Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory45.3 B

Variable types

Categorical2
Numeric2
Text1

Alerts

anals_trget_year has constant value ""Constant
anals_trget_mt has constant value ""Constant
all_kwrd_rank_co is highly overall correlated with fq_coHigh correlation
fq_co is highly overall correlated with all_kwrd_rank_coHigh correlation
kwrd_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:01:50.487717
Analysis finished2023-12-10 10:01:51.834412
Duration1.35 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

anals_trget_year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 100
100.0%

Length

2023-12-10T19:01:51.970535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:52.224406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 100
100.0%

anals_trget_mt
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 100
100.0%

Length

2023-12-10T19:01:52.408056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:52.601506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 100
100.0%

all_kwrd_rank_co
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.22
Minimum1
Maximum982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:01:52.813546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum982
Range981
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation162.00078
Coefficient of variation (CV)2.044948
Kurtosis27.965859
Mean79.22
Median Absolute Deviation (MAD)25.5
Skewness5.3302158
Sum7922
Variance26244.254
MonotonicityNot monotonic
2023-12-10T19:01:53.545037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 3
 
3.0%
92 3
 
3.0%
86 3
 
3.0%
83 3
 
3.0%
69 3
 
3.0%
982 3
 
3.0%
73 2
 
2.0%
35 2
 
2.0%
62 2
 
2.0%
38 2
 
2.0%
Other values (61) 74
74.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
982 3
3.0%
99 2
2.0%
98 1
 
1.0%
97 1
 
1.0%
95 2
2.0%
92 3
3.0%
91 1
 
1.0%
89 2
2.0%
86 3
3.0%
83 3
3.0%

kwrd_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:01:54.164831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.24
Min length2

Characters and Unicode

Total characters224
Distinct characters139
Distinct categories1 ?
Distinct scripts1 ?
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 row다양
2nd row폭발
3rd row어린이
4th row사람
5th row독자
ValueCountFrequency (%)
다양 1
 
1.0%
원리 1
 
1.0%
읽기 1
 
1.0%
가족 1
 
1.0%
이용 1
 
1.0%
어른 1
 
1.0%
초등학교 1
 
1.0%
시대 1
 
1.0%
사실 1
 
1.0%
마지막 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:01:55.216022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 9
 
4.0%
상 6
 
2.7%
학 5
 
2.2%
사 5
 
2.2%
리 5
 
2.2%
생 4
 
1.8%
기 4
 
1.8%
시 4
 
1.8%
고 3
 
1.3%
습 3
 
1.3%
Other values (129) 176
78.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 224
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 9
 
4.0%
상 6
 
2.7%
학 5
 
2.2%
사 5
 
2.2%
리 5
 
2.2%
생 4
 
1.8%
기 4
 
1.8%
시 4
 
1.8%
고 3
 
1.3%
습 3
 
1.3%
Other values (129) 176
78.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 224
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 9
 
4.0%
상 6
 
2.7%
학 5
 
2.2%
사 5
 
2.2%
리 5
 
2.2%
생 4
 
1.8%
기 4
 
1.8%
시 4
 
1.8%
고 3
 
1.3%
습 3
 
1.3%
Other values (129) 176
78.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 224
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 9
 
4.0%
상 6
 
2.7%
학 5
 
2.2%
사 5
 
2.2%
리 5
 
2.2%
생 4
 
1.8%
기 4
 
1.8%
시 4
 
1.8%
고 3
 
1.3%
습 3
 
1.3%
Other values (129) 176
78.6%

fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.67
Minimum25
Maximum505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:01:55.558525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile128.95
Q1139.5
median172
Q3222.5
95-th percentile334.1
Maximum505
Range480
Interquartile range (IQR)83

Descriptive statistics

Standard deviation78.213261
Coefficient of variation (CV)0.40594416
Kurtosis2.8897134
Mean192.67
Median Absolute Deviation (MAD)36
Skewness1.2586475
Sum19267
Variance6117.3142
MonotonicityNot monotonic
2023-12-10T19:01:55.905011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144 3
 
3.0%
132 3
 
3.0%
135 3
 
3.0%
136 3
 
3.0%
151 3
 
3.0%
25 3
 
3.0%
145 2
 
2.0%
208 2
 
2.0%
158 2
 
2.0%
204 2
 
2.0%
Other values (61) 74
74.0%
ValueCountFrequency (%)
25 3
3.0%
128 2
2.0%
129 1
 
1.0%
130 1
 
1.0%
131 2
2.0%
132 3
3.0%
133 1
 
1.0%
134 2
2.0%
135 3
3.0%
136 3
3.0%
ValueCountFrequency (%)
505 1
1.0%
422 1
1.0%
398 1
1.0%
386 1
1.0%
374 1
1.0%
332 1
1.0%
328 1
1.0%
318 1
1.0%
303 1
1.0%
302 1
1.0%

Interactions

2023-12-10T19:01:51.158130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:50.796250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:51.305953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:50.971499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:01:56.077417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
all_kwrd_rank_cokwrd_nmfq_co
all_kwrd_rank_co1.0001.0001.000
kwrd_nm1.0001.0001.000
fq_co1.0001.0001.000
2023-12-10T19:01:56.247698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
all_kwrd_rank_cofq_co
all_kwrd_rank_co1.000-1.000
fq_co-1.0001.000

Missing values

2023-12-10T19:01:51.535188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:01:51.757145image/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

anals_trget_yearanals_trget_mtall_kwrd_rank_cokwrd_nmfq_co
02021111다양505
1202111982폭발25
22021113어린이422
32021114사람398
42021115독자386
52021116시리즈374
62021117세계332
7202111982기법25
82021119사랑328
920211110아이318
anals_trget_yearanals_trget_mtall_kwrd_rank_cokwrd_nmfq_co
9020211191게임133
9120211192사이132
9220211192순간132
9320211192머리132
9420211195인간131
9520211195선물131
9620211197생생130
9720211198질문129
9820211199학교128
9920211199그림책128