Overview

Dataset statistics

Number of variables10
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory84.0 B

Variable types

Text1
Numeric9

Alerts

ic_ali is highly overall correlated with ic_ali_nc and 5 other fieldsHigh correlation
ic_ali_nc is highly overall correlated with ic_ali and 5 other fieldsHigh correlation
ic_cv is highly overall correlated with ic_ali and 3 other fieldsHigh correlation
ic_rezedu is highly overall correlated with ic_sbv and 2 other fieldsHigh correlation
ic_sbv is highly overall correlated with ic_ali and 6 other fieldsHigh correlation
ic_segsoc is highly overall correlated with ic_ali and 6 other fieldsHigh correlation
plp is highly overall correlated with ic_ali and 5 other fieldsHigh correlation
plp_e is highly overall correlated with ic_ali and 4 other fieldsHigh correlation
Estados has unique valuesUnique
plp_e has unique valuesUnique
plp has unique valuesUnique
ic_rezedu has unique valuesUnique
ic_asalud has unique valuesUnique
ic_segsoc has unique valuesUnique
ic_cv has unique valuesUnique
ic_sbv has unique valuesUnique
ic_ali has unique valuesUnique
ic_ali_nc has unique valuesUnique

Reproduction

Analysis started2024-02-03 19:35:38.409893
Analysis finished2024-02-03 19:36:04.460831
Duration26.05 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Estados
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2024-02-03T19:36:04.805822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length14
Mean length8.6875
Min length4

Characters and Unicode

Total characters278
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st rowAguascalientes
2nd rowBaja California
3rd rowBaja California Sur
4th rowCampeche
5th rowCoahuila
ValueCountFrequency (%)
baja 2
 
5.1%
california 2
 
5.1%
durango 1
 
2.6%
morelos 1
 
2.6%
michoacán 1
 
2.6%
edomex 1
 
2.6%
jalisco 1
 
2.6%
hidalgo 1
 
2.6%
guerrero 1
 
2.6%
guanajuato 1
 
2.6%
Other values (27) 27
69.2%
2024-02-03T19:36:05.747373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 51
18.3%
o 22
 
7.9%
u 17
 
6.1%
i 17
 
6.1%
r 14
 
5.0%
l 13
 
4.7%
e 13
 
4.7%
n 13
 
4.7%
c 12
 
4.3%
s 10
 
3.6%
Other values (39) 96
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 224
80.6%
Uppercase Letter 47
 
16.9%
Space Separator 7
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 51
22.8%
o 22
9.8%
u 17
 
7.6%
i 17
 
7.6%
r 14
 
6.2%
l 13
 
5.8%
e 13
 
5.8%
n 13
 
5.8%
c 12
 
5.4%
s 10
 
4.5%
Other values (17) 42
18.8%
Uppercase Letter
ValueCountFrequency (%)
C 8
17.0%
S 4
 
8.5%
M 4
 
8.5%
D 3
 
6.4%
T 3
 
6.4%
P 2
 
4.3%
Q 2
 
4.3%
E 2
 
4.3%
N 2
 
4.3%
O 2
 
4.3%
Other values (11) 15
31.9%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 271
97.5%
Common 7
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 51
18.8%
o 22
 
8.1%
u 17
 
6.3%
i 17
 
6.3%
r 14
 
5.2%
l 13
 
4.8%
e 13
 
4.8%
n 13
 
4.8%
c 12
 
4.4%
s 10
 
3.7%
Other values (38) 89
32.8%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273
98.2%
None 5
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 51
18.7%
o 22
 
8.1%
u 17
 
6.2%
i 17
 
6.2%
r 14
 
5.1%
l 13
 
4.8%
e 13
 
4.8%
n 13
 
4.8%
c 12
 
4.4%
s 10
 
3.7%
Other values (35) 91
33.3%
None
ValueCountFrequency (%)
á 2
40.0%
ó 1
20.0%
é 1
20.0%
í 1
20.0%

plp_e
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14960361
Minimum0.044712505
Maximum0.50196397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:06.236005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.044712505
5-th percentile0.055509932
Q10.076684551
median0.12237307
Q30.17553972
95-th percentile0.34258509
Maximum0.50196397
Range0.45725147
Interquartile range (IQR)0.098855165

Descriptive statistics

Standard deviation0.099165628
Coefficient of variation (CV)0.66285583
Kurtosis4.4015968
Mean0.14960361
Median Absolute Deviation (MAD)0.051420193
Skewness1.8931581
Sum4.7873157
Variance0.0098338217
MonotonicityNot monotonic
2024-02-03T19:36:06.668707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.08632552139 1
 
3.1%
0.04471250503 1
 
3.1%
0.1303767923 1
 
3.1%
0.2647012205 1
 
3.1%
0.1794117647 1
 
3.1%
0.1179616232 1
 
3.1%
0.1902106568 1
 
3.1%
0.0684599694 1
 
3.1%
0.07140864714 1
 
3.1%
0.1629851688 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.04471250503 1
3.1%
0.05271141449 1
3.1%
0.05779962796 1
3.1%
0.05936952715 1
3.1%
0.0650833224 1
3.1%
0.06513701609 1
3.1%
0.0684599694 1
3.1%
0.07140864714 1
3.1%
0.07844318593 1
3.1%
0.08632552139 1
3.1%
ValueCountFrequency (%)
0.5019639713 1
3.1%
0.3519327187 1
3.1%
0.3349370313 1
3.1%
0.2647012205 1
3.1%
0.2082373272 1
3.1%
0.2033147593 1
3.1%
0.1902106568 1
3.1%
0.1794117647 1
3.1%
0.174249034 1
3.1%
0.1711864407 1
3.1%

plp
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50352611
Minimum0.28604745
Maximum0.82635785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:07.082834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.28604745
5-th percentile0.31140831
Q10.39812186
median0.47890505
Q30.59793221
95-th percentile0.70883797
Maximum0.82635785
Range0.5403104
Interquartile range (IQR)0.19981035

Descriptive statistics

Standard deviation0.13214678
Coefficient of variation (CV)0.26244275
Kurtosis-0.3776483
Mean0.50352611
Median Absolute Deviation (MAD)0.09912699
Skewness0.42250433
Sum16.112836
Variance0.017462771
MonotonicityNot monotonic
2024-02-03T19:36:07.366356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.3965813803 1
 
3.1%
0.2860474467 1
 
3.1%
0.5461820607 1
 
3.1%
0.6636550959 1
 
3.1%
0.6504411765 1
 
3.1%
0.4584775087 1
 
3.1%
0.5822490706 1
 
3.1%
0.380290668 1
 
3.1%
0.3792654579 1
 
3.1%
0.5321868097 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.2860474467 1
3.1%
0.3036141377 1
3.1%
0.3177853622 1
3.1%
0.3630472855 1
3.1%
0.3792654579 1
3.1%
0.380290668 1
3.1%
0.3872335798 1
3.1%
0.3965813803 1
3.1%
0.3986353497 1
3.1%
0.4172999192 1
3.1%
ValueCountFrequency (%)
0.8263578491 1
3.1%
0.7135694647 1
3.1%
0.7049667469 1
3.1%
0.6636550959 1
3.1%
0.6504411765 1
3.1%
0.6382488479 1
3.1%
0.6242054094 1
3.1%
0.6103729104 1
3.1%
0.5937853107 1
3.1%
0.5822490706 1
3.1%

ic_rezedu
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20118462
Minimum0.092819615
Maximum0.3071922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:07.662644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.092819615
5-th percentile0.15137107
Q10.17611241
median0.19118883
Q30.2115736
95-th percentile0.28905418
Maximum0.3071922
Range0.21437258
Interquartile range (IQR)0.035461198

Descriptive statistics

Standard deviation0.046687441
Coefficient of variation (CV)0.23206267
Kurtosis0.53657153
Mean0.20118462
Median Absolute Deviation (MAD)0.017827693
Skewness0.55297393
Sum6.437908
Variance0.0021797172
MonotonicityNot monotonic
2024-02-03T19:36:07.917305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1765211782 1
 
3.1%
0.1921994371 1
 
3.1%
0.2547515839 1
 
3.1%
0.272467903 1
 
3.1%
0.1460294118 1
 
3.1%
0.171437559 1
 
3.1%
0.1809169765 1
 
3.1%
0.157827639 1
 
3.1%
0.1557415156 1
 
3.1%
0.1967497633 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.09281961471 1
3.1%
0.1460294118 1
3.1%
0.1557415156 1
3.1%
0.1577809199 1
3.1%
0.157827639 1
3.1%
0.1700770662 1
3.1%
0.171437559 1
3.1%
0.1748860895 1
3.1%
0.1765211782 1
3.1%
0.1784471016 1
3.1%
ValueCountFrequency (%)
0.3071921983 1
3.1%
0.2912126786 1
3.1%
0.2872881356 1
3.1%
0.272467903 1
3.1%
0.2688015526 1
3.1%
0.2547515839 1
3.1%
0.2439983444 1
3.1%
0.2146699908 1
3.1%
0.2105414747 1
3.1%
0.207046542 1
3.1%

ic_asalud
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1415095
Minimum0.088829284
Maximum0.225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:08.165852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.088829284
5-th percentile0.10775508
Q10.1237135
median0.13307214
Q30.15281097
95-th percentile0.19446458
Maximum0.225
Range0.13617072
Interquartile range (IQR)0.029097473

Descriptive statistics

Standard deviation0.02905709
Coefficient of variation (CV)0.20533666
Kurtosis1.1236967
Mean0.1415095
Median Absolute Deviation (MAD)0.01547877
Skewness0.95444029
Sum4.5283041
Variance0.00084431445
MonotonicityNot monotonic
2024-02-03T19:36:08.449914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1173941088 1
 
3.1%
0.1684760756 1
 
3.1%
0.1150383461 1
 
3.1%
0.1933745443 1
 
3.1%
0.1223529412 1
 
3.1%
0.1270839887 1
 
3.1%
0.1322800496 1
 
3.1%
0.1432942376 1
 
3.1%
0.1238493724 1
 
3.1%
0.08882928369 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.08882928369 1
3.1%
0.1024275646 1
3.1%
0.1121139626 1
3.1%
0.1150383461 1
3.1%
0.1157308187 1
3.1%
0.1173941088 1
3.1%
0.1223529412 1
3.1%
0.123305873 1
3.1%
0.1238493724 1
3.1%
0.1249548138 1
3.1%
ValueCountFrequency (%)
0.225 1
3.1%
0.1957968476 1
3.1%
0.1933745443 1
3.1%
0.1762433005 1
3.1%
0.1749114289 1
3.1%
0.1684760756 1
3.1%
0.1649151172 1
3.1%
0.1576340739 1
3.1%
0.1512032693 1
3.1%
0.1496681566 1
3.1%

ic_segsoc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56348725
Minimum0.29327123
Maximum0.82744142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:08.721722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.29327123
5-th percentile0.38140334
Q10.44515984
median0.57238728
Q30.65742853
95-th percentile0.78876708
Maximum0.82744142
Range0.53417019
Interquartile range (IQR)0.21226869

Descriptive statistics

Standard deviation0.13549819
Coefficient of variation (CV)0.24046364
Kurtosis-0.68325174
Mean0.56348725
Median Absolute Deviation (MAD)0.1120301
Skewness0.1120863
Sum18.031592
Variance0.018359761
MonotonicityNot monotonic
2024-02-03T19:36:08.994004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.4052891851 1
 
3.1%
0.434579815 1
 
3.1%
0.5788596199 1
 
3.1%
0.6978919005 1
 
3.1%
0.6730882353 1
 
3.1%
0.4584775087 1
 
3.1%
0.6404894672 1
 
3.1%
0.4279704233 1
 
3.1%
0.3952580195 1
 
3.1%
0.5883559482 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.2932712301 1
3.1%
0.3644698379 1
3.1%
0.3952580195 1
3.1%
0.4052891851 1
3.1%
0.4148653773 1
3.1%
0.4279704233 1
3.1%
0.4316764317 1
3.1%
0.434579815 1
3.1%
0.4486865149 1
3.1%
0.4584775087 1
3.1%
ValueCountFrequency (%)
0.8274414195 1
3.1%
0.8110938163 1
3.1%
0.7704997574 1
3.1%
0.7386232719 1
3.1%
0.7036723164 1
3.1%
0.6978919005 1
3.1%
0.6825377041 1
3.1%
0.6730882353 1
3.1%
0.6522086216 1
3.1%
0.6404894672 1
3.1%

ic_cv
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12964219
Minimum0.054436348
Maximum0.32605531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:09.257156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.054436348
5-th percentile0.059833893
Q10.085233929
median0.12242619
Q30.15039304
95-th percentile0.25204559
Maximum0.32605531
Range0.27161896
Interquartile range (IQR)0.065159115

Descriptive statistics

Standard deviation0.063814458
Coefficient of variation (CV)0.49223528
Kurtosis1.8602997
Mean0.12964219
Median Absolute Deviation (MAD)0.035158519
Skewness1.3383326
Sum4.14855
Variance0.0040722851
MonotonicityNot monotonic
2024-02-03T19:36:09.548141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.06353472372 1
 
3.1%
0.1247285887 1
 
3.1%
0.2177392464 1
 
3.1%
0.1776826755 1
 
3.1%
0.1016176471 1
 
3.1%
0.07297892419 1
 
3.1%
0.1228314746 1
 
3.1%
0.1254462009 1
 
3.1%
0.09986052999 1
 
3.1%
0.1083938151 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.05443634805 1
3.1%
0.05531065458 1
3.1%
0.06353472372 1
3.1%
0.06429602414 1
3.1%
0.06830122592 1
3.1%
0.06915036183 1
3.1%
0.07297892419 1
3.1%
0.0818164581 1
3.1%
0.08637308637 1
3.1%
0.08816225166 1
3.1%
ValueCountFrequency (%)
0.326055313 1
3.1%
0.2588085468 1
3.1%
0.2465122579 1
3.1%
0.2177392464 1
3.1%
0.1927243331 1
3.1%
0.1776826755 1
3.1%
0.1752545027 1
3.1%
0.1566569227 1
3.1%
0.1483050847 1
3.1%
0.1416289593 1
3.1%

ic_sbv
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23897198
Minimum0.027845884
Maximum0.6257252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:09.818457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.027845884
5-th percentile0.062612316
Q10.11304271
median0.18873197
Q30.28895064
95-th percentile0.5345152
Maximum0.6257252
Range0.59787932
Interquartile range (IQR)0.17590794

Descriptive statistics

Standard deviation0.15804823
Coefficient of variation (CV)0.6613672
Kurtosis0.18077898
Mean0.23897198
Median Absolute Deviation (MAD)0.079690614
Skewness1.0018385
Sum7.6471035
Variance0.024979243
MonotonicityNot monotonic
2024-02-03T19:36:10.081733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.03870135455 1
 
3.1%
0.2447929232 1
 
3.1%
0.5051683895 1
 
3.1%
0.4149627516 1
 
3.1%
0.1092647059 1
 
3.1%
0.1495753382 1
 
3.1%
0.5069702602 1
 
3.1%
0.1756756757 1
 
3.1%
0.1045095305 1
 
3.1%
0.282265699 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.02784588441 1
3.1%
0.03870135455 1
3.1%
0.08217582966 1
3.1%
0.08958608959 1
3.1%
0.09355727595 1
3.1%
0.1045095305 1
3.1%
0.1088180113 1
3.1%
0.1092647059 1
3.1%
0.1143020431 1
3.1%
0.1350018961 1
3.1%
ValueCountFrequency (%)
0.6257252016 1
3.1%
0.5451713396 1
3.1%
0.5257965389 1
3.1%
0.5069702602 1
3.1%
0.5051683895 1
3.1%
0.4149627516 1
3.1%
0.3171082949 1
3.1%
0.3090054816 1
3.1%
0.282265699 1
3.1%
0.2610859729 1
3.1%

ic_ali
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21123412
Minimum0.12399299
Maximum0.45570012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:10.636448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.12399299
5-th percentile0.14560715
Q10.18051835
median0.19921827
Q30.22719981
95-th percentile0.29091171
Maximum0.45570012
Range0.33170713
Interquartile range (IQR)0.046681463

Descriptive statistics

Standard deviation0.060161039
Coefficient of variation (CV)0.28480739
Kurtosis8.0964888
Mean0.21123412
Median Absolute Deviation (MAD)0.026389985
Skewness2.2635634
Sum6.7594919
Variance0.0036193506
MonotonicityNot monotonic
2024-02-03T19:36:10.888735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1831864115 1
 
3.1%
0.1671089666 1
 
3.1%
0.2150716906 1
 
3.1%
0.2236487557 1
 
3.1%
0.2252941176 1
 
3.1%
0.1907832652 1
 
3.1%
0.4557001239 1
 
3.1%
0.2329168791 1
 
3.1%
0.2188749419 1
 
3.1%
0.2060586936 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.1239929947 1
3.1%
0.1426677913 1
3.1%
0.1480120719 1
3.1%
0.1507358194 1
3.1%
0.1671089666 1
3.1%
0.1685627607 1
3.1%
0.16967479 1
3.1%
0.1725141471 1
3.1%
0.1831864115 1
3.1%
0.1842669526 1
3.1%
ValueCountFrequency (%)
0.4557001239 1
3.1%
0.308051507 1
3.1%
0.276888242 1
3.1%
0.2609240407 1
3.1%
0.2604519774 1
3.1%
0.2531682028 1
3.1%
0.2384615385 1
3.1%
0.2329168791 1
3.1%
0.2252941176 1
3.1%
0.2236487557 1
3.1%

ic_ali_nc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22932764
Minimum0.14640981
Maximum0.48358116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:36:11.156895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.14640981
5-th percentile0.15625739
Q10.18921352
median0.21359625
Q30.25032458
95-th percentile0.32275636
Maximum0.48358116
Range0.33717136
Interquartile range (IQR)0.061111058

Descriptive statistics

Standard deviation0.066590909
Coefficient of variation (CV)0.29037455
Kurtosis5.8556865
Mean0.22932764
Median Absolute Deviation (MAD)0.029920205
Skewness1.9579231
Sum7.3384844
Variance0.0044343491
MonotonicityNot monotonic
2024-02-03T19:36:11.419191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1902816599 1
 
3.1%
0.1813429835 1
 
3.1%
0.249305324 1
 
3.1%
0.2402916469 1
 
3.1%
0.2533823529 1
 
3.1%
0.194400755 1
 
3.1%
0.4835811648 1
 
3.1%
0.2353391127 1
 
3.1%
0.2213854021 1
 
3.1%
0.2139476175 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.1464098074 1
3.1%
0.1543104579 1
3.1%
0.1578503434 1
3.1%
0.1595942277 1
3.1%
0.17197573 1
3.1%
0.1748315728 1
3.1%
0.1813429835 1
3.1%
0.1860091128 1
3.1%
0.1902816599 1
3.1%
0.194400755 1
3.1%
ValueCountFrequency (%)
0.4835811648 1
3.1%
0.3463987548 1
3.1%
0.3034125829 1
3.1%
0.2918552036 1
3.1%
0.2884886453 1
3.1%
0.2861463134 1
3.1%
0.2793785311 1
3.1%
0.2533823529 1
3.1%
0.249305324 1
3.1%
0.2402916469 1
3.1%

Interactions

2024-02-03T19:36:01.560648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:38.744523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:43.362676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:46.927075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:49.664914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:52.036994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:54.891078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:56.916273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:59.215907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:01.781904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:39.041603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:43.718562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:47.355748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:49.876102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:52.397485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:55.122480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:57.143774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:59.421859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:02.034688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:40.001971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:44.068583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:47.800237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:50.102685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:52.720447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:55.332204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:57.356962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:59.887605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:02.280722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:40.637231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:44.574479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:48.213537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:50.348975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:53.081070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:55.543550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:57.569279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:00.103565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:02.523641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:41.206715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:44.867057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:48.534028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:50.789043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:53.408537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:55.777773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:57.802245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:00.358767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:02.750252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:41.876797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:45.239868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:48.770900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:50.989282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:53.707326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:56.013372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:58.033253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:00.587641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:02.992395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:42.187326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:45.572126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:48.984427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:51.232906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:54.043558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:56.236943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:58.280168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:00.819419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:03.242469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:42.554037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:46.015165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:49.189178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:51.475704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:54.421329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:56.463315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:58.526067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:01.048403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:03.483931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:42.979086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:46.478566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:49.417547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:51.709742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:54.637934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:56.676567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:35:58.854316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:01.316286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-03T19:36:11.677482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ic_aliic_ali_ncic_asaludic_cvic_rezeduic_sbvic_segsocplpplp_e
ic_ali1.0000.968-0.0660.6040.2730.6510.5670.5710.598
ic_ali_nc0.9681.000-0.0070.7140.3500.7500.6530.6580.673
ic_asalud-0.066-0.0071.0000.1970.2210.1540.2480.1020.096
ic_cv0.6040.7140.1971.0000.4780.8460.5200.4250.446
ic_rezedu0.2730.3500.2210.4781.0000.6230.6740.5620.493
ic_sbv0.6510.7500.1540.8460.6231.0000.7290.6400.676
ic_segsoc0.5670.6530.2480.5200.6740.7291.0000.9120.869
plp0.5710.6580.1020.4250.5620.6400.9121.0000.962
plp_e0.5980.6730.0960.4460.4930.6760.8690.9621.000

Missing values

2024-02-03T19:36:03.854276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-03T19:36:04.244357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Estadosplp_eplpic_rezeduic_asaludic_segsocic_cvic_sbvic_aliic_ali_nc
0Aguascalientes0.0863260.3965810.1765210.1173940.4052890.0635350.0387010.1831860.190282
1Baja California0.0447130.2860470.1921990.1684760.4345800.1247290.2447930.1671090.181343
2Baja California Sur0.0578000.3036140.1700770.1233060.3644700.1566570.1690140.1939940.204358
3Campeche0.1461240.5166800.1827720.1024280.5882540.1752550.3090050.2609240.288489
4Coahuila0.0965650.4353320.1577810.1266740.2932710.0553110.0821760.1696750.174832
5Colima0.0651370.3872340.2070470.1121140.5213140.1224450.1684430.2032410.213245
6Chiapas0.5019640.8263580.3071920.1496680.8274410.2465120.5451710.1946360.235135
7Chihuahua0.1119830.4470800.2026080.1269140.4316760.0863730.0895860.1928750.197978
8CDMX0.0593700.3630470.0928200.1957970.4486870.0683010.0278460.1239930.146410
9Durango0.1223530.4815060.1748860.1483520.5125970.0691500.1088180.1842670.186009
Estadosplp_eplpic_rezeduic_asaludic_segsocic_cvic_sbvic_aliic_ali_nc
22Quintana Roo0.1223930.4173000.1814070.1649150.5033140.1927240.2551330.1725140.201617
23San Luis Potosí0.1629850.5321870.1967500.0888290.5883560.1083940.2822660.2060590.213948
24Sinaloa0.0714090.3792650.1557420.1238490.3952580.0998610.1045100.2188750.221385
25Sonora0.0684600.3802910.1578280.1432940.4279700.1254460.1756760.2329170.235339
26Tabasco0.1902110.5822490.1809170.1322800.6404890.1228310.5069700.4557000.483581
27Tamaulipas0.1179620.4584780.1714380.1270840.4584780.0729790.1495750.1907830.194401
28Tlaxcala0.1794120.6504410.1460290.1223530.6730880.1016180.1092650.2252940.253382
29Veracruz0.2647010.6636550.2724680.1933750.6978920.1776830.4149630.2236490.240292
30Yucatán0.1303770.5461820.2547520.1150380.5788600.2177390.5051680.2150720.249305
31Zacatecas0.2033150.6103730.1981710.1157310.6362340.0544360.1143020.1507360.159594