Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory59.3 B

Variable types

Numeric2
DateTime1
Text3
Categorical1

Alerts

sccnt_ym has constant value ""Constant
origin_ty has constant value ""Constant
seq has unique valuesUnique
origin_sn_id has unique valuesUnique
kwrd_nm has unique valuesUnique
srchwrd_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:49:16.054358
Analysis finished2023-12-10 09:49:17.914464
Duration1.86 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51700.35
Minimum51514
Maximum54460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:18.043367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51514
5-th percentile51525.9
Q151567.5
median51618
Q351669.5
95-th percentile51708.1
Maximum54460
Range2946
Interquartile range (IQR)102

Descriptive statistics

Standard deviation490.6886
Coefficient of variation (CV)0.0094910111
Kurtosis29.028386
Mean51700.35
Median Absolute Deviation (MAD)51.5
Skewness5.4762292
Sum5170035
Variance240775.3
MonotonicityNot monotonic
2023-12-10T18:49:18.285904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51514 1
 
1.0%
51641 1
 
1.0%
51662 1
 
1.0%
51660 1
 
1.0%
51658 1
 
1.0%
51656 1
 
1.0%
51654 1
 
1.0%
51652 1
 
1.0%
51650 1
 
1.0%
51648 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
51514 1
1.0%
51518 1
1.0%
51520 1
1.0%
51522 1
1.0%
51524 1
1.0%
51526 1
1.0%
51530 1
1.0%
51532 1
1.0%
51534 1
1.0%
51536 1
1.0%
ValueCountFrequency (%)
54460 1
1.0%
54458 1
1.0%
54456 1
1.0%
51712 1
1.0%
51710 1
1.0%
51708 1
1.0%
51706 1
1.0%
51704 1
1.0%
51702 1
1.0%
51700 1
1.0%

sccnt_ym
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2021-11-01 00:00:00
Maximum2021-11-01 00:00:00
2023-12-10T18:49:18.558121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:18.727840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

origin_sn_id
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:49:19.120242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters1600
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowKC483PC19N000808
2nd rowKC483PC19N000210
3rd rowKC483PC19N000816
4th rowKC483PC19N000158
5th rowKC483PC19N001047
ValueCountFrequency (%)
kc483pc19n000808 1
 
1.0%
kc483pc19n000107 1
 
1.0%
kc483pc19n000789 1
 
1.0%
kc483pc19n001271 1
 
1.0%
kc483pc19n000871 1
 
1.0%
kc483pc19n000211 1
 
1.0%
kc483pc19n000127 1
 
1.0%
kc483pc19n000451 1
 
1.0%
kc483pc19n000409 1
 
1.0%
kc483pc19n000035 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:49:19.940943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 310
19.4%
C 200
12.5%
1 179
11.2%
3 135
8.4%
4 128
8.0%
8 121
 
7.6%
9 117
 
7.3%
K 100
 
6.2%
P 100
 
6.2%
N 100
 
6.2%
Other values (4) 110
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1100
68.8%
Uppercase Letter 500
31.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 310
28.2%
1 179
16.3%
3 135
12.3%
4 128
11.6%
8 121
 
11.0%
9 117
 
10.6%
2 38
 
3.5%
5 28
 
2.5%
6 25
 
2.3%
7 19
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
C 200
40.0%
K 100
20.0%
P 100
20.0%
N 100
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1100
68.8%
Latin 500
31.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 310
28.2%
1 179
16.3%
3 135
12.3%
4 128
11.6%
8 121
 
11.0%
9 117
 
10.6%
2 38
 
3.5%
5 28
 
2.5%
6 25
 
2.3%
7 19
 
1.7%
Latin
ValueCountFrequency (%)
C 200
40.0%
K 100
20.0%
P 100
20.0%
N 100
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 310
19.4%
C 200
12.5%
1 179
11.2%
3 135
8.4%
4 128
8.0%
8 121
 
7.6%
9 117
 
7.3%
K 100
 
6.2%
P 100
 
6.2%
N 100
 
6.2%
Other values (4) 110
 
6.9%

kwrd_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:49:20.474299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.38
Min length4

Characters and Unicode

Total characters538
Distinct characters118
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

Unique100 ?
Unique (%)100.0%

Sample

1st row(유)중앙시장
2nd row흥덕시장
3rd row가경터미널시장
4th row가락타운상가시장
5th row가리봉시장
ValueCountFrequency (%)
유)중앙시장 1
 
1.0%
고성공룡시장 1
 
1.0%
공주산성시장 1
 
1.0%
공릉동도깨비시장 1
 
1.0%
공덕시장 1
 
1.0%
골드테마거리 1
 
1.0%
곤양종합시장 1
 
1.0%
곡천공설시장 1
 
1.0%
곡성기차마을전통시장 1
 
1.0%
고흥전통시장 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:49:21.315001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 93
 
17.3%
장 93
 
17.3%
가 17
 
3.2%
광 17
 
3.2%
고 14
 
2.6%
경 14
 
2.6%
강 12
 
2.2%
전 12
 
2.2%
통 11
 
2.0%
성 10
 
1.9%
Other values (108) 245
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 534
99.3%
Decimal Number 2
 
0.4%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 93
 
17.4%
장 93
 
17.4%
가 17
 
3.2%
광 17
 
3.2%
고 14
 
2.6%
경 14
 
2.6%
강 12
 
2.2%
전 12
 
2.2%
통 11
 
2.1%
성 10
 
1.9%
Other values (104) 241
45.1%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
2 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 534
99.3%
Common 4
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 93
 
17.4%
장 93
 
17.4%
가 17
 
3.2%
광 17
 
3.2%
고 14
 
2.6%
경 14
 
2.6%
강 12
 
2.2%
전 12
 
2.2%
통 11
 
2.1%
성 10
 
1.9%
Other values (104) 241
45.1%
Common
ValueCountFrequency (%)
5 1
25.0%
( 1
25.0%
) 1
25.0%
2 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 534
99.3%
ASCII 4
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 93
 
17.4%
장 93
 
17.4%
가 17
 
3.2%
광 17
 
3.2%
고 14
 
2.6%
경 14
 
2.6%
강 12
 
2.2%
전 12
 
2.2%
통 11
 
2.1%
성 10
 
1.9%
Other values (104) 241
45.1%
ASCII
ValueCountFrequency (%)
5 1
25.0%
( 1
25.0%
) 1
25.0%
2 1
25.0%

srchwrd_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:49:21.922040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.35
Min length4

Characters and Unicode

Total characters535
Distinct characters116
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

Unique100 ?
Unique (%)100.0%

Sample

1st row중앙시장
2nd row흥덕시장
3rd row가경터미널시장
4th row가락타운상가시장
5th row가리봉시장
ValueCountFrequency (%)
중앙시장 1
 
1.0%
고성공룡시장 1
 
1.0%
공주산성시장 1
 
1.0%
공릉동도깨비시장 1
 
1.0%
공덕시장 1
 
1.0%
골드테마거리 1
 
1.0%
곤양종합시장 1
 
1.0%
곡천공설시장 1
 
1.0%
곡성기차마을전통시장 1
 
1.0%
고흥전통시장 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:49:22.679301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 93
 
17.4%
장 93
 
17.4%
광 17
 
3.2%
가 17
 
3.2%
고 14
 
2.6%
경 14
 
2.6%
강 12
 
2.2%
전 12
 
2.2%
통 11
 
2.1%
성 10
 
1.9%
Other values (106) 242
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 533
99.6%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 93
 
17.4%
장 93
 
17.4%
광 17
 
3.2%
가 17
 
3.2%
고 14
 
2.6%
경 14
 
2.6%
강 12
 
2.3%
전 12
 
2.3%
통 11
 
2.1%
성 10
 
1.9%
Other values (104) 240
45.0%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
2 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 533
99.6%
Common 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 93
 
17.4%
장 93
 
17.4%
광 17
 
3.2%
가 17
 
3.2%
고 14
 
2.6%
경 14
 
2.6%
강 12
 
2.3%
전 12
 
2.3%
통 11
 
2.1%
성 10
 
1.9%
Other values (104) 240
45.0%
Common
ValueCountFrequency (%)
5 1
50.0%
2 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 533
99.6%
ASCII 2
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 93
 
17.4%
장 93
 
17.4%
광 17
 
3.2%
가 17
 
3.2%
고 14
 
2.6%
경 14
 
2.6%
강 12
 
2.3%
전 12
 
2.3%
통 11
 
2.1%
성 10
 
1.9%
Other values (104) 240
45.0%
ASCII
ValueCountFrequency (%)
5 1
50.0%
2 1
50.0%

sccnt
Real number (ℝ)

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2064.18
Minimum11
Maximum97700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:23.134176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile16.9
Q160
median405
Q31135
95-th percentile5772.5
Maximum97700
Range97689
Interquartile range (IQR)1075

Descriptive statistics

Standard deviation9916.0705
Coefficient of variation (CV)4.8038788
Kurtosis89.799568
Mean2064.18
Median Absolute Deviation (MAD)363.5
Skewness9.2750788
Sum206418
Variance98328454
MonotonicityNot monotonic
2023-12-10T18:49:23.401220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 5
 
5.0%
60 3
 
3.0%
17 2
 
2.0%
370 2
 
2.0%
120 2
 
2.0%
340 2
 
2.0%
450 2
 
2.0%
14 2
 
2.0%
1480 2
 
2.0%
100 2
 
2.0%
Other values (69) 76
76.0%
ValueCountFrequency (%)
11 1
 
1.0%
13 1
 
1.0%
14 2
 
2.0%
15 1
 
1.0%
17 2
 
2.0%
20 2
 
2.0%
23 1
 
1.0%
24 1
 
1.0%
27 1
 
1.0%
30 5
5.0%
ValueCountFrequency (%)
97700 1
1.0%
14090 1
1.0%
11130 1
1.0%
8970 1
1.0%
8290 1
1.0%
5640 1
1.0%
5420 1
1.0%
3500 1
1.0%
3160 1
1.0%
3070 1
1.0%

origin_ty
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
쇼핑
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
쇼핑 100
100.0%

Length

2023-12-10T18:49:23.716301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:23.883036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
쇼핑 100
100.0%

Interactions

2023-12-10T18:49:17.267482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:16.969080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:17.405213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:17.120330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:49:23.976511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seqorigin_sn_idkwrd_nmsrchwrd_nmsccnt
seq1.0001.0001.0001.0000.000
origin_sn_id1.0001.0001.0001.0001.000
kwrd_nm1.0001.0001.0001.0001.000
srchwrd_nm1.0001.0001.0001.0001.000
sccnt0.0001.0001.0001.0001.000
2023-12-10T18:49:24.200896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seqsccnt
seq1.000-0.051
sccnt-0.0511.000

Missing values

2023-12-10T18:49:17.617876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:49:17.840787image/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

seqsccnt_ymorigin_sn_idkwrd_nmsrchwrd_nmsccntorigin_ty
0515142021-11KC483PC19N000808(유)중앙시장중앙시장8970쇼핑
1544562021-11KC483PC19N000210흥덕시장흥덕시장23쇼핑
2515182021-11KC483PC19N000816가경터미널시장가경터미널시장620쇼핑
3515202021-11KC483PC19N000158가락타운상가시장가락타운상가시장24쇼핑
4515222021-11KC483PC19N001047가리봉시장가리봉시장770쇼핑
5515242021-11KC483PC19N000215가야벽산상가가야벽산상가47쇼핑
6515262021-11KC483PC19N000218가야시장가야시장400쇼핑
7544582021-11KC483PC19N001370흥부시장흥부시장30쇼핑
8515302021-11KC483PC19N000824가음정대상가가음정대상가220쇼핑
9515322021-11KC483PC19N000344가음정시장가음정시장1130쇼핑
seqsccnt_ymorigin_sn_idkwrd_nmsrchwrd_nmsccntorigin_ty
90516952021-11KC483PC19N000109광양상설시장광양상설시장58쇼핑
91516962021-11KC483PC19N000105광영상설시장광영상설시장15쇼핑
92516982021-11KC483PC19N001209광장시장광장시장97700쇼핑
93517002021-11KC483PC19N000224광주양동시장광주양동시장3160쇼핑
94517022021-11KC483PC19N000794광천전통시장광천전통시장1380쇼핑
95517042021-11KC483PC19N001307광탄경매시장광탄경매시장80쇼핑
96517062021-11KC483PC19N001203광희패션몰광희패션몰1210쇼핑
97517082021-11KC483PC19N000161괴목시장괴목시장17쇼핑
98517102021-11KC483PC19N000864괴산전통시장괴산전통시장470쇼핑
99517122021-11KC483PC19N000150괴정골목시장괴정골목시장230쇼핑