Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory94.3 B

Variable types

Categorical8
Numeric3

Alerts

ldgs_addr is highly overall correlated with min_prc and 8 other fieldsHigh correlation
ota_nm is highly overall correlated with min_prc and 8 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with min_prc and 8 other fieldsHigh correlation
wkday_nm is highly overall correlated with min_prc and 8 other fieldsHigh correlation
gugun_nm is highly overall correlated with min_prc and 8 other fieldsHigh correlation
ldgmnt_de is highly overall correlated with min_prc and 8 other fieldsHigh correlation
ldgs_nm is highly overall correlated with min_prc and 8 other fieldsHigh correlation
max_prc is highly overall correlated with min_prc and 8 other fieldsHigh correlation
min_prc is highly overall correlated with avrg_prc and 8 other fieldsHigh correlation
avrg_prc is highly overall correlated with min_prc and 8 other fieldsHigh correlation
ldgs_nm is highly imbalanced (80.6%)Imbalance
ota_nm is highly imbalanced (80.6%)Imbalance
ctprvn_nm is highly imbalanced (80.6%)Imbalance
gugun_nm is highly imbalanced (80.6%)Imbalance
ldgs_addr is highly imbalanced (80.6%)Imbalance

Reproduction

Analysis started2023-12-10 10:12:11.202104
Analysis finished2023-12-10 10:12:14.282095
Duration3.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ldgs_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
호텔마누
97 
호텔인터시티
 
3

Length

Max length6
Median length4
Mean length4.06
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row호텔마누
2nd row호텔인터시티
3rd row호텔마누
4th row호텔마누
5th row호텔마누

Common Values

ValueCountFrequency (%)
호텔마누 97
97.0%
호텔인터시티 3
 
3.0%

Length

2023-12-10T19:12:14.430353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:14.632311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
호텔마누 97
97.0%
호텔인터시티 3
 
3.0%

ota_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
GOODCHOICE
97 
EXPEDIA
 
3

Length

Max length10
Median length10
Mean length9.91
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGOODCHOICE
2nd rowEXPEDIA
3rd rowGOODCHOICE
4th rowGOODCHOICE
5th rowGOODCHOICE

Common Values

ValueCountFrequency (%)
GOODCHOICE 97
97.0%
EXPEDIA 3
 
3.0%

Length

2023-12-10T19:12:14.815851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:14.998855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
goodchoice 97
97.0%
expedia 3
 
3.0%

ctprvn_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
97 
대전
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 97
97.0%
대전 3
 
3.0%

Length

2023-12-10T19:12:15.183510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:15.371356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 97
97.0%
대전 3
 
3.0%

gugun_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중구
97 
유성구
 
3

Length

Max length3
Median length2
Mean length2.03
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 97
97.0%
유성구 3
 
3.0%

Length

2023-12-10T19:12:15.547781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:15.759991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 97
97.0%
유성구 3
 
3.0%

ldgs_addr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울 중구 남대문로5가 84-16
97 
대전 유성구 봉명동 545-5
 
3

Length

Max length18
Median length18
Mean length17.94
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울 중구 남대문로5가 84-16
2nd row대전 유성구 봉명동 545-5
3rd row서울 중구 남대문로5가 84-16
4th row서울 중구 남대문로5가 84-16
5th row서울 중구 남대문로5가 84-16

Common Values

ValueCountFrequency (%)
서울 중구 남대문로5가 84-16 97
97.0%
대전 유성구 봉명동 545-5 3
 
3.0%

Length

2023-12-10T19:12:15.978334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:16.209105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 97
24.2%
중구 97
24.2%
남대문로5가 97
24.2%
84-16 97
24.2%
대전 3
 
0.8%
유성구 3
 
0.8%
봉명동 3
 
0.8%
545-5 3
 
0.8%

extrc_de
Real number (ℝ)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220868
Minimum20220819
Maximum20220915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:16.396399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220819
5-th percentile20220820
Q120220826
median20220901
Q320220907
95-th percentile20220913
Maximum20220915
Range96
Interquartile range (IQR)81.25

Descriptive statistics

Standard deviation41.279989
Coefficient of variation (CV)2.0414549 × 10-6
Kurtosis-1.9979149
Mean20220868
Median Absolute Deviation (MAD)13
Skewness-0.078846939
Sum2.0220868 × 109
Variance1704.0375
MonotonicityNot monotonic
2023-12-10T19:12:16.656511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20220906 5
 
5.0%
20220907 5
 
5.0%
20220820 4
 
4.0%
20220830 4
 
4.0%
20220908 4
 
4.0%
20220905 4
 
4.0%
20220904 4
 
4.0%
20220902 4
 
4.0%
20220901 4
 
4.0%
20220831 4
 
4.0%
Other values (18) 58
58.0%
ValueCountFrequency (%)
20220819 3
3.0%
20220820 4
4.0%
20220821 3
3.0%
20220822 4
4.0%
20220823 4
4.0%
20220824 4
4.0%
20220825 3
3.0%
20220826 4
4.0%
20220827 3
3.0%
20220828 4
4.0%
ValueCountFrequency (%)
20220915 1
 
1.0%
20220914 2
 
2.0%
20220913 3
3.0%
20220912 3
3.0%
20220911 3
3.0%
20220910 3
3.0%
20220909 3
3.0%
20220908 4
4.0%
20220907 5
5.0%
20220906 5
5.0%

ldgmnt_de
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20220915
28 
20220914
26 
20220913
23 
20220916
20 
20220917
3 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220913
2nd row20220917
3rd row20220913
4th row20220913
5th row20220913

Common Values

ValueCountFrequency (%)
20220915 28
28.0%
20220914 26
26.0%
20220913 23
23.0%
20220916 20
20.0%
20220917 3
 
3.0%

Length

2023-12-10T19:12:16.879202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:17.063917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220915 28
28.0%
20220914 26
26.0%
20220913 23
23.0%
20220916 20
20.0%
20220917 3
 
3.0%

wkday_nm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Thursday
28 
Wednesday
26 
Tuesday
23 
Friday
20 
Saturday
3 

Length

Max length9
Median length8
Mean length7.63
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowSaturday
3rd rowTuesday
4th rowTuesday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Thursday 28
28.0%
Wednesday 26
26.0%
Tuesday 23
23.0%
Friday 20
20.0%
Saturday 3
 
3.0%

Length

2023-12-10T19:12:17.283599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:17.531026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thursday 28
28.0%
wednesday 26
26.0%
tuesday 23
23.0%
friday 20
20.0%
saturday 3
 
3.0%

min_prc
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105991.62
Minimum89000
Maximum172745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:17.765573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89000
5-th percentile99000
Q199000
median99000
Q399000
95-th percentile126000
Maximum172745
Range83745
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14947.559
Coefficient of variation (CV)0.14102586
Kurtosis6.3509694
Mean105991.62
Median Absolute Deviation (MAD)0
Skewness2.2734741
Sum10599162
Variance2.2342952 × 108
MonotonicityNot monotonic
2023-12-10T19:12:17.973978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
99000 73
73.0%
126000 20
 
20.0%
89000 3
 
3.0%
172745 2
 
2.0%
133672 1
 
1.0%
106000 1
 
1.0%
ValueCountFrequency (%)
89000 3
 
3.0%
99000 73
73.0%
106000 1
 
1.0%
126000 20
 
20.0%
133672 1
 
1.0%
172745 2
 
2.0%
ValueCountFrequency (%)
172745 2
 
2.0%
133672 1
 
1.0%
126000 20
 
20.0%
106000 1
 
1.0%
99000 73
73.0%
89000 3
 
3.0%

max_prc
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
152000
74 
168000
20 
255005
 
3
142000
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row152000
2nd row255005
3rd row152000
4th row152000
5th row152000

Common Values

ValueCountFrequency (%)
152000 74
74.0%
168000 20
 
20.0%
255005 3
 
3.0%
142000 3
 
3.0%

Length

2023-12-10T19:12:18.213564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:18.394596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
152000 74
74.0%
168000 20
 
20.0%
255005 3
 
3.0%
142000 3
 
3.0%

avrg_prc
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130165.01
Minimum116250
Maximum200919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:18.577680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116250
5-th percentile119000
Q1119000
median126250
Q3126250
95-th percentile144750
Maximum200919
Range84669
Interquartile range (IQR)7250

Descriptive statistics

Standard deviation15061.452
Coefficient of variation (CV)0.11571045
Kurtosis10.8285
Mean130165.01
Median Absolute Deviation (MAD)875
Skewness2.940425
Sum13016501
Variance2.2684735 × 108
MonotonicityNot monotonic
2023-12-10T19:12:18.761575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
126250 50
50.0%
119000 23
23.0%
144750 20
 
20.0%
116250 3
 
3.0%
197166 2
 
2.0%
200919 1
 
1.0%
128000 1
 
1.0%
ValueCountFrequency (%)
116250 3
 
3.0%
119000 23
23.0%
126250 50
50.0%
128000 1
 
1.0%
144750 20
 
20.0%
197166 2
 
2.0%
200919 1
 
1.0%
ValueCountFrequency (%)
200919 1
 
1.0%
197166 2
 
2.0%
144750 20
 
20.0%
128000 1
 
1.0%
126250 50
50.0%
119000 23
23.0%
116250 3
 
3.0%

Interactions

2023-12-10T19:12:13.006757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.155929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.531253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:13.155301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.289867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.698738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:13.673297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.419578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:12.864736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:12:18.910527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_nmota_nmctprvn_nmgugun_nmldgs_addrextrc_deldgmnt_dewkday_nmmin_prcmax_prcavrg_prc
ldgs_nm1.0000.9630.9630.9630.9630.2811.0001.0001.0001.0001.000
ota_nm0.9631.0000.9630.9630.9630.2811.0001.0001.0001.0001.000
ctprvn_nm0.9630.9631.0000.9630.9630.2811.0001.0001.0001.0001.000
gugun_nm0.9630.9630.9631.0000.9630.2811.0001.0001.0001.0001.000
ldgs_addr0.9630.9630.9630.9631.0000.2811.0001.0001.0001.0001.000
extrc_de0.2810.2810.2810.2810.2811.0000.0000.0000.0660.3980.615
ldgmnt_de1.0001.0001.0001.0001.0000.0001.0001.0000.9540.8400.875
wkday_nm1.0001.0001.0001.0001.0000.0001.0001.0000.9540.8400.875
min_prc1.0001.0001.0001.0001.0000.0660.9540.9541.0001.0000.836
max_prc1.0001.0001.0001.0001.0000.3980.8400.8401.0001.0001.000
avrg_prc1.0001.0001.0001.0001.0000.6150.8750.8750.8361.0001.000
2023-12-10T19:12:19.177825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_addrota_nmctprvn_nmwkday_nmgugun_nmldgmnt_deldgs_nmmax_prc
ldgs_addr1.0000.8260.8260.9850.8260.9850.8260.990
ota_nm0.8261.0000.8260.9850.8260.9850.8260.990
ctprvn_nm0.8260.8261.0000.9850.8260.9850.8260.990
wkday_nm0.9850.9850.9851.0000.9851.0000.9850.810
gugun_nm0.8260.8260.8260.9851.0000.9850.8260.990
ldgmnt_de0.9850.9850.9851.0000.9851.0000.9850.810
ldgs_nm0.8260.8260.8260.9850.8260.9851.0000.990
max_prc0.9900.9900.9900.8100.9900.8100.9901.000
2023-12-10T19:12:19.413428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
extrc_demin_prcavrg_prcldgs_nmota_nmctprvn_nmgugun_nmldgs_addrldgmnt_dewkday_nmmax_prc
extrc_de1.000-0.175-0.1820.1890.1890.1890.1890.1890.0000.0000.168
min_prc-0.1751.0000.8400.9790.9790.9790.9790.9790.6940.6940.990
avrg_prc-0.1820.8401.0000.9900.9900.9900.9900.9900.8640.8640.827
ldgs_nm0.1890.9790.9901.0000.8260.8260.8260.8260.9850.9850.990
ota_nm0.1890.9790.9900.8261.0000.8260.8260.8260.9850.9850.990
ctprvn_nm0.1890.9790.9900.8260.8261.0000.8260.8260.9850.9850.990
gugun_nm0.1890.9790.9900.8260.8260.8261.0000.8260.9850.9850.990
ldgs_addr0.1890.9790.9900.8260.8260.8260.8261.0000.9850.9850.990
ldgmnt_de0.0000.6940.8640.9850.9850.9850.9850.9851.0001.0000.810
wkday_nm0.0000.6940.8640.9850.9850.9850.9850.9851.0001.0000.810
max_prc0.1680.9900.8270.9900.9900.9900.9900.9900.8100.8101.000

Missing values

2023-12-10T19:12:13.889236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:12:14.169360image/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

ldgs_nmota_nmctprvn_nmgugun_nmldgs_addrextrc_deldgmnt_dewkday_nmmin_prcmax_prcavrg_prc
0호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082020220913Tuesday99000152000126250
1호텔인터시티EXPEDIA대전유성구대전 유성구 봉명동 545-52022090620220917Saturday133672255005200919
2호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082220220913Tuesday99000152000126250
3호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082320220913Tuesday99000152000126250
4호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082420220913Tuesday99000152000126250
5호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082520220913Tuesday99000152000126250
6호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082620220913Tuesday99000152000126250
7호텔인터시티EXPEDIA대전유성구대전 유성구 봉명동 545-52022090720220917Saturday172745255005197166
8호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082820220913Tuesday99000152000119000
9호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082920220913Tuesday99000152000119000
ldgs_nmota_nmctprvn_nmgugun_nmldgs_addrextrc_deldgmnt_dewkday_nmmin_prcmax_prcavrg_prc
90호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022082920220916Friday126000168000144750
91호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022083020220916Friday126000168000144750
92호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022083120220916Friday126000168000144750
93호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022090120220916Friday126000168000144750
94호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022090220220916Friday126000168000144750
95호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022090320220916Friday126000168000144750
96호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022090420220916Friday126000168000144750
97호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022090520220916Friday126000168000144750
98호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022090620220916Friday126000168000144750
99호텔마누GOODCHOICE서울중구서울 중구 남대문로5가 84-162022090720220916Friday126000168000144750