Overview

Dataset statistics

Number of variables13
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory121.5 B

Variable types

Text1
Categorical2
Numeric10

Dataset

Description2022년 한국보훈복지의료공단 대구보훈병원에서 등록한 암등록 환자의 현황을 진료과별 월별 인원수를 제공합니다.
URLhttps://www.data.go.kr/data/15066384/fileData.do

Alerts

4월 has constant value ""Constant
2월 is highly overall correlated with 3월 and 9 other fieldsHigh correlation
3월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
5월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
6월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
7월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
8월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
9월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
10월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
11월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
12월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
1월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
구분 has unique valuesUnique
2월 has 17 (70.8%) zerosZeros
3월 has 14 (58.3%) zerosZeros
5월 has 18 (75.0%) zerosZeros
6월 has 11 (45.8%) zerosZeros
7월 has 12 (50.0%) zerosZeros
8월 has 15 (62.5%) zerosZeros
9월 has 17 (70.8%) zerosZeros
10월 has 13 (54.2%) zerosZeros
11월 has 16 (66.7%) zerosZeros
12월 has 16 (66.7%) zerosZeros

Reproduction

Analysis started2023-12-12 21:59:44.594277
Analysis finished2023-12-12 21:59:54.325089
Duration9.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T06:59:54.464605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.125
Min length2

Characters and Unicode

Total characters99
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row내과
2nd row소화기내과
3rd row내분비내과
4th row순환기내과
5th row호흡기내과
ValueCountFrequency (%)
내과 1
 
4.2%
소화기내과 1
 
4.2%
핵의학과 1
 
4.2%
치과 1
 
4.2%
가정의학과 1
 
4.2%
재활의학과 1
 
4.2%
비뇨기과 1
 
4.2%
피부과 1
 
4.2%
이비인후과 1
 
4.2%
안과 1
 
4.2%
Other values (14) 14
58.3%
2023-12-13T06:59:54.828327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 24
24.2%
내 8
 
8.1%
학 5
 
5.1%
의 5
 
5.1%
신 4
 
4.0%
외 4
 
4.0%
기 4
 
4.0%
비 3
 
3.0%
부 3
 
3.0%
소 3
 
3.0%
Other values (32) 36
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 24
24.2%
내 8
 
8.1%
학 5
 
5.1%
의 5
 
5.1%
신 4
 
4.0%
외 4
 
4.0%
기 4
 
4.0%
비 3
 
3.0%
부 3
 
3.0%
소 3
 
3.0%
Other values (32) 36
36.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 24
24.2%
내 8
 
8.1%
학 5
 
5.1%
의 5
 
5.1%
신 4
 
4.0%
외 4
 
4.0%
기 4
 
4.0%
비 3
 
3.0%
부 3
 
3.0%
소 3
 
3.0%
Other values (32) 36
36.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 24
24.2%
내 8
 
8.1%
학 5
 
5.1%
의 5
 
5.1%
신 4
 
4.0%
외 4
 
4.0%
기 4
 
4.0%
비 3
 
3.0%
부 3
 
3.0%
소 3
 
3.0%
Other values (32) 36
36.4%

1월
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
19 
2
3 
13
2 

Length

Max length2
Median length1
Mean length1.0833333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
79.2%
2 3
 
12.5%
13 2
 
8.3%

Length

2023-12-13T06:59:54.951473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:59:55.036601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
79.2%
2 3
 
12.5%
13 2
 
8.3%

2월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5416667
Minimum0
Maximum23
Zeros17
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:55.115537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.25
95-th percentile11.85
Maximum23
Range23
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation5.5870905
Coefficient of variation (CV)2.1981995
Kurtosis7.5430107
Mean2.5416667
Median Absolute Deviation (MAD)0
Skewness2.6745829
Sum61
Variance31.21558
MonotonicityNot monotonic
2023-12-13T06:59:55.215382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 17
70.8%
12 1
 
4.2%
7 1
 
4.2%
2 1
 
4.2%
5 1
 
4.2%
23 1
 
4.2%
11 1
 
4.2%
1 1
 
4.2%
ValueCountFrequency (%)
0 17
70.8%
1 1
 
4.2%
2 1
 
4.2%
5 1
 
4.2%
7 1
 
4.2%
11 1
 
4.2%
12 1
 
4.2%
23 1
 
4.2%
ValueCountFrequency (%)
23 1
 
4.2%
12 1
 
4.2%
11 1
 
4.2%
7 1
 
4.2%
5 1
 
4.2%
2 1
 
4.2%
1 1
 
4.2%
0 17
70.8%

3월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.125
Minimum0
Maximum45
Zeros14
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:55.314855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile34.25
Maximum45
Range45
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.525843
Coefficient of variation (CV)2.0450356
Kurtosis4.2079705
Mean6.125
Median Absolute Deviation (MAD)0
Skewness2.2573976
Sum147
Variance156.89674
MonotonicityNot monotonic
2023-12-13T06:59:55.405365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14
58.3%
1 3
 
12.5%
5 2
 
8.3%
45 1
 
4.2%
9 1
 
4.2%
15 1
 
4.2%
35 1
 
4.2%
30 1
 
4.2%
ValueCountFrequency (%)
0 14
58.3%
1 3
 
12.5%
5 2
 
8.3%
9 1
 
4.2%
15 1
 
4.2%
30 1
 
4.2%
35 1
 
4.2%
45 1
 
4.2%
ValueCountFrequency (%)
45 1
 
4.2%
35 1
 
4.2%
30 1
 
4.2%
15 1
 
4.2%
9 1
 
4.2%
5 2
 
8.3%
1 3
 
12.5%
0 14
58.3%

4월
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
24 

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 24
100.0%

Length

2023-12-13T06:59:55.525849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:59:55.606715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24
100.0%

5월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0833333
Minimum0
Maximum8
Zeros18
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:55.679163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile5.85
Maximum8
Range8
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation2.244155
Coefficient of variation (CV)2.0715277
Kurtosis3.6001847
Mean1.0833333
Median Absolute Deviation (MAD)0
Skewness2.1026353
Sum26
Variance5.0362319
MonotonicityNot monotonic
2023-12-13T06:59:55.778966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
75.0%
3 2
 
8.3%
8 1
 
4.2%
1 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%
ValueCountFrequency (%)
0 18
75.0%
1 1
 
4.2%
3 2
 
8.3%
5 1
 
4.2%
6 1
 
4.2%
8 1
 
4.2%
ValueCountFrequency (%)
8 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%
3 2
 
8.3%
1 1
 
4.2%
0 18
75.0%

6월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.208333
Minimum0
Maximum52
Zeros11
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:55.873061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37.25
95-th percentile51.25
Maximum52
Range52
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation18.299392
Coefficient of variation (CV)1.7925935
Kurtosis1.3764987
Mean10.208333
Median Absolute Deviation (MAD)1
Skewness1.7323675
Sum245
Variance334.86775
MonotonicityNot monotonic
2023-12-13T06:59:55.963753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11
45.8%
1 3
 
12.5%
52 2
 
8.3%
45 1
 
4.2%
47 1
 
4.2%
2 1
 
4.2%
20 1
 
4.2%
4 1
 
4.2%
7 1
 
4.2%
5 1
 
4.2%
ValueCountFrequency (%)
0 11
45.8%
1 3
 
12.5%
2 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%
7 1
 
4.2%
8 1
 
4.2%
20 1
 
4.2%
45 1
 
4.2%
47 1
 
4.2%
ValueCountFrequency (%)
52 2
8.3%
47 1
 
4.2%
45 1
 
4.2%
20 1
 
4.2%
8 1
 
4.2%
7 1
 
4.2%
5 1
 
4.2%
4 1
 
4.2%
2 1
 
4.2%
1 3
12.5%

7월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2083333
Minimum0
Maximum34
Zeros12
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:56.046610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q34
95-th percentile17.7
Maximum34
Range34
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.1666297
Coefficient of variation (CV)1.9405853
Kurtosis7.3687217
Mean4.2083333
Median Absolute Deviation (MAD)0.5
Skewness2.6271175
Sum101
Variance66.693841
MonotonicityNot monotonic
2023-12-13T06:59:56.139518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12
50.0%
1 3
 
12.5%
2 2
 
8.3%
4 2
 
8.3%
34 1
 
4.2%
12 1
 
4.2%
6 1
 
4.2%
16 1
 
4.2%
18 1
 
4.2%
ValueCountFrequency (%)
0 12
50.0%
1 3
 
12.5%
2 2
 
8.3%
4 2
 
8.3%
6 1
 
4.2%
12 1
 
4.2%
16 1
 
4.2%
18 1
 
4.2%
34 1
 
4.2%
ValueCountFrequency (%)
34 1
 
4.2%
18 1
 
4.2%
16 1
 
4.2%
12 1
 
4.2%
6 1
 
4.2%
4 2
 
8.3%
2 2
 
8.3%
1 3
 
12.5%
0 12
50.0%

8월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7083333
Minimum0
Maximum84
Zeros15
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:56.252038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile28.45
Maximum84
Range84
Interquartile range (IQR)5

Descriptive statistics

Standard deviation17.957629
Coefficient of variation (CV)2.6769137
Kurtosis16.153048
Mean6.7083333
Median Absolute Deviation (MAD)0
Skewness3.8654494
Sum161
Variance322.47645
MonotonicityNot monotonic
2023-12-13T06:59:56.351947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 15
62.5%
5 2
 
8.3%
14 2
 
8.3%
1 2
 
8.3%
84 1
 
4.2%
6 1
 
4.2%
31 1
 
4.2%
ValueCountFrequency (%)
0 15
62.5%
1 2
 
8.3%
5 2
 
8.3%
6 1
 
4.2%
14 2
 
8.3%
31 1
 
4.2%
84 1
 
4.2%
ValueCountFrequency (%)
84 1
 
4.2%
31 1
 
4.2%
14 2
 
8.3%
6 1
 
4.2%
5 2
 
8.3%
1 2
 
8.3%
0 15
62.5%

9월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8333333
Minimum0
Maximum24
Zeros17
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:56.462437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile13.85
Maximum24
Range24
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation6.004829
Coefficient of variation (CV)2.1193514
Kurtosis6.4746123
Mean2.8333333
Median Absolute Deviation (MAD)0
Skewness2.5372983
Sum68
Variance36.057971
MonotonicityNot monotonic
2023-12-13T06:59:56.566391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 17
70.8%
24 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%
2 1
 
4.2%
14 1
 
4.2%
13 1
 
4.2%
4 1
 
4.2%
ValueCountFrequency (%)
0 17
70.8%
2 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%
6 1
 
4.2%
13 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
ValueCountFrequency (%)
24 1
 
4.2%
14 1
 
4.2%
13 1
 
4.2%
6 1
 
4.2%
5 1
 
4.2%
4 1
 
4.2%
2 1
 
4.2%
0 17
70.8%

10월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.708333
Minimum0
Maximum261
Zeros13
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:56.678766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313.25
95-th percentile83.65
Maximum261
Range261
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation55.45345
Coefficient of variation (CV)2.6778326
Kurtosis16.624766
Mean20.708333
Median Absolute Deviation (MAD)0
Skewness3.9210794
Sum497
Variance3075.0851
MonotonicityNot monotonic
2023-12-13T06:59:56.769612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 13
54.2%
1 2
 
8.3%
261 1
 
4.2%
29 1
 
4.2%
88 1
 
4.2%
7 1
 
4.2%
14 1
 
4.2%
13 1
 
4.2%
59 1
 
4.2%
20 1
 
4.2%
ValueCountFrequency (%)
0 13
54.2%
1 2
 
8.3%
4 1
 
4.2%
7 1
 
4.2%
13 1
 
4.2%
14 1
 
4.2%
20 1
 
4.2%
29 1
 
4.2%
59 1
 
4.2%
88 1
 
4.2%
ValueCountFrequency (%)
261 1
4.2%
88 1
4.2%
59 1
4.2%
29 1
4.2%
20 1
4.2%
14 1
4.2%
13 1
4.2%
7 1
4.2%
4 1
4.2%
1 2
8.3%

11월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4166667
Minimum0
Maximum12
Zeros16
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:56.855374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9.1
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.1473476
Coefficient of variation (CV)2.2216572
Kurtosis6.9740321
Mean1.4166667
Median Absolute Deviation (MAD)0
Skewness2.7284829
Sum34
Variance9.9057971
MonotonicityNot monotonic
2023-12-13T06:59:56.947731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 16
66.7%
1 3
 
12.5%
12 1
 
4.2%
4 1
 
4.2%
10 1
 
4.2%
2 1
 
4.2%
3 1
 
4.2%
ValueCountFrequency (%)
0 16
66.7%
1 3
 
12.5%
2 1
 
4.2%
3 1
 
4.2%
4 1
 
4.2%
10 1
 
4.2%
12 1
 
4.2%
ValueCountFrequency (%)
12 1
 
4.2%
10 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
2 1
 
4.2%
1 3
 
12.5%
0 16
66.7%

12월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9583333
Minimum0
Maximum37
Zeros16
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T06:59:57.027352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.25
95-th percentile22.1
Maximum37
Range37
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation9.0864408
Coefficient of variation (CV)2.2955219
Kurtosis7.8006891
Mean3.9583333
Median Absolute Deviation (MAD)0
Skewness2.7943808
Sum95
Variance82.563406
MonotonicityNot monotonic
2023-12-13T06:59:57.394809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 16
66.7%
23 1
 
4.2%
7 1
 
4.2%
3 1
 
4.2%
1 1
 
4.2%
2 1
 
4.2%
17 1
 
4.2%
37 1
 
4.2%
5 1
 
4.2%
ValueCountFrequency (%)
0 16
66.7%
1 1
 
4.2%
2 1
 
4.2%
3 1
 
4.2%
5 1
 
4.2%
7 1
 
4.2%
17 1
 
4.2%
23 1
 
4.2%
37 1
 
4.2%
ValueCountFrequency (%)
37 1
 
4.2%
23 1
 
4.2%
17 1
 
4.2%
7 1
 
4.2%
5 1
 
4.2%
3 1
 
4.2%
2 1
 
4.2%
1 1
 
4.2%
0 16
66.7%

Interactions

2023-12-13T06:59:52.960872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:44.922021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.038045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.941479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.710197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.519448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.407868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.349789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.324952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.053240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.079365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:44.984148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.135900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.015869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.771935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.619493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.480788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.412256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.390240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.126082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.165722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:45.045333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.228490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.088643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.834657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.693342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.563145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.476597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.450775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.194751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.267950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:45.117440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.332393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.173332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.915353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.777439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.670286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.559735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.523670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.266288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.364775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:45.493869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.420504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.250488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.984848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.859740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.746068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.629876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.609003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.375486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.480751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:45.578070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.522283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.330800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.083062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.956966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.881037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.702427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.683406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.483687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.558115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:45.654397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.609667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.403389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.166425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.028519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.974905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.768987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.742636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.582157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.643049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:45.722677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.695667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.474472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.259485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.095816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.076338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.836373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.806953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.690462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.744421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:45.801287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.770935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.546050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.343012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.187699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.188744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.916567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.878124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.779717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:53.856137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:45.927107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:46.859439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:47.620040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:48.440332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:49.299716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:50.268522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.248537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:51.954189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:52.863181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:59:57.519277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1월2월3월5월6월7월8월9월10월11월12월
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1월1.0001.0000.9900.8790.9750.6790.9950.8220.6720.7590.9220.760
2월1.0000.9901.0000.9300.9800.7380.9960.9690.8230.8920.9600.939
3월1.0000.8790.9301.0000.9301.0000.9591.0000.9331.0000.9610.958
5월1.0000.9750.9800.9301.0000.8010.9840.9920.8910.8910.9600.913
6월1.0000.6790.7381.0000.8011.0000.8160.8050.9810.9740.9030.936
7월1.0000.9950.9960.9590.9840.8161.0000.9850.8460.9220.9840.939
8월1.0000.8220.9691.0000.9920.8050.9851.0000.8060.8910.8850.892
9월1.0000.6720.8230.9330.8910.9810.8460.8061.0000.9660.8230.978
10월1.0000.7590.8921.0000.8910.9740.9220.8910.9661.0000.8920.966
11월1.0000.9220.9600.9610.9600.9030.9840.8850.8230.8921.0000.824
12월1.0000.7600.9390.9580.9130.9360.9390.8920.9780.9660.8241.000
2023-12-13T06:59:57.649053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2월3월5월6월7월8월9월10월11월12월1월
2월1.0000.8590.7940.8250.8330.8920.8610.8260.7720.8120.810
3월0.8591.0000.7110.9160.9410.9500.8960.8920.8780.8090.811
5월0.7940.7111.0000.7460.6670.7150.8110.6800.7220.7510.743
6월0.8250.9160.7461.0000.9000.8930.8380.8680.8190.8200.618
7월0.8330.9410.6670.9001.0000.9230.8350.8500.8740.7490.846
8월0.8920.9500.7150.8930.9231.0000.8740.9390.9310.7910.867
9월0.8610.8960.8110.8380.8350.8741.0000.8430.7970.8420.609
10월0.8260.8920.6800.8680.8500.9390.8431.0000.9040.8220.724
11월0.7720.8780.7220.8190.8740.9310.7970.9041.0000.7100.611
12월0.8120.8090.7510.8200.7490.7910.8420.8220.7101.0000.726
1월0.8100.8110.7430.6180.8460.8670.6090.7240.6110.7261.000

Missing values

2023-12-13T06:59:54.031047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:59:54.264416image/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

구분1월2월3월4월5월6월7월8월9월10월11월12월
0내과13124508453484242611223
1소화기내과079034712562947
2내분비내과000012000000
3순환기내과001001100000
4호흡기내과000000000000
5신장내과000000000100
6감염내과000000000000
7신경과000000000000
8정신건강의학과000000000000
9외과22150320614588103
구분1월2월3월4월5월6월7월8월9월10월11월12월
14소아청소년과000000000000
15안과000000000000
16이비인후과0050074521420
17피부과2510054601310
18비뇨기과132335065216311459117
19재활의학과000000000000
20가정의학과21130055218141320337
21치과000000100000
22핵의학과015008214405
23응급의학과000000000000