Overview

Dataset statistics

Number of variables6
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory53.7 B

Variable types

Categorical4
Numeric2

Dataset

DescriptionSample
Author한국스마트그리드
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KSG2019111100AMUS02a

Alerts

toc_no has constant value ""Constant
mesure_ym has constant value ""Constant
legal_dong_cd has constant value ""Constant
all_pwrer_use_am has constant value ""Constant
cnsmr_no has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:14:35.305676
Analysis finished2023-12-10 06:14:36.234093
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

toc_no
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
A000000002
200 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA000000002
2nd rowA000000002
3rd rowA000000002
4th rowA000000002
5th rowA000000002

Common Values

ValueCountFrequency (%)
A000000002 200
100.0%

Length

2023-12-10T15:14:36.323956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:14:36.474873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a000000002 200
100.0%

cnsmr_no
Real number (ℝ)

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4107203 × 109
Minimum3.41072 × 109
Maximum3.4107206 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:14:36.964039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.41072 × 109
5-th percentile3.41072 × 109
Q13.4107201 × 109
median3.4107203 × 109
Q33.4107205 × 109
95-th percentile3.4107206 × 109
Maximum3.4107206 × 109
Range615
Interquartile range (IQR)334.75

Descriptive statistics

Standard deviation187.01616
Coefficient of variation (CV)5.4831867 × 10-8
Kurtosis-1.303755
Mean3.4107203 × 109
Median Absolute Deviation (MAD)167.5
Skewness0.024330996
Sum6.8214406 × 1011
Variance34975.045
MonotonicityStrictly increasing
2023-12-10T15:14:37.240487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3410720004 1
 
0.5%
3410720432 1
 
0.5%
3410720400 1
 
0.5%
3410720403 1
 
0.5%
3410720404 1
 
0.5%
3410720406 1
 
0.5%
3410720409 1
 
0.5%
3410720411 1
 
0.5%
3410720412 1
 
0.5%
3410720423 1
 
0.5%
Other values (190) 190
95.0%
ValueCountFrequency (%)
3410720004 1
0.5%
3410720011 1
0.5%
3410720014 1
0.5%
3410720019 1
0.5%
3410720020 1
0.5%
3410720023 1
0.5%
3410720028 1
0.5%
3410720031 1
0.5%
3410720032 1
0.5%
3410720033 1
0.5%
ValueCountFrequency (%)
3410720619 1
0.5%
3410720618 1
0.5%
3410720617 1
0.5%
3410720613 1
0.5%
3410720611 1
0.5%
3410720609 1
0.5%
3410720604 1
0.5%
3410720603 1
0.5%
3410720599 1
0.5%
3410720596 1
0.5%

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

2023-12-10T15:14:37.485319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:14:37.648395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201910 200
100.0%

legal_dong_cd
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2653010400
200 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2653010400
2nd row2653010400
3rd row2653010400
4th row2653010400
5th row2653010400

Common Values

ValueCountFrequency (%)
2653010400 200
100.0%

Length

2023-12-10T15:14:37.834667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:14:38.118445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2653010400 200
100.0%

sg_pwrer_use_am
Real number (ℝ)

Distinct196
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.4335
Minimum0
Maximum1118.3
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:14:38.340708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.2
Q1117.55
median200.25
Q3273.575
95-th percentile438.085
Maximum1118.3
Range1118.3
Interquartile range (IQR)156.025

Descriptive statistics

Standard deviation131.96901
Coefficient of variation (CV)0.63619911
Kurtosis11.079449
Mean207.4335
Median Absolute Deviation (MAD)81.15
Skewness2.0907312
Sum41486.7
Variance17415.819
MonotonicityNot monotonic
2023-12-10T15:14:38.702211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145.0 2
 
1.0%
331.0 2
 
1.0%
0.0 2
 
1.0%
221.1 2
 
1.0%
262.5 1
 
0.5%
164.2 1
 
0.5%
48.6 1
 
0.5%
130.7 1
 
0.5%
261.2 1
 
0.5%
272.7 1
 
0.5%
Other values (186) 186
93.0%
ValueCountFrequency (%)
0.0 2
1.0%
1.4 1
0.5%
2.4 1
0.5%
12.5 1
0.5%
28.3 1
0.5%
31.0 1
0.5%
36.3 1
0.5%
37.5 1
0.5%
42.3 1
0.5%
44.3 1
0.5%
ValueCountFrequency (%)
1118.3 1
0.5%
691.5 1
0.5%
540.6 1
0.5%
501.0 1
0.5%
487.2 1
0.5%
464.2 1
0.5%
457.6 1
0.5%
453.4 1
0.5%
453.3 1
0.5%
439.7 1
0.5%

all_pwrer_use_am
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
200 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 200
100.0%

Length

2023-12-10T15:14:38.913321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:14:39.061165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 200
100.0%

Interactions

2023-12-10T15:14:35.711728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:14:35.431266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:14:35.833621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:14:35.587454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:14:39.173732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
cnsmr_nosg_pwrer_use_am
cnsmr_no1.0000.185
sg_pwrer_use_am0.1851.000
2023-12-10T15:14:39.338849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
cnsmr_nosg_pwrer_use_am
cnsmr_no1.000-0.092
sg_pwrer_use_am-0.0921.000

Missing values

2023-12-10T15:14:36.003553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:14:36.167175image/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
0A00000000234107200042019102653010400262.50
1A00000000234107200112019102653010400266.00
2A00000000234107200142019102653010400184.60
3A00000000234107200192019102653010400259.20
4A0000000023410720020201910265301040059.60
5A00000000234107200232019102653010400313.70
6A00000000234107200282019102653010400157.90
7A00000000234107200312019102653010400108.60
8A00000000234107200322019102653010400306.10
9A0000000023410720033201910265301040060.80
toc_nocnsmr_nomesure_ymlegal_dong_cdsg_pwrer_use_amall_pwrer_use_am
190A00000000234107205962019102653010400221.10
191A00000000234107205992019102653010400167.60
192A00000000234107206032019102653010400128.20
193A00000000234107206042019102653010400187.20
194A00000000234107206092019102653010400271.60
195A00000000234107206112019102653010400307.40
196A00000000234107206132019102653010400187.90
197A00000000234107206172019102653010400309.80
198A00000000234107206182019102653010400145.00
199A00000000234107206192019102653010400107.10