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
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
co is highly overall correlated with nox and 3 other fieldsHigh correlation
nox is highly overall correlated with co and 3 other fieldsHigh correlation
hc is highly overall correlated with co and 3 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co has unique valuesUnique
nox has unique valuesUnique
pm has unique valuesUnique
co2 has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:32:29.282955
Analysis finished2023-12-10 10:32:47.967667
Duration18.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

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
2023-12-10T19:32:48.190312image/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
2023-12-10T19:32:48.563638image/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

2023-12-10T19:32:48.826010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:32:48.986155image/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
2023-12-10T19:32:49.532080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.04
Min length8

Characters and Unicode

Total characters804
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[0114-1]
2nd row[0114-1]
3rd row[0115-1]
4th row[0115-1]
5th row[0116-2]
ValueCountFrequency (%)
0114-1 2
 
2.0%
2609-1 2
 
2.0%
3005-3 2
 
2.0%
2313-2 2
 
2.0%
2316-0 2
 
2.0%
2317-0 2
 
2.0%
2320-2 2
 
2.0%
2602-3 2
 
2.0%
2602-4 2
 
2.0%
2605-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T19:32:50.457726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 136
16.9%
0 104
12.9%
2 104
12.9%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
3 44
 
5.5%
7 34
 
4.2%
9 26
 
3.2%
6 24
 
3.0%
Other values (3) 32
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 504
62.7%
Open Punctuation 100
 
12.4%
Dash Punctuation 100
 
12.4%
Close Punctuation 100
 
12.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 136
27.0%
0 104
20.6%
2 104
20.6%
3 44
 
8.7%
7 34
 
6.7%
9 26
 
5.2%
6 24
 
4.8%
5 14
 
2.8%
4 12
 
2.4%
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 804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 136
16.9%
0 104
12.9%
2 104
12.9%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
3 44
 
5.5%
7 34
 
4.2%
9 26
 
3.2%
6 24
 
3.0%
Other values (3) 32
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 136
16.9%
0 104
12.9%
2 104
12.9%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
3 44
 
5.5%
7 34
 
4.2%
9 26
 
3.2%
6 24
 
3.0%
Other values (3) 32
 
4.0%

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

2023-12-10T19:32:50.810452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:32:51.009126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%
Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:32:51.510504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.14
Min length5

Characters and Unicode

Total characters514
Distinct characters82
Distinct categories3 ?
Distinct scripts3 ?
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 row태인-금구
2nd row태인-금구
3rd row정읍-태인
4th row정읍-태인
5th row금산-전주
ValueCountFrequency (%)
태인-금구 2
 
2.0%
천천-장계 2
 
2.0%
부안ic-화호 2
 
2.0%
고창-흥덕 2
 
2.0%
보안-부안 2
 
2.0%
부안-죽산 2
 
2.0%
군산-익산 2
 
2.0%
군산-대야 2
 
2.0%
군산-전주 2
 
2.0%
공덕-완주 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T19:32:52.244593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.5%
22
 
4.3%
22
 
4.3%
16
 
3.1%
14
 
2.7%
12
 
2.3%
12
 
2.3%
10
 
1.9%
10
 
1.9%
10
 
1.9%
Other values (72) 286
55.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
79.0%
Dash Punctuation 100
 
19.5%
Uppercase Letter 8
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
5.4%
22
 
5.4%
16
 
3.9%
14
 
3.4%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (69) 268
66.0%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
79.0%
Common 100
 
19.5%
Latin 8
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
5.4%
22
 
5.4%
16
 
3.9%
14
 
3.4%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (69) 268
66.0%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
79.0%
ASCII 108
 
21.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
22
 
5.4%
22
 
5.4%
16
 
3.9%
14
 
3.4%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (69) 268
66.0%

연장
Real number (ℝ)

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.122
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:32:52.546760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.3
median6.15
Q38.7
95-th percentile14.6
Maximum18.9
Range18
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation4.0080369
Coefficient of variation (CV)0.56276845
Kurtosis0.63460065
Mean7.122
Median Absolute Deviation (MAD)2.45
Skewness0.94936942
Sum712.2
Variance16.06436
MonotonicityNot monotonic
2023-12-10T19:32:52.832026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
6.0 4
 
4.0%
3.2 4
 
4.0%
4.9 4
 
4.0%
2.4 4
 
4.0%
6.5 4
 
4.0%
5.4 4
 
4.0%
8.0 4
 
4.0%
8.7 4
 
4.0%
6.3 2
 
2.0%
7.5 2
 
2.0%
Other values (32) 64
64.0%
ValueCountFrequency (%)
0.9 2
2.0%
1.0 2
2.0%
2.4 4
4.0%
2.7 2
2.0%
3.2 4
4.0%
3.3 2
2.0%
3.4 2
2.0%
3.6 2
2.0%
3.7 2
2.0%
4.1 2
2.0%
ValueCountFrequency (%)
18.9 2
2.0%
17.3 2
2.0%
14.6 2
2.0%
13.8 2
2.0%
13.0 2
2.0%
12.9 2
2.0%
11.9 2
2.0%
11.7 2
2.0%
11.5 2
2.0%
11.1 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

2023-12-10T19:32:53.159721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:32:53.481296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 100
100.0%

측정시간
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

2023-12-10T19:32:53.754143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:32:53.936038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.698112
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:32:54.325352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38415
Q135.52961
median35.72265
Q335.87978
95-th percentile35.97701
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.35017

Descriptive statistics

Standard deviation0.194329
Coefficient of variation (CV)0.0054436772
Kurtosis-1.0353721
Mean35.698112
Median Absolute Deviation (MAD)0.16868
Skewness-0.16909629
Sum3569.8112
Variance0.037763758
MonotonicityNot monotonic
2023-12-10T19:32:54.683437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.66929 2
 
2.0%
35.71626 2
 
2.0%
35.69967 2
 
2.0%
35.75539 2
 
2.0%
35.97701 2
 
2.0%
35.9615 2
 
2.0%
35.9389 2
 
2.0%
35.87978 2
 
2.0%
35.85422 2
 
2.0%
35.77224 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
35.31836 2
2.0%
35.36379 2
2.0%
35.38415 2
2.0%
35.40351 2
2.0%
35.41493 2
2.0%
35.42787 2
2.0%
35.44376 2
2.0%
35.44964 2
2.0%
35.467 2
2.0%
35.48235 2
2.0%
ValueCountFrequency (%)
36.05245 2
2.0%
35.97732 2
2.0%
35.97701 2
2.0%
35.97553 2
2.0%
35.9615 2
2.0%
35.9389 2
2.0%
35.9258 2
2.0%
35.91702 2
2.0%
35.91078 2
2.0%
35.9058 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.09958
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:32:55.075070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5004
5-th percentile126.64598
Q1126.88133
median127.06941
Q3127.31509
95-th percentile127.59682
Maximum127.67801
Range1.17761
Interquartile range (IQR)0.43376

Descriptive statistics

Standard deviation0.29977612
Coefficient of variation (CV)0.0023585925
Kurtosis-0.79550916
Mean127.09958
Median Absolute Deviation (MAD)0.215435
Skewness0.20628666
Sum12709.958
Variance0.089865724
MonotonicityNot monotonic
2023-12-10T19:32:55.579671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.96828 2
 
2.0%
127.11512 2
 
2.0%
126.69676 2
 
2.0%
126.75919 2
 
2.0%
126.91023 2
 
2.0%
126.77112 2
 
2.0%
126.84434 2
 
2.0%
126.98112 2
 
2.0%
127.21711 2
 
2.0%
127.4985 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.5004 2
2.0%
126.59317 2
2.0%
126.64598 2
2.0%
126.69676 2
2.0%
126.6981 2
2.0%
126.75919 2
2.0%
126.77112 2
2.0%
126.77892 2
2.0%
126.7879 2
2.0%
126.83701 2
2.0%
ValueCountFrequency (%)
127.67801 2
2.0%
127.65033 2
2.0%
127.59682 2
2.0%
127.57067 2
2.0%
127.55201 2
2.0%
127.53885 2
2.0%
127.53076 2
2.0%
127.52057 2
2.0%
127.4985 2
2.0%
127.42317 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2577.4416
Minimum36.23
Maximum13695.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:32:56.102233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.23
5-th percentile122.0835
Q1651.0375
median1805.02
Q34131.99
95-th percentile7337.279
Maximum13695.22
Range13658.99
Interquartile range (IQR)3480.9525

Descriptive statistics

Standard deviation2580.0681
Coefficient of variation (CV)1.001019
Kurtosis5.2722839
Mean2577.4416
Median Absolute Deviation (MAD)1336.655
Skewness1.9368154
Sum257744.16
Variance6656751.6
MonotonicityNot monotonic
2023-12-10T19:32:56.403449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3961.66 1
 
1.0%
2429.08 1
 
1.0%
495.98 1
 
1.0%
433.62 1
 
1.0%
434.36 1
 
1.0%
522.59 1
 
1.0%
593.24 1
 
1.0%
7336.47 1
 
1.0%
6687.07 1
 
1.0%
661.8 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
36.23 1
1.0%
49.51 1
1.0%
105.64 1
1.0%
106.2 1
1.0%
115.31 1
1.0%
122.44 1
1.0%
174.05 1
1.0%
182.74 1
1.0%
192.21 1
1.0%
216.42 1
1.0%
ValueCountFrequency (%)
13695.22 1
1.0%
13607.97 1
1.0%
7377.73 1
1.0%
7365.08 1
1.0%
7352.65 1
1.0%
7336.47 1
1.0%
7133.14 1
1.0%
6992.28 1
1.0%
6687.07 1
1.0%
6267.62 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.1286
Minimum27.77
Maximum13455.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:32:57.040107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.77
5-th percentile133.7465
Q1560.57
median1483.015
Q33042.415
95-th percentile7804.4865
Maximum13455.9
Range13428.13
Interquartile range (IQR)2481.845

Descriptive statistics

Standard deviation2647.0974
Coefficient of variation (CV)1.1084401
Kurtosis5.1009697
Mean2388.1286
Median Absolute Deviation (MAD)1102.025
Skewness2.1110491
Sum238812.86
Variance7007124.9
MonotonicityNot monotonic
2023-12-10T19:32:57.393668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3029.92 1
 
1.0%
2701.75 1
 
1.0%
380.37 1
 
1.0%
398.5 1
 
1.0%
397.48 1
 
1.0%
384.84 1
 
1.0%
584.36 1
 
1.0%
8113.55 1
 
1.0%
7577.96 1
 
1.0%
717.3 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
27.77 1
1.0%
47.84 1
1.0%
74.24 1
1.0%
95.2 1
1.0%
126.84 1
1.0%
134.11 1
1.0%
138.57 1
1.0%
149.22 1
1.0%
149.8 1
1.0%
155.08 1
1.0%
ValueCountFrequency (%)
13455.9 1
1.0%
13260.63 1
1.0%
8787.39 1
1.0%
8133.86 1
1.0%
8113.55 1
1.0%
7788.22 1
1.0%
7577.96 1
1.0%
7572.9 1
1.0%
7434.48 1
1.0%
7395.61 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.3629
Minimum3.64
Maximum1631.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:32:57.652265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.64
5-th percentile13.224
Q168.62
median204.83
Q3411.4525
95-th percentile908.98
Maximum1631.58
Range1627.94
Interquartile range (IQR)342.8325

Descriptive statistics

Standard deviation312.88501
Coefficient of variation (CV)1.0486726
Kurtosis5.1197022
Mean298.3629
Median Absolute Deviation (MAD)149.57
Skewness2.0253374
Sum29836.29
Variance97897.027
MonotonicityNot monotonic
2023-12-10T19:32:57.952721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.99 2
 
2.0%
412.48 1
 
1.0%
875.23 1
 
1.0%
51.91 1
 
1.0%
45.5 1
 
1.0%
45.43 1
 
1.0%
51.97 1
 
1.0%
998.48 1
 
1.0%
895.61 1
 
1.0%
82.55 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
3.64 1
1.0%
5.73 1
1.0%
10.39 1
1.0%
12.24 1
1.0%
12.92 1
1.0%
13.24 1
1.0%
17.24 1
1.0%
18.46 1
1.0%
19.33 1
1.0%
20.7 1
1.0%
ValueCountFrequency (%)
1631.58 1
1.0%
1592.58 1
1.0%
998.48 1
1.0%
988.58 1
1.0%
951.16 1
1.0%
906.76 1
1.0%
895.61 1
1.0%
875.23 1
1.0%
790.49 1
1.0%
773.86 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.611
Minimum2.14
Maximum616.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:32:58.225612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.14
5-th percentile7.62
Q133.425
median78.42
Q3151.545
95-th percentile494.3755
Maximum616.19
Range614.05
Interquartile range (IQR)118.12

Descriptive statistics

Standard deviation142.99133
Coefficient of variation (CV)1.1205251
Kurtosis2.9056499
Mean127.611
Median Absolute Deviation (MAD)53.36
Skewness1.8888884
Sum12761.1
Variance20446.519
MonotonicityNot monotonic
2023-12-10T19:32:58.569086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.16 1
 
1.0%
151.05 1
 
1.0%
25.69 1
 
1.0%
24.7 1
 
1.0%
24.25 1
 
1.0%
16.25 1
 
1.0%
36.35 1
 
1.0%
500.02 1
 
1.0%
476.71 1
 
1.0%
49.74 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2.14 1
1.0%
4.44 1
1.0%
6.07 1
1.0%
6.4 1
1.0%
7.24 1
1.0%
7.64 1
1.0%
8.17 1
1.0%
8.31 1
1.0%
8.6 1
1.0%
11.12 1
1.0%
ValueCountFrequency (%)
616.19 1
1.0%
569.87 1
1.0%
508.89 1
1.0%
500.02 1
1.0%
496.38 1
1.0%
494.27 1
1.0%
493.29 1
1.0%
476.71 1
1.0%
469.19 1
1.0%
431.17 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean667376.17
Minimum9460.44
Maximum3643475.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:32:58.831973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9460.44
5-th percentile29034.233
Q1163001.31
median456290.55
Q31086591.5
95-th percentile1833967.4
Maximum3643475.2
Range3634014.8
Interquartile range (IQR)923590.16

Descriptive statistics

Standard deviation680164.87
Coefficient of variation (CV)1.0191627
Kurtosis5.5872532
Mean667376.17
Median Absolute Deviation (MAD)343880.89
Skewness1.9915595
Sum66737617
Variance4.6262425 × 1011
MonotonicityNot monotonic
2023-12-10T19:32:59.091449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016296.07 1
 
1.0%
602157.17 1
 
1.0%
118550.32 1
 
1.0%
111252.64 1
 
1.0%
111505.57 1
 
1.0%
135180.41 1
 
1.0%
149986.39 1
 
1.0%
1830376.51 1
 
1.0%
1708416.19 1
 
1.0%
164158.84 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
9460.44 1
1.0%
11825.7 1
1.0%
26484.63 1
1.0%
27748.68 1
1.0%
28889.14 1
1.0%
29041.87 1
1.0%
45157.65 1
1.0%
47163.23 1
1.0%
50124.46 1
1.0%
55986.01 1
1.0%
ValueCountFrequency (%)
3643475.23 1
1.0%
3599264.76 1
1.0%
2002357.71 1
1.0%
1978997.99 1
1.0%
1902193.68 1
1.0%
1830376.51 1
1.0%
1821581.46 1
1.0%
1758091.09 1
1.0%
1708416.19 1
1.0%
1646018.78 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:32:59.763604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.96
Min length8

Characters and Unicode

Total characters1096
Distinct characters104
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 row전북 정읍 옹동 오성
2nd row전북 정읍 옹동 오성
3rd row전북 정읍 정우 우산
4th row전북 정읍 정우 우산
5th row전북 김제 금구 대화
ValueCountFrequency (%)
전북 100
25.1%
순창 14
 
3.5%
장수 12
 
3.0%
완주 12
 
3.0%
김제 10
 
2.5%
정읍 10
 
2.5%
임실 8
 
2.0%
부안 8
 
2.0%
군산 6
 
1.5%
무주 6
 
1.5%
Other values (96) 212
53.3%
2023-12-10T19:33:00.742027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
102
 
9.3%
100
 
9.1%
34
 
3.1%
24
 
2.2%
22
 
2.0%
18
 
1.6%
18
 
1.6%
16
 
1.5%
16
 
1.5%
Other values (94) 448
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 798
72.8%
Space Separator 298
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
34
 
4.3%
24
 
3.0%
22
 
2.8%
18
 
2.3%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (93) 432
54.1%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 798
72.8%
Common 298
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
34
 
4.3%
24
 
3.0%
22
 
2.8%
18
 
2.3%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (93) 432
54.1%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 798
72.8%
ASCII 298
 
27.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
34
 
4.3%
24
 
3.0%
22
 
2.8%
18
 
2.3%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (93) 432
54.1%

Interactions

2023-12-10T19:32:45.243283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:30.283305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:31.955716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:34.113588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:35.964633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:37.615492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:39.472848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:41.277251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:43.058141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:45.406557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:30.455076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:32.117817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:34.304157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:36.116112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:37.801444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:39.639025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:41.466595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:43.232951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:45.616740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:30.629775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:32.710273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:34.493873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:36.301467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:38.166941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:39.808232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:41.657039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:43.942567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:45.823471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:30.802779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:32.974538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:34.739443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:36.519791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:38.384982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:39.980622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:41.848999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:44.126180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:45.996496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:30.970267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:33.147753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:34.933748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:36.695666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:38.563690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:40.146362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:42.038319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:44.298643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:46.250663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:31.129810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:33.307028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:35.121411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:36.873060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:38.722779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:40.338813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:42.243015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:44.465650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:46.450057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:31.313243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:33.489980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:35.314013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:37.054851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:38.914580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:40.530417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:42.428411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:44.644179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:46.686750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:31.479013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:33.705573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:35.546954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:37.243282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:39.105279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:40.733539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:42.630937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:44.869497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:46.859566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:31.626055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:33.912675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:35.770546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:37.411437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:39.274461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:40.945105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:42.819316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:32:45.055853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:33:01.185184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.5740.8220.8820.5170.5620.6140.6720.5641.000
지점1.0001.0000.0001.0001.0001.0001.0000.9800.9920.9770.9250.9861.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9800.9920.9770.9250.9861.000
연장0.5741.0000.0001.0001.0000.5010.5210.4380.5110.4990.3510.4661.000
좌표위치위도0.8221.0000.0001.0000.5011.0000.8020.5830.5300.5470.5240.5991.000
좌표위치경도0.8821.0000.0001.0000.5210.8021.0000.4000.4480.4460.0000.4321.000
co0.5170.9800.0000.9800.4380.5830.4001.0000.8860.8980.8860.9990.980
nox0.5620.9920.0000.9920.5110.5300.4480.8861.0000.9780.9200.9080.992
hc0.6140.9770.0000.9770.4990.5470.4460.8980.9781.0000.8960.8900.977
pm0.6720.9250.0000.9250.3510.5240.0000.8860.9200.8961.0000.8790.925
co20.5640.9860.0000.9860.4660.5990.4320.9990.9080.8900.8791.0000.986
주소1.0001.0000.0001.0001.0001.0001.0000.9800.9920.9770.9250.9861.000
2023-12-10T19:33:01.506762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.0000.0240.054-0.386-0.042-0.039-0.041-0.094-0.0410.000
연장0.0241.000-0.001-0.103-0.080-0.057-0.063-0.073-0.0760.000
좌표위치위도0.054-0.0011.000-0.0230.3980.4270.4200.4070.4020.000
좌표위치경도-0.386-0.103-0.0231.000-0.295-0.279-0.293-0.233-0.2920.000
co-0.042-0.0800.398-0.2951.0000.9850.9900.9380.9980.000
nox-0.039-0.0570.427-0.2790.9851.0000.9960.9720.9830.000
hc-0.041-0.0630.420-0.2930.9900.9961.0000.9620.9880.000
pm-0.094-0.0730.407-0.2330.9380.9720.9621.0000.9340.000
co2-0.041-0.0760.402-0.2920.9980.9830.9880.9341.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T19:32:47.180267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:32:47.720111image/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

기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0114-1]1태인-금구11.120210101035.66929126.968283961.663029.92412.48130.161016296.07전북 정읍 옹동 오성
12건기연[0114-1]2태인-금구11.120210101035.66929126.968283516.32669.41357.84119.76906307.92전북 정읍 옹동 오성
23건기연[0115-1]1정읍-태인6.420210101035.62947126.903444609.443496.58480.75167.091188602.95전북 정읍 정우 우산
34건기연[0115-1]2정읍-태인6.420210101035.62947126.903444489.173580.32484.66209.731149397.22전북 정읍 정우 우산
45건기연[0116-2]1금산-전주4.320210101035.78758127.03517352.657572.9988.58493.291821581.46전북 김제 금구 대화
56건기연[0116-2]2금산-전주4.320210101035.78758127.03517133.147395.61951.16494.271758091.09전북 김제 금구 대화
67건기연[0117-3]1김제IC-전주5.120210101035.79995127.058226992.286651.97790.49317.71902193.68전북 완주 이서 이성
78건기연[0117-3]2김제IC-전주5.120210101035.79995127.058226267.625544.98719.92250.331646018.78전북 완주 이서 이성
89건기연[0120-1]1전주-삼례3.220210101035.91078127.05472746.522691.28392.91139.62624551.03전북 완주 삼례 후정
910건기연[0120-1]2전주-삼례3.220210101035.91078127.05472686.982151.88314.2105.02675591.77전북 완주 삼례 후정
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2911-0]1화호-김제11.720210101035.7375126.83701343.48403.2159.3132.2778452.29전북 김제 부량 대평
9192건기연[2911-0]2화호-김제11.720210101035.7375126.83701336.8478.9767.7234.0872196.04전북 김제 부량 대평
9293건기연[2912-1]1만경-백산4.620210101035.84681126.849011904.981608.95236.981.15472631.02전북 김제 만경 대동
9394건기연[2912-1]2만경-백산4.620210101035.84681126.849011772.251716.07209.5174.45476324.24전북 김제 만경 대동
9495건기연[3003-0]1변산-하서3.620210101035.72216126.645984463.722735.97409.5560.041167923.96전북 부안 하서 청호
9596건기연[3003-0]2변산-하서3.620210101035.72216126.645983724.32396.47342.7789.69975059.9전북 부안 하서 청호
9697건기연[3005-3]1부안IC-화호3.720210101035.72314126.78791279.54865.78125.7537.18332530.13전북 부안 백산 덕신
9798건기연[3005-3]2부안IC-화호3.720210101035.72314126.78791262.41003.38137.3749.43327254.58전북 부안 백산 덕신
9899건기연[3006-1]1신태인-태인6.520210101035.68032126.915271668.581427.61187.4776.57440445.03전북 정읍 신태인 궁사
99100건기연[3006-1]2신태인-태인6.520210101035.68032126.915271507.321252.53171.4470.23392554.56전북 정읍 신태인 궁사