Overview

Dataset statistics

Number of variables14
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory129.0 B

Variable types

Text3
Numeric11

Dataset

Description광주광역시 전체인구, 급수인구, 시설용량, 보급률, 1인 1일 급수량, 누수율, 유수율 등 상수도 전반에 관한 자료입니다.
Author광주광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15099880/fileData.do

Alerts

2010 is highly overall correlated with 2011 and 9 other fieldsHigh correlation
2011 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2012 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2013 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2014 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2015 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2016 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2017 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2019 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2020 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
2021 is highly overall correlated with 2010 and 9 other fieldsHigh correlation
구 분 has unique valuesUnique
2010 has unique valuesUnique
2012 has unique valuesUnique
2013 has unique valuesUnique
2014 has unique valuesUnique
2015 has unique valuesUnique
2016 has unique valuesUnique
2017 has unique valuesUnique
2018 has unique valuesUnique
2019 has unique valuesUnique
2020 has unique valuesUnique
2021 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:22:26.954625
Analysis finished2023-12-12 13:22:39.993357
Duration13.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T22:22:40.115788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.9090909
Min length2

Characters and Unicode

Total characters108
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row전체인구
2nd row급수인구
3rd row시설용량
4th row보급률
5th row1인 1일 급수량
ValueCountFrequency (%)
정원 3
 
9.4%
연간 2
 
6.2%
전체인구 1
 
3.1%
관 1
 
3.1%
보호구역 1
 
3.1%
부채비율 1
 
3.1%
부채현황(원금 1
 
3.1%
부채현황 1
 
3.1%
상근인력 1
 
3.1%
공무원 1
 
3.1%
Other values (19) 19
59.4%
2023-12-12T22:22:40.483704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
 
9.3%
원 7
 
6.5%
수 6
 
5.6%
량 5
 
4.6%
인 4
 
3.7%
정 4
 
3.7%
급 4
 
3.7%
율 3
 
2.8%
연 3
 
2.8%
일 3
 
2.8%
Other values (40) 59
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 94
87.0%
Space Separator 10
 
9.3%
Decimal Number 2
 
1.9%
Close Punctuation 1
 
0.9%
Open Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 7
 
7.4%
수 6
 
6.4%
량 5
 
5.3%
인 4
 
4.3%
정 4
 
4.3%
급 4
 
4.3%
율 3
 
3.2%
연 3
 
3.2%
일 3
 
3.2%
생 3
 
3.2%
Other values (36) 52
55.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94
87.0%
Common 14
 
13.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 7
 
7.4%
수 6
 
6.4%
량 5
 
5.3%
인 4
 
4.3%
정 4
 
4.3%
급 4
 
4.3%
율 3
 
3.2%
연 3
 
3.2%
일 3
 
3.2%
생 3
 
3.2%
Other values (36) 52
55.3%
Common
ValueCountFrequency (%)
10
71.4%
1 2
 
14.3%
) 1
 
7.1%
( 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 94
87.0%
ASCII 14
 
13.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10
71.4%
1 2
 
14.3%
) 1
 
7.1%
( 1
 
7.1%
Hangul
ValueCountFrequency (%)
원 7
 
7.4%
수 6
 
6.4%
량 5
 
5.3%
인 4
 
4.3%
정 4
 
4.3%
급 4
 
4.3%
율 3
 
3.2%
연 3
 
3.2%
일 3
 
3.2%
생 3
 
3.2%
Other values (36) 52
55.3%

단 위
Text

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T22:22:40.641502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.6818182
Min length1

Characters and Unicode

Total characters37
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)22.7%

Sample

1st row천명
2nd row천명
3rd row천㎥
4th row%
5th rowℓ
ValueCountFrequency (%)
천㎥ 4
18.2%
4
18.2%
명 3
13.6%
천명 2
9.1%
원/㎥ 2
9.1%
억원 2
9.1%
ℓ 1
 
4.5%
천전 1
 
4.5%
㎞ 1
 
4.5%
백만원 1
 
4.5%
2023-12-12T22:22:40.965711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 7
18.9%
㎥ 6
16.2%
명 5
13.5%
원 5
13.5%
% 4
10.8%
/ 2
 
5.4%
억 2
 
5.4%
ℓ 1
 
2.7%
전 1
 
2.7%
㎞ 1
 
2.7%
Other values (3) 3
8.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22
59.5%
Other Symbol 8
 
21.6%
Other Punctuation 6
 
16.2%
Lowercase Letter 1
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 7
31.8%
명 5
22.7%
원 5
22.7%
억 2
 
9.1%
전 1
 
4.5%
백 1
 
4.5%
만 1
 
4.5%
Other Symbol
ValueCountFrequency (%)
㎥ 6
75.0%
㎞ 1
 
12.5%
㎢ 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
% 4
66.7%
/ 2
33.3%
Lowercase Letter
ValueCountFrequency (%)
ℓ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22
59.5%
Common 15
40.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 7
31.8%
명 5
22.7%
원 5
22.7%
억 2
 
9.1%
전 1
 
4.5%
백 1
 
4.5%
만 1
 
4.5%
Common
ValueCountFrequency (%)
㎥ 6
40.0%
% 4
26.7%
/ 2
 
13.3%
ℓ 1
 
6.7%
㎞ 1
 
6.7%
㎢ 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22
59.5%
CJK Compat 8
 
21.6%
ASCII 6
 
16.2%
Letterlike Symbols 1
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 7
31.8%
명 5
22.7%
원 5
22.7%
억 2
 
9.1%
전 1
 
4.5%
백 1
 
4.5%
만 1
 
4.5%
CJK Compat
ValueCountFrequency (%)
㎥ 6
75.0%
㎞ 1
 
12.5%
㎢ 1
 
12.5%
ASCII
ValueCountFrequency (%)
% 4
66.7%
/ 2
33.3%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 1
100.0%

2010
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18200.163
Minimum1.9
Maximum170131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:41.120673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile13.866
Q180.7
median367.5
Q31289.25
95-th percentile131890.25
Maximum170131
Range170129.1
Interquartile range (IQR)1208.55

Descriptive statistics

Standard deviation47186.118
Coefficient of variation (CV)2.5926206
Kurtosis5.925996
Mean18200.163
Median Absolute Deviation (MAD)291.5
Skewness2.6191537
Sum400403.58
Variance2.2265297 × 109
MonotonicityNot monotonic
2023-12-12T22:22:41.273894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1468.0 1
 
4.5%
83635.0 1
 
4.5%
26.9 1
 
4.5%
1.9 1
 
4.5%
75.0 1
 
4.5%
80.0 1
 
4.5%
77.0 1
 
4.5%
329.0 1
 
4.5%
406.0 1
 
4.5%
522.27 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1.9 1
4.5%
13.18 1
4.5%
26.9 1
4.5%
75.0 1
4.5%
77.0 1
4.5%
80.0 1
4.5%
82.8 1
4.5%
99.39 1
4.5%
132.0 1
4.5%
319.0 1
4.5%
ValueCountFrequency (%)
170131.0 1
4.5%
134430.0 1
4.5%
83635.0 1
4.5%
5248.0 1
4.5%
1468.0 1
4.5%
1459.0 1
4.5%
780.0 1
4.5%
622.14 1
4.5%
522.27 1
4.5%
466.0 1
4.5%

2011
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18690.93
Minimum1.3
Maximum173423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:41.434710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile12.65
Q178.7675
median361.5
Q31296.75
95-th percentile136083
Maximum173423
Range173421.7
Interquartile range (IQR)1217.9825

Descriptive statistics

Standard deviation48406.583
Coefficient of variation (CV)2.5898434
Kurtosis5.8053896
Mean18690.93
Median Absolute Deviation (MAD)321.5
Skewness2.6028287
Sum411200.47
Variance2.3431973 × 109
MonotonicityNot monotonic
2023-12-12T22:22:41.579404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
323.0 2
 
9.1%
1478.0 1
 
4.5%
86911.0 1
 
4.5%
26.9 1
 
4.5%
1.3 1
 
4.5%
39.0 1
 
4.5%
41.0 1
 
4.5%
77.0 1
 
4.5%
400.0 1
 
4.5%
524.11 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
1.3 1
4.5%
11.9 1
4.5%
26.9 1
4.5%
39.0 1
4.5%
41.0 1
4.5%
77.0 1
4.5%
84.07 1
4.5%
99.45 1
4.5%
134.0 1
4.5%
323.0 2
9.1%
ValueCountFrequency (%)
173423.0 1
4.5%
138671.0 1
4.5%
86911.0 1
4.5%
5282.0 1
4.5%
1478.0 1
4.5%
1469.0 1
4.5%
780.0 1
4.5%
626.74 1
4.5%
524.11 1
4.5%
475.0 1
4.5%

2012
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18678.371
Minimum0.78
Maximum172044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:41.731674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile11.2995
Q178.88
median357.5
Q31302
95-th percentile138619.2
Maximum172044
Range172043.22
Interquartile range (IQR)1223.12

Descriptive statistics

Standard deviation48541.964
Coefficient of variation (CV)2.5988328
Kurtosis5.6937005
Mean18678.371
Median Absolute Deviation (MAD)337.05
Skewness2.5908015
Sum410924.17
Variance2.3563222 × 109
MonotonicityNot monotonic
2023-12-12T22:22:41.883463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1484.0 1
 
4.5%
86962.0 1
 
4.5%
26.9 1
 
4.5%
0.78 1
 
4.5%
13.0 1
 
4.5%
14.0 1
 
4.5%
77.0 1
 
4.5%
319.0 1
 
4.5%
396.0 1
 
4.5%
525.98 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.78 1
4.5%
11.21 1
4.5%
13.0 1
4.5%
14.0 1
4.5%
26.9 1
4.5%
77.0 1
4.5%
84.52 1
4.5%
99.5 1
4.5%
135.0 1
4.5%
318.0 1
4.5%
ValueCountFrequency (%)
172044.0 1
4.5%
141338.0 1
4.5%
86962.0 1
4.5%
3733.0 1
4.5%
1484.0 1
4.5%
1476.0 1
4.5%
780.0 1
4.5%
615.28 1
4.5%
525.98 1
4.5%
471.0 1
4.5%

2013
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19064.194
Minimum1.2
Maximum174179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:42.033243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile11.9375
Q178.9625
median357
Q31306.5
95-th percentile141636.3
Maximum174179
Range174177.8
Interquartile range (IQR)1227.5375

Descriptive statistics

Standard deviation49438.022
Coefficient of variation (CV)2.5932396
Kurtosis5.573229
Mean19064.194
Median Absolute Deviation (MAD)319
Skewness2.5735633
Sum419412.26
Variance2.444118 × 109
MonotonicityNot monotonic
2023-12-12T22:22:42.163858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1488.0 1
 
4.5%
90152.0 1
 
4.5%
26.9 1
 
4.5%
1.2 1
 
4.5%
37.0 1
 
4.5%
39.0 1
 
4.5%
77.0 1
 
4.5%
315.0 1
 
4.5%
392.0 1
 
4.5%
544.05 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1.2 1
4.5%
11.15 1
4.5%
26.9 1
4.5%
37.0 1
4.5%
39.0 1
4.5%
77.0 1
4.5%
84.85 1
4.5%
99.56 1
4.5%
136.0 1
4.5%
315.0 1
4.5%
ValueCountFrequency (%)
174179.0 1
4.5%
144346.0 1
4.5%
90152.0 1
4.5%
3798.0 1
4.5%
1488.0 1
4.5%
1482.0 1
4.5%
780.0 1
4.5%
624.55 1
4.5%
544.05 1
4.5%
477.0 1
4.5%

2014
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19101.234
Minimum1.9
Maximum171970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:42.267919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile11.4625
Q179.035
median353
Q31310.25
95-th percentile142432.4
Maximum171970
Range171968.1
Interquartile range (IQR)1231.215

Descriptive statistics

Standard deviation49350.583
Coefficient of variation (CV)2.5836332
Kurtosis5.3801516
Mean19101.234
Median Absolute Deviation (MAD)281.39
Skewness2.5463574
Sum420227.15
Variance2.43548 × 109
MonotonicityNot monotonic
2023-12-12T22:22:42.436978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1493.0 1
 
4.5%
92375.0 1
 
4.5%
26.9 1
 
4.5%
1.9 1
 
4.5%
74.0 1
 
4.5%
76.0 1
 
4.5%
77.0 1
 
4.5%
312.0 1
 
4.5%
389.0 1
 
4.5%
560.18 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1.9 1
4.5%
10.65 1
4.5%
26.9 1
4.5%
74.0 1
4.5%
76.0 1
4.5%
77.0 1
4.5%
85.14 1
4.5%
99.6 1
4.5%
138.0 1
4.5%
312.0 1
4.5%
ValueCountFrequency (%)
171970.0 1
4.5%
145067.0 1
4.5%
92375.0 1
4.5%
3780.0 1
4.5%
1493.0 1
4.5%
1487.0 1
4.5%
780.0 1
4.5%
636.78 1
4.5%
560.18 1
4.5%
471.0 1
4.5%

2015
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19332.845
Minimum2.8
Maximum173271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:42.551805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile10.959
Q1106.335
median354
Q31310.25
95-th percentile144804.1
Maximum173271
Range173268.2
Interquartile range (IQR)1203.915

Descriptive statistics

Standard deviation49918.356
Coefficient of variation (CV)2.5820492
Kurtosis5.3426478
Mean19332.845
Median Absolute Deviation (MAD)278.595
Skewness2.5417726
Sum425322.58
Variance2.4918423 × 109
MonotonicityNot monotonic
2023-12-12T22:22:42.665195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1490.0 1
 
4.5%
93544.0 1
 
4.5%
26.9 1
 
4.5%
2.8 1
 
4.5%
126.0 1
 
4.5%
130.0 1
 
4.5%
77.0 1
 
4.5%
312.0 1
 
4.5%
389.0 1
 
4.5%
575.86 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
2.8 1
4.5%
10.12 1
4.5%
26.9 1
4.5%
77.0 1
4.5%
85.93 1
4.5%
99.78 1
4.5%
126.0 1
4.5%
130.0 1
4.5%
139.0 1
4.5%
312.0 1
4.5%
ValueCountFrequency (%)
173271.0 1
4.5%
147502.0 1
4.5%
93544.0 1
4.5%
3847.0 1
4.5%
1490.0 1
4.5%
1487.0 1
4.5%
780.0 1
4.5%
634.19 1
4.5%
575.86 1
4.5%
474.0 1
4.5%

2016
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19945.225
Minimum2.7
Maximum178468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:42.783184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile10.7025
Q1106.365
median353.5
Q31304.5
95-th percentile150436.1
Maximum178468
Range178465.3
Interquartile range (IQR)1198.135

Descriptive statistics

Standard deviation51561.939
Coefficient of variation (CV)2.5851772
Kurtosis5.3445119
Mean19945.225
Median Absolute Deviation (MAD)274.99
Skewness2.5432406
Sum438794.94
Variance2.6586336 × 109
MonotonicityNot monotonic
2023-12-12T22:22:42.921625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1489.0 1
 
4.5%
95965.0 1
 
4.5%
26.9 1
 
4.5%
2.7 1
 
4.5%
126.0 1
 
4.5%
163.0 1
 
4.5%
76.0 1
 
4.5%
303.0 1
 
4.5%
379.0 1
 
4.5%
624.49 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
2.7 1
4.5%
9.85 1
4.5%
26.9 1
4.5%
76.0 1
4.5%
86.2 1
4.5%
99.82 1
4.5%
126.0 1
4.5%
139.0 1
4.5%
163.0 1
4.5%
303.0 1
4.5%
ValueCountFrequency (%)
178468.0 1
4.5%
153303.0 1
4.5%
95965.0 1
4.5%
3847.0 1
4.5%
1489.0 1
4.5%
1486.0 1
4.5%
760.0 1
4.5%
625.98 1
4.5%
624.49 1
4.5%
487.0 1
4.5%

2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20266.123
Minimum2.5
Maximum178558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:43.041754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile10.1515
Q1127
median377
Q31302.25
95-th percentile152717.7
Maximum178558
Range178555.5
Interquartile range (IQR)1175.25

Descriptive statistics

Standard deviation52174.835
Coefficient of variation (CV)2.5744852
Kurtosis5.1306638
Mean20266.123
Median Absolute Deviation (MAD)283.71
Skewness2.5118718
Sum445854.71
Variance2.7222134 × 109
MonotonicityNot monotonic
2023-12-12T22:22:43.165479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1485.0 1
 
4.5%
100652.0 1
 
4.5%
26.9 1
 
4.5%
2.5 1
 
4.5%
126.0 1
 
4.5%
165.0 1
 
4.5%
130.0 1
 
4.5%
295.0 1
 
4.5%
425.0 1
 
4.5%
651.01 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
2.5 1
4.5%
9.27 1
4.5%
26.9 1
4.5%
86.7 1
4.5%
99.88 1
4.5%
126.0 1
4.5%
130.0 1
4.5%
140.0 1
4.5%
165.0 1
4.5%
295.0 1
4.5%
ValueCountFrequency (%)
178558.0 1
4.5%
155458.0 1
4.5%
100652.0 1
4.5%
3836.0 1
4.5%
1485.0 1
4.5%
1483.0 1
4.5%
760.0 1
4.5%
651.01 1
4.5%
647.45 1
4.5%
489.0 1
4.5%

2018
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T22:22:43.341502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.0909091
Min length2

Characters and Unicode

Total characters90
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row1482
2nd row1481
3rd row760
4th row99.91
5th row338
ValueCountFrequency (%)
1482 1
 
4.5%
1481 1
 
4.5%
1.6 1
 
4.5%
72 1
 
4.5%
117 1
 
4.5%
130 1
 
4.5%
291 1
 
4.5%
421 1
 
4.5%
653.45 1
 
4.5%
666.71 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T22:22:43.662193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16
17.8%
6 10
11.1%
3 10
11.1%
8 8
8.9%
9 8
8.9%
2 7
7.8%
0 7
7.8%
. 7
7.8%
4 6
 
6.7%
7 6
 
6.7%
Other values (2) 5
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82
91.1%
Other Punctuation 8
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
19.5%
6 10
12.2%
3 10
12.2%
8 8
9.8%
9 8
9.8%
2 7
8.5%
0 7
8.5%
4 6
 
7.3%
7 6
 
7.3%
5 4
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 7
87.5%
, 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 90
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16
17.8%
6 10
11.1%
3 10
11.1%
8 8
8.9%
9 8
8.9%
2 7
7.8%
0 7
7.8%
. 7
7.8%
4 6
 
6.7%
7 6
 
6.7%
Other values (2) 5
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16
17.8%
6 10
11.1%
3 10
11.1%
8 8
8.9%
9 8
8.9%
2 7
7.8%
0 7
7.8%
. 7
7.8%
4 6
 
6.7%
7 6
 
6.7%
Other values (2) 5
 
5.6%

2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20940.864
Minimum1.08
Maximum180111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:43.782601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.08
5-th percentile8.6315
Q191.1325
median383
Q31299.25
95-th percentile156487.85
Maximum180111
Range180109.92
Interquartile range (IQR)1208.1175

Descriptive statistics

Standard deviation53570.84
Coefficient of variation (CV)2.5581963
Kurtosis4.7694937
Mean20940.864
Median Absolute Deviation (MAD)331.515
Skewness2.4582416
Sum460699.01
Variance2.8698349 × 109
MonotonicityNot monotonic
2023-12-12T22:22:43.980503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1480.0 1
 
4.5%
110448.0 1
 
4.5%
26.9 1
 
4.5%
1.08 1
 
4.5%
32.0 1
 
4.5%
79.0 1
 
4.5%
132.0 1
 
4.5%
301.0 1
 
4.5%
433.0 1
 
4.5%
655.2 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1.08 1
4.5%
7.67 1
4.5%
26.9 1
4.5%
32.0 1
4.5%
79.0 1
4.5%
88.2 1
4.5%
99.93 1
4.5%
132.0 1
4.5%
137.0 1
4.5%
301.0 1
4.5%
ValueCountFrequency (%)
180111.0 1
4.5%
158911.0 1
4.5%
110448.0 1
4.5%
3996.0 1
4.5%
1480.0 1
4.5%
1479.0 1
4.5%
760.0 1
4.5%
695.03 1
4.5%
655.2 1
4.5%
493.0 1
4.5%

2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20902.016
Minimum1.14
Maximum176828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:44.105311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile6.266
Q193.125
median380.5
Q31293.25
95-th percentile154900.65
Maximum176828
Range176826.86
Interquartile range (IQR)1200.125

Descriptive statistics

Standard deviation53227.05
Coefficient of variation (CV)2.5465032
Kurtosis4.5377423
Mean20902.016
Median Absolute Deviation (MAD)349.34
Skewness2.4240791
Sum459844.35
Variance2.8331189 × 109
MonotonicityNot monotonic
2023-12-12T22:22:44.234273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1472.0 1
 
4.5%
114728.0 1
 
4.5%
26.9 1
 
4.5%
1.14 1
 
4.5%
32.0 1
 
4.5%
88.0 1
 
4.5%
132.0 1
 
4.5%
301.0 1
 
4.5%
433.0 1
 
4.5%
649.65 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1.14 1
4.5%
5.18 1
4.5%
26.9 1
4.5%
32.0 1
4.5%
88.0 1
4.5%
90.85 1
4.5%
99.95 1
4.5%
132.0 1
4.5%
137.0 1
4.5%
301.0 1
4.5%
ValueCountFrequency (%)
176828.0 1
4.5%
157015.0 1
4.5%
114728.0 1
4.5%
4032.0 1
4.5%
1472.0 1
4.5%
1471.0 1
4.5%
760.0 1
4.5%
730.68 1
4.5%
649.65 1
4.5%
483.0 1
4.5%

2021
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21341.875
Minimum1.18
Maximum179933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T22:22:44.359267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile6.2755
Q196.2375
median385
Q31286.5
95-th percentile157889.8
Maximum179933
Range179931.82
Interquartile range (IQR)1190.2625

Descriptive statistics

Standard deviation54321.223
Coefficient of variation (CV)2.5452882
Kurtosis4.4845614
Mean21341.875
Median Absolute Deviation (MAD)355.375
Skewness2.4163126
Sum469521.26
Variance2.9507953 × 109
MonotonicityNot monotonic
2023-12-12T22:22:44.475078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1463.0 1
 
4.5%
118309.0 1
 
4.5%
26.9 1
 
4.5%
1.18 1
 
4.5%
28.8 1
 
4.5%
95.0 1
 
4.5%
132.0 1
 
4.5%
301.0 1
 
4.5%
433.0 1
 
4.5%
654.83 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1.18 1
4.5%
5.19 1
4.5%
26.9 1
4.5%
28.8 1
4.5%
90.86 1
4.5%
95.0 1
4.5%
99.95 1
4.5%
132.0 1
4.5%
137.0 1
4.5%
301.0 1
4.5%
ValueCountFrequency (%)
179933.0 1
4.5%
159973.0 1
4.5%
118309.0 1
4.5%
4046.0 1
4.5%
1463.0 1
4.5%
1462.0 1
4.5%
760.0 1
4.5%
739.55 1
4.5%
654.83 1
4.5%
493.0 1
4.5%

Interactions

2023-12-12T22:22:38.310525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:27.365371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:28.738884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.766048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.772502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.954123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.968939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.258361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.353171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.353962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.334712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:38.386094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:27.448605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:28.843643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.857597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.871860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.046048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:33.054835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.354990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.447160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.441653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.432988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:38.761410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:27.549067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:28.955303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.944514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.959255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.144900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:33.137305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.450559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.544879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.528370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.546108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:38.850813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:27.643908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.047761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.033452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.062996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.232991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:33.230484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.534779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.633451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.619135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.637546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:38.941170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:27.740658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.154472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.124859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.178164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.314895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:33.325271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.644139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.735368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.709783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.716709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:39.060079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:27.866857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.246032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.226810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.306241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.406604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:33.694656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.750486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.840100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.805187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.814462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:39.170392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:27.964816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.330828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.313926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.419095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.511155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:33.779103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.844813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.922339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.889750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.898650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:39.255034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:28.049262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.415480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.409226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.529995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.612096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:33.880573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.933790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.006199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.968403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.983275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:39.354579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:28.413357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.502547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.495711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.637451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.697320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:33.964348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.059919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.085823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.058839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:38.062993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:39.461246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:28.536506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.595490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.590443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.726834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.782290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.061083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.164261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.169457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.146450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:38.147902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:39.555550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:28.632931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:29.678272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:30.690667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:31.840947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:32.869458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:34.161775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:35.268441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:36.255427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:37.244896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:22:38.228547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:22:44.587824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분단 위201020112012201320142015201620172018201920202021
구 분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
단 위1.0001.0000.3620.3620.3620.3620.3620.3620.3620.3621.0000.3620.3620.362
20101.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20111.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20121.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20131.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20141.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20151.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20161.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20171.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20181.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20191.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20201.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20211.0000.3621.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T22:22:44.993814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20102011201220132014201520162017201920202021
20101.0000.9990.9940.9980.9980.9860.9830.9770.9920.9920.991
20110.9991.0000.9961.0001.0000.9830.9780.9760.9960.9960.994
20120.9940.9961.0000.9950.9950.9720.9660.9650.9920.9920.989
20130.9981.0000.9951.0001.0000.9830.9790.9760.9970.9970.994
20140.9981.0000.9951.0001.0000.9830.9790.9760.9970.9970.994
20150.9860.9830.9720.9830.9831.0000.9990.9910.9800.9800.983
20160.9830.9780.9660.9790.9790.9991.0000.9920.9750.9750.980
20170.9770.9760.9650.9760.9760.9910.9921.0000.9820.9820.988
20190.9920.9960.9920.9970.9970.9800.9750.9821.0001.0000.999
20200.9920.9960.9920.9970.9970.9800.9750.9821.0001.0000.999
20210.9910.9940.9890.9940.9940.9830.9800.9880.9990.9991.000

Missing values

2023-12-12T22:22:39.701921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:22:39.907578image/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

구 분단 위201020112012201320142015201620172018201920202021
0전체인구천명1468.01478.01484.01488.01493.01490.01489.01485.014821480.01472.01463.0
1급수인구천명1459.01469.01476.01482.01487.01487.01486.01483.014811479.01471.01462.0
2시설용량천㎥780.0780.0780.0780.0780.0780.0760.0760.0760760.0760.0760.0
3보급률%99.3999.4599.599.5699.699.7899.8299.8899.9199.9399.9599.95
41인 1일 급수량ℓ319.0323.0318.0322.0317.0319.0328.0329.0338333.0328.0337.0
5급수전수천전132.0134.0135.0136.0138.0139.0139.0140.0138137.0137.0137.0
6연간 총생산량천㎥170131.0173423.0172044.0174179.0171970.0173271.0178468.0178558.0182,942180111.0176828.0179933.0
7연간 조정량천㎥134430.0138671.0141338.0144346.0145067.0147502.0153303.0155458.0160495158911.0157015.0159973.0
8일일 생산량천㎥466.0475.0471.0477.0471.0474.0487.0489.0501493.0483.0493.0
9유수율%82.884.0784.5284.8585.1485.9386.286.787.6888.290.8590.86
구 분단 위201020112012201320142015201620172018201920202021
12총괄원가백만원83635.086911.086962.090152.092375.093544.095965.0100652.0107003110448.0114728.0118309.0
13생산원가원/㎥622.14626.74615.28624.55636.78634.19625.98647.45666.71695.03730.68739.55
14판매단가원/㎥522.27524.11525.98544.05560.18575.86624.49651.01653.45655.2649.65654.83
15정원명406.0400.0396.0392.0389.0389.0379.0425.0421433.0433.0433.0
16공무원 정원명329.0323.0319.0315.0312.0312.0303.0295.0291301.0301.0301.0
17상근인력 정원명77.077.077.077.077.077.076.0130.0130132.0132.0132.0
18부채현황억원80.041.014.039.076.0130.0163.0165.011779.088.095.0
19부채현황(원금)억원75.039.013.037.074.0126.0126.0126.07232.032.028.8
20부채비율%1.91.30.781.21.92.82.72.51.61.081.141.18
21보호구역 면적㎢26.926.926.926.926.926.926.926.926.926.926.926.9