Overview

Dataset statistics

Number of variables8
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory71.3 B

Variable types

Categorical3
Text1
Numeric4

Alerts

PROGRM_BEGIN_TIME is highly overall correlated with PROGRM_END_TIME and 1 other fieldsHigh correlation
PROGRM_END_TIME is highly overall correlated with PROGRM_BEGIN_TIME and 2 other fieldsHigh correlation
WTCHNG_RT is highly overall correlated with DAIL_AVRG_REACH_RTHigh correlation
DAIL_AVRG_REACH_RT is highly overall correlated with WTCHNG_RTHigh correlation
BRDCST_DE is highly overall correlated with PROGRM_BEGIN_TIME and 2 other fieldsHigh correlation
BRDCST_END_DE is highly overall correlated with PROGRM_END_TIME and 1 other fieldsHigh correlation
BRDCST_DE is highly imbalanced (76.0%)Imbalance
BRDCST_END_DE is highly imbalanced (63.7%)Imbalance
WTCHNG_RT has 3 (3.0%) zerosZeros
DAIL_AVRG_REACH_RT has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:43:26.379374
Analysis finished2023-12-10 09:43:30.735778
Duration4.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BRDCST_DE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
20210701
97 
20210702
 
4

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210701
2nd row20210701
3rd row20210701
4th row20210701
5th row20210701

Common Values

ValueCountFrequency (%)
20210701 97
96.0%
20210702 4
 
4.0%

Length

2023-12-10T18:43:30.851520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:31.459535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210701 97
96.0%
20210702 4
 
4.0%

BRDCST_END_DE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
20210701
94 
20210702
 
7

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210701
2nd row20210701
3rd row20210701
4th row20210701
5th row20210701

Common Values

ValueCountFrequency (%)
20210701 94
93.1%
20210702 7
 
6.9%

Length

2023-12-10T18:43:31.681048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:31.867969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210701 94
93.1%
20210702 7
 
6.9%

CHNNEL_NM
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
롯데홈쇼핑
26 
공영쇼핑
25 
홈&쇼핑
23 
현대홈쇼핑
17 
CJ오쇼핑
10 

Length

Max length5
Median length5
Mean length4.5247525
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
롯데홈쇼핑 26
25.7%
공영쇼핑 25
24.8%
홈&쇼핑 23
22.8%
현대홈쇼핑 17
16.8%
CJ오쇼핑 10
 
9.9%

Length

2023-12-10T18:43:32.058270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:32.320556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
롯데홈쇼핑 26
25.7%
공영쇼핑 25
24.8%
홈&쇼핑 23
22.8%
현대홈쇼핑 17
16.8%
cj오쇼핑 10
 
9.9%

PROGRM_NM
Text

Distinct54
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-10T18:43:32.770240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length3.8613861
Min length2

Characters and Unicode

Total characters390
Distinct characters128
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)32.7%

Sample

1st row레포츠기기
2nd row신선수산
3rd row의류
4th row건강식품
5th row가공축산
ValueCountFrequency (%)
건강식품 9
 
8.9%
렌탈 7
 
6.9%
식품 6
 
5.9%
의류 5
 
5.0%
일반식품 4
 
4.0%
보험 3
 
3.0%
이미용 3
 
3.0%
신선수산 3
 
3.0%
주방 3
 
3.0%
생활 3
 
3.0%
Other values (44) 55
54.5%
2023-12-10T18:43:33.561391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
품 33
 
8.5%
식 22
 
5.6%
건 12
 
3.1%
강 12
 
3.1%
1 9
 
2.3%
산 9
 
2.3%
의 8
 
2.1%
가 8
 
2.1%
부 8
 
2.1%
E 7
 
1.8%
Other values (118) 262
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 350
89.7%
Uppercase Letter 24
 
6.2%
Decimal Number 12
 
3.1%
Other Punctuation 2
 
0.5%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 33
 
9.4%
식 22
 
6.3%
건 12
 
3.4%
강 12
 
3.4%
산 9
 
2.6%
의 8
 
2.3%
가 8
 
2.3%
부 8
 
2.3%
탈 7
 
2.0%
렌 7
 
2.0%
Other values (102) 224
64.0%
Uppercase Letter
ValueCountFrequency (%)
E 7
29.2%
T 3
12.5%
S 3
12.5%
W 2
 
8.3%
B 2
 
8.3%
K 2
 
8.3%
L 2
 
8.3%
Y 2
 
8.3%
H 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
1 9
75.0%
2 2
 
16.7%
3 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
' 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 350
89.7%
Latin 24
 
6.2%
Common 16
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 33
 
9.4%
식 22
 
6.3%
건 12
 
3.4%
강 12
 
3.4%
산 9
 
2.6%
의 8
 
2.3%
가 8
 
2.3%
부 8
 
2.3%
탈 7
 
2.0%
렌 7
 
2.0%
Other values (102) 224
64.0%
Latin
ValueCountFrequency (%)
E 7
29.2%
T 3
12.5%
S 3
12.5%
W 2
 
8.3%
B 2
 
8.3%
K 2
 
8.3%
L 2
 
8.3%
Y 2
 
8.3%
H 1
 
4.2%
Common
ValueCountFrequency (%)
1 9
56.2%
2 2
 
12.5%
) 1
 
6.2%
( 1
 
6.2%
3 1
 
6.2%
/ 1
 
6.2%
' 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 350
89.7%
ASCII 40
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
품 33
 
9.4%
식 22
 
6.3%
건 12
 
3.4%
강 12
 
3.4%
산 9
 
2.6%
의 8
 
2.3%
가 8
 
2.3%
부 8
 
2.3%
탈 7
 
2.0%
렌 7
 
2.0%
Other values (102) 224
64.0%
ASCII
ValueCountFrequency (%)
1 9
22.5%
E 7
17.5%
T 3
 
7.5%
S 3
 
7.5%
W 2
 
5.0%
B 2
 
5.0%
K 2
 
5.0%
L 2
 
5.0%
Y 2
 
5.0%
2 2
 
5.0%
Other values (6) 6
15.0%

PROGRM_BEGIN_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114779.24
Minimum3
Maximum235503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:33.848658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile20003
Q160003
median113503
Q3174003
95-th percentile224503
Maximum235503
Range235500
Interquartile range (IQR)114000

Descriptive statistics

Standard deviation66743.571
Coefficient of variation (CV)0.58149516
Kurtosis-1.1879935
Mean114779.24
Median Absolute Deviation (MAD)58500
Skewness0.10096121
Sum11592703
Variance4.4547042 × 109
MonotonicityNot monotonic
2023-12-10T18:43:34.122713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20003 5
 
5.0%
92503 5
 
5.0%
81503 4
 
4.0%
71503 4
 
4.0%
60003 4
 
4.0%
124003 4
 
4.0%
184003 4
 
4.0%
113503 3
 
3.0%
214503 3
 
3.0%
10003 3
 
3.0%
Other values (44) 62
61.4%
ValueCountFrequency (%)
3 1
 
1.0%
10003 3
3.0%
20003 5
5.0%
23503 1
 
1.0%
25003 1
 
1.0%
30003 1
 
1.0%
30503 1
 
1.0%
31503 1
 
1.0%
34503 1
 
1.0%
35003 1
 
1.0%
ValueCountFrequency (%)
235503 1
 
1.0%
235003 1
 
1.0%
225503 2
2.0%
224503 2
2.0%
214503 3
3.0%
214003 1
 
1.0%
204503 2
2.0%
204003 2
2.0%
194003 2
2.0%
193503 1
 
1.0%

PROGRM_END_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117659.99
Minimum5959
Maximum235959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:34.416370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5959
5-th percentile15959
Q155959
median123459
Q3173959
95-th percentile224459
Maximum235959
Range230000
Interquartile range (IQR)118000

Descriptive statistics

Standard deviation66969.993
Coefficient of variation (CV)0.56918237
Kurtosis-1.1702346
Mean117659.99
Median Absolute Deviation (MAD)60500
Skewness0.060514521
Sum11883659
Variance4.4849799 × 109
MonotonicityNot monotonic
2023-12-10T18:43:34.682875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92459 5
 
5.0%
183959 4
 
4.0%
123959 4
 
4.0%
55959 4
 
4.0%
81459 4
 
4.0%
71459 4
 
4.0%
15959 4
 
4.0%
173959 3
 
3.0%
214459 3
 
3.0%
5959 3
 
3.0%
Other values (44) 63
62.4%
ValueCountFrequency (%)
5959 3
3.0%
15959 4
4.0%
23459 1
 
1.0%
24959 1
 
1.0%
25959 1
 
1.0%
30459 2
2.0%
34459 1
 
1.0%
34959 1
 
1.0%
35959 2
2.0%
43459 1
 
1.0%
ValueCountFrequency (%)
235959 1
 
1.0%
235459 1
 
1.0%
234959 1
 
1.0%
225459 2
2.0%
224459 2
2.0%
214459 3
3.0%
213959 1
 
1.0%
204459 2
2.0%
203959 2
2.0%
193959 2
2.0%

WTCHNG_RT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030078614
Minimum0
Maximum0.12629
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:34.948896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00209
Q10.0083
median0.02516
Q30.04557
95-th percentile0.07809
Maximum0.12629
Range0.12629
Interquartile range (IQR)0.03727

Descriptive statistics

Standard deviation0.025828263
Coefficient of variation (CV)0.85869192
Kurtosis1.2152082
Mean0.030078614
Median Absolute Deviation (MAD)0.01702
Skewness1.1433662
Sum3.03794
Variance0.00066709915
MonotonicityNot monotonic
2023-12-10T18:43:35.241005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
0.00495 1
 
1.0%
0.07316 1
 
1.0%
0.00436 1
 
1.0%
0.05181 1
 
1.0%
0.00768 1
 
1.0%
0.00131 1
 
1.0%
0.00613 1
 
1.0%
0.00049 1
 
1.0%
0.00814 1
 
1.0%
Other values (89) 89
88.1%
ValueCountFrequency (%)
0.0 3
3.0%
0.00049 1
 
1.0%
0.00131 1
 
1.0%
0.00209 1
 
1.0%
0.00234 1
 
1.0%
0.00261 1
 
1.0%
0.00293 1
 
1.0%
0.00326 1
 
1.0%
0.00396 1
 
1.0%
0.00425 1
 
1.0%
ValueCountFrequency (%)
0.12629 1
1.0%
0.10008 1
1.0%
0.09082 1
1.0%
0.08547 1
1.0%
0.08174 1
1.0%
0.07809 1
1.0%
0.07583 1
1.0%
0.07316 1
1.0%
0.07163 1
1.0%
0.07031 1
1.0%

DAIL_AVRG_REACH_RT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3959497
Minimum0
Maximum2.11184
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:35.513720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04272
Q10.12905
median0.2948
Q30.53808
95-th percentile1.11282
Maximum2.11184
Range2.11184
Interquartile range (IQR)0.40903

Descriptive statistics

Standard deviation0.3884805
Coefficient of variation (CV)0.98113598
Kurtosis5.2718734
Mean0.3959497
Median Absolute Deviation (MAD)0.17325
Skewness2.1017782
Sum39.99092
Variance0.1509171
MonotonicityNot monotonic
2023-12-10T18:43:35.786176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04593 3
 
3.0%
0.0 3
 
3.0%
0.15453 1
 
1.0%
0.11199 1
 
1.0%
0.59345 1
 
1.0%
0.29669 1
 
1.0%
0.09805 1
 
1.0%
0.11741 1
 
1.0%
0.02964 1
 
1.0%
0.12155 1
 
1.0%
Other values (87) 87
86.1%
ValueCountFrequency (%)
0.0 3
3.0%
0.02964 1
 
1.0%
0.04185 1
 
1.0%
0.04272 1
 
1.0%
0.04395 1
 
1.0%
0.04593 3
3.0%
0.04897 1
 
1.0%
0.05031 1
 
1.0%
0.0698 1
 
1.0%
0.08701 1
 
1.0%
ValueCountFrequency (%)
2.11184 1
1.0%
1.75275 1
1.0%
1.57306 1
1.0%
1.48988 1
1.0%
1.47851 1
1.0%
1.11282 1
1.0%
1.10393 1
1.0%
1.00627 1
1.0%
0.81872 1
1.0%
0.77985 1
1.0%

Interactions

2023-12-10T18:43:29.411814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:27.101934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:27.905694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:28.665288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:29.582361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:27.271696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:28.076273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:28.845322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:29.779420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:27.436529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:28.254478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:29.000918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:29.959203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:27.744248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:28.446991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:43:29.177115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:43:35.980823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEWTCHNG_RTDAIL_AVRG_REACH_RT
BRDCST_DE1.0000.8440.0000.0000.7170.7530.0000.000
BRDCST_END_DE0.8441.0000.0000.0000.6340.8780.1650.354
CHNNEL_NM0.0000.0001.0000.9170.0000.0000.1990.000
PROGRM_NM0.0000.0000.9171.0000.4860.7240.4100.713
PROGRM_BEGIN_TIME0.7170.6340.0000.4861.0000.9820.0000.400
PROGRM_END_TIME0.7530.8780.0000.7240.9821.0000.0000.416
WTCHNG_RT0.0000.1650.1990.4100.0000.0001.0000.783
DAIL_AVRG_REACH_RT0.0000.3540.0000.7130.4000.4160.7831.000
2023-12-10T18:43:36.207111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRDCST_END_DECHNNEL_NMBRDCST_DE
BRDCST_END_DE1.0000.0000.640
CHNNEL_NM0.0001.0000.000
BRDCST_DE0.6400.0001.000
2023-12-10T18:43:36.355001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PROGRM_BEGIN_TIMEPROGRM_END_TIMEWTCHNG_RTDAIL_AVRG_REACH_RTBRDCST_DEBRDCST_END_DECHNNEL_NM
PROGRM_BEGIN_TIME1.0000.8290.1940.4880.5370.4710.000
PROGRM_END_TIME0.8291.0000.1340.4440.5370.7700.000
WTCHNG_RT0.1940.1341.0000.6400.0000.1570.110
DAIL_AVRG_REACH_RT0.4880.4440.6401.0000.0000.3400.000
BRDCST_DE0.5370.5370.0000.0001.0000.6400.000
BRDCST_END_DE0.4710.7700.1570.3400.6401.0000.000
CHNNEL_NM0.0000.0000.1100.0000.0000.0001.000

Missing values

2023-12-10T18:43:30.324742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:43:30.641988image/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

BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEWTCHNG_RTDAIL_AVRG_REACH_RT
02021070120210701공영쇼핑레포츠기기20003259590.029580.1827
12021070120210701공영쇼핑신선수산30003349590.021130.04593
22021070120210701공영쇼핑의류35003359590.045930.04593
32021070120210701공영쇼핑건강식품40003459590.042860.04593
42021070120210701공영쇼핑가공축산50003559590.011340.11266
52021070120210701공영쇼핑가공농산60003704590.004610.11149
62021070120210701공영쇼핑이미용70503819590.012090.40051
72021070120210701공영쇼핑주방용품82003924590.01380.34363
82021070120210701공영쇼핑가공축산925031029590.018280.38532
92021070120210701공영쇼핑신선농산1030031134590.022580.67592
BRDCST_DEBRDCST_END_DECHNNEL_NMPROGRM_NMPROGRM_BEGIN_TIMEPROGRM_END_TIMEWTCHNG_RTDAIL_AVRG_REACH_RT
912021070120210701CJ오쇼핑순환20003529590.039680.64256
922021070120210701CJ오쇼핑1촌1명품/1사1명품53003559590.003260.04897
932021070120210701CJ오쇼핑건강식품1부60003714590.051970.3897
942021070120210701CJ오쇼핑온스타일베스트1부71503814590.034510.31567
952021070120210701CJ오쇼핑건강식품2부81503924590.049950.63816
962021070120210701CJ오쇼핑건강식품3부925031024590.028250.38541
972021070120210701CJ오쇼핑WEEKLYBEST1부1025031134590.041840.67418
982021070120210701CJ오쇼핑WEEKLYBEST2부1135031234590.03540.31603
992021070120210701CJ오쇼핑쇼핑릴레이1부1235031434590.085471.57306
1002021070120210701CJ오쇼핑보험1부1435031529590.025570.37795