Overview

Dataset statistics

Number of variables6
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory51.7 B

Variable types

Categorical2
Text2
Numeric2

Dataset

DescriptionSample
Author한국스마트그리드
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KSG2019111100EMUS02a

Alerts

mesure_ym has constant value ""Constant
cnsmr_no has unique valuesUnique
sg_pwrer_use_am has 23 (11.5%) zerosZeros
all_pwrer_use_am has 142 (71.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:57:45.837503
Analysis finished2023-12-10 06:57:46.541723
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

toc_no
Categorical

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
E000000004
106 
E000000002
56 
E000000001
25 
E000000003
13 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE000000001
2nd rowE000000001
3rd rowE000000001
4th rowE000000001
5th rowE000000001

Common Values

ValueCountFrequency (%)
E000000004 106
53.0%
E000000002 56
28.0%
E000000001 25
 
12.5%
E000000003 13
 
6.5%

Length

2023-12-10T15:57:46.611404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:57:46.715827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e000000004 106
53.0%
e000000002 56
28.0%
e000000001 25
 
12.5%
e000000003 13
 
6.5%

cnsmr_no
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:57:46.994862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length9.395
Min length3

Characters and Unicode

Total characters1879
Distinct characters30
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st row2344
2nd row2611
3rd row2626
4th row2714
5th row2744
ValueCountFrequency (%)
2344 1
 
0.5%
1169 1
 
0.5%
1223 1
 
0.5%
1127 1
 
0.5%
1129 1
 
0.5%
1134 1
 
0.5%
1144 1
 
0.5%
1145 1
 
0.5%
1149 1
 
0.5%
1155 1
 
0.5%
Other values (190) 190
95.0%
2023-12-10T15:57:47.426188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 680
36.2%
1 293
15.6%
2 136
 
7.2%
3 103
 
5.5%
5 86
 
4.6%
C 84
 
4.5%
4 81
 
4.3%
6 80
 
4.3%
M 67
 
3.6%
I 58
 
3.1%
Other values (20) 211
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1586
84.4%
Uppercase Letter 293
 
15.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 84
28.7%
M 67
22.9%
I 58
19.8%
K 17
 
5.8%
S 15
 
5.1%
O 8
 
2.7%
A 5
 
1.7%
P 5
 
1.7%
J 5
 
1.7%
H 5
 
1.7%
Other values (10) 24
 
8.2%
Decimal Number
ValueCountFrequency (%)
0 680
42.9%
1 293
18.5%
2 136
 
8.6%
3 103
 
6.5%
5 86
 
5.4%
4 81
 
5.1%
6 80
 
5.0%
7 57
 
3.6%
8 43
 
2.7%
9 27
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1586
84.4%
Latin 293
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 84
28.7%
M 67
22.9%
I 58
19.8%
K 17
 
5.8%
S 15
 
5.1%
O 8
 
2.7%
A 5
 
1.7%
P 5
 
1.7%
J 5
 
1.7%
H 5
 
1.7%
Other values (10) 24
 
8.2%
Common
ValueCountFrequency (%)
0 680
42.9%
1 293
18.5%
2 136
 
8.6%
3 103
 
6.5%
5 86
 
5.4%
4 81
 
5.1%
6 80
 
5.0%
7 57
 
3.6%
8 43
 
2.7%
9 27
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 680
36.2%
1 293
15.6%
2 136
 
7.2%
3 103
 
5.5%
5 86
 
4.6%
C 84
 
4.5%
4 81
 
4.3%
6 80
 
4.3%
M 67
 
3.6%
I 58
 
3.1%
Other values (20) 211
 
11.2%

mesure_ym
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
201910
200 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201910
2nd row201910
3rd row201910
4th row201910
5th row201910

Common Values

ValueCountFrequency (%)
201910 200
100.0%

Length

2023-12-10T15:57:47.557043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:57:47.657293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201910 200
100.0%

legal_dong_cd
Text

Distinct76
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:57:47.867611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.91
Min length1

Characters and Unicode

Total characters1982
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

Unique51 ?
Unique (%)25.5%

Sample

1st row4125010700
2nd row2820011100
3rd row4719011300
4th row2820011100
5th row4220038024
ValueCountFrequency (%)
2729000000 39
19.7%
2771000000 21
 
10.6%
2717000000 13
 
6.6%
2723000000 10
 
5.1%
2726000000 8
 
4.0%
2714000000 7
 
3.5%
3171025000 5
 
2.5%
2720000000 5
 
2.5%
4719011300 4
 
2.0%
4313011900 4
 
2.0%
Other values (65) 82
41.4%
2023-12-10T15:57:48.237623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 945
47.7%
2 265
 
13.4%
1 215
 
10.8%
7 182
 
9.2%
3 113
 
5.7%
4 94
 
4.7%
9 64
 
3.2%
5 47
 
2.4%
6 30
 
1.5%
8 25
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1980
99.9%
Space Separator 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 945
47.7%
2 265
 
13.4%
1 215
 
10.9%
7 182
 
9.2%
3 113
 
5.7%
4 94
 
4.7%
9 64
 
3.2%
5 47
 
2.4%
6 30
 
1.5%
8 25
 
1.3%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 945
47.7%
2 265
 
13.4%
1 215
 
10.8%
7 182
 
9.2%
3 113
 
5.7%
4 94
 
4.7%
9 64
 
3.2%
5 47
 
2.4%
6 30
 
1.5%
8 25
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 945
47.7%
2 265
 
13.4%
1 215
 
10.8%
7 182
 
9.2%
3 113
 
5.7%
4 94
 
4.7%
9 64
 
3.2%
5 47
 
2.4%
6 30
 
1.5%
8 25
 
1.3%

sg_pwrer_use_am
Real number (ℝ)

ZEROS 

Distinct178
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57145531
Minimum0
Maximum1.1285514 × 1010
Zeros23
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:57:48.397456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15259.0985
median42284.93
Q3188245.9
95-th percentile1807446.2
Maximum1.1285514 × 1010
Range1.1285514 × 1010
Interquartile range (IQR)182986.8

Descriptive statistics

Standard deviation7.9797162 × 108
Coefficient of variation (CV)13.96385
Kurtosis199.98343
Mean57145531
Median Absolute Deviation (MAD)42284.93
Skewness14.141265
Sum1.1429106 × 1010
Variance6.367587 × 1017
MonotonicityNot monotonic
2023-12-10T15:57:48.540174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
11.5%
13.0 1
 
0.5%
14784.5629 1
 
0.5%
107830.32 1
 
0.5%
66443.04 1
 
0.5%
48439.53 1
 
0.5%
8154.816 1
 
0.5%
54212.787 1
 
0.5%
166266.26 1
 
0.5%
426062.9792 1
 
0.5%
Other values (168) 168
84.0%
ValueCountFrequency (%)
0.0 23
11.5%
0.01 1
 
0.5%
13.0 1
 
0.5%
17.391 1
 
0.5%
78.7377 1
 
0.5%
133.76 1
 
0.5%
290.034 1
 
0.5%
484.16 1
 
0.5%
701.3068 1
 
0.5%
701.5135 1
 
0.5%
ValueCountFrequency (%)
11285514198.0 1
0.5%
69500196.07 1
0.5%
15077273.91 1
0.5%
9429182.7 1
0.5%
7951308.705 1
0.5%
4834009.8 1
0.5%
2877599.193 1
0.5%
2310617.5 1
0.5%
2201523.25 1
0.5%
1959497.5 1
0.5%

all_pwrer_use_am
Real number (ℝ)

ZEROS 

Distinct59
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1369215.2
Minimum0
Maximum2.3043608 × 108
Zeros142
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:57:48.980844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36699.6588
95-th percentile714923.95
Maximum2.3043608 × 108
Range2.3043608 × 108
Interquartile range (IQR)6699.6588

Descriptive statistics

Standard deviation16326812
Coefficient of variation (CV)11.924212
Kurtosis197.62818
Mean1369215.2
Median Absolute Deviation (MAD)0
Skewness14.021077
Sum2.7384303 × 108
Variance2.6656481 × 1014
MonotonicityNot monotonic
2023-12-10T15:57:49.125391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 142
71.0%
271510.05 1
 
0.5%
564373.011 1
 
0.5%
464591.477 1
 
0.5%
7951308.705 1
 
0.5%
162234.834 1
 
0.5%
1466.825 1
 
0.5%
33057.415 1
 
0.5%
11529.867 1
 
0.5%
47653.337 1
 
0.5%
Other values (49) 49
 
24.5%
ValueCountFrequency (%)
0.0 142
71.0%
17.391 1
 
0.5%
1027.401 1
 
0.5%
1029.101 1
 
0.5%
1466.825 1
 
0.5%
1822.867 1
 
0.5%
2429.0 1
 
0.5%
5044.379 1
 
0.5%
5650.743 1
 
0.5%
9846.406 1
 
0.5%
ValueCountFrequency (%)
230436079.0 1
0.5%
15077273.91 1
0.5%
7951308.705 1
0.5%
2877599.193 1
0.5%
2310617.5 1
0.5%
2201523.25 1
0.5%
1959497.5 1
0.5%
1799443.5 1
0.5%
977603.0 1
0.5%
848047.0 1
0.5%

Interactions

2023-12-10T15:57:46.193733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:46.012920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:46.275910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:57:46.100779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:57:49.243875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
toc_nolegal_dong_cdsg_pwrer_use_amall_pwrer_use_am
toc_no1.0001.0000.2160.361
legal_dong_cd1.0001.0001.0001.000
sg_pwrer_use_am0.2161.0001.0000.000
all_pwrer_use_am0.3611.0000.0001.000
2023-12-10T15:57:49.369089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
sg_pwrer_use_amall_pwrer_use_amtoc_no
sg_pwrer_use_am1.0000.1670.142
all_pwrer_use_am0.1671.0000.240
toc_no0.1420.2401.000

Missing values

2023-12-10T15:57:46.390185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:57:46.497949image/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

toc_nocnsmr_nomesure_ymlegal_dong_cdsg_pwrer_use_amall_pwrer_use_am
0E0000000012344201910412501070013.00.0
1E0000000012611201910282001110013258.080.0
2E0000000012626201910471901130018005.120.0
3E000000001271420191028200111009522.720.0
4E0000000012744201910422003802414782.320.0
5E0000000013463920191041220256240.00.0
6E00000000135479201910412731040083043.00.0
7E000000001610201910412731050028233.320.0
8E0000000017333201910302301130020148.480.0
9E000000001261320191028200107000.010.0
toc_nocnsmr_nomesure_ymlegal_dong_cdsg_pwrer_use_amall_pwrer_use_am
190E00000000414082019102771000000174207.30.0
191E0000000041411201910271100000036863.18830.0
192E0000000041417201910271400000084926.920.0
193E0000000041420201910271700000023603.470.0
194E0000000041424201910271700000099050.70350.0
195E00000000414432019102729000000121141.1880.0
196E000000004145020191027200000002538.16730.0
197E0000000041465201910277100000014619.740.0
198E000000004100120191027710000000.00.0
199E00000000410052019102723000000687167.040.0