Overview

Dataset statistics

Number of variables9
Number of observations186
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.1 KiB
Average record size in memory77.7 B

Variable types

Numeric5
Categorical4

Alerts

SRCHWRD_TY_NM has constant value ""Constant
LWPRT_CTGRY_NM is highly overall correlated with SEQ_NO and 2 other fieldsHigh correlation
SRCHWRD_NM is highly overall correlated with SEQ_NO and 2 other fieldsHigh correlation
UPPER_CTGRY_NM is highly overall correlated with SEQ_NO and 2 other fieldsHigh correlation
SEQ_NO is highly overall correlated with SRCHWRD_NM and 2 other fieldsHigh correlation
MOBILE_SCCNT_VALUE is highly overall correlated with PC_SCCNT_VALUE and 1 other fieldsHigh correlation
PC_SCCNT_VALUE is highly overall correlated with MOBILE_SCCNT_VALUE and 1 other fieldsHigh correlation
SCCNT_SM_VALUE is highly overall correlated with MOBILE_SCCNT_VALUE and 1 other fieldsHigh correlation
SEQ_NO has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:06:03.991142
Analysis finished2023-12-10 10:06:09.418057
Duration5.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SEQ_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean431374.83
Minimum260332
Maximum568890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T19:06:09.651970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum260332
5-th percentile260341.25
Q1350129.25
median440182.5
Q3528532.75
95-th percentile568880.75
Maximum568890
Range308558
Interquartile range (IQR)178403.5

Descriptive statistics

Standard deviation115609.86
Coefficient of variation (CV)0.26800326
Kurtosis-1.6233398
Mean431374.83
Median Absolute Deviation (MAD)89202
Skewness-0.17689105
Sum80235719
Variance1.336564 × 1010
MonotonicityNot monotonic
2023-12-10T19:06:10.088375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
527946 1
 
0.5%
568881 1
 
0.5%
260351 1
 
0.5%
260352 1
 
0.5%
350134 1
 
0.5%
352409 1
 
0.5%
568880 1
 
0.5%
527966 1
 
0.5%
528538 1
 
0.5%
528539 1
 
0.5%
Other values (176) 176
94.6%
ValueCountFrequency (%)
260332 1
0.5%
260333 1
0.5%
260334 1
0.5%
260335 1
0.5%
260336 1
0.5%
260337 1
0.5%
260338 1
0.5%
260339 1
0.5%
260340 1
0.5%
260341 1
0.5%
ValueCountFrequency (%)
568890 1
0.5%
568889 1
0.5%
568888 1
0.5%
568887 1
0.5%
568886 1
0.5%
568885 1
0.5%
568884 1
0.5%
568883 1
0.5%
568882 1
0.5%
568881 1
0.5%

SRCHWRD_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
돼지갈비
62 
팽현숙순대국
31 
부대찌개
31 
다이어트도시락
31 
곱창
31 

Length

Max length7
Median length6.5
Mean length4.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row돼지갈비
2nd row돼지갈비
3rd row팽현숙순대국
4th row부대찌개
5th row다이어트도시락

Common Values

ValueCountFrequency (%)
돼지갈비 62
33.3%
팽현숙순대국 31
16.7%
부대찌개 31
16.7%
다이어트도시락 31
16.7%
곱창 31
16.7%

Length

2023-12-10T19:06:10.347044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:10.568797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
돼지갈비 62
33.3%
팽현숙순대국 31
16.7%
부대찌개 31
16.7%
다이어트도시락 31
16.7%
곱창 31
16.7%

UPPER_CTGRY_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
축산식품
93 
냉동/간편조리식품
62 
가공식품
31 

Length

Max length9
Median length4
Mean length5.6666667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row축산식품
2nd row축산식품
3rd row냉동/간편조리식품
4th row가공식품
5th row냉동/간편조리식품

Common Values

ValueCountFrequency (%)
축산식품 93
50.0%
냉동/간편조리식품 62
33.3%
가공식품 31
 
16.7%

Length

2023-12-10T19:06:10.835169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:11.019923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
축산식품 93
50.0%
냉동/간편조리식품 62
33.3%
가공식품 31
 
16.7%

LWPRT_CTGRY_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
축산가공식품
62 
냉동/간편조리식품
62 
돼지양념육
31 
쿠킹박스
31 

Length

Max length9
Median length6
Mean length6.5
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row돼지양념육
2nd row축산가공식품
3rd row냉동/간편조리식품
4th row쿠킹박스
5th row냉동/간편조리식품

Common Values

ValueCountFrequency (%)
축산가공식품 62
33.3%
냉동/간편조리식품 62
33.3%
돼지양념육 31
16.7%
쿠킹박스 31
16.7%

Length

2023-12-10T19:06:11.287999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:11.675953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
축산가공식품 62
33.3%
냉동/간편조리식품 62
33.3%
돼지양념육 31
16.7%
쿠킹박스 31
16.7%

SRCHWRD_TY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
인기검색어
186 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인기검색어 186
100.0%

Length

2023-12-10T19:06:11.946274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:12.118386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인기검색어 186
100.0%

MOBILE_SCCNT_VALUE
Real number (ℝ)

HIGH CORRELATION 

Distinct143
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean609.82258
Minimum66
Maximum12878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T19:06:12.334435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile106.5
Q1208
median283
Q3507.75
95-th percentile1568.25
Maximum12878
Range12812
Interquartile range (IQR)299.75

Descriptive statistics

Standard deviation1333.579
Coefficient of variation (CV)2.1868311
Kurtosis50.094501
Mean609.82258
Median Absolute Deviation (MAD)126
Skewness6.6308424
Sum113427
Variance1778433
MonotonicityNot monotonic
2023-12-10T19:06:12.635351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219 3
 
1.6%
265 3
 
1.6%
138 3
 
1.6%
256 3
 
1.6%
106 3
 
1.6%
260 2
 
1.1%
118 2
 
1.1%
208 2
 
1.1%
275 2
 
1.1%
285 2
 
1.1%
Other values (133) 161
86.6%
ValueCountFrequency (%)
66 1
 
0.5%
67 1
 
0.5%
71 1
 
0.5%
99 1
 
0.5%
100 1
 
0.5%
102 2
1.1%
106 3
1.6%
108 1
 
0.5%
111 1
 
0.5%
113 1
 
0.5%
ValueCountFrequency (%)
12878 1
0.5%
8584 1
0.5%
7710 1
0.5%
5986 1
0.5%
2629 1
0.5%
1805 1
0.5%
1764 1
0.5%
1595 1
0.5%
1573 1
0.5%
1571 1
0.5%

PC_SCCNT_VALUE
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3810.1828
Minimum1173
Maximum22471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T19:06:12.886861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1173
5-th percentile1785
Q12302.25
median3768
Q34639.5
95-th percentile6150.5
Maximum22471
Range21298
Interquartile range (IQR)2337.25

Descriptive statistics

Standard deviation2138.5609
Coefficient of variation (CV)0.56127513
Kurtosis32.078657
Mean3810.1828
Median Absolute Deviation (MAD)1267
Skewness4.239637
Sum708694
Variance4573442.5
MonotonicityNot monotonic
2023-12-10T19:06:13.112036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2462 2
 
1.1%
3050 2
 
1.1%
1910 2
 
1.1%
1883 2
 
1.1%
2301 2
 
1.1%
2174 2
 
1.1%
2640 2
 
1.1%
2654 2
 
1.1%
2409 2
 
1.1%
3280 2
 
1.1%
Other values (144) 166
89.2%
ValueCountFrequency (%)
1173 1
0.5%
1350 1
0.5%
1380 1
0.5%
1561 1
0.5%
1621 1
0.5%
1630 1
0.5%
1645 1
0.5%
1649 1
0.5%
1713 1
0.5%
1777 1
0.5%
ValueCountFrequency (%)
22471 1
0.5%
12269 1
0.5%
11211 1
0.5%
9133 1
0.5%
7549 1
0.5%
7188 1
0.5%
6882 1
0.5%
6330 1
0.5%
6277 1
0.5%
6210 1
0.5%

SCCNT_SM_VALUE
Real number (ℝ)

HIGH CORRELATION 

Distinct152
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4420.0054
Minimum1287
Maximum35349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T19:06:13.374828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1287
5-th percentile2034
Q12550
median4148.5
Q35118.25
95-th percentile7476.75
Maximum35349
Range34062
Interquartile range (IQR)2568.25

Descriptive statistics

Standard deviation3318.7993
Coefficient of variation (CV)0.75085867
Kurtosis44.851311
Mean4420.0054
Median Absolute Deviation (MAD)1483.5
Skewness5.6278039
Sum822121
Variance11014429
MonotonicityNot monotonic
2023-12-10T19:06:13.720815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2665 4
 
2.2%
2564 2
 
1.1%
2417 2
 
1.1%
2611 2
 
1.1%
2566 2
 
1.1%
2172 2
 
1.1%
2112 2
 
1.1%
2550 2
 
1.1%
2424 2
 
1.1%
2404 2
 
1.1%
Other values (142) 164
88.2%
ValueCountFrequency (%)
1287 1
0.5%
1456 1
0.5%
1491 1
0.5%
1632 1
0.5%
1734 1
0.5%
1738 1
0.5%
1876 2
1.1%
1957 1
0.5%
2028 1
0.5%
2052 1
0.5%
ValueCountFrequency (%)
35349 1
0.5%
20853 1
0.5%
18921 1
0.5%
11881 1
0.5%
10897 1
0.5%
10178 1
0.5%
8687 1
0.5%
7781 1
0.5%
7532 1
0.5%
7480 1
0.5%

SCCNT_DE
Real number (ℝ)

Distinct31
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210116
Minimum20210101
Maximum20210131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-10T19:06:13.957431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210101
5-th percentile20210102
Q120210108
median20210116
Q320210124
95-th percentile20210130
Maximum20210131
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.968413
Coefficient of variation (CV)4.4375861 × 10-7
Kurtosis-1.2024973
Mean20210116
Median Absolute Deviation (MAD)8
Skewness0
Sum3.7590816 × 109
Variance80.432432
MonotonicityIncreasing
2023-12-10T19:06:14.218631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20210101 6
 
3.2%
20210102 6
 
3.2%
20210131 6
 
3.2%
20210130 6
 
3.2%
20210129 6
 
3.2%
20210128 6
 
3.2%
20210127 6
 
3.2%
20210126 6
 
3.2%
20210125 6
 
3.2%
20210124 6
 
3.2%
Other values (21) 126
67.7%
ValueCountFrequency (%)
20210101 6
3.2%
20210102 6
3.2%
20210103 6
3.2%
20210104 6
3.2%
20210105 6
3.2%
20210106 6
3.2%
20210107 6
3.2%
20210108 6
3.2%
20210109 6
3.2%
20210110 6
3.2%
ValueCountFrequency (%)
20210131 6
3.2%
20210130 6
3.2%
20210129 6
3.2%
20210128 6
3.2%
20210127 6
3.2%
20210126 6
3.2%
20210125 6
3.2%
20210124 6
3.2%
20210123 6
3.2%
20210122 6
3.2%

Interactions

2023-12-10T19:06:08.026170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:04.565095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:05.591442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.449368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:07.152611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:08.245833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:05.010100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:05.748697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.612146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:07.316040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:08.434999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:05.159067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:05.914487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.757071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:07.472410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:08.636676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:05.303386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.122144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.876008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:07.674111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:08.806238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:05.435877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:06.293000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:07.021217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:07.853063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:06:14.765908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOSRCHWRD_NMUPPER_CTGRY_NMLWPRT_CTGRY_NMMOBILE_SCCNT_VALUEPC_SCCNT_VALUESCCNT_SM_VALUESCCNT_DE
SEQ_NO1.0001.0001.0000.9900.2360.6440.4830.000
SRCHWRD_NM1.0001.0001.0000.8740.3660.6480.4820.000
UPPER_CTGRY_NM1.0001.0001.0001.0000.3350.4900.4480.000
LWPRT_CTGRY_NM0.9900.8741.0001.0000.2370.4860.3100.000
MOBILE_SCCNT_VALUE0.2360.3660.3350.2371.0000.9750.9190.000
PC_SCCNT_VALUE0.6440.6480.4900.4860.9751.0000.8780.000
SCCNT_SM_VALUE0.4830.4820.4480.3100.9190.8781.0000.000
SCCNT_DE0.0000.0000.0000.0000.0000.0000.0001.000
2023-12-10T19:06:14.983164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LWPRT_CTGRY_NMSRCHWRD_NMUPPER_CTGRY_NM
LWPRT_CTGRY_NM1.0000.8600.997
SRCHWRD_NM0.8601.0000.995
UPPER_CTGRY_NM0.9970.9951.000
2023-12-10T19:06:15.188487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOMOBILE_SCCNT_VALUEPC_SCCNT_VALUESCCNT_SM_VALUESCCNT_DESRCHWRD_NMUPPER_CTGRY_NMLWPRT_CTGRY_NM
SEQ_NO1.000-0.148-0.275-0.2590.1670.9970.9970.864
MOBILE_SCCNT_VALUE-0.1481.0000.5830.6600.0100.2430.2360.163
PC_SCCNT_VALUE-0.2750.5831.0000.9890.0140.4850.3690.349
SCCNT_SM_VALUE-0.2590.6600.9891.0000.0370.3490.2010.200
SCCNT_DE0.1670.0100.0140.0371.0000.0000.0000.000
SRCHWRD_NM0.9970.2430.4850.3490.0001.0000.9950.860
UPPER_CTGRY_NM0.9970.2360.3690.2010.0000.9951.0000.997
LWPRT_CTGRY_NM0.8640.1630.3490.2000.0000.8600.9971.000

Missing values

2023-12-10T19:06:09.029419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:06:09.328702image/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

SEQ_NOSRCHWRD_NMUPPER_CTGRY_NMLWPRT_CTGRY_NMSRCHWRD_TY_NMMOBILE_SCCNT_VALUEPC_SCCNT_VALUESCCNT_SM_VALUESCCNT_DE
0527946돼지갈비축산식품돼지양념육인기검색어1022462256420210101
1528518돼지갈비축산식품축산가공식품인기검색어1022462256420210101
2352389팽현숙순대국냉동/간편조리식품냉동/간편조리식품인기검색어2081920212820210101
3260332부대찌개가공식품쿠킹박스인기검색어1384013415120210101
4350114다이어트도시락냉동/간편조리식품냉동/간편조리식품인기검색어3253542386720210101
5568860곱창축산식품축산가공식품인기검색어2043578378220210101
6568861곱창축산식품축산가공식품인기검색어2684420468820210102
7350115다이어트도시락냉동/간편조리식품냉동/간편조리식품인기검색어4004214461420210102
8528519돼지갈비축산식품축산가공식품인기검색어1062559266520210102
9527947돼지갈비축산식품돼지양념육인기검색어1062559266520210102
SEQ_NOSRCHWRD_NMUPPER_CTGRY_NMLWPRT_CTGRY_NMSRCHWRD_TY_NMMOBILE_SCCNT_VALUEPC_SCCNT_VALUESCCNT_SM_VALUESCCNT_DE
176350143다이어트도시락냉동/간편조리식품냉동/간편조리식품인기검색어3443452379620210130
177527975돼지갈비축산식품돼지양념육인기검색어1433250339320210130
178352418팽현숙순대국냉동/간편조리식품냉동/간편조리식품인기검색어711561163220210130
179568889곱창축산식품축산가공식품인기검색어2154389460420210130
180568890곱창축산식품축산가공식품인기검색어2024082428420210131
181352419팽현숙순대국냉동/간편조리식품냉동/간편조리식품인기검색어662680274620210131
182527976돼지갈비축산식품돼지양념육인기검색어1403199333920210131
183350144다이어트도시락냉동/간편조리식품냉동/간편조리식품인기검색어4845263574720210131
184260362부대찌개가공식품쿠킹박스인기검색어1686330649820210131
185528548돼지갈비축산식품축산가공식품인기검색어1403199333920210131