Overview

Dataset statistics

Number of variables10
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 KiB
Average record size in memory85.7 B

Variable types

Categorical7
Text1
Numeric2

Dataset

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

Alerts

A000000001 has constant value ""Constant
4311110700 has constant value ""Constant
202108 has constant value ""Constant
100 주택용전력 has constant value ""Constant
주택용 고압 has constant value ""Constant
4 단일계약아파트 has constant value ""Constant
106.967 is highly overall correlated with 15963 and 1 other fieldsHigh correlation
15963 is highly overall correlated with 106.967 and 1 other fieldsHigh correlation
1 is highly overall correlated with 106.967 and 1 other fieldsHigh correlation
CB0100101000101 has unique valuesUnique
106.967 has unique valuesUnique
15963 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:34:56.716929
Analysis finished2023-12-10 06:34:58.264544
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

A000000001
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
A000000001
199 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA000000001
2nd rowA000000001
3rd rowA000000001
4th rowA000000001
5th rowA000000001

Common Values

ValueCountFrequency (%)
A000000001 199
100.0%

Length

2023-12-10T15:34:58.360837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:34:58.503910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a000000001 199
100.0%

CB0100101000101
Text

UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:34:58.825869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters2985
Distinct characters12
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

Unique199 ?
Unique (%)100.0%

Sample

1st rowCB0100101000102
2nd rowCB0100101000104
3rd rowCB0100101000105
4th rowCB0100101000106
5th rowCB0100101000201
ValueCountFrequency (%)
cb0100101000102 1
 
0.5%
cb0100105000502 1
 
0.5%
cb0100104001001 1
 
0.5%
cb0100104001101 1
 
0.5%
cb0100104001102 1
 
0.5%
cb0100104001104 1
 
0.5%
cb0100104001106 1
 
0.5%
cb0100104001203 1
 
0.5%
cb0100104001205 1
 
0.5%
cb0100104001206 1
 
0.5%
Other values (189) 189
95.0%
2023-12-10T15:34:59.372545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1524
51.1%
1 582
 
19.5%
C 199
 
6.7%
B 199
 
6.7%
2 100
 
3.4%
3 91
 
3.0%
5 84
 
2.8%
4 80
 
2.7%
6 73
 
2.4%
8 23
 
0.8%
Other values (2) 30
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2587
86.7%
Uppercase Letter 398
 
13.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1524
58.9%
1 582
 
22.5%
2 100
 
3.9%
3 91
 
3.5%
5 84
 
3.2%
4 80
 
3.1%
6 73
 
2.8%
8 23
 
0.9%
7 20
 
0.8%
9 10
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
C 199
50.0%
B 199
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2587
86.7%
Latin 398
 
13.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1524
58.9%
1 582
 
22.5%
2 100
 
3.9%
3 91
 
3.5%
5 84
 
3.2%
4 80
 
3.1%
6 73
 
2.8%
8 23
 
0.9%
7 20
 
0.8%
9 10
 
0.4%
Latin
ValueCountFrequency (%)
C 199
50.0%
B 199
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1524
51.1%
1 582
 
19.5%
C 199
 
6.7%
B 199
 
6.7%
2 100
 
3.4%
3 91
 
3.0%
5 84
 
2.8%
4 80
 
2.7%
6 73
 
2.4%
8 23
 
0.8%
Other values (2) 30
 
1.0%

4311110700
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
4311110700
199 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4311110700
2nd row4311110700
3rd row4311110700
4th row4311110700
5th row4311110700

Common Values

ValueCountFrequency (%)
4311110700 199
100.0%

Length

2023-12-10T15:34:59.608204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:34:59.831398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4311110700 199
100.0%

202108
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
202108
199 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202108
2nd row202108
3rd row202108
4th row202108
5th row202108

Common Values

ValueCountFrequency (%)
202108 199
100.0%

Length

2023-12-10T15:35:00.044157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:00.192285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202108 199
100.0%

106.967
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.06885
Minimum11.172
Maximum767.014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:00.396144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.172
5-th percentile153.5542
Q1214.8695
median288.35
Q3357.887
95-th percentile468.3912
Maximum767.014
Range755.842
Interquartile range (IQR)143.0175

Descriptive statistics

Standard deviation109.30726
Coefficient of variation (CV)0.36549195
Kurtosis1.7554437
Mean299.06885
Median Absolute Deviation (MAD)72.161
Skewness0.80530974
Sum59514.701
Variance11948.077
MonotonicityNot monotonic
2023-12-10T15:35:00.670945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
283.304 1
 
0.5%
288.35 1
 
0.5%
389.213 1
 
0.5%
341.969 1
 
0.5%
361.664 1
 
0.5%
356.585 1
 
0.5%
408.339 1
 
0.5%
273.716 1
 
0.5%
362.215 1
 
0.5%
413.08 1
 
0.5%
Other values (189) 189
95.0%
ValueCountFrequency (%)
11.172 1
0.5%
90.658 1
0.5%
94.231 1
0.5%
105.042 1
0.5%
117.899 1
0.5%
117.941 1
0.5%
131.01 1
0.5%
150.04 1
0.5%
151.538 1
0.5%
152.134 1
0.5%
ValueCountFrequency (%)
767.014 1
0.5%
648.06 1
0.5%
641.123 1
0.5%
593.513 1
0.5%
587.959 1
0.5%
540.17 1
0.5%
503.567 1
0.5%
484.452 1
0.5%
482.906 1
0.5%
479.589 1
0.5%

100 주택용전력
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
100 주택용전력
199 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100 주택용전력
2nd row100 주택용전력
3rd row100 주택용전력
4th row100 주택용전력
5th row100 주택용전력

Common Values

ValueCountFrequency (%)
100 주택용전력 199
100.0%

Length

2023-12-10T15:35:01.379057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:01.667181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 199
50.0%
주택용전력 199
50.0%

주택용 고압
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
주택용 고압
199 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주택용 고압
2nd row주택용 고압
3rd row주택용 고압
4th row주택용 고압
5th row주택용 고압

Common Values

ValueCountFrequency (%)
주택용 고압 199
100.0%

Length

2023-12-10T15:35:01.881781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:02.053531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주택용 199
50.0%
고압 199
50.0%

4 단일계약아파트
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
4 단일계약아파트
199 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4 단일계약아파트
2nd row4 단일계약아파트
3rd row4 단일계약아파트
4th row4 단일계약아파트
5th row4 단일계약아파트

Common Values

ValueCountFrequency (%)
4 단일계약아파트 199
100.0%

Length

2023-12-10T15:35:02.222177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:02.393389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 199
50.0%
단일계약아파트 199
50.0%

1
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
107 
2
77 
3
15 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 107
53.8%
2 77
38.7%
3 15
 
7.5%

Length

2023-12-10T15:35:02.564155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:35:02.736728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 107
53.8%
2 77
38.7%
3 15
 
7.5%

15963
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35957.96
Minimum7836
Maximum132965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:35:02.931443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7836
5-th percentile19915.4
Q125116
median31346
Q341626
95-th percentile61018
Maximum132965
Range125129
Interquartile range (IQR)16510

Descriptive statistics

Standard deviation16339.962
Coefficient of variation (CV)0.4544185
Kurtosis8.6818836
Mean35957.96
Median Absolute Deviation (MAD)7652
Skewness2.340958
Sum7155634
Variance2.6699437 × 108
MonotonicityNot monotonic
2023-12-10T15:35:03.183520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30918 1
 
0.5%
31346 1
 
0.5%
46739 1
 
0.5%
39026 1
 
0.5%
42243 1
 
0.5%
41415 1
 
0.5%
49861 1
 
0.5%
30108 1
 
0.5%
42331 1
 
0.5%
50637 1
 
0.5%
Other values (189) 189
95.0%
ValueCountFrequency (%)
7836 1
0.5%
14581 1
0.5%
14881 1
0.5%
15800 1
0.5%
16891 1
0.5%
16894 1
0.5%
18002 1
0.5%
19616 1
0.5%
19746 1
0.5%
19793 1
0.5%
ValueCountFrequency (%)
132965 1
0.5%
104306 1
0.5%
102634 1
0.5%
91166 1
0.5%
89826 1
0.5%
78312 1
0.5%
69495 1
0.5%
64886 1
0.5%
64516 1
0.5%
63718 1
0.5%

Interactions

2023-12-10T15:34:57.369060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:57.048185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:57.583758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:34:57.219428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:35:03.348180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
106.967115963
106.9671.0000.9130.993
10.9131.0001.000
159630.9931.0001.000
2023-12-10T15:35:03.497251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
106.967159631
106.9671.0001.0000.859
159631.0001.0000.979
10.8590.9791.000

Missing values

2023-12-10T15:34:57.857394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:34:58.163764image/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

A000000001CB01001010001014311110700202108106.967100 주택용전력주택용 고압4 단일계약아파트115963
0A000000001CB01001010001024311110700202108283.304100 주택용전력주택용 고압4 단일계약아파트130918
1A000000001CB01001010001044311110700202108216.245100 주택용전력주택용 고압4 단일계약아파트125230
2A000000001CB01001010001054311110700202108348.381100 주택용전력주택용 고압4 단일계약아파트240071
3A000000001CB0100101000106431111070020210890.658100 주택용전력주택용 고압4 단일계약아파트114581
4A000000001CB01001010002014311110700202108203.313100 주택용전력주택용 고압4 단일계약아파트124134
5A000000001CB01001010002034311110700202108369.23100 주택용전력주택용 고압4 단일계약아파트243476
6A000000001CB01001010002064311110700202108105.042100 주택용전력주택용 고압4 단일계약아파트115800
7A000000001CB01001010002084311110700202108287.225100 주택용전력주택용 고압4 단일계약아파트131252
8A000000001CB01001010003024311110700202108203.122100 주택용전력주택용 고압4 단일계약아파트124118
9A000000001CB01001010003074311110700202108425.732100 주택용전력주택용 고압4 단일계약아파트252704
A000000001CB01001010001014311110700202108106.967100 주택용전력주택용 고압4 단일계약아파트115963
189A000000001CB01001060011014311110700202108341.402100 주택용전력주택용 고압4 단일계약아파트238932
190A000000001CB01001060011034311110700202108234.574100 주택용전력주택용 고압4 단일계약아파트126788
191A000000001CB01001060012034311110700202108241.12100 주택용전력주택용 고압4 단일계약아파트127341
192A000000001CB01001060012054311110700202108302.677100 주택용전력주택용 고압4 단일계약아파트232612
193A000000001CB01001060015014311110700202108189.843100 주택용전력주택용 고압4 단일계약아파트122993
194A000000001CB01001060015054311110700202108339.025100 주택용전력주택용 고압4 단일계약아파트238545
195A000000001CB01001070001014311110700202108284.894100 주택용전력주택용 고압4 단일계약아파트131055
196A000000001CB01001070001044311110700202108321.851100 주택용전력주택용 고압4 단일계약아파트235743
197A000000001CB01001070002034311110700202108237.267100 주택용전력주택용 고압4 단일계약아파트127014
198A000000001CB01001070002044311110700202108248.94100 주택용전력주택용 고압4 단일계약아파트128005