Overview

Dataset statistics

Number of variables11
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory103.5 B

Variable types

Text1
Numeric10

Dataset

Description한국도로공사 고속도로 하계휴가철 특별교통대책 결과보고 정보를 제공한다. (일자,합계,1종,6종,소계1,2종,소계2,3종,4종,5종,소계3)
URLhttps://www.data.go.kr/data/15062673/fileData.do

Alerts

합계 is highly overall correlated with 1종 and 2 other fieldsHigh correlation
1종 is highly overall correlated with 합계 and 2 other fieldsHigh correlation
6종 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
소계1 is highly overall correlated with 합계 and 2 other fieldsHigh correlation
2종 is highly overall correlated with 6종 and 5 other fieldsHigh correlation
소계2 is highly overall correlated with 6종 and 5 other fieldsHigh correlation
3종 is highly overall correlated with 6종 and 5 other fieldsHigh correlation
4종 is highly overall correlated with 6종 and 5 other fieldsHigh correlation
5종 is highly overall correlated with 6종 and 5 other fieldsHigh correlation
소계3 is highly overall correlated with 6종 and 5 other fieldsHigh correlation
일자 has unique valuesUnique
합계 has unique valuesUnique
1종 has unique valuesUnique
6종 has unique valuesUnique
소계1 has unique valuesUnique
2종 has unique valuesUnique
소계2 has unique valuesUnique
3종 has unique valuesUnique
4종 has unique valuesUnique
5종 has unique valuesUnique
소계3 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:53:04.573144
Analysis finished2023-12-12 13:53:16.457857
Duration11.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T22:53:16.616931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters312
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row2015-07-24(금)
2nd row2015-07-25(토)
3rd row2015-07-26(일)
4th row2015-07-27(월)
5th row2015-07-28(화)
ValueCountFrequency (%)
2015-07-24(금 1
 
4.2%
2015-07-25(토 1
 
4.2%
2015-08-15(토 1
 
4.2%
2015-08-14(금 1
 
4.2%
2015-08-13(목 1
 
4.2%
2015-08-12(수 1
 
4.2%
2015-08-11(화 1
 
4.2%
2015-08-10(월 1
 
4.2%
2015-08-09(일 1
 
4.2%
2015-08-08(토 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T22:53:16.961337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59
18.9%
- 48
15.4%
1 34
10.9%
2 32
10.3%
5 27
8.7%
( 24
7.7%
) 24
7.7%
8 18
 
5.8%
7 10
 
3.2%
일 4
 
1.3%
Other values (10) 32
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192
61.5%
Dash Punctuation 48
 
15.4%
Open Punctuation 24
 
7.7%
Close Punctuation 24
 
7.7%
Other Letter 24
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59
30.7%
1 34
17.7%
2 32
16.7%
5 27
14.1%
8 18
 
9.4%
7 10
 
5.2%
3 4
 
2.1%
6 3
 
1.6%
4 3
 
1.6%
9 2
 
1.0%
Other Letter
ValueCountFrequency (%)
일 4
16.7%
토 4
16.7%
금 4
16.7%
월 3
12.5%
화 3
12.5%
수 3
12.5%
목 3
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288
92.3%
Hangul 24
 
7.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59
20.5%
- 48
16.7%
1 34
11.8%
2 32
11.1%
5 27
9.4%
( 24
8.3%
) 24
8.3%
8 18
 
6.2%
7 10
 
3.5%
3 4
 
1.4%
Other values (3) 8
 
2.8%
Hangul
ValueCountFrequency (%)
일 4
16.7%
토 4
16.7%
금 4
16.7%
월 3
12.5%
화 3
12.5%
수 3
12.5%
목 3
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288
92.3%
Hangul 24
 
7.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59
20.5%
- 48
16.7%
1 34
11.8%
2 32
11.1%
5 27
9.4%
( 24
8.3%
) 24
8.3%
8 18
 
6.2%
7 10
 
3.5%
3 4
 
1.4%
Other values (3) 8
 
2.8%
Hangul
ValueCountFrequency (%)
일 4
16.7%
토 4
16.7%
금 4
16.7%
월 3
12.5%
화 3
12.5%
수 3
12.5%
목 3
12.5%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4321930.8
Minimum3659149
Maximum5179335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:17.091224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3659149
5-th percentile3717009.9
Q14164924.2
median4316908
Q34468908.5
95-th percentile4789528.5
Maximum5179335
Range1520186
Interquartile range (IQR)303984.25

Descriptive statistics

Standard deviation339101.62
Coefficient of variation (CV)0.078460677
Kurtosis1.1791934
Mean4321930.8
Median Absolute Deviation (MAD)156863.5
Skewness0.14160603
Sum1.0372634 × 108
Variance1.1498991 × 1011
MonotonicityNot monotonic
2023-12-12T22:53:17.226679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4148130 1
 
4.2%
4346645 1
 
4.2%
3742090 1
 
4.2%
4433233 1
 
4.2%
5179335 1
 
4.2%
4494626 1
 
4.2%
4169804 1
 
4.2%
4268170 1
 
4.2%
4303015 1
 
4.2%
3712584 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
3659149 1
4.2%
3712584 1
4.2%
3742090 1
4.2%
4103019 1
4.2%
4148130 1
4.2%
4150285 1
4.2%
4169804 1
4.2%
4219457 1
4.2%
4225540 1
4.2%
4268170 1
4.2%
ValueCountFrequency (%)
5179335 1
4.2%
4800337 1
4.2%
4728280 1
4.2%
4603447 1
4.2%
4573146 1
4.2%
4494626 1
4.2%
4460336 1
4.2%
4433233 1
4.2%
4419518 1
4.2%
4352377 1
4.2%

1종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3622856.4
Minimum3297258
Maximum4388306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:17.365700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3297258
5-th percentile3333949.9
Q13387688.5
median3571295
Q33754291
95-th percentile4125523.6
Maximum4388306
Range1091048
Interquartile range (IQR)366602.5

Descriptive statistics

Standard deviation281801.66
Coefficient of variation (CV)0.077784386
Kurtosis0.99087423
Mean3622856.4
Median Absolute Deviation (MAD)184367
Skewness1.1168699
Sum86948554
Variance7.9412177 × 1010
MonotonicityNot monotonic
2023-12-12T22:53:17.512170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3351222 1
 
4.2%
3557905 1
 
4.2%
3373654 1
 
4.2%
3909353 1
 
4.2%
4388306 1
 
4.2%
3605526 1
 
4.2%
3331913 1
 
4.2%
3388449 1
 
4.2%
3483432 1
 
4.2%
3345492 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
3297258 1
4.2%
3331913 1
4.2%
3345492 1
4.2%
3351222 1
4.2%
3373654 1
4.2%
3385407 1
4.2%
3388449 1
4.2%
3414034 1
4.2%
3419312 1
4.2%
3483432 1
4.2%
ValueCountFrequency (%)
4388306 1
4.2%
4155703 1
4.2%
3954507 1
4.2%
3911401 1
4.2%
3909353 1
4.2%
3758257 1
4.2%
3752969 1
4.2%
3731280 1
4.2%
3690180 1
4.2%
3605526 1
4.2%

6종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228151.96
Minimum189225
Maximum258326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:17.667018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189225
5-th percentile191064.25
Q1219433.5
median232817.5
Q3238885.75
95-th percentile246782.45
Maximum258326
Range69101
Interquartile range (IQR)19452.25

Descriptive statistics

Standard deviation18610.482
Coefficient of variation (CV)0.081570556
Kurtosis0.21919726
Mean228151.96
Median Absolute Deviation (MAD)10379
Skewness-0.93469966
Sum5475647
Variance3.4635004 × 108
MonotonicityNot monotonic
2023-12-12T22:53:17.805601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
230008 1
 
4.2%
233164 1
 
4.2%
191451 1
 
4.2%
218985 1
 
4.2%
246974 1
 
4.2%
245697 1
 
4.2%
230705 1
 
4.2%
232471 1
 
4.2%
237856 1
 
4.2%
190996 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
189225 1
4.2%
190996 1
4.2%
191451 1
4.2%
202654 1
4.2%
213066 1
4.2%
218985 1
4.2%
219583 1
4.2%
230008 1
4.2%
230705 1
4.2%
230744 1
4.2%
ValueCountFrequency (%)
258326 1
4.2%
246974 1
4.2%
245697 1
4.2%
245077 1
4.2%
244418 1
4.2%
241975 1
4.2%
237856 1
4.2%
236533 1
4.2%
236174 1
4.2%
235149 1
4.2%

소계1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3851008.4
Minimum3486483
Maximum4635280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:18.087701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3486483
5-th percentile3540407.5
Q13619828.2
median3801913
Q33982726.8
95-th percentile4360404.9
Maximum4635280
Range1148797
Interquartile range (IQR)362898.5

Descriptive statistics

Standard deviation290142.42
Coefficient of variation (CV)0.075341934
Kurtosis0.9364146
Mean3851008.4
Median Absolute Deviation (MAD)183176.5
Skewness1.0665801
Sum92424201
Variance8.4182623 × 1010
MonotonicityNot monotonic
2023-12-12T22:53:18.344377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3581230 1
 
4.2%
3791069 1
 
4.2%
3565105 1
 
4.2%
4128338 1
 
4.2%
4635280 1
 
4.2%
3851223 1
 
4.2%
3562618 1
 
4.2%
3620920 1
 
4.2%
3721288 1
 
4.2%
3536488 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
3486483 1
4.2%
3536488 1
4.2%
3562618 1
4.2%
3565105 1
4.2%
3581230 1
4.2%
3616553 1
4.2%
3620920 1
4.2%
3650208 1
4.2%
3655845 1
4.2%
3721288 1
4.2%
ValueCountFrequency (%)
4635280 1
4.2%
4386447 1
4.2%
4212833 1
4.2%
4156478 1
4.2%
4128338 1
4.2%
3997387 1
4.2%
3977840 1
4.2%
3933934 1
4.2%
3925329 1
4.2%
3851223 1
4.2%

2종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119290.62
Minimum36058
Maximum168336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:18.488495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36058
5-th percentile36333.95
Q186584.75
median145059
Q3153909
95-th percentile167332.3
Maximum168336
Range132278
Interquartile range (IQR)67324.25

Descriptive statistics

Standard deviation46893.199
Coefficient of variation (CV)0.39310045
Kurtosis-0.81706755
Mean119290.62
Median Absolute Deviation (MAD)20347.5
Skewness-0.83034322
Sum2862975
Variance2.1989721 × 109
MonotonicityNot monotonic
2023-12-12T22:53:18.619104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
146571 1
 
4.2%
146884 1
 
4.2%
36058 1
 
4.2%
71701 1
 
4.2%
143547 1
 
4.2%
167554 1
 
4.2%
156438 1
 
4.2%
166076 1
 
4.2%
153663 1
 
4.2%
36221 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
36058 1
4.2%
36221 1
4.2%
36974 1
4.2%
37147 1
4.2%
71701 1
4.2%
78985 1
4.2%
89118 1
4.2%
90185 1
4.2%
114239 1
4.2%
125381 1
4.2%
ValueCountFrequency (%)
168336 1
4.2%
167554 1
4.2%
166076 1
4.2%
159836 1
4.2%
156438 1
4.2%
154647 1
4.2%
153663 1
4.2%
152311 1
4.2%
150001 1
4.2%
147781 1
4.2%

소계2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119290.62
Minimum36058
Maximum168336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:18.767952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36058
5-th percentile36333.95
Q186584.75
median145059
Q3153909
95-th percentile167332.3
Maximum168336
Range132278
Interquartile range (IQR)67324.25

Descriptive statistics

Standard deviation46893.199
Coefficient of variation (CV)0.39310045
Kurtosis-0.81706755
Mean119290.62
Median Absolute Deviation (MAD)20347.5
Skewness-0.83034322
Sum2862975
Variance2.1989721 × 109
MonotonicityNot monotonic
2023-12-12T22:53:18.893275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
146571 1
 
4.2%
146884 1
 
4.2%
36058 1
 
4.2%
71701 1
 
4.2%
143547 1
 
4.2%
167554 1
 
4.2%
156438 1
 
4.2%
166076 1
 
4.2%
153663 1
 
4.2%
36221 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
36058 1
4.2%
36221 1
4.2%
36974 1
4.2%
37147 1
4.2%
71701 1
4.2%
78985 1
4.2%
89118 1
4.2%
90185 1
4.2%
114239 1
4.2%
125381 1
4.2%
ValueCountFrequency (%)
168336 1
4.2%
167554 1
4.2%
166076 1
4.2%
159836 1
4.2%
156438 1
4.2%
154647 1
4.2%
153663 1
4.2%
152311 1
4.2%
150001 1
4.2%
147781 1
4.2%

3종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152542.83
Minimum91553
Maximum191951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:19.027081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91553
5-th percentile93186.25
Q1127783.5
median164845
Q3178872.25
95-th percentile188017.85
Maximum191951
Range100398
Interquartile range (IQR)51088.75

Descriptive statistics

Standard deviation33913.223
Coefficient of variation (CV)0.22231935
Kurtosis-0.88322877
Mean152542.83
Median Absolute Deviation (MAD)18010.5
Skewness-0.73874439
Sum3661028
Variance1.1501067 × 109
MonotonicityNot monotonic
2023-12-12T22:53:19.144017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
178517 1
 
4.2%
166490 1
 
4.2%
96032 1
 
4.2%
119009 1
 
4.2%
163200 1
 
4.2%
191951 1
 
4.2%
181756 1
 
4.2%
187456 1
 
4.2%
176185 1
 
4.2%
94338 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
91553 1
4.2%
92983 1
4.2%
94338 1
4.2%
96032 1
4.2%
119009 1
4.2%
122775 1
4.2%
129453 1
4.2%
133867 1
4.2%
146347 1
4.2%
153871 1
4.2%
ValueCountFrequency (%)
191951 1
4.2%
188117 1
4.2%
187456 1
4.2%
182368 1
4.2%
181756 1
4.2%
179938 1
4.2%
178517 1
4.2%
177306 1
4.2%
176185 1
4.2%
175069 1
4.2%

4종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84144.792
Minimum19946
Maximum123521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:19.271020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19946
5-th percentile20731.55
Q162878.25
median98428.5
Q3113642.5
95-th percentile122910.15
Maximum123521
Range103575
Interquartile range (IQR)50764.25

Descriptive statistics

Standard deviation35775.934
Coefficient of variation (CV)0.42517111
Kurtosis-0.73029609
Mean84144.792
Median Absolute Deviation (MAD)21093.5
Skewness-0.79587934
Sum2019475
Variance1.2799175 × 109
MonotonicityNot monotonic
2023-12-12T22:53:19.403086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
104260 1
 
4.2%
99172 1
 
4.2%
21962 1
 
4.2%
44492 1
 
4.2%
97685 1
 
4.2%
119749 1
 
4.2%
113252 1
 
4.2%
123521 1
 
4.2%
102640 1
 
4.2%
20967 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
19946 1
4.2%
20690 1
4.2%
20967 1
4.2%
21962 1
4.2%
44492 1
4.2%
61526 1
4.2%
63329 1
4.2%
64639 1
4.2%
77536 1
4.2%
89877 1
4.2%
ValueCountFrequency (%)
123521 1
4.2%
123468 1
4.2%
119749 1
4.2%
119723 1
4.2%
115756 1
4.2%
114814 1
4.2%
113252 1
4.2%
104802 1
4.2%
104275 1
4.2%
104260 1
4.2%

5종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114944.17
Minimum20612
Maximum170622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:19.541617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20612
5-th percentile22009.05
Q183705.5
median138587.5
Q3150305.5
95-th percentile169289.8
Maximum170622
Range150010
Interquartile range (IQR)66600

Descriptive statistics

Standard deviation50532.98
Coefficient of variation (CV)0.43963066
Kurtosis-0.50972542
Mean114944.17
Median Absolute Deviation (MAD)21502
Skewness-0.91946806
Sum2758660
Variance2.553582 × 109
MonotonicityNot monotonic
2023-12-12T22:53:19.652934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
137552 1
 
4.2%
143030 1
 
4.2%
22933 1
 
4.2%
69693 1
 
4.2%
139623 1
 
4.2%
164149 1
 
4.2%
155740 1
 
4.2%
170197 1
 
4.2%
149239 1
 
4.2%
24570 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
20612 1
4.2%
21846 1
4.2%
22933 1
4.2%
24570 1
4.2%
69693 1
4.2%
78547 1
4.2%
85425 1
4.2%
92637 1
4.2%
108847 1
4.2%
125060 1
4.2%
ValueCountFrequency (%)
170622 1
4.2%
170197 1
4.2%
164149 1
4.2%
156030 1
4.2%
155740 1
4.2%
153505 1
4.2%
149239 1
4.2%
148057 1
4.2%
146661 1
4.2%
143150 1
4.2%

소계3
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean351631.79
Minimum132111
Maximum482207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:53:19.777772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132111
5-th percentile136172.4
Q1278660.25
median404600
Q3441856.5
95-th percentile480375.25
Maximum482207
Range350096
Interquartile range (IQR)163196.25

Descriptive statistics

Standard deviation119938.96
Coefficient of variation (CV)0.34109248
Kurtosis-0.67994233
Mean351631.79
Median Absolute Deviation (MAD)62385
Skewness-0.83930748
Sum8439163
Variance1.4385354 × 1010
MonotonicityNot monotonic
2023-12-12T22:53:19.883788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
420329 1
 
4.2%
408692 1
 
4.2%
140927 1
 
4.2%
233194 1
 
4.2%
400508 1
 
4.2%
475849 1
 
4.2%
450748 1
 
4.2%
481174 1
 
4.2%
428064 1
 
4.2%
139875 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
132111 1
4.2%
135519 1
4.2%
139875 1
4.2%
140927 1
4.2%
233194 1
4.2%
262848 1
4.2%
283931 1
4.2%
285419 1
4.2%
332730 1
4.2%
368808 1
4.2%
ValueCountFrequency (%)
482207 1
4.2%
481174 1
4.2%
475849 1
4.2%
458121 1
4.2%
450748 1
4.2%
448257 1
4.2%
439723 1
4.2%
428064 1
4.2%
425758 1
4.2%
423021 1
4.2%

Interactions

2023-12-12T22:53:15.109414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:04.873139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.775574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.041996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.050410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:09.091833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.269584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.334444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:12.460294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.012853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.230026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:04.963502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.884708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.134790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.165605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:09.219516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.375955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.456413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:12.637906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.124446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.348903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.046972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.963119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.222257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.280038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:09.315614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.485729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.567369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:12.767807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.233240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.455921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.139151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:06.053887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.332524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.372695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:09.436960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.591784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.685780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:12.883866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.340509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.551830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.221428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:06.151428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.423918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.463542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:09.572552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.687144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.810967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:13.017697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.442204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.649679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.305298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:06.627424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.525548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.565748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:09.754861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.799685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.909842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:13.131088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.549293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.756160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.391620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:06.726923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.611528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.657501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:09.865738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.908121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:12.016606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:13.277175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.641830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.858193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.490893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:06.799169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.693132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.742371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:09.964272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.004223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:12.122634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:13.692348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.744251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.963399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.575536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:06.872354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.813816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.838969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.071196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.107891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:12.211730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:13.799438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:14.864923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:16.077152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:05.680391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:06.956705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:07.947899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:08.963161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:10.180331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:11.223353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:12.313621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:13.908102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:53:15.001245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:53:19.981332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자합계1종6종소계12종소계23종4종5종소계3
일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0000.7690.7550.8210.4350.4350.7240.6580.6220.706
1종1.0000.7691.0000.6780.9910.7230.7230.7370.4920.6670.908
6종1.0000.7550.6781.0000.7640.0000.0000.7180.7670.7820.629
소계11.0000.8210.9910.7641.0000.6420.6420.7750.5930.5900.866
2종1.0000.4350.7230.0000.6421.0001.0000.9830.9390.9710.996
소계21.0000.4350.7230.0000.6421.0001.0000.9830.9390.9710.996
3종1.0000.7240.7370.7180.7750.9830.9831.0000.9580.9030.985
4종1.0000.6580.4920.7670.5930.9390.9390.9581.0000.9630.947
5종1.0000.6220.6670.7820.5900.9710.9710.9030.9631.0000.961
소계31.0000.7060.9080.6290.8660.9960.9960.9850.9470.9611.000
2023-12-12T22:53:20.108303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계1종6종소계12종소계23종4종5종소계3
합계1.0000.8560.7850.8730.2740.2740.2610.3110.3120.302
1종0.8561.0000.5100.997-0.117-0.117-0.143-0.086-0.097-0.104
6종0.7850.5101.0000.5380.6510.6510.6280.6680.6680.660
소계10.8730.9970.5381.000-0.077-0.077-0.106-0.051-0.061-0.069
2종0.274-0.1170.651-0.0771.0001.0000.9730.9680.9880.983
소계20.274-0.1170.651-0.0771.0001.0000.9730.9680.9880.983
3종0.261-0.1430.628-0.1060.9730.9731.0000.9820.9730.987
4종0.311-0.0860.668-0.0510.9680.9680.9821.0000.9750.989
5종0.312-0.0970.668-0.0610.9880.9880.9730.9751.0000.994
소계30.302-0.1040.660-0.0690.9830.9830.9870.9890.9941.000

Missing values

2023-12-12T22:53:16.232318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:53:16.393324image/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

일자합계1종6종소계12종소계23종4종5종소계3
02015-07-24(금)414813033512222300083581230146571146571178517104260137552420329
12015-07-25(토)41502853563103213066377616990185901851338676463985425283931
22015-07-26(일)3659149329725818922534864833714737147929832069021846135519
32015-07-27(월)422554034140342361743650208152311152311175069104802143150423021
42015-07-28(화)430638834193122365333655845168336168336188117123468170622482207
52015-07-29(수)421945733854072311463616553154647154647179938114814153505448257
62015-07-30(목)446033636004042419753842379159836159836182368119723156030458121
72015-07-31(금)480033739545072583264212833147781147781177306115756146661439723
82015-08-01(토)47282804155703230744438644778985789851227756152678547262848
92015-08-02(일)4103019373128020265439339343697436974915531994620612132111
일자합계1종6종소계12종소계23종4종5종소계3
142015-08-07(금)457314637529692444183997387150001150001173426104275148057425758
152015-08-08(토)43523773758257219583397784089118891181294536332992637285419
162015-08-09(일)3712584334549219099635364883622136221943382096724570139875
172015-08-10(월)430301534834322378563721288153663153663176185102640149239428064
182015-08-11(화)426817033884492324713620920166076166076187456123521170197481174
192015-08-12(수)416980433319132307053562618156438156438181756113252155740450748
202015-08-13(목)449462636055262456973851223167554167554191951119749164149475849
212015-08-14(금)51793354388306246974463528014354714354716320097685139623400508
222015-08-15(토)44332333909353218985412833871701717011190094449269693233194
232015-08-16(일)3742090337365419145135651053605836058960322196222933140927