Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory143.3 B

Variable types

Categorical3
Numeric13

Alerts

anals_trget_year has constant value ""Constant
two_area_nm is highly overall correlated with all_lon_fq_co and 7 other fieldsHigh correlation
one_area_nm is highly overall correlated with setler_base_mnuri_setle_fq_co and 2 other fieldsHigh correlation
setler_base_mnuri_setle_fq_co is highly overall correlated with mrhst_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
mrhst_base_mnuri_setle_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
all_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
age_seven_below_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
age_thirteen_below_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 10 other fieldsHigh correlation
age_nineteen_below_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 10 other fieldsHigh correlation
n20s_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
n30s_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
n40s_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
n50s_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
n60s_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 10 other fieldsHigh correlation
n70s_above_lon_fq_co is highly overall correlated with setler_base_mnuri_setle_fq_co and 11 other fieldsHigh correlation
one_area_nm is highly imbalanced (80.6%)Imbalance
all_lon_fq_co has unique valuesUnique
age_thirteen_below_lon_fq_co has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:58:38.608730
Analysis finished2023-12-10 09:59:15.555311
Duration36.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

anals_trget_year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 100
100.0%

Length

2023-12-10T18:59:15.671377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:15.970791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 100
100.0%

anals_trget_mt
Real number (ℝ)

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.05
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:16.162090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median7
Q310
95-th percentile12
Maximum12
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2235716
Coefficient of variation (CV)0.4572442
Kurtosis-1.236526
Mean7.05
Median Absolute Deviation (MAD)3
Skewness-0.021946665
Sum705
Variance10.391414
MonotonicityNot monotonic
2023-12-10T18:59:16.389824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 10
10.0%
10 10
10.0%
12 10
10.0%
4 9
9.0%
5 9
9.0%
6 9
9.0%
7 9
9.0%
8 9
9.0%
11 9
9.0%
3 8
8.0%
ValueCountFrequency (%)
2 10
10.0%
3 8
8.0%
4 9
9.0%
5 9
9.0%
6 9
9.0%
7 9
9.0%
8 9
9.0%
9 8
8.0%
10 10
10.0%
11 9
9.0%
ValueCountFrequency (%)
12 10
10.0%
11 9
9.0%
10 10
10.0%
9 8
8.0%
8 9
9.0%
7 9
9.0%
6 9
9.0%
5 9
9.0%
4 9
9.0%
3 8
8.0%

one_area_nm
Categorical

HIGH CORRELATION  IMBALANCE 

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

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 97
97.0%
충청북도 3
 
3.0%

Length

2023-12-10T18:59:16.648059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:16.833567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 97
97.0%
충청북도 3
 
3.0%

two_area_nm
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
고성군
11 
삼척시
11 
속초시
11 
양구군
11 
양양군
11 
Other values (6)
45 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row강릉시
2nd row충주시
3rd row강릉시
4th row강릉시
5th row강릉시

Common Values

ValueCountFrequency (%)
고성군 11
11.0%
삼척시 11
11.0%
속초시 11
11.0%
양구군 11
11.0%
양양군 11
11.0%
영월군 11
11.0%
원주시 11
11.0%
동해시 10
10.0%
강릉시 9
9.0%
충주시 3
 
3.0%

Length

2023-12-10T18:59:17.042681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고성군 11
11.0%
삼척시 11
11.0%
속초시 11
11.0%
양구군 11
11.0%
양양군 11
11.0%
영월군 11
11.0%
원주시 11
11.0%
동해시 10
10.0%
강릉시 9
9.0%
충주시 3
 
3.0%

setler_base_mnuri_setle_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean651.79
Minimum64
Maximum3276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:17.300065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile96.85
Q1191
median323
Q31073.25
95-th percentile1898.1
Maximum3276
Range3212
Interquartile range (IQR)882.25

Descriptive statistics

Standard deviation712.49128
Coefficient of variation (CV)1.0931301
Kurtosis3.7615992
Mean651.79
Median Absolute Deviation (MAD)179
Skewness1.9084137
Sum65179
Variance507643.82
MonotonicityNot monotonic
2023-12-10T18:59:17.579873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 2
 
2.0%
434 2
 
2.0%
1344 1
 
1.0%
152 1
 
1.0%
226 1
 
1.0%
188 1
 
1.0%
242 1
 
1.0%
206 1
 
1.0%
256 1
 
1.0%
210 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
64 1
1.0%
72 1
1.0%
87 1
1.0%
92 1
1.0%
94 1
1.0%
97 1
1.0%
99 1
1.0%
108 1
1.0%
113 1
1.0%
116 1
1.0%
ValueCountFrequency (%)
3276 1
1.0%
3252 1
1.0%
3237 1
1.0%
2434 1
1.0%
1976 1
1.0%
1894 1
1.0%
1806 1
1.0%
1734 1
1.0%
1682 1
1.0%
1588 1
1.0%

mrhst_base_mnuri_setle_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.89
Minimum27
Maximum3422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:17.867017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile66.9
Q1137.75
median241
Q3891.5
95-th percentile1828.15
Maximum3422
Range3395
Interquartile range (IQR)753.75

Descriptive statistics

Standard deviation750.97878
Coefficient of variation (CV)1.1998575
Kurtosis3.4647245
Mean625.89
Median Absolute Deviation (MAD)145.5
Skewness1.8525806
Sum62589
Variance563969.13
MonotonicityNot monotonic
2023-12-10T18:59:18.161430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1514 2
 
2.0%
96 2
 
2.0%
215 2
 
2.0%
1417 1
 
1.0%
162 1
 
1.0%
132 1
 
1.0%
187 1
 
1.0%
180 1
 
1.0%
138 1
 
1.0%
98 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
27 1
1.0%
30 1
1.0%
53 1
1.0%
63 1
1.0%
65 1
1.0%
67 1
1.0%
77 1
1.0%
79 1
1.0%
80 1
1.0%
91 1
1.0%
ValueCountFrequency (%)
3422 1
1.0%
3319 1
1.0%
3288 1
1.0%
2384 1
1.0%
2040 1
1.0%
1817 1
1.0%
1804 1
1.0%
1739 1
1.0%
1640 1
1.0%
1625 1
1.0%

all_lon_fq_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12810.47
Minimum735
Maximum64327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:18.451303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum735
5-th percentile938.6
Q13297.75
median7321
Q315215.25
95-th percentile53346.4
Maximum64327
Range63592
Interquartile range (IQR)11917.5

Descriptive statistics

Standard deviation15694.153
Coefficient of variation (CV)1.2251036
Kurtosis3.2114225
Mean12810.47
Median Absolute Deviation (MAD)4684.5
Skewness2.0235465
Sum1281047
Variance2.4630643 × 108
MonotonicityNot monotonic
2023-12-10T18:59:18.810500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10441 1
 
1.0%
3527 1
 
1.0%
3306 1
 
1.0%
3630 1
 
1.0%
4813 1
 
1.0%
4327 1
 
1.0%
3665 1
 
1.0%
3550 1
 
1.0%
3759 1
 
1.0%
4329 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
735 1
1.0%
737 1
1.0%
785 1
1.0%
809 1
1.0%
912 1
1.0%
940 1
1.0%
987 1
1.0%
1057 1
1.0%
1082 1
1.0%
1249 1
1.0%
ValueCountFrequency (%)
64327 1
1.0%
63101 1
1.0%
58877 1
1.0%
54598 1
1.0%
53715 1
1.0%
53327 1
1.0%
51508 1
1.0%
50871 1
1.0%
50659 1
1.0%
47694 1
1.0%

age_seven_below_lon_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean920.34
Minimum14
Maximum5458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:19.753495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30.7
Q1139.25
median575
Q31006.25
95-th percentile3928.55
Maximum5458
Range5444
Interquartile range (IQR)867

Descriptive statistics

Standard deviation1212.045
Coefficient of variation (CV)1.3169535
Kurtosis3.363132
Mean920.34
Median Absolute Deviation (MAD)435
Skewness2.019318
Sum92034
Variance1469053
MonotonicityNot monotonic
2023-12-10T18:59:20.050812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141 2
 
2.0%
369 1
 
1.0%
99 1
 
1.0%
346 1
 
1.0%
450 1
 
1.0%
367 1
 
1.0%
334 1
 
1.0%
198 1
 
1.0%
281 1
 
1.0%
340 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
14 1
1.0%
16 1
1.0%
19 1
1.0%
20 1
1.0%
25 1
1.0%
31 1
1.0%
34 1
1.0%
35 1
1.0%
40 1
1.0%
42 1
1.0%
ValueCountFrequency (%)
5458 1
1.0%
4462 1
1.0%
4320 1
1.0%
4169 1
1.0%
4015 1
1.0%
3924 1
1.0%
3915 1
1.0%
3828 1
1.0%
3679 1
1.0%
3619 1
1.0%

age_thirteen_below_lon_fq_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.16
Minimum77
Maximum10341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:20.436613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile122.65
Q1361.75
median1153.5
Q32319.25
95-th percentile8117.5
Maximum10341
Range10264
Interquartile range (IQR)1957.5

Descriptive statistics

Standard deviation2476.1963
Coefficient of variation (CV)1.2479822
Kurtosis3.2707273
Mean1984.16
Median Absolute Deviation (MAD)825.5
Skewness1.9849083
Sum198416
Variance6131548.3
MonotonicityNot monotonic
2023-12-10T18:59:20.884232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1416 1
 
1.0%
362 1
 
1.0%
522 1
 
1.0%
488 1
 
1.0%
826 1
 
1.0%
649 1
 
1.0%
489 1
 
1.0%
546 1
 
1.0%
512 1
 
1.0%
571 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
77 1
1.0%
92 1
1.0%
98 1
1.0%
100 1
1.0%
116 1
1.0%
123 1
1.0%
125 1
1.0%
133 1
1.0%
134 1
1.0%
137 1
1.0%
ValueCountFrequency (%)
10341 1
1.0%
10021 1
1.0%
9486 1
1.0%
9479 1
1.0%
8374 1
1.0%
8104 1
1.0%
7636 1
1.0%
7277 1
1.0%
7186 1
1.0%
7164 1
1.0%

age_nineteen_below_lon_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean790.29
Minimum15
Maximum5902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:21.179737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile35.6
Q1196.75
median433
Q3784
95-th percentile3278.85
Maximum5902
Range5887
Interquartile range (IQR)587.25

Descriptive statistics

Standard deviation1088.6421
Coefficient of variation (CV)1.3775223
Kurtosis7.7741642
Mean790.29
Median Absolute Deviation (MAD)289
Skewness2.7002099
Sum79029
Variance1185141.6
MonotonicityNot monotonic
2023-12-10T18:59:21.499317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237 2
 
2.0%
552 2
 
2.0%
17 2
 
2.0%
50 2
 
2.0%
245 1
 
1.0%
136 1
 
1.0%
132 1
 
1.0%
150 1
 
1.0%
196 1
 
1.0%
148 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
15 1
1.0%
17 2
2.0%
22 1
1.0%
28 1
1.0%
36 1
1.0%
38 1
1.0%
40 1
1.0%
50 2
2.0%
77 1
1.0%
94 1
1.0%
ValueCountFrequency (%)
5902 1
1.0%
5213 1
1.0%
4100 1
1.0%
3817 1
1.0%
3333 1
1.0%
3276 1
1.0%
3089 1
1.0%
2825 1
1.0%
2457 1
1.0%
2409 1
1.0%

n20s_lon_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean727.72
Minimum25
Maximum4385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:21.787700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile79.6
Q1144
median323.5
Q3752
95-th percentile3354.55
Maximum4385
Range4360
Interquartile range (IQR)608

Descriptive statistics

Standard deviation1005.9107
Coefficient of variation (CV)1.3822771
Kurtosis4.028628
Mean727.72
Median Absolute Deviation (MAD)219.5
Skewness2.2383707
Sum72772
Variance1011856.3
MonotonicityNot monotonic
2023-12-10T18:59:22.070810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106 2
 
2.0%
144 2
 
2.0%
204 2
 
2.0%
626 2
 
2.0%
34 2
 
2.0%
130 1
 
1.0%
114 1
 
1.0%
193 1
 
1.0%
258 1
 
1.0%
160 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
25 1
1.0%
26 1
1.0%
32 1
1.0%
34 2
2.0%
82 1
1.0%
85 1
1.0%
88 1
1.0%
99 1
1.0%
104 1
1.0%
106 2
2.0%
ValueCountFrequency (%)
4385 1
1.0%
3911 1
1.0%
3697 1
1.0%
3525 1
1.0%
3498 1
1.0%
3347 1
1.0%
3229 1
1.0%
3162 1
1.0%
3088 1
1.0%
3016 1
1.0%

n30s_lon_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2613
Minimum133
Maximum12301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:22.358869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum133
5-th percentile215.9
Q1753.5
median1585.5
Q33019.5
95-th percentile10818.1
Maximum12301
Range12168
Interquartile range (IQR)2266

Descriptive statistics

Standard deviation3045.0097
Coefficient of variation (CV)1.1653309
Kurtosis3.0602943
Mean2613
Median Absolute Deviation (MAD)1044
Skewness2.0072754
Sum261300
Variance9272084.3
MonotonicityNot monotonic
2023-12-10T18:59:22.717525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
614 2
 
2.0%
1678 1
 
1.0%
1013 1
 
1.0%
906 1
 
1.0%
1148 1
 
1.0%
1045 1
 
1.0%
894 1
 
1.0%
754 1
 
1.0%
949 1
 
1.0%
1002 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
133 1
1.0%
166 1
1.0%
180 1
1.0%
194 1
1.0%
214 1
1.0%
216 1
1.0%
223 1
1.0%
245 1
1.0%
246 1
1.0%
279 1
1.0%
ValueCountFrequency (%)
12301 1
1.0%
11368 1
1.0%
11355 1
1.0%
11038 1
1.0%
10953 1
1.0%
10811 1
1.0%
10240 1
1.0%
10136 1
1.0%
10059 1
1.0%
9782 1
1.0%

n40s_lon_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3539.31
Minimum210
Maximum18618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:23.028190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile269.6
Q1670.5
median1956
Q34135
95-th percentile15205.1
Maximum18618
Range18408
Interquartile range (IQR)3464.5

Descriptive statistics

Standard deviation4493.9462
Coefficient of variation (CV)1.2697238
Kurtosis3.3056295
Mean3539.31
Median Absolute Deviation (MAD)1373
Skewness2.0404938
Sum353931
Variance20195553
MonotonicityNot monotonic
2023-12-10T18:59:23.395117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
827 2
 
2.0%
666 1
 
1.0%
901 1
 
1.0%
930 1
 
1.0%
1220 1
 
1.0%
1131 1
 
1.0%
1115 1
 
1.0%
1133 1
 
1.0%
1071 1
 
1.0%
1197 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
210 1
1.0%
221 1
1.0%
226 1
1.0%
232 1
1.0%
243 1
1.0%
271 1
1.0%
281 1
1.0%
353 1
1.0%
354 1
1.0%
386 1
1.0%
ValueCountFrequency (%)
18618 1
1.0%
17629 1
1.0%
17310 1
1.0%
15494 1
1.0%
15492 1
1.0%
15190 1
1.0%
14222 1
1.0%
14089 1
1.0%
13975 1
1.0%
13897 1
1.0%

n50s_lon_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1211.87
Minimum80
Maximum6041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:23.793380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile97.9
Q1259
median497
Q31601.5
95-th percentile5129
Maximum6041
Range5961
Interquartile range (IQR)1342.5

Descriptive statistics

Standard deviation1516.3472
Coefficient of variation (CV)1.2512458
Kurtosis2.6914521
Mean1211.87
Median Absolute Deviation (MAD)338
Skewness1.9019932
Sum121187
Variance2299308.9
MonotonicityNot monotonic
2023-12-10T18:59:24.205400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
259 2
 
2.0%
80 2
 
2.0%
322 2
 
2.0%
5129 2
 
2.0%
361 2
 
2.0%
234 1
 
1.0%
312 1
 
1.0%
296 1
 
1.0%
384 1
 
1.0%
359 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
80 2
2.0%
82 1
1.0%
83 1
1.0%
96 1
1.0%
98 1
1.0%
116 1
1.0%
123 1
1.0%
129 1
1.0%
138 1
1.0%
147 1
1.0%
ValueCountFrequency (%)
6041 1
1.0%
5668 1
1.0%
5568 1
1.0%
5288 1
1.0%
5129 2
2.0%
4932 1
1.0%
4906 1
1.0%
4850 1
1.0%
4667 1
1.0%
3974 1
1.0%

n60s_lon_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean542.86
Minimum14
Maximum2461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:24.497310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile43.9
Q1178.25
median270
Q3691.75
95-th percentile2183.2
Maximum2461
Range2447
Interquartile range (IQR)513.5

Descriptive statistics

Standard deviation623.27885
Coefficient of variation (CV)1.1481392
Kurtosis2.9283047
Mean542.86
Median Absolute Deviation (MAD)164.5
Skewness1.9543531
Sum54286
Variance388476.53
MonotonicityNot monotonic
2023-12-10T18:59:24.836376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
258 2
 
2.0%
115 2
 
2.0%
261 2
 
2.0%
38 2
 
2.0%
199 2
 
2.0%
42 2
 
2.0%
270 2
 
2.0%
110 1
 
1.0%
324 1
 
1.0%
219 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
14 1
1.0%
38 2
2.0%
42 2
2.0%
44 1
1.0%
46 1
1.0%
56 1
1.0%
61 1
1.0%
63 1
1.0%
70 1
1.0%
71 1
1.0%
ValueCountFrequency (%)
2461 1
1.0%
2419 1
1.0%
2374 1
1.0%
2289 1
1.0%
2244 1
1.0%
2180 1
1.0%
2114 1
1.0%
2055 1
1.0%
2010 1
1.0%
1933 1
1.0%

n70s_above_lon_fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.69
Minimum1
Maximum911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:25.111450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.85
Q138
median107
Q3274.75
95-th percentile806.3
Maximum911
Range910
Interquartile range (IQR)236.75

Descriptive statistics

Standard deviation238.86447
Coefficient of variation (CV)1.1726863
Kurtosis1.6512434
Mean203.69
Median Absolute Deviation (MAD)88.5
Skewness1.5863786
Sum20369
Variance57056.236
MonotonicityNot monotonic
2023-12-10T18:59:25.425431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 4
 
4.0%
26 3
 
3.0%
38 2
 
2.0%
42 2
 
2.0%
25 2
 
2.0%
18 2
 
2.0%
40 2
 
2.0%
19 2
 
2.0%
39 2
 
2.0%
56 2
 
2.0%
Other values (75) 77
77.0%
ValueCountFrequency (%)
1 1
 
1.0%
3 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
10 1
 
1.0%
13 4
4.0%
14 1
 
1.0%
17 1
 
1.0%
18 2
2.0%
19 2
2.0%
ValueCountFrequency (%)
911 1
1.0%
869 1
1.0%
853 1
1.0%
821 1
1.0%
812 1
1.0%
806 1
1.0%
746 1
1.0%
745 1
1.0%
735 1
1.0%
700 1
1.0%

Interactions

2023-12-10T18:59:12.601760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:39.812351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:42.610930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:45.857062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:48.707314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:51.474599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:53.584329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:56.803999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:59.052425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:01.227268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:03.404062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:05.717053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:09.726156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:12.749121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:39.973709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:42.821801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:46.036761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:48.897282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:51.634611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:53.802874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:57.048996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:59.214070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:01.374004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:03.570518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:05.883039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:09.895508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:12.912559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:40.259136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:43.124129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:46.245471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:49.113747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:51.811223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:53.957366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:57.279680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:59.428595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:01.526699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:03.735304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:06.055778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:10.079775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:13.069481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:40.597408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:43.332656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:46.591627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:49.395766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:51.966607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:54.119209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:57.546096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:59.615989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:01.698342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:03.899617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:06.262843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:10.344478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:13.247646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:40.799222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:43.524985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:46.837302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:49.612214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:52.118926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:54.296174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:57.756088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:59.805314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:01.890446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:04.047910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:06.449807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:10.598075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:13.421118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:40.984011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:43.798617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:47.016423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:49.851304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:52.255894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:54.499254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:57.937895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:59.946138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:02.049917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:04.222584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:06.641263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:10.902027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:13.633657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:41.230417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:44.450223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:47.194347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:50.050810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:52.398772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:54.653923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:58.084248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:00.098638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:02.226550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:04.430047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:06.820304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:11.154837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:13.841245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:41.496380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:44.686721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:47.413875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:50.295336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:52.581945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:54.815062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:58.242653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:00.356103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:02.421915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:04.637759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:07.369076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:11.417681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:14.007861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:41.745033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:44.890620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:47.597481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:50.457535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:52.722646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:54.965353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:58.353058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:00.477814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:02.579459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:04.790776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:08.697622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:11.636324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:14.173737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:41.936838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:45.147684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:47.833741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:50.637162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:52.878835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:55.173695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:58.486382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:00.624709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:02.726771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:04.965026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:09.016260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:11.850200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:14.341574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:42.113992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:45.358004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:48.058745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:50.858257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:53.079203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:55.358440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:58.628316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:00.783342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:02.942243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:05.155151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:09.188316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:12.158092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:14.495924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:42.279501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:45.516998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:48.274798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:51.085616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:53.275440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:55.515202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:58.751182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:00.959417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:03.094203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:05.343269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:09.409918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:12.324193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:14.655876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:42.440712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:45.683311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:48.494663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:51.265492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:53.439029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:55.771821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:58.906516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:01.108122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:03.263751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:05.566262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:09.576451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:12.461549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:59:25.623081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
anals_trget_mtone_area_nmtwo_area_nmsetler_base_mnuri_setle_fq_comrhst_base_mnuri_setle_fq_coall_lon_fq_coage_seven_below_lon_fq_coage_thirteen_below_lon_fq_coage_nineteen_below_lon_fq_con20s_lon_fq_con30s_lon_fq_con40s_lon_fq_con50s_lon_fq_con60s_lon_fq_con70s_above_lon_fq_co
anals_trget_mt1.0000.0000.0000.0680.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
one_area_nm0.0001.0001.0000.7660.7990.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
two_area_nm0.0001.0001.0000.6710.7170.8240.7880.7780.6490.7950.8320.7910.8340.7700.814
setler_base_mnuri_setle_fq_co0.0680.7660.6711.0000.9880.7720.6160.6130.5870.5920.7920.7870.7940.7940.649
mrhst_base_mnuri_setle_fq_co0.1020.7990.7170.9881.0000.7960.6070.6830.5850.6480.8070.7730.8140.8480.727
all_lon_fq_co0.0000.0000.8240.7720.7961.0000.8780.9500.8330.9000.9910.9910.9860.9580.893
age_seven_below_lon_fq_co0.0000.0000.7880.6160.6070.8781.0000.9010.9300.8510.8470.8470.8450.8220.860
age_thirteen_below_lon_fq_co0.0000.0000.7780.6130.6830.9500.9011.0000.8590.9260.9200.9460.9160.8960.958
age_nineteen_below_lon_fq_co0.0000.0000.6490.5870.5850.8330.9300.8591.0000.8910.8720.8350.8940.8720.876
n20s_lon_fq_co0.0000.0000.7950.5920.6480.9000.8510.9260.8911.0000.8880.8970.8890.8900.877
n30s_lon_fq_co0.0000.0000.8320.7920.8070.9910.8470.9200.8720.8881.0000.9860.9950.9830.927
n40s_lon_fq_co0.0000.0000.7910.7870.7730.9910.8470.9460.8350.8970.9861.0000.9840.9640.893
n50s_lon_fq_co0.0000.0000.8340.7940.8140.9860.8450.9160.8940.8890.9950.9841.0000.9830.930
n60s_lon_fq_co0.0000.0000.7700.7940.8480.9580.8220.8960.8720.8900.9830.9640.9831.0000.932
n70s_above_lon_fq_co0.0000.0000.8140.6490.7270.8930.8600.9580.8760.8770.9270.8930.9300.9321.000
2023-12-10T18:59:25.932962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
two_area_nmone_area_nm
two_area_nm1.0000.953
one_area_nm0.9531.000
2023-12-10T18:59:26.190765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
anals_trget_mtsetler_base_mnuri_setle_fq_comrhst_base_mnuri_setle_fq_coall_lon_fq_coage_seven_below_lon_fq_coage_thirteen_below_lon_fq_coage_nineteen_below_lon_fq_con20s_lon_fq_con30s_lon_fq_con40s_lon_fq_con50s_lon_fq_con60s_lon_fq_con70s_above_lon_fq_coone_area_nmtwo_area_nm
anals_trget_mt1.0000.1290.167-0.0420.060-0.043-0.061-0.154-0.016-0.053-0.059-0.037-0.1070.0000.000
setler_base_mnuri_setle_fq_co0.1291.0000.9840.6930.6600.6790.5810.5630.6160.7010.6590.6910.7440.5710.390
mrhst_base_mnuri_setle_fq_co0.1670.9841.0000.7170.6800.7020.6320.5880.6510.7210.6900.7120.7540.6000.435
all_lon_fq_co-0.0420.6930.7171.0000.9490.9850.8930.9420.9750.9930.9400.9080.9470.0000.572
age_seven_below_lon_fq_co0.0600.6600.6800.9491.0000.9620.8260.8750.9250.9490.9030.8380.8990.0000.507
age_thirteen_below_lon_fq_co-0.0430.6790.7020.9850.9621.0000.8720.9220.9550.9870.9380.8980.9380.0000.471
age_nineteen_below_lon_fq_co-0.0610.5810.6320.8930.8260.8721.0000.8520.8660.8770.9300.8520.8580.0000.359
n20s_lon_fq_co-0.1540.5630.5880.9420.8750.9220.8521.0000.9520.9290.8970.8400.8660.0000.544
n30s_lon_fq_co-0.0160.6160.6510.9750.9250.9550.8660.9521.0000.9640.9030.8540.8910.0000.585
n40s_lon_fq_co-0.0530.7010.7210.9930.9490.9870.8770.9290.9641.0000.9340.9090.9510.0000.525
n50s_lon_fq_co-0.0590.6590.6900.9400.9030.9380.9300.8970.9030.9341.0000.9350.9430.0000.588
n60s_lon_fq_co-0.0370.6910.7120.9080.8380.8980.8520.8400.8540.9090.9351.0000.9300.0000.497
n70s_above_lon_fq_co-0.1070.7440.7540.9470.8990.9380.8580.8660.8910.9510.9430.9301.0000.0000.519
one_area_nm0.0000.5710.6000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.953
two_area_nm0.0000.3900.4350.5720.5070.4710.3590.5440.5850.5250.5880.4970.5190.9531.000

Missing values

2023-12-10T18:59:14.920034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:59:15.380493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

anals_trget_yearanals_trget_mtone_area_nmtwo_area_nmsetler_base_mnuri_setle_fq_comrhst_base_mnuri_setle_fq_coall_lon_fq_coage_seven_below_lon_fq_coage_thirteen_below_lon_fq_coage_nineteen_below_lon_fq_con20s_lon_fq_con30s_lon_fq_con40s_lon_fq_con50s_lon_fq_con60s_lon_fq_con70s_above_lon_fq_co
020192강원도강릉시13441417104413691416741543167832731053434352
1201910충청북도충주시1065992533030891239082752122525918550
220194강원도강릉시15881625119986291653552476212335191117632246
320195강원도강릉시16821739105765731232551392194432251008590323
420196강원도강릉시14301514120436961741662478201238191064556273
520197강원도강릉시12911335130567171891860512203539891285602470
620198강원도강릉시13501477153347792106836626277147921356695331
7201911충청북도충주시144813725922512954462113918140025920841
8201910강원도강릉시117012037009681114426216316001876311277106
9201911강원도강릉시146614887166648115520514015921887366303252
anals_trget_yearanals_trget_mtone_area_nmtwo_area_nmsetler_base_mnuri_setle_fq_comrhst_base_mnuri_setle_fq_coall_lon_fq_coage_seven_below_lon_fq_coage_thirteen_below_lon_fq_coage_nineteen_below_lon_fq_con20s_lon_fq_con30s_lon_fq_con40s_lon_fq_con50s_lon_fq_con60s_lon_fq_con70s_above_lon_fq_co
9020194강원도원주시19762040515083679810424093347108111519048502114735
9120195강원도원주시18941804506594462716430893162100591408949322180745
9220196강원도원주시18061817537154015837433333498110381549449062010812
9320197강원도원주시149315776310141691002159024385113681762956682244911
9420198강원도원주시143215016432743201034152133911123011861860412374821
9520199강원도원주시1098119547694361972772218308897821389746671933700
96201910강원도원주시1201126642059391555442457266286031172039741645619
97201911강원도원주시13491514533275458763628253229109531397551292055746
98201912강원도원주시32523319508713828718638173016102401422251292289853
9920192강원도인제군87272702175403114858227431696320