Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory683.6 KiB
Average record size in memory70.0 B

Variable types

Numeric5
Categorical1
Text1

Dataset

Description한국지역난방공사 기후환경시스템의 배출량 산정결과(온실가스) 자료입니다. 기준연월별 파라미터, 버전 등의 정보를 제공합니다.
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15124177/fileData.do

Alerts

배출활동순번 is highly imbalanced (80.0%)Imbalance

Reproduction

Analysis started2023-12-11 23:34:05.693134
Analysis finished2023-12-11 23:34:09.356584
Duration3.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월
Real number (ℝ)

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202128.27
Minimum202001
Maximum202304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:34:09.453237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202001
5-th percentile202003
Q1202011
median202109
Q3202207
95-th percentile202302
Maximum202304
Range303
Interquartile range (IQR)196

Descriptive statistics

Standard deviation97.25612
Coefficient of variation (CV)0.00048116039
Kurtosis-1.0878852
Mean202128.27
Median Absolute Deviation (MAD)98
Skewness0.17958462
Sum2.0212828 × 109
Variance9458.7529
MonotonicityNot monotonic
2023-12-12T08:34:09.596905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
202302 311
 
3.1%
202112 286
 
2.9%
202101 278
 
2.8%
202206 277
 
2.8%
202212 273
 
2.7%
202201 268
 
2.7%
202111 266
 
2.7%
202106 266
 
2.7%
202003 264
 
2.6%
202104 263
 
2.6%
Other values (30) 7248
72.5%
ValueCountFrequency (%)
202001 254
2.5%
202002 228
2.3%
202003 264
2.6%
202004 251
2.5%
202005 250
2.5%
202006 220
2.2%
202007 241
2.4%
202008 246
2.5%
202009 223
2.2%
202010 249
2.5%
ValueCountFrequency (%)
202304 210
2.1%
202303 262
2.6%
202302 311
3.1%
202301 253
2.5%
202212 273
2.7%
202211 241
2.4%
202210 228
2.3%
202209 259
2.6%
202208 253
2.5%
202207 255
2.5%

사업장순번
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.2973
Minimum4
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:34:09.721588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q19
median13
Q319
95-th percentile24
Maximum26
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9533736
Coefficient of variation (CV)0.44771296
Kurtosis-0.94461362
Mean13.2973
Median Absolute Deviation (MAD)5
Skewness0.29026194
Sum132973
Variance35.442657
MonotonicityNot monotonic
2023-12-12T08:34:09.834504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
13 1272
 
12.7%
14 783
 
7.8%
9 703
 
7.0%
5 655
 
6.6%
19 604
 
6.0%
22 574
 
5.7%
11 544
 
5.4%
7 503
 
5.0%
4 501
 
5.0%
8 474
 
4.7%
Other values (10) 3387
33.9%
ValueCountFrequency (%)
4 501
 
5.0%
5 655
6.6%
6 255
 
2.5%
7 503
 
5.0%
8 474
 
4.7%
9 703
7.0%
10 471
 
4.7%
11 544
5.4%
12 467
 
4.7%
13 1272
12.7%
ValueCountFrequency (%)
26 150
 
1.5%
24 356
3.6%
23 192
 
1.9%
22 574
5.7%
21 454
4.5%
20 466
4.7%
19 604
6.0%
16 270
 
2.7%
15 306
 
3.1%
14 783
7.8%

배출시설순번
Real number (ℝ)

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.2661
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:34:09.953721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q322
95-th percentile44.05
Maximum80
Range79
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.226722
Coefficient of variation (CV)0.93610161
Kurtosis2.7931792
Mean16.2661
Median Absolute Deviation (MAD)7
Skewness1.6274485
Sum162661
Variance231.85308
MonotonicityNot monotonic
2023-12-12T08:34:10.158134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 692
 
6.9%
1 569
 
5.7%
4 540
 
5.4%
8 540
 
5.4%
7 487
 
4.9%
5 457
 
4.6%
2 453
 
4.5%
10 394
 
3.9%
9 356
 
3.6%
6 340
 
3.4%
Other values (48) 5172
51.7%
ValueCountFrequency (%)
1 569
5.7%
2 453
4.5%
3 692
6.9%
4 540
5.4%
5 457
4.6%
6 340
3.4%
7 487
4.9%
8 540
5.4%
9 356
3.6%
10 394
3.9%
ValueCountFrequency (%)
80 27
0.3%
77 44
0.4%
70 54
0.5%
66 18
 
0.2%
64 34
0.3%
63 52
0.5%
62 48
0.5%
61 42
0.4%
60 32
0.3%
59 40
0.4%

배출활동순번
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9247 
2
 
586
3
 
102
4
 
33
5
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9247
92.5%
2 586
 
5.9%
3 102
 
1.0%
4 33
 
0.3%
5 32
 
0.3%

Length

2023-12-12T08:34:10.295994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:34:10.391365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9247
92.5%
2 586
 
5.9%
3 102
 
1.0%
4 33
 
0.3%
5 32
 
0.3%

파라미터ID
Real number (ℝ)

Distinct259
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54989.485
Minimum1
Maximum200233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:34:10.527985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile132
Q1202
median353
Q3200002
95-th percentile200029
Maximum200233
Range200232
Interquartile range (IQR)199800

Descriptive statistics

Standard deviation87324.84
Coefficient of variation (CV)1.588028
Kurtosis-0.87802233
Mean54989.485
Median Absolute Deviation (MAD)222
Skewness1.0555387
Sum5.4989485 × 108
Variance7.6256277 × 109
MonotonicityNot monotonic
2023-12-12T08:34:10.680316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200029 260
 
2.6%
200002 255
 
2.5%
211 253
 
2.5%
200011 249
 
2.5%
190 249
 
2.5%
200018 248
 
2.5%
198 248
 
2.5%
200014 246
 
2.5%
200022 244
 
2.4%
209 237
 
2.4%
Other values (249) 7511
75.1%
ValueCountFrequency (%)
1 7
0.1%
5 8
0.1%
9 6
0.1%
13 2
 
< 0.1%
17 5
0.1%
21 3
 
< 0.1%
24 4
< 0.1%
26 7
0.1%
29 6
0.1%
32 3
 
< 0.1%
ValueCountFrequency (%)
200233 2
 
< 0.1%
200232 2
 
< 0.1%
200231 6
0.1%
200230 2
 
< 0.1%
200229 5
0.1%
200228 3
< 0.1%
200227 5
0.1%
200217 3
< 0.1%
200216 3
< 0.1%
200215 5
0.1%

파라미터값
Text

Distinct1039
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:34:10.886394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.8659
Min length2

Characters and Unicode

Total characters28659
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique868 ?
Unique (%)8.7%

Sample

1st row56100
2nd row0
3rd row0
4th row0
5th row377952
ValueCountFrequency (%)
0 4353
43.5%
1 1122
 
11.2%
56100 297
 
3.0%
39 292
 
2.9%
44 265
 
2.6%
10 239
 
2.4%
3 230
 
2.3%
4 198
 
2.0%
38 162
 
1.6%
74100 162
 
1.6%
Other values (1029) 2680
26.8%
2023-12-12T08:34:11.245871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9999
34.9%
0 6549
22.9%
1 2838
 
9.9%
3 1869
 
6.5%
4 1634
 
5.7%
5 1274
 
4.4%
6 1044
 
3.6%
9 964
 
3.4%
2 945
 
3.3%
7 824
 
2.9%
Other values (3) 719
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18658
65.1%
Space Separator 9999
34.9%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6549
35.1%
1 2838
15.2%
3 1869
 
10.0%
4 1634
 
8.8%
5 1274
 
6.8%
6 1044
 
5.6%
9 964
 
5.2%
2 945
 
5.1%
7 824
 
4.4%
8 717
 
3.8%
Space Separator
ValueCountFrequency (%)
9999
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28659
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9999
34.9%
0 6549
22.9%
1 2838
 
9.9%
3 1869
 
6.5%
4 1634
 
5.7%
5 1274
 
4.4%
6 1044
 
3.6%
9 964
 
3.4%
2 945
 
3.3%
7 824
 
2.9%
Other values (3) 719
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9999
34.9%
0 6549
22.9%
1 2838
 
9.9%
3 1869
 
6.5%
4 1634
 
5.7%
5 1274
 
4.4%
6 1044
 
3.6%
9 964
 
3.4%
2 945
 
3.3%
7 824
 
2.9%
Other values (3) 719
 
2.5%

파라미터버전순번
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3409
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:34:11.361523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98973994
Coefficient of variation (CV)0.73811615
Kurtosis41.877675
Mean1.3409
Median Absolute Deviation (MAD)0
Skewness5.6305359
Sum13409
Variance0.97958515
MonotonicityNot monotonic
2023-12-12T08:34:11.465524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 7999
80.0%
2 1344
 
13.4%
3 440
 
4.4%
4 69
 
0.7%
5 46
 
0.5%
9 25
 
0.2%
11 24
 
0.2%
6 18
 
0.2%
10 14
 
0.1%
8 12
 
0.1%
ValueCountFrequency (%)
1 7999
80.0%
2 1344
 
13.4%
3 440
 
4.4%
4 69
 
0.7%
5 46
 
0.5%
6 18
 
0.2%
7 9
 
0.1%
8 12
 
0.1%
9 25
 
0.2%
10 14
 
0.1%
ValueCountFrequency (%)
11 24
 
0.2%
10 14
 
0.1%
9 25
 
0.2%
8 12
 
0.1%
7 9
 
0.1%
6 18
 
0.2%
5 46
 
0.5%
4 69
 
0.7%
3 440
 
4.4%
2 1344
13.4%

Interactions

2023-12-12T08:34:08.328239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:06.339862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:06.791380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.228268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.733336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:08.431907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:06.423231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:06.881422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.315048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.835021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:08.521562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:06.504809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:06.962513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.404146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.940554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:08.635273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:06.597106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.056158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.514171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:08.077040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:08.738328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:06.687961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.143038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:07.619927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:34:08.218461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:34:11.545011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월사업장순번배출시설순번배출활동순번파라미터ID파라미터버전순번
기준연월1.0000.0980.0400.0420.0000.049
사업장순번0.0981.0000.7090.4620.3300.237
배출시설순번0.0400.7091.0000.2960.2610.144
배출활동순번0.0420.4620.2961.0000.1080.164
파라미터ID0.0000.3300.2610.1081.0000.190
파라미터버전순번0.0490.2370.1440.1640.1901.000
2023-12-12T08:34:11.684001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월사업장순번배출시설순번파라미터ID파라미터버전순번배출활동순번
기준연월1.0000.042-0.000-0.005-0.0060.035
사업장순번0.0421.000-0.1250.0860.0210.209
배출시설순번-0.000-0.1251.000-0.1310.0290.127
파라미터ID-0.0050.086-0.1311.000-0.1210.132
파라미터버전순번-0.0060.0210.029-0.1211.0000.062
배출활동순번0.0350.2090.1270.1320.0621.000

Missing values

2023-12-12T08:34:09.162444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:34:09.298181image/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

기준연월사업장순번배출시설순번배출활동순번파라미터ID파라미터값파라미터버전순번
1367720200613341211561001
98004202304418120000501
57723202112722111201
79505202208216120000501
4153220210515412000113779521
16604202007191110093741001
863402022111427120201
10039202005419120001801
112320200111211209392
837772022101410122301
기준연월사업장순번배출시설순번배출활동순번파라미터ID파라미터값파라미터버전순번
30392202101102012027215
49212020022471100011281
72888202206611112801
900692023015231132382
89476202212218151301
9957520230415311008301
63989202202143110170621
909642023011118121411
19581202008236120000801
7377620220613331209392