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 2 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 2 other fieldsHigh correlation
기본키 is highly overall correlated with 지점 and 1 other fieldsHigh correlation
측정시간 is highly overall correlated with 강수량(mm)High correlation
강수량(mm) is highly overall correlated with 측정시간 and 2 other fieldsHigh correlation
기본키 has unique valuesUnique
강수량(mm) has 23 (23.0%) zerosZeros
강우량(mm) has 82 (82.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:42:35.845454
Analysis finished2023-12-10 13:42:38.553112
Duration2.71 seconds
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
2023-12-10T22:42:38.664536image/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
2023-12-10T22:42:38.909818image/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
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T22:42:39.155938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:39.396503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 100
100.0%

측정시간
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1064.95
Minimum15
Maximum2345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:39.756775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile30
Q1311.25
median1137.5
Q31733.75
95-th percentile2230.75
Maximum2345
Range2330
Interquartile range (IQR)1422.5

Descriptive statistics

Standard deviation775.47655
Coefficient of variation (CV)0.72818119
Kurtosis-1.471811
Mean1064.95
Median Absolute Deviation (MAD)770
Skewness0.064845264
Sum106495
Variance601363.89
MonotonicityNot monotonic
2023-12-10T22:42:40.084969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 3
 
3.0%
45 3
 
3.0%
100 3
 
3.0%
30 3
 
3.0%
330 2
 
2.0%
430 2
 
2.0%
400 2
 
2.0%
345 2
 
2.0%
315 2
 
2.0%
215 2
 
2.0%
Other values (71) 76
76.0%
ValueCountFrequency (%)
15 3
3.0%
30 3
3.0%
45 3
3.0%
100 3
3.0%
115 2
2.0%
130 2
2.0%
145 2
2.0%
200 2
2.0%
215 2
2.0%
230 1
 
1.0%
ValueCountFrequency (%)
2345 1
1.0%
2330 1
1.0%
2315 1
1.0%
2300 1
1.0%
2245 1
1.0%
2230 1
1.0%
2215 1
1.0%
2200 1
1.0%
2145 1
1.0%
2130 1
1.0%

지점
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-3019E-6
76 
A-0010-3352E-9
20 
A-0010-1185E-8
 
4

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-0010-3019E-6

Common Values

ValueCountFrequency (%)
A-0010-3019E-6 76
76.0%
A-0010-3352E-9 20
 
20.0%
A-0010-1185E-8 4
 
4.0%

Length

2023-12-10T22:42:40.303975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:40.537004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a-0010-3019e-6 76
76.0%
a-0010-3352e-9 20
 
20.0%
a-0010-1185e-8 4
 
4.0%

주소
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청주시 흥덕구 강서1동
76 
충남 천안시 구성동
20 
대구 동구 안심3동
 
4

Length

Max length15
Median length15
Mean length13.8
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 row충북 청주시 흥덕구 강서1동

Common Values

ValueCountFrequency (%)
충북 청주시 흥덕구 강서1동 76
76.0%
충남 천안시 구성동 20
 
20.0%
대구 동구 안심3동 4
 
4.0%

Length

2023-12-10T22:42:40.740261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:40.896687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충북 76
20.2%
청주시 76
20.2%
흥덕구 76
20.2%
강서1동 76
20.2%
충남 20
 
5.3%
천안시 20
 
5.3%
구성동 20
 
5.3%
대구 4
 
1.1%
동구 4
 
1.1%
안심3동 4
 
1.1%

강수량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.06
Minimum0
Maximum7
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:41.057569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median4
Q34
95-th percentile5.05
Maximum7
Range7
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.8630918
Coefficient of variation (CV)0.60885353
Kurtosis-0.57602017
Mean3.06
Median Absolute Deviation (MAD)0
Skewness-0.67239055
Sum306
Variance3.4711111
MonotonicityNot monotonic
2023-12-10T22:42:41.250518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 63
63.0%
0 23
 
23.0%
2 3
 
3.0%
3 3
 
3.0%
6 3
 
3.0%
1 2
 
2.0%
7 2
 
2.0%
5 1
 
1.0%
ValueCountFrequency (%)
0 23
 
23.0%
1 2
 
2.0%
2 3
 
3.0%
3 3
 
3.0%
4 63
63.0%
5 1
 
1.0%
6 3
 
3.0%
7 2
 
2.0%
ValueCountFrequency (%)
7 2
 
2.0%
6 3
 
3.0%
5 1
 
1.0%
4 63
63.0%
3 3
 
3.0%
2 3
 
3.0%
1 2
 
2.0%
0 23
 
23.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.562
Minimum0
Maximum5.6
Zeros82
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:41.447120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.53
Maximum5.6
Range5.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4308139
Coefficient of variation (CV)2.5459321
Kurtosis5.7870421
Mean0.562
Median Absolute Deviation (MAD)0
Skewness2.6204345
Sum56.2
Variance2.0472283
MonotonicityNot monotonic
2023-12-10T22:42:41.664114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 82
82.0%
5.6 3
 
3.0%
3.6 2
 
2.0%
1.1 1
 
1.0%
1.6 1
 
1.0%
2.1 1
 
1.0%
2.7 1
 
1.0%
3.1 1
 
1.0%
0.3 1
 
1.0%
0.8 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
0.0 82
82.0%
0.3 1
 
1.0%
0.7 1
 
1.0%
0.8 1
 
1.0%
1.1 1
 
1.0%
1.6 1
 
1.0%
1.9 1
 
1.0%
2.1 1
 
1.0%
2.7 1
 
1.0%
2.8 1
 
1.0%
ValueCountFrequency (%)
5.6 3
3.0%
5.5 1
 
1.0%
5.1 1
 
1.0%
4.5 1
 
1.0%
3.6 2
2.0%
3.1 1
 
1.0%
2.8 1
 
1.0%
2.7 1
 
1.0%
2.1 1
 
1.0%
1.9 1
 
1.0%

Interactions

2023-12-10T22:42:37.782238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:36.109534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:36.523967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:37.306116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:37.893710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:36.210662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:36.930671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:37.421671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:37.993780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:36.315695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:37.061587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:37.544883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:38.125657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:36.427027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:37.183943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:37.686375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:42:41.816584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키측정시간지점주소강수량(mm)강우량(mm)
기본키1.0000.9780.8740.8740.7160.515
측정시간0.9781.0000.6310.6310.7800.659
지점0.8740.6311.0001.0000.6450.557
주소0.8740.6311.0001.0000.6450.557
강수량(mm)0.7160.7800.6450.6451.0000.870
강우량(mm)0.5150.6590.5570.5570.8701.000
2023-12-10T22:42:41.991046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소지점
주소1.0001.000
지점1.0001.000
2023-12-10T22:42:42.122209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키측정시간강수량(mm)강우량(mm)지점주소
기본키1.0000.3130.3130.2080.7780.778
측정시간0.3131.0000.688-0.2820.4590.459
강수량(mm)0.3130.6881.0000.2100.5050.505
강우량(mm)0.208-0.2820.2101.0000.2830.283
지점0.7780.4590.5050.2831.0001.000
주소0.7780.4590.5050.2831.0001.000

Missing values

2023-12-10T22:42:38.303328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:42:38.483878image/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)
012021020115A-0010-1185E-8대구 동구 안심3동00.0
122021020130A-0010-1185E-8대구 동구 안심3동00.0
232021020145A-0010-1185E-8대구 동구 안심3동00.0
3420210201100A-0010-1185E-8대구 동구 안심3동00.0
452021020115A-0010-3019E-6충북 청주시 흥덕구 강서1동00.0
562021020130A-0010-3019E-6충북 청주시 흥덕구 강서1동00.0
672021020145A-0010-3019E-6충북 청주시 흥덕구 강서1동00.0
7820210201100A-0010-3019E-6충북 청주시 흥덕구 강서1동00.0
8920210201115A-0010-3019E-6충북 청주시 흥덕구 강서1동00.0
91020210201130A-0010-3019E-6충북 청주시 흥덕구 강서1동00.0
기본키측정일측정시간지점주소강수량(mm)강우량(mm)
909120210201315A-0010-3352E-9충남 천안시 구성동10.8
919220210201330A-0010-3352E-9충남 천안시 구성동21.9
929320210201345A-0010-3352E-9충남 천안시 구성동32.8
939420210201400A-0010-3352E-9충남 천안시 구성동43.6
949520210201430A-0010-3352E-9충남 천안시 구성동54.5
959620210201445A-0010-3352E-9충남 천안시 구성동65.1
969720210201500A-0010-3352E-9충남 천안시 구성동65.5
979820210201515A-0010-3352E-9충남 천안시 구성동65.6
989920210201530A-0010-3352E-9충남 천안시 구성동75.6
9910020210201545A-0010-3352E-9충남 천안시 구성동75.6