Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells112
Missing cells (%)6.6%
Duplicate rows1
Duplicate rows (%)1.0%
Total size in memory13.9 KiB
Average record size in memory142.3 B

Variable types

Categorical12
Numeric3
Unsupported1
DateTime1

Alerts

base_ymd has constant value ""Constant
Dataset has 1 (1.0%) duplicate rowsDuplicates
eng_lang_nm is highly overall correlated with entrp_nm and 6 other fieldsHigh correlation
city_do_kor_lang_nm is highly overall correlated with xpos_lo and 13 other fieldsHigh correlation
gov_dn_jan_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
kor_lang_nm is highly overall correlated with entrp_nm and 6 other fieldsHigh correlation
gov_dn_kor_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
entrp_nm is highly overall correlated with kor_lang_nm and 6 other fieldsHigh correlation
city_do_cd is highly overall correlated with xpos_lo and 13 other fieldsHigh correlation
city_gn_gu_kor_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
chg_lang_nm is highly overall correlated with entrp_nm and 6 other fieldsHigh correlation
city_gn_gu_jan_lang_nm is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
jan_lang_nm is highly overall correlated with entrp_nm and 6 other fieldsHigh correlation
city_do_jan_lang_nm is highly overall correlated with xpos_lo and 13 other fieldsHigh correlation
xpos_lo is highly overall correlated with city_gn_gu_cd and 7 other fieldsHigh correlation
ypos_la is highly overall correlated with city_gn_gu_cd and 7 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with xpos_lo and 8 other fieldsHigh correlation
city_do_cd is highly imbalanced (80.6%)Imbalance
city_do_kor_lang_nm is highly imbalanced (80.6%)Imbalance
city_do_jan_lang_nm is highly imbalanced (80.6%)Imbalance
xpos_lo has 3 (3.0%) missing valuesMissing
ypos_la has 3 (3.0%) missing valuesMissing
chb_lang_nm has 100 (100.0%) missing valuesMissing
city_gn_gu_cd has 3 (3.0%) missing valuesMissing
base_ymd has 3 (3.0%) missing valuesMissing
chb_lang_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:08:33.551565
Analysis finished2023-12-10 10:08:39.316884
Duration5.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Categorical

HIGH CORRELATION 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
아리따움
19 
더페이스샵
18 
스킨푸드
7 
네이처리퍼블릭
6 
이니스프리
5 
Other values (35)
45 

Length

Max length13
Median length9
Mean length4.87
Min length2

Unique

Unique31 ?
Unique (%)31.0%

Sample

1st row강릉교동점
2nd row<NA>
3rd row강릉점
4th row더페이스샵
5th row더페이스샵

Common Values

ValueCountFrequency (%)
아리따움 19
19.0%
더페이스샵 18
18.0%
스킨푸드 7
 
7.0%
네이처리퍼블릭 6
 
6.0%
이니스프리 5
 
5.0%
미샤 5
 
5.0%
에뛰드 4
 
4.0%
<NA> 3
 
3.0%
올리브영 2
 
2.0%
유리가면 1
 
1.0%
Other values (30) 30
30.0%

Length

2023-12-10T19:08:39.586703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아리따움 19
18.6%
더페이스샵 18
17.6%
스킨푸드 7
 
6.9%
네이처리퍼블릭 6
 
5.9%
이니스프리 5
 
4.9%
미샤 5
 
4.9%
에뛰드 4
 
3.9%
na 3
 
2.9%
올리브영 2
 
2.0%
엄선영스킨케어 1
 
1.0%
Other values (32) 32
31.4%

xpos_lo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94
Distinct (%)96.9%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean1093222.2
Minimum1037104
Maximum1147384.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:40.345224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1037104
5-th percentile1037900.4
Q11039838
median1095986
Q31124341.9
95-th percentile1147228.4
Maximum1147384.5
Range110280.5
Interquartile range (IQR)84503.88

Descriptive statistics

Standard deviation43322.523
Coefficient of variation (CV)0.039628288
Kurtosis-1.6385922
Mean1093222.2
Median Absolute Deviation (MAD)46907
Skewness-0.22490997
Sum1.0604255 × 108
Variance1.876841 × 109
MonotonicityNot monotonic
2023-12-10T19:08:40.790394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1037104.0 2
 
2.0%
1142893.0 2
 
2.0%
1039758.0 2
 
2.0%
1042884.0 1
 
1.0%
1038326.0 1
 
1.0%
1042602.0 1
 
1.0%
1039962.25 1
 
1.0%
1039699.0 1
 
1.0%
1038161.0 1
 
1.0%
1085753.0 1
 
1.0%
Other values (84) 84
84.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
1037104.0 2
2.0%
1037852.5 1
1.0%
1037860.0 1
1.0%
1037899.25 1
1.0%
1037900.75 1
1.0%
1038121.313 1
1.0%
1038161.0 1
1.0%
1038164.0 1
1.0%
1038326.0 1
1.0%
1038910.0 1
1.0%
ValueCountFrequency (%)
1147384.5 1
1.0%
1147320.38 1
1.0%
1147303.0 1
1.0%
1147279.0 1
1.0%
1147242.0 1
1.0%
1147225.0 1
1.0%
1147208.0 1
1.0%
1147179.0 1
1.0%
1147177.0 1
1.0%
1147176.0 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct94
Distinct (%)96.9%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean1959570.3
Minimum1909840
Maximum2042541.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:41.100545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1909840
5-th percentile1924694.6
Q11927928
median1948224
Q31974683.2
95-th percentile2023068.6
Maximum2042541.9
Range132701.88
Interquartile range (IQR)46755.25

Descriptive statistics

Standard deviation34656.214
Coefficient of variation (CV)0.017685619
Kurtosis-0.62778968
Mean1959570.3
Median Absolute Deviation (MAD)23498
Skewness0.75658925
Sum1.9007832 × 108
Variance1.2010532 × 109
MonotonicityNot monotonic
2023-12-10T19:08:41.359789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1924698.0 2
 
2.0%
1948407.0 2
 
2.0%
1928077.0 2
 
2.0%
2011877.0 1
 
1.0%
1928552.0 1
 
1.0%
1925026.0 1
 
1.0%
1927897.76 1
 
1.0%
1925855.0 1
 
1.0%
1927382.875 1
 
1.0%
1909840.0 1
 
1.0%
Other values (84) 84
84.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
1909840.0 1
1.0%
1924314.38 1
1.0%
1924350.0 1
1.0%
1924391.0 1
1.0%
1924681.0 1
1.0%
1924698.0 2
2.0%
1924726.0 1
1.0%
1925026.0 1
1.0%
1925780.0 1
1.0%
1925801.574 1
1.0%
ValueCountFrequency (%)
2042541.88 1
1.0%
2025009.75 1
1.0%
2023778.857 1
1.0%
2023505.468 1
1.0%
2023107.0 1
1.0%
2023059.0 1
1.0%
2023056.0 1
1.0%
2023040.0 1
1.0%
2023030.928 1
1.0%
2023027.0 1
1.0%

kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
아리따움
19 
더페이스샵
18 
스킨푸드
7 
네이처리퍼블릭
6 
미샤
5 
Other values (36)
45 

Length

Max length14
Median length13
Mean length5.36
Min length2

Unique

Unique33 ?
Unique (%)33.0%

Sample

1st row올리브영(강릉교동점)
2nd row<NA>
3rd row올리브영(강릉점)
4th row더페이스샵
5th row더페이스샵

Common Values

ValueCountFrequency (%)
아리따움 19
19.0%
더페이스샵 18
18.0%
스킨푸드 7
 
7.0%
네이처리퍼블릭 6
 
6.0%
미샤 5
 
5.0%
이니스프리 5
 
5.0%
에뛰드 4
 
4.0%
<NA> 3
 
3.0%
유리가면 1
 
1.0%
에코뷰티라인샵 1
 
1.0%
Other values (31) 31
31.0%

Length

2023-12-10T19:08:41.648395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아리따움 19
18.6%
더페이스샵 18
17.6%
스킨푸드 7
 
6.9%
네이처리퍼블릭 6
 
5.9%
미샤 5
 
4.9%
이니스프리 5
 
4.9%
에뛰드 4
 
3.9%
na 3
 
2.9%
웰니스테라피 1
 
1.0%
미샤원주점 1
 
1.0%
Other values (33) 33
32.4%

eng_lang_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
24 
ARITAUM
20 
THE FACE SHOP
18 
Olive Young
8 
Skin Food
7 
Other values (5)
23 

Length

Max length15
Median length11
Mean length8.3
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowOlive Young
2nd row<NA>
3rd rowOlive Young
4th rowTHE FACE SHOP
5th rowTHE FACE SHOP

Common Values

ValueCountFrequency (%)
<NA> 24
24.0%
ARITAUM 20
20.0%
THE FACE SHOP 18
18.0%
Olive Young 8
 
8.0%
Skin Food 7
 
7.0%
MISSHA 6
 
6.0%
Nature Republic 6
 
6.0%
Etude 5
 
5.0%
Innisfree 5
 
5.0%
The Body Shop 1
 
1.0%

Length

2023-12-10T19:08:41.887101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:42.110663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
15.1%
aritaum 20
12.6%
the 19
11.9%
shop 19
11.9%
face 18
11.3%
olive 8
 
5.0%
young 8
 
5.0%
skin 7
 
4.4%
food 7
 
4.4%
missha 6
 
3.8%
Other values (5) 23
14.5%

jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
24 
アリタウム
20 
THE FACE SHOP
18 
オリ?ブヤング
8 
スキンフ?ド
7 
Other values (6)
23 

Length

Max length13
Median length7
Mean length7.04
Min length4

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st rowオリ?ブヤング
2nd row<NA>
3rd rowオリ?ブヤング
4th rowTHE FACE SHOP
5th rowTHE FACE SHOP

Common Values

ValueCountFrequency (%)
<NA> 24
24.0%
アリタウム 20
20.0%
THE FACE SHOP 18
18.0%
オリ?ブヤング 8
 
8.0%
スキンフ?ド 7
 
7.0%
MISSHA 6
 
6.0%
ネイチャ?リパブリック 6
 
6.0%
イニスフリ? 5
 
5.0%
Etude 4
 
4.0%
The Body Shop 1
 
1.0%

Length

2023-12-10T19:08:42.356523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 24
17.3%
アリタウム 20
14.4%
the 19
13.7%
shop 19
13.7%
face 18
12.9%
オリ?ブヤング 8
 
5.8%
スキンフ?ド 7
 
5.0%
missha 6
 
4.3%
ネイチャ?リパブリック 6
 
4.3%
イニスフリ 5
 
3.6%
Other values (3) 7
 
5.0%

chg_lang_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
24 
ARITAUM
20 
菲?小?
18 
Olive Young
8 
思??
7 
Other values (5)
23 

Length

Max length11
Median length4
Mean length5.22
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowOlive Young
2nd row<NA>
3rd rowOlive Young
4th row菲?小?
5th row菲?小?

Common Values

ValueCountFrequency (%)
<NA> 24
24.0%
ARITAUM 20
20.0%
菲?小? 18
18.0%
Olive Young 8
 
8.0%
思?? 7
 
7.0%
?? 6
 
6.0%
自然?? 6
 
6.0%
??之屋 5
 
5.0%
Innisfree 5
 
5.0%
美?小? 1
 
1.0%

Length

2023-12-10T19:08:42.634408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:42.842783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
22.2%
aritaum 20
18.5%
菲?小 18
16.7%
olive 8
 
7.4%
young 8
 
7.4%
思 7
 
6.5%
6
 
5.6%
自然 6
 
5.6%
之屋 5
 
4.6%
innisfree 5
 
4.6%

chb_lang_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

city_do_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
42
97 
<NA>
 
3

Length

Max length4
Median length2
Mean length2.06
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42
2nd row<NA>
3rd row42
4th row42
5th row42

Common Values

ValueCountFrequency (%)
42 97
97.0%
<NA> 3
 
3.0%

Length

2023-12-10T19:08:43.111296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:43.290164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42 97
97.0%
na 3
 
3.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)9.3%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean42201.753
Minimum42130
Maximum42830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:43.434473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42130
5-th percentile42130
Q142130
median42150
Q342210
95-th percentile42760
Maximum42830
Range700
Interquartile range (IQR)80

Descriptive statistics

Standard deviation158.23907
Coefficient of variation (CV)0.0037495854
Kurtosis10.655414
Mean42201.753
Median Absolute Deviation (MAD)20
Skewness3.4251173
Sum4093570
Variance25039.605
MonotonicityNot monotonic
2023-12-10T19:08:43.645108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
42130 32
32.0%
42150 25
25.0%
42170 12
 
12.0%
42210 12
 
12.0%
42230 10
 
10.0%
42800 3
 
3.0%
42820 1
 
1.0%
42830 1
 
1.0%
42750 1
 
1.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
42130 32
32.0%
42150 25
25.0%
42170 12
 
12.0%
42210 12
 
12.0%
42230 10
 
10.0%
42750 1
 
1.0%
42800 3
 
3.0%
42820 1
 
1.0%
42830 1
 
1.0%
ValueCountFrequency (%)
42830 1
 
1.0%
42820 1
 
1.0%
42800 3
 
3.0%
42750 1
 
1.0%
42230 10
 
10.0%
42210 12
 
12.0%
42170 12
 
12.0%
42150 25
25.0%
42130 32
32.0%

city_do_kor_lang_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원도
97 
<NA>
 
3

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row<NA>
3rd row강원도
4th row강원도
5th row강원도

Common Values

ValueCountFrequency (%)
강원도 97
97.0%
<NA> 3
 
3.0%

Length

2023-12-10T19:08:43.855186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:44.089635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 97
97.0%
na 3
 
3.0%

city_gn_gu_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
원주시
32 
강릉시
25 
동해시
12 
속초시
12 
삼척시
10 
Other values (5)
9 

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row강릉시
2nd row<NA>
3rd row강릉시
4th row강릉시
5th row강릉시

Common Values

ValueCountFrequency (%)
원주시 32
32.0%
강릉시 25
25.0%
동해시 12
 
12.0%
속초시 12
 
12.0%
삼척시 10
 
10.0%
<NA> 3
 
3.0%
양구군 3
 
3.0%
고성군 1
 
1.0%
양양군 1
 
1.0%
영월군 1
 
1.0%

Length

2023-12-10T19:08:44.295142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:44.556213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원주시 32
32.0%
강릉시 25
25.0%
동해시 12
 
12.0%
속초시 12
 
12.0%
삼척시 10
 
10.0%
na 3
 
3.0%
양구군 3
 
3.0%
고성군 1
 
1.0%
양양군 1
 
1.0%
영월군 1
 
1.0%

gov_dn_kor_lang_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중앙동
20 
천곡동
8 
금호동
8 
남양동
8 
단계동
7 
Other values (25)
49 

Length

Max length5
Median length3
Mean length3.18
Min length2

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row교1동
2nd row<NA>
3rd row중앙동
4th row중앙동
5th row교1동

Common Values

ValueCountFrequency (%)
중앙동 20
20.0%
천곡동 8
 
8.0%
금호동 8
 
8.0%
남양동 8
 
8.0%
단계동 7
 
7.0%
반곡관설동 6
 
6.0%
단구동 5
 
5.0%
교1동 5
 
5.0%
무실동 4
 
4.0%
<NA> 3
 
3.0%
Other values (20) 26
26.0%

Length

2023-12-10T19:08:44.772978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중앙동 20
20.0%
금호동 8
 
8.0%
남양동 8
 
8.0%
천곡동 8
 
8.0%
단계동 7
 
7.0%
반곡관설동 6
 
6.0%
단구동 5
 
5.0%
교1동 5
 
5.0%
무실동 4
 
4.0%
na 3
 
3.0%
Other values (20) 26
26.0%

city_do_jan_lang_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
江原道
97 
<NA>
 
3

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row江原道
2nd row<NA>
3rd row江原道
4th row江原道
5th row江原道

Common Values

ValueCountFrequency (%)
江原道 97
97.0%
<NA> 3
 
3.0%

Length

2023-12-10T19:08:44.976907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:45.167393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
江原道 97
97.0%
na 3
 
3.0%

city_gn_gu_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
原州市
32 
江陵市
25 
東海市
12 
束草市
12 
三陟市
10 
Other values (5)
9 

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row江陵市
2nd row<NA>
3rd row江陵市
4th row江陵市
5th row江陵市

Common Values

ValueCountFrequency (%)
原州市 32
32.0%
江陵市 25
25.0%
東海市 12
 
12.0%
束草市 12
 
12.0%
三陟市 10
 
10.0%
<NA> 3
 
3.0%
楊口郡 3
 
3.0%
高城郡 1
 
1.0%
襄陽郡 1
 
1.0%
寧越郡 1
 
1.0%

Length

2023-12-10T19:08:45.415721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:45.650527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
原州市 32
32.0%
江陵市 25
25.0%
東海市 12
 
12.0%
束草市 12
 
12.0%
三陟市 10
 
10.0%
na 3
 
3.0%
楊口郡 3
 
3.0%
高城郡 1
 
1.0%
襄陽郡 1
 
1.0%
寧越郡 1
 
1.0%

gov_dn_jan_lang_nm
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
中央洞
20 
泉谷洞
8 
琴湖洞
8 
南陽洞
8 
丹?洞
7 
Other values (25)
49 

Length

Max length5
Median length3
Mean length3.18
Min length2

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row校1洞
2nd row<NA>
3rd row中央洞
4th row中央洞
5th row校1洞

Common Values

ValueCountFrequency (%)
中央洞 20
20.0%
泉谷洞 8
 
8.0%
琴湖洞 8
 
8.0%
南陽洞 8
 
8.0%
丹?洞 7
 
7.0%
盤谷?雪洞 6
 
6.0%
丹邱洞 5
 
5.0%
校1洞 5
 
5.0%
戊?洞 4
 
4.0%
<NA> 3
 
3.0%
Other values (20) 26
26.0%

Length

2023-12-10T19:08:45.890870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
中央洞 20
20.0%
琴湖洞 8
 
8.0%
南陽洞 8
 
8.0%
泉谷洞 8
 
8.0%
丹?洞 7
 
7.0%
盤谷?雪洞 6
 
6.0%
丹邱洞 5
 
5.0%
校1洞 5
 
5.0%
戊?洞 4
 
4.0%
na 3
 
3.0%
Other values (20) 26
26.0%

base_ymd
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)1.0%
Missing3
Missing (%)3.0%
Memory size932.0 B
Minimum2020-12-31 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T19:08:46.061790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:46.233184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:08:37.172912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:36.133283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:36.650114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:37.329800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:36.309105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:36.835010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:37.497030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:36.473670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:36.993905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:08:46.391078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmxpos_loypos_lakor_lang_nmeng_lang_nmjan_lang_nmchg_lang_nmcity_gn_gu_cdcity_gn_gu_kor_lang_nmgov_dn_kor_lang_nmcity_gn_gu_jan_lang_nmgov_dn_jan_lang_nm
entrp_nm1.0000.6340.7351.0001.0001.0001.0000.0000.0000.9260.0000.926
xpos_lo0.6341.0000.9430.6340.0000.0000.0000.7500.9700.9780.9700.978
ypos_la0.7350.9431.0000.7410.0000.0000.0000.8950.9910.9980.9910.998
kor_lang_nm1.0000.6340.7411.0001.0001.0001.0000.0000.0000.9230.0000.923
eng_lang_nm1.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.000
jan_lang_nm1.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.000
chg_lang_nm1.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.000
city_gn_gu_cd0.0000.7500.8950.0000.0000.0000.0001.0001.0001.0001.0001.000
city_gn_gu_kor_lang_nm0.0000.9700.9910.0000.0000.0000.0001.0001.0000.9951.0000.995
gov_dn_kor_lang_nm0.9260.9780.9980.9230.0000.0000.0001.0000.9951.0000.9951.000
city_gn_gu_jan_lang_nm0.0000.9700.9910.0000.0000.0000.0001.0001.0000.9951.0000.995
gov_dn_jan_lang_nm0.9260.9780.9980.9230.0000.0000.0001.0000.9951.0000.9951.000
2023-12-10T19:08:46.641806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
eng_lang_nmcity_do_kor_lang_nmgov_dn_jan_lang_nmkor_lang_nmgov_dn_kor_lang_nmentrp_nmcity_do_cdcity_gn_gu_kor_lang_nmchg_lang_nmcity_gn_gu_jan_lang_nmjan_lang_nmcity_do_jan_lang_nm
eng_lang_nm1.0001.0000.0000.9220.0000.9301.0000.0001.0000.0000.9931.000
city_do_kor_lang_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
gov_dn_jan_lang_nm0.0001.0001.0000.4151.0000.4271.0000.8520.0000.8520.0001.000
kor_lang_nm0.9221.0000.4151.0000.4150.9911.0000.0000.9220.0000.9291.000
gov_dn_kor_lang_nm0.0001.0001.0000.4151.0000.4271.0000.8520.0000.8520.0001.000
entrp_nm0.9301.0000.4270.9910.4271.0001.0000.0000.9300.0000.9371.000
city_do_cd1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_gn_gu_kor_lang_nm0.0001.0000.8520.0000.8520.0001.0001.0000.0001.0000.0001.000
chg_lang_nm1.0001.0000.0000.9220.0000.9301.0000.0001.0000.0000.9931.000
city_gn_gu_jan_lang_nm0.0001.0000.8520.0000.8520.0001.0001.0000.0001.0000.0001.000
jan_lang_nm0.9931.0000.0000.9290.0000.9371.0000.0000.9930.0001.0001.000
city_do_jan_lang_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:08:46.937405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
xpos_loypos_lacity_gn_gu_cdentrp_nmkor_lang_nmeng_lang_nmjan_lang_nmchg_lang_nmcity_do_cdcity_do_kor_lang_nmcity_gn_gu_kor_lang_nmgov_dn_kor_lang_nmcity_do_jan_lang_nmcity_gn_gu_jan_lang_nmgov_dn_jan_lang_nm
xpos_lo1.0000.3890.6710.2790.2590.0000.0000.0001.0001.0000.9260.7741.0000.9260.774
ypos_la0.3891.0000.6300.1940.1620.0000.0000.0001.0001.0000.8590.8511.0000.8590.851
city_gn_gu_cd0.6710.6301.0000.0000.0000.0000.0000.0001.0001.0000.9730.8551.0000.9730.855
entrp_nm0.2790.1940.0001.0000.9910.9300.9370.9301.0001.0000.0000.4271.0000.0000.427
kor_lang_nm0.2590.1620.0000.9911.0000.9220.9290.9221.0001.0000.0000.4151.0000.0000.415
eng_lang_nm0.0000.0000.0000.9300.9221.0000.9931.0001.0001.0000.0000.0001.0000.0000.000
jan_lang_nm0.0000.0000.0000.9370.9290.9931.0000.9931.0001.0000.0000.0001.0000.0000.000
chg_lang_nm0.0000.0000.0000.9300.9221.0000.9931.0001.0001.0000.0000.0001.0000.0000.000
city_do_cd1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_kor_lang_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_gn_gu_kor_lang_nm0.9260.8590.9730.0000.0000.0000.0000.0001.0001.0001.0000.8521.0001.0000.852
gov_dn_kor_lang_nm0.7740.8510.8550.4270.4150.0000.0000.0001.0001.0000.8521.0001.0000.8521.000
city_do_jan_lang_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
city_gn_gu_jan_lang_nm0.9260.8590.9730.0000.0000.0000.0000.0001.0001.0001.0000.8521.0001.0000.852
gov_dn_jan_lang_nm0.7740.8510.8550.4270.4150.0000.0000.0001.0001.0000.8521.0001.0000.8521.000

Missing values

2023-12-10T19:08:37.906154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:08:38.397709image/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.
2023-12-10T19:08:38.839329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

entrp_nmxpos_loypos_lakor_lang_nmeng_lang_nmjan_lang_nmchg_lang_nmchb_lang_nmcity_do_cdcity_gn_gu_cdcity_do_kor_lang_nmcity_gn_gu_kor_lang_nmgov_dn_kor_lang_nmcity_do_jan_lang_nmcity_gn_gu_jan_lang_nmgov_dn_jan_lang_nmbase_ymd
0강릉교동점1121328.01974803.0올리브영(강릉교동점)Olive Youngオリ?ブヤングOlive Young<NA>4242150강원도강릉시교1동江原道江陵市校1洞2020-12-31
1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2강릉점1122960.01973650.0올리브영(강릉점)Olive Youngオリ?ブヤングOlive Young<NA>4242150강원도강릉시중앙동江原道江陵市中央洞2020-12-31
3더페이스샵1122941.01973530.0더페이스샵THE FACE SHOPTHE FACE SHOP菲?小?<NA>4242150강원도강릉시중앙동江原道江陵市中央洞2020-12-31
4더페이스샵1121399.9841974855.062더페이스샵THE FACE SHOPTHE FACE SHOP菲?小?<NA>4242150강원도강릉시교1동江原道江陵市校1洞2020-12-31
5더페이스샵1116608.01988665.0더페이스샵THE FACE SHOPTHE FACE SHOP菲?小?<NA>4242150강원도강릉시주문진읍江原道江陵市注文津邑2020-12-31
6아리따움1123458.01973680.0아리따움ARITAUMアリタウムARITAUM<NA>4242150강원도강릉시중앙동江原道江陵市中央洞2020-12-31
7<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8아리따움1122980.01973653.0아리따움ARITAUMアリタウムARITAUM<NA>4242150강원도강릉시중앙동江原道江陵市中央洞2020-12-31
9스킨푸드1123048.01973648.0스킨푸드Skin Foodスキンフ?ド思??<NA>4242150강원도강릉시중앙동江原道江陵市中央洞2020-12-31
entrp_nmxpos_loypos_lakor_lang_nmeng_lang_nmjan_lang_nmchg_lang_nmchb_lang_nmcity_do_cdcity_gn_gu_cdcity_do_kor_lang_nmcity_gn_gu_kor_lang_nmgov_dn_kor_lang_nmcity_do_jan_lang_nmcity_gn_gu_jan_lang_nmgov_dn_jan_lang_nmbase_ymd
90스킨푸드1037900.751927493.469스킨푸드Skin Foodスキンフ?ド思??<NA>4242130강원도원주시단계동江原道原州市丹?洞2020-12-31
91미샤1037104.01924698.0미샤MISSHAMISSHA??<NA>4242130강원도원주시무실동江原道原州市戊?洞2020-12-31
92미샤1039776.01925832.0미샤MISSHAMISSHA??<NA>4242130강원도원주시단구동江原道原州市丹邱洞2020-12-31
93미샤/원주점1039772.411927974.67미샤원주점MISSHAMISSHA??<NA>4242130강원도원주시중앙동江原道原州市中央洞2020-12-31
94에뛰드하우스1039809.661928066.76에뛰드하우스EtudeETUDE HOUSE??之屋<NA>4242130강원도원주시중앙동江原道原州市中央洞2020-12-31
95네이처리퍼블릭1039796.9741927981.566네이처리퍼블릭Nature Republicネイチャ?リパブリック自然??<NA>4242130강원도원주시중앙동江原道原州市中央洞2020-12-31
96네이처리퍼블릭1039758.01928077.0네이처리퍼블릭Nature Republicネイチャ?リパブリック自然??<NA>4242130강원도원주시중앙동江原道原州市中央洞2020-12-31
97네이처리퍼블릭1037899.251927470.219네이처리퍼블릭Nature Republicネイチャ?リパブリック自然??<NA>4242130강원도원주시단계동江原道原州市丹?洞2020-12-31
98네이처리퍼블릭1041460.01924350.0네이처리퍼블릭Nature Republicネイチャ?リパブリック自然??<NA>4242130강원도원주시반곡관설동江原道原州市盤谷?雪洞2020-12-31
99란에스테틱1040853.811924314.38란에스테틱<NA><NA><NA><NA>4242130강원도원주시반곡관설동江原道原州市盤谷?雪洞2020-12-31

Duplicate rows

Most frequently occurring

entrp_nmxpos_loypos_lakor_lang_nmeng_lang_nmjan_lang_nmchg_lang_nmcity_do_cdcity_gn_gu_cdcity_do_kor_lang_nmcity_gn_gu_kor_lang_nmgov_dn_kor_lang_nmcity_do_jan_lang_nmcity_gn_gu_jan_lang_nmgov_dn_jan_lang_nmbase_ymd# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3