Overview

Dataset statistics

Number of variables13
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory120.7 B

Variable types

Categorical5
Numeric8

Dataset

Description11년 ~13년 산업용 업종별 고객호수 추이
Author한국전력공사
URLhttps://www.data.go.kr/data/15053169/fileData.do

Alerts

석유정제 has constant value ""Constant
철강 is highly overall correlated with 기계장비 and 9 other fieldsHigh correlation
기계장비 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
섬유 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
반도체 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
자동차 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
조립금속 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
화학제품 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
연도 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
조선 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
요업 is highly overall correlated with 철강 and 9 other fieldsHigh correlation
펄프.종이 is highly overall correlated with 철강 and 9 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 23:01:37.121693
Analysis finished2023-12-12 23:01:43.821246
Duration6.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
2011
12 
2012
12 
2013
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2011 12
33.3%
2012 12
33.3%
2013 12
33.3%

Length

2023-12-13T08:01:43.882364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:43.976321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2011 12
33.3%
2012 12
33.3%
2013 12
33.3%

월분
Real number (ℝ)

Distinct12
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:01:44.074981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.5010203
Coefficient of variation (CV)0.5386185
Kurtosis-1.217232
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum234
Variance12.257143
MonotonicityNot monotonic
2023-12-13T08:01:44.175815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 3
8.3%
2 3
8.3%
3 3
8.3%
4 3
8.3%
5 3
8.3%
6 3
8.3%
7 3
8.3%
8 3
8.3%
9 3
8.3%
10 3
8.3%
Other values (2) 6
16.7%
ValueCountFrequency (%)
1 3
8.3%
2 3
8.3%
3 3
8.3%
4 3
8.3%
5 3
8.3%
6 3
8.3%
7 3
8.3%
8 3
8.3%
9 3
8.3%
10 3
8.3%
ValueCountFrequency (%)
12 3
8.3%
11 3
8.3%
10 3
8.3%
9 3
8.3%
8 3
8.3%
7 3
8.3%
6 3
8.3%
5 3
8.3%
4 3
8.3%
3 3
8.3%

철강
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.111111
Minimum87
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:01:44.289263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum87
5-th percentile87.75
Q190
median93
Q396.25
95-th percentile98
Maximum98
Range11
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation3.4705107
Coefficient of variation (CV)0.037272788
Kurtosis-1.1836738
Mean93.111111
Median Absolute Deviation (MAD)3
Skewness-0.1552888
Sum3352
Variance12.044444
MonotonicityNot monotonic
2023-12-13T08:01:44.393660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
97 5
13.9%
92 4
11.1%
98 4
11.1%
89 3
8.3%
90 3
8.3%
93 3
8.3%
94 3
8.3%
95 3
8.3%
87 2
 
5.6%
88 2
 
5.6%
Other values (2) 4
11.1%
ValueCountFrequency (%)
87 2
5.6%
88 2
5.6%
89 3
8.3%
90 3
8.3%
91 2
5.6%
92 4
11.1%
93 3
8.3%
94 3
8.3%
95 3
8.3%
96 2
5.6%
ValueCountFrequency (%)
98 4
11.1%
97 5
13.9%
96 2
 
5.6%
95 3
8.3%
94 3
8.3%
93 3
8.3%
92 4
11.1%
91 2
 
5.6%
90 3
8.3%
89 3
8.3%

기계장비
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean364.02778
Minimum340
Maximum387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:01:44.516457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum340
5-th percentile342.75
Q1351.75
median364.5
Q3375.25
95-th percentile385.25
Maximum387
Range47
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation14.185478
Coefficient of variation (CV)0.038968119
Kurtosis-1.1929858
Mean364.02778
Median Absolute Deviation (MAD)12
Skewness-0.025031561
Sum13105
Variance201.22778
MonotonicityIncreasing
2023-12-13T08:01:44.635697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
367 2
 
5.6%
342 1
 
2.8%
369 1
 
2.8%
370 1
 
2.8%
371 1
 
2.8%
373 1
 
2.8%
374 1
 
2.8%
375 1
 
2.8%
376 1
 
2.8%
340 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
340 1
2.8%
342 1
2.8%
343 1
2.8%
344 1
2.8%
346 1
2.8%
347 1
2.8%
349 1
2.8%
350 1
2.8%
351 1
2.8%
352 1
2.8%
ValueCountFrequency (%)
387 1
2.8%
386 1
2.8%
385 1
2.8%
384 1
2.8%
382 1
2.8%
381 1
2.8%
380 1
2.8%
378 1
2.8%
376 1
2.8%
375 1
2.8%

조선
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
31
18 
30
10 
29
5 
32
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29
2nd row29
3rd row29
4th row29
5th row29

Common Values

ValueCountFrequency (%)
31 18
50.0%
30 10
27.8%
29 5
 
13.9%
32 3
 
8.3%

Length

2023-12-13T08:01:44.754725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:44.856690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31 18
50.0%
30 10
27.8%
29 5
 
13.9%
32 3
 
8.3%

석유정제
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
4
36 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 36
100.0%

Length

2023-12-13T08:01:44.995250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:45.143394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 36
100.0%

섬유
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.13889
Minimum343
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:01:45.222299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum343
5-th percentile344.75
Q1348
median352.5
Q3356
95-th percentile359.25
Maximum360
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.0435406
Coefficient of variation (CV)0.014322589
Kurtosis-1.1144193
Mean352.13889
Median Absolute Deviation (MAD)3.5
Skewness-0.13583588
Sum12677
Variance25.437302
MonotonicityNot monotonic
2023-12-13T08:01:45.346113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
356 4
 
11.1%
352 3
 
8.3%
345 3
 
8.3%
359 3
 
8.3%
354 3
 
8.3%
346 2
 
5.6%
360 2
 
5.6%
348 2
 
5.6%
349 2
 
5.6%
350 2
 
5.6%
Other values (7) 10
27.8%
ValueCountFrequency (%)
343 1
 
2.8%
344 1
 
2.8%
345 3
8.3%
346 2
5.6%
347 1
 
2.8%
348 2
5.6%
349 2
5.6%
350 2
5.6%
351 1
 
2.8%
352 3
8.3%
ValueCountFrequency (%)
360 2
5.6%
359 3
8.3%
358 2
5.6%
356 4
11.1%
355 2
5.6%
354 3
8.3%
353 2
5.6%
352 3
8.3%
351 1
 
2.8%
350 2
5.6%

반도체
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.61111
Minimum122
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:01:45.472350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile122.75
Q1126.75
median132.5
Q3138.25
95-th percentile143.25
Maximum145
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9375903
Coefficient of variation (CV)0.052315302
Kurtosis-1.1287753
Mean132.61111
Median Absolute Deviation (MAD)6
Skewness0.14653982
Sum4774
Variance48.130159
MonotonicityIncreasing
2023-12-13T08:01:45.593210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
122 2
 
5.6%
130 2
 
5.6%
143 2
 
5.6%
140 2
 
5.6%
135 2
 
5.6%
123 2
 
5.6%
133 2
 
5.6%
132 2
 
5.6%
134 2
 
5.6%
127 2
 
5.6%
Other values (14) 16
44.4%
ValueCountFrequency (%)
122 2
5.6%
123 2
5.6%
124 1
2.8%
125 2
5.6%
126 2
5.6%
127 2
5.6%
128 1
2.8%
129 1
2.8%
130 2
5.6%
131 1
2.8%
ValueCountFrequency (%)
145 1
2.8%
144 1
2.8%
143 2
5.6%
142 1
2.8%
141 1
2.8%
140 2
5.6%
139 1
2.8%
138 1
2.8%
137 1
2.8%
136 1
2.8%

요업
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
109
18 
110
10 
108
8 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row108
2nd row109
3rd row108
4th row109
5th row108

Common Values

ValueCountFrequency (%)
109 18
50.0%
110 10
27.8%
108 8
22.2%

Length

2023-12-13T08:01:45.725222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:45.827914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
109 18
50.0%
110 10
27.8%
108 8
22.2%

자동차
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.80556
Minimum114
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:01:45.938994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile114.75
Q1119.75
median127.5
Q3133.25
95-th percentile138.25
Maximum140
Range26
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation7.9957826
Coefficient of variation (CV)0.06305546
Kurtosis-1.2553535
Mean126.80556
Median Absolute Deviation (MAD)6.5
Skewness-0.12235079
Sum4565
Variance63.93254
MonotonicityIncreasing
2023-12-13T08:01:46.064914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
114 2
 
5.6%
131 2
 
5.6%
118 2
 
5.6%
136 2
 
5.6%
122 2
 
5.6%
134 2
 
5.6%
133 2
 
5.6%
127 2
 
5.6%
115 2
 
5.6%
130 2
 
5.6%
Other values (16) 16
44.4%
ValueCountFrequency (%)
114 2
5.6%
115 2
5.6%
116 1
2.8%
117 1
2.8%
118 2
5.6%
119 1
2.8%
120 1
2.8%
121 1
2.8%
122 2
5.6%
123 1
2.8%
ValueCountFrequency (%)
140 1
2.8%
139 1
2.8%
138 1
2.8%
137 1
2.8%
136 2
5.6%
135 1
2.8%
134 2
5.6%
133 2
5.6%
132 1
2.8%
131 2
5.6%

조립금속
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.47222
Minimum231
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:01:46.182062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum231
5-th percentile232
Q1235
median240
Q3244
95-th percentile246.25
Maximum247
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.8549598
Coefficient of variation (CV)0.020273582
Kurtosis-1.2735809
Mean239.47222
Median Absolute Deviation (MAD)4
Skewness-0.099268245
Sum8621
Variance23.570635
MonotonicityIncreasing
2023-12-13T08:01:46.356282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
235 3
 
8.3%
241 3
 
8.3%
244 3
 
8.3%
245 3
 
8.3%
240 2
 
5.6%
246 2
 
5.6%
233 2
 
5.6%
234 2
 
5.6%
236 2
 
5.6%
237 2
 
5.6%
Other values (7) 12
33.3%
ValueCountFrequency (%)
231 1
 
2.8%
232 2
5.6%
233 2
5.6%
234 2
5.6%
235 3
8.3%
236 2
5.6%
237 2
5.6%
238 2
5.6%
239 1
 
2.8%
240 2
5.6%
ValueCountFrequency (%)
247 2
5.6%
246 2
5.6%
245 3
8.3%
244 3
8.3%
243 2
5.6%
242 2
5.6%
241 3
8.3%
240 2
5.6%
239 1
 
2.8%
238 2
5.6%

펄프.종이
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
64
13 
65
12 
66
8 
67
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row64
2nd row64
3rd row64
4th row64
5th row64

Common Values

ValueCountFrequency (%)
64 13
36.1%
65 12
33.3%
66 8
22.2%
67 3
 
8.3%

Length

2023-12-13T08:01:46.497835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:46.628170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
64 13
36.1%
65 12
33.3%
66 8
22.2%
67 3
 
8.3%

화학제품
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.30556
Minimum124
Maximum137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:01:46.756926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124
5-th percentile124.75
Q1128
median132
Q3134.25
95-th percentile137
Maximum137
Range13
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation4.1114436
Coefficient of variation (CV)0.031312031
Kurtosis-1.0799368
Mean131.30556
Median Absolute Deviation (MAD)3
Skewness-0.30485654
Sum4727
Variance16.903968
MonotonicityIncreasing
2023-12-13T08:01:46.885299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
132 4
11.1%
134 4
11.1%
137 4
11.1%
125 3
8.3%
130 3
8.3%
133 3
8.3%
136 3
8.3%
124 2
 
5.6%
127 2
 
5.6%
128 2
 
5.6%
Other values (4) 6
16.7%
ValueCountFrequency (%)
124 2
5.6%
125 3
8.3%
126 1
 
2.8%
127 2
5.6%
128 2
5.6%
129 2
5.6%
130 3
8.3%
131 1
 
2.8%
132 4
11.1%
133 3
8.3%
ValueCountFrequency (%)
137 4
11.1%
136 3
8.3%
135 2
5.6%
134 4
11.1%
133 3
8.3%
132 4
11.1%
131 1
 
2.8%
130 3
8.3%
129 2
5.6%
128 2
5.6%

Interactions

2023-12-13T08:01:42.784061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:37.613601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.349888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.071597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.713144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:40.459147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.341046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.039612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.877085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:37.692619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.447431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.143115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.790341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:40.526680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.430631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.121403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.957455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:37.797268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.520172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.224882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.882109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:40.885082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.541292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.211711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:43.044956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:37.882008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.593450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.311350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.964654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:40.953707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.643935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.290987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:43.155252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:37.968465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.681737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.398674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:40.052482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.036982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.733859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.381397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:43.245316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.049368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.768470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.474574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:40.134277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.105709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.806009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.464021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:43.347374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.131882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.871816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.547451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:40.246394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.173069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.878169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.564053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:43.452157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.245010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:38.974359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:39.637809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:40.365362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.254638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:41.954620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:42.663209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:01:47.305135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월분철강기계장비조선섬유반도체요업자동차조립금속펄프.종이화학제품
연도1.0000.0000.9610.9600.6150.9551.0000.9680.9350.9480.8420.958
월분0.0001.0000.0000.0000.0000.0000.3420.0000.0000.2780.0000.165
철강0.9610.0001.0000.9690.8620.9710.9510.9320.9400.9710.8730.954
기계장비0.9600.0000.9691.0000.9350.9490.9710.8930.9770.9890.9380.937
조선0.6150.0000.8620.9351.0000.8970.9660.5200.9970.9190.9670.931
섬유0.9550.0000.9710.9490.8971.0000.9460.9030.9310.9660.8880.956
반도체1.0000.3420.9510.9710.9660.9461.0000.8930.9800.9640.9400.976
요업0.9680.0000.9320.8930.5200.9030.8931.0000.8970.9340.7420.876
자동차0.9350.0000.9400.9770.9970.9310.9800.8971.0000.9540.9980.948
조립금속0.9480.2780.9710.9890.9190.9660.9640.9340.9541.0000.9430.913
펄프.종이0.8420.0000.8730.9380.9670.8880.9400.7420.9980.9431.0000.861
화학제품0.9580.1650.9540.9370.9310.9560.9760.8760.9480.9130.8611.000
2023-12-13T08:01:47.497876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조선펄프.종이요업연도
조선1.0000.7540.5120.622
펄프.종이0.7541.0000.7760.898
요업0.5120.7761.0000.779
연도0.6220.8980.7791.000
2023-12-13T08:01:47.599730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월분철강기계장비섬유반도체자동차조립금속화학제품연도조선요업펄프.종이
월분1.0000.3150.3320.3210.3280.3330.3220.3280.0000.0000.0000.000
철강0.3151.0000.9950.9920.9950.9940.9940.9930.8540.6430.8070.659
기계장비0.3320.9951.0000.9970.9990.9990.9980.9960.8540.7720.7330.769
섬유0.3210.9920.9971.0000.9960.9960.9960.9930.8450.7150.7350.687
반도체0.3280.9950.9990.9961.0000.9990.9970.9960.8880.8150.7440.770
자동차0.3330.9940.9990.9960.9991.0000.9970.9970.8110.8770.7500.880
조립금속0.3220.9940.9980.9960.9970.9971.0000.9950.8360.7480.8080.778
화학제품0.3280.9930.9960.9930.9960.9970.9951.0000.8490.7550.7180.641
연도0.0000.8540.8540.8450.8880.8110.8360.8491.0000.6220.7790.898
조선0.0000.6430.7720.7150.8150.8770.7480.7550.6221.0000.5120.754
요업0.0000.8070.7330.7350.7440.7500.8080.7180.7790.5121.0000.776
펄프.종이0.0000.6590.7690.6870.7700.8800.7780.6410.8980.7540.7761.000

Missing values

2023-12-13T08:01:43.589046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:01:43.751826image/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

연도월분철강기계장비조선석유정제섬유반도체요업자동차조립금속펄프.종이화학제품
0201118734029434312210811423164124
1201128734229434412210911423264124
2201138834329434512310811523264125
3201148834429434512310911523364125
4201158934629434512410811623364125
5201168934730434612510811723464126
6201178934930434612510811823464127
7201189035030434712610811823564127
8201199035130434812610811923564128
92011109035230434812710812023564128
연도월분철강기계장비조선석유정제섬유반도체요업자동차조립금속펄프.종이화학제품
26201339637531435613811013324466134
27201349737631435613911013424466135
28201359737831435614011013424466135
29201369738031435814011013524566136
30201379738131435814111013624566136
31201389738231435914211013624566136
32201399838431435914311013724666137
332013109838532435914311013824667137
342013119838632436014411013924767137
352013129838732436014511014024767137