Overview

Dataset statistics

Number of variables16
Number of observations199
Missing cells130
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 KiB
Average record size in memory138.7 B

Variable types

Numeric8
Categorical8

Dataset

DescriptionSample
Author(재)인천테크노파크
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ICTCHNC000001

Alerts

인천 has constant value ""Constant
20210801 has constant value ""Constant
2020 has constant value ""Constant
중구 is highly overall correlated with 2811062000 and 1 other fieldsHigh correlation
여행업 is highly overall correlated with Unnamed: 2 and 4 other fieldsHigh correlation
Unnamed: 3 is highly overall correlated with Unnamed: 2 and 5 other fieldsHigh correlation
영종동 is highly overall correlated with 2811062000 and 1 other fieldsHigh correlation
T&E is highly overall correlated with Unnamed: 2 and 4 other fieldsHigh correlation
2811062000 is highly overall correlated with 중구 and 1 other fieldsHigh correlation
Unnamed: 2 is highly overall correlated with Unnamed: 3 and 2 other fieldsHigh correlation
0.083 is highly overall correlated with 0.00535714 and 1 other fieldsHigh correlation
0.00535714 is highly overall correlated with 0.083 and 2 other fieldsHigh correlation
0.0108303 is highly overall correlated with 0.083 and 1 other fieldsHigh correlation
10 is highly overall correlated with 11 and 3 other fieldsHigh correlation
11 is highly overall correlated with 10 and 3 other fieldsHigh correlation
Unnamed: 2 has 130 (65.3%) missing valuesMissing

Reproduction

Analysis started2023-12-10 06:23:57.621389
Analysis finished2023-12-10 06:24:10.498119
Duration12.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2811062000
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8123097 × 109
Minimum2.811062 × 109
Maximum2.818583 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:10.593577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.811062 × 109
5-th percentile2.8110622 × 109
Q12.8110628 × 109
median2.8110628 × 109
Q32.8110628 × 109
95-th percentile2.818582 × 109
Maximum2.818583 × 109
Range7521000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2803755.7
Coefficient of variation (CV)0.00099695836
Kurtosis1.2913159
Mean2.8123097 × 109
Median Absolute Deviation (MAD)0
Skewness1.8106459
Sum5.5964963 × 1011
Variance7.8610459 × 1012
MonotonicityIncreasing
2023-12-10T15:24:10.796680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2811062800 140
70.4%
2818582000 25
 
12.6%
2811062200 11
 
5.5%
2811063000 11
 
5.5%
2818583000 8
 
4.0%
2811062000 4
 
2.0%
ValueCountFrequency (%)
2811062000 4
 
2.0%
2811062200 11
 
5.5%
2811062800 140
70.4%
2811063000 11
 
5.5%
2818582000 25
 
12.6%
2818583000 8
 
4.0%
ValueCountFrequency (%)
2818583000 8
 
4.0%
2818582000 25
 
12.6%
2811063000 11
 
5.5%
2811062800 140
70.4%
2811062200 11
 
5.5%
2811062000 4
 
2.0%

인천
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
인천
199 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천 199
100.0%

Length

2023-12-10T15:24:11.033000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:11.181399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천 199
100.0%

Unnamed: 2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)8.7%
Missing130
Missing (%)65.3%
Infinite0
Infinite (%)0.0%
Mean3877.7826
Minimum2101
Maximum4040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:11.309211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2101
5-th percentile2199
Q14004
median4010
Q34010
95-th percentile4040
Maximum4040
Range1939
Interquartile range (IQR)6

Descriptive statistics

Standard deviation489.54706
Coefficient of variation (CV)0.12624407
Kurtosis9.6738978
Mean3877.7826
Median Absolute Deviation (MAD)6
Skewness-3.3725361
Sum267567
Variance239656.32
MonotonicityNot monotonic
2023-12-10T15:24:11.477345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4004 24
 
12.1%
4010 23
 
11.6%
4040 10
 
5.0%
4020 7
 
3.5%
2101 3
 
1.5%
2199 2
 
1.0%
(Missing) 130
65.3%
ValueCountFrequency (%)
2101 3
 
1.5%
2199 2
 
1.0%
4004 24
12.1%
4010 23
11.6%
4020 7
 
3.5%
4040 10
5.0%
ValueCountFrequency (%)
4040 10
5.0%
4020 7
 
3.5%
4010 23
11.6%
4004 24
12.1%
2199 2
 
1.0%
2101 3
 
1.5%

Unnamed: 3
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
130 
대형할인점
24 
편의점
23 
면세점
 
10
슈퍼마켓
 
7
Other values (2)
 
5

Length

Max length5
Median length4
Mean length3.9798995
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row편의점
5th row슈퍼마켓

Common Values

ValueCountFrequency (%)
<NA> 130
65.3%
대형할인점 24
 
12.1%
편의점 23
 
11.6%
면세점 10
 
5.0%
슈퍼마켓 7
 
3.5%
골프경기장 3
 
1.5%
기타레져업 2
 
1.0%

Length

2023-12-10T15:24:11.691384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:11.889247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 130
65.3%
대형할인점 24
 
12.1%
편의점 23
 
11.6%
면세점 10
 
5.0%
슈퍼마켓 7
 
3.5%
골프경기장 3
 
1.5%
기타레져업 2
 
1.0%

0.083
Real number (ℝ)

HIGH CORRELATION 

Distinct198
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9194884
Minimum0.0019
Maximum51.7771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:12.115886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0019
5-th percentile0.0056724
Q10.02379335
median0.0812267
Q30.2356445
95-th percentile7.843789
Maximum51.7771
Range51.7752
Interquartile range (IQR)0.21185115

Descriptive statistics

Standard deviation7.3770094
Coefficient of variation (CV)3.8432165
Kurtosis24.989335
Mean1.9194884
Median Absolute Deviation (MAD)0.06696
Skewness4.9390204
Sum381.97819
Variance54.420268
MonotonicityNot monotonic
2023-12-10T15:24:12.384897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00356667 2
 
1.0%
0.0784667 1
 
0.5%
0.006558 1
 
0.5%
0.024258 1
 
0.5%
0.131216 1
 
0.5%
0.00636667 1
 
0.5%
0.117568 1
 
0.5%
23.5437 1
 
0.5%
0.00970667 1
 
0.5%
0.218107 1
 
0.5%
Other values (188) 188
94.5%
ValueCountFrequency (%)
0.0019 1
0.5%
0.00196 1
0.5%
0.00262 1
0.5%
0.00356667 2
1.0%
0.00361333 1
0.5%
0.00372 1
0.5%
0.00375 1
0.5%
0.004 1
0.5%
0.005604 1
0.5%
0.00568 1
0.5%
ValueCountFrequency (%)
51.7771 1
0.5%
46.6713 1
0.5%
39.8327 1
0.5%
35.9012 1
0.5%
33.7671 1
0.5%
33.4023 1
0.5%
23.5437 1
0.5%
18.631 1
0.5%
15.9218 1
0.5%
11.7163 1
0.5%

0.00535714
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44322467
Minimum0.00535714
Maximum13.6607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:12.701819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.00535714
5-th percentile0.00535714
Q10.01339285
median0.0482143
Q30.2080355
95-th percentile2.120361
Maximum13.6607
Range13.655343
Interquartile range (IQR)0.19464265

Descriptive statistics

Standard deviation1.512484
Coefficient of variation (CV)3.4124545
Kurtosis53.555641
Mean0.44322467
Median Absolute Deviation (MAD)0.03928573
Skewness6.7884942
Sum88.201709
Variance2.2876078
MonotonicityNot monotonic
2023-12-10T15:24:13.054046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00535714 13
 
6.5%
0.0107143 11
 
5.5%
0.00714286 10
 
5.0%
0.0125 8
 
4.0%
0.00892857 8
 
4.0%
0.0214286 7
 
3.5%
0.075 5
 
2.5%
0.0142857 4
 
2.0%
0.0446429 4
 
2.0%
0.0160714 4
 
2.0%
Other values (94) 125
62.8%
ValueCountFrequency (%)
0.00535714 13
6.5%
0.00714286 10
5.0%
0.00892857 8
4.0%
0.0107143 11
5.5%
0.0125 8
4.0%
0.0142857 4
 
2.0%
0.0160714 4
 
2.0%
0.0178571 2
 
1.0%
0.0196429 4
 
2.0%
0.0214286 7
3.5%
ValueCountFrequency (%)
13.6607 1
0.5%
13.0696 1
0.5%
4.16964 1
0.5%
4.13214 1
0.5%
3.88571 1
0.5%
3.87143 1
0.5%
3.56607 1
0.5%
3.53929 1
0.5%
3.36071 1
0.5%
2.625 1
0.5%

0.0108303
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65772908
Minimum0.0108303
Maximum23.2816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:13.328696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0108303
5-th percentile0.0108303
Q10.0198556
median0.064982
Q30.299639
95-th percentile3.138264
Maximum23.2816
Range23.27077
Interquartile range (IQR)0.2797834

Descriptive statistics

Standard deviation2.4513646
Coefficient of variation (CV)3.7270126
Kurtosis64.587979
Mean0.65772908
Median Absolute Deviation (MAD)0.0541517
Skewness7.5802527
Sum130.88809
Variance6.0091882
MonotonicityNot monotonic
2023-12-10T15:24:13.537269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0108303 28
 
14.1%
0.0144404 15
 
7.5%
0.0288809 9
 
4.5%
0.0216607 7
 
3.5%
0.0180505 7
 
3.5%
0.0361011 6
 
3.0%
0.032491 5
 
2.5%
0.0252708 5
 
2.5%
0.064982 4
 
2.0%
0.0397112 4
 
2.0%
Other values (84) 109
54.8%
ValueCountFrequency (%)
0.0108303 28
14.1%
0.0144404 15
7.5%
0.0180505 7
 
3.5%
0.0216607 7
 
3.5%
0.0252708 5
 
2.5%
0.0288809 9
 
4.5%
0.032491 5
 
2.5%
0.0361011 6
 
3.0%
0.0397112 4
 
2.0%
0.0433213 3
 
1.5%
ValueCountFrequency (%)
23.2816 1
0.5%
21.8917 1
0.5%
6.72563 1
0.5%
6.70758 1
0.5%
5.24188 1
0.5%
4.42599 1
0.5%
4.15162 1
0.5%
4.04693 1
0.5%
3.74729 1
0.5%
3.37545 1
0.5%

20210801
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
20210801
199 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210801
2nd row20210801
3rd row20210801
4th row20210801
5th row20210801

Common Values

ValueCountFrequency (%)
20210801 199
100.0%

Length

2023-12-10T15:24:13.722494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:13.859457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210801 199
100.0%

중구
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
중구
166 
연수구
33 

Length

Max length3
Median length2
Mean length2.1658291
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 166
83.4%
연수구 33
 
16.6%

Length

2023-12-10T15:24:14.020268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:14.182015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 166
83.4%
연수구 33
 
16.6%

영종동
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
운서동
140 
송도1동
25 
영종1동
 
11
용유동
 
11
송도2동
 
8

Length

Max length4
Median length3
Mean length3.2211055
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영종동
2nd row영종동
3rd row영종동
4th row영종동
5th row영종1동

Common Values

ValueCountFrequency (%)
운서동 140
70.4%
송도1동 25
 
12.6%
영종1동 11
 
5.5%
용유동 11
 
5.5%
송도2동 8
 
4.0%
영종동 4
 
2.0%

Length

2023-12-10T15:24:14.339826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:14.516368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운서동 140
70.4%
송도1동 25
 
12.6%
영종1동 11
 
5.5%
용유동 11
 
5.5%
송도2동 8
 
4.0%
영종동 4
 
2.0%

2020
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2020
199 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 199
100.0%

Length

2023-12-10T15:24:14.696087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:14.857360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 199
100.0%

01
Real number (ℝ)

Distinct12
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4773869
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:15.031871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7319588
Coefficient of variation (CV)0.68133926
Kurtosis-1.3533451
Mean5.4773869
Median Absolute Deviation (MAD)3
Skewness0.30329353
Sum1090
Variance13.927516
MonotonicityNot monotonic
2023-12-10T15:24:15.241158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 36
18.1%
2 31
15.6%
3 16
8.0%
10 16
8.0%
9 15
7.5%
11 13
 
6.5%
8 13
 
6.5%
12 12
 
6.0%
4 12
 
6.0%
5 12
 
6.0%
Other values (2) 23
11.6%
ValueCountFrequency (%)
1 36
18.1%
2 31
15.6%
3 16
8.0%
4 12
 
6.0%
5 12
 
6.0%
6 11
 
5.5%
7 12
 
6.0%
8 13
 
6.5%
9 15
7.5%
10 16
8.0%
ValueCountFrequency (%)
12 12
6.0%
11 13
6.5%
10 16
8.0%
9 15
7.5%
8 13
6.5%
7 12
6.0%
6 11
5.5%
5 12
6.0%
4 12
6.0%
3 16
8.0%

10
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.01005
Minimum10
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:15.435429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q120
median30
Q340
95-th percentile85
Maximum99
Range89
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.175343
Coefficient of variation (CV)0.6453553
Kurtosis-0.52918294
Mean39.01005
Median Absolute Deviation (MAD)10
Skewness0.84611495
Sum7763
Variance633.79788
MonotonicityNot monotonic
2023-12-10T15:24:15.603470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
30 63
31.7%
40 37
18.6%
10 36
18.1%
80 29
14.6%
20 18
 
9.0%
85 13
 
6.5%
99 2
 
1.0%
50 1
 
0.5%
ValueCountFrequency (%)
10 36
18.1%
20 18
 
9.0%
30 63
31.7%
40 37
18.6%
50 1
 
0.5%
80 29
14.6%
85 13
 
6.5%
99 2
 
1.0%
ValueCountFrequency (%)
99 2
 
1.0%
85 13
 
6.5%
80 29
14.6%
50 1
 
0.5%
40 37
18.6%
30 63
31.7%
20 18
 
9.0%
10 36
18.1%

T&E
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
생활
63 
쇼핑
37 
T&E
36 
음식
29 
문화
18 
Other values (3)
16 

Length

Max length3
Median length2
Mean length2.1859296
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row생활
2nd row생활
3rd row쇼핑
4th row생활
5th row생활

Common Values

ValueCountFrequency (%)
생활 63
31.7%
쇼핑 37
18.6%
T&E 36
18.1%
음식 29
14.6%
문화 18
 
9.0%
유흥 13
 
6.5%
기타 2
 
1.0%
내구재 1
 
0.5%

Length

2023-12-10T15:24:15.790922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:15.994334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활 63
31.7%
쇼핑 37
18.6%
t&e 36
18.1%
음식 29
14.6%
문화 18
 
9.0%
유흥 13
 
6.5%
기타 2
 
1.0%
내구재 1
 
0.5%

11
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.201005
Minimum10
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:24:16.190230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q140
median40
Q380
95-th percentile86
Maximum91
Range81
Interquartile range (IQR)40

Descriptive statistics

Standard deviation26.058787
Coefficient of variation (CV)0.52963933
Kurtosis-1.3151568
Mean49.201005
Median Absolute Deviation (MAD)29
Skewness-0.0008503347
Sum9791
Variance679.0604
MonotonicityNot monotonic
2023-12-10T15:24:16.384713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
40 64
32.2%
80 29
14.6%
10 22
 
11.1%
70 15
 
7.5%
83 12
 
6.0%
86 11
 
5.5%
22 11
 
5.5%
11 9
 
4.5%
71 7
 
3.5%
50 7
 
3.5%
Other values (5) 12
 
6.0%
ValueCountFrequency (%)
10 22
 
11.1%
11 9
 
4.5%
21 5
 
2.5%
22 11
 
5.5%
31 1
 
0.5%
40 64
32.2%
44 3
 
1.5%
50 7
 
3.5%
61 1
 
0.5%
70 15
 
7.5%
ValueCountFrequency (%)
91 2
 
1.0%
86 11
 
5.5%
83 12
 
6.0%
80 29
14.6%
71 7
 
3.5%
70 15
 
7.5%
61 1
 
0.5%
50 7
 
3.5%
44 3
 
1.5%
40 64
32.2%

여행업
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
유통업영리
64 
일반음식
29 
숙박업
22 
의료기관
15 
음식료품
12 
Other values (10)
57 

Length

Max length8
Median length5
Mean length4.2110553
Min length2

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row보건/위생
2nd row의료기관
3rd row신변잡화
4th row유통업영리
5th row유통업영리

Common Values

ValueCountFrequency (%)
유통업영리 64
32.2%
일반음식 29
14.6%
숙박업 22
 
11.1%
의료기관 15
 
7.5%
음식료품 12
 
6.0%
휴게 11
 
5.5%
문화/취미 11
 
5.5%
여행업 9
 
4.5%
보건/위생 7
 
3.5%
서적/문구 7
 
3.5%
Other values (5) 12
 
6.0%

Length

2023-12-10T15:24:16.613617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
유통업영리 64
32.2%
일반음식 29
14.6%
숙박업 22
 
11.1%
의료기관 15
 
7.5%
음식료품 12
 
6.0%
휴게 11
 
5.5%
문화/취미 11
 
5.5%
여행업 9
 
4.5%
보건/위생 7
 
3.5%
서적/문구 7
 
3.5%
Other values (5) 12
 
6.0%

Interactions

2023-12-10T15:24:08.050083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:23:58.780306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:00.191017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:01.966979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:03.167500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:04.392301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:05.664494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:06.892808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:08.232103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:23:58.960361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:00.366285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:02.122075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:03.327649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:04.546088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:05.850174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:07.061347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:08.405976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:23:59.159239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:00.908479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:02.276429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:03.510307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:04.686498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:06.016599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:07.209550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:08.636500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:23:59.318613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:01.049356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:02.452348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:03.642796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:04.820244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:06.155751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:07.340488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:08.815551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:23:59.522821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:01.226169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:02.609178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:03.800969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:04.982497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:06.306613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:07.476463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:08.986974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:23:59.690352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:01.379471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:02.755625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:03.930959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:05.121203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:06.436064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:07.595747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:09.137220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:23:59.855374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:01.537113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:02.894704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:04.085808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:05.256933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:06.594963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:07.743109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:09.272020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:00.005191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:01.700863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:03.020642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:04.226356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:05.449485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:06.740919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:07.869940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:24:16.786170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2811062000Unnamed: 2Unnamed: 30.0830.005357140.0108303중구영종동0110T&E11여행업
28110620001.0000.0000.5220.0000.0000.0001.0001.0000.1150.3060.3060.2260.198
Unnamed: 20.0001.0001.0000.0000.0000.0000.0000.0000.0001.0001.0000.9160.916
Unnamed: 30.5221.0001.0000.5310.8830.7980.5260.5230.0001.0001.0001.0001.000
0.0830.0000.0000.5311.0000.8220.9380.0000.0000.2110.0500.0500.0000.000
0.005357140.0000.0000.8830.8221.0000.9010.0000.0000.0000.1960.1960.0000.000
0.01083030.0000.0000.7980.9380.9011.0000.0000.0000.0000.1390.1390.0000.000
중구1.0000.0000.5260.0000.0000.0001.0001.0000.0430.3020.3020.2270.201
영종동1.0000.0000.5230.0000.0000.0001.0001.0000.0000.2530.2530.2520.197
010.1150.0000.0000.2110.0000.0000.0430.0001.0000.0000.0000.0000.000
100.3061.0001.0000.0500.1960.1390.3020.2530.0001.0001.0000.9160.985
T&E0.3061.0001.0000.0500.1960.1390.3020.2530.0001.0001.0000.9160.985
110.2260.9161.0000.0000.0000.0000.2270.2520.0000.9160.9161.0001.000
여행업0.1980.9161.0000.0000.0000.0000.2010.1970.0000.9850.9851.0001.000
2023-12-10T15:24:17.038854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중구여행업Unnamed: 3영종동T&E
중구1.0000.1760.3680.9900.223
여행업0.1761.0000.9700.0890.918
Unnamed: 30.3680.9701.0000.2090.977
영종동0.9900.0890.2091.0000.142
T&E0.2230.9180.9770.1421.000
2023-12-10T15:24:17.217256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2811062000Unnamed: 20.0830.005357140.0108303011011Unnamed: 3중구영종동T&E여행업
28110620001.000-0.246-0.218-0.261-0.296-0.0900.091-0.0420.3680.9820.9900.2230.176
Unnamed: 2-0.2461.0000.0730.4450.4010.036-0.0510.4690.9700.0000.0000.9930.890
0.083-0.2180.0731.0000.7490.761-0.152-0.150-0.2510.3470.0000.0000.0100.000
0.00535714-0.2610.4450.7491.0000.966-0.0620.1190.0890.5790.0000.0000.1190.000
0.0108303-0.2960.4010.7610.9661.000-0.0760.1040.0990.4670.0000.0000.0610.000
01-0.0900.036-0.152-0.062-0.0761.000-0.031-0.0760.0000.0290.0000.0000.000
100.091-0.051-0.1500.1190.104-0.0311.0000.7440.9770.2230.1421.0000.918
11-0.0420.469-0.2510.0890.099-0.0760.7441.0000.9700.2220.1260.7610.984
Unnamed: 30.3680.9700.3470.5790.4670.0000.9770.9701.0000.3680.2090.9770.970
중구0.9820.0000.0000.0000.0000.0290.2230.2220.3681.0000.9900.2230.176
영종동0.9900.0000.0000.0000.0000.0000.1420.1260.2090.9901.0000.1420.089
T&E0.2230.9930.0100.1190.0610.0001.0000.7610.9770.2230.1421.0000.918
여행업0.1760.8900.0000.0000.0000.0000.9180.9840.9700.1760.0890.9181.000

Missing values

2023-12-10T15:24:09.585296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:24:10.297332image/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

2811062000인천Unnamed: 2Unnamed: 30.0830.005357140.010830320210801중구영종동20200110T&E11여행업
02811062000인천<NA><NA>0.9383480.4232140.78700420210801중구영종동2020130생활71보건/위생
12811062000인천<NA><NA>0.1431430.0750.13718420210801중구영종동2020130생활70의료기관
22811062000인천<NA><NA>0.2177620.0750.14440420210801중구영종동2020140쇼핑44신변잡화
32811062000인천4010편의점0.0162330.01250.0108320210801중구영종동2020230생활40유통업영리
42811062200인천4020슈퍼마켓0.0280010.0232140.02888120210801중구영종1동2020130생활40유통업영리
52811062200인천4010편의점0.0721780.20.30324920210801중구영종1동2020130생활40유통업영리
62811062200인천<NA><NA>0.0132530.01250.01805120210801중구영종1동2020185유흥86휴게
72811062200인천<NA><NA>0.1139970.0607140.10108320210801중구영종1동2020180음식80일반음식
82811062200인천4010편의점0.073750.0303570.02527120210801중구영종1동2020230생활40유통업영리
92811062200인천<NA><NA>0.012680.0071430.0144420210801중구영종1동2020280음식80일반음식
2811062000인천Unnamed: 2Unnamed: 30.0830.005357140.010830320210801중구영종동20200110T&E11여행업
1892818582000인천<NA><NA>0.003720.0053570.0108320210801연수구송도1동20201180음식80일반음식
1902818582000인천4004대형할인점0.0312910.0178570.02888120210801연수구송도1동20201240쇼핑40유통업영리
1912818583000인천<NA><NA>0.3410070.0446430.06498220210801연수구송도2동2020110T&E10숙박업
1922818583000인천4010편의점0.0085950.0214290.02888120210801연수구송도2동2020130생활40유통업영리
1932818583000인천<NA><NA>0.2011670.0535710.09025320210801연수구송도2동2020180음식80일반음식
1942818583000인천<NA><NA>0.0305490.0107140.0108320210801연수구송도2동2020210T&E10숙박업
1952818583000인천4020슈퍼마켓0.069990.0285710.02888120210801연수구송도2동2020230생활40유통업영리
1962818583000인천4010편의점0.003750.0071430.0108320210801연수구송도2동2020230생활40유통업영리
1972818583000인천<NA><NA>0.0542930.0071430.0108320210801연수구송도2동2020230생활70의료기관
1982818583000인천<NA><NA>0.0361270.0142860.02166120210801연수구송도2동2020280음식80일반음식