Overview

Dataset statistics

Number of variables14
Number of observations36
Missing cells121
Missing cells (%)24.0%
Duplicate rows1
Duplicate rows (%)2.8%
Total size in memory4.5 KiB
Average record size in memory128.7 B

Variable types

Text1
Categorical2
Numeric10
Unsupported1

Dataset

Description광주환경공단의 2014년 주암원수 일별공급량(운영일지)에 대한 데이터로 2014년동안의 일별 수치를 제공합니다.
Author광주환경공단
URLhttps://www.data.go.kr/data/15100434/fileData.do

Alerts

Dataset has 1 (2.8%) duplicate rowsDuplicates
12월 is highly overall correlated with 02월 and 10 other fieldsHigh correlation
01월 is highly overall correlated with 03월 and 5 other fieldsHigh correlation
02월 is highly overall correlated with 04월 and 6 other fieldsHigh correlation
03월 is highly overall correlated with 04월 and 6 other fieldsHigh correlation
04월 is highly overall correlated with 02월 and 5 other fieldsHigh correlation
05월 is highly overall correlated with 02월 and 2 other fieldsHigh correlation
06월 is highly overall correlated with 02월 and 2 other fieldsHigh correlation
07월 is highly overall correlated with 02월 and 6 other fieldsHigh correlation
08월 is highly overall correlated with 07월 and 3 other fieldsHigh correlation
09월 is highly overall correlated with 02월 and 4 other fieldsHigh correlation
10월 is highly overall correlated with 02월 and 6 other fieldsHigh correlation
11월 is highly overall correlated with 03월 and 3 other fieldsHigh correlation
01월 is highly imbalanced (77.0%)Imbalance
일자 has 5 (13.9%) missing valuesMissing
02월 has 29 (80.6%) missing valuesMissing
03월 has 7 (19.4%) missing valuesMissing
04월 has 6 (16.7%) missing valuesMissing
05월 has 5 (13.9%) missing valuesMissing
06월 has 6 (16.7%) missing valuesMissing
07월 has 5 (13.9%) missing valuesMissing
08월 has 5 (13.9%) missing valuesMissing
09월 has 6 (16.7%) missing valuesMissing
10월 has 5 (13.9%) missing valuesMissing
11월 has 6 (16.7%) missing valuesMissing
Unnamed: 13 has 36 (100.0%) missing valuesMissing
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported
07월 has 12 (33.3%) zerosZeros
08월 has 24 (66.7%) zerosZeros
10월 has 7 (19.4%) zerosZeros
11월 has 1 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-12 19:15:29.098616
Analysis finished2023-12-12 19:15:42.138365
Duration13.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Text

MISSING 

Distinct31
Distinct (%)100.0%
Missing5
Missing (%)13.9%
Memory size420.0 B
2023-12-13T04:15:42.289512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters93
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row01일
2nd row02일
3rd row03일
4th row04일
5th row05일
ValueCountFrequency (%)
02일 1
 
3.2%
18일 1
 
3.2%
31일 1
 
3.2%
30일 1
 
3.2%
29일 1
 
3.2%
28일 1
 
3.2%
27일 1
 
3.2%
26일 1
 
3.2%
25일 1
 
3.2%
24일 1
 
3.2%
Other values (21) 21
67.7%
2023-12-13T04:15:42.621417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 31
33.3%
1 14
15.1%
2 13
14.0%
0 12
 
12.9%
3 5
 
5.4%
4 3
 
3.2%
5 3
 
3.2%
6 3
 
3.2%
7 3
 
3.2%
8 3
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
66.7%
Other Letter 31
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
22.6%
2 13
21.0%
0 12
19.4%
3 5
 
8.1%
4 3
 
4.8%
5 3
 
4.8%
6 3
 
4.8%
7 3
 
4.8%
8 3
 
4.8%
9 3
 
4.8%
Other Letter
ValueCountFrequency (%)
일 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 62
66.7%
Hangul 31
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
22.6%
2 13
21.0%
0 12
19.4%
3 5
 
8.1%
4 3
 
4.8%
5 3
 
4.8%
6 3
 
4.8%
7 3
 
4.8%
8 3
 
4.8%
9 3
 
4.8%
Hangul
ValueCountFrequency (%)
일 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62
66.7%
Hangul 31
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 31
100.0%
ASCII
ValueCountFrequency (%)
1 14
22.6%
2 13
21.0%
0 12
19.4%
3 5
 
8.1%
4 3
 
4.8%
5 3
 
4.8%
6 3
 
4.8%
7 3
 
4.8%
8 3
 
4.8%
9 3
 
4.8%

01월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
<NA>
34 
1627
 
1
5050
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)5.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 34
94.4%
1627 1
 
2.8%
5050 1
 
2.8%

Length

2023-12-13T04:15:42.755402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:42.858746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 34
94.4%
1627 1
 
2.8%
5050 1
 
2.8%

02월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing29
Missing (%)80.6%
Infinite0
Infinite (%)0.0%
Mean15807.143
Minimum225
Maximum31120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:42.957322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum225
5-th percentile338.4
Q11697.5
median14160
Q330875
95-th percentile31099
Maximum31120
Range30895
Interquartile range (IQR)29177.5

Descriptive statistics

Standard deviation14914.767
Coefficient of variation (CV)0.94354603
Kurtosis-2.5778511
Mean15807.143
Median Absolute Deviation (MAD)13935
Skewness0.065276586
Sum110650
Variance2.2245027 × 108
MonotonicityStrictly decreasing
2023-12-13T04:15:43.104808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
31120 1
 
2.8%
31050 1
 
2.8%
30700 1
 
2.8%
14160 1
 
2.8%
2792 1
 
2.8%
603 1
 
2.8%
225 1
 
2.8%
(Missing) 29
80.6%
ValueCountFrequency (%)
225 1
2.8%
603 1
2.8%
2792 1
2.8%
14160 1
2.8%
30700 1
2.8%
31050 1
2.8%
31120 1
2.8%
ValueCountFrequency (%)
31120 1
2.8%
31050 1
2.8%
30700 1
2.8%
14160 1
2.8%
2792 1
2.8%
603 1
2.8%
225 1
2.8%

03월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing7
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean33250.31
Minimum14303
Maximum49451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:43.260525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14303
5-th percentile32131
Q132396
median33147
Q333420
95-th percentile39691.2
Maximum49451
Range35148
Interquartile range (IQR)1024

Descriptive statistics

Standard deviation5099.4752
Coefficient of variation (CV)0.15336624
Kurtosis10.174636
Mean33250.31
Median Absolute Deviation (MAD)602
Skewness-0.47479832
Sum964259
Variance26004647
MonotonicityNot monotonic
2023-12-13T04:15:43.400982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
34800 1
 
2.8%
32858 1
 
2.8%
32545 1
 
2.8%
32396 1
 
2.8%
32113 1
 
2.8%
32158 1
 
2.8%
32335 1
 
2.8%
32263 1
 
2.8%
32429 1
 
2.8%
32652 1
 
2.8%
Other values (19) 19
52.8%
(Missing) 7
 
19.4%
ValueCountFrequency (%)
14303 1
2.8%
32113 1
2.8%
32158 1
2.8%
32212 1
2.8%
32263 1
2.8%
32335 1
2.8%
32391 1
2.8%
32396 1
2.8%
32418 1
2.8%
32429 1
2.8%
ValueCountFrequency (%)
49451 1
2.8%
42952 1
2.8%
34800 1
2.8%
33882 1
2.8%
33682 1
2.8%
33641 1
2.8%
33507 1
2.8%
33420 1
2.8%
33389 1
2.8%
33347 1
2.8%

04월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)96.7%
Missing6
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean33046.233
Minimum31907
Maximum33881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:43.559350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31907
5-th percentile32018.5
Q132950.5
median33145
Q333272.75
95-th percentile33711.25
Maximum33881
Range1974
Interquartile range (IQR)322.25

Descriptive statistics

Standard deviation508.27658
Coefficient of variation (CV)0.015380772
Kurtosis0.40004588
Mean33046.233
Median Absolute Deviation (MAD)175
Skewness-0.92450383
Sum991387
Variance258345.08
MonotonicityNot monotonic
2023-12-13T04:15:43.726407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
33049 2
 
5.6%
33432 1
 
2.8%
32833 1
 
2.8%
32719 1
 
2.8%
32157 1
 
2.8%
32295 1
 
2.8%
31987 1
 
2.8%
31907 1
 
2.8%
32057 1
 
2.8%
33066 1
 
2.8%
Other values (19) 19
52.8%
(Missing) 6
 
16.7%
ValueCountFrequency (%)
31907 1
2.8%
31987 1
2.8%
32057 1
2.8%
32157 1
2.8%
32295 1
2.8%
32719 1
2.8%
32833 1
2.8%
32931 1
2.8%
33009 1
2.8%
33049 2
5.6%
ValueCountFrequency (%)
33881 1
2.8%
33772 1
2.8%
33637 1
2.8%
33528 1
2.8%
33450 1
2.8%
33434 1
2.8%
33432 1
2.8%
33273 1
2.8%
33272 1
2.8%
33259 1
2.8%

05월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)96.8%
Missing5
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean32213.355
Minimum30906
Maximum33515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:43.908070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30906
5-th percentile30986
Q132039
median32313
Q332482.5
95-th percentile33073.5
Maximum33515
Range2609
Interquartile range (IQR)443.5

Descriptive statistics

Standard deviation634.26065
Coefficient of variation (CV)0.01968937
Kurtosis0.28179138
Mean32213.355
Median Absolute Deviation (MAD)194
Skewness-0.59448881
Sum998614
Variance402286.57
MonotonicityNot monotonic
2023-12-13T04:15:44.106848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
32391 2
 
5.6%
33515 1
 
2.8%
32119 1
 
2.8%
31959 1
 
2.8%
32313 1
 
2.8%
32615 1
 
2.8%
32237 1
 
2.8%
32303 1
 
2.8%
32333 1
 
2.8%
32261 1
 
2.8%
Other values (20) 20
55.6%
(Missing) 5
 
13.9%
ValueCountFrequency (%)
30906 1
2.8%
30959 1
2.8%
31013 1
2.8%
31037 1
2.8%
31452 1
2.8%
31506 1
2.8%
31839 1
2.8%
31959 1
2.8%
32119 1
2.8%
32232 1
2.8%
ValueCountFrequency (%)
33515 1
2.8%
33124 1
2.8%
33023 1
2.8%
32811 1
2.8%
32760 1
2.8%
32755 1
2.8%
32615 1
2.8%
32500 1
2.8%
32465 1
2.8%
32459 1
2.8%

06월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing6
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean31177.833
Minimum30394
Maximum32666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:44.253639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30394
5-th percentile30464.25
Q130610.5
median30723.5
Q332063
95-th percentile32553.6
Maximum32666
Range2272
Interquartile range (IQR)1452.5

Descriptive statistics

Standard deviation794.77692
Coefficient of variation (CV)0.02549173
Kurtosis-1.0897546
Mean31177.833
Median Absolute Deviation (MAD)215.5
Skewness0.83506152
Sum935335
Variance631670.35
MonotonicityNot monotonic
2023-12-13T04:15:44.413551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
30692 1
 
2.8%
32586 1
 
2.8%
32220 1
 
2.8%
32077 1
 
2.8%
32021 1
 
2.8%
30394 1
 
2.8%
30676 1
 
2.8%
30467 1
 
2.8%
30484 1
 
2.8%
30644 1
 
2.8%
Other values (20) 20
55.6%
(Missing) 6
 
16.7%
ValueCountFrequency (%)
30394 1
2.8%
30462 1
2.8%
30467 1
2.8%
30484 1
2.8%
30532 1
2.8%
30561 1
2.8%
30564 1
2.8%
30609 1
2.8%
30615 1
2.8%
30626 1
2.8%
ValueCountFrequency (%)
32666 1
2.8%
32586 1
2.8%
32514 1
2.8%
32310 1
2.8%
32276 1
2.8%
32265 1
2.8%
32220 1
2.8%
32077 1
2.8%
32021 1
2.8%
31314 1
2.8%

07월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)64.5%
Missing5
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean17919.387
Minimum0
Maximum32817
Zeros12
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:44.562605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31086
Q332199.5
95-th percentile32562
Maximum32817
Range32817
Interquartile range (IQR)32199.5

Descriptive statistics

Standard deviation15513.928
Coefficient of variation (CV)0.86576222
Kurtosis-1.9886974
Mean17919.387
Median Absolute Deviation (MAD)1731
Skewness-0.23211838
Sum555501
Variance2.4068197 × 108
MonotonicityNot monotonic
2023-12-13T04:15:44.717982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 12
33.3%
31086 1
 
2.8%
12437 1
 
2.8%
7904 1
 
2.8%
21439 1
 
2.8%
31749 1
 
2.8%
32033 1
 
2.8%
31917 1
 
2.8%
31406 1
 
2.8%
32366 1
 
2.8%
Other values (10) 10
27.8%
(Missing) 5
13.9%
ValueCountFrequency (%)
0 12
33.3%
7904 1
 
2.8%
12437 1
 
2.8%
21439 1
 
2.8%
31086 1
 
2.8%
31406 1
 
2.8%
31512 1
 
2.8%
31749 1
 
2.8%
31917 1
 
2.8%
31920 1
 
2.8%
ValueCountFrequency (%)
32817 1
2.8%
32583 1
2.8%
32541 1
2.8%
32489 1
2.8%
32460 1
2.8%
32459 1
2.8%
32412 1
2.8%
32366 1
2.8%
32033 1
2.8%
31971 1
2.8%

08월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)25.8%
Missing5
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean4688.2258
Minimum0
Maximum33282
Zeros24
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:44.852621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile31830
Maximum33282
Range33282
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10193.904
Coefficient of variation (CV)2.1743628
Kurtosis3.4663236
Mean4688.2258
Median Absolute Deviation (MAD)0
Skewness2.1520788
Sum145335
Variance1.0391567 × 108
MonotonicityNot monotonic
2023-12-13T04:15:44.968365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 24
66.7%
4504 1
 
2.8%
14901 1
 
2.8%
32613 1
 
2.8%
33282 1
 
2.8%
15249 1
 
2.8%
13739 1
 
2.8%
31047 1
 
2.8%
(Missing) 5
 
13.9%
ValueCountFrequency (%)
0 24
66.7%
4504 1
 
2.8%
13739 1
 
2.8%
14901 1
 
2.8%
15249 1
 
2.8%
31047 1
 
2.8%
32613 1
 
2.8%
33282 1
 
2.8%
ValueCountFrequency (%)
33282 1
 
2.8%
32613 1
 
2.8%
31047 1
 
2.8%
15249 1
 
2.8%
14901 1
 
2.8%
13739 1
 
2.8%
4504 1
 
2.8%
0 24
66.7%

09월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing6
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean28185.1
Minimum23885
Maximum34170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:45.147859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23885
5-th percentile25024.95
Q125735.25
median28718
Q330443.25
95-th percentile31547.35
Maximum34170
Range10285
Interquartile range (IQR)4708

Descriptive statistics

Standard deviation2620.505
Coefficient of variation (CV)0.092974835
Kurtosis-0.90462041
Mean28185.1
Median Absolute Deviation (MAD)2430.5
Skewness0.24064511
Sum845553
Variance6867046.5
MonotonicityNot monotonic
2023-12-13T04:15:45.667394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25874 1
 
2.8%
34170 1
 
2.8%
28479 1
 
2.8%
23885 1
 
2.8%
24777 1
 
2.8%
25328 1
 
2.8%
25694 1
 
2.8%
25918 1
 
2.8%
25519 1
 
2.8%
25605 1
 
2.8%
Other values (20) 20
55.6%
(Missing) 6
 
16.7%
ValueCountFrequency (%)
23885 1
2.8%
24777 1
2.8%
25328 1
2.8%
25519 1
2.8%
25530 1
2.8%
25605 1
2.8%
25694 1
2.8%
25720 1
2.8%
25781 1
2.8%
25874 1
2.8%
ValueCountFrequency (%)
34170 1
2.8%
31933 1
2.8%
31076 1
2.8%
31029 1
2.8%
30977 1
2.8%
30901 1
2.8%
30546 1
2.8%
30466 1
2.8%
30375 1
2.8%
29700 1
2.8%

10월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)80.6%
Missing5
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean24976.774
Minimum0
Maximum34871
Zeros7
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:45.826756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116432
median33872
Q334236
95-th percentile34679
Maximum34871
Range34871
Interquartile range (IQR)17804

Descriptive statistics

Standard deviation14416.832
Coefficient of variation (CV)0.57720952
Kurtosis-0.60900155
Mean24976.774
Median Absolute Deviation (MAD)615
Skewness-1.1313908
Sum774280
Variance2.0784504 × 108
MonotonicityNot monotonic
2023-12-13T04:15:45.976547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 7
19.4%
33978 1
 
2.8%
30564 1
 
2.8%
30527 1
 
2.8%
30545 1
 
2.8%
18843 1
 
2.8%
14021 1
 
2.8%
34551 1
 
2.8%
34487 1
 
2.8%
34316 1
 
2.8%
Other values (15) 15
41.7%
(Missing) 5
 
13.9%
ValueCountFrequency (%)
0 7
19.4%
14021 1
 
2.8%
18843 1
 
2.8%
30527 1
 
2.8%
30545 1
 
2.8%
30564 1
 
2.8%
33794 1
 
2.8%
33823 1
 
2.8%
33833 1
 
2.8%
33872 1
 
2.8%
ValueCountFrequency (%)
34871 1
2.8%
34807 1
2.8%
34551 1
2.8%
34487 1
2.8%
34396 1
2.8%
34366 1
2.8%
34316 1
2.8%
34301 1
2.8%
34171 1
2.8%
34149 1
2.8%

11월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)100.0%
Missing6
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean27543.767
Minimum0
Maximum31275
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T04:15:46.148149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7206.05
Q130079.5
median30518
Q330775
95-th percentile31056.1
Maximum31275
Range31275
Interquartile range (IQR)695.5

Descriptive statistics

Standard deviation8165.5851
Coefficient of variation (CV)0.29645855
Kurtosis7.8135356
Mean27543.767
Median Absolute Deviation (MAD)288.5
Skewness-2.9118789
Sum826313
Variance66676780
MonotonicityNot monotonic
2023-12-13T04:15:46.308111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
31129 1
 
2.8%
27084 1
 
2.8%
29305 1
 
2.8%
29182 1
 
2.8%
15923 1
 
2.8%
0 1
 
2.8%
74 1
 
2.8%
20007 1
 
2.8%
30486 1
 
2.8%
30497 1
 
2.8%
Other values (20) 20
55.6%
(Missing) 6
 
16.7%
ValueCountFrequency (%)
0 1
2.8%
74 1
2.8%
15923 1
2.8%
20007 1
2.8%
27084 1
2.8%
29182 1
2.8%
29305 1
2.8%
30070 1
2.8%
30108 1
2.8%
30261 1
2.8%
ValueCountFrequency (%)
31275 1
2.8%
31129 1
2.8%
30967 1
2.8%
30917 1
2.8%
30840 1
2.8%
30811 1
2.8%
30802 1
2.8%
30780 1
2.8%
30760 1
2.8%
30755 1
2.8%

12월
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
31 
<NA>
5 

Length

Max length4
Median length1
Mean length1.4166667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 31
86.1%
<NA> 5
 
13.9%

Length

2023-12-13T04:15:46.532231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:46.677155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
86.1%
na 5
 
13.9%

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36
Missing (%)100.0%
Memory size456.0 B

Interactions

2023-12-13T04:15:40.663129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:29.548613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.535121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:32.009051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:33.292370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.463978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:35.666292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.893275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.061824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:39.162509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:40.746345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:29.650386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.630631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:32.123712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:33.420236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.584055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:35.781183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.994044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.158421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:39.276478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:40.842128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:29.735111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.735790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:32.256545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:33.548563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.696695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:35.905243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:37.131938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.284231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:39.724735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:40.939883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:29.831874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.847884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:32.398248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:33.657447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.823142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.035784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:37.261704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.403489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:39.844962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:41.049187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:29.929313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.956237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:32.540232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:33.772212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.951433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.154587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:37.382825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.528843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:39.985739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:41.147133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.024083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:31.056979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:32.647056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:33.884476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:35.050151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.287293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:37.492065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.636784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:40.088362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:41.238625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.114248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:31.187366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:32.767578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.002525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:35.167354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.423056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:37.604223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.746696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:40.200225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:41.320719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.222826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:31.284793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:32.906527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.109301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:35.300909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.550089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:37.718602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.856167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:40.304311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:41.401772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.323946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:31.395571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:33.022334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.208062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:35.415382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.663137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:37.833218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:38.950949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:40.441798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:41.495146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:30.429838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:31.520876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:33.147541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:34.328412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:35.555751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:36.794711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:37.948426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:39.054941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:15:40.561352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:15:46.784863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자01월02월03월04월05월06월07월08월09월10월11월
일자1.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
01월0.0001.000NaNNaNNaN0.000NaNNaN0.000NaN0.000NaN
02월1.000NaN1.000NaN0.3441.0001.000NaNNaN1.000NaNNaN
03월1.000NaNNaN1.0000.1580.0000.0000.0000.0000.1580.0000.000
04월1.000NaN0.3440.1581.0000.0000.7090.3390.0000.8620.7750.737
05월1.0000.0001.0000.0000.0001.0000.5090.5770.0000.7940.6900.251
06월1.000NaN1.0000.0000.7090.5091.0000.0000.0000.9110.1730.000
07월1.000NaNNaN0.0000.3390.5770.0001.0000.3800.0000.7090.000
08월1.0000.000NaN0.0000.0000.0000.0000.3801.0000.0000.7150.529
09월1.000NaN1.0000.1580.8620.7940.9110.0000.0001.0000.8330.795
10월1.0000.000NaN0.0000.7750.6900.1730.7090.7150.8331.0000.736
11월1.000NaNNaN0.0000.7370.2510.0000.0000.5290.7950.7361.000
2023-12-13T04:15:46.974288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
12월01월
12월1.0001.000
01월1.0001.000
2023-12-13T04:15:47.119010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
02월03월04월05월06월07월08월09월10월11월01월12월
02월1.000-0.100-0.5710.5000.8930.500NaN0.643-0.7140.0710.0001.000
03월-0.1001.0000.735-0.0150.0070.529-0.3520.5190.7360.6441.0001.000
04월-0.5710.7351.0000.0660.0940.587-0.4280.4190.7610.714NaN1.000
05월0.500-0.0150.0661.0000.1490.190-0.018-0.014-0.138-0.1131.0001.000
06월0.8930.0070.0940.1491.0000.394-0.2780.5860.117-0.004NaN1.000
07월0.5000.5290.5870.1900.3941.000-0.5230.7630.4610.4301.0001.000
08월NaN-0.352-0.428-0.018-0.278-0.5231.000-0.301-0.575-0.4261.0001.000
09월0.6430.5190.419-0.0140.5860.763-0.3011.0000.3910.283NaN1.000
10월-0.7140.7360.761-0.1380.1170.461-0.5750.3911.0000.7291.0001.000
11월0.0710.6440.714-0.113-0.0040.430-0.4260.2830.7291.000NaN1.000
01월0.0001.000NaN1.000NaN1.0001.000NaN1.000NaN1.0001.000
12월1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T04:15:41.632335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:15:41.813843image/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.
2023-12-13T04:15:41.991775image/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

일자01월02월03월04월05월06월07월08월09월10월11월12월Unnamed: 13
001일<NA>31120<NA>3293132232322653236603090133999305620<NA>
102일<NA>31050<NA>3327332280326663241203102933978305810<NA>
203일<NA>30700143033304932500325143245903107633833301080<NA>
304일<NA>14160348003308832811313143246003097734110304950<NA>
405일<NA><NA>336823322632755323103254103046634149308400<NA>
506일<NA><NA>338823377232465306723258303054634301307550<NA>
607일<NA><NA>336413388133023322763281703037534059304310<NA>
708일<NA><NA>335073345033124311523248903193333823305390<NA>
809일<NA><NA>334203311331452308633197102970033794308020<NA>
910일<NA><NA>331973322331506306263192002934133872307600<NA>
일자01월02월03월04월05월06월07월08월09월10월11월12월Unnamed: 13
2627일<NA><NA>3215832295322373202100247770159230<NA>
2728일<NA><NA>32113321573261532077002388518843291820<NA>
2829일<NA><NA>32396327193231332220002847930545293050<NA>
2930일<NA><NA>325453283331959325860137393417030527270840<NA>
3031일5050<NA>32858<NA>32119<NA>031047<NA>30564<NA>0<NA>
31<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
34<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
35<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

일자01월02월03월04월05월06월07월08월09월10월11월12월# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>5