Overview

Dataset statistics

Number of variables13
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory118.2 B

Variable types

DateTime1
Numeric6
Categorical6

Dataset

Description용인도시공사가 사업 운영 및 행정 처리 과정에서 발송한 월별 문자 발송건수입니다. 각 통신사별(SKT, LGU+, KT, 기타) SMS, LMS, MMS 월별 발송 이력을 정리한 자료로, 2019년 5월부터 제공합니다.
Author용인도시공사
URLhttps://www.data.go.kr/data/15112563/fileData.do

Alerts

장문문자(일반번호) has constant value ""Constant
멀티문자(일반번호) has constant value ""Constant
멀티문자(LG유플러스) is highly overall correlated with 멀티문자(SKT) and 1 other fieldsHigh correlation
멀티문자(SKT) is highly overall correlated with 멀티문자(KT) and 1 other fieldsHigh correlation
멀티문자(KT) is highly overall correlated with 멀티문자(SKT) and 1 other fieldsHigh correlation
단문문자(SKT) is highly overall correlated with 단문문자(KT) and 1 other fieldsHigh correlation
단문문자(KT) is highly overall correlated with 단문문자(SKT) and 1 other fieldsHigh correlation
단문문자(LG유플러스) is highly overall correlated with 단문문자(SKT) and 1 other fieldsHigh correlation
장문문자(SKT) is highly overall correlated with 장문문자(KT) and 1 other fieldsHigh correlation
장문문자(KT) is highly overall correlated with 장문문자(SKT) and 1 other fieldsHigh correlation
장문문자(LG유플러스) is highly overall correlated with 장문문자(SKT) and 1 other fieldsHigh correlation
단문문자(일반번호) is highly imbalanced (81.7%)Imbalance
멀티문자(SKT) is highly imbalanced (81.7%)Imbalance
멀티문자(KT) is highly imbalanced (84.6%)Imbalance
멀티문자(LG유플러스) is highly imbalanced (84.6%)Imbalance
발송연월 has unique valuesUnique
장문문자(SKT) has unique valuesUnique
장문문자(KT) has unique valuesUnique
장문문자(LG유플러스) has unique valuesUnique
장문문자(LG유플러스) has 1 (1.7%) zerosZeros

Reproduction

Analysis started2024-05-18 09:05:35.110675
Analysis finished2024-05-18 09:05:46.955429
Duration11.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발송연월
Date

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
Minimum2019-05-01 00:00:00
Maximum2024-04-01 00:00:00
2024-05-18T18:05:47.156400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:47.604281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

단문문자(SKT)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.98333
Minimum27
Maximum2397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-05-18T18:05:48.055454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile51.7
Q1109.75
median165.5
Q3241.75
95-th percentile613.2
Maximum2397
Range2370
Interquartile range (IQR)132

Descriptive statistics

Standard deviation323.72543
Coefficient of variation (CV)1.3322948
Kurtosis33.970841
Mean242.98333
Median Absolute Deviation (MAD)70
Skewness5.3187502
Sum14579
Variance104798.15
MonotonicityNot monotonic
2024-05-18T18:05:48.660612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 2
 
3.3%
226 2
 
3.3%
276 2
 
3.3%
195 1
 
1.7%
30 1
 
1.7%
489 1
 
1.7%
607 1
 
1.7%
592 1
 
1.7%
112 1
 
1.7%
76 1
 
1.7%
Other values (47) 47
78.3%
ValueCountFrequency (%)
27 1
1.7%
30 1
1.7%
46 1
1.7%
52 1
1.7%
57 1
1.7%
74 1
1.7%
76 1
1.7%
77 1
1.7%
79 1
1.7%
87 1
1.7%
ValueCountFrequency (%)
2397 1
1.7%
798 1
1.7%
731 1
1.7%
607 1
1.7%
592 1
1.7%
489 1
1.7%
347 1
1.7%
343 1
1.7%
337 1
1.7%
321 1
1.7%

단문문자(KT)
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.5
Minimum9
Maximum1328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-05-18T18:05:49.192094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15
Q151.25
median74.5
Q3118
95-th percentile232.75
Maximum1328
Range1319
Interquartile range (IQR)66.75

Descriptive statistics

Standard deviation171.13732
Coefficient of variation (CV)1.5919751
Kurtosis45.442043
Mean107.5
Median Absolute Deviation (MAD)28.5
Skewness6.3731764
Sum6450
Variance29287.983
MonotonicityNot monotonic
2024-05-18T18:05:49.670505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156 2
 
3.3%
15 2
 
3.3%
74 2
 
3.3%
44 2
 
3.3%
75 2
 
3.3%
55 2
 
3.3%
82 2
 
3.3%
1328 1
 
1.7%
63 1
 
1.7%
123 1
 
1.7%
Other values (43) 43
71.7%
ValueCountFrequency (%)
9 1
1.7%
13 1
1.7%
15 2
3.3%
17 1
1.7%
18 1
1.7%
19 1
1.7%
22 1
1.7%
28 1
1.7%
35 1
1.7%
42 1
1.7%
ValueCountFrequency (%)
1328 1
1.7%
278 1
1.7%
266 1
1.7%
231 1
1.7%
230 1
1.7%
188 1
1.7%
170 1
1.7%
156 2
3.3%
136 1
1.7%
128 1
1.7%

단문문자(LG유플러스)
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.416667
Minimum7
Maximum771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-05-18T18:05:50.120016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15
Q135
median58.5
Q396
95-th percentile241.5
Maximum771
Range764
Interquartile range (IQR)61

Descriptive statistics

Standard deviation107.43706
Coefficient of variation (CV)1.2432447
Kurtosis28.206293
Mean86.416667
Median Absolute Deviation (MAD)25
Skewness4.7129542
Sum5185
Variance11542.722
MonotonicityNot monotonic
2024-05-18T18:05:50.536906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
38 3
 
5.0%
114 3
 
5.0%
35 2
 
3.3%
51 2
 
3.3%
15 2
 
3.3%
65 2
 
3.3%
55 2
 
3.3%
57 2
 
3.3%
34 2
 
3.3%
147 1
 
1.7%
Other values (39) 39
65.0%
ValueCountFrequency (%)
7 1
1.7%
9 1
1.7%
15 2
3.3%
16 1
1.7%
18 1
1.7%
22 1
1.7%
23 1
1.7%
24 1
1.7%
26 1
1.7%
32 1
1.7%
ValueCountFrequency (%)
771 1
1.7%
268 1
1.7%
251 1
1.7%
241 1
1.7%
208 1
1.7%
187 1
1.7%
151 1
1.7%
147 1
1.7%
129 1
1.7%
128 1
1.7%

단문문자(일반번호)
Categorical

IMBALANCE 

Distinct4
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
0
57 
22340
 
1
2
 
1
3
 
1

Length

Max length5
Median length1
Mean length1.0666667
Min length1

Unique

Unique3 ?
Unique (%)5.0%

Sample

1st row22340
2nd row2
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 57
95.0%
22340 1
 
1.7%
2 1
 
1.7%
3 1
 
1.7%

Length

2024-05-18T18:05:50.905342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:05:51.269321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 57
95.0%
22340 1
 
1.7%
2 1
 
1.7%
3 1
 
1.7%

장문문자(SKT)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20700.4
Minimum2098
Maximum85774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-05-18T18:05:51.693350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2098
5-th percentile3556.8
Q113265.25
median19104
Q327297
95-th percentile37062.15
Maximum85774
Range83676
Interquartile range (IQR)14031.75

Descriptive statistics

Standard deviation13070.684
Coefficient of variation (CV)0.63142182
Kurtosis9.281582
Mean20700.4
Median Absolute Deviation (MAD)6494
Skewness2.1142139
Sum1242024
Variance1.7084279 × 108
MonotonicityNot monotonic
2024-05-18T18:05:52.143662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14396 1
 
1.7%
2492 1
 
1.7%
9608 1
 
1.7%
3597 1
 
1.7%
4307 1
 
1.7%
7254 1
 
1.7%
8498 1
 
1.7%
13664 1
 
1.7%
13263 1
 
1.7%
10780 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
2098 1
1.7%
2492 1
1.7%
2793 1
1.7%
3597 1
1.7%
4307 1
1.7%
4489 1
1.7%
7193 1
1.7%
7254 1
1.7%
8498 1
1.7%
9608 1
1.7%
ValueCountFrequency (%)
85774 1
1.7%
44145 1
1.7%
41511 1
1.7%
36828 1
1.7%
33910 1
1.7%
33582 1
1.7%
32601 1
1.7%
32353 1
1.7%
30714 1
1.7%
30407 1
1.7%

장문문자(KT)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12674.067
Minimum1247
Maximum52988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-05-18T18:05:52.573010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1247
5-th percentile2225.75
Q18097.5
median12153.5
Q316639.75
95-th percentile23081.25
Maximum52988
Range51741
Interquartile range (IQR)8542.25

Descriptive statistics

Standard deviation7987.3237
Coefficient of variation (CV)0.63021001
Kurtosis9.9031199
Mean12674.067
Median Absolute Deviation (MAD)4400.5
Skewness2.1866341
Sum760444
Variance63797340
MonotonicityNot monotonic
2024-05-18T18:05:53.016535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10281 1
 
1.7%
1669 1
 
1.7%
6092 1
 
1.7%
2254 1
 
1.7%
2644 1
 
1.7%
4363 1
 
1.7%
5111 1
 
1.7%
8315 1
 
1.7%
7832 1
 
1.7%
6379 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
1247 1
1.7%
1669 1
1.7%
1689 1
1.7%
2254 1
1.7%
2644 1
1.7%
3062 1
1.7%
3949 1
1.7%
4363 1
1.7%
5111 1
1.7%
5967 1
1.7%
ValueCountFrequency (%)
52988 1
1.7%
26398 1
1.7%
25442 1
1.7%
22957 1
1.7%
20681 1
1.7%
20328 1
1.7%
20201 1
1.7%
19257 1
1.7%
18736 1
1.7%
18378 1
1.7%

장문문자(LG유플러스)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8809.5667
Minimum0
Maximum35980
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-05-18T18:05:53.421780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1198.9
Q15551.5
median8393.5
Q311930.25
95-th percentile16569.75
Maximum35980
Range35980
Interquartile range (IQR)6378.75

Descriptive statistics

Standard deviation5667.7405
Coefficient of variation (CV)0.64336201
Kurtosis7.7169668
Mean8809.5667
Median Absolute Deviation (MAD)3286.5
Skewness1.8323063
Sum528574
Variance32123282
MonotonicityNot monotonic
2024-05-18T18:05:53.712229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
1.7%
1207 1
 
1.7%
4783 1
 
1.7%
1451 1
 
1.7%
1698 1
 
1.7%
2758 1
 
1.7%
3241 1
 
1.7%
6302 1
 
1.7%
5998 1
 
1.7%
4803 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
0 1
1.7%
908 1
1.7%
1045 1
1.7%
1207 1
1.7%
1451 1
1.7%
1698 1
1.7%
2102 1
1.7%
2686 1
1.7%
2758 1
1.7%
3241 1
1.7%
ValueCountFrequency (%)
35980 1
1.7%
18004 1
1.7%
17496 1
1.7%
16521 1
1.7%
15660 1
1.7%
14743 1
1.7%
14289 1
1.7%
13915 1
1.7%
13764 1
1.7%
13033 1
1.7%

장문문자(일반번호)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
0
60 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 60
100.0%

Length

2024-05-18T18:05:54.168857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:05:54.442044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 60
100.0%

멀티문자(SKT)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
0
57 
603
 
1
3
 
1
698
 
1

Length

Max length3
Median length1
Mean length1.0666667
Min length1

Unique

Unique3 ?
Unique (%)5.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 57
95.0%
603 1
 
1.7%
3 1
 
1.7%
698 1
 
1.7%

Length

2024-05-18T18:05:54.952088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:05:55.443077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 57
95.0%
603 1
 
1.7%
3 1
 
1.7%
698 1
 
1.7%

멀티문자(KT)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
0
58 
403
 
1
512
 
1

Length

Max length3
Median length1
Mean length1.0666667
Min length1

Unique

Unique2 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 58
96.7%
403 1
 
1.7%
512 1
 
1.7%

Length

2024-05-18T18:05:56.026588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:05:56.493084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 58
96.7%
403 1
 
1.7%
512 1
 
1.7%

멀티문자(LG유플러스)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
0
58 
221
 
1
274
 
1

Length

Max length3
Median length1
Mean length1.0666667
Min length1

Unique

Unique2 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 58
96.7%
221 1
 
1.7%
274 1
 
1.7%

Length

2024-05-18T18:05:57.083385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:05:57.559086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 58
96.7%
221 1
 
1.7%
274 1
 
1.7%

멀티문자(일반번호)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
0
60 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 60
100.0%

Length

2024-05-18T18:05:57.920445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:05:58.264821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 60
100.0%

Interactions

2024-05-18T18:05:44.295540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:36.182846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:38.038990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:39.757004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:41.583245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:42.998542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:44.450556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:36.577210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:38.319952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:40.096267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:41.824876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:43.208423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:44.623403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:36.944275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:38.628949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:40.437821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:42.068909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:43.478017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:44.859699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:37.301664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:39.018656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:40.787366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:42.330458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:43.803427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:45.016184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:37.527660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:39.237274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:41.046414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:42.572695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:43.978560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:45.329164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:37.792145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:39.490668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:41.312557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:42.805606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:05:44.126688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T18:05:58.524977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발송연월단문문자(SKT)단문문자(KT)단문문자(LG유플러스)단문문자(일반번호)장문문자(SKT)장문문자(KT)장문문자(LG유플러스)멀티문자(SKT)멀티문자(KT)멀티문자(LG유플러스)
발송연월1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
단문문자(SKT)1.0001.0000.7740.9570.0000.0000.0000.0000.0000.0000.000
단문문자(KT)1.0000.7741.0000.8400.4720.0000.0000.0000.0000.1350.135
단문문자(LG유플러스)1.0000.9570.8401.0000.0000.0000.0000.0000.0000.0660.066
단문문자(일반번호)1.0000.0000.4720.0001.0000.0000.0000.0000.0000.0000.000
장문문자(SKT)1.0000.0000.0000.0000.0001.0000.9500.9850.0000.0000.000
장문문자(KT)1.0000.0000.0000.0000.0000.9501.0000.9010.0000.0890.089
장문문자(LG유플러스)1.0000.0000.0000.0000.0000.9850.9011.0000.1840.3280.328
멀티문자(SKT)1.0000.0000.0000.0000.0000.0000.0000.1841.0001.0001.000
멀티문자(KT)1.0000.0000.1350.0660.0000.0000.0890.3281.0001.0001.000
멀티문자(LG유플러스)1.0000.0000.1350.0660.0000.0000.0890.3281.0001.0001.000
2024-05-18T18:05:58.952027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
멀티문자(LG유플러스)멀티문자(SKT)단문문자(일반번호)멀티문자(KT)
멀티문자(LG유플러스)1.0000.9910.0001.000
멀티문자(SKT)0.9911.0000.0000.991
단문문자(일반번호)0.0000.0001.0000.000
멀티문자(KT)1.0000.9910.0001.000
2024-05-18T18:05:59.327456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단문문자(SKT)단문문자(KT)단문문자(LG유플러스)장문문자(SKT)장문문자(KT)장문문자(LG유플러스)단문문자(일반번호)멀티문자(SKT)멀티문자(KT)멀티문자(LG유플러스)
단문문자(SKT)1.0000.8020.894-0.170-0.164-0.1120.0000.0000.0000.000
단문문자(KT)0.8021.0000.846-0.074-0.067-0.0780.1970.0000.1230.123
단문문자(LG유플러스)0.8940.8461.000-0.030-0.029-0.0180.0000.0000.0350.035
장문문자(SKT)-0.170-0.074-0.0301.0000.9940.9710.0000.0000.0000.000
장문문자(KT)-0.164-0.067-0.0290.9941.0000.9690.0000.0000.0390.039
장문문자(LG유플러스)-0.112-0.078-0.0180.9710.9691.0000.0000.1160.2210.221
단문문자(일반번호)0.0000.1970.0000.0000.0000.0001.0000.0000.0000.000
멀티문자(SKT)0.0000.0000.0000.0000.0000.1160.0001.0000.9910.991
멀티문자(KT)0.0000.1230.0350.0000.0390.2210.0000.9911.0001.000
멀티문자(LG유플러스)0.0000.1230.0350.0000.0390.2210.0000.9911.0001.000

Missing values

2024-05-18T18:05:45.751479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T18:05:46.714418image/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

발송연월단문문자(SKT)단문문자(KT)단문문자(LG유플러스)단문문자(일반번호)장문문자(SKT)장문문자(KT)장문문자(LG유플러스)장문문자(일반번호)멀티문자(SKT)멀티문자(KT)멀티문자(LG유플러스)멀티문자(일반번호)
02024-04195156151223401439610281000000
12024-03188156952144499478715800000
22024-021451245231633010228736800000
32024-01157825102120713569986400000
42023-12149814201970812805929800000
52023-111428051036828229571566000000
62023-10122645402138613637938400000
72023-09343230129032353206811474306034032210
82023-08196101102028646179531248800000
92023-0726997650154119823689100000
발송연월단문문자(SKT)단문문자(KT)단문문자(LG유플러스)단문문자(일반번호)장문문자(SKT)장문문자(KT)장문문자(LG유플러스)장문문자(일반번호)멀티문자(SKT)멀티문자(KT)멀티문자(LG유플러스)멀티문자(일반번호)
502020-021037457030714183781206900000
512020-01795632032601192571221600000
522019-12101575702558514609909200000
532019-1177152202501314674949700000
542019-1012263480154709137574800000
552019-092397132877101826110800668300000
562019-08927033030027165061018700000
572019-07150907602156712735755300000
582019-0693553801893312093755900000
592019-0513295350127657328463900000