Overview

Dataset statistics

Number of variables9
Number of observations149
Missing cells149
Missing cells (%)11.1%
Duplicate rows1
Duplicate rows (%)0.7%
Total size in memory11.5 KiB
Average record size in memory78.9 B

Variable types

Categorical6
Unsupported1
Numeric2

Dataset

DescriptionSample
Author(주)넥스트이지
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=NXEVISITRFUSN

Alerts

제주방문 has constant value ""Constant
제주공항 has constant value ""Constant
20200825 has constant value ""Constant
50 has constant value ""Constant
Dataset has 1 (0.7%) duplicate rowsDuplicates
00 is highly overall correlated with 322High correlation
322 is highly overall correlated with 00High correlation
Unnamed: 2 has 149 (100.0%) missing valuesMissing
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
00 has 27 (18.1%) zerosZeros

Reproduction

Analysis started2023-12-10 06:37:39.429728
Analysis finished2023-12-10 06:37:40.605630
Duration1.18 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제주방문
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
제주방문
149 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주방문 149
100.0%

Length

2023-12-10T15:37:40.723886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:40.968960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주방문 149
100.0%

제주공항
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
제주공항
149 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주공항 149
100.0%

Length

2023-12-10T15:37:41.126475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:41.377189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주공항 149
100.0%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing149
Missing (%)100.0%
Memory size1.4 KiB

20200825
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
20200825
149 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200825
2nd row20200825
3rd row20200825
4th row20200825
5th row20200825

Common Values

ValueCountFrequency (%)
20200825 149
100.0%

Length

2023-12-10T15:37:41.556902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:41.777172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200825 149
100.0%

00
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2147651
Minimum0
Maximum5
Zeros27
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:37:41.926569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5577347
Coefficient of variation (CV)0.7033408
Kurtosis-1.1407821
Mean2.2147651
Median Absolute Deviation (MAD)1
Skewness0.081787505
Sum330
Variance2.4265373
MonotonicityIncreasing
2023-12-10T15:37:42.131520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 28
18.8%
2 28
18.8%
3 28
18.8%
4 28
18.8%
0 27
18.1%
5 10
 
6.7%
ValueCountFrequency (%)
0 27
18.1%
1 28
18.8%
2 28
18.8%
3 28
18.8%
4 28
18.8%
5 10
 
6.7%
ValueCountFrequency (%)
5 10
 
6.7%
4 28
18.8%
3 28
18.8%
2 28
18.8%
1 28
18.8%
0 27
18.1%

10-
Categorical

Distinct8
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
10
22 
20
22 
30
22 
40
22 
50
21 
Other values (3)
40 

Length

Max length3
Median length2
Mean length2.0671141
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row20
4th row20
5th row30

Common Values

ValueCountFrequency (%)
10 22
14.8%
20 22
14.8%
30 22
14.8%
40 22
14.8%
50 21
14.1%
60 20
13.4%
70 10
6.7%
10- 10
6.7%

Length

2023-12-10T15:37:42.339269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:42.534684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 32
21.5%
20 22
14.8%
30 22
14.8%
40 22
14.8%
50 21
14.1%
60 20
13.4%
70 10
 
6.7%

2
Categorical

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2
79 
1
70 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 79
53.0%
1 70
47.0%

Length

2023-12-10T15:37:42.752014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:42.923150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 79
53.0%
1 70
47.0%

50
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
50
149 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row50

Common Values

ValueCountFrequency (%)
50 149
100.0%

Length

2023-12-10T15:37:43.189014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:43.349703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50 149
100.0%

322
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378.12752
Minimum9
Maximum2237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:37:43.546891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile26.8
Q197
median211
Q3373
95-th percentile1582.4
Maximum2237
Range2228
Interquartile range (IQR)276

Descriptive statistics

Standard deviation474.22931
Coefficient of variation (CV)1.2541518
Kurtosis4.3238596
Mean378.12752
Median Absolute Deviation (MAD)121
Skewness2.2321762
Sum56341
Variance224893.44
MonotonicityNot monotonic
2023-12-10T15:37:43.761361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265 3
 
2.0%
60 2
 
1.3%
237 2
 
1.3%
125 2
 
1.3%
54 2
 
1.3%
18 2
 
1.3%
187 2
 
1.3%
526 2
 
1.3%
140 2
 
1.3%
155 2
 
1.3%
Other values (124) 128
85.9%
ValueCountFrequency (%)
9 1
0.7%
13 1
0.7%
16 1
0.7%
18 2
1.3%
19 1
0.7%
22 1
0.7%
24 1
0.7%
31 1
0.7%
46 1
0.7%
48 2
1.3%
ValueCountFrequency (%)
2237 1
0.7%
2093 1
0.7%
1891 1
0.7%
1855 1
0.7%
1828 1
0.7%
1816 1
0.7%
1598 1
0.7%
1586 1
0.7%
1577 1
0.7%
1549 1
0.7%

Interactions

2023-12-10T15:37:40.006893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:39.704406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:40.137326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:39.873427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:37:43.917046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
0010-2322
001.0000.0000.2470.556
10-0.0001.0000.0000.482
20.2470.0001.0000.343
3220.5560.4820.3431.000
2023-12-10T15:37:44.148546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10-2
10-1.0000.000
20.0001.000
2023-12-10T15:37:44.280999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
0032210-2
001.000-0.5340.0000.174
322-0.5341.0000.2520.255
10-0.0000.2521.0000.000
20.1740.2550.0001.000

Missing values

2023-12-10T15:37:40.312438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:37:40.521604image/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

제주방문제주공항Unnamed: 2202008250010-250322
0제주방문제주공항<NA>20200825010250604
1제주방문제주공항<NA>20200825010250677
2제주방문제주공항<NA>202008250202501855
3제주방문제주공항<NA>202008250202502093
4제주방문제주공항<NA>202008250302501586
5제주방문제주공항<NA>202008250302501577
6제주방문제주공항<NA>202008250402501343
7제주방문제주공항<NA>202008250402501297
8제주방문제주공항<NA>202008250502501252
9제주방문제주공항<NA>202008250502501080
제주방문제주공항Unnamed: 2202008250010-250322
139제주방문제주공항<NA>20200825510-25019
140제주방문제주공항<NA>2020082551025060
141제주방문제주공항<NA>2020082551025087
142제주방문제주공항<NA>20200825520250268
143제주방문제주공항<NA>20200825520250303
144제주방문제주공항<NA>20200825530250231
145제주방문제주공항<NA>20200825530250210
146제주방문제주공항<NA>20200825540250197
147제주방문제주공항<NA>20200825540250193
148제주방문제주공항<NA>20200825550250190

Duplicate rows

Most frequently occurring

제주방문제주공항202008250010-250322# duplicates
0제주방문제주공항202008251202505262