Overview

Dataset statistics

Number of variables7
Number of observations142
Missing cells63
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory62.9 B

Variable types

Numeric6
Text1

Dataset

Description대전광역시 인재개발원 나라배움터 사이버(직무)교육과정으로 4차 산업혁명과 인문학 특강 등 131개 과정, 외국어(영어 등 4개 과정), 자격증(공인중개사 등 7개 과정) 월별 이수현황
Author대전광역시
URLhttps://www.data.go.kr/data/15081823/fileData.do

Alerts

2월 is highly overall correlated with 3월 and 3 other fieldsHigh correlation
3월 is highly overall correlated with 2월 and 3 other fieldsHigh correlation
4월 is highly overall correlated with 2월 and 3 other fieldsHigh correlation
5월 is highly overall correlated with 2월 and 3 other fieldsHigh correlation
6월 is highly overall correlated with 2월 and 3 other fieldsHigh correlation
2월 has 17 (12.0%) missing valuesMissing
3월 has 8 (5.6%) missing valuesMissing
4월 has 10 (7.0%) missing valuesMissing
5월 has 14 (9.9%) missing valuesMissing
6월 has 14 (9.9%) missing valuesMissing
번호 has unique valuesUnique
과정명 has unique valuesUnique
2월 has 3 (2.1%) zerosZeros
3월 has 3 (2.1%) zerosZeros
4월 has 2 (1.4%) zerosZeros
6월 has 2 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-11 23:26:30.489720
Analysis finished2023-12-11 23:26:34.947410
Duration4.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.5
Minimum1
Maximum142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:26:35.015181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.05
Q136.25
median71.5
Q3106.75
95-th percentile134.95
Maximum142
Range141
Interquartile range (IQR)70.5

Descriptive statistics

Standard deviation41.135953
Coefficient of variation (CV)0.57532802
Kurtosis-1.2
Mean71.5
Median Absolute Deviation (MAD)35.5
Skewness0
Sum10153
Variance1692.1667
MonotonicityStrictly increasing
2023-12-12T08:26:35.155401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
99 1
 
0.7%
93 1
 
0.7%
94 1
 
0.7%
95 1
 
0.7%
96 1
 
0.7%
97 1
 
0.7%
98 1
 
0.7%
100 1
 
0.7%
91 1
 
0.7%
Other values (132) 132
93.0%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
142 1
0.7%
141 1
0.7%
140 1
0.7%
139 1
0.7%
138 1
0.7%
137 1
0.7%
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%

과정명
Text

UNIQUE 

Distinct142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T08:26:35.493057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length24
Mean length14.34507
Min length2

Characters and Unicode

Total characters2037
Distinct characters340
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)100.0%

Sample

1st row사례로 배우는 정부혁신
2nd row에너지로 바꾸는 세상
3rd row지방정부의 사회적가치 실현과 사회적 경제
4th row라이프스타일 의학으로 건강지키기
5th row세상을 변화시키는 IT트렌드
ValueCountFrequency (%)
및 14
 
3.0%
이해 10
 
2.1%
위한 6
 
1.3%
교육 5
 
1.1%
이해와 4
 
0.9%
배우는 4
 
0.9%
보고서 4
 
0.9%
디지털 4
 
0.9%
아동학대 4
 
0.9%
실무 3
 
0.6%
Other values (362) 409
87.6%
2023-12-12T08:26:35.926845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
325
 
16.0%
의 46
 
2.3%
이 42
 
2.1%
공 36
 
1.8%
기 35
 
1.7%
지 30
 
1.5%
로 28
 
1.4%
사 26
 
1.3%
는 26
 
1.3%
리 24
 
1.2%
Other values (330) 1419
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1599
78.5%
Space Separator 325
 
16.0%
Open Punctuation 28
 
1.4%
Close Punctuation 28
 
1.4%
Other Punctuation 22
 
1.1%
Uppercase Letter 18
 
0.9%
Decimal Number 12
 
0.6%
Connector Punctuation 2
 
0.1%
Math Symbol 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 46
 
2.9%
이 42
 
2.6%
공 36
 
2.3%
기 35
 
2.2%
지 30
 
1.9%
로 28
 
1.8%
사 26
 
1.6%
는 26
 
1.6%
리 24
 
1.5%
정 22
 
1.4%
Other values (300) 1284
80.3%
Uppercase Letter
ValueCountFrequency (%)
I 4
22.2%
A 3
16.7%
M 2
11.1%
D 2
11.1%
P 2
11.1%
T 1
 
5.6%
Z 1
 
5.6%
C 1
 
5.6%
S 1
 
5.6%
F 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 11
50.0%
! 5
22.7%
' 4
 
18.2%
& 1
 
4.5%
? 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 5
41.7%
4 4
33.3%
9 2
 
16.7%
2 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 23
82.1%
[ 4
 
14.3%
「 1
 
3.6%
Close Punctuation
ValueCountFrequency (%)
) 23
82.1%
] 4
 
14.3%
」 1
 
3.6%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
325
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1598
78.4%
Common 420
 
20.6%
Latin 18
 
0.9%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 46
 
2.9%
이 42
 
2.6%
공 36
 
2.3%
기 35
 
2.2%
지 30
 
1.9%
로 28
 
1.8%
사 26
 
1.6%
는 26
 
1.6%
리 24
 
1.5%
정 22
 
1.4%
Other values (299) 1283
80.3%
Common
ValueCountFrequency (%)
325
77.4%
( 23
 
5.5%
) 23
 
5.5%
, 11
 
2.6%
1 5
 
1.2%
! 5
 
1.2%
' 4
 
1.0%
[ 4
 
1.0%
] 4
 
1.0%
4 4
 
1.0%
Other values (10) 12
 
2.9%
Latin
ValueCountFrequency (%)
I 4
22.2%
A 3
16.7%
M 2
11.1%
D 2
11.1%
P 2
11.1%
T 1
 
5.6%
Z 1
 
5.6%
C 1
 
5.6%
S 1
 
5.6%
F 1
 
5.6%
Han
ValueCountFrequency (%)
家 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1598
78.4%
ASCII 436
 
21.4%
None 2
 
0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
325
74.5%
( 23
 
5.3%
) 23
 
5.3%
, 11
 
2.5%
1 5
 
1.1%
! 5
 
1.1%
I 4
 
0.9%
' 4
 
0.9%
[ 4
 
0.9%
] 4
 
0.9%
Other values (18) 28
 
6.4%
Hangul
ValueCountFrequency (%)
의 46
 
2.9%
이 42
 
2.6%
공 36
 
2.3%
기 35
 
2.2%
지 30
 
1.9%
로 28
 
1.8%
사 26
 
1.6%
는 26
 
1.6%
리 24
 
1.5%
정 22
 
1.4%
Other values (299) 1283
80.3%
None
ValueCountFrequency (%)
「 1
50.0%
」 1
50.0%
CJK
ValueCountFrequency (%)
家 1
100.0%

2월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct58
Distinct (%)46.4%
Missing17
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean87.088
Minimum0
Maximum2342
Zeros3
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:26:36.062869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median12
Q338
95-th percentile482.8
Maximum2342
Range2342
Interquartile range (IQR)31

Descriptive statistics

Standard deviation258.7275
Coefficient of variation (CV)2.9708743
Kurtosis47.634231
Mean87.088
Median Absolute Deviation (MAD)8
Skewness6.1448096
Sum10886
Variance66939.92
MonotonicityNot monotonic
2023-12-12T08:26:36.186084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
4.9%
10 7
 
4.9%
9 7
 
4.9%
7 7
 
4.9%
12 6
 
4.2%
4 6
 
4.2%
8 5
 
3.5%
3 5
 
3.5%
14 5
 
3.5%
0 3
 
2.1%
Other values (48) 67
47.2%
(Missing) 17
 
12.0%
ValueCountFrequency (%)
0 3
2.1%
1 7
4.9%
2 3
2.1%
3 5
3.5%
4 6
4.2%
5 3
2.1%
6 3
2.1%
7 7
4.9%
8 5
3.5%
9 7
4.9%
ValueCountFrequency (%)
2342 1
0.7%
833 1
0.7%
814 1
0.7%
689 1
0.7%
685 1
0.7%
588 1
0.7%
484 1
0.7%
478 2
1.4%
331 1
0.7%
267 1
0.7%

3월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct63
Distinct (%)47.0%
Missing8
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean53.828358
Minimum0
Maximum577
Zeros3
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:26:36.310781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median13
Q346
95-th percentile313.4
Maximum577
Range577
Interquartile range (IQR)40

Descriptive statistics

Standard deviation108.67227
Coefficient of variation (CV)2.0188665
Kurtosis11.086366
Mean53.828358
Median Absolute Deviation (MAD)11
Skewness3.306256
Sum7213
Variance11809.662
MonotonicityNot monotonic
2023-12-12T08:26:36.424912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9
 
6.3%
6 9
 
6.3%
7 8
 
5.6%
9 8
 
5.6%
5 6
 
4.2%
4 6
 
4.2%
1 5
 
3.5%
13 5
 
3.5%
8 4
 
2.8%
31 3
 
2.1%
Other values (53) 71
50.0%
(Missing) 8
 
5.6%
ValueCountFrequency (%)
0 3
 
2.1%
1 5
3.5%
2 9
6.3%
3 3
 
2.1%
4 6
4.2%
5 6
4.2%
6 9
6.3%
7 8
5.6%
8 4
2.8%
9 8
5.6%
ValueCountFrequency (%)
577 2
1.4%
519 1
0.7%
435 1
0.7%
353 1
0.7%
352 1
0.7%
329 1
0.7%
305 1
0.7%
302 1
0.7%
285 1
0.7%
200 1
0.7%

4월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct62
Distinct (%)47.0%
Missing10
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean46.045455
Minimum0
Maximum764
Zeros2
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:26:36.552929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median12
Q336.5
95-th percentile212
Maximum764
Range764
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation112.29676
Coefficient of variation (CV)2.4388241
Kurtosis25.002491
Mean46.045455
Median Absolute Deviation (MAD)9
Skewness4.7511131
Sum6078
Variance12610.563
MonotonicityNot monotonic
2023-12-12T08:26:36.689604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 12
 
8.5%
5 11
 
7.7%
8 8
 
5.6%
6 8
 
5.6%
4 6
 
4.2%
7 4
 
2.8%
1 4
 
2.8%
3 4
 
2.8%
9 3
 
2.1%
13 3
 
2.1%
Other values (52) 69
48.6%
(Missing) 10
 
7.0%
ValueCountFrequency (%)
0 2
 
1.4%
1 4
 
2.8%
2 12
8.5%
3 4
 
2.8%
4 6
4.2%
5 11
7.7%
6 8
5.6%
7 4
 
2.8%
8 8
5.6%
9 3
 
2.1%
ValueCountFrequency (%)
764 1
0.7%
736 1
0.7%
499 1
0.7%
410 1
0.7%
279 1
0.7%
240 1
0.7%
234 1
0.7%
194 1
0.7%
164 1
0.7%
149 1
0.7%

5월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)43.0%
Missing14
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean50.039062
Minimum0
Maximum967
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:26:36.817871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15.75
median11
Q334.5
95-th percentile195.05
Maximum967
Range967
Interquartile range (IQR)28.75

Descriptive statistics

Standard deviation131.31158
Coefficient of variation (CV)2.6241814
Kurtosis32.366839
Mean50.039062
Median Absolute Deviation (MAD)8
Skewness5.384006
Sum6405
Variance17242.731
MonotonicityNot monotonic
2023-12-12T08:26:37.229681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 11
 
7.7%
8 9
 
6.3%
6 7
 
4.9%
7 7
 
4.9%
4 7
 
4.9%
10 6
 
4.2%
5 6
 
4.2%
1 5
 
3.5%
15 4
 
2.8%
17 3
 
2.1%
Other values (45) 63
44.4%
(Missing) 14
 
9.9%
ValueCountFrequency (%)
0 1
 
0.7%
1 5
3.5%
2 2
 
1.4%
3 11
7.7%
4 7
4.9%
5 6
4.2%
6 7
4.9%
7 7
4.9%
8 9
6.3%
9 3
 
2.1%
ValueCountFrequency (%)
967 1
0.7%
895 1
0.7%
508 1
0.7%
362 1
0.7%
288 1
0.7%
254 1
0.7%
201 1
0.7%
184 1
0.7%
125 1
0.7%
124 1
0.7%

6월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct55
Distinct (%)43.0%
Missing14
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean42.53125
Minimum0
Maximum580
Zeros2
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T08:26:37.343683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median15
Q339.25
95-th percentile223.95
Maximum580
Range580
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation82.629897
Coefficient of variation (CV)1.9428043
Kurtosis17.986121
Mean42.53125
Median Absolute Deviation (MAD)10.5
Skewness3.9052852
Sum5444
Variance6827.6998
MonotonicityNot monotonic
2023-12-12T08:26:37.472875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 8
 
5.6%
8 8
 
5.6%
11 7
 
4.9%
4 6
 
4.2%
7 6
 
4.2%
15 6
 
4.2%
6 6
 
4.2%
5 5
 
3.5%
9 5
 
3.5%
25 5
 
3.5%
Other values (45) 66
46.5%
(Missing) 14
 
9.9%
ValueCountFrequency (%)
0 2
 
1.4%
1 2
 
1.4%
2 4
2.8%
3 8
5.6%
4 6
4.2%
5 5
3.5%
6 6
4.2%
7 6
4.2%
8 8
5.6%
9 5
3.5%
ValueCountFrequency (%)
580 1
0.7%
396 1
0.7%
361 1
0.7%
292 1
0.7%
248 1
0.7%
228 1
0.7%
225 1
0.7%
222 1
0.7%
162 1
0.7%
152 1
0.7%

Interactions

2023-12-12T08:26:34.053405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:30.808736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:31.396721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.235292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.813418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.498140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:34.134428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:30.893692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:31.469768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.351354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.930365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.609763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:34.214501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:31.018497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:31.549512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.445090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.010064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.703989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:34.307261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:31.124619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:31.949266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.538185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.118682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.804134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:34.408313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:31.230422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.063242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.640536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.257409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.907084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:34.498140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:31.317035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.153332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:32.726645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.390675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:26:33.978552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:26:37.563256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호2월3월4월5월6월
번호1.0000.4190.4880.2550.3760.301
2월0.4191.0000.9970.7890.7460.816
3월0.4880.9971.0000.8610.8320.889
4월0.2550.7890.8611.0000.9600.837
5월0.3760.7460.8320.9601.0000.849
6월0.3010.8160.8890.8370.8491.000
2023-12-12T08:26:37.660487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호2월3월4월5월6월
번호1.000-0.162-0.081-0.249-0.327-0.369
2월-0.1621.0000.6430.6720.6330.643
3월-0.0810.6431.0000.8680.8850.855
4월-0.2490.6720.8681.0000.8900.886
5월-0.3270.6330.8850.8901.0000.915
6월-0.3690.6430.8550.8860.9151.000

Missing values

2023-12-12T08:26:34.619555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:26:34.765508image/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.
2023-12-12T08:26:34.882934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

번호과정명2월3월4월5월6월
01사례로 배우는 정부혁신2<NA><NA><NA><NA>
12에너지로 바꾸는 세상106<NA><NA><NA><NA>
23지방정부의 사회적가치 실현과 사회적 경제1<NA><NA><NA><NA>
34라이프스타일 의학으로 건강지키기100<NA><NA><NA>
45세상을 변화시키는 IT트렌드510<NA><NA><NA>
56주택법0<NA><NA><NA><NA>
67경관계획의 이론과 실제1076<NA><NA>
78도로 포장 점검 및 관리031<NA><NA>
89도시정책 기본이해(마이크로러닝)0<NA><NA><NA><NA>
910사례로 배우는 알기 쉬운 지역공동체421<NA><NA>
번호과정명2월3월4월5월6월
132133중국어<NA>10367
133134일본어<NA>161486
134135기타 외국어<NA>9543
135136공인중개사<NA>45251511
136137소방설비기사<NA>35121711
137138주택관리사<NA>9261
138139한국사능력검정<NA>16670
139140한국어능력검정<NA>6232
140141컴퓨터 활용능력1급<NA>12234
141142요양보호사<NA>7711