연금공단 급여 미청구자 관리 현황과 관련된 데이터로 상담일자,LMS,메일(개인),우편(개인),전화,우편((현)기관),우편((전)기관),메일(기관),방문,기타,계 등의 항목을 제공합니다.(단위 : 건 수)
Author사립학교교직원연금공단
URLhttps://www.data.go.kr/data/15081264/fileData.do

Alerts

장문메시지 is highly overall correlated with 계High correlation
계 is highly overall correlated with 장문메시지High correlation
우편((현)기관) is highly overall correlated with 우편((전)기관)High correlation
우편((전)기관) is highly overall correlated with 우편((현)기관)High correlation
우편((현)기관) is highly imbalanced (95.9%)Imbalance
우편((전)기관) is highly imbalanced (95.3%)Imbalance
방문 is highly imbalanced (92.8%)Imbalance
상담일자 has unique valuesUnique
장문메시지 has 103 (28.3%) zerosZeros
메일(개인) has 217 (59.6%) zerosZeros
우편(개인) has 260 (71.4%) zerosZeros
전화 has 212 (58.2%) zerosZeros
메일(기관) has 295 (81.0%) zerosZeros
기타 has 329 (90.4%) zerosZeros

Reproduction

Analysis started2023-12-11 23:15:55.005205
Analysis finished2023-12-11 23:16:00.169327
Duration5.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상담일자
Date

UNIQUE 

Distinct364
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2020-01-15 00:00:00
Maximum2022-12-30 00:00:00
2023-12-12T08:16:00.240017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:16:00.623756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

장문메시지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct151
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.73901
Minimum0
Maximum5521
Zeros103
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T08:16:00.743677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.5
Q393.25
95-th percentile629.5
Maximum5521
Range5521
Interquartile range (IQR)93.25

Descriptive statistics

Standard deviation469.01965
Coefficient of variation (CV)3.1114682
Kurtosis61.579899
Mean150.73901
Median Absolute Deviation (MAD)8.5
Skewness6.9317326
Sum54869
Variance219979.43
MonotonicityNot monotonic
2023-12-12T08:16:00.866352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
28.3%
1 24
 
6.6%
2 15
 
4.1%
6 9
 
2.5%
5 9
 
2.5%
4 7
 
1.9%
3 6
 
1.6%
12 6
 
1.6%
14 5
 
1.4%
16 5
 
1.4%
Other values (141) 175
48.1%
ValueCountFrequency (%)
0 103
28.3%
1 24
 
6.6%
2 15
 
4.1%
3 6
 
1.6%
4 7
 
1.9%
5 9
 
2.5%
6 9
 
2.5%
7 4
 
1.1%
8 5
 
1.4%
9 2
 
0.5%
ValueCountFrequency (%)
5521 1
0.3%
3685 1
0.3%
2899 1
0.3%
2342 1
0.3%
2288 1
0.3%
2161 1
0.3%
1466 1
0.3%
1461 1
0.3%
1188 1
0.3%
1168 1
0.3%

메일(개인)
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.401099
Minimum0
Maximum4515
Zeros217
Zeros (%)59.6%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T08:16:01.006449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile214.3
Maximum4515
Range4515
Interquartile range (IQR)8

Descriptive statistics

Standard deviation344.5403
Coefficient of variation (CV)5.037058
Kurtosis97.305286
Mean68.401099
Median Absolute Deviation (MAD)0
Skewness9.0895527
Sum24898
Variance118708.02
MonotonicityNot monotonic
2023-12-12T08:16:01.135785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 217
59.6%
1 17
 
4.7%
3 11
 
3.0%
5 10
 
2.7%
2 6
 
1.6%
4 5
 
1.4%
6 4
 
1.1%
8 4
 
1.1%
11 3
 
0.8%
95 2
 
0.5%
Other values (73) 85
 
23.4%
ValueCountFrequency (%)
0 217
59.6%
1 17
 
4.7%
2 6
 
1.6%
3 11
 
3.0%
4 5
 
1.4%
5 10
 
2.7%
6 4
 
1.1%
7 2
 
0.5%
8 4
 
1.1%
9 1
 
0.3%
ValueCountFrequency (%)
4515 1
0.3%
3080 1
0.3%
1971 1
0.3%
1838 1
0.3%
1667 1
0.3%
1163 1
0.3%
818 1
0.3%
692 1
0.3%
601 1
0.3%
535 1
0.3%

우편(개인)
Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.494505
Minimum0
Maximum2406
Zeros260
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T08:16:01.284437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile157.6
Maximum2406
Range2406
Interquartile range (IQR)1

Descriptive statistics

Standard deviation170.94227
Coefficient of variation (CV)5.2606515
Kurtosis114.45664
Mean32.494505
Median Absolute Deviation (MAD)0
Skewness9.642103
Sum11828
Variance29221.259
MonotonicityNot monotonic
2023-12-12T08:16:01.434403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 260
71.4%
1 26
 
7.1%
2 10
 
2.7%
4 8
 
2.2%
3 4
 
1.1%
14 3
 
0.8%
19 2
 
0.5%
5 2
 
0.5%
7 2
 
0.5%
10 2
 
0.5%
Other values (42) 45
 
12.4%
ValueCountFrequency (%)
0 260
71.4%
1 26
 
7.1%
2 10
 
2.7%
3 4
 
1.1%
4 8
 
2.2%
5 2
 
0.5%
7 2
 
0.5%
8 2
 
0.5%
9 1
 
0.3%
10 2
 
0.5%
ValueCountFrequency (%)
2406 1
0.3%
1269 1
0.3%
1064 1
0.3%
745 1
0.3%
718 1
0.3%
465 1
0.3%
459 1
0.3%
449 1
0.3%
401 1
0.3%
324 1
0.3%

전화
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)12.4%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.4447514
Minimum0
Maximum144
Zeros212
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T08:16:01.615099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile33.95
Maximum144
Range144
Interquartile range (IQR)3

Descriptive statistics

Standard deviation17.547355
Coefficient of variation (CV)2.7227358
Kurtosis23.381863
Mean6.4447514
Median Absolute Deviation (MAD)0
Skewness4.4478755
Sum2333
Variance307.90968
MonotonicityNot monotonic
2023-12-12T08:16:01.754826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 212
58.2%
1 41
 
11.3%
2 12
 
3.3%
4 9
 
2.5%
3 8
 
2.2%
5 7
 
1.9%
14 5
 
1.4%
10 4
 
1.1%
7 4
 
1.1%
9 4
 
1.1%
Other values (35) 56
 
15.4%
ValueCountFrequency (%)
0 212
58.2%
1 41
 
11.3%
2 12
 
3.3%
3 8
 
2.2%
4 9
 
2.5%
5 7
 
1.9%
6 3
 
0.8%
7 4
 
1.1%
8 3
 
0.8%
9 4
 
1.1%
ValueCountFrequency (%)
144 1
0.3%
122 1
0.3%
110 1
0.3%
95 1
0.3%
89 1
0.3%
79 1
0.3%
78 1
0.3%
77 1
0.3%
73 1
0.3%
63 1
0.3%

우편((현)기관)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
361 
4
 
1
2
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 361
99.2%
4 1
 
0.3%
2 1
 
0.3%
1 1
 
0.3%

Length

2023-12-12T08:16:01.873195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:16:02.018573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 361
99.2%
4 1
 
0.3%
2 1
 
0.3%
1 1
 
0.3%

우편((전)기관)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
360 
1
 
1
100
 
1
67
 
1
50
 
1

Length

Max length3
Median length1
Mean length1.010989
Min length1

Unique

Unique4 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 360
98.9%
1 1
 
0.3%
100 1
 
0.3%
67 1
 
0.3%
50 1
 
0.3%

Length

2023-12-12T08:16:02.175308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:16:02.307622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 360
98.9%
1 1
 
0.3%
100 1
 
0.3%
67 1
 
0.3%
50 1
 
0.3%

메일(기관)
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4120879
Minimum0
Maximum1287
Zeros295
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T08:16:02.441728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum1287
Range1287
Interquartile range (IQR)0

Descriptive statistics

Standard deviation77.701523
Coefficient of variation (CV)8.2555033
Kurtosis213.58617
Mean9.4120879
Median Absolute Deviation (MAD)0
Skewness13.87549
Sum3426
Variance6037.5267
MonotonicityNot monotonic
2023-12-12T08:16:02.601205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 295
81.0%
1 14
 
3.8%
2 12
 
3.3%
5 6
 
1.6%
8 4
 
1.1%
3 3
 
0.8%
16 3
 
0.8%
6 2
 
0.5%
11 2
 
0.5%
4 2
 
0.5%
Other values (21) 21
 
5.8%
ValueCountFrequency (%)
0 295
81.0%
1 14
 
3.8%
2 12
 
3.3%
3 3
 
0.8%
4 2
 
0.5%
5 6
 
1.6%
6 2
 
0.5%
8 4
 
1.1%
9 1
 
0.3%
10 1
 
0.3%
ValueCountFrequency (%)
1287 1
0.3%
633 1
0.3%
198 1
0.3%
197 1
0.3%
175 1
0.3%
158 1
0.3%
77 1
0.3%
70 1
0.3%
68 1
0.3%
66 1
0.3%

방문
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
359 
1
 
4
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 359
98.6%
1 4
 
1.1%
7 1
 
0.3%

Length

2023-12-12T08:16:02.782264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:16:02.942931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 359
98.6%
1 4
 
1.1%
7 1
 
0.3%

기타
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9230769
Minimum0
Maximum911
Zeros329
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T08:16:03.081438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum911
Range911
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64.707476
Coefficient of variation (CV)9.3466354
Kurtosis159.3452
Mean6.9230769
Median Absolute Deviation (MAD)0
Skewness12.309816
Sum2520
Variance4187.0574
MonotonicityNot monotonic
2023-12-12T08:16:03.240243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 329
90.4%
1 18
 
4.9%
2 3
 
0.8%
3 2
 
0.5%
112 1
 
0.3%
6 1
 
0.3%
5 1
 
0.3%
10 1
 
0.3%
192 1
 
0.3%
142 1
 
0.3%
Other values (6) 6
 
1.6%
ValueCountFrequency (%)
0 329
90.4%
1 18
 
4.9%
2 3
 
0.8%
3 2
 
0.5%
4 1
 
0.3%
5 1
 
0.3%
6 1
 
0.3%
10 1
 
0.3%
87 1
 
0.3%
108 1
 
0.3%
ValueCountFrequency (%)
911 1
0.3%
774 1
0.3%
192 1
0.3%
142 1
0.3%
139 1
0.3%
112 1
0.3%
108 1
0.3%
87 1
0.3%
10 1
0.3%
6 1
0.3%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275.02473
Minimum1
Maximum11325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T08:16:03.404428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15.75
median42
Q3189.75
95-th percentile1058.9
Maximum11325
Range11324
Interquartile range (IQR)184

Descriptive statistics

Standard deviation871.87382
Coefficient of variation (CV)3.1701652
Kurtosis83.259696
Mean275.02473
Median Absolute Deviation (MAD)40
Skewness8.0303372
Sum100109
Variance760163.96
MonotonicityNot monotonic
2023-12-12T08:16:03.568527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 39
 
10.7%
2 20
 
5.5%
3 13
 
3.6%
4 11
 
3.0%
5 8
 
2.2%
6 7
 
1.9%
9 7
 
1.9%
8 6
 
1.6%
25 6
 
1.6%
23 4
 
1.1%
Other values (184) 243
66.8%
ValueCountFrequency (%)
1 39
10.7%
2 20
5.5%
3 13
 
3.6%
4 11
 
3.0%
5 8
 
2.2%
6 7
 
1.9%
7 4
 
1.1%
8 6
 
1.6%
9 7
 
1.9%
10 4
 
1.1%
ValueCountFrequency (%)
11325 1
0.3%
6765 1
0.3%
4870 1
0.3%
4069 1
0.3%
3999 1
0.3%
3955 1
0.3%
2704 1
0.3%
2417 1
0.3%
2006 1
0.3%
1836 1
0.3%

Interactions

2023-12-12T08:15:59.295721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:55.408036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.038112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.713087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.379310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.058786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.677546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.375246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:55.493083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.131386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.807709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.468502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.158029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.755683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.456122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:55.580256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.215656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.886793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.574084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.257470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.833633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.556908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:55.677840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.292501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.985394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.671725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.346845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.921642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.641374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:55.773255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.379211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.104934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.771011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.437363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.031235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.715399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:55.860502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.488460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.208413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.863498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.522408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.125261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.782984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:55.956362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:56.597806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.288182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:57.953886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:58.590038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:15:59.205847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:16:03.748894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장문메시지메일(개인)우편(개인)전화우편((현)기관)우편((전)기관)메일(기관)방문기타계
장문메시지1.0000.9290.5120.0000.0000.0000.8820.0000.0000.950
메일(개인)0.9291.0000.4220.0000.0000.0000.7360.0000.0000.986
우편(개인)0.5120.4221.0000.0000.0000.0000.0000.0000.0000.804
전화0.0000.0000.0001.0000.0000.0000.0000.3610.0000.000
우편((현)기관)0.0000.0000.0000.0001.0000.8400.6050.0000.0000.000
우편((전)기관)0.0000.0000.0000.0000.8401.0000.3240.0000.0000.000
메일(기관)0.8820.7360.0000.0000.6050.3241.0000.0000.0000.732
방문0.0000.0000.0000.3610.0000.0000.0001.0000.0000.000
기타0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
계0.9500.9860.8040.0000.0000.0000.7320.0000.0001.000
2023-12-12T08:16:03.891409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편((전)기관)우편((현)기관)방문
우편((전)기관)1.0000.8130.000
우편((현)기관)0.8131.0000.000
방문0.0000.0001.000
2023-12-12T08:16:03.999055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장문메시지메일(개인)우편(개인)전화메일(기관)기타계우편((현)기관)우편((전)기관)방문
장문메시지1.0000.4790.041-0.2310.178-0.1130.7810.0000.0000.000
메일(개인)0.4791.0000.0460.0010.439-0.0110.4560.0000.0000.000
우편(개인)0.0410.0461.000-0.0240.004-0.0610.2350.0000.0000.000
전화-0.2310.001-0.0241.0000.1310.007-0.0760.0000.0000.230
메일(기관)0.1780.4390.0040.1311.0000.0060.1580.2740.2690.000
기타-0.113-0.011-0.0610.0070.0061.0000.0380.0000.0000.000
계0.7810.4560.235-0.0760.1580.0381.0000.0000.0000.000
우편((현)기관)0.0000.0000.0000.0000.2740.0000.0001.0000.8130.000
우편((전)기관)0.0000.0000.0000.0000.2690.0000.0000.8131.0000.000
방문0.0000.0000.0000.2300.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T08:15:59.890580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:16:00.109620image/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

상담일자장문메시지메일(개인)우편(개인)전화우편((현)기관)우편((전)기관)메일(기관)방문기타계
02020-01-15001200000012
12020-02-056000000006
22020-02-061000000001
32020-02-070010000001
42020-02-11109107450000001836
52020-02-12140000000014
62020-02-18140410000019
72020-02-19003300000033
82020-02-200020000002
92020-02-250100000001
상담일자장문메시지메일(개인)우편(개인)전화우편((현)기관)우편((전)기관)메일(기관)방문기타계
3542022-12-06107000000017
3552022-12-070001000001
3562022-12-093200001006
3572022-12-140020000002
3582022-12-150000000011
3592022-12-200001000001
3602022-12-2162149100000001112
3612022-12-260001000001
3622022-12-270001000001
3632022-12-302481990000000447