Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory683.6 KiB
Average record size in memory70.0 B

Variable types

Numeric5
Categorical1
Text1

Dataset

Description경상북도 구미시 유수율제고블록시스템의 유량일집계 테이블 데이터로 유량계에 대한 일별 유량값 데이터를 제공합니다.
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15049701/fileData.do

Alerts

월 is highly overall correlated with 년도High correlation
유량값 is highly overall correlated with 종침High correlation
종침 is highly overall correlated with 유량값High correlation
년도 is highly overall correlated with 월High correlation
유량값 has 502 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-12 22:54:53.442579
Analysis finished2023-12-12 22:54:57.135764
Duration3.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유량계 코드
Real number (ℝ)

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.6416
Minimum2
Maximum457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:54:57.213799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile28
Q1241
median307
Q3383
95-th percentile418
Maximum457
Range455
Interquartile range (IQR)142

Descriptive statistics

Standard deviation115.45205
Coefficient of variation (CV)0.39998408
Kurtosis0.093990714
Mean288.6416
Median Absolute Deviation (MAD)72
Skewness-0.87443491
Sum2886416
Variance13329.175
MonotonicityNot monotonic
2023-12-13T07:54:57.358047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247 138
 
1.4%
200 135
 
1.4%
417 131
 
1.3%
229 130
 
1.3%
141 130
 
1.3%
235 129
 
1.3%
380 129
 
1.3%
377 128
 
1.3%
283 128
 
1.3%
322 127
 
1.3%
Other values (77) 8695
87.0%
ValueCountFrequency (%)
2 109
1.1%
5 115
1.1%
8 107
1.1%
11 105
1.1%
28 112
1.1%
38 93
0.9%
80 125
1.2%
89 116
1.2%
92 117
1.2%
100 114
1.1%
ValueCountFrequency (%)
457 100
1.0%
454 95
0.9%
451 105
1.1%
449 113
1.1%
418 99
1.0%
417 131
1.3%
416 114
1.1%
415 109
1.1%
414 116
1.2%
413 107
1.1%

년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
6470 
2019
3340 
2021
 
190

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2019

Common Values

ValueCountFrequency (%)
2020 6470
64.7%
2019 3340
33.4%
2021 190
 
1.9%

Length

2023-12-13T07:54:57.517553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:54:57.636864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 6470
64.7%
2019 3340
33.4%
2021 190
 
1.9%

월
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5591
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:54:57.736086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.480994
Coefficient of variation (CV)0.53071213
Kurtosis-1.2353809
Mean6.5591
Median Absolute Deviation (MAD)3
Skewness-0.05407028
Sum65591
Variance12.117319
MonotonicityNot monotonic
2023-12-13T07:54:57.843438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 946
9.5%
4 887
8.9%
1 881
8.8%
7 873
8.7%
8 870
8.7%
12 855
8.6%
10 852
8.5%
3 843
8.4%
6 840
8.4%
11 815
8.2%
Other values (2) 1338
13.4%
ValueCountFrequency (%)
1 881
8.8%
2 809
8.1%
3 843
8.4%
4 887
8.9%
5 529
5.3%
6 840
8.4%
7 873
8.7%
8 870
8.7%
9 946
9.5%
10 852
8.5%
ValueCountFrequency (%)
12 855
8.6%
11 815
8.2%
10 852
8.5%
9 946
9.5%
8 870
8.7%
7 873
8.7%
6 840
8.4%
5 529
5.3%
4 887
8.9%
3 843
8.4%

일자
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.9643
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:54:57.972734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7676059
Coefficient of variation (CV)0.54920077
Kurtosis-1.1643638
Mean15.9643
Median Absolute Deviation (MAD)7
Skewness-0.035772063
Sum159643
Variance76.870913
MonotonicityNot monotonic
2023-12-13T07:54:58.122934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 409
 
4.1%
18 371
 
3.7%
29 365
 
3.6%
14 351
 
3.5%
21 348
 
3.5%
22 347
 
3.5%
2 345
 
3.5%
23 341
 
3.4%
24 341
 
3.4%
16 337
 
3.4%
Other values (21) 6445
64.5%
ValueCountFrequency (%)
1 305
3.0%
2 345
3.5%
3 323
3.2%
4 327
3.3%
5 307
3.1%
6 299
3.0%
7 316
3.2%
8 310
3.1%
9 274
2.7%
10 325
3.2%
ValueCountFrequency (%)
31 199
2.0%
30 311
3.1%
29 365
3.6%
28 307
3.1%
27 317
3.2%
26 304
3.0%
25 317
3.2%
24 341
3.4%
23 341
3.4%
22 347
3.5%

유량값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6307
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51906.154
Minimum-99998227
Maximum99999783
Zeros502
Zeros (%)5.0%
Negative8
Negative (%)0.1%
Memory size166.0 KiB
2023-12-13T07:54:58.300352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99998227
5-th percentile0
Q1844
median2123.5
Q36965.25
95-th percentile50590.735
Maximum99999783
Range1.9999801 × 108
Interquartile range (IQR)6121.25

Descriptive statistics

Standard deviation2007685.6
Coefficient of variation (CV)38.679145
Kurtosis1820.86
Mean51906.154
Median Absolute Deviation (MAD)1816.5
Skewness16.148604
Sum5.1906154 × 108
Variance4.0308016 × 1012
MonotonicityNot monotonic
2023-12-13T07:54:58.478840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 502
 
5.0%
913.0 11
 
0.1%
253.0 11
 
0.1%
59.9993 11
 
0.1%
60.0 10
 
0.1%
365.0 10
 
0.1%
874.0 9
 
0.1%
250.0 9
 
0.1%
911.0 9
 
0.1%
855.0 8
 
0.1%
Other values (6297) 9410
94.1%
ValueCountFrequency (%)
-99998227.0 1
 
< 0.1%
-9995797.0 1
 
< 0.1%
-3952964.0 1
 
< 0.1%
-2396072.0 1
 
< 0.1%
-62946.0 1
 
< 0.1%
-21.0 1
 
< 0.1%
-2.0 1
 
< 0.1%
-0.45 1
 
< 0.1%
0.0 502
5.0%
0.1 2
 
< 0.1%
ValueCountFrequency (%)
99999783.0 1
< 0.1%
88536884.99 1
< 0.1%
63029016.1 1
< 0.1%
62321992.0 1
< 0.1%
38320504.27 1
< 0.1%
33684172.51 1
< 0.1%
21594660.0 1
< 0.1%
20753790.0 1
< 0.1%
20456052.0 1
< 0.1%
13802603.0 1
< 0.1%

시침
Text

Distinct9523
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:54:58.884000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.6471
Min length1

Characters and Unicode

Total characters76471
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9472 ?
Unique (%)94.7%

Sample

1st row18429548
2nd row3968416.874
3rd row6229031
4th row5957960
5th row1201274
ValueCountFrequency (%)
2926236672 110
 
1.1%
0 61
 
0.6%
91761 54
 
0.5%
90875 46
 
0.5%
22532554 24
 
0.2%
1093706 22
 
0.2%
6113669 21
 
0.2%
28099 18
 
0.2%
3773205 11
 
0.1%
20663264 11
 
0.1%
Other values (9513) 9622
96.2%
2023-12-13T07:54:59.406120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 8780
11.5%
1 8381
11.0%
3 8070
10.6%
6 7660
10.0%
4 7585
9.9%
5 7127
9.3%
7 7124
9.3%
8 6979
9.1%
9 6659
8.7%
0 6599
8.6%
Other values (3) 1507
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74964
98.0%
Other Punctuation 1495
 
2.0%
Space Separator 8
 
< 0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8780
11.7%
1 8381
11.2%
3 8070
10.8%
6 7660
10.2%
4 7585
10.1%
5 7127
9.5%
7 7124
9.5%
8 6979
9.3%
9 6659
8.9%
0 6599
8.8%
Other Punctuation
ValueCountFrequency (%)
. 1495
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76471
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8780
11.5%
1 8381
11.0%
3 8070
10.6%
6 7660
10.0%
4 7585
9.9%
5 7127
9.3%
7 7124
9.3%
8 6979
9.1%
9 6659
8.7%
0 6599
8.6%
Other values (3) 1507
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8780
11.5%
1 8381
11.0%
3 8070
10.6%
6 7660
10.0%
4 7585
9.9%
5 7127
9.3%
7 7124
9.3%
8 6979
9.1%
9 6659
8.7%
0 6599
8.6%
Other values (3) 1507
 
2.0%

종침
Real number (ℝ)

HIGH CORRELATION 

Distinct9551
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54610721
Minimum0
Maximum2.9262367 × 109
Zeros36
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:54:59.591637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile416152.4
Q12397714.2
median4961721.5
Q318172909
95-th percentile1.2551119 × 108
Maximum2.9262367 × 109
Range2.9262367 × 109
Interquartile range (IQR)15775195

Descriptive statistics

Standard deviation3.053844 × 108
Coefficient of variation (CV)5.5920229
Kurtosis81.517875
Mean54610721
Median Absolute Deviation (MAD)3553659.5
Skewness8.9997644
Sum5.4610721 × 1011
Variance9.3259633 × 1016
MonotonicityNot monotonic
2023-12-13T07:54:59.721928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2926236672.0 108
 
1.1%
91761.0 56
 
0.6%
90875.0 45
 
0.4%
0.0 36
 
0.4%
22532554.0 24
 
0.2%
1093706.0 23
 
0.2%
6113669.0 20
 
0.2%
28099.0 18
 
0.2%
3773205.0 11
 
0.1%
4888309.0 10
 
0.1%
Other values (9541) 9649
96.5%
ValueCountFrequency (%)
0.0 36
0.4%
202.0 1
 
< 0.1%
927.0 1
 
< 0.1%
1415.0 1
 
< 0.1%
1619.0 1
 
< 0.1%
1773.0 1
 
< 0.1%
2498.0 1
 
< 0.1%
2722.0 1
 
< 0.1%
3674.0 1
 
< 0.1%
4110.0 1
 
< 0.1%
ValueCountFrequency (%)
2926236672.0 108
1.1%
457010168.0 1
 
< 0.1%
439350220.0 1
 
< 0.1%
408916935.8 1
 
< 0.1%
408276245.3 1
 
< 0.1%
405845745.0 1
 
< 0.1%
405659111.5 1
 
< 0.1%
405478694.9 1
 
< 0.1%
404739688.0 1
 
< 0.1%
404189353.4 1
 
< 0.1%

Interactions

2023-12-13T07:54:56.496198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.219438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.657209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:55.163800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:55.987393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:56.572837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.290914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.785064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:55.251438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:56.071626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:56.658054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.366303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.877049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:55.333978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:56.183265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:56.746296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.454947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.968783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:55.434866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:56.294996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:56.848568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:54.558512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:55.066239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:55.557004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:56.401400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:54:59.826837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유량계 코드년도월일자유량값종침
유량계 코드1.0000.0220.0000.0000.4440.591
년도0.0221.0000.7980.2010.2080.032
월0.0000.7981.0000.2240.1190.050
일자0.0000.2010.2241.0000.0750.000
유량값0.4440.2080.1190.0751.0000.182
종침0.5910.0320.0500.0000.1821.000
2023-12-13T07:54:59.950445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유량계 코드월일자유량값종침년도
유량계 코드1.000-0.0200.0070.1780.2290.013
월-0.0201.000-0.0170.025-0.0220.688
일자0.007-0.0171.000-0.0010.0020.140
유량값0.1780.025-0.0011.0000.6110.159
종침0.229-0.0220.0020.6111.0000.009
년도0.0130.6880.1400.1590.0091.000

Missing values

2023-12-13T07:54:56.973195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:54:57.089582image/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

유량계 코드년도월일자유량값시침종침
118731120201152016.01842954818431564.0
2799741520207312194.55773968416.8743970611.432
1499114120202207109.062290316236140.0
2263625620205311118.059579605959078.0
90383201992429.012012741201703.0
94640720199119666.754346898601.0146908267.77
1198341520201161029.41593719536.7793720566.195
712239820191121928.018840731885001.0
64322201911132015.056358395637854.0
257152442020754391.012493041253695.0
유량계 코드년도월일자유량값시침종침
6849253201911181395.034354133436808.0
3009823820208245284.01666006316665347.0
312389220212180.09176191761.0
188183862020440.060434866043486.0
180983432020327277.014337501434027.0
2424734020206181011.0248606249617.0
2950431620208181832.054246285426460.0
281633102020822244.076001307602374.0
2127638202051513831.01683170716845538.0
2773108201910216687.077231277739814.0