Overview

Dataset statistics

Number of variables21
Number of observations814
Missing cells392
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory146.4 KiB
Average record size in memory184.2 B

Variable types

Numeric14
Categorical4
Text2
DateTime1

Alerts

last_load_dttm has constant value ""Constant
water10 is highly imbalanced (55.8%)Imbalance
water11 has 382 (46.9%) missing valuesMissing
skey has unique valuesUnique
water04 has 20 (2.5%) zerosZeros
water05 has 10 (1.2%) zerosZeros
water11 has 276 (33.9%) zerosZeros

Reproduction

Analysis started2024-04-20 17:17:57.464505
Analysis finished2024-04-20 17:17:57.901517
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct814
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191527.5
Minimum191121
Maximum191934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:17:58.088256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191121
5-th percentile191161.65
Q1191324.25
median191527.5
Q3191730.75
95-th percentile191893.35
Maximum191934
Range813
Interquartile range (IQR)406.5

Descriptive statistics

Standard deviation235.12585
Coefficient of variation (CV)0.0012276349
Kurtosis-1.2
Mean191527.5
Median Absolute Deviation (MAD)203.5
Skewness0
Sum1.5590338 × 108
Variance55284.167
MonotonicityNot monotonic
2024-04-21T02:17:58.538643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191859 1
 
0.1%
191377 1
 
0.1%
191367 1
 
0.1%
191368 1
 
0.1%
191369 1
 
0.1%
191370 1
 
0.1%
191371 1
 
0.1%
191372 1
 
0.1%
191373 1
 
0.1%
191374 1
 
0.1%
Other values (804) 804
98.8%
ValueCountFrequency (%)
191121 1
0.1%
191122 1
0.1%
191123 1
0.1%
191124 1
0.1%
191125 1
0.1%
191126 1
0.1%
191127 1
0.1%
191128 1
0.1%
191129 1
0.1%
191130 1
0.1%
ValueCountFrequency (%)
191934 1
0.1%
191933 1
0.1%
191932 1
0.1%
191931 1
0.1%
191930 1
0.1%
191929 1
0.1%
191928 1
0.1%
191927 1
0.1%
191926 1
0.1%
191925 1
0.1%

inspec_yy
Real number (ℝ)

Distinct11
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.4459
Minimum2005
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:17:58.909745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2013
Q12015
median2017
Q32019
95-th percentile2020
Maximum2021
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0016645
Coefficient of variation (CV)0.0014885916
Kurtosis1.88002
Mean2016.4459
Median Absolute Deviation (MAD)2
Skewness-1.0441096
Sum1641387
Variance9.0099897
MonotonicityNot monotonic
2024-04-21T02:17:59.271545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2019 100
12.3%
2020 100
12.3%
2018 100
12.3%
2017 100
12.3%
2016 100
12.3%
2015 84
10.3%
2014 84
10.3%
2013 84
10.3%
2021 30
 
3.7%
2008 23
 
2.8%
ValueCountFrequency (%)
2005 9
 
1.1%
2008 23
 
2.8%
2013 84
10.3%
2014 84
10.3%
2015 84
10.3%
2016 100
12.3%
2017 100
12.3%
2018 100
12.3%
2019 100
12.3%
2020 100
12.3%
ValueCountFrequency (%)
2021 30
 
3.7%
2020 100
12.3%
2019 100
12.3%
2018 100
12.3%
2017 100
12.3%
2016 100
12.3%
2015 84
10.3%
2014 84
10.3%
2013 84
10.3%
2008 23
 
2.8%

inspec_qt
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
1
249 
3
213 
2
180 
4
172 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 249
30.6%
3 213
26.2%
2 180
22.1%
4 172
21.1%

Length

2024-04-21T02:17:59.576467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:17:59.759200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 249
30.6%
3 213
26.2%
2 180
22.1%
4 172
21.1%

site
Categorical

Distinct30
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
동천하류
 
35
북내항
 
35
남항
 
35
남외항
 
35
5부두
 
35
Other values (25)
639 

Length

Max length7
Median length6
Mean length3.4520885
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동천하류
2nd row발전소앞
3rd row부산대교
4th row자갈치시장
5th row다대포어시장

Common Values

ValueCountFrequency (%)
동천하류 35
 
4.3%
북내항 35
 
4.3%
남항 35
 
4.3%
남외항 35
 
4.3%
5부두 35
 
4.3%
북외항 35
 
4.3%
감천항 34
 
4.2%
다대포어시장 34
 
4.2%
다대포항 34
 
4.2%
발전소앞 34
 
4.2%
Other values (20) 468
57.5%

Length

2024-04-21T02:17:59.972043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동천하류 35
 
4.3%
남항 35
 
4.3%
남외항 35
 
4.3%
5부두 35
 
4.3%
북외항 35
 
4.3%
북내항 35
 
4.3%
발전소앞 34
 
4.2%
다대포항 34
 
4.2%
다대포어시장 34
 
4.2%
감천항 34
 
4.2%
Other values (20) 468
57.5%

water01
Real number (ℝ)

Distinct54
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.375921
Minimum20
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:00.198148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median30
Q342
95-th percentile63
Maximum88
Range68
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.215135
Coefficient of variation (CV)0.41352012
Kurtosis0.4577056
Mean34.375921
Median Absolute Deviation (MAD)10
Skewness1.0284961
Sum27982
Variance202.07007
MonotonicityNot monotonic
2024-04-21T02:18:00.450609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 209
25.7%
30 61
 
7.5%
32 45
 
5.5%
28 36
 
4.4%
36 35
 
4.3%
40 31
 
3.8%
26 31
 
3.8%
23 25
 
3.1%
29 20
 
2.5%
58 19
 
2.3%
Other values (44) 302
37.1%
ValueCountFrequency (%)
20 209
25.7%
22 15
 
1.8%
23 25
 
3.1%
24 15
 
1.8%
25 1
 
0.1%
26 31
 
3.8%
28 36
 
4.4%
29 20
 
2.5%
30 61
 
7.5%
31 5
 
0.6%
ValueCountFrequency (%)
88 2
 
0.2%
87 1
 
0.1%
79 1
 
0.1%
76 2
 
0.2%
75 2
 
0.2%
72 1
 
0.1%
71 4
 
0.5%
68 11
1.4%
67 4
 
0.5%
66 3
 
0.4%

water02
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
I
250 
II
222 
III
188 
IV
91 
V
63 

Length

Max length3
Median length2
Mean length1.8464373
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIV
2nd rowII
3rd rowII
4th rowI
5th rowII

Common Values

ValueCountFrequency (%)
I 250
30.7%
II 222
27.3%
III 188
23.1%
IV 91
 
11.2%
V 63
 
7.7%

Length

2024-04-21T02:18:00.707438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:18:00.919323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 250
30.7%
ii 222
27.3%
iii 188
23.1%
iv 91
 
11.2%
v 63
 
7.7%

water03
Text

Distinct723
Distinct (%)88.9%
Missing1
Missing (%)0.1%
Memory size6.5 KiB
2024-04-21T02:18:02.321279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.9114391
Min length3

Characters and Unicode

Total characters3993
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique641 ?
Unique (%)78.8%

Sample

1st row861.7
2nd row96.2
3rd row110.7
4th row54.8
5th row174.5
ValueCountFrequency (%)
143.2 4
 
0.5%
108.1 3
 
0.4%
113.1 3
 
0.4%
94.4 3
 
0.4%
130.8 3
 
0.4%
151.6 3
 
0.4%
34.3 3
 
0.4%
295.9 2
 
0.2%
173.8 2
 
0.2%
1,077.9 2
 
0.2%
Other values (713) 785
96.6%
2024-04-21T02:18:03.968304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 813
20.4%
1 590
14.8%
2 403
10.1%
3 294
 
7.4%
4 282
 
7.1%
6 267
 
6.7%
7 266
 
6.7%
8 265
 
6.6%
5 262
 
6.6%
9 244
 
6.1%
Other values (2) 307
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3102
77.7%
Other Punctuation 891
 
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 590
19.0%
2 403
13.0%
3 294
9.5%
4 282
9.1%
6 267
8.6%
7 266
8.6%
8 265
8.5%
5 262
8.4%
9 244
7.9%
0 229
 
7.4%
Other Punctuation
ValueCountFrequency (%)
. 813
91.2%
, 78
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3993
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 813
20.4%
1 590
14.8%
2 403
10.1%
3 294
 
7.4%
4 282
 
7.1%
6 267
 
6.7%
7 266
 
6.7%
8 265
 
6.6%
5 262
 
6.6%
9 244
 
6.1%
Other values (2) 307
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 813
20.4%
1 590
14.8%
2 403
10.1%
3 294
 
7.4%
4 282
 
7.1%
6 267
 
6.7%
7 266
 
6.7%
8 265
 
6.6%
5 262
 
6.6%
9 244
 
6.1%
Other values (2) 307
 
7.7%

water04
Real number (ℝ)

ZEROS 

Distinct378
Distinct (%)46.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean29.266544
Minimum0
Maximum327.3
Zeros20
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:04.210723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.62
Q19.6
median19.2
Q332.1
95-th percentile96.26
Maximum327.3
Range327.3
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation38.528785
Coefficient of variation (CV)1.3164788
Kurtosis19.951249
Mean29.266544
Median Absolute Deviation (MAD)10.9
Skewness3.8843664
Sum23793.7
Variance1484.4673
MonotonicityNot monotonic
2024-04-21T02:18:04.461211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 20
 
2.5%
2.0 19
 
2.3%
1.0 12
 
1.5%
22.0 11
 
1.4%
21.0 10
 
1.2%
8.0 10
 
1.2%
27.0 10
 
1.2%
6.0 9
 
1.1%
20.0 9
 
1.1%
4.0 9
 
1.1%
Other values (368) 694
85.3%
ValueCountFrequency (%)
0.0 20
2.5%
0.3 1
 
0.1%
0.7 3
 
0.4%
0.8 1
 
0.1%
0.9 1
 
0.1%
1.0 12
1.5%
1.1 1
 
0.1%
1.2 2
 
0.2%
1.9 1
 
0.1%
2.0 19
2.3%
ValueCountFrequency (%)
327.3 2
0.2%
289.9 2
0.2%
275.4 1
0.1%
252.0 1
0.1%
211.6 1
0.1%
207.0 1
0.1%
200.3 1
0.1%
195.0 1
0.1%
190.0 2
0.2%
179.9 1
0.1%

water05
Real number (ℝ)

ZEROS 

Distinct442
Distinct (%)54.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.834674
Minimum0
Maximum41.51
Zeros10
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:04.844949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.176
Q10.79
median1.93
Q34.5
95-th percentile14.348
Maximum41.51
Range41.51
Interquartile range (IQR)3.71

Descriptive statistics

Standard deviation5.5357933
Coefficient of variation (CV)1.4436151
Kurtosis14.603193
Mean3.834674
Median Absolute Deviation (MAD)1.45
Skewness3.387322
Sum3117.59
Variance30.645008
MonotonicityNot monotonic
2024-04-21T02:18:05.260147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 12
 
1.5%
0.0 10
 
1.2%
1.5 7
 
0.9%
1.0 7
 
0.9%
0.3 7
 
0.9%
0.5 7
 
0.9%
1.1 7
 
0.9%
0.38 7
 
0.9%
0.24 6
 
0.7%
1.03 6
 
0.7%
Other values (432) 737
90.5%
ValueCountFrequency (%)
0.0 10
1.2%
0.02 2
 
0.2%
0.03 1
 
0.1%
0.05 1
 
0.1%
0.06 2
 
0.2%
0.07 1
 
0.1%
0.08 2
 
0.2%
0.09 4
 
0.5%
0.1 3
 
0.4%
0.12 1
 
0.1%
ValueCountFrequency (%)
41.51 2
0.2%
39.63 1
0.1%
35.54 2
0.2%
34.9 1
0.1%
31.82 2
0.2%
31.26 1
0.1%
29.1 1
0.1%
26.4 1
0.1%
26.24 1
0.1%
24.24 2
0.2%

water06
Real number (ℝ)

Distinct363
Distinct (%)44.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean92.662977
Minimum43.9
Maximum143.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:05.902872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43.9
5-th percentile68.92
Q186
median94.3
Q399.8
95-th percentile110.58
Maximum143.8
Range99.9
Interquartile range (IQR)13.8

Descriptive statistics

Standard deviation12.553097
Coefficient of variation (CV)0.13547047
Kurtosis1.4447115
Mean92.662977
Median Absolute Deviation (MAD)6.6
Skewness-0.48680292
Sum75335
Variance157.58024
MonotonicityNot monotonic
2024-04-21T02:18:06.340760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.5 8
 
1.0%
95.7 8
 
1.0%
96.4 7
 
0.9%
94.1 7
 
0.9%
94.4 7
 
0.9%
96.7 7
 
0.9%
99.5 6
 
0.7%
96.3 6
 
0.7%
98.7 6
 
0.7%
93.4 6
 
0.7%
Other values (353) 745
91.5%
ValueCountFrequency (%)
43.9 2
0.2%
52.2 1
0.1%
53.4 1
0.1%
55.2 1
0.1%
55.8 1
0.1%
56.0 1
0.1%
56.3 1
0.1%
57.4 2
0.2%
57.8 1
0.1%
58.7 1
0.1%
ValueCountFrequency (%)
143.8 1
0.1%
134.5 1
0.1%
128.7 1
0.1%
125.2 1
0.1%
124.6 1
0.1%
123.3 1
0.1%
123.2 1
0.1%
122.9 1
0.1%
122.7 1
0.1%
121.8 1
0.1%

water07
Real number (ℝ)

Distinct67
Distinct (%)8.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.1227552
Minimum0.4
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:06.751373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1
Q12.1
median3
Q34
95-th percentile5.54
Maximum8.5
Range8.1
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.4603752
Coefficient of variation (CV)0.46765601
Kurtosis0.6645066
Mean3.1227552
Median Absolute Deviation (MAD)1
Skewness0.73301474
Sum2538.8
Variance2.1326958
MonotonicityNot monotonic
2024-04-21T02:18:07.164983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 59
 
7.2%
2.5 57
 
7.0%
3.5 53
 
6.5%
2.0 48
 
5.9%
4.0 36
 
4.4%
4.5 32
 
3.9%
5.0 32
 
3.9%
1.5 30
 
3.7%
2.8 26
 
3.2%
5.5 25
 
3.1%
Other values (57) 415
51.0%
ValueCountFrequency (%)
0.4 2
 
0.2%
0.5 10
1.2%
0.6 9
1.1%
0.7 3
 
0.4%
0.8 8
 
1.0%
0.9 5
 
0.6%
1.0 7
 
0.9%
1.1 2
 
0.2%
1.2 7
 
0.9%
1.3 21
2.6%
ValueCountFrequency (%)
8.5 5
0.6%
7.9 1
 
0.1%
7.5 2
 
0.2%
7.2 1
 
0.1%
7.1 1
 
0.1%
7.0 2
 
0.2%
6.9 1
 
0.1%
6.8 3
0.4%
6.6 1
 
0.1%
6.5 6
0.7%

water08
Real number (ℝ)

Distinct18
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1192875
Minimum7.1
Maximum8.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:07.540802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.1
5-th percentile7.8
Q18
median8.1
Q38.2
95-th percentile8.4
Maximum8.9
Range1.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.17657109
Coefficient of variation (CV)0.021747117
Kurtosis4.1919827
Mean8.1192875
Median Absolute Deviation (MAD)0.1
Skewness-0.93435128
Sum6609.1
Variance0.031177351
MonotonicityNot monotonic
2024-04-21T02:18:07.896245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
8.1 223
27.4%
8.2 212
26.0%
8.0 137
16.8%
8.3 103
12.7%
7.9 48
 
5.9%
8.4 35
 
4.3%
7.8 15
 
1.8%
7.6 11
 
1.4%
8.5 9
 
1.1%
7.7 9
 
1.1%
Other values (8) 12
 
1.5%
ValueCountFrequency (%)
7.1 1
 
0.1%
7.2 1
 
0.1%
7.3 1
 
0.1%
7.4 1
 
0.1%
7.5 4
 
0.5%
7.6 11
 
1.4%
7.7 9
 
1.1%
7.8 15
 
1.8%
7.9 48
 
5.9%
8.0 137
16.8%
ValueCountFrequency (%)
8.9 1
 
0.1%
8.7 2
 
0.2%
8.6 1
 
0.1%
8.5 9
 
1.1%
8.4 35
 
4.3%
8.3 103
12.7%
8.2 212
26.0%
8.1 223
27.4%
8.0 137
16.8%
7.9 48
 
5.9%

water09
Text

Distinct97
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-04-21T02:18:08.622481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.6707617
Min length1

Characters and Unicode

Total characters2174
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)3.2%

Sample

1st row7,000
2nd row78
3rd row78
4th row45
5th row130
ValueCountFrequency (%)
2 60
 
7.4%
0 41
 
5.0%
130 36
 
4.4%
5 28
 
3.4%
170 27
 
3.3%
350 26
 
3.2%
330 24
 
2.9%
20 22
 
2.7%
49 22
 
2.7%
8 22
 
2.7%
Other values (87) 506
62.2%
2024-04-21T02:18:09.744267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 659
30.3%
3 251
 
11.5%
1 243
 
11.2%
2 241
 
11.1%
4 173
 
8.0%
7 139
 
6.4%
, 135
 
6.2%
5 123
 
5.7%
9 106
 
4.9%
8 62
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2039
93.8%
Other Punctuation 135
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 659
32.3%
3 251
 
12.3%
1 243
 
11.9%
2 241
 
11.8%
4 173
 
8.5%
7 139
 
6.8%
5 123
 
6.0%
9 106
 
5.2%
8 62
 
3.0%
6 42
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2174
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 659
30.3%
3 251
 
11.5%
1 243
 
11.2%
2 241
 
11.1%
4 173
 
8.0%
7 139
 
6.4%
, 135
 
6.2%
5 123
 
5.7%
9 106
 
4.9%
8 62
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 659
30.3%
3 251
 
11.5%
1 243
 
11.2%
2 241
 
11.1%
4 173
 
8.0%
7 139
 
6.4%
, 135
 
6.2%
5 123
 
5.7%
9 106
 
4.9%
8 62
 
2.9%

water10
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
<NA>
562 
0.0
234 
0.1
 
15
0.2
 
2
1.4
 
1

Length

Max length4
Median length4
Mean length3.6904177
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 562
69.0%
0.0 234
28.7%
0.1 15
 
1.8%
0.2 2
 
0.2%
1.4 1
 
0.1%

Length

2024-04-21T02:18:09.966136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:18:10.162611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 562
69.0%
0.0 234
28.7%
0.1 15
 
1.8%
0.2 2
 
0.2%
1.4 1
 
0.1%

water11
Real number (ℝ)

MISSING  ZEROS 

Distinct61
Distinct (%)14.1%
Missing382
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean0.25550926
Minimum0
Maximum5.71
Zeros276
Zeros (%)33.9%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:10.480343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile1.354
Maximum5.71
Range5.71
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.66339017
Coefficient of variation (CV)2.5963449
Kurtosis27.929229
Mean0.25550926
Median Absolute Deviation (MAD)0
Skewness4.6883137
Sum110.38
Variance0.44008651
MonotonicityNot monotonic
2024-04-21T02:18:10.900536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 276
33.9%
0.1 34
 
4.2%
0.2 25
 
3.1%
0.3 11
 
1.4%
0.4 10
 
1.2%
0.5 6
 
0.7%
0.6 5
 
0.6%
1.0 3
 
0.4%
1.1 3
 
0.4%
1.9 3
 
0.4%
Other values (51) 56
 
6.9%
(Missing) 382
46.9%
ValueCountFrequency (%)
0.0 276
33.9%
0.1 34
 
4.2%
0.11 1
 
0.1%
0.13 1
 
0.1%
0.14 1
 
0.1%
0.18 1
 
0.1%
0.2 25
 
3.1%
0.24 1
 
0.1%
0.25 1
 
0.1%
0.26 1
 
0.1%
ValueCountFrequency (%)
5.71 1
0.1%
5.42 1
0.1%
4.87 1
0.1%
4.2 1
0.1%
2.9 1
0.1%
2.52 1
0.1%
2.5 1
0.1%
2.48 1
0.1%
2.34 1
0.1%
2.3 1
0.1%

water12
Real number (ℝ)

Distinct63
Distinct (%)7.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.5757688
Minimum0.1
Maximum10.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:11.321759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.7
median1.1
Q32
95-th percentile4.3
Maximum10.9
Range10.8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.408425
Coefficient of variation (CV)0.89380181
Kurtosis9.5000101
Mean1.5757688
Median Absolute Deviation (MAD)0.5
Skewness2.4709655
Sum1281.1
Variance1.9836609
MonotonicityNot monotonic
2024-04-21T02:18:11.759855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 67
 
8.2%
1.0 59
 
7.2%
0.6 51
 
6.3%
0.4 49
 
6.0%
0.9 47
 
5.8%
0.7 43
 
5.3%
1.2 38
 
4.7%
1.4 37
 
4.5%
1.1 32
 
3.9%
0.5 32
 
3.9%
Other values (53) 358
44.0%
ValueCountFrequency (%)
0.1 5
 
0.6%
0.2 25
 
3.1%
0.3 17
 
2.1%
0.4 49
6.0%
0.5 32
3.9%
0.6 51
6.3%
0.7 43
5.3%
0.8 67
8.2%
0.9 47
5.8%
1.0 59
7.2%
ValueCountFrequency (%)
10.9 1
0.1%
10.8 2
0.2%
10.6 1
0.1%
7.7 1
0.1%
6.8 1
0.1%
6.6 2
0.2%
6.4 2
0.2%
6.2 1
0.1%
5.9 1
0.1%
5.8 1
0.1%

water13
Real number (ℝ)

Distinct74
Distinct (%)9.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.9392374
Minimum0.1
Maximum12.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:12.172451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile5.9
Q17.1
median8.1
Q38.8
95-th percentile9.9
Maximum12.4
Range12.3
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.3440428
Coefficient of variation (CV)0.16929118
Kurtosis4.5114042
Mean7.9392374
Median Absolute Deviation (MAD)0.8
Skewness-0.79514093
Sum6454.6
Variance1.8064511
MonotonicityNot monotonic
2024-04-21T02:18:12.615927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 40
 
4.9%
7.6 33
 
4.1%
8.3 31
 
3.8%
7.3 30
 
3.7%
8.1 30
 
3.7%
8.5 28
 
3.4%
8.2 27
 
3.3%
8.8 27
 
3.3%
8.0 26
 
3.2%
6.9 26
 
3.2%
Other values (64) 515
63.3%
ValueCountFrequency (%)
0.1 2
0.2%
0.4 1
0.1%
1.2 1
0.1%
3.2 1
0.1%
3.5 1
0.1%
4.4 1
0.1%
4.6 1
0.1%
4.8 1
0.1%
4.9 1
0.1%
5.0 2
0.2%
ValueCountFrequency (%)
12.4 2
0.2%
12.1 2
0.2%
11.8 1
 
0.1%
11.6 1
 
0.1%
11.2 2
0.2%
11.1 3
0.4%
11.0 1
 
0.1%
10.9 1
 
0.1%
10.8 1
 
0.1%
10.7 1
 
0.1%

water14
Real number (ℝ)

Distinct202
Distinct (%)24.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean17.442312
Minimum5.9
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:13.032393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile9.5
Q112.6
median16.6
Q321.7
95-th percentile28.44
Maximum30
Range24.1
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation5.7800941
Coefficient of variation (CV)0.33138348
Kurtosis-0.71708412
Mean17.442312
Median Absolute Deviation (MAD)4.1
Skewness0.49027274
Sum14180.6
Variance33.409488
MonotonicityNot monotonic
2024-04-21T02:18:13.471236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.4 14
 
1.7%
16.8 13
 
1.6%
12.2 13
 
1.6%
16.9 12
 
1.5%
16.5 12
 
1.5%
16.7 12
 
1.5%
16.6 11
 
1.4%
12.3 11
 
1.4%
17.3 10
 
1.2%
12.6 10
 
1.2%
Other values (192) 695
85.4%
ValueCountFrequency (%)
5.9 1
 
0.1%
7.2 1
 
0.1%
7.3 1
 
0.1%
7.6 1
 
0.1%
7.7 2
 
0.2%
8.1 3
0.4%
8.2 1
 
0.1%
8.5 6
0.7%
8.6 2
 
0.2%
8.7 1
 
0.1%
ValueCountFrequency (%)
30.0 2
0.2%
29.9 1
 
0.1%
29.8 1
 
0.1%
29.6 1
 
0.1%
29.5 1
 
0.1%
29.4 1
 
0.1%
29.3 2
0.2%
29.2 1
 
0.1%
29.1 2
0.2%
29.0 3
0.4%

water15
Real number (ℝ)

Distinct38
Distinct (%)4.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean48.544895
Minimum0
Maximum62
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:13.857200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q148
median51
Q352
95-th percentile53
Maximum62
Range62
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.879112
Coefficient of variation (CV)0.14170619
Kurtosis18.242075
Mean48.544895
Median Absolute Deviation (MAD)1
Skewness-3.8275628
Sum39467
Variance47.322181
MonotonicityNot monotonic
2024-04-21T02:18:14.266349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
52 180
22.1%
51 141
17.3%
50 126
15.5%
53 90
11.1%
49 68
 
8.4%
48 43
 
5.3%
47 27
 
3.3%
46 22
 
2.7%
45 13
 
1.6%
40 11
 
1.4%
Other values (28) 92
11.3%
ValueCountFrequency (%)
0 2
0.2%
6 3
0.4%
7 2
0.2%
9 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
0.2%
19 2
0.2%
22 1
 
0.1%
23 1
 
0.1%
ValueCountFrequency (%)
62 1
 
0.1%
54 2
 
0.2%
53 90
11.1%
52 180
22.1%
51 141
17.3%
50 126
15.5%
49 68
 
8.4%
48 43
 
5.3%
47 27
 
3.3%
46 22
 
2.7%

water16
Real number (ℝ)

Distinct125
Distinct (%)15.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean31.675154
Minimum0.2
Maximum35.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T02:18:14.671256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile22.9
Q131.6
median33.1
Q334.1
95-th percentile34.74
Maximum35.3
Range35.1
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation4.7321454
Coefficient of variation (CV)0.14939613
Kurtosis15.930075
Mean31.675154
Median Absolute Deviation (MAD)1.1
Skewness-3.6040926
Sum25751.9
Variance22.3932
MonotonicityNot monotonic
2024-04-21T02:18:15.112473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.6 30
 
3.7%
34.1 30
 
3.7%
32.8 29
 
3.6%
33.1 29
 
3.6%
32.9 27
 
3.3%
34.2 25
 
3.1%
34.4 25
 
3.1%
33.3 24
 
2.9%
33.4 23
 
2.8%
34.5 23
 
2.8%
Other values (115) 548
67.3%
ValueCountFrequency (%)
0.2 2
0.2%
3.4 3
0.4%
3.6 2
0.2%
5.2 1
 
0.1%
8.9 1
 
0.1%
9.5 1
 
0.1%
10.8 2
0.2%
11.3 2
0.2%
14.0 1
 
0.1%
14.2 1
 
0.1%
ValueCountFrequency (%)
35.3 3
 
0.4%
35.2 6
 
0.7%
35.1 6
 
0.7%
35.0 6
 
0.7%
34.9 8
 
1.0%
34.8 12
 
1.5%
34.7 18
2.2%
34.6 30
3.7%
34.5 23
2.8%
34.4 25
3.1%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Minimum2021-05-01 06:13:03
Maximum2021-05-01 06:13:03
2024-04-21T02:18:15.465270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:18:15.754226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Sample

skeyinspec_yyinspec_qtsitewater01water02water03water04water05water06water07water08water09water10water11water12water13water14water15water16last_load_dttm
019185920192동천하류56IV861.777.90.4872.73.58.17,000<NA><NA>1.65.718.65133.42021-05-01 06:13:03
119186020192발전소앞30II96.224.30.3486.43.08.278<NA><NA>0.87.118.25234.32021-05-01 06:13:03
219186120192부산대교30II110.723.10.4289.55.08.378<NA><NA>0.86.917.05234.52021-05-01 06:13:03
319186220192자갈치시장20I54.820.00.4496.15.08.345<NA><NA>0.77.616.65435.12021-05-01 06:13:03
419186320192다대포어시장30II174.533.00.6986.52.58.2130<NA><NA>1.26.817.75334.92021-05-01 06:13:03
519186420192송도해수욕장30II40.610.30.4687.84.08.32<NA><NA>0.87.617.55334.92021-05-01 06:13:03
619186520192광안리해수욕장36III515.154.51.4390.75.18.220<NA><NA>0.97.517.55334.72021-05-01 06:13:03
719186620192해운대해수욕장30II72.423.50.6187.25.18.345<NA><NA>0.87.115.95334.82021-05-01 06:13:03
819186720193고리20I8.23.81.56103.23.08.40<NA>1.121.27.326.15032.62021-05-01 06:13:03
919186820193남항30II61.86.61.8784.62.58.2330<NA>1.820.46.223.65032.82021-05-01 06:13:03
skeyinspec_yyinspec_qtsitewater01water02water03water04water05water06water07water08water09water10water11water12water13water14water15water16last_load_dttm
80419119020204북외항20I128.320.33.6292.14.08.023<NA><NA>0.67.316.65032.62021-05-01 06:13:03
80519119120204수영만36III982.174.90.8693.65.18.12,400<NA><NA>0.97.017.14831.52021-05-01 06:13:03
80619119220204이기대34III260.419.91.9189.65.58.045<NA><NA>0.77.417.45032.72021-05-01 06:13:03
80719119320204해운대46III847.495.71.4288.85.38.0350<NA><NA>0.86.715.64832.92021-05-01 06:13:03
80819119420204다대포항20I150.518.75.1399.33.77.626<NA><NA>0.78.016.45032.92021-05-01 06:13:03
80919119520204동천하류65V693.966.30.6578.61.58.09,200<NA><NA>1.06.016.74831.22021-05-01 06:13:03
81019193120201북외항26II134.220.80.4293.12.08.15<NA><NA>0.78.112.45133.32021-05-01 06:13:03
81119193220201수영만36III933.274.50.0594.12.38.01,100<NA><NA>1.37.012.64529.42021-05-01 06:13:03
81219193320201신외항29II150.519.30.093.41.88.12<NA><NA>1.48.411.05032.52021-05-01 06:13:03
81319193420201이기대22I237.923.90.096.43.07.9130<NA><NA>1.18.114.05032.92021-05-01 06:13:03