Overview

Dataset statistics

Number of variables8
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory74.5 B

Variable types

Numeric5
Categorical3

Dataset

Description주요 도시의 월별 폭염 일수 통계정보를 제공하는 서비스
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2986

Alerts

합계(일) is highly overall correlated with 7월(일) and 1 other fieldsHigh correlation
7월(일) is highly overall correlated with 합계(일)High correlation
8월(일) is highly overall correlated with 합계(일)High correlation
5월(일) is highly overall correlated with 9월(일)High correlation
9월(일) is highly overall correlated with 5월(일)High correlation
5월(일) is highly imbalanced (52.2%)Imbalance
9월(일) is highly imbalanced (70.3%)Imbalance
6월(일) has 16 (42.1%) zerosZeros
7월(일) has 10 (26.3%) zerosZeros
8월(일) has 2 (5.3%) zerosZeros

Reproduction

Analysis started2024-01-09 20:26:46.348410
Analysis finished2024-01-09 20:26:48.878597
Duration2.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

Distinct6
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.6053
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T05:26:48.922568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7481956
Coefficient of variation (CV)0.00086604132
Kurtosis-1.3062768
Mean2018.6053
Median Absolute Deviation (MAD)1.5
Skewness-0.089456912
Sum76707
Variance3.0561878
MonotonicityNot monotonic
2024-01-10T05:26:49.017432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 7
18.4%
2020 7
18.4%
2016 6
15.8%
2017 6
15.8%
2018 6
15.8%
2019 6
15.8%
ValueCountFrequency (%)
2016 6
15.8%
2017 6
15.8%
2018 6
15.8%
2019 6
15.8%
2020 7
18.4%
2021 7
18.4%
ValueCountFrequency (%)
2021 7
18.4%
2020 7
18.4%
2019 6
15.8%
2018 6
15.8%
2017 6
15.8%
2016 6
15.8%

지역
Categorical

Distinct7
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size436.0 B
서울
6 
강릉
6 
대전
6 
대구
6 
광주
6 
Other values (2)
8 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전국
2nd row서울
3rd row강릉
4th row대전
5th row대구

Common Values

ValueCountFrequency (%)
서울 6
15.8%
강릉 6
15.8%
대전 6
15.8%
대구 6
15.8%
광주 6
15.8%
부산 6
15.8%
전국 2
 
5.3%

Length

2024-01-10T05:26:49.118742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:26:49.213266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 6
15.8%
강릉 6
15.8%
대전 6
15.8%
대구 6
15.8%
광주 6
15.8%
부산 6
15.8%
전국 2
 
5.3%

합계(일)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.092105
Minimum3
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T05:26:49.311434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.85
Q112
median17
Q329
95-th percentile37.45
Maximum43
Range40
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.973195
Coefficient of variation (CV)0.57475041
Kurtosis-0.75750443
Mean19.092105
Median Absolute Deviation (MAD)7.5
Skewness0.44186249
Sum725.5
Variance120.41102
MonotonicityNot monotonic
2024-01-10T05:26:49.412856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
13.0 3
 
7.9%
12.0 3
 
7.9%
29.0 3
 
7.9%
18.0 3
 
7.9%
9.0 2
 
5.3%
23.0 2
 
5.3%
14.0 2
 
5.3%
3.0 2
 
5.3%
4.0 2
 
5.3%
31.0 2
 
5.3%
Other values (14) 14
36.8%
ValueCountFrequency (%)
3.0 2
5.3%
4.0 2
5.3%
6.0 1
 
2.6%
7.7 1
 
2.6%
9.0 2
5.3%
11.8 1
 
2.6%
12.0 3
7.9%
13.0 3
7.9%
14.0 2
5.3%
15.0 1
 
2.6%
ValueCountFrequency (%)
43.0 1
 
2.6%
40.0 1
 
2.6%
37.0 1
 
2.6%
35.0 1
 
2.6%
33.0 1
 
2.6%
32.0 1
 
2.6%
31.0 2
5.3%
29.0 3
7.9%
24.0 1
 
2.6%
23.0 2
5.3%

5월(일)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
0
31 
2
 
3
1
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.6%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
81.6%
2 3
 
7.9%
1 3
 
7.9%
3 1
 
2.6%

Length

2024-01-10T05:26:49.525489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:26:49.626248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
81.6%
2 3
 
7.9%
1 3
 
7.9%
3 1
 
2.6%

6월(일)
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5526316
Minimum0
Maximum7
Zeros16
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T05:26:49.706757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5.15
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9102479
Coefficient of variation (CV)1.2303291
Kurtosis0.88801572
Mean1.5526316
Median Absolute Deviation (MAD)1
Skewness1.2554216
Sum59
Variance3.6490469
MonotonicityNot monotonic
2024-01-10T05:26:49.801265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 16
42.1%
2.0 6
 
15.8%
1.0 5
 
13.2%
3.0 3
 
7.9%
4.0 2
 
5.3%
5.0 2
 
5.3%
0.1 1
 
2.6%
6.0 1
 
2.6%
1.9 1
 
2.6%
7.0 1
 
2.6%
ValueCountFrequency (%)
0.0 16
42.1%
0.1 1
 
2.6%
1.0 5
 
13.2%
1.9 1
 
2.6%
2.0 6
 
15.8%
3.0 3
 
7.9%
4.0 2
 
5.3%
5.0 2
 
5.3%
6.0 1
 
2.6%
7.0 1
 
2.6%
ValueCountFrequency (%)
7.0 1
 
2.6%
6.0 1
 
2.6%
5.0 2
 
5.3%
4.0 2
 
5.3%
3.0 3
 
7.9%
2.0 6
 
15.8%
1.9 1
 
2.6%
1.0 5
 
13.2%
0.1 1
 
2.6%
0.0 16
42.1%

7월(일)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0842105
Minimum0
Maximum21
Zeros10
Zeros (%)26.3%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T05:26:49.901627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.025
median5.5
Q312.25
95-th percentile17.45
Maximum21
Range21
Interquartile range (IQR)12.225

Descriptive statistics

Standard deviation6.5379361
Coefficient of variation (CV)0.92288846
Kurtosis-0.88799004
Mean7.0842105
Median Absolute Deviation (MAD)5.5
Skewness0.54658281
Sum269.2
Variance42.744609
MonotonicityNot monotonic
2024-01-10T05:26:49.997144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 10
26.3%
5.0 4
 
10.5%
16.0 4
 
10.5%
9.0 3
 
7.9%
6.0 2
 
5.3%
13.0 2
 
5.3%
4.0 2
 
5.3%
10.0 2
 
5.3%
20.0 1
 
2.6%
1.0 1
 
2.6%
Other values (7) 7
18.4%
ValueCountFrequency (%)
0.0 10
26.3%
0.1 1
 
2.6%
1.0 1
 
2.6%
3.0 1
 
2.6%
4.0 2
 
5.3%
5.0 4
 
10.5%
6.0 2
 
5.3%
7.0 1
 
2.6%
8.1 1
 
2.6%
9.0 3
 
7.9%
ValueCountFrequency (%)
21.0 1
 
2.6%
20.0 1
 
2.6%
17.0 1
 
2.6%
16.0 4
10.5%
15.0 1
 
2.6%
13.0 2
5.3%
10.0 2
5.3%
9.0 3
7.9%
8.1 1
 
2.6%
7.0 1
 
2.6%

8월(일)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.084211
Minimum0
Maximum24
Zeros2
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T05:26:50.093077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7
Q15
median9
Q314.25
95-th percentile21.3
Maximum24
Range24
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation6.8190324
Coefficient of variation (CV)0.67620885
Kurtosis-0.73736054
Mean10.084211
Median Absolute Deviation (MAD)4
Skewness0.5888898
Sum383.2
Variance46.499203
MonotonicityNot monotonic
2024-01-10T05:26:50.193714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5.0 4
 
10.5%
9.0 4
 
10.5%
21.0 3
 
7.9%
10.0 3
 
7.9%
3.0 3
 
7.9%
12.0 3
 
7.9%
6.0 2
 
5.3%
0.0 2
 
5.3%
19.0 2
 
5.3%
16.0 1
 
2.6%
Other values (11) 11
28.9%
ValueCountFrequency (%)
0.0 2
5.3%
2.0 1
 
2.6%
3.0 3
7.9%
3.5 1
 
2.6%
4.0 1
 
2.6%
5.0 4
10.5%
5.7 1
 
2.6%
6.0 2
5.3%
7.0 1
 
2.6%
8.0 1
 
2.6%
ValueCountFrequency (%)
24.0 1
 
2.6%
23.0 1
 
2.6%
21.0 3
7.9%
20.0 1
 
2.6%
19.0 2
5.3%
16.0 1
 
2.6%
15.0 1
 
2.6%
12.0 3
7.9%
11.0 1
 
2.6%
10.0 3
7.9%

9월(일)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size436.0 B
0
36 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36
94.7%
1 2
 
5.3%

Length

2024-01-10T05:26:50.295646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:26:50.377980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 36
94.7%
1 2
 
5.3%

Interactions

2024-01-10T05:26:48.073806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:46.615120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:46.965929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.353660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.726688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:48.371727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:46.680040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.040145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.426508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.798908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:48.449259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:46.756022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.121731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.510547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.871495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:48.529912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:46.833859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.200075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.588472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.945564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:48.606925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:46.895043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.276308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:47.656961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:26:48.008711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:26:50.436669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도지역합계(일)5월(일)6월(일)7월(일)8월(일)9월(일)
기준년도1.0000.0000.0000.2040.3200.5780.4650.106
지역0.0001.0000.4260.0000.2020.3230.4020.000
합계(일)0.0000.4261.0000.4140.5030.7930.6660.414
5월(일)0.2040.0000.4141.0000.7750.5510.3040.866
6월(일)0.3200.2020.5030.7751.0000.3270.4240.000
7월(일)0.5780.3230.7930.5510.3271.0000.1890.362
8월(일)0.4650.4020.6660.3040.4240.1891.0000.000
9월(일)0.1060.0000.4140.8660.0000.3620.0001.000
2024-01-10T05:26:50.535619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
9월(일)5월(일)지역
9월(일)1.0000.6480.000
5월(일)0.6481.0000.000
지역0.0000.0001.000
2024-01-10T05:26:50.615483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도합계(일)6월(일)7월(일)8월(일)지역5월(일)9월(일)
기준년도1.000-0.3280.085-0.148-0.4560.0000.1630.000
합계(일)-0.3281.0000.3310.7440.7240.2360.0000.000
6월(일)0.0850.3311.0000.1640.1320.0750.4090.000
7월(일)-0.1480.7440.1641.0000.1620.1500.3490.317
8월(일)-0.4560.7240.1320.1621.0000.2360.0000.000
지역0.0000.2360.0750.1500.2361.0000.0000.000
5월(일)0.1630.0000.4090.3490.0000.0001.0000.648
9월(일)0.0000.0000.0000.3170.0000.0000.6481.000

Missing values

2024-01-10T05:26:48.715518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:26:48.832879image/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

기준년도지역합계(일)5월(일)6월(일)7월(일)8월(일)9월(일)
02021전국11.800.18.13.50
12021서울18.000.015.03.00
22021강릉9.022.05.00.00
32021대전21.000.016.05.00
42021대구23.002.013.08.00
52021광주14.000.09.05.00
62021부산3.000.00.03.00
72016서울24.000.04.020.00
82016강릉12.000.03.09.00
92016대전29.000.06.023.00
기준년도지역합계(일)5월(일)6월(일)7월(일)8월(일)9월(일)
282019대구29.025.06.016.00
292019광주12.000.00.012.00
302019부산3.000.00.03.00
312020전국7.701.90.15.70
322020서울4.002.00.02.00
332020강릉16.003.01.012.00
342020대전13.003.00.010.00
352020대구31.007.00.024.00
362020광주13.002.00.011.00
372020부산4.000.00.04.00