Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.9 KiB
Average record size in memory142.3 B

Variable types

Categorical4
Text1
Numeric11

Alerts

file_name has constant value ""Constant
base_ymd has constant value ""Constant
signgu_cd is highly overall correlated with residnt_2030_cnt and 4 other fieldsHigh correlation
residnt_2030_cnt is highly overall correlated with signgu_cd and 6 other fieldsHigh correlation
ibllbr_fclt_cnt is highly overall correlated with signgu_cd and 4 other fieldsHigh correlation
museum_fclt_cnt is highly overall correlated with fclt_cnt and 1 other fieldsHigh correlation
artgr_fclt_cnt is highly overall correlated with fclt_cnt and 1 other fieldsHigh correlation
fclt_cnt is highly overall correlated with residnt_2030_cnt and 4 other fieldsHigh correlation
ibllbr_rate is highly overall correlated with residnt_2030_cnt and 3 other fieldsHigh correlation
museum_rate is highly overall correlated with residnt_2030_cnt and 4 other fieldsHigh correlation
artgr_rate is highly overall correlated with artgr_fclt_cntHigh correlation
prfplc_rate is highly overall correlated with signgu_cd and 5 other fieldsHigh correlation
fclt_rate is highly overall correlated with signgu_cd and 5 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with signgu_cdHigh correlation
prfplc_fclt_cnt is highly overall correlated with fclt_cntHigh correlation
signgu_cd has unique valuesUnique
residnt_2030_cnt has unique valuesUnique
ibllbr_rate has unique valuesUnique
fclt_rate has unique valuesUnique
museum_fclt_cnt has 14 (14.0%) zerosZeros
artgr_fclt_cnt has 53 (53.0%) zerosZeros
museum_rate has 14 (14.0%) zerosZeros
artgr_rate has 53 (53.0%) zerosZeros
prfplc_rate has 12 (12.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:19:30.888225
Analysis finished2023-12-10 10:19:51.845457
Duration20.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
40 
경상남도
22 
경상북도
19 
강원도
16 
충청북도
 
3

Length

Max length4
Median length3
Mean length3.44
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row충청북도
3rd row강원도
4th row강원도
5th row강원도

Common Values

ValueCountFrequency (%)
경기도 40
40.0%
경상남도 22
22.0%
경상북도 19
19.0%
강원도 16
 
16.0%
충청북도 3
 
3.0%

Length

2023-12-10T19:19:51.962250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:52.165099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 40
40.0%
경상남도 22
22.0%
경상북도 19
19.0%
강원도 16
 
16.0%
충청북도 3
 
3.0%
Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:19:52.621864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length4.02
Min length3

Characters and Unicode

Total characters402
Distinct characters93
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row춘천시
2nd row괴산군
3rd row강릉시
4th row동해시
5th row태백시
ValueCountFrequency (%)
창원시 5
 
4.0%
수원시 4
 
3.2%
성남시 3
 
2.4%
용인시 3
 
2.4%
고양시 3
 
2.4%
고성군 2
 
1.6%
안산시 2
 
1.6%
포항시 2
 
1.6%
안양시 2
 
1.6%
군위군 1
 
0.8%
Other values (97) 97
78.2%
2023-12-10T19:19:53.414053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
 
16.7%
36
 
9.0%
27
 
6.7%
24
 
6.0%
17
 
4.2%
13
 
3.2%
12
 
3.0%
12
 
3.0%
11
 
2.7%
10
 
2.5%
Other values (83) 173
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 378
94.0%
Space Separator 24
 
6.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
67
17.7%
36
 
9.5%
27
 
7.1%
17
 
4.5%
13
 
3.4%
12
 
3.2%
12
 
3.2%
11
 
2.9%
10
 
2.6%
9
 
2.4%
Other values (82) 164
43.4%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 378
94.0%
Common 24
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
67
17.7%
36
 
9.5%
27
 
7.1%
17
 
4.5%
13
 
3.4%
12
 
3.2%
12
 
3.2%
11
 
2.9%
10
 
2.6%
9
 
2.4%
Other values (82) 164
43.4%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 378
94.0%
ASCII 24
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
67
17.7%
36
 
9.5%
27
 
7.1%
17
 
4.5%
13
 
3.4%
12
 
3.2%
12
 
3.2%
11
 
2.9%
10
 
2.6%
9
 
2.4%
Other values (82) 164
43.4%
ASCII
ValueCountFrequency (%)
24
100.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44349.84
Minimum41111
Maximum48890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:53.656024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41111
5-th percentile41132.9
Q141445
median42775
Q347782.5
95-th percentile48840.5
Maximum48890
Range7779
Interquartile range (IQR)6337.5

Descriptive statistics

Standard deviation3132.8314
Coefficient of variation (CV)0.070639069
Kurtosis-1.7665619
Mean44349.84
Median Absolute Deviation (MAD)1603
Skewness0.32906346
Sum4434984
Variance9814632.7
MonotonicityNot monotonic
2023-12-10T19:19:54.217423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42110 1
 
1.0%
48170 1
 
1.0%
48820 1
 
1.0%
48740 1
 
1.0%
48730 1
 
1.0%
48720 1
 
1.0%
48330 1
 
1.0%
48310 1
 
1.0%
48270 1
 
1.0%
48250 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
41111 1
1.0%
41113 1
1.0%
41115 1
1.0%
41117 1
1.0%
41131 1
1.0%
41133 1
1.0%
41135 1
1.0%
41150 1
1.0%
41171 1
1.0%
41173 1
1.0%
ValueCountFrequency (%)
48890 1
1.0%
48880 1
1.0%
48870 1
1.0%
48860 1
1.0%
48850 1
1.0%
48840 1
1.0%
48820 1
1.0%
48740 1
1.0%
48730 1
1.0%
48720 1
1.0%

residnt_2030_cnt
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52420.99
Minimum2171
Maximum255142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:54.462522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2171
5-th percentile4598.1
Q17737
median41849.5
Q379729.25
95-th percentile139275.7
Maximum255142
Range252971
Interquartile range (IQR)71992.25

Descriptive statistics

Standard deviation52046.924
Coefficient of variation (CV)0.99286419
Kurtosis2.2910369
Mean52420.99
Median Absolute Deviation (MAD)34945
Skewness1.3850347
Sum5242099
Variance2.7088823 × 109
MonotonicityNot monotonic
2023-12-10T19:19:54.681796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71508 1
 
1.0%
86792 1
 
1.0%
7822 1
 
1.0%
10080 1
 
1.0%
11187 1
 
1.0%
3800 1
 
1.0%
90165 1
 
1.0%
61316 1
 
1.0%
18140 1
 
1.0%
138922 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2171 1
1.0%
2846 1
1.0%
3426 1
1.0%
3800 1
1.0%
4296 1
1.0%
4614 1
1.0%
4751 1
1.0%
4792 1
1.0%
4870 1
1.0%
5096 1
1.0%
ValueCountFrequency (%)
255142 1
1.0%
234176 1
1.0%
174576 1
1.0%
152874 1
1.0%
145996 1
1.0%
138922 1
1.0%
135987 1
1.0%
130609 1
1.0%
126747 1
1.0%
124267 1
1.0%

ibllbr_fclt_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.56
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:54.868789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum16
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1982318
Coefficient of variation (CV)0.70136663
Kurtosis2.346961
Mean4.56
Median Absolute Deviation (MAD)2
Skewness1.4024466
Sum456
Variance10.228687
MonotonicityNot monotonic
2023-12-10T19:19:55.039748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 18
18.0%
3 17
17.0%
1 13
13.0%
5 12
12.0%
6 10
10.0%
4 8
8.0%
7 8
8.0%
8 5
 
5.0%
9 2
 
2.0%
15 2
 
2.0%
Other values (4) 5
 
5.0%
ValueCountFrequency (%)
1 13
13.0%
2 18
18.0%
3 17
17.0%
4 8
8.0%
5 12
12.0%
6 10
10.0%
7 8
8.0%
8 5
 
5.0%
9 2
 
2.0%
10 1
 
1.0%
ValueCountFrequency (%)
16 1
 
1.0%
15 2
 
2.0%
12 1
 
1.0%
11 2
 
2.0%
10 1
 
1.0%
9 2
 
2.0%
8 5
5.0%
7 8
8.0%
6 10
10.0%
5 12
12.0%

museum_fclt_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.56
Minimum0
Maximum21
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:55.253464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile9.1
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8067457
Coefficient of variation (CV)1.0693106
Kurtosis6.1638727
Mean3.56
Median Absolute Deviation (MAD)1.5
Skewness2.161073
Sum356
Variance14.491313
MonotonicityNot monotonic
2023-12-10T19:19:55.395944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 24
24.0%
1 18
18.0%
0 14
14.0%
3 8
 
8.0%
4 7
 
7.0%
6 7
 
7.0%
5 6
 
6.0%
9 5
 
5.0%
7 5
 
5.0%
11 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
0 14
14.0%
1 18
18.0%
2 24
24.0%
3 8
 
8.0%
4 7
 
7.0%
5 6
 
6.0%
6 7
 
7.0%
7 5
 
5.0%
8 1
 
1.0%
9 5
 
5.0%
ValueCountFrequency (%)
21 1
 
1.0%
19 1
 
1.0%
14 1
 
1.0%
13 1
 
1.0%
11 1
 
1.0%
9 5
5.0%
8 1
 
1.0%
7 5
5.0%
6 7
7.0%
5 6
6.0%

artgr_fclt_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86
Minimum0
Maximum5
Zeros53
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:55.563355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.25
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation1.163589
Coefficient of variation (CV)1.3530105
Kurtosis2.3138746
Mean0.86
Median Absolute Deviation (MAD)0
Skewness1.5346685
Sum86
Variance1.3539394
MonotonicityNot monotonic
2023-12-10T19:19:55.739863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 53
53.0%
1 22
22.0%
2 17
 
17.0%
3 4
 
4.0%
4 2
 
2.0%
5 2
 
2.0%
ValueCountFrequency (%)
0 53
53.0%
1 22
22.0%
2 17
 
17.0%
3 4
 
4.0%
4 2
 
2.0%
5 2
 
2.0%
ValueCountFrequency (%)
5 2
 
2.0%
4 2
 
2.0%
3 4
 
4.0%
2 17
 
17.0%
1 22
22.0%
0 53
53.0%

prfplc_fclt_cnt
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
72 
2
12 
0
12 
3
 
3
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 72
72.0%
2 12
 
12.0%
0 12
 
12.0%
3 3
 
3.0%
4 1
 
1.0%

Length

2023-12-10T19:19:55.933671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:56.105202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 72
72.0%
2 12
 
12.0%
0 12
 
12.0%
3 3
 
3.0%
4 1
 
1.0%

fclt_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.07
Minimum2
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:56.279802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median8
Q313
95-th percentile26
Maximum38
Range36
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.632115
Coefficient of variation (CV)0.65860129
Kurtosis3.1635672
Mean10.07
Median Absolute Deviation (MAD)3
Skewness1.6630799
Sum1007
Variance43.984949
MonotonicityNot monotonic
2023-12-10T19:19:56.487661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5 12
12.0%
6 9
9.0%
7 9
9.0%
10 9
9.0%
4 9
9.0%
8 7
 
7.0%
14 7
 
7.0%
13 6
 
6.0%
9 5
 
5.0%
11 5
 
5.0%
Other values (13) 22
22.0%
ValueCountFrequency (%)
2 1
 
1.0%
3 5
5.0%
4 9
9.0%
5 12
12.0%
6 9
9.0%
7 9
9.0%
8 7
7.0%
9 5
5.0%
10 9
9.0%
11 5
5.0%
ValueCountFrequency (%)
38 1
 
1.0%
27 2
2.0%
26 3
3.0%
25 1
 
1.0%
24 1
 
1.0%
21 1
 
1.0%
19 1
 
1.0%
18 1
 
1.0%
17 3
3.0%
15 1
 
1.0%

ibllbr_rate
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00017277569
Minimum3.2132 × 10-5
Maximum0.000768285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:56.722815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.2132 × 10-5
5-th percentile4.332645 × 10-5
Q16.472375 × 10-5
median0.0001313965
Q30.000217675
95-th percentile0.00041085435
Maximum0.000768285
Range0.000736153
Interquartile range (IQR)0.00015295125

Descriptive statistics

Standard deviation0.00014342692
Coefficient of variation (CV)0.83013367
Kurtosis3.6766304
Mean0.00017277569
Median Absolute Deviation (MAD)7.24275 × 10-5
Skewness1.7342096
Sum0.017277569
Variance2.057128 × 10-8
MonotonicityNot monotonic
2023-12-10T19:19:56.976691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000139844 1
 
1.0%
8.0652e-05 1
 
1.0%
0.000383533 1
 
1.0%
0.000297619 1
 
1.0%
0.000178778 1
 
1.0%
0.000263157 1
 
1.0%
6.6544e-05 1
 
1.0%
9.7853e-05 1
 
1.0%
0.000220507 1
 
1.0%
6.4784e-05 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
3.2132e-05 1
1.0%
3.2273e-05 1
1.0%
3.3478e-05 1
1.0%
4.0235e-05 1
1.0%
4.2081e-05 1
1.0%
4.3392e-05 1
1.0%
4.6671e-05 1
1.0%
4.7609e-05 1
1.0%
4.904e-05 1
1.0%
5.0916e-05 1
1.0%
ValueCountFrequency (%)
0.000768285 1
1.0%
0.000698324 1
1.0%
0.000583771 1
1.0%
0.000460617 1
1.0%
0.000421691 1
1.0%
0.000410284 1
1.0%
0.000400962 1
1.0%
0.000392464 1
1.0%
0.000383533 1
1.0%
0.0003803 1
1.0%

museum_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00022633402
Minimum0
Maximum0.00344093
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:57.225063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.805225 × 10-5
median8.67285 × 10-5
Q30.0002959375
95-th percentile0.0007723208
Maximum0.00344093
Range0.00344093
Interquartile range (IQR)0.00027788525

Descriptive statistics

Standard deviation0.00041536065
Coefficient of variation (CV)1.8351667
Kurtosis36.350355
Mean0.00022633402
Median Absolute Deviation (MAD)7.88135 × 10-5
Skewness5.1646952
Sum0.022633402
Variance1.7252447 × 10-7
MonotonicityNot monotonic
2023-12-10T19:19:57.448664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.000153828 1
 
1.0%
0.000100775 1
 
1.0%
0.00017298 1
 
1.0%
0.000767067 1
 
1.0%
0.000297619 1
 
1.0%
8.9389e-05 1
 
1.0%
0.000526315 1
 
1.0%
2.2181e-05 1
 
1.0%
0.000114162 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 14
14.0%
7.353e-06 1
 
1.0%
1.0158e-05 1
 
1.0%
1.1119e-05 1
 
1.0%
1.1667e-05 1
 
1.0%
1.2039e-05 1
 
1.0%
1.2516e-05 1
 
1.0%
1.3699e-05 1
 
1.0%
1.5677e-05 1
 
1.0%
1.6066e-05 1
 
1.0%
ValueCountFrequency (%)
0.00344093 1
1.0%
0.00130039 1
1.0%
0.00100993 1
1.0%
0.000981161 1
1.0%
0.000872143 1
1.0%
0.000767067 1
1.0%
0.000758917 1
1.0%
0.00070274 1
1.0%
0.000614628 1
1.0%
0.000590368 1
1.0%

artgr_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.656958 × 10-5
Minimum0
Maximum0.000392464
Zeros53
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:57.661818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.833575 × 10-5
95-th percentile0.0002126065
Maximum0.000392464
Range0.000392464
Interquartile range (IQR)2.833575 × 10-5

Descriptive statistics

Standard deviation7.8017551 × 10-5
Coefficient of variation (CV)2.1334003
Kurtosis8.4065162
Mean3.656958 × 10-5
Median Absolute Deviation (MAD)0
Skewness2.9243999
Sum0.003656958
Variance6.0867383 × 10-9
MonotonicityNot monotonic
2023-12-10T19:19:57.933197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.0 53
53.0%
2.5458e-05 1
 
1.0%
4.0414e-05 1
 
1.0%
1.6482e-05 1
 
1.0%
2.1919e-05 1
 
1.0%
1.5779e-05 1
 
1.0%
7.838e-06 1
 
1.0%
1.9404e-05 1
 
1.0%
3.6057e-05 1
 
1.0%
0.000333722 1
 
1.0%
Other values (38) 38
38.0%
ValueCountFrequency (%)
0.0 53
53.0%
6.849e-06 1
 
1.0%
7.656e-06 1
 
1.0%
7.838e-06 1
 
1.0%
8.047e-06 1
 
1.0%
9.813e-06 1
 
1.0%
1.1521e-05 1
 
1.0%
1.2039e-05 1
 
1.0%
1.4238e-05 1
 
1.0%
1.4396e-05 1
 
1.0%
ValueCountFrequency (%)
0.000392464 1
1.0%
0.000333722 1
1.0%
0.000327707 1
1.0%
0.000291885 1
1.0%
0.000252972 1
1.0%
0.000210482 1
1.0%
0.00017674 1
1.0%
0.000174428 1
1.0%
0.000168321 1
1.0%
0.000140232 1
1.0%

prfplc_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.532372 × 10-5
Minimum0
Maximum0.000460617
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:58.179257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.196875 × 10-5
median3.49465 × 10-5
Q30.0001268255
95-th percentile0.00023429315
Maximum0.000460617
Range0.000460617
Interquartile range (IQR)0.00011485675

Descriptive statistics

Standard deviation9.0357528 × 10-5
Coefficient of variation (CV)1.1995893
Kurtosis3.3245508
Mean7.532372 × 10-5
Median Absolute Deviation (MAD)2.9001 × 10-5
Skewness1.7286285
Sum0.007532372
Variance8.1644828 × 10-9
MonotonicityNot monotonic
2023-12-10T19:19:58.421794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
12.0%
1.3984e-05 1
 
1.0%
5.5126e-05 1
 
1.0%
0.00015921 1
 
1.0%
0.00017298 1
 
1.0%
0.000127844 1
 
1.0%
9.9206e-05 1
 
1.0%
8.9389e-05 1
 
1.0%
0.000263157 1
 
1.0%
1.109e-05 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.0 12
12.0%
5.728e-06 1
 
1.0%
7.353e-06 1
 
1.0%
7.889e-06 1
 
1.0%
8.047e-06 1
 
1.0%
8.54e-06 1
 
1.0%
9.702e-06 1
 
1.0%
1.0158e-05 1
 
1.0%
1.109e-05 1
 
1.0%
1.1119e-05 1
 
1.0%
ValueCountFrequency (%)
0.000460617 1
1.0%
0.00035137 1
1.0%
0.000336643 1
1.0%
0.000291885 1
1.0%
0.000263157 1
1.0%
0.000232774 1
1.0%
0.000216731 1
1.0%
0.000210482 1
1.0%
0.000208681 1
1.0%
0.000205338 1
1.0%

fclt_rate
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0005110042
Minimum4.8198 × 10-5
Maximum0.004260199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:58.638804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.8198 × 10-5
5-th percentile6.964435 × 10-5
Q10.00012384775
median0.00030683
Q30.00069255175
95-th percentile0.0015464341
Maximum0.004260199
Range0.004212001
Interquartile range (IQR)0.000568704

Descriptive statistics

Standard deviation0.00060044837
Coefficient of variation (CV)1.1750361
Kurtosis14.72498
Mean0.0005110042
Median Absolute Deviation (MAD)0.000214488
Skewness3.0785843
Sum0.05110042
Variance3.6053824 × 10-7
MonotonicityNot monotonic
2023-12-10T19:19:58.880800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000363595 1
 
1.0%
0.000207392 1
 
1.0%
0.001278445 1
 
1.0%
0.000694444 1
 
1.0%
0.000357557 1
 
1.0%
0.001052631 1
 
1.0%
9.9817e-05 1
 
1.0%
0.000228325 1
 
1.0%
0.000551267 1
 
1.0%
0.000194353 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
4.8198e-05 1
1.0%
4.841e-05 1
1.0%
5.3099e-05 1
1.0%
5.633e-05 1
1.0%
6.2754e-05 1
1.0%
7.0007e-05 1
1.0%
7.0136e-05 1
1.0%
7.8058e-05 1
1.0%
8.0531e-05 1
1.0%
8.1734e-05 1
1.0%
ValueCountFrequency (%)
0.004260199 1
1.0%
0.001962323 1
1.0%
0.00185154 1
1.0%
0.001751313 1
1.0%
0.001733853 1
1.0%
0.00153657 1
1.0%
0.001405481 1
1.0%
0.001396648 1
1.0%
0.001395429 1
1.0%
0.001391348 1
1.0%

file_name
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KC_600_MIL_CLT_STATN_BIZAEA_2021
100 

Length

Max length32
Median length32
Mean length32
Min length32

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_600_MIL_CLT_STATN_BIZAEA_2021
2nd rowKC_600_MIL_CLT_STATN_BIZAEA_2021
3rd rowKC_600_MIL_CLT_STATN_BIZAEA_2021
4th rowKC_600_MIL_CLT_STATN_BIZAEA_2021
5th rowKC_600_MIL_CLT_STATN_BIZAEA_2021

Common Values

ValueCountFrequency (%)
KC_600_MIL_CLT_STATN_BIZAEA_2021 100
100.0%

Length

2023-12-10T19:19:59.142399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:59.299968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_600_mil_clt_statn_bizaea_2021 100
100.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200101
2nd row20200101
3rd row20200101
4th row20200101
5th row20200101

Common Values

ValueCountFrequency (%)
20200101 100
100.0%

Length

2023-12-10T19:19:59.622198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:59.778970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200101 100
100.0%

Interactions

2023-12-10T19:19:49.541631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:31.761035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:33.613333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:35.704582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:37.723097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:39.112731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:40.715782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.223171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.059743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.107482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.944973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.719184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:31.925333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:33.770362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:35.972998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:37.879939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:39.249322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:40.930980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.371952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.562437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.286974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.092235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.843867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:32.087822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:33.893453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:36.132922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.010313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:39.372391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.081914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.546407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.704630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.456195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.213640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.996379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:32.275514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:34.034459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:36.280131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.171906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:39.520272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.204114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.725070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.860223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.638150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.379307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.139669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:32.471258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:34.174975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:36.514499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.265432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:39.657903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.329604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.877805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.989892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.781427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.517636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.375197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:32.623461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:34.330628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:36.699231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.392860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:39.797809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.465319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.049351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.131476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.948596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.666589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.521016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:32.778687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:34.485725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:36.865390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.515372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:40.016600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.585785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.195617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.275839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.094422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.813375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.695029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:32.946290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:34.657110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:37.080548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.634264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:40.149128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.736576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.362227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.469907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.267329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.967451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.847304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:33.120019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:35.174987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:37.244458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.747519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:40.289418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.861032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.589103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.646847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.429093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.110493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:51.004432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:33.317351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:35.346290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:37.424680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.862293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:40.434089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.002171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.759359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.819162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.630556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.281283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:51.134349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:33.470929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:35.490024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:37.584809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:38.991740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:40.579348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.115649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.897801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.961928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.773009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.411950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:19:59.970823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_nmsigngu_nmsigngu_cdresidnt_2030_cntibllbr_fclt_cntmuseum_fclt_cntartgr_fclt_cntprfplc_fclt_cntfclt_cntibllbr_ratemuseum_rateartgr_rateprfplc_ratefclt_rate
ctprvn_nm1.0000.8491.0000.3840.3160.0870.1170.2410.0000.4770.6010.4040.5190.400
signgu_nm0.8491.0000.9151.0000.9871.0000.9601.0001.0001.0001.0000.0000.6770.000
signgu_cd1.0000.9151.0000.3410.3870.2880.0210.1810.2630.5400.4510.5020.5920.485
residnt_2030_cnt0.3841.0000.3411.0000.8090.4840.3240.4780.6510.5750.0000.0000.5970.408
ibllbr_fclt_cnt0.3160.9870.3870.8091.0000.5880.5430.5140.6900.0000.0000.0000.4010.000
museum_fclt_cnt0.0871.0000.2880.4840.5881.0000.6150.4920.8760.0000.5890.4050.0000.497
artgr_fclt_cnt0.1170.9600.0210.3240.5430.6151.0000.4690.6860.0000.0000.6750.0000.000
prfplc_fclt_cnt0.2411.0000.1810.4780.5140.4920.4691.0000.7280.0000.0000.0000.0000.000
fclt_cnt0.0001.0000.2630.6510.6900.8760.6860.7281.0000.0000.1750.2220.0000.212
ibllbr_rate0.4771.0000.5400.5750.0000.0000.0000.0000.0001.0000.5470.7200.8240.680
museum_rate0.6011.0000.4510.0000.0000.5890.0000.0000.1750.5471.0000.8010.6660.813
artgr_rate0.4040.0000.5020.0000.0000.4050.6750.0000.2220.7200.8011.0000.6800.692
prfplc_rate0.5190.6770.5920.5970.4010.0000.0000.0000.0000.8240.6660.6801.0000.771
fclt_rate0.4000.0000.4850.4080.0000.4970.0000.0000.2120.6800.8130.6920.7711.000
2023-12-10T19:20:00.323897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_nmprfplc_fclt_cnt
ctprvn_nm1.0000.090
prfplc_fclt_cnt0.0901.000
2023-12-10T19:20:00.496826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
signgu_cdresidnt_2030_cntibllbr_fclt_cntmuseum_fclt_cntartgr_fclt_cntfclt_cntibllbr_ratemuseum_rateartgr_rateprfplc_ratefclt_ratectprvn_nmprfplc_fclt_cnt
signgu_cd1.000-0.590-0.5410.121-0.260-0.3110.4370.494-0.1640.5440.5370.9890.108
residnt_2030_cnt-0.5901.0000.8390.0590.2980.560-0.815-0.6840.056-0.873-0.8670.2430.314
ibllbr_fclt_cnt-0.5410.8391.0000.2250.3400.761-0.412-0.4810.097-0.661-0.5590.2120.344
museum_fclt_cnt0.1210.0590.2251.0000.2950.7140.0980.6050.2260.0690.3290.0450.324
artgr_fclt_cnt-0.2600.2980.3400.2951.0000.543-0.180-0.0630.920-0.249-0.0410.0750.338
fclt_cnt-0.3110.5600.7610.7140.5431.000-0.1910.0390.376-0.331-0.1180.0000.552
ibllbr_rate0.437-0.815-0.4120.098-0.180-0.1911.0000.660-0.0230.8020.9040.3120.000
museum_rate0.494-0.684-0.4810.605-0.0630.0390.6601.0000.0700.6850.8590.2610.000
artgr_rate-0.1640.0560.0970.2260.9200.376-0.0230.0701.000-0.0770.1290.1720.000
prfplc_rate0.544-0.873-0.6610.069-0.249-0.3310.8020.685-0.0771.0000.8710.3240.000
fclt_rate0.537-0.867-0.5590.329-0.041-0.1180.9040.8590.1290.8711.0000.2820.000
ctprvn_nm0.9890.2430.2120.0450.0750.0000.3120.2610.1720.3240.2821.0000.090
prfplc_fclt_cnt0.1080.3140.3440.3240.3380.5520.0000.0000.0000.0000.0000.0901.000

Missing values

2023-12-10T19:19:51.336272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:19:51.701774image/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

ctprvn_nmsigngu_nmsigngu_cdresidnt_2030_cntibllbr_fclt_cntmuseum_fclt_cntartgr_fclt_cntprfplc_fclt_cntfclt_cntibllbr_ratemuseum_rateartgr_rateprfplc_ratefclt_ratefile_namebase_ymd
0강원도춘천시4211071508101141260.000140.0001540.0000560.0000140.000364KC_600_MIL_CLT_STATN_BIZAEA_202120200101
1충청북도괴산군437605388130150.0001860.0005570.00.0001860.000928KC_600_MIL_CLT_STATN_BIZAEA_202120200101
2강원도강릉시421504681641922270.0000850.0004060.0000430.0000430.000577KC_600_MIL_CLT_STATN_BIZAEA_202120200101
3강원도동해시4217018638301150.0001610.00.0000540.0000540.000268KC_600_MIL_CLT_STATN_BIZAEA_202120200101
4강원도태백시421907482320160.0004010.0002670.00.0001340.000802KC_600_MIL_CLT_STATN_BIZAEA_202120200101
5강원도속초시4221018234321170.0001650.000110.0000550.0000550.000384KC_600_MIL_CLT_STATN_BIZAEA_202120200101
6강원도삼척시4223013145340180.0002280.0003040.00.0000760.000609KC_600_MIL_CLT_STATN_BIZAEA_202120200101
7충청북도음성군43770192464601110.0002080.0003120.00.0000520.000572KC_600_MIL_CLT_STATN_BIZAEA_202120200101
8강원도횡성군427307312310150.000410.0001370.00.0001370.000684KC_600_MIL_CLT_STATN_BIZAEA_202120200101
9강원도영월군42750610322121260.0003280.0034410.0003280.0001640.00426KC_600_MIL_CLT_STATN_BIZAEA_202120200101
ctprvn_nmsigngu_nmsigngu_cdresidnt_2030_cntibllbr_fclt_cntmuseum_fclt_cntartgr_fclt_cntprfplc_fclt_cntfclt_cntibllbr_ratemuseum_rateartgr_rateprfplc_ratefclt_ratefile_namebase_ymd
90경상북도문경시47280118575701130.0004220.000590.00.0000840.001096KC_600_MIL_CLT_STATN_BIZAEA_202120200101
91경상북도경산시47290691373811130.0000430.0001160.0000140.0000140.000188KC_600_MIL_CLT_STATN_BIZAEA_202120200101
92경상북도군위군477202846120140.0003510.0007030.00.0003510.001405KC_600_MIL_CLT_STATN_BIZAEA_202120200101
93경상북도의성군477306414220150.0003120.0003120.00.0001560.00078KC_600_MIL_CLT_STATN_BIZAEA_202120200101
94경상북도청송군477503426221160.0005840.0005840.0002920.0002920.001751KC_600_MIL_CLT_STATN_BIZAEA_202120200101
95경상북도영양군477602171110130.0004610.0004610.00.0004610.001382KC_600_MIL_CLT_STATN_BIZAEA_202120200101
96경상북도영덕군477704870100120.0002050.00.00.0002050.000411KC_600_MIL_CLT_STATN_BIZAEA_202120200101
97경상북도청도군478205658211040.0003530.0001770.0001770.00.000707KC_600_MIL_CLT_STATN_BIZAEA_202120200101
98경상북도고령군478304792120140.0002090.0004170.00.0002090.000835KC_600_MIL_CLT_STATN_BIZAEA_202120200101
99경상북도성주군478406747200130.0002960.00.00.0001480.000445KC_600_MIL_CLT_STATN_BIZAEA_202120200101