Overview

Dataset statistics

Number of variables14
Number of observations31
Missing cells4
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory125.3 B

Variable types

Text4
Categorical1
Numeric9

Alerts

AREA_NM has constant value ""Constant
ALL_KWRD_RANK_CO is highly overall correlated with ALL_KWRD_OCCU_RT and 7 other fieldsHigh correlation
ALL_KWRD_OCCU_RT is highly overall correlated with ALL_KWRD_RANK_CO and 7 other fieldsHigh correlation
ALL_KWRD_CO is highly overall correlated with ALL_KWRD_RANK_CO and 7 other fieldsHigh correlation
PASSNGR_KWRD_RANK_CO is highly overall correlated with ALL_KWRD_RANK_CO and 7 other fieldsHigh correlation
PASSNGR_KWRD_OCCU_RT is highly overall correlated with ALL_KWRD_RANK_CO and 7 other fieldsHigh correlation
PASSNGR_KWRD_CO is highly overall correlated with ALL_KWRD_RANK_CO and 7 other fieldsHigh correlation
LCLS_KWRD_RANK_CO is highly overall correlated with ALL_KWRD_RANK_CO and 7 other fieldsHigh correlation
LCLS_KWRD_OCCU_RT is highly overall correlated with ALL_KWRD_RANK_CO and 7 other fieldsHigh correlation
LCLS_KWRD_CO is highly overall correlated with ALL_KWRD_RANK_CO and 7 other fieldsHigh correlation
PASSNGR_KWRD_RANK_CO has 1 (3.2%) missing valuesMissing
PASSNGR_KWRD_NM has 1 (3.2%) missing valuesMissing
PASSNGR_KWRD_OCCU_RT has 1 (3.2%) missing valuesMissing
PASSNGR_KWRD_CO has 1 (3.2%) missing valuesMissing
AREA_ID has unique valuesUnique
ALL_KWRD_NM has unique valuesUnique
LCLS_KWRD_NM has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:00:58.768371
Analysis finished2023-12-10 10:01:18.692203
Duration19.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

AREA_ID
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-10T19:01:18.962323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters186
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st rowTOT_01
2nd rowTOT_02
3rd rowTOT_03
4th rowTOT_04
5th rowTOT_05
ValueCountFrequency (%)
tot_01 1
 
3.2%
tot_17 1
 
3.2%
tot_30 1
 
3.2%
tot_29 1
 
3.2%
tot_28 1
 
3.2%
tot_27 1
 
3.2%
tot_26 1
 
3.2%
tot_25 1
 
3.2%
tot_24 1
 
3.2%
tot_23 1
 
3.2%
Other values (21) 21
67.7%
2023-12-10T19:01:19.612077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 62
33.3%
O 31
16.7%
_ 31
16.7%
1 14
 
7.5%
2 13
 
7.0%
0 12
 
6.5%
3 5
 
2.7%
4 3
 
1.6%
5 3
 
1.6%
6 3
 
1.6%
Other values (3) 9
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 93
50.0%
Decimal Number 62
33.3%
Connector Punctuation 31
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
22.6%
2 13
21.0%
0 12
19.4%
3 5
 
8.1%
4 3
 
4.8%
5 3
 
4.8%
6 3
 
4.8%
7 3
 
4.8%
8 3
 
4.8%
9 3
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
T 62
66.7%
O 31
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93
50.0%
Common 93
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 31
33.3%
1 14
15.1%
2 13
14.0%
0 12
 
12.9%
3 5
 
5.4%
4 3
 
3.2%
5 3
 
3.2%
6 3
 
3.2%
7 3
 
3.2%
8 3
 
3.2%
Latin
ValueCountFrequency (%)
T 62
66.7%
O 31
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 62
33.3%
O 31
16.7%
_ 31
16.7%
1 14
 
7.5%
2 13
 
7.0%
0 12
 
6.5%
3 5
 
2.7%
4 3
 
1.6%
5 3
 
1.6%
6 3
 
1.6%
Other values (3) 9
 
4.8%

AREA_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
전국
31 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전국 31
100.0%

Length

2023-12-10T19:01:19.880002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:20.071420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전국 31
100.0%

ALL_KWRD_RANK_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.967742
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:20.281475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q18.5
median16
Q323.5
95-th percentile29.5
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0387457
Coefficient of variation (CV)0.56606287
Kurtosis-1.2242473
Mean15.967742
Median Absolute Deviation (MAD)8
Skewness-0.019002164
Sum495
Variance81.698925
MonotonicityIncreasing
2023-12-10T19:01:20.514935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
30 2
 
6.5%
1 1
 
3.2%
17 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
23 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
1 1
3.2%
2 1
3.2%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 1
3.2%
ValueCountFrequency (%)
30 2
6.5%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%
21 1
3.2%

ALL_KWRD_NM
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-10T19:01:20.955016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2903226
Min length1

Characters and Unicode

Total characters71
Distinct characters57
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

Unique31 ?
Unique (%)100.0%

Sample

1st row바다
2nd row먹거리
3rd row해변
4th row공원
5th row해산물
ValueCountFrequency (%)
바다 1
 
3.2%
재래시장 1
 
3.2%
관광 1
 
3.2%
휴식 1
 
3.2%
휴양림 1
 
3.2%
도시 1
 
3.2%
체험 1
 
3.2%
해수욕장 1
 
3.2%
자연경관 1
 
3.2%
온천 1
 
3.2%
Other values (21) 21
67.7%
2023-12-10T19:01:21.625669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 4
 
5.6%
시 3
 
4.2%
장 3
 
4.2%
해 3
 
4.2%
관 3
 
4.2%
휴 2
 
2.8%
리 2
 
2.8%
물 2
 
2.8%
천 1
 
1.4%
온 1
 
1.4%
Other values (47) 47
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 4
 
5.6%
시 3
 
4.2%
장 3
 
4.2%
해 3
 
4.2%
관 3
 
4.2%
휴 2
 
2.8%
리 2
 
2.8%
물 2
 
2.8%
천 1
 
1.4%
온 1
 
1.4%
Other values (47) 47
66.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 4
 
5.6%
시 3
 
4.2%
장 3
 
4.2%
해 3
 
4.2%
관 3
 
4.2%
휴 2
 
2.8%
리 2
 
2.8%
물 2
 
2.8%
천 1
 
1.4%
온 1
 
1.4%
Other values (47) 47
66.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 4
 
5.6%
시 3
 
4.2%
장 3
 
4.2%
해 3
 
4.2%
관 3
 
4.2%
휴 2
 
2.8%
리 2
 
2.8%
물 2
 
2.8%
천 1
 
1.4%
온 1
 
1.4%
Other values (47) 47
66.2%

ALL_KWRD_OCCU_RT
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5225806
Minimum0.7
Maximum4.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:21.853132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.7
Q10.9
median1.3
Q31.8
95-th percentile3.05
Maximum4.2
Range3.5
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.82004458
Coefficient of variation (CV)0.5385886
Kurtosis2.7396876
Mean1.5225806
Median Absolute Deviation (MAD)0.5
Skewness1.5361456
Sum47.2
Variance0.67247312
MonotonicityDecreasing
2023-12-10T19:01:22.071933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.6 4
12.9%
0.8 4
12.9%
0.7 3
9.7%
0.9 3
9.7%
1.9 2
 
6.5%
1.8 2
 
6.5%
1.7 2
 
6.5%
1.0 2
 
6.5%
1.3 2
 
6.5%
1.1 1
 
3.2%
Other values (6) 6
19.4%
ValueCountFrequency (%)
0.7 3
9.7%
0.8 4
12.9%
0.9 3
9.7%
1.0 2
6.5%
1.1 1
 
3.2%
1.2 1
 
3.2%
1.3 2
6.5%
1.6 4
12.9%
1.7 2
6.5%
1.8 2
6.5%
ValueCountFrequency (%)
4.2 1
 
3.2%
3.2 1
 
3.2%
2.9 1
 
3.2%
2.5 1
 
3.2%
2.3 1
 
3.2%
1.9 2
6.5%
1.8 2
6.5%
1.7 2
6.5%
1.6 4
12.9%
1.3 2
6.5%

ALL_KWRD_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1476.129
Minimum693
Maximum4068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:22.303762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum693
5-th percentile695.5
Q1856
median1271
Q31736
95-th percentile2967.5
Maximum4068
Range3375
Interquartile range (IQR)880

Descriptive statistics

Standard deviation792.00702
Coefficient of variation (CV)0.53654322
Kurtosis2.786986
Mean1476.129
Median Absolute Deviation (MAD)442
Skewness1.5538287
Sum45760
Variance627275.12
MonotonicityDecreasing
2023-12-10T19:01:23.005982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
693 2
 
6.5%
4068 1
 
3.2%
1217 1
 
3.2%
698 1
 
3.2%
745 1
 
3.2%
774 1
 
3.2%
793 1
 
3.2%
818 1
 
3.2%
855 1
 
3.2%
857 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
693 2
6.5%
698 1
3.2%
745 1
3.2%
774 1
3.2%
793 1
3.2%
818 1
3.2%
855 1
3.2%
857 1
3.2%
867 1
3.2%
960 1
3.2%
ValueCountFrequency (%)
4068 1
3.2%
3071 1
3.2%
2864 1
3.2%
2409 1
3.2%
2215 1
3.2%
1861 1
3.2%
1810 1
3.2%
1759 1
3.2%
1713 1
3.2%
1642 1
3.2%

PASSNGR_KWRD_RANK_CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:23.314091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-10T19:01:23.620625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.2%
17 1
 
3.2%
30 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
23 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
1 1
3.2%
2 1
3.2%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 1
3.2%
ValueCountFrequency (%)
30 1
3.2%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%
21 1
3.2%

PASSNGR_KWRD_NM
Text

MISSING 

Distinct30
Distinct (%)100.0%
Missing1
Missing (%)3.2%
Memory size380.0 B
2023-12-10T19:01:24.161623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3333333
Min length1

Characters and Unicode

Total characters70
Distinct characters54
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

Unique30 ?
Unique (%)100.0%

Sample

1st row바다
2nd row해변
3rd row먹거리
4th row힐링
5th row해산물
ValueCountFrequency (%)
바다 1
 
3.3%
해변 1
 
3.3%
관광 1
 
3.3%
볼거리 1
 
3.3%
박물관 1
 
3.3%
온천 1
 
3.3%
축제 1
 
3.3%
경관 1
 
3.3%
해수욕장 1
 
3.3%
체험 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T19:01:24.972474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 4
 
5.7%
해 3
 
4.3%
리 3
 
4.3%
산 3
 
4.3%
장 3
 
4.3%
경 2
 
2.9%
거 2
 
2.9%
휴 2
 
2.9%
물 2
 
2.9%
시 2
 
2.9%
Other values (44) 44
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 4
 
5.7%
해 3
 
4.3%
리 3
 
4.3%
산 3
 
4.3%
장 3
 
4.3%
경 2
 
2.9%
거 2
 
2.9%
휴 2
 
2.9%
물 2
 
2.9%
시 2
 
2.9%
Other values (44) 44
62.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 4
 
5.7%
해 3
 
4.3%
리 3
 
4.3%
산 3
 
4.3%
장 3
 
4.3%
경 2
 
2.9%
거 2
 
2.9%
휴 2
 
2.9%
물 2
 
2.9%
시 2
 
2.9%
Other values (44) 44
62.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 4
 
5.7%
해 3
 
4.3%
리 3
 
4.3%
산 3
 
4.3%
장 3
 
4.3%
경 2
 
2.9%
거 2
 
2.9%
휴 2
 
2.9%
물 2
 
2.9%
시 2
 
2.9%
Other values (44) 44
62.9%

PASSNGR_KWRD_OCCU_RT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)50.0%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean1.7066667
Minimum0.8
Maximum5.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:25.199887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile0.845
Q11
median1.4
Q32
95-th percentile3.685
Maximum5.7
Range4.9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0760507
Coefficient of variation (CV)0.63049844
Kurtosis5.973576
Mean1.7066667
Median Absolute Deviation (MAD)0.5
Skewness2.2409407
Sum51.2
Variance1.1578851
MonotonicityDecreasing
2023-12-10T19:01:25.488164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2.0 4
12.9%
1.0 4
12.9%
0.9 4
12.9%
1.4 3
9.7%
1.8 2
 
6.5%
1.2 2
 
6.5%
1.1 2
 
6.5%
0.8 2
 
6.5%
5.7 1
 
3.2%
4.0 1
 
3.2%
Other values (5) 5
16.1%
ValueCountFrequency (%)
0.8 2
6.5%
0.9 4
12.9%
1.0 4
12.9%
1.1 2
6.5%
1.2 2
6.5%
1.4 3
9.7%
1.5 1
 
3.2%
1.8 2
6.5%
1.9 1
 
3.2%
2.0 4
12.9%
ValueCountFrequency (%)
5.7 1
 
3.2%
4.0 1
 
3.2%
3.3 1
 
3.2%
2.7 1
 
3.2%
2.5 1
 
3.2%
2.0 4
12.9%
1.9 1
 
3.2%
1.8 2
6.5%
1.5 1
 
3.2%
1.4 3
9.7%

PASSNGR_KWRD_CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean811.96667
Minimum389
Maximum2702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:25.797886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum389
5-th percentile397.2
Q1461.5
median661.5
Q3932.75
95-th percentile1768.8
Maximum2702
Range2313
Interquartile range (IQR)471.25

Descriptive statistics

Standard deviation513.98232
Coefficient of variation (CV)0.63300914
Kurtosis5.7282247
Mean811.96667
Median Absolute Deviation (MAD)225
Skewness2.2094216
Sum24359
Variance264177.83
MonotonicityStrictly decreasing
2023-12-10T19:01:26.150627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2702 1
 
3.2%
584 1
 
3.2%
389 1
 
3.2%
390 1
 
3.2%
406 1
 
3.2%
419 1
 
3.2%
423 1
 
3.2%
452 1
 
3.2%
455 1
 
3.2%
458 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
389 1
3.2%
390 1
3.2%
406 1
3.2%
419 1
3.2%
423 1
3.2%
452 1
3.2%
455 1
3.2%
458 1
3.2%
472 1
3.2%
475 1
3.2%
ValueCountFrequency (%)
2702 1
3.2%
1911 1
3.2%
1595 1
3.2%
1304 1
3.2%
1215 1
3.2%
958 1
3.2%
936 1
3.2%
933 1
3.2%
932 1
3.2%
903 1
3.2%

LCLS_KWRD_RANK_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.935484
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:26.434152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18.5
median16
Q323.5
95-th percentile29.5
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0918846
Coefficient of variation (CV)0.57054337
Kurtosis-1.2013507
Mean15.935484
Median Absolute Deviation (MAD)8
Skewness-0.037076326
Sum494
Variance82.662366
MonotonicityIncreasing
2023-12-10T19:01:26.759619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 2
 
6.5%
30 2
 
6.5%
3 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
23 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
1 2
6.5%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 1
3.2%
11 1
3.2%
ValueCountFrequency (%)
30 2
6.5%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%
21 1
3.2%

LCLS_KWRD_NM
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-10T19:01:27.187341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2258065
Min length1

Characters and Unicode

Total characters69
Distinct characters55
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

Unique31 ?
Unique (%)100.0%

Sample

1st row먹거리
2nd row공원
3rd row바다
4th row축제
5th row해산물
ValueCountFrequency (%)
먹거리 1
 
3.2%
캠핑 1
 
3.2%
낚시 1
 
3.2%
쇼핑 1
 
3.2%
마을 1
 
3.2%
체험 1
 
3.2%
휴양림 1
 
3.2%
해수욕장 1
 
3.2%
도시 1
 
3.2%
리조트 1
 
3.2%
Other values (21) 21
67.7%
2023-12-10T19:01:27.810216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 4
 
5.8%
산 4
 
5.8%
장 3
 
4.3%
해 3
 
4.3%
물 2
 
2.9%
리 2
 
2.9%
핑 2
 
2.9%
관 2
 
2.9%
먹 1
 
1.4%
천 1
 
1.4%
Other values (45) 45
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4
 
5.8%
산 4
 
5.8%
장 3
 
4.3%
해 3
 
4.3%
물 2
 
2.9%
리 2
 
2.9%
핑 2
 
2.9%
관 2
 
2.9%
먹 1
 
1.4%
천 1
 
1.4%
Other values (45) 45
65.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4
 
5.8%
산 4
 
5.8%
장 3
 
4.3%
해 3
 
4.3%
물 2
 
2.9%
리 2
 
2.9%
핑 2
 
2.9%
관 2
 
2.9%
먹 1
 
1.4%
천 1
 
1.4%
Other values (45) 45
65.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 4
 
5.8%
산 4
 
5.8%
장 3
 
4.3%
해 3
 
4.3%
물 2
 
2.9%
리 2
 
2.9%
핑 2
 
2.9%
관 2
 
2.9%
먹 1
 
1.4%
천 1
 
1.4%
Other values (45) 45
65.2%

LCLS_KWRD_OCCU_RT
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3774194
Minimum0.6
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:28.037935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.6
Q10.8
median1.3
Q31.8
95-th percentile2.9
Maximum3
Range2.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.70176429
Coefficient of variation (CV)0.50947759
Kurtosis0.26441446
Mean1.3774194
Median Absolute Deviation (MAD)0.5
Skewness0.91845544
Sum42.7
Variance0.49247312
MonotonicityDecreasing
2023-12-10T19:01:28.305902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.6 5
16.1%
1.8 3
9.7%
1.6 3
9.7%
1.3 3
9.7%
0.9 3
9.7%
3.0 2
 
6.5%
1.2 2
 
6.5%
0.8 2
 
6.5%
0.7 2
 
6.5%
2.8 1
 
3.2%
Other values (5) 5
16.1%
ValueCountFrequency (%)
0.6 5
16.1%
0.7 2
 
6.5%
0.8 2
 
6.5%
0.9 3
9.7%
1.1 1
 
3.2%
1.2 2
 
6.5%
1.3 3
9.7%
1.5 1
 
3.2%
1.6 3
9.7%
1.8 3
9.7%
ValueCountFrequency (%)
3.0 2
6.5%
2.8 1
 
3.2%
2.2 1
 
3.2%
2.0 1
 
3.2%
1.9 1
 
3.2%
1.8 3
9.7%
1.6 3
9.7%
1.5 1
 
3.2%
1.3 3
9.7%
1.2 2
6.5%

LCLS_KWRD_CO
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean685.32258
Minimum303
Maximum1477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:01:28.557031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum303
5-th percentile306.5
Q1405
median653
Q3877
95-th percentile1421.5
Maximum1477
Range1174
Interquartile range (IQR)472

Descriptive statistics

Standard deviation339.9619
Coefficient of variation (CV)0.49606114
Kurtosis0.28887749
Mean685.32258
Median Absolute Deviation (MAD)235
Skewness0.92406859
Sum21245
Variance115574.09
MonotonicityDecreasing
2023-12-10T19:01:28.787249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1477 2
 
6.5%
303 2
 
6.5%
1366 1
 
3.2%
310 1
 
3.2%
320 1
 
3.2%
321 1
 
3.2%
356 1
 
3.2%
360 1
 
3.2%
392 1
 
3.2%
418 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
303 2
6.5%
310 1
3.2%
320 1
3.2%
321 1
3.2%
356 1
3.2%
360 1
3.2%
392 1
3.2%
418 1
3.2%
434 1
3.2%
449 1
3.2%
ValueCountFrequency (%)
1477 2
6.5%
1366 1
3.2%
1067 1
3.2%
1000 1
3.2%
952 1
3.2%
894 1
3.2%
880 1
3.2%
874 1
3.2%
802 1
3.2%
801 1
3.2%

Interactions

2023-12-10T19:01:15.795123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:59.636873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.361037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:03.057680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:05.455626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:07.218883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:09.213286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:10.794688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:12.504297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:16.022852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:59.889072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.547522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:03.296479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:05.685044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:07.377222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:09.420099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:10.996939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:12.853103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:16.214967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.128455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.713363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:03.523869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:05.881667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:07.545666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:09.599989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:11.218494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:13.405795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:16.414514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.313869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.873710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:03.759352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:06.062095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:07.728626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:09.762773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:11.395995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:13.917728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:16.612717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.497964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:02.061798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:03.935115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:06.233199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:07.888312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:09.923540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:11.584197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:14.563906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:16.805870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.665269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:02.232910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:04.110280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:06.407973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:08.030921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:10.070567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:11.770884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:14.856092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:16.972613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:00.835777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:02.389755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:04.672944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:06.638511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:08.172784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:10.210994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:11.928334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:15.151912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:17.289693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.016539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:02.569514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:04.972259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:06.873586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:08.833107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:10.448224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:12.097337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:15.394297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:17.555981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:01.200218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:02.817423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:05.179789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:07.055319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:09.027808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:10.622593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:12.288367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:15.589733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:01:28.967229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AREA_IDALL_KWRD_RANK_COALL_KWRD_NMALL_KWRD_OCCU_RTALL_KWRD_COPASSNGR_KWRD_RANK_COPASSNGR_KWRD_NMPASSNGR_KWRD_OCCU_RTPASSNGR_KWRD_COLCLS_KWRD_RANK_COLCLS_KWRD_NMLCLS_KWRD_OCCU_RTLCLS_KWRD_CO
AREA_ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ALL_KWRD_RANK_CO1.0001.0001.0000.8390.8271.0001.0000.8230.7841.0001.0000.9090.921
ALL_KWRD_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ALL_KWRD_OCCU_RT1.0000.8391.0001.0001.0000.8341.0000.9550.9610.8391.0000.8930.903
ALL_KWRD_CO1.0000.8271.0001.0001.0000.8271.0000.9550.9610.8271.0000.8880.903
PASSNGR_KWRD_RANK_CO1.0001.0001.0000.8340.8271.0001.0000.8230.7841.0001.0000.9070.921
PASSNGR_KWRD_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
PASSNGR_KWRD_OCCU_RT1.0000.8231.0000.9550.9550.8231.0001.0001.0000.8231.0000.7980.834
PASSNGR_KWRD_CO1.0000.7841.0000.9610.9610.7841.0001.0001.0000.7841.0000.7650.801
LCLS_KWRD_RANK_CO1.0001.0001.0000.8390.8271.0001.0000.8230.7841.0001.0000.9090.921
LCLS_KWRD_NM1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
LCLS_KWRD_OCCU_RT1.0000.9091.0000.8930.8880.9071.0000.7980.7650.9091.0001.0000.992
LCLS_KWRD_CO1.0000.9211.0000.9030.9030.9211.0000.8340.8010.9211.0000.9921.000
2023-12-10T19:01:29.285415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ALL_KWRD_RANK_COALL_KWRD_OCCU_RTALL_KWRD_COPASSNGR_KWRD_RANK_COPASSNGR_KWRD_OCCU_RTPASSNGR_KWRD_COLCLS_KWRD_RANK_COLCLS_KWRD_OCCU_RTLCLS_KWRD_CO
ALL_KWRD_RANK_CO1.000-0.997-1.0001.000-0.996-1.0001.000-0.996-1.000
ALL_KWRD_OCCU_RT-0.9971.0000.997-0.9970.9950.997-0.9970.9920.997
ALL_KWRD_CO-1.0000.9971.000-1.0000.9961.000-1.0000.9961.000
PASSNGR_KWRD_RANK_CO1.000-0.997-1.0001.000-0.996-1.0001.000-0.997-1.000
PASSNGR_KWRD_OCCU_RT-0.9960.9950.996-0.9961.0000.996-0.9960.9940.996
PASSNGR_KWRD_CO-1.0000.9971.000-1.0000.9961.000-1.0000.9971.000
LCLS_KWRD_RANK_CO1.000-0.997-1.0001.000-0.996-1.0001.000-0.996-1.000
LCLS_KWRD_OCCU_RT-0.9960.9920.996-0.9970.9940.997-0.9961.0000.996
LCLS_KWRD_CO-1.0000.9971.000-1.0000.9961.000-1.0000.9961.000

Missing values

2023-12-10T19:01:17.820575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:01:18.200090image/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.
2023-12-10T19:01:18.529183image/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

AREA_IDAREA_NMALL_KWRD_RANK_COALL_KWRD_NMALL_KWRD_OCCU_RTALL_KWRD_COPASSNGR_KWRD_RANK_COPASSNGR_KWRD_NMPASSNGR_KWRD_OCCU_RTPASSNGR_KWRD_COLCLS_KWRD_RANK_COLCLS_KWRD_NMLCLS_KWRD_OCCU_RTLCLS_KWRD_CO
0TOT_01전국1바다4.240681바다5.727021먹거리3.01477
1TOT_02전국2먹거리3.230712해변4.019111공원3.01477
2TOT_03전국3해변2.928643먹거리3.315953바다2.81366
3TOT_04전국4공원2.524094힐링2.713044축제2.21067
4TOT_05전국5해산물2.322155해산물2.512155해산물2.01000
5TOT_06전국6힐링1.918616펜션2.09586해변1.9952
6TOT_07전국7계곡1.918107계곡2.09367한우1.8894
7TOT_08전국8펜션1.817598공원2.09338맛집1.8880
8TOT_09전국9호텔1.817139호텔2.09329계곡1.8874
9TOT_10전국10산1.7164210회1.990310산1.6802
AREA_IDAREA_NMALL_KWRD_RANK_COALL_KWRD_NMALL_KWRD_OCCU_RTALL_KWRD_COPASSNGR_KWRD_RANK_COPASSNGR_KWRD_NMPASSNGR_KWRD_OCCU_RTPASSNGR_KWRD_COLCLS_KWRD_RANK_COLCLS_KWRD_NMLCLS_KWRD_OCCU_RTLCLS_KWRD_CO
21TOT_22전국22박물관0.986722체험1.047222온천0.9434
22TOT_23전국23온천0.985723해수욕장1.045823리조트0.8418
23TOT_24전국24자연경관0.985524경관1.045524도시0.8392
24TOT_25전국25해수욕장0.881825축제0.945225해수욕장0.7360
25TOT_26전국26체험0.879326온천0.942326휴양림0.7356
26TOT_27전국27도시0.877427박물관0.941927체험0.6321
27TOT_28전국28휴양림0.874528볼거리0.940628마을0.6320
28TOT_29전국29휴식0.769829관광0.839029쇼핑0.6310
29TOT_30전국30관광0.769330휴양림0.838930낚시0.6303
30TOT_31전국30마을0.7693<NA><NA><NA><NA>30관광0.6303