Overview

Dataset statistics

Number of variables10
Number of observations85
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory90.6 B

Variable types

Categorical2
Numeric8

Alerts

avrg_edc_ct is highly overall correlated with elesch_edc_ct and 2 other fieldsHigh correlation
elesch_edc_ct is highly overall correlated with avrg_edc_ct and 2 other fieldsHigh correlation
mskul_edc_ct is highly overall correlated with avrg_edc_ct and 2 other fieldsHigh correlation
hgschl_edc_ct is highly overall correlated with avrg_edc_ct and 2 other fieldsHigh correlation
tot_lon_co is highly overall correlated with elesch_lon_co and 3 other fieldsHigh correlation
elesch_lon_co is highly overall correlated with tot_lon_co and 3 other fieldsHigh correlation
mskul_lon_co is highly overall correlated with tot_lon_co and 3 other fieldsHigh correlation
hgschl_lon_co is highly overall correlated with tot_lon_co and 3 other fieldsHigh correlation
area_nm is highly overall correlated with tot_lon_co and 3 other fieldsHigh correlation
tot_lon_co has unique valuesUnique
elesch_lon_co has unique valuesUnique
mskul_lon_co has unique valuesUnique
hgschl_lon_co has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:47:07.326984
Analysis finished2023-12-10 09:47:21.115008
Duration13.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

anals_trget_year
Categorical

Distinct5
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
2014
17 
2015
17 
2016
17 
2017
17 
2018
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2014 17
20.0%
2015 17
20.0%
2016 17
20.0%
2017 17
20.0%
2018 17
20.0%

Length

2023-12-10T18:47:21.216300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:47:21.411965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 17
20.0%
2015 17
20.0%
2016 17
20.0%
2017 17
20.0%
2018 17
20.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
강원도
 
5
경기도
 
5
경상남도
 
5
경상북도
 
5
광주광역시
 
5
Other values (12)
60 

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row경기도
3rd row경상남도
4th row경상북도
5th row광주광역시

Common Values

ValueCountFrequency (%)
강원도 5
 
5.9%
경기도 5
 
5.9%
경상남도 5
 
5.9%
경상북도 5
 
5.9%
광주광역시 5
 
5.9%
대구광역시 5
 
5.9%
대전광역시 5
 
5.9%
부산광역시 5
 
5.9%
서울특별시 5
 
5.9%
세종특별자치시 5
 
5.9%
Other values (7) 35
41.2%

Length

2023-12-10T18:47:21.667947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원도 5
 
5.9%
세종특별자치시 5
 
5.9%
충청남도 5
 
5.9%
제주특별자치도 5
 
5.9%
전라북도 5
 
5.9%
전라남도 5
 
5.9%
인천광역시 5
 
5.9%
울산광역시 5
 
5.9%
서울특별시 5
 
5.9%
경기도 5
 
5.9%
Other values (7) 35
41.2%

avrg_edc_ct
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231870.59
Minimum158000
Maximum411000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T18:47:21.980748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum158000
5-th percentile167800
Q1193000
median226000
Q3260000
95-th percentile332200
Maximum411000
Range253000
Interquartile range (IQR)67000

Descriptive statistics

Standard deviation50275.855
Coefficient of variation (CV)0.21682722
Kurtosis1.8697332
Mean231870.59
Median Absolute Deviation (MAD)34000
Skewness1.2010488
Sum19709000
Variance2.5276616 × 109
MonotonicityNot monotonic
2023-12-10T18:47:22.265825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190000 4
 
4.7%
203000 3
 
3.5%
265000 3
 
3.5%
276000 3
 
3.5%
209000 2
 
2.4%
186000 2
 
2.4%
228000 2
 
2.4%
188000 2
 
2.4%
262000 2
 
2.4%
244000 2
 
2.4%
Other values (57) 60
70.6%
ValueCountFrequency (%)
158000 1
1.2%
162000 1
1.2%
164000 1
1.2%
165000 1
1.2%
167000 1
1.2%
171000 1
1.2%
177000 1
1.2%
180000 1
1.2%
181000 1
1.2%
183000 1
1.2%
ValueCountFrequency (%)
411000 1
1.2%
391000 1
1.2%
352000 1
1.2%
338000 1
1.2%
335000 1
1.2%
321000 1
1.2%
303000 1
1.2%
300000 1
1.2%
288000 1
1.2%
287000 1
1.2%

elesch_edc_ct
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223164.71
Minimum145000
Maximum365000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T18:47:22.625570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum145000
5-th percentile163600
Q1200000
median217000
Q3245000
95-th percentile293600
Maximum365000
Range220000
Interquartile range (IQR)45000

Descriptive statistics

Standard deviation39403.241
Coefficient of variation (CV)0.17656574
Kurtosis1.6431769
Mean223164.71
Median Absolute Deviation (MAD)23000
Skewness0.90769726
Sum18969000
Variance1.5526154 × 109
MonotonicityNot monotonic
2023-12-10T18:47:22.899231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204000 4
 
4.7%
203000 3
 
3.5%
217000 3
 
3.5%
191000 3
 
3.5%
212000 3
 
3.5%
195000 2
 
2.4%
253000 2
 
2.4%
216000 2
 
2.4%
252000 2
 
2.4%
218000 2
 
2.4%
Other values (53) 59
69.4%
ValueCountFrequency (%)
145000 1
1.2%
155000 1
1.2%
161000 1
1.2%
163000 2
2.4%
166000 1
1.2%
177000 1
1.2%
179000 1
1.2%
182000 1
1.2%
183000 1
1.2%
184000 1
1.2%
ValueCountFrequency (%)
365000 1
1.2%
341000 1
1.2%
300000 1
1.2%
299000 1
1.2%
296000 1
1.2%
284000 1
1.2%
283000 1
1.2%
282000 1
1.2%
277000 1
1.2%
268000 1
1.2%

mskul_edc_ct
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259929.41
Minimum188000
Maximum417000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T18:47:23.163327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum188000
5-th percentile202000
Q1225000
median254000
Q3291000
95-th percentile346200
Maximum417000
Range229000
Interquartile range (IQR)66000

Descriptive statistics

Standard deviation48151
Coefficient of variation (CV)0.18524645
Kurtosis1.2387677
Mean259929.41
Median Absolute Deviation (MAD)33000
Skewness1.0543989
Sum22094000
Variance2.3185188 × 109
MonotonicityNot monotonic
2023-12-10T18:47:23.472400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226000 4
 
4.7%
291000 3
 
3.5%
202000 3
 
3.5%
240000 2
 
2.4%
228000 2
 
2.4%
255000 2
 
2.4%
264000 2
 
2.4%
214000 2
 
2.4%
236000 2
 
2.4%
221000 2
 
2.4%
Other values (52) 61
71.8%
ValueCountFrequency (%)
188000 1
 
1.2%
191000 1
 
1.2%
195000 1
 
1.2%
199000 1
 
1.2%
202000 3
3.5%
204000 1
 
1.2%
212000 2
2.4%
213000 1
 
1.2%
214000 2
2.4%
215000 1
 
1.2%
ValueCountFrequency (%)
417000 1
1.2%
415000 1
1.2%
370000 1
1.2%
355000 1
1.2%
349000 1
1.2%
335000 1
1.2%
334000 1
1.2%
332000 2
2.4%
320000 1
1.2%
316000 1
1.2%

hgschl_edc_ct
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221505.88
Minimum105000
Maximum484000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T18:47:23.772084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105000
5-th percentile134200
Q1166000
median212000
Q3258000
95-th percentile380600
Maximum484000
Range379000
Interquartile range (IQR)92000

Descriptive statistics

Standard deviation76935.753
Coefficient of variation (CV)0.34733052
Kurtosis1.4088387
Mean221505.88
Median Absolute Deviation (MAD)46000
Skewness1.1101606
Sum18828000
Variance5.9191101 × 109
MonotonicityNot monotonic
2023-12-10T18:47:24.159803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141000 3
 
3.5%
175000 2
 
2.4%
185000 2
 
2.4%
237000 2
 
2.4%
201000 2
 
2.4%
212000 2
 
2.4%
206000 2
 
2.4%
182000 2
 
2.4%
146000 2
 
2.4%
302000 2
 
2.4%
Other values (63) 64
75.3%
ValueCountFrequency (%)
105000 1
 
1.2%
107000 1
 
1.2%
121000 1
 
1.2%
125000 1
 
1.2%
134000 1
 
1.2%
135000 1
 
1.2%
137000 1
 
1.2%
138000 1
 
1.2%
141000 3
3.5%
142000 1
 
1.2%
ValueCountFrequency (%)
484000 1
1.2%
445000 1
1.2%
417000 1
1.2%
385000 1
1.2%
383000 1
1.2%
371000 1
1.2%
319000 1
1.2%
318000 1
1.2%
314000 1
1.2%
312000 1
1.2%

tot_lon_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6770562.2
Minimum474816
Maximum39759818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T18:47:24.664884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum474816
5-th percentile1127148.4
Q11985876
median2851470
Q34323428
95-th percentile32656704
Maximum39759818
Range39285002
Interquartile range (IQR)2337552

Descriptive statistics

Standard deviation9626167.8
Coefficient of variation (CV)1.4217679
Kurtosis4.6447874
Mean6770562.2
Median Absolute Deviation (MAD)1144486
Skewness2.3520798
Sum5.7549779 × 108
Variance9.2663107 × 1013
MonotonicityNot monotonic
2023-12-10T18:47:25.005754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2259253 1
 
1.2%
2566944 1
 
1.2%
3676335 1
 
1.2%
1424230 1
 
1.2%
1073498 1
 
1.2%
23891304 1
 
1.2%
10168886 1
 
1.2%
2576175 1
 
1.2%
11673939 1
 
1.2%
1433847 1
 
1.2%
Other values (75) 75
88.2%
ValueCountFrequency (%)
474816 1
1.2%
654693 1
1.2%
999455 1
1.2%
1048675 1
1.2%
1073498 1
1.2%
1341750 1
1.2%
1360650 1
1.2%
1362451 1
1.2%
1364556 1
1.2%
1368001 1
1.2%
ValueCountFrequency (%)
39759818 1
1.2%
39207194 1
1.2%
38674800 1
1.2%
36316738 1
1.2%
34504934 1
1.2%
25263786 1
1.2%
24746657 1
1.2%
24624773 1
1.2%
23891304 1
1.2%
23878196 1
1.2%

elesch_lon_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1067823.7
Minimum79500
Maximum6111230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T18:47:25.349737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79500
5-th percentile192348.8
Q1304922
median429137
Q3773821
95-th percentile5144689.8
Maximum6111230
Range6031730
Interquartile range (IQR)468899

Descriptive statistics

Standard deviation1496416.9
Coefficient of variation (CV)1.4013707
Kurtosis4.5486214
Mean1067823.7
Median Absolute Deviation (MAD)182910
Skewness2.3492636
Sum90765016
Variance2.2392634 × 1012
MonotonicityNot monotonic
2023-12-10T18:47:25.667938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
359531 1
 
1.2%
416420 1
 
1.2%
575743 1
 
1.2%
251255 1
 
1.2%
170927 1
 
1.2%
3778828 1
 
1.2%
1545895 1
 
1.2%
446069 1
 
1.2%
1700173 1
 
1.2%
225616 1
 
1.2%
Other values (75) 75
88.2%
ValueCountFrequency (%)
79500 1
1.2%
124448 1
1.2%
150680 1
1.2%
170927 1
1.2%
184032 1
1.2%
225616 1
1.2%
225621 1
1.2%
232721 1
1.2%
236144 1
1.2%
236650 1
1.2%
ValueCountFrequency (%)
6111230 1
1.2%
6071230 1
1.2%
6041350 1
1.2%
5547475 1
1.2%
5404028 1
1.2%
4107337 1
1.2%
4081877 1
1.2%
4056255 1
1.2%
3778828 1
1.2%
3615143 1
1.2%

mskul_lon_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208466.89
Minimum13174
Maximum1171964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T18:47:26.018911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13174
5-th percentile32167.8
Q165988
median103743
Q3172002
95-th percentile972753.8
Maximum1171964
Range1158790
Interquartile range (IQR)106014

Descriptive statistics

Standard deviation272608.99
Coefficient of variation (CV)1.3076848
Kurtosis5.3151919
Mean208466.89
Median Absolute Deviation (MAD)47491
Skewness2.4497418
Sum17719686
Variance7.4315662 × 1010
MonotonicityNot monotonic
2023-12-10T18:47:26.398418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96746 1
 
1.2%
91392 1
 
1.2%
111374 1
 
1.2%
47907 1
 
1.2%
30749 1
 
1.2%
609165 1
 
1.2%
333444 1
 
1.2%
95670 1
 
1.2%
253082 1
 
1.2%
41589 1
 
1.2%
Other values (75) 75
88.2%
ValueCountFrequency (%)
13174 1
1.2%
19270 1
1.2%
28263 1
1.2%
29434 1
1.2%
30749 1
1.2%
37843 1
1.2%
41589 1
1.2%
43632 1
1.2%
44372 1
1.2%
44845 1
1.2%
ValueCountFrequency (%)
1171964 1
1.2%
1124680 1
1.2%
1122104 1
1.2%
1109809 1
1.2%
1037691 1
1.2%
713005 1
1.2%
705178 1
1.2%
690458 1
1.2%
609165 1
1.2%
551755 1
1.2%

hgschl_lon_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164097.48
Minimum5728
Maximum938604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-10T18:47:26.695590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5728
5-th percentile18429.8
Q162815
median89276
Q3130862
95-th percentile766510.6
Maximum938604
Range932876
Interquartile range (IQR)68047

Descriptive statistics

Standard deviation213248.27
Coefficient of variation (CV)1.2995219
Kurtosis6.0405458
Mean164097.48
Median Absolute Deviation (MAD)39969
Skewness2.5520904
Sum13948286
Variance4.5474826 × 1010
MonotonicityNot monotonic
2023-12-10T18:47:26.944061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65323 1
 
1.2%
89276 1
 
1.2%
105639 1
 
1.2%
42089 1
 
1.2%
15380 1
 
1.2%
472916 1
 
1.2%
282486 1
 
1.2%
76087 1
 
1.2%
210749 1
 
1.2%
40249 1
 
1.2%
Other values (75) 75
88.2%
ValueCountFrequency (%)
5728 1
1.2%
9249 1
1.2%
14421 1
1.2%
14484 1
1.2%
15380 1
1.2%
30629 1
1.2%
31520 1
1.2%
35219 1
1.2%
35339 1
1.2%
36602 1
1.2%
ValueCountFrequency (%)
938604 1
1.2%
924762 1
1.2%
908398 1
1.2%
848224 1
1.2%
834245 1
1.2%
495573 1
1.2%
494700 1
1.2%
487833 1
1.2%
472916 1
1.2%
422190 1
1.2%

Interactions

2023-12-10T18:47:18.711333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:08.102293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:09.659899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:11.307295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:12.670666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:14.185198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:15.798992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:17.272633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:18.856916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:08.268282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:09.809171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:11.484155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:12.837961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:14.426957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:15.967625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:17.440578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:19.339552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:08.411423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:09.962680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:11.670391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:12.989138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:14.627909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:16.142080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:17.621494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:19.493537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:08.573337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:10.100920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:11.827298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:13.233808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:14.810566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:16.363564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:17.781404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:19.672601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:08.739683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:10.269151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:12.006155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:13.430506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:15.025503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:16.557548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:18.004948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:19.852107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:09.044258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:10.454241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:12.189691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:13.640638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:15.236894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:16.739835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:18.246738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:20.013196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:09.210955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:10.675526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:12.370719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:13.828075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:15.421343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:16.940825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:18.408203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:20.196826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:09.410625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:11.115217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:12.532381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:14.017164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:15.607404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:17.111632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:18.556496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:47:27.175272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
anals_trget_yeararea_nmavrg_edc_ctelesch_edc_ctmskul_edc_cthgschl_edc_cttot_lon_coelesch_lon_comskul_lon_cohgschl_lon_co
anals_trget_year1.0000.0000.0000.0000.1430.3030.0000.0000.0000.000
area_nm0.0001.0000.7570.7130.8060.6180.8600.9170.8830.878
avrg_edc_ct0.0000.7571.0000.8710.9380.9620.7970.7190.9020.702
elesch_edc_ct0.0000.7130.8711.0000.7730.8460.7420.7560.7670.744
mskul_edc_ct0.1430.8060.9380.7731.0000.8780.8180.7420.8890.731
hgschl_edc_ct0.3030.6180.9620.8460.8781.0000.7240.7880.9190.779
tot_lon_co0.0000.8600.7970.7420.8180.7241.0000.9470.8770.978
elesch_lon_co0.0000.9170.7190.7560.7420.7880.9471.0000.9320.963
mskul_lon_co0.0000.8830.9020.7670.8890.9190.8770.9321.0000.929
hgschl_lon_co0.0000.8780.7020.7440.7310.7790.9780.9630.9291.000
2023-12-10T18:47:27.457223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
anals_trget_yeararea_nm
anals_trget_year1.0000.000
area_nm0.0001.000
2023-12-10T18:47:28.071162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
avrg_edc_ctelesch_edc_ctmskul_edc_cthgschl_edc_cttot_lon_coelesch_lon_comskul_lon_cohgschl_lon_coanals_trget_yeararea_nm
avrg_edc_ct1.0000.9220.9250.9590.3510.3770.2930.3210.0000.410
elesch_edc_ct0.9221.0000.8060.8250.3060.3480.2780.2550.0000.365
mskul_edc_ct0.9250.8061.0000.8500.2570.2750.2060.2380.0750.465
hgschl_edc_ct0.9590.8250.8501.0000.4290.4450.3560.4010.1920.285
tot_lon_co0.3510.3060.2570.4291.0000.9780.9650.9780.0000.553
elesch_lon_co0.3770.3480.2750.4450.9781.0000.9660.9590.0000.689
mskul_lon_co0.2930.2780.2060.3560.9650.9661.0000.9660.0000.581
hgschl_lon_co0.3210.2550.2380.4010.9780.9590.9661.0000.0000.584
anals_trget_year0.0000.0000.0750.1920.0000.0000.0000.0001.0000.000
area_nm0.4100.3650.4650.2850.5530.6890.5810.5840.0001.000

Missing values

2023-12-10T18:47:20.628148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:47:21.007486image/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_yeararea_nmavrg_edc_ctelesch_edc_ctmskul_edc_cthgschl_edc_cttot_lon_coelesch_lon_comskul_lon_cohgschl_lon_co
02014강원도16700016100020400014100022592533595319674665323
12014경기도2600002450002910002530003450493454040281122104834245
22014경상남도2030002060002330001680003876749665697172105127379
32014경상북도19100020300022500014100022849743905629153063854
42014광주광역시23100023600027300018500013680012256214363236602
52014대구광역시24200022100026700024900095893291331585244983182954
62014대전광역시25700026100027100023700022669334279949662063814
72014부산광역시22700020100026800022500096531431507653356986259741
82014서울특별시335000300000349000371000246247734056255705178487833
92014세종특별자치시18600018400021500016000047481679500131745728
anals_trget_yeararea_nmavrg_edc_ctelesch_edc_ctmskul_edc_cthgschl_edc_cttot_lon_coelesch_lon_comskul_lon_cohgschl_lon_co
752018부산광역시276000241000316000302000102238331557886308080261563
762018서울특별시411000365000415000484000238781963615143551755422190
772018세종특별자치시28800026200033400031100010486751506802826314421
782018울산광역시26500025100029100026800019067963049225740048270
792018인천광역시277000245000300000314000372367256700210597697308
802018전라남도19000019100022000016600028405524272559491779794
812018전라북도20900019600024500020100019858762361445239556395
822018제주특별자치도23200021500028200021800015758052462274505036701
832018충청남도1870001770002020001910003995956664674118686106374
842018충청북도24400022500029100023600030318094754969224787313