Overview

Dataset statistics

Number of variables8
Number of observations98
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory68.3 B

Variable types

Categorical5
Text1
Numeric2

Alerts

dmn_nm has constant value ""Constant
chatbot_ctgry_nm has constant value ""Constant
convrs_partn_cl_nm has constant value ""Constant
area_nm is highly overall correlated with base_de and 1 other fieldsHigh correlation
hotel_grad_no is highly overall correlated with base_de and 1 other fieldsHigh correlation
base_de is highly overall correlated with hotel_grad_no and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 10:01:11.523515
Analysis finished2023-12-10 10:01:17.169738
Duration5.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

dmn_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
숙박
98 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
숙박 98
100.0%

Length

2023-12-10T19:01:17.383881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:17.629968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박 98
100.0%

chatbot_ctgry_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
호텔
98 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
호텔 98
100.0%

Length

2023-12-10T19:01:17.803649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:17.981591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
호텔 98
100.0%

convrs_partn_cl_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
고객
98 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고객 98
100.0%

Length

2023-12-10T19:01:18.183064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:18.395608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고객 98
100.0%

convrs_ctgry_nm
Text

Distinct79
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size916.0 B
2023-12-10T19:01:18.897853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.4387755
Min length1

Characters and Unicode

Total characters435
Distinct characters153
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

Unique68 ?
Unique (%)69.4%

Sample

1st row맛집
2nd row지도
3rd row대중교통
4th row객실예약
5th row에약수정
ValueCountFrequency (%)
객실예약 7
 
7.1%
교통안내 4
 
4.1%
지도 3
 
3.1%
라페스타메뉴 2
 
2.0%
디럭스더블 2
 
2.0%
연회장예약 2
 
2.0%
기본정보 2
 
2.0%
대중교통 2
 
2.0%
디럭스더블객실크기 2
 
2.0%
객실타입 2
 
2.0%
Other values (69) 70
71.4%
2023-12-10T19:01:19.727468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
실 16
 
3.7%
객 15
 
3.4%
스 14
 
3.2%
약 12
 
2.8%
예 11
 
2.5%
로 10
 
2.3%
시 8
 
1.8%
정 7
 
1.6%
메 7
 
1.6%
라 7
 
1.6%
Other values (143) 328
75.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 431
99.1%
Decimal Number 4
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 16
 
3.7%
객 15
 
3.5%
스 14
 
3.2%
약 12
 
2.8%
예 11
 
2.6%
로 10
 
2.3%
시 8
 
1.9%
정 7
 
1.6%
메 7
 
1.6%
라 7
 
1.6%
Other values (141) 324
75.2%
Decimal Number
ValueCountFrequency (%)
1 3
75.0%
9 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 431
99.1%
Common 4
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 16
 
3.7%
객 15
 
3.5%
스 14
 
3.2%
약 12
 
2.8%
예 11
 
2.6%
로 10
 
2.3%
시 8
 
1.9%
정 7
 
1.6%
메 7
 
1.6%
라 7
 
1.6%
Other values (141) 324
75.2%
Common
ValueCountFrequency (%)
1 3
75.0%
9 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 431
99.1%
ASCII 4
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
실 16
 
3.7%
객 15
 
3.5%
스 14
 
3.2%
약 12
 
2.8%
예 11
 
2.6%
로 10
 
2.3%
시 8
 
1.9%
정 7
 
1.6%
메 7
 
1.6%
라 7
 
1.6%
Other values (141) 324
75.2%
ASCII
ValueCountFrequency (%)
1 3
75.0%
9 1
 
25.0%

text_fq_rt
Real number (ℝ)

Distinct24
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.345408
Minimum2.27
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2023-12-10T19:01:19.993857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.27
5-th percentile3.57
Q14.17
median7.735
Q322.22
95-th percentile50
Maximum100
Range97.73
Interquartile range (IQR)18.05

Descriptive statistics

Standard deviation20.992328
Coefficient of variation (CV)1.2102527
Kurtosis5.6191289
Mean17.345408
Median Absolute Deviation (MAD)4.165
Skewness2.2578378
Sum1699.85
Variance440.67782
MonotonicityNot monotonic
2023-12-10T19:01:20.226052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3.57 17
17.3%
50.0 11
11.2%
5.88 11
11.2%
4.35 8
 
8.2%
4.17 6
 
6.1%
11.11 5
 
5.1%
25.0 4
 
4.1%
16.67 4
 
4.1%
11.76 3
 
3.1%
7.14 3
 
3.1%
Other values (14) 26
26.5%
ValueCountFrequency (%)
2.27 3
 
3.1%
3.57 17
17.3%
4.17 6
 
6.1%
4.35 8
8.2%
4.55 1
 
1.0%
5.88 11
11.2%
7.14 3
 
3.1%
8.33 3
 
3.1%
8.7 2
 
2.0%
11.11 5
 
5.1%
ValueCountFrequency (%)
100.0 3
 
3.1%
50.0 11
11.2%
34.09 1
 
1.0%
33.3 3
 
3.1%
27.27 2
 
2.0%
25.0 4
 
4.1%
22.22 2
 
2.0%
21.74 1
 
1.0%
20.83 1
 
1.0%
17.86 1
 
1.0%

hotel_grad_no
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
5
47 
4
42 
3
9 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 47
48.0%
4 42
42.9%
3 9
 
9.2%

Length

2023-12-10T19:01:20.479916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:20.650454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 47
48.0%
4 42
42.9%
3 9
 
9.2%

area_nm
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
서울
45 
서울 강남구
33 
서울 중구
9 
부산
6 
인천
 
3

Length

Max length6
Median length2
Mean length3.622449
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울 중구
2nd row서울
3rd row서울 중구
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 45
45.9%
서울 강남구 33
33.7%
서울 중구 9
 
9.2%
부산 6
 
6.1%
인천 3
 
3.1%
제주 2
 
2.0%

Length

2023-12-10T19:01:20.976066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:21.279667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 87
62.1%
강남구 33
 
23.6%
중구 9
 
6.4%
부산 6
 
4.3%
인천 3
 
2.1%
제주 2
 
1.4%

base_de
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210791
Minimum20210131
Maximum20211231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2023-12-10T19:01:21.494195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210131
5-th percentile20210131
Q120210430
median20210831
Q320211130
95-th percentile20211130
Maximum20211231
Range1100
Interquartile range (IQR)700

Descriptive statistics

Standard deviation372.9053
Coefficient of variation (CV)1.8450802 × 10-5
Kurtosis-1.0337186
Mean20210791
Median Absolute Deviation (MAD)299
Skewness-0.71906644
Sum1.9806575 × 109
Variance139058.36
MonotonicityNot monotonic
2023-12-10T19:01:21.716990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20211130 32
32.7%
20210831 21
21.4%
20210228 17
17.3%
20210131 7
 
7.1%
20211031 7
 
7.1%
20210731 5
 
5.1%
20210930 4
 
4.1%
20211231 3
 
3.1%
20210430 2
 
2.0%
ValueCountFrequency (%)
20210131 7
 
7.1%
20210228 17
17.3%
20210430 2
 
2.0%
20210731 5
 
5.1%
20210831 21
21.4%
20210930 4
 
4.1%
20211031 7
 
7.1%
20211130 32
32.7%
20211231 3
 
3.1%
ValueCountFrequency (%)
20211231 3
 
3.1%
20211130 32
32.7%
20211031 7
 
7.1%
20210930 4
 
4.1%
20210831 21
21.4%
20210731 5
 
5.1%
20210430 2
 
2.0%
20210228 17
17.3%
20210131 7
 
7.1%

Interactions

2023-12-10T19:01:16.220820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:15.752529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:16.446590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:16.014592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:01:21.868608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
convrs_ctgry_nmtext_fq_rthotel_grad_noarea_nmbase_de
convrs_ctgry_nm1.0000.0000.0000.0000.000
text_fq_rt0.0001.0000.5930.8020.676
hotel_grad_no0.0000.5931.0001.0000.623
area_nm0.0000.8021.0001.0000.790
base_de0.0000.6760.6230.7901.000
2023-12-10T19:01:22.063242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
area_nmhotel_grad_no
area_nm1.0000.984
hotel_grad_no0.9841.000
2023-12-10T19:01:22.214577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
text_fq_rtbase_dehotel_grad_noarea_nm
text_fq_rt1.0000.0200.2930.410
base_de0.0201.0000.5190.620
hotel_grad_no0.2930.5191.0000.984
area_nm0.4100.6200.9841.000

Missing values

2023-12-10T19:01:16.742407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:01:17.000388image/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

dmn_nmchatbot_ctgry_nmconvrs_partn_cl_nmconvrs_ctgry_nmtext_fq_rthotel_grad_noarea_nmbase_de
0숙박호텔고객맛집50.04서울 중구20211231
1숙박호텔고객지도100.05서울20211231
2숙박호텔고객대중교통50.04서울 중구20211231
3숙박호텔고객객실예약50.05서울20210731
4숙박호텔고객에약수정50.05서울20210731
5숙박호텔고객객실예약50.03부산20210731
6숙박호텔고객객실예약100.04서울 중구20210731
7숙박호텔고객객실타입50.03부산20210731
8숙박호텔고객캐슬테라스이용요금22.225서울20210131
9숙박호텔고객레스토랑11.115서울20210131
dmn_nmchatbot_ctgry_nmconvrs_partn_cl_nmconvrs_ctgry_nmtext_fq_rthotel_grad_noarea_nmbase_de
88숙박호텔고객체크인3.574서울 강남구20210831
89숙박호텔고객택시3.574서울 강남구20210831
90숙박호텔고객파티오나인프로모션3.574서울 강남구20210831
91숙박호텔고객호텔소개3.574서울 강남구20210831
92숙박호텔고객벙커룸3.574서울 강남구20210831
93숙박호텔고객벙커룸객실샤워부스3.574서울 강남구20210831
94숙박호텔고객벙커룸객실전망3.574서울 강남구20210831
95숙박호텔고객돌잔치3.574서울 강남구20210831
96숙박호텔고객룸어메니티3.574서울 강남구20210831
97숙박호텔고객냉난방3.574서울 강남구20210831