Overview

Dataset statistics

Number of variables9
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory80.2 B

Variable types

Categorical2
Numeric7

Dataset

Description강원특별자치도내 소비자물가지수를 품목성질별로 데이터를 제공하고 있습니다. - 제공 데이터 : 농축수산물, 공업제품, 전기수도가스, 집세, 공공서비스, 개인서비스에 대한 월별 소비자물가지수 월별 전국 소비자물가 정보 및 도내 품목성질별 지수 정보 수록
URLhttps://www.data.go.kr/data/3037866/fileData.do

Alerts

총지수 is highly overall correlated with 농축수산물 and 5 other fieldsHigh correlation
농축수산물 is highly overall correlated with 총지수 and 1 other fieldsHigh correlation
공업제품 is highly overall correlated with 총지수 and 2 other fieldsHigh correlation
전기수도가스 is highly overall correlated with 총지수 and 2 other fieldsHigh correlation
집세 is highly overall correlated with 총지수 and 2 other fieldsHigh correlation
공공서비스 is highly overall correlated with 집세 and 2 other fieldsHigh correlation
개인서비스 is highly overall correlated with 총지수 and 3 other fieldsHigh correlation
연도 is highly overall correlated with 총지수 and 5 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 10:06:28.808808
Analysis finished2023-12-12 10:06:36.114257
Duration7.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size996.0 B
2014년
12 
2015년
12 
2016년
12 
2017년
12 
2018년
12 
Other values (4)
48 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014년
2nd row2014년
3rd row2014년
4th row2014년
5th row2014년

Common Values

ValueCountFrequency (%)
2014년 12
11.1%
2015년 12
11.1%
2016년 12
11.1%
2017년 12
11.1%
2018년 12
11.1%
2019년 12
11.1%
2020년 12
11.1%
2021년 12
11.1%
2022년 12
11.1%

Length

2023-12-12T19:06:36.205999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:06:36.378086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014년 12
11.1%
2015년 12
11.1%
2016년 12
11.1%
2017년 12
11.1%
2018년 12
11.1%
2019년 12
11.1%
2020년 12
11.1%
2021년 12
11.1%
2022년 12
11.1%

월
Categorical

Distinct12
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size996.0 B
01월
9 
02월
9 
03월
9 
04월
9 
05월
9 
Other values (7)
63 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01월
2nd row02월
3rd row03월
4th row04월
5th row05월

Common Values

ValueCountFrequency (%)
01월 9
8.3%
02월 9
8.3%
03월 9
8.3%
04월 9
8.3%
05월 9
8.3%
06월 9
8.3%
07월 9
8.3%
08월 9
8.3%
09월 9
8.3%
10월 9
8.3%
Other values (2) 18
16.7%

Length

2023-12-12T19:06:36.618986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01월 9
8.3%
02월 9
8.3%
03월 9
8.3%
04월 9
8.3%
05월 9
8.3%
06월 9
8.3%
07월 9
8.3%
08월 9
8.3%
09월 9
8.3%
10월 9
8.3%
Other values (2) 18
16.7%

총지수
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.27537
Minimum100.2
Maximum110.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T19:06:36.816356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100.2
5-th percentile100.535
Q1102.85
median105.315
Q3108.1
95-th percentile109.9
Maximum110.66
Range10.46
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation2.8238303
Coefficient of variation (CV)0.026823276
Kurtosis-0.9791432
Mean105.27537
Median Absolute Deviation (MAD)2.685
Skewness-0.018720546
Sum11369.74
Variance7.9740176
MonotonicityNot monotonic
2023-12-12T19:06:36.988096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108.4 5
 
4.6%
108.3 5
 
4.6%
108.5 4
 
3.7%
108.1 4
 
3.7%
102.5 4
 
3.7%
100.5 3
 
2.8%
108.0 2
 
1.9%
101.3 2
 
1.9%
105.98 2
 
1.9%
105.77 2
 
1.9%
Other values (69) 75
69.4%
ValueCountFrequency (%)
100.2 1
 
0.9%
100.3 2
1.9%
100.5 3
2.8%
100.6 1
 
0.9%
100.8 1
 
0.9%
101.01 1
 
0.9%
101.2 1
 
0.9%
101.3 2
1.9%
101.5 1
 
0.9%
101.63 1
 
0.9%
ValueCountFrequency (%)
110.66 1
 
0.9%
110.55 1
 
0.9%
110.43 1
 
0.9%
110.39 1
 
0.9%
110.27 1
 
0.9%
109.97 1
 
0.9%
109.77 1
 
0.9%
109.04 1
 
0.9%
108.5 4
3.7%
108.4 5
4.6%

농축수산물
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.49926
Minimum99.76
Maximum131.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T19:06:37.143789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99.76
5-th percentile102.545
Q1108.4375
median111.575
Q3113.955
95-th percentile121.5105
Maximum131.16
Range31.4
Interquartile range (IQR)5.5175

Descriptive statistics

Standard deviation5.3102776
Coefficient of variation (CV)0.047626124
Kurtosis2.2584124
Mean111.49926
Median Absolute Deviation (MAD)2.645
Skewness0.7238785
Sum12041.92
Variance28.199048
MonotonicityNot monotonic
2023-12-12T19:06:37.331975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.6 2
 
1.9%
107.7 2
 
1.9%
110.78 2
 
1.9%
111.68 2
 
1.9%
109.9 2
 
1.9%
115.66 2
 
1.9%
111.6 2
 
1.9%
113.25 2
 
1.9%
121.57 1
 
0.9%
114.57 1
 
0.9%
Other values (90) 90
83.3%
ValueCountFrequency (%)
99.76 1
0.9%
100.3 1
0.9%
101.4 1
0.9%
101.6 2
1.9%
102.3 1
0.9%
103.0 1
0.9%
104.1 1
0.9%
104.2 1
0.9%
105.21 1
0.9%
105.4 1
0.9%
ValueCountFrequency (%)
131.16 1
0.9%
129.57 1
0.9%
123.78 1
0.9%
122.4 1
0.9%
121.95 1
0.9%
121.57 1
0.9%
121.4 1
0.9%
119.05 1
0.9%
118.5 1
0.9%
118.12 1
0.9%

공업제품
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.32769
Minimum98.3
Maximum113.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T19:06:37.459812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.3
5-th percentile99.228
Q1101.2225
median102.67
Q3108.4
95-th percentile112.0975
Maximum113.98
Range15.68
Interquartile range (IQR)7.1775

Descriptive statistics

Standard deviation4.2721409
Coefficient of variation (CV)0.040949254
Kurtosis-0.91036079
Mean104.32769
Median Absolute Deviation (MAD)2.145
Skewness0.66620687
Sum11267.39
Variance18.251188
MonotonicityNot monotonic
2023-12-12T19:06:37.607283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110.3 4
 
3.7%
102.67 3
 
2.8%
100.8 2
 
1.9%
108.2 2
 
1.9%
101.3 2
 
1.9%
109.0 2
 
1.9%
107.9 2
 
1.9%
108.7 2
 
1.9%
108.9 2
 
1.9%
110.5 2
 
1.9%
Other values (83) 85
78.7%
ValueCountFrequency (%)
98.3 1
0.9%
98.41 1
0.9%
98.63 1
0.9%
98.89 1
0.9%
98.92 1
0.9%
99.2 1
0.9%
99.28 1
0.9%
99.37 1
0.9%
99.57 1
0.9%
99.59 1
0.9%
ValueCountFrequency (%)
113.98 1
0.9%
113.63 1
0.9%
112.79 1
0.9%
112.71 1
0.9%
112.17 1
0.9%
112.15 1
0.9%
112.0 1
0.9%
111.8 1
0.9%
111.5 1
0.9%
110.7 1
0.9%

전기수도가스
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.982407
Minimum80.62
Maximum123.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T19:06:37.775121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80.62
5-th percentile83.3515
Q190.38
median93.98
Q3106.505
95-th percentile113.6
Maximum123.55
Range42.93
Interquartile range (IQR)16.125

Descriptive statistics

Standard deviation10.430995
Coefficient of variation (CV)0.10645784
Kurtosis-0.62227696
Mean97.982407
Median Absolute Deviation (MAD)4.7
Skewness0.54993595
Sum10582.1
Variance108.80567
MonotonicityNot monotonic
2023-12-12T19:06:37.896003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
113.6 6
 
5.6%
90.38 6
 
5.6%
91.56 6
 
5.6%
98.6 5
 
4.6%
89.67 4
 
3.7%
90.76 4
 
3.7%
92.0 4
 
3.7%
89.28 4
 
3.7%
91.32 4
 
3.7%
101.86 4
 
3.7%
Other values (38) 61
56.5%
ValueCountFrequency (%)
80.62 2
 
1.9%
81.0 2
 
1.9%
82.9 2
 
1.9%
84.19 3
2.8%
89.28 4
3.7%
89.36 2
 
1.9%
89.54 2
 
1.9%
89.67 4
3.7%
90.29 1
 
0.9%
90.38 6
5.6%
ValueCountFrequency (%)
123.55 1
 
0.9%
123.19 2
 
1.9%
114.38 1
 
0.9%
114.1 1
 
0.9%
113.6 6
5.6%
113.4 4
3.7%
113.3 1
 
0.9%
112.4 2
 
1.9%
110.0 2
 
1.9%
109.6 1
 
0.9%

집세
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.76204
Minimum99.96
Maximum111.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T19:06:38.043297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99.96
5-th percentile100.3105
Q1101.365
median102.27
Q3102.9725
95-th percentile111.265
Maximum111.9
Range11.94
Interquartile range (IQR)1.6075

Descriptive statistics

Standard deviation3.8489602
Coefficient of variation (CV)0.037094108
Kurtosis-0.2193403
Mean103.76204
Median Absolute Deviation (MAD)0.8
Skewness1.2283684
Sum11206.3
Variance14.814495
MonotonicityNot monotonic
2023-12-12T19:06:38.199541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.6 3
 
2.8%
101.9 3
 
2.8%
101.5 3
 
2.8%
110.7 3
 
2.8%
102.71 2
 
1.9%
102.74 2
 
1.9%
103.0 2
 
1.9%
111.8 2
 
1.9%
102.6 2
 
1.9%
101.1 2
 
1.9%
Other values (77) 84
77.8%
ValueCountFrequency (%)
99.96 1
0.9%
100.08 1
0.9%
100.14 1
0.9%
100.2 1
0.9%
100.27 1
0.9%
100.3 1
0.9%
100.33 1
0.9%
100.35 1
0.9%
100.46 1
0.9%
100.5 1
0.9%
ValueCountFrequency (%)
111.9 1
 
0.9%
111.8 2
1.9%
111.6 1
 
0.9%
111.4 1
 
0.9%
111.3 1
 
0.9%
111.2 1
 
0.9%
111.1 1
 
0.9%
111.0 1
 
0.9%
110.9 1
 
0.9%
110.7 3
2.8%

공공서비스
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.72463
Minimum95.43
Maximum103.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T19:06:38.383765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95.43
5-th percentile100.9
Q1101.1
median101.865
Q3102.215
95-th percentile102.82
Maximum103.25
Range7.82
Interquartile range (IQR)1.115

Descriptive statistics

Standard deviation0.90464474
Coefficient of variation (CV)0.0088930748
Kurtosis20.775576
Mean101.72463
Median Absolute Deviation (MAD)0.55
Skewness-3.0541097
Sum10986.26
Variance0.8183821
MonotonicityNot monotonic
2023-12-12T19:06:38.593663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.2 8
 
7.4%
101.0 7
 
6.5%
102.2 6
 
5.6%
101.5 4
 
3.7%
102.0 3
 
2.8%
100.93 3
 
2.8%
100.9 3
 
2.8%
101.1 3
 
2.8%
102.19 3
 
2.8%
101.8 3
 
2.8%
Other values (55) 65
60.2%
ValueCountFrequency (%)
95.43 1
 
0.9%
100.34 1
 
0.9%
100.75 1
 
0.9%
100.8 1
 
0.9%
100.83 1
 
0.9%
100.9 3
2.8%
100.91 1
 
0.9%
100.92 1
 
0.9%
100.93 3
2.8%
101.0 7
6.5%
ValueCountFrequency (%)
103.25 1
0.9%
103.19 1
0.9%
103.17 1
0.9%
103.14 1
0.9%
103.13 1
0.9%
102.82 2
1.9%
102.79 1
0.9%
102.78 1
0.9%
102.7 1
0.9%
102.63 1
0.9%

개인서비스
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.75407
Minimum100.96
Maximum113.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T19:06:38.757793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100.96
5-th percentile102.114
Q1105.2525
median108.15
Q3110.025
95-th percentile113.116
Maximum113.76
Range12.8
Interquartile range (IQR)4.7725

Descriptive statistics

Standard deviation3.4564665
Coefficient of variation (CV)0.032077363
Kurtosis-0.9525117
Mean107.75407
Median Absolute Deviation (MAD)2.45
Skewness-0.17425174
Sum11637.44
Variance11.947161
MonotonicityNot monotonic
2023-12-12T19:06:38.938822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.48 2
 
1.9%
111.13 2
 
1.9%
110.92 2
 
1.9%
107.9 2
 
1.9%
106.1 2
 
1.9%
108.1 2
 
1.9%
106.3 2
 
1.9%
108.2 2
 
1.9%
108.4 2
 
1.9%
109.3 2
 
1.9%
Other values (84) 88
81.5%
ValueCountFrequency (%)
100.96 1
0.9%
101.27 1
0.9%
101.5 1
0.9%
101.69 1
0.9%
101.8 1
0.9%
102.1 1
0.9%
102.14 1
0.9%
102.36 1
0.9%
102.49 1
0.9%
102.5 1
0.9%
ValueCountFrequency (%)
113.76 1
0.9%
113.68 1
0.9%
113.47 1
0.9%
113.36 1
0.9%
113.34 1
0.9%
113.27 1
0.9%
112.83 1
0.9%
112.73 1
0.9%
112.7 1
0.9%
112.61 1
0.9%

Interactions

2023-12-12T19:06:34.950597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:29.208531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:30.224221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.025225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.847301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:33.018272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:33.940140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:35.047522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:29.331274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:30.336637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.153060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.946308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:33.139381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:34.172271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:35.176238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:29.451681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:30.439705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.265940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:32.052200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:33.254827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:34.318612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:35.310869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:29.586994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:30.565568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.399540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:32.195941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:33.364772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:34.463103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:35.426275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:29.803046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:30.659628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.504344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:32.306894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:33.476091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:34.577289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:35.536872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:29.950506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:30.772213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.611148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:32.454775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:33.619102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:34.694939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:35.662427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:30.101889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:30.896325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:31.723823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:32.552915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:33.783643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:06:34.825587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:06:39.423954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월총지수농축수산물공업제품전기수도가스집세공공서비스개인서비스
연도1.0000.0000.8700.7940.8840.8380.9320.7330.850
월0.0001.0000.0000.1080.0000.2430.0000.0000.000
총지수0.8700.0001.0000.6820.9260.7630.7700.6450.861
농축수산물0.7940.1080.6821.0000.5660.4620.4780.6790.564
공업제품0.8840.0000.9260.5661.0000.8160.8100.5900.813
전기수도가스0.8380.2430.7630.4620.8161.0000.8280.4960.718
집세0.9320.0000.7700.4780.8100.8281.0000.4740.766
공공서비스0.7330.0000.6450.6790.5900.4960.4741.0000.837
개인서비스0.8500.0000.8610.5640.8130.7180.7660.8371.000
2023-12-12T19:06:39.553123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월연도
월1.0000.000
연도0.0001.000
2023-12-12T19:06:39.648456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총지수농축수산물공업제품전기수도가스집세공공서비스개인서비스연도월
총지수1.0000.6690.8910.5790.5510.3840.6410.6360.000
농축수산물0.6691.0000.4510.1230.2250.2500.6850.3680.032
공업제품0.8910.4511.0000.6640.4300.3090.3510.6630.000
전기수도가스0.5790.1230.6641.0000.165-0.062-0.1200.6120.098
집세0.5510.2250.4300.1651.0000.5000.4880.7600.000
공공서비스0.3840.2500.309-0.0620.5001.0000.5550.5280.000
개인서비스0.6410.6850.351-0.1200.4880.5551.0000.6010.000
연도0.6360.3680.6630.6120.7600.5280.6011.0000.000
월0.0000.0320.0000.0980.0000.0000.0000.0001.000

Missing values

2023-12-12T19:06:35.837053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:06:36.037087image/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

연도월총지수농축수산물공업제품전기수도가스집세공공서비스개인서비스
02014년01월108.0111.1110.3113.6109.5101.2106.5
12014년02월108.2112.0110.5113.6109.7101.2106.6
22014년03월108.4112.3110.7113.6109.9101.2107.1
32014년04월108.3111.3110.3113.6110.1101.2107.5
42014년05월108.5111.7110.5113.6110.1101.2107.9
52014년06월108.3110.8110.3113.6110.3101.2107.6
62014년07월108.4110.7110.3113.3110.4101.3108.1
72014년08월108.5111.9110.1113.4110.4101.0108.6
82014년09월108.4113.7109.7113.4110.5101.0108.2
92014년10월108.1109.2109.3113.4110.6101.5108.4
연도월총지수농축수산물공업제품전기수도가스집세공공서비스개인서비스
982022년03월107.07111.68109.63101.86101.18101.76106.39
992022년04월108.23112.21111.5106.04101.24102.13107.05
1002022년05월109.04114.19112.15108.37101.35102.15107.99
1012022년06월109.77112.86113.63108.37101.37102.19108.89
1022022년07월110.27115.8113.98105.29101.46102.23109.54
1032022년08월109.97118.5112.17105.29101.48102.24109.99
1042022년09월110.39121.4111.8114.38101.53102.19109.91
1052022년10월110.55118.12112.0123.19101.56102.2110.13
1062022년11월110.43112.92112.79123.19101.6102.21110.3
1072022년12월110.66113.25112.71123.55101.63102.34110.92