Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory62.3 B

Variable types

Numeric4
Categorical3

Alerts

측정일 has constant value ""Constant
주소 is highly overall correlated with 기본키 and 1 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 1 other fieldsHigh correlation
기본키 is highly overall correlated with 측정시간 and 4 other fieldsHigh correlation
측정시간 is highly overall correlated with 기본키 and 2 other fieldsHigh correlation
강수량(mm) is highly overall correlated with 기본키 and 2 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 기본키 and 2 other fieldsHigh correlation
기본키 has unique valuesUnique
강수량(mm) has 59 (59.0%) zerosZeros
강우량(mm) has 60 (60.0%) zerosZeros

Reproduction

Analysis started2024-04-22 00:16:15.461657
Analysis finished2024-04-22 00:16:17.210597
Duration1.75 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T09:16:17.281314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2024-04-22T09:16:17.410553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2024-04-22T09:16:17.542047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:16:17.905499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 100
100.0%

측정시간
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.5
Minimum15
Maximum1715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T09:16:18.008982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile45
Q1315
median622.5
Q31103.75
95-th percentile1600.75
Maximum1715
Range1700
Interquartile range (IQR)788.75

Descriptive statistics

Standard deviation487.2695
Coefficient of variation (CV)0.68101957
Kurtosis-0.92722288
Mean715.5
Median Absolute Deviation (MAD)377.5
Skewness0.44267443
Sum71550
Variance237431.57
MonotonicityNot monotonic
2024-04-22T09:16:18.163568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
2.0%
430 2
 
2.0%
900 2
 
2.0%
30 2
 
2.0%
830 2
 
2.0%
815 2
 
2.0%
800 2
 
2.0%
645 2
 
2.0%
630 2
 
2.0%
615 2
 
2.0%
Other values (58) 80
80.0%
ValueCountFrequency (%)
15 2
2.0%
30 2
2.0%
45 2
2.0%
100 2
2.0%
115 2
2.0%
130 2
2.0%
145 2
2.0%
200 2
2.0%
215 2
2.0%
230 2
2.0%
ValueCountFrequency (%)
1715 1
1.0%
1700 1
1.0%
1645 1
1.0%
1630 1
1.0%
1615 1
1.0%
1600 1
1.0%
1545 1
1.0%
1530 1
1.0%
1515 1
1.0%
1500 1
1.0%

지점
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-3019E-6
64 
A-0010-1185E-8
36 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-0010-1185E-8
2nd rowA-0010-1185E-8
3rd rowA-0010-1185E-8
4th rowA-0010-1185E-8
5th rowA-0010-1185E-8

Common Values

ValueCountFrequency (%)
A-0010-3019E-6 64
64.0%
A-0010-1185E-8 36
36.0%

Length

2024-04-22T09:16:18.297847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:16:18.389082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a-0010-3019e-6 64
64.0%
a-0010-1185e-8 36
36.0%

주소
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청주시 흥덕구 강서1동
64 
대구 동구 안심3동
36 

Length

Max length15
Median length15
Mean length13.2
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구 동구 안심3동
2nd row대구 동구 안심3동
3rd row대구 동구 안심3동
4th row대구 동구 안심3동
5th row대구 동구 안심3동

Common Values

ValueCountFrequency (%)
충북 청주시 흥덕구 강서1동 64
64.0%
대구 동구 안심3동 36
36.0%

Length

2024-04-22T09:16:18.490726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:16:18.592895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충북 64
17.6%
청주시 64
17.6%
흥덕구 64
17.6%
강서1동 64
17.6%
대구 36
9.9%
동구 36
9.9%
안심3동 36
9.9%

강수량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.86
Minimum0
Maximum43
Zeros59
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T09:16:18.698845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile38.05
Maximum43
Range43
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.368108
Coefficient of variation (CV)1.8029312
Kurtosis1.8929678
Mean6.86
Median Absolute Deviation (MAD)0
Skewness1.8038465
Sum686
Variance152.9701
MonotonicityNot monotonic
2024-04-22T09:16:18.821788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 59
59.0%
1 4
 
4.0%
2 3
 
3.0%
4 3
 
3.0%
7 3
 
3.0%
5 2
 
2.0%
6 2
 
2.0%
35 2
 
2.0%
38 1
 
1.0%
29 1
 
1.0%
Other values (20) 20
 
20.0%
ValueCountFrequency (%)
0 59
59.0%
1 4
 
4.0%
2 3
 
3.0%
3 1
 
1.0%
4 3
 
3.0%
5 2
 
2.0%
6 2
 
2.0%
7 3
 
3.0%
8 1
 
1.0%
9 1
 
1.0%
ValueCountFrequency (%)
43 1
1.0%
42 1
1.0%
41 1
1.0%
40 1
1.0%
39 1
1.0%
38 1
1.0%
35 2
2.0%
33 1
1.0%
32 1
1.0%
29 1
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.467
Minimum0
Maximum40.4
Zeros60
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-22T09:16:18.987392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.375
95-th percentile31.795
Maximum40.4
Range40.4
Interquartile range (IQR)5.375

Descriptive statistics

Standard deviation10.499255
Coefficient of variation (CV)1.9204783
Kurtosis3.0874202
Mean5.467
Median Absolute Deviation (MAD)0
Skewness2.0632226
Sum546.7
Variance110.23435
MonotonicityNot monotonic
2024-04-22T09:16:19.125050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 60
60.0%
1.5 2
 
2.0%
25.1 1
 
1.0%
8.1 1
 
1.0%
11.7 1
 
1.0%
15.0 1
 
1.0%
17.0 1
 
1.0%
18.8 1
 
1.0%
20.7 1
 
1.0%
22.1 1
 
1.0%
Other values (30) 30
30.0%
ValueCountFrequency (%)
0.0 60
60.0%
0.6 1
 
1.0%
0.8 1
 
1.0%
0.9 1
 
1.0%
1.2 1
 
1.0%
1.5 2
 
2.0%
2.4 1
 
1.0%
2.8 1
 
1.0%
3.0 1
 
1.0%
3.1 1
 
1.0%
ValueCountFrequency (%)
40.4 1
1.0%
38.9 1
1.0%
37.2 1
1.0%
35.5 1
1.0%
33.6 1
1.0%
31.7 1
1.0%
29.8 1
1.0%
28.3 1
1.0%
26.8 1
1.0%
25.1 1
1.0%

Interactions

2024-04-22T09:16:16.650762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:15.670381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:15.990223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.324655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.728313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:15.744052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.065745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.409617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.823933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:15.825092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.153741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.491003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.900432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:15.905083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.229521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:16:16.558978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T09:16:19.224549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키측정시간지점주소강수량(mm)강우량(mm)
기본키1.0000.9480.9960.9960.8850.833
측정시간0.9481.0000.5620.5620.9240.908
지점0.9960.5621.0000.9990.5290.489
주소0.9960.5620.9991.0000.5290.489
강수량(mm)0.8850.9240.5290.5291.0000.993
강우량(mm)0.8330.9080.4890.4890.9931.000
2024-04-22T09:16:19.321492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소지점
주소1.0000.978
지점0.9781.000
2024-04-22T09:16:19.407250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키측정시간강수량(mm)강우량(mm)지점주소
기본키1.0000.8230.8890.8830.9020.902
측정시간0.8231.0000.8670.8660.4150.415
강수량(mm)0.8890.8671.0000.9930.3890.389
강우량(mm)0.8830.8660.9931.0000.3590.359
지점0.9020.4150.3890.3591.0000.978
주소0.9020.4150.3890.3590.9781.000

Missing values

2024-04-22T09:16:17.046161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T09:16:17.171663image/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

기본키측정일측정시간지점주소강수량(mm)강우량(mm)
012021030115A-0010-1185E-8대구 동구 안심3동00.0
122021030130A-0010-1185E-8대구 동구 안심3동00.0
232021030145A-0010-1185E-8대구 동구 안심3동00.0
3420210301100A-0010-1185E-8대구 동구 안심3동00.0
4520210301115A-0010-1185E-8대구 동구 안심3동00.0
5620210301130A-0010-1185E-8대구 동구 안심3동00.0
6720210301145A-0010-1185E-8대구 동구 안심3동00.0
7820210301200A-0010-1185E-8대구 동구 안심3동00.0
8920210301215A-0010-1185E-8대구 동구 안심3동00.0
91020210301230A-0010-1185E-8대구 동구 안심3동00.0
기본키측정일측정시간지점주소강수량(mm)강우량(mm)
9091202103011445A-0010-3019E-6충북 청주시 흥덕구 강서1동2923.6
9192202103011500A-0010-3019E-6충북 청주시 흥덕구 강서1동3225.1
9293202103011530A-0010-3019E-6충북 청주시 흥덕구 강서1동3528.3
9394202103011545A-0010-3019E-6충북 청주시 흥덕구 강서1동3529.8
9495202103011600A-0010-3019E-6충북 청주시 흥덕구 강서1동3831.7
9596202103011615A-0010-3019E-6충북 청주시 흥덕구 강서1동3933.6
9697202103011630A-0010-3019E-6충북 청주시 흥덕구 강서1동4035.5
9798202103011645A-0010-3019E-6충북 청주시 흥덕구 강서1동4137.2
9899202103011700A-0010-3019E-6충북 청주시 흥덕구 강서1동4238.9
99100202103011715A-0010-3019E-6충북 청주시 흥덕구 강서1동4340.4