Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric9
Categorical5
Text2

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시분 has constant value ""Constant
기본키 is highly overall correlated with 측정구간High correlation
연장 is highly overall correlated with 측정구간High correlation
좌표위치위도 is highly overall correlated with co and 4 other fieldsHigh correlation
좌표위치경도 is highly overall correlated with 측정구간High correlation
co is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
nox is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
hc is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
nox has unique valuesUnique
co2 has unique valuesUnique
pm has 6 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:34:00.951872
Analysis finished2023-12-10 13:34:12.535082
Duration11.58 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
2023-12-10T22:34:12.658029image/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
2023-12-10T22:34:12.902875image/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%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T22:34:13.104170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:34:13.239309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:34:13.487996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0122-2]
2nd row[0122-2]
3rd row[0123-2]
4th row[0123-2]
5th row[0124-0]
ValueCountFrequency (%)
0122-2 2
 
2.0%
3204-0 2
 
2.0%
3706-0 2
 
2.0%
2915-2 2
 
2.0%
2915-4 2
 
2.0%
2918-0 2
 
2.0%
2921-3 2
 
2.0%
2922-0 2
 
2.0%
2923-0 2
 
2.0%
2924-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:34:14.014038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 118
14.8%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 70
8.8%
4 44
 
5.5%
6 28
 
3.5%
7 18
 
2.2%
Other values (3) 38
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118
23.6%
2 106
21.2%
1 78
15.6%
3 70
14.0%
4 44
 
8.8%
6 28
 
5.6%
7 18
 
3.6%
9 18
 
3.6%
5 14
 
2.8%
8 6
 
1.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 118
14.8%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 70
8.8%
4 44
 
5.5%
6 28
 
3.5%
7 18
 
2.2%
Other values (3) 38
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 118
14.8%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 70
8.8%
4 44
 
5.5%
6 28
 
3.5%
7 18
 
2.2%
Other values (3) 38
 
4.8%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T22:34:14.208465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:34:14.352590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
공주-유성
 
4
신평-인주
 
4
부여-논산
 
2
논산-반포
 
2
금남-조치원
 
2
Other values (43)
86 

Length

Max length8
Median length5
Mean length5.12
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연무-논산
2nd row연무-논산
3rd row두마-금남
4th row두마-금남
5th row논산-반포

Common Values

ValueCountFrequency (%)
공주-유성 4
 
4.0%
신평-인주 4
 
4.0%
부여-논산 2
 
2.0%
논산-반포 2
 
2.0%
금남-조치원 2
 
2.0%
전동-쌍전 2
 
2.0%
천안-성환 2
 
2.0%
성환-천안 2
 
2.0%
둔포-평택 2
 
2.0%
장항-마서 2
 
2.0%
Other values (38) 76
76.0%

Length

2023-12-10T22:34:14.520366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공주-유성 4
 
4.0%
신평-인주 4
 
4.0%
태안-서산 2
 
2.0%
연무-논산 2
 
2.0%
당진-송악 2
 
2.0%
화양-한산 2
 
2.0%
구룡-부여 2
 
2.0%
청양-홍성 2
 
2.0%
홍성-고북 2
 
2.0%
고북-서산 2
 
2.0%
Other values (38) 76
76.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.57
Minimum1.8
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:34:14.713061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.7
Q15.3
median6.65
Q39.5
95-th percentile14.2
Maximum17.6
Range15.8
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.2491335
Coefficient of variation (CV)0.42921183
Kurtosis0.71031104
Mean7.57
Median Absolute Deviation (MAD)2
Skewness0.77149306
Sum757
Variance10.556869
MonotonicityNot monotonic
2023-12-10T22:34:14.943600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4.9 6
 
6.0%
6.6 6
 
6.0%
9.7 4
 
4.0%
9.3 4
 
4.0%
6.0 4
 
4.0%
4.2 2
 
2.0%
4.0 2
 
2.0%
6.4 2
 
2.0%
2.2 2
 
2.0%
7.2 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
1.8 2
 
2.0%
2.2 2
 
2.0%
2.7 2
 
2.0%
3.6 2
 
2.0%
4.0 2
 
2.0%
4.2 2
 
2.0%
4.3 2
 
2.0%
4.4 2
 
2.0%
4.9 6
6.0%
5.2 2
 
2.0%
ValueCountFrequency (%)
17.6 2
2.0%
14.6 2
2.0%
14.2 2
2.0%
12.4 2
2.0%
12.2 2
2.0%
11.5 2
2.0%
11.4 2
2.0%
10.6 2
2.0%
10.2 2
2.0%
10.0 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T22:34:15.164316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:34:15.303038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 100
100.0%

측정시분
Categorical

CONSTANT 

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

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 (%)
0 100
100.0%

Length

2023-12-10T22:34:15.456187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:34:15.596430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.512798
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:34:15.772770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25196
median36.51428
Q336.76405
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.51209

Descriptive statistics

Standard deviation0.28602506
Coefficient of variation (CV)0.0078335565
Kurtosis-1.355159
Mean36.512798
Median Absolute Deviation (MAD)0.26062
Skewness-0.07746355
Sum3651.2798
Variance0.081810337
MonotonicityNot monotonic
2023-12-10T22:34:16.003549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.87015 2
 
2.0%
36.07008 2
 
2.0%
36.27491 2
 
2.0%
36.47481 2
 
2.0%
36.58635 2
 
2.0%
36.72496 2
 
2.0%
36.83292 2
 
2.0%
36.75792 2
 
2.0%
36.78489 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.02784 2
2.0%
36.05892 2
2.0%
36.07008 2
2.0%
36.08997 2
2.0%
36.1228 2
2.0%
36.13314 2
2.0%
36.14511 2
2.0%
36.16232 2
2.0%
36.18861 2
2.0%
36.1897 2
2.0%
ValueCountFrequency (%)
36.95295 2
2.0%
36.9261 2
2.0%
36.90325 2
2.0%
36.89991 2
2.0%
36.89461 2
2.0%
36.89111 2
2.0%
36.87646 2
2.0%
36.87015 2
2.0%
36.86711 2
2.0%
36.83292 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.94368
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:34:16.233232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.71493
median126.87882
Q3127.20167
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.48674

Descriptive statistics

Standard deviation0.30407213
Coefficient of variation (CV)0.002395331
Kurtosis-0.53441001
Mean126.94368
Median Absolute Deviation (MAD)0.246255
Skewness-0.13261952
Sum12694.368
Variance0.092459863
MonotonicityNot monotonic
2023-12-10T22:34:16.465134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
126.75145 2
 
2.0%
126.78607 2
 
2.0%
126.85936 2
 
2.0%
126.79526 2
 
2.0%
126.61312 2
 
2.0%
126.5301 2
 
2.0%
126.44223 2
 
2.0%
126.18913 2
 
2.0%
126.37641 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.18913 2
2.0%
126.37641 2
2.0%
126.44223 2
2.0%
126.5301 2
2.0%
126.53713 2
2.0%
126.60118 2
2.0%
126.61312 2
2.0%
126.64274 2
2.0%
126.64887 2
2.0%
126.66796 2
2.0%
ValueCountFrequency (%)
127.49717 2
2.0%
127.49544 2
2.0%
127.47469 2
2.0%
127.42036 2
2.0%
127.28963 2
2.0%
127.28536 2
2.0%
127.27513 2
2.0%
127.25961 2
2.0%
127.25821 2
2.0%
127.24084 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.9074
Minimum0
Maximum236.78
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:34:16.679773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.398
Q119.0325
median44.83
Q376.355
95-th percentile137.1885
Maximum236.78
Range236.78
Interquartile range (IQR)57.3225

Descriptive statistics

Standard deviation46.592847
Coefficient of variation (CV)0.86431263
Kurtosis3.5944883
Mean53.9074
Median Absolute Deviation (MAD)28.54
Skewness1.6357501
Sum5390.74
Variance2170.8934
MonotonicityNot monotonic
2023-12-10T22:34:16.898422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.35 2
 
2.0%
47.88 1
 
1.0%
57.81 1
 
1.0%
78.16 1
 
1.0%
137.06 1
 
1.0%
79.0 1
 
1.0%
94.4 1
 
1.0%
32.93 1
 
1.0%
7.86 1
 
1.0%
5.4 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.52 1
1.0%
2.63 1
1.0%
5.28 1
1.0%
5.36 1
1.0%
5.4 1
1.0%
5.76 1
1.0%
6.18 1
1.0%
6.35 1
1.0%
6.45 1
1.0%
ValueCountFrequency (%)
236.78 1
1.0%
232.0 1
1.0%
188.41 1
1.0%
165.69 1
1.0%
139.63 1
1.0%
137.06 1
1.0%
129.75 1
1.0%
123.94 1
1.0%
114.01 1
1.0%
102.54 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.7142
Minimum0
Maximum273.17
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:34:17.479016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.1165
Q113.3925
median31.76
Q351.805
95-th percentile104.4365
Maximum273.17
Range273.17
Interquartile range (IQR)38.4125

Descriptive statistics

Standard deviation43.457098
Coefficient of variation (CV)1.0673696
Kurtosis10.058111
Mean40.7142
Median Absolute Deviation (MAD)20.07
Skewness2.7129146
Sum4071.42
Variance1888.5193
MonotonicityNot monotonic
2023-12-10T22:34:17.865556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.29 1
 
1.0%
35.29 1
 
1.0%
103.21 1
 
1.0%
127.74 1
 
1.0%
97.93 1
 
1.0%
77.65 1
 
1.0%
23.7 1
 
1.0%
4.16 1
 
1.0%
3.17 1
 
1.0%
184.08 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.28 1
1.0%
1.64 1
1.0%
3.02 1
1.0%
3.05 1
1.0%
3.12 1
1.0%
3.17 1
1.0%
3.72 1
1.0%
4.05 1
1.0%
4.16 1
1.0%
ValueCountFrequency (%)
273.17 1
1.0%
207.51 1
1.0%
184.08 1
1.0%
138.27 1
1.0%
127.74 1
1.0%
103.21 1
1.0%
99.36 1
1.0%
97.93 1
1.0%
97.9 1
1.0%
84.94 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8184
Minimum0
Maximum29.88
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:34:18.341975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.48
Q11.82
median4.585
Q38.5375
95-th percentile16.2785
Maximum29.88
Range29.88
Interquartile range (IQR)6.7175

Descriptive statistics

Standard deviation5.5181366
Coefficient of variation (CV)0.94839417
Kurtosis5.4246554
Mean5.8184
Median Absolute Deviation (MAD)3.22
Skewness2.0101448
Sum581.84
Variance30.449832
MonotonicityNot monotonic
2023-12-10T22:34:18.835651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.48 3
 
3.0%
11.1 2
 
2.0%
6.88 1
 
1.0%
5.52 1
 
1.0%
12.54 1
 
1.0%
17.58 1
 
1.0%
10.5 1
 
1.0%
9.56 1
 
1.0%
3.12 1
 
1.0%
0.66 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.04 1
 
1.0%
0.26 1
 
1.0%
0.48 3
3.0%
0.5 1
 
1.0%
0.55 1
 
1.0%
0.57 1
 
1.0%
0.63 1
 
1.0%
0.66 1
 
1.0%
0.75 1
 
1.0%
ValueCountFrequency (%)
29.88 1
1.0%
26.8 1
1.0%
24.62 1
1.0%
17.61 1
1.0%
17.58 1
1.0%
16.21 1
1.0%
14.2 1
1.0%
12.54 1
1.0%
12.38 1
1.0%
12.06 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9308
Minimum0
Maximum16.12
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:34:19.240024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4075
median1.105
Q32.425
95-th percentile5.942
Maximum16.12
Range16.12
Interquartile range (IQR)2.0175

Descriptive statistics

Standard deviation2.4802141
Coefficient of variation (CV)1.2845526
Kurtosis12.610403
Mean1.9308
Median Absolute Deviation (MAD)0.835
Skewness3.0486401
Sum193.08
Variance6.151462
MonotonicityNot monotonic
2023-12-10T22:34:19.488333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27 6
 
6.0%
0.0 6
 
6.0%
0.13 5
 
5.0%
0.4 4
 
4.0%
1.38 3
 
3.0%
0.28 2
 
2.0%
1.08 2
 
2.0%
0.94 2
 
2.0%
0.41 2
 
2.0%
0.14 2
 
2.0%
Other values (60) 66
66.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.13 5
5.0%
0.14 2
 
2.0%
0.27 6
6.0%
0.28 2
 
2.0%
0.4 4
4.0%
0.41 2
 
2.0%
0.53 2
 
2.0%
0.54 2
 
2.0%
0.56 2
 
2.0%
ValueCountFrequency (%)
16.12 1
1.0%
12.13 1
1.0%
7.99 1
1.0%
7.71 1
1.0%
6.36 1
1.0%
5.92 1
1.0%
5.78 1
1.0%
4.86 1
1.0%
4.82 1
1.0%
4.71 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13312.158
Minimum0
Maximum59681.29
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:34:19.707152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1431.5095
Q14302.965
median11368.275
Q319315.583
95-th percentile30718.622
Maximum59681.29
Range59681.29
Interquartile range (IQR)15012.618

Descriptive statistics

Standard deviation11419.657
Coefficient of variation (CV)0.8578366
Kurtosis3.8765451
Mean13312.158
Median Absolute Deviation (MAD)7159.99
Skewness1.6813353
Sum1331215.8
Variance1.3040856 × 108
MonotonicityNot monotonic
2023-12-10T22:34:19.887250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12084.29 1
 
1.0%
13692.57 1
 
1.0%
19296.46 1
 
1.0%
33790.02 1
 
1.0%
20572.47 1
 
1.0%
24219.02 1
 
1.0%
8544.5 1
 
1.0%
2080.28 1
 
1.0%
1428.08 1
 
1.0%
55100.81 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.0 1
1.0%
138.68 1
1.0%
640.96 1
1.0%
1261.32 1
1.0%
1428.08 1
1.0%
1431.69 1
1.0%
1525.54 1
1.0%
1660.5 1
1.0%
1705.45 1
1.0%
1758.51 1
1.0%
ValueCountFrequency (%)
59681.29 1
1.0%
55100.81 1
1.0%
49110.26 1
1.0%
42457.37 1
1.0%
33790.02 1
1.0%
30556.97 1
1.0%
29373.75 1
1.0%
29320.68 1
1.0%
26935.16 1
1.0%
26724.09 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:34:20.283288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.94
Min length8

Characters and Unicode

Total characters1094
Distinct characters109
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충남 논산 은진 토양
2nd row충남 논산 은진 토양
3rd row충남 공주 반포 온천
4th row충남 공주 반포 온천
5th row충남 논산 연산 천호
ValueCountFrequency (%)
충남 100
25.1%
천안 12
 
3.0%
서천 10
 
2.5%
금산 10
 
2.5%
공주 8
 
2.0%
세종 8
 
2.0%
서산 8
 
2.0%
부여 8
 
2.0%
예산 8
 
2.0%
아산 6
 
1.5%
Other values (95) 220
55.3%
2023-12-10T22:34:20.869321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
충 100
 
9.1%
남 100
 
9.1%
산 52
 
4.8%
천 28
 
2.6%
서 26
 
2.4%
성 24
 
2.2%
신 18
 
1.6%
안 16
 
1.5%
부 14
 
1.3%
Other values (99) 418
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
72.8%
Space Separator 298
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
충 100
 
12.6%
남 100
 
12.6%
산 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 24
 
3.0%
신 18
 
2.3%
안 16
 
2.0%
부 14
 
1.8%
금 12
 
1.5%
Other values (98) 406
51.0%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
72.8%
Common 298
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
충 100
 
12.6%
남 100
 
12.6%
산 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 24
 
3.0%
신 18
 
2.3%
안 16
 
2.0%
부 14
 
1.8%
금 12
 
1.5%
Other values (98) 406
51.0%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
72.8%
ASCII 298
 
27.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
충 100
 
12.6%
남 100
 
12.6%
산 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 24
 
3.0%
신 18
 
2.3%
안 16
 
2.0%
부 14
 
1.8%
금 12
 
1.5%
Other values (98) 406
51.0%

Interactions

2023-12-10T22:34:10.786542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:01.664583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:03.043413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.125553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.016067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.038903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.007324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.832778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:09.122020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:10.921776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:01.754385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:03.166791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.211409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.161889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.136317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.095063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.947845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:09.275895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:11.071313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:01.897648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:03.297295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.313465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.302673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.261042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.207118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:08.053099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:09.423915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:11.202854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:02.008468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:03.393161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.412565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.417682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.363195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.296847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:08.162257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:09.897224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:11.319184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:02.106905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:03.513182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.511231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.527995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.490659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.391787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:08.349678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:10.046025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:11.448354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:02.209743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:03.633403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.611162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.622779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.605230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.470547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:08.518695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:10.191001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:11.581028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:02.327547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:03.772763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.691705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.715408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.701398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.549509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:08.681842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:10.347209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:11.704836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:02.792471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:03.895572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.780125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.828188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.802575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.647668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:08.858697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:10.491916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:11.852379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:02.928536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.024044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:04.909604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:05.938462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:06.912943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:07.741015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:08.997395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:34:10.637436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:34:21.101084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9980.6930.8840.8220.2720.3890.3730.4370.2901.000
지점1.0001.0000.0001.0001.0001.0001.0000.6810.7790.7320.7600.7451.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.000
측정구간0.9981.0000.0001.0000.9981.0000.9980.6850.8020.7630.7710.7481.000
연장0.6931.0000.0000.9981.0000.7220.8640.2150.2880.1070.4570.1531.000
좌표위치위도0.8841.0000.0001.0000.7221.0000.8410.3020.3890.2600.3540.3771.000
좌표위치경도0.8221.0000.0000.9980.8640.8411.0000.3780.4780.4140.3310.4461.000
co0.2720.6810.0000.6850.2150.3020.3781.0000.9750.9240.8500.9960.681
nox0.3890.7790.1120.8020.2880.3890.4780.9751.0000.9040.9310.9760.779
hc0.3730.7320.0000.7630.1070.2600.4140.9240.9041.0000.8670.9080.732
pm0.4370.7600.0000.7710.4570.3540.3310.8500.9310.8671.0000.8480.760
co20.2900.7450.0000.7480.1530.3770.4460.9960.9760.9080.8481.0000.745
주소1.0001.0000.0001.0001.0001.0001.0000.6810.7790.7320.7600.7451.000
2023-12-10T22:34:21.278144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:34:21.422613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.1380.273-0.279-0.157-0.202-0.176-0.274-0.1660.0000.744
연장-0.1381.0000.098-0.1650.1320.1370.1280.1290.1420.0000.741
좌표위치위도0.2730.0981.000-0.1680.5340.5560.5550.4620.5380.0000.760
좌표위치경도-0.279-0.165-0.1681.0000.2150.1880.2170.1210.2000.0000.739
co-0.1570.1320.5340.2151.0000.9680.9850.7910.9960.0000.233
nox-0.2020.1370.5560.1880.9681.0000.9920.8900.9680.1040.325
hc-0.1760.1280.5550.2170.9850.9921.0000.8520.9800.0000.284
pm-0.2740.1290.4620.1210.7910.8900.8521.0000.7950.0000.321
co2-0.1660.1420.5380.2000.9960.9680.9800.7951.0000.0000.278
방향0.0000.0000.0000.0000.0000.1040.0000.0000.0001.0000.000
측정구간0.7440.7410.7600.7390.2330.3250.2840.3210.2780.0001.000

Missing values

2023-12-10T22:34:12.084939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:34:12.422371image/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

기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0122-2]1연무-논산11.520210101036.14511127.1050147.8862.296.884.3512084.29충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210101036.14511127.1050141.946.716.213.549414.57충남 논산 은진 토양
23건기연[0123-2]1두마-금남4.920210101036.37685127.25961139.6397.916.214.7129373.75충남 공주 반포 온천
34건기연[0123-2]2두마-금남4.920210101036.37685127.2596172.162.918.623.2918004.07충남 공주 반포 온천
45건기연[0124-0]1논산-반포10.220210101036.24966127.2285449.3833.635.11.8612905.77충남 논산 연산 천호
56건기연[0124-0]2논산-반포10.220210101036.24966127.2285473.6644.226.671.9519372.95충남 논산 연산 천호
67건기연[0127-2]1금남-조치원12.220210101036.56218127.28536123.9484.9412.384.029320.68충남 세종 연서 봉암
78건기연[0127-2]2금남-조치원12.220210101036.56218127.2853698.5782.4211.14.8626724.09충남 세종 연서 봉암
89건기연[0127-7]1공주-유성5.820210101036.40916127.2582119.520.113.01.384591.21충남 공주 반포 성강
910건기연[0127-7]2공주-유성5.820210101036.40916127.2582120.0122.653.141.094304.21충남 공주 반포 성강
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3602-0]1보령-청양9.320210101036.39222126.6679615.938.81.370.144207.42충남 보령 청라 나원
9192건기연[3602-0]2보령-청양9.320210101036.39222126.6679633.2117.443.050.47916.49충남 보령 청라 나원
9293건기연[3604-3]1청양-정산14.620210101036.46306126.8459316.248.591.360.04299.23충남 청양 대치 시전
9394건기연[3604-3]2청양-정산14.620210101036.46306126.8459346.8922.484.620.419781.27충남 청양 대치 시전
9495건기연[3606-0]1공주-어진동4.420210101036.48872127.2016763.9735.025.490.5316953.03충남 세종 장군 은용
9596건기연[3606-0]2공주-어진동4.420210101036.48872127.20167101.5951.889.20.5624101.86충남 세종 장군 은용
9697건기연[3706-0]1진천-음성9.320210101036.1228127.4971730.6116.162.820.47301.76충남 금산 군북 내부
9798건기연[3706-0]2진천-음성9.320210101036.1228127.4971751.7928.064.820.8412332.09충남 금산 군북 내부
9899건기연[3707-0]1추부-군서1.820210101036.21913127.4954412.487.361.120.283283.48충남 금산 추부 요광
99100건기연[3707-0]2추부-군서1.820210101036.21913127.495446.453.720.570.131705.45충남 금산 추부 요광