Overview

Dataset statistics

Number of variables4
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory36.9 B

Variable types

Categorical1
Text1
Numeric2

Alerts

에스컬레이터 외부 is highly overall correlated with 호선High correlation
호선 is highly overall correlated with 에스컬레이터 외부High correlation
에스컬레이터 외부 has 12 (17.6%) zerosZeros
에스컬레이터내부 has 5 (7.4%) zerosZeros

Reproduction

Analysis started2024-03-18 05:23:23.796333
Analysis finished2024-03-18 05:23:24.423010
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

호선
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size676.0 B
1
30 
2
27 
7
11 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 30
44.1%
2 27
39.7%
7 11
 
16.2%

Length

2024-03-18T14:23:24.498570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:23:24.624186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
44.1%
2 27
39.7%
7 11
 
16.2%

역사명
Text

Distinct66
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size676.0 B
2024-03-18T14:23:24.845301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6.5
Mean length4.0882353
Min length2

Characters and Unicode

Total characters278
Distinct characters122
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)94.1%

Sample

1st row계 양
2nd row귤 현
3rd row박 촌
4th row임 학
5th row계 산
ValueCountFrequency (%)
동 3
 
3.6%
인천시청 2
 
2.4%
부평구청 2
 
2.4%
수 2
 
2.4%
학 2
 
2.4%
계 2
 
2.4%
산 2
 
2.4%
가재울 1
 
1.2%
가정(루원시티 1
 
1.2%
가정중앙시장 1
 
1.2%
Other values (66) 66
78.6%
2024-03-18T14:23:25.186684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
5.8%
시 11
 
4.0%
천 10
 
3.6%
인 8
 
2.9%
장 8
 
2.9%
부 8
 
2.9%
청 7
 
2.5%
동 7
 
2.5%
구 6
 
2.2%
가 5
 
1.8%
Other values (112) 192
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
92.1%
Space Separator 16
 
5.8%
Close Punctuation 3
 
1.1%
Open Punctuation 3
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 11
 
4.3%
천 10
 
3.9%
인 8
 
3.1%
장 8
 
3.1%
부 8
 
3.1%
청 7
 
2.7%
동 7
 
2.7%
구 6
 
2.3%
가 5
 
2.0%
원 5
 
2.0%
Other values (109) 181
70.7%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
92.1%
Common 22
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 11
 
4.3%
천 10
 
3.9%
인 8
 
3.1%
장 8
 
3.1%
부 8
 
3.1%
청 7
 
2.7%
동 7
 
2.7%
구 6
 
2.3%
가 5
 
2.0%
원 5
 
2.0%
Other values (109) 181
70.7%
Common
ValueCountFrequency (%)
16
72.7%
) 3
 
13.6%
( 3
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
92.1%
ASCII 22
 
7.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16
72.7%
) 3
 
13.6%
( 3
 
13.6%
Hangul
ValueCountFrequency (%)
시 11
 
4.3%
천 10
 
3.9%
인 8
 
3.1%
장 8
 
3.1%
부 8
 
3.1%
청 7
 
2.7%
동 7
 
2.7%
구 6
 
2.3%
가 5
 
2.0%
원 5
 
2.0%
Other values (109) 181
70.7%

에스컬레이터 외부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0882353
Minimum0
Maximum10
Zeros12
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-03-18T14:23:25.299360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q34
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5900804
Coefficient of variation (CV)0.83869269
Kurtosis0.12386092
Mean3.0882353
Median Absolute Deviation (MAD)2
Skewness0.93543905
Sum210
Variance6.7085162
MonotonicityNot monotonic
2024-03-18T14:23:25.393762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 27
39.7%
4 12
17.6%
0 12
17.6%
8 7
 
10.3%
6 3
 
4.4%
5 2
 
2.9%
1 2
 
2.9%
9 1
 
1.5%
3 1
 
1.5%
10 1
 
1.5%
ValueCountFrequency (%)
0 12
17.6%
1 2
 
2.9%
2 27
39.7%
3 1
 
1.5%
4 12
17.6%
5 2
 
2.9%
6 3
 
4.4%
8 7
 
10.3%
9 1
 
1.5%
10 1
 
1.5%
ValueCountFrequency (%)
10 1
 
1.5%
9 1
 
1.5%
8 7
 
10.3%
6 3
 
4.4%
5 2
 
2.9%
4 12
17.6%
3 1
 
1.5%
2 27
39.7%
1 2
 
2.9%
0 12
17.6%

에스컬레이터내부
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8235294
Minimum0
Maximum20
Zeros5
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-03-18T14:23:25.493445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median5
Q38
95-th percentile10.65
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5699458
Coefficient of variation (CV)0.61302099
Kurtosis3.9496399
Mean5.8235294
Median Absolute Deviation (MAD)1
Skewness1.4406633
Sum396
Variance12.744513
MonotonicityNot monotonic
2024-03-18T14:23:25.602185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 26
38.2%
8 13
19.1%
6 11
16.2%
0 5
 
7.4%
10 4
 
5.9%
5 3
 
4.4%
2 2
 
2.9%
16 2
 
2.9%
11 1
 
1.5%
20 1
 
1.5%
ValueCountFrequency (%)
0 5
 
7.4%
2 2
 
2.9%
4 26
38.2%
5 3
 
4.4%
6 11
16.2%
8 13
19.1%
10 4
 
5.9%
11 1
 
1.5%
16 2
 
2.9%
20 1
 
1.5%
ValueCountFrequency (%)
20 1
 
1.5%
16 2
 
2.9%
11 1
 
1.5%
10 4
 
5.9%
8 13
19.1%
6 11
16.2%
5 3
 
4.4%
4 26
38.2%
2 2
 
2.9%
0 5
 
7.4%

Interactions

2024-03-18T14:23:24.100915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:23:23.948263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:23:24.191394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:23:24.020233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:23:25.683577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선역사명에스컬레이터 외부에스컬레이터내부
호선1.0000.0000.8830.452
역사명0.0001.0000.9740.671
에스컬레이터 외부0.8830.9741.0000.000
에스컬레이터내부0.4520.6710.0001.000
2024-03-18T14:23:25.779080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
에스컬레이터 외부에스컬레이터내부호선
에스컬레이터 외부1.0000.1510.607
에스컬레이터내부0.1511.0000.353
호선0.6070.3531.000

Missing values

2024-03-18T14:23:24.306238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:23:24.381421image/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

호선역사명에스컬레이터 외부에스컬레이터내부
01계 양40
11귤 현00
21박 촌40
31임 학04
41계 산26
51경인교대26
61작 전22
71갈 산24
81부평구청28
91부평시장24
호선역사명에스컬레이터 외부에스컬레이터내부
587부천종합운동장610
597춘의84
607신중동88
617부천시청84
627상동84
637삼산체육관104
647굴포천68
657부평구청411
667산곡86
677석남820