Overview

Dataset statistics

Number of variables7
Number of observations192
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory61.7 B

Variable types

Categorical3
Numeric4

Dataset

DescriptionSample
Author한국스마트그리드
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KSG2019111100EMUS01a

Alerts

mesure_de has constant value ""Constant
toc_no is highly overall correlated with legal_dong_cd and 3 other fieldsHigh correlation
cnsmr_no is highly overall correlated with legal_dong_cd and 2 other fieldsHigh correlation
legal_dong_cd is highly overall correlated with toc_no and 1 other fieldsHigh correlation
sg_pwrer_use_am is highly overall correlated with toc_no and 1 other fieldsHigh correlation
all_pwrer_use_am is highly overall correlated with toc_noHigh correlation
mesure_tm has 8 (4.2%) zerosZeros
sg_pwrer_use_am has 23 (12.0%) zerosZeros
all_pwrer_use_am has 108 (56.2%) zerosZeros

Reproduction

Analysis started2023-12-10 06:16:51.696589
Analysis finished2023-12-10 06:16:54.684876
Duration2.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

toc_no
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
E000000001
48 
E000000002
48 
E000000003
48 
E000000004
48 

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 (%)
E000000001 48
25.0%
E000000002 48
25.0%
E000000003 48
25.0%
E000000004 48
25.0%

Length

2023-12-10T15:16:54.789770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:54.964528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e000000001 48
25.0%
e000000002 48
25.0%
e000000003 48
25.0%
e000000004 48
25.0%

cnsmr_no
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
7333
24 
2714
24 
IMC0000008AM03200001
24 
IMC0000012SK00200002
24 
C20180516131706082
24 
Other values (3)
72 

Length

Max length20
Median length19
Mean length11.5
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7333
2nd row7333
3rd row7333
4th row7333
5th row7333

Common Values

ValueCountFrequency (%)
7333 24
12.5%
2714 24
12.5%
IMC0000008AM03200001 24
12.5%
IMC0000012SK00200002 24
12.5%
C20180516131706082 24
12.5%
C20190713131750096 24
12.5%
1003 24
12.5%
1467 24
12.5%

Length

2023-12-10T15:16:55.186615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:55.405059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7333 24
12.5%
2714 24
12.5%
imc0000008am03200001 24
12.5%
imc0000012sk00200002 24
12.5%
c20180516131706082 24
12.5%
c20190713131750096 24
12.5%
1003 24
12.5%
1467 24
12.5%

mesure_de
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
20191001
192 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20191001
2nd row20191001
3rd row20191001
4th row20191001
5th row20191001

Common Values

ValueCountFrequency (%)
20191001 192
100.0%

Length

2023-12-10T15:16:55.634380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:55.793576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20191001 192
100.0%

mesure_tm
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros8
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T15:16:55.955569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9402838
Coefficient of variation (CV)0.60350294
Kurtosis-1.2042178
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum2208
Variance48.167539
MonotonicityNot monotonic
2023-12-10T15:16:56.176934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 8
 
4.2%
13 8
 
4.2%
23 8
 
4.2%
22 8
 
4.2%
21 8
 
4.2%
20 8
 
4.2%
19 8
 
4.2%
18 8
 
4.2%
17 8
 
4.2%
16 8
 
4.2%
Other values (14) 112
58.3%
ValueCountFrequency (%)
0 8
4.2%
1 8
4.2%
2 8
4.2%
3 8
4.2%
4 8
4.2%
5 8
4.2%
6 8
4.2%
7 8
4.2%
8 8
4.2%
9 8
4.2%
ValueCountFrequency (%)
23 8
4.2%
22 8
4.2%
21 8
4.2%
20 8
4.2%
19 8
4.2%
18 8
4.2%
17 8
4.2%
16 8
4.2%
15 8
4.2%
14 8
4.2%

legal_dong_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5402635 × 109
Minimum2.729 × 109
Maximum5.0110253 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T15:16:56.380573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.729 × 109
5-th percentile2.729 × 109
Q12.807758 × 109
median2.9215112 × 109
Q34.4445313 × 109
95-th percentile5.0110253 × 109
Maximum5.0110253 × 109
Range2.2820253 × 109
Interquartile range (IQR)1.6367733 × 109

Descriptive statistics

Standard deviation9.3716326 × 108
Coefficient of variation (CV)0.26471568
Kurtosis-1.4969122
Mean3.5402635 × 109
Median Absolute Deviation (MAD)1.715112 × 108
Skewness0.60661042
Sum6.7973059 × 1011
Variance8.7827498 × 1017
MonotonicityNot monotonic
2023-12-10T15:16:56.570280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3023011300 24
12.5%
2820011100 24
12.5%
2820010700 24
12.5%
5011025342 24
12.5%
4315038023 24
12.5%
4833011300 24
12.5%
2729000000 24
12.5%
2771000000 24
12.5%
ValueCountFrequency (%)
2729000000 24
12.5%
2771000000 24
12.5%
2820010700 24
12.5%
2820011100 24
12.5%
3023011300 24
12.5%
4315038023 24
12.5%
4833011300 24
12.5%
5011025342 24
12.5%
ValueCountFrequency (%)
5011025342 24
12.5%
4833011300 24
12.5%
4315038023 24
12.5%
3023011300 24
12.5%
2820011100 24
12.5%
2820010700 24
12.5%
2771000000 24
12.5%
2729000000 24
12.5%

sg_pwrer_use_am
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.96875
Minimum0
Maximum966.6984
Zeros23
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T15:16:56.780204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.076
median43
Q3448.12075
95-th percentile888.948
Maximum966.6984
Range966.6984
Interquartile range (IQR)431.04475

Descriptive statistics

Standard deviation311.09721
Coefficient of variation (CV)1.1830197
Kurtosis-0.63617038
Mean262.96875
Median Absolute Deviation (MAD)43
Skewness0.86659876
Sum50490
Variance96781.471
MonotonicityNot monotonic
2023-12-10T15:16:57.009301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
12.0%
28.62 21
 
10.9%
46.68 9
 
4.7%
42.0 5
 
2.6%
23.34 4
 
2.1%
30.0 4
 
2.1%
14.31 3
 
1.6%
602.0 2
 
1.0%
618.0 2
 
1.0%
12.0 2
 
1.0%
Other values (116) 117
60.9%
ValueCountFrequency (%)
0.0 23
12.0%
2.028 1
 
0.5%
2.052 1
 
0.5%
2.124 1
 
0.5%
2.148 1
 
0.5%
2.184 1
 
0.5%
2.316 1
 
0.5%
2.388 1
 
0.5%
2.868 1
 
0.5%
3.48 1
 
0.5%
ValueCountFrequency (%)
966.6984 1
0.5%
959.04 1
0.5%
943.92 1
0.5%
932.76 1
0.5%
921.96 1
0.5%
920.88 1
0.5%
913.14 1
0.5%
909.4416 1
0.5%
901.44 1
0.5%
897.66 1
0.5%

all_pwrer_use_am
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.67356
Minimum0
Maximum646
Zeros108
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T15:16:57.253310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3383.392
95-th percentile602
Maximum646
Range646
Interquartile range (IQR)383.392

Descriptive statistics

Standard deviation219.29453
Coefficient of variation (CV)1.5053833
Kurtosis-0.55035407
Mean145.67356
Median Absolute Deviation (MAD)0
Skewness1.0701525
Sum27969.323
Variance48090.093
MonotonicityNot monotonic
2023-12-10T15:16:57.534685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 108
56.2%
42.0 5
 
2.6%
30.0 4
 
2.1%
12.0 2
 
1.0%
618.0 2
 
1.0%
628.0 2
 
1.0%
602.0 2
 
1.0%
11.0 1
 
0.5%
22.0 1
 
0.5%
23.0 1
 
0.5%
Other values (64) 64
33.3%
ValueCountFrequency (%)
0.0 108
56.2%
11.0 1
 
0.5%
12.0 2
 
1.0%
14.0 1
 
0.5%
16.0 1
 
0.5%
18.0 1
 
0.5%
22.0 1
 
0.5%
23.0 1
 
0.5%
29.0 1
 
0.5%
30.0 4
 
2.1%
ValueCountFrequency (%)
646.0 1
0.5%
628.0 2
1.0%
619.0 1
0.5%
618.0 2
1.0%
610.0 1
0.5%
609.0 1
0.5%
608.0 1
0.5%
602.0 2
1.0%
601.0 1
0.5%
592.0 1
0.5%

Interactions

2023-12-10T15:16:53.708141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:52.059143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:53.163005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:53.984008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:52.324461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:53.429803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:54.161115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:52.469018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:53.567109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:16:58.078492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
toc_nocnsmr_nomesure_tmlegal_dong_cdsg_pwrer_use_amall_pwrer_use_am
toc_no1.0001.0000.0000.9140.7730.856
cnsmr_no1.0001.0000.0001.0000.8290.881
mesure_tm0.0000.0001.0000.0000.0000.000
legal_dong_cd0.9141.0000.0001.0000.5900.765
sg_pwrer_use_am0.7730.8290.0000.5901.0000.932
all_pwrer_use_am0.8560.8810.0000.7650.9321.000
2023-12-10T15:16:58.279107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
toc_nocnsmr_no
toc_no1.0000.989
cnsmr_no0.9891.000
2023-12-10T15:16:58.445118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
mesure_tmlegal_dong_cdsg_pwrer_use_amall_pwrer_use_amtoc_nocnsmr_no
mesure_tm1.0000.0000.0600.0120.0000.000
legal_dong_cd0.0001.000-0.2020.4500.6380.989
sg_pwrer_use_am0.060-0.2021.0000.4840.6150.599
all_pwrer_use_am0.0120.4500.4841.0000.5280.490
toc_no0.0000.6380.6150.5281.0000.989
cnsmr_no0.0000.9890.5990.4900.9891.000

Missing values

2023-12-10T15:16:54.400992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:16:54.603901image/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_demesure_tmlegal_dong_cdsg_pwrer_use_amall_pwrer_use_am
0E0000000017333201910010302301130028.620.0
1E0000000017333201910011302301130028.620.0
2E0000000017333201910012302301130028.620.0
3E0000000017333201910013302301130028.620.0
4E0000000017333201910014302301130028.620.0
5E0000000017333201910015302301130028.620.0
6E0000000017333201910016302301130014.310.0
7E0000000017333201910017302301130028.620.0
8E0000000017333201910018302301130028.620.0
9E0000000017333201910019302301130028.620.0
toc_nocnsmr_nomesure_demesure_tmlegal_dong_cdsg_pwrer_use_amall_pwrer_use_am
182E00000000414672019100114277100000020.4720.0
183E00000000414672019100115277100000088.0440.0
184E00000000414672019100116277100000070.440.0
185E00000000414672019100117277100000017.8440.0
186E0000000041467201910011827710000007.5960.0
187E0000000041467201910011927710000004.6440.0
188E0000000041467201910012027710000004.8480.0
189E0000000041467201910012127710000004.320.0
190E0000000041467201910012227710000003.9720.0
191E0000000041467201910012327710000003.480.0