Overview

Dataset statistics

Number of variables13
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory112.3 B

Variable types

Categorical9
Numeric4

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO011TOTALCGIAIRCL

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
공기청정기 has constant value ""Constant
전체 has constant value ""Constant
전체.1 has constant value ""Constant
40 is highly overall correlated with 2High correlation
2 is highly overall correlated with 40High correlation
344 is highly overall correlated with 243 and 2 other fieldsHigh correlation
243 is highly overall correlated with 344 and 2 other fieldsHigh correlation
344.1 is highly overall correlated with 344 and 2 other fieldsHigh correlation
119 is highly overall correlated with 344 and 2 other fieldsHigh correlation
대구광역시 is highly overall correlated with 북구High correlation
북구 is highly overall correlated with 대구광역시High correlation
344 has 1 (1.0%) zerosZeros
243 has 1 (1.0%) zerosZeros
344.1 has 1 (1.0%) zerosZeros
119 has 1 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:44:08.366732
Analysis finished2023-12-10 06:44:12.015935
Duration3.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MT
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
MT
99 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
MT 99
100.0%

Length

2023-12-10T15:44:12.114629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:12.244534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mt 99
100.0%

201901
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
201901
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201901
2nd row201901
3rd row201901
4th row201901
5th row201901

Common Values

ValueCountFrequency (%)
201901 99
100.0%

Length

2023-12-10T15:44:12.380864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:12.510812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

344
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.0404
Minimum0
Maximum1335
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:44:12.696113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8
Q143
median115
Q3236
95-th percentile748.9
Maximum1335
Range1335
Interquartile range (IQR)193

Descriptive statistics

Standard deviation268.37112
Coefficient of variation (CV)1.3217622
Kurtosis6.6626754
Mean203.0404
Median Absolute Deviation (MAD)84
Skewness2.5271111
Sum20101
Variance72023.06
MonotonicityNot monotonic
2023-12-10T15:44:12.953613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 4
 
4.0%
15 2
 
2.0%
45 2
 
2.0%
32 2
 
2.0%
162 2
 
2.0%
5 2
 
2.0%
54 2
 
2.0%
82 2
 
2.0%
237 2
 
2.0%
58 2
 
2.0%
Other values (75) 77
77.8%
ValueCountFrequency (%)
0 1
1.0%
4 1
1.0%
5 2
2.0%
7 1
1.0%
9 2
2.0%
11 1
1.0%
13 1
1.0%
15 2
2.0%
16 1
1.0%
19 1
1.0%
ValueCountFrequency (%)
1335 1
1.0%
1228 1
1.0%
1178 1
1.0%
1076 1
1.0%
838 1
1.0%
739 1
1.0%
679 1
1.0%
667 1
1.0%
597 1
1.0%
589 1
1.0%

243
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.38384
Minimum0
Maximum3577
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:44:13.165067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q144.5
median124
Q3304
95-th percentile755
Maximum3577
Range3577
Interquartile range (IQR)259.5

Descriptive statistics

Standard deviation441.55788
Coefficient of variation (CV)1.7426442
Kurtosis35.218136
Mean253.38384
Median Absolute Deviation (MAD)94
Skewness5.2803096
Sum25085
Variance194973.36
MonotonicityNot monotonic
2023-12-10T15:44:13.391420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 4
 
4.0%
11 3
 
3.0%
60 3
 
3.0%
167 2
 
2.0%
104 2
 
2.0%
25 2
 
2.0%
35 2
 
2.0%
36 2
 
2.0%
31 2
 
2.0%
2 2
 
2.0%
Other values (73) 75
75.8%
ValueCountFrequency (%)
0 1
 
1.0%
2 2
2.0%
11 3
3.0%
12 1
 
1.0%
13 1
 
1.0%
14 1
 
1.0%
20 1
 
1.0%
24 4
4.0%
25 2
2.0%
28 1
 
1.0%
ValueCountFrequency (%)
3577 1
1.0%
2096 1
1.0%
957 1
1.0%
801 1
1.0%
773 1
1.0%
753 1
1.0%
714 1
1.0%
698 1
1.0%
588 1
1.0%
551 1
1.0%

344.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.0404
Minimum0
Maximum1335
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:44:13.648322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8
Q143
median115
Q3236
95-th percentile748.9
Maximum1335
Range1335
Interquartile range (IQR)193

Descriptive statistics

Standard deviation268.37112
Coefficient of variation (CV)1.3217622
Kurtosis6.6626754
Mean203.0404
Median Absolute Deviation (MAD)84
Skewness2.5271111
Sum20101
Variance72023.06
MonotonicityNot monotonic
2023-12-10T15:44:13.935296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 4
 
4.0%
15 2
 
2.0%
45 2
 
2.0%
32 2
 
2.0%
162 2
 
2.0%
5 2
 
2.0%
54 2
 
2.0%
82 2
 
2.0%
237 2
 
2.0%
58 2
 
2.0%
Other values (75) 77
77.8%
ValueCountFrequency (%)
0 1
1.0%
4 1
1.0%
5 2
2.0%
7 1
1.0%
9 2
2.0%
11 1
1.0%
13 1
1.0%
15 2
2.0%
16 1
1.0%
19 1
1.0%
ValueCountFrequency (%)
1335 1
1.0%
1228 1
1.0%
1178 1
1.0%
1076 1
1.0%
838 1
1.0%
739 1
1.0%
679 1
1.0%
667 1
1.0%
597 1
1.0%
589 1
1.0%

공기청정기
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
공기청정기
99 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공기청정기 99
100.0%

Length

2023-12-10T15:44:14.126415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:14.282946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공기청정기 99
100.0%

전체
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
99 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 99
100.0%

Length

2023-12-10T15:44:14.435285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:14.557572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 99
100.0%

전체.1
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
99 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 99
100.0%

Length

2023-12-10T15:44:14.710660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:14.865710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 99
100.0%

2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
63 
1
36 

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 63
63.6%
1 36
36.4%

Length

2023-12-10T15:44:15.006954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:15.155228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 63
63.6%
1 36
36.4%

40
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
50
28 
30
26 
40
25 
60
10 
20
10 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row40
4th row40
5th row40

Common Values

ValueCountFrequency (%)
50 28
28.3%
30 26
26.3%
40 25
25.3%
60 10
 
10.1%
20 10
 
10.1%

Length

2023-12-10T15:44:15.319480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:15.489375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50 28
28.3%
30 26
26.3%
40 25
25.3%
60 10
 
10.1%
20 10
 
10.1%

대구광역시
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
24 
서울특별시
11 
인천광역시
8 
경상남도
8 
광주광역시
7 
Other values (10)
41 

Length

Max length7
Median length5
Mean length4.1414141
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row인천광역시
2nd row인천광역시
3rd row광주광역시
4th row광주광역시
5th row울산광역시

Common Values

ValueCountFrequency (%)
경기도 24
24.2%
서울특별시 11
11.1%
인천광역시 8
 
8.1%
경상남도 8
 
8.1%
광주광역시 7
 
7.1%
강원도 6
 
6.1%
전라북도 6
 
6.1%
울산광역시 5
 
5.1%
충청남도 5
 
5.1%
경상북도 4
 
4.0%
Other values (5) 15
15.2%

Length

2023-12-10T15:44:15.683637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 24
24.2%
서울특별시 11
11.1%
인천광역시 8
 
8.1%
경상남도 8
 
8.1%
광주광역시 7
 
7.1%
강원도 6
 
6.1%
전라북도 6
 
6.1%
울산광역시 5
 
5.1%
충청남도 5
 
5.1%
경상북도 4
 
4.0%
Other values (5) 15
15.2%

북구
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
15 
서구
7 
구리시
 
5
도봉구
 
4
창원시
 
4
Other values (27)
64 

Length

Max length4
Median length3
Mean length2.7474747
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row중구
2nd row서구
3rd row서구
4th row광산구
5th row전체

Common Values

ValueCountFrequency (%)
전체 15
 
15.2%
서구 7
 
7.1%
구리시 5
 
5.1%
도봉구 4
 
4.0%
창원시 4
 
4.0%
전주시 4
 
4.0%
천안시 4
 
4.0%
화성시 3
 
3.0%
광명시 3
 
3.0%
파주시 3
 
3.0%
Other values (22) 47
47.5%

Length

2023-12-10T15:44:15.895604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 15
 
15.2%
서구 7
 
7.1%
구리시 5
 
5.1%
도봉구 4
 
4.0%
창원시 4
 
4.0%
전주시 4
 
4.0%
천안시 4
 
4.0%
광주시 3
 
3.0%
양주시 3
 
3.0%
춘천시 3
 
3.0%
Other values (22) 47
47.5%

119
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.55556
Minimum0
Maximum482
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:44:16.124980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.8
Q149.5
median83
Q3140.5
95-th percentile252.3
Maximum482
Range482
Interquartile range (IQR)91

Descriptive statistics

Standard deviation79.972983
Coefficient of variation (CV)0.76488507
Kurtosis4.0746213
Mean104.55556
Median Absolute Deviation (MAD)44
Skewness1.5304525
Sum10351
Variance6395.678
MonotonicityNot monotonic
2023-12-10T15:44:16.384336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 4
 
4.0%
58 2
 
2.0%
75 2
 
2.0%
80 2
 
2.0%
115 2
 
2.0%
175 2
 
2.0%
21 2
 
2.0%
48 2
 
2.0%
18 2
 
2.0%
255 2
 
2.0%
Other values (73) 77
77.8%
ValueCountFrequency (%)
0 1
1.0%
2 1
1.0%
7 1
1.0%
10 1
1.0%
13 1
1.0%
15 1
1.0%
17 1
1.0%
18 2
2.0%
20 1
1.0%
21 2
2.0%
ValueCountFrequency (%)
482 1
1.0%
302 1
1.0%
266 1
1.0%
255 2
2.0%
252 1
1.0%
250 1
1.0%
243 1
1.0%
234 1
1.0%
228 1
1.0%
210 1
1.0%

Interactions

2023-12-10T15:44:10.993774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:08.954664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.501630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.402746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:11.158349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.095787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.989892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.557378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:11.310797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.232390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.131960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.709798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:11.450490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.367630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.264516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.885456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:44:16.540340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
344243344.1240대구광역시북구119
3441.0000.3031.0000.0000.1860.0000.0000.665
2430.3031.0000.3030.0310.1580.0000.0000.385
344.11.0000.3031.0000.0000.1860.0000.0000.665
20.0000.0310.0001.0001.0000.0000.0000.117
400.1860.1580.1861.0001.0000.0000.0000.000
대구광역시0.0000.0000.0000.0000.0001.0000.9730.000
북구0.0000.0000.0000.0000.0000.9731.0000.752
1190.6650.3850.6650.1170.0000.0000.7521.000
2023-12-10T15:44:17.088411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
40북구2대구광역시
401.0000.0000.9840.000
북구0.0001.0000.0000.695
20.9840.0001.0000.000
대구광역시0.0000.6950.0001.000
2023-12-10T15:44:17.237668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
344243344.1119240대구광역시북구
3441.0000.6441.0000.6630.0000.1020.0000.000
2430.6441.0000.6440.7060.0320.0560.0000.000
344.11.0000.6441.0000.6630.0000.1020.0000.000
1190.6630.7060.6631.0000.0800.0000.0000.317
20.0000.0320.0000.0801.0000.9840.0000.000
400.1020.0560.1020.0000.9841.0000.0000.000
대구광역시0.0000.0000.0000.0000.0000.0001.0000.695
북구0.0000.0000.0000.3170.0000.0000.6951.000

Missing values

2023-12-10T15:44:11.664348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:44:11.932531image/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

MT201901344243344.1공기청정기전체전체.1240대구광역시북구119
0MT2019011313013공기청정기전체전체240인천광역시중구58
1MT201901161316공기청정기전체전체240인천광역시서구21
2MT201901302430공기청정기전체전체240광주광역시서구26
3MT201901656865공기청정기전체전체240광주광역시광산구66
4MT201901111111공기청정기전체전체240울산광역시전체26
5MT2019015145공기청정기전체전체240울산광역시북구13
6MT2019014254공기청정기전체전체240세종특별자치시전체22
7MT2019018039380공기청정기전체전체240경기도광명시138
8MT2019012532096253공기청정기전체전체240경기도구리시183
9MT201901171336171공기청정기전체전체240경기도파주시125
MT201901344243344.1공기청정기전체전체.1240대구광역시북구119
89MT20190120858208공기청정기전체전체130경기도화성시126
90MT20190119231192공기청정기전체전체130경기도광주시174
91MT201901302513302공기청정기전체전체130경기도양주시250
92MT201901104473104공기청정기전체전체130강원도춘천시110
93MT20190111785121178공기청정기전체전체130강원도원주시101
94MT201901155227155공기청정기전체전체130충청남도천안시168
95MT2019018317483공기청정기전체전체130전라북도전주시89
96MT20190126497264공기청정기전체전체130전라북도군산시80
97MT2019014112341공기청정기전체전체130경상북도포항시48
98MT201901172714172공기청정기전체전체130경상북도경주시115