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 5 other fieldsHigh correlation
ic_rezedu is highly overall correlated with ic_cv and 3 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 4 other fieldsHigh correlation
plp_e is highly overall correlated with ic_ali and 6 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 19:37:38.174266
Analysis finished2024-02-03 19:38:10.416786
Duration32.24 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
2024-02-03T19:38:10.732507image/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%
2024-02-03T19:38:11.762195image/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%
Mean0.1418114
Minimum0.036449663
Maximum0.48869155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:12.113322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.036449663
5-th percentile0.046442469
Q10.070432417
median0.1264554
Q30.15248658
95-th percentile0.35045988
Maximum0.48869155
Range0.45224189
Interquartile range (IQR)0.082054167

Descriptive statistics

Standard deviation0.10231648
Coefficient of variation (CV)0.72149684
Kurtosis3.8867973
Mean0.1418114
Median Absolute Deviation (MAD)0.052211115
Skewness1.9026623
Sum4.5379647
Variance0.010468661
MonotonicityNot monotonic
2024-02-03T19:38:12.584927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.06720638793 1
 
3.1%
0.03644966267 1
 
3.1%
0.1256001746 1
 
3.1%
0.2966832706 1
 
3.1%
0.1273106324 1
 
3.1%
0.130189566 1
 
3.1%
0.2003395586 1
 
3.1%
0.07150776053 1
 
3.1%
0.05142242643 1
 
3.1%
0.178157337 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.03644966267 1
3.1%
0.04035585353 1
3.1%
0.05142242643 1
3.1%
0.05447061076 1
3.1%
0.05925672204 1
3.1%
0.05927835052 1
3.1%
0.06499597963 1
3.1%
0.06720638793 1
3.1%
0.07150776053 1
3.1%
0.0737351062 1
3.1%
ValueCountFrequency (%)
0.4886915499 1
3.1%
0.383555984 1
3.1%
0.3233812508 1
3.1%
0.2966832706 1
3.1%
0.2003395586 1
3.1%
0.1889013453 1
3.1%
0.178157337 1
3.1%
0.1609060156 1
3.1%
0.1496801071 1
3.1%
0.1478200692 1
3.1%

plp
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4938884
Minimum0.25412584
Maximum0.81129457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:13.050830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.25412584
5-th percentile0.31633771
Q10.38133437
median0.50981346
Q30.56775715
95-th percentile0.71286057
Maximum0.81129457
Range0.55716874
Interquartile range (IQR)0.18642279

Descriptive statistics

Standard deviation0.13607803
Coefficient of variation (CV)0.27552384
Kurtosis-0.42829454
Mean0.4938884
Median Absolute Deviation (MAD)0.10502645
Skewness0.40909408
Sum15.804429
Variance0.01851723
MonotonicityNot monotonic
2024-02-03T19:38:13.511503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.3676389043 1
 
3.1%
0.2982563743 1
 
3.1%
0.5259711916 1
 
3.1%
0.6967561657 1
 
3.1%
0.6107018763 1
 
3.1%
0.5049349836 1
 
3.1%
0.6068837784 1
 
3.1%
0.3783259424 1
 
3.1%
0.3831264053 1
 
3.1%
0.5399576113 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.2541258381 1
3.1%
0.2982563743 1
3.1%
0.3311315336 1
3.1%
0.3552835052 1
3.1%
0.3581092653 1
3.1%
0.3676389043 1
3.1%
0.3762547221 1
3.1%
0.3783259424 1
3.1%
0.382337175 1
3.1%
0.3831264053 1
3.1%
ValueCountFrequency (%)
0.8112945747 1
3.1%
0.7325437268 1
3.1%
0.6967561657 1
3.1%
0.6945570709 1
3.1%
0.6608744395 1
3.1%
0.6107018763 1
3.1%
0.6068837784 1
3.1%
0.5833333333 1
3.1%
0.5625650945 1
3.1%
0.5412560671 1
3.1%

ic_rezedu
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20884048
Minimum0.11779684
Maximum0.31219648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:14.003732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.11779684
5-th percentile0.15726118
Q10.18144554
median0.19918755
Q30.21912673
95-th percentile0.30531585
Maximum0.31219648
Range0.19439964
Interquartile range (IQR)0.037681181

Descriptive statistics

Standard deviation0.046577112
Coefficient of variation (CV)0.22302722
Kurtosis0.33772285
Mean0.20884048
Median Absolute Deviation (MAD)0.019379627
Skewness0.75972993
Sum6.6828953
Variance0.0021694274
MonotonicityNot monotonic
2024-02-03T19:38:14.535173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1700121992 1
 
3.1%
0.2049417331 1
 
3.1%
0.2469445657 1
 
3.1%
0.3000850443 1
 
3.1%
0.1619179986 1
 
3.1%
0.1801660661 1
 
3.1%
0.1839790091 1
 
3.1%
0.157289357 1
 
3.1%
0.1680516179 1
 
3.1%
0.218925321 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.1177968373 1
3.1%
0.1572267311 1
3.1%
0.157289357 1
3.1%
0.1619179986 1
3.1%
0.1627127385 1
3.1%
0.1680516179 1
3.1%
0.1700121992 1
3.1%
0.1801660661 1
3.1%
0.1818720379 1
3.1%
0.1839790091 1
3.1%
ValueCountFrequency (%)
0.3121964756 1
3.1%
0.3117090552 1
3.1%
0.3000850443 1
3.1%
0.2751687095 1
3.1%
0.2744047619 1
3.1%
0.2539140336 1
3.1%
0.2469445657 1
3.1%
0.2197309417 1
3.1%
0.218925321 1
3.1%
0.2174851592 1
3.1%

ic_asalud
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14167314
Minimum0.082658023
Maximum0.21264881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:15.102168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.082658023
5-th percentile0.10596268
Q10.11782857
median0.134525
Q30.15922045
95-th percentile0.20073184
Maximum0.21264881
Range0.12999079
Interquartile range (IQR)0.041391881

Descriptive statistics

Standard deviation0.031270459
Coefficient of variation (CV)0.22072256
Kurtosis-0.065254896
Mean0.14167314
Median Absolute Deviation (MAD)0.019558601
Skewness0.66415257
Sum4.5335406
Variance0.0009778416
MonotonicityNot monotonic
2024-02-03T19:38:15.590935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1147831873 1
 
3.1%
0.1652501533 1
 
3.1%
0.1301833261 1
 
3.1%
0.1572105455 1
 
3.1%
0.1359277276 1
 
3.1%
0.1151496162 1
 
3.1%
0.1264083964 1
 
3.1%
0.1255543237 1
 
3.1%
0.1346172646 1
 
3.1%
0.08265802269 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.08265802269 1
3.1%
0.1040474906 1
3.1%
0.1075296545 1
3.1%
0.1124336359 1
3.1%
0.1127266917 1
3.1%
0.1147831873 1
3.1%
0.1151496162 1
3.1%
0.1164877188 1
3.1%
0.1182755153 1
3.1%
0.1255543237 1
3.1%
ValueCountFrequency (%)
0.2126488095 1
3.1%
0.2062780269 1
3.1%
0.1961940499 1
3.1%
0.1853092784 1
3.1%
0.1837552358 1
3.1%
0.1749687804 1
3.1%
0.1670588235 1
3.1%
0.1652501533 1
3.1%
0.1572105455 1
3.1%
0.1568988819 1
3.1%

ic_segsoc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55372831
Minimum0.26852012
Maximum0.83210767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:16.074311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.26852012
5-th percentile0.35422518
Q10.45390693
median0.54109186
Q30.65041705
95-th percentile0.79080659
Maximum0.83210767
Range0.56358756
Interquartile range (IQR)0.19651013

Descriptive statistics

Standard deviation0.14201839
Coefficient of variation (CV)0.25647667
Kurtosis-0.63128346
Mean0.55372831
Median Absolute Deviation (MAD)0.10022298
Skewness0.10262429
Sum17.719306
Variance0.020169224
MonotonicityNot monotonic
2024-02-03T19:38:16.537178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.3754020184 1
 
3.1%
0.4249539998 1
 
3.1%
0.5573985159 1
 
3.1%
0.7205685822 1
 
3.1%
0.6450312717 1
 
3.1%
0.4449318502 1
 
3.1%
0.6375983948 1
 
3.1%
0.3970343681 1
 
3.1%
0.4054159742 1
 
3.1%
0.592569505 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.2685201174 1
3.1%
0.3372872615 1
3.1%
0.3680834769 1
3.1%
0.3754020184 1
3.1%
0.3970343681 1
3.1%
0.4054159742 1
3.1%
0.4249539998 1
3.1%
0.4449318502 1
3.1%
0.4568986179 1
3.1%
0.4766818547 1
3.1%
ValueCountFrequency (%)
0.8321076731 1
3.1%
0.8056272269 1
3.1%
0.7786806225 1
3.1%
0.7221132287 1
3.1%
0.7205685822 1
3.1%
0.7007885731 1
3.1%
0.6745535714 1
3.1%
0.6665743945 1
3.1%
0.6450312717 1
3.1%
0.6375983948 1
3.1%

ic_cv
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12285215
Minimum0.045933345
Maximum0.30202451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:17.028122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.045933345
5-th percentile0.050908867
Q10.088763755
median0.11064904
Q30.13492256
95-th percentile0.24620861
Maximum0.30202451
Range0.25609117
Interquartile range (IQR)0.046158805

Descriptive statistics

Standard deviation0.061650766
Coefficient of variation (CV)0.50182896
Kurtosis1.5757781
Mean0.12285215
Median Absolute Deviation (MAD)0.022072187
Skewness1.3232263
Sum3.9312688
Variance0.0038008169
MonotonicityNot monotonic
2024-02-03T19:38:17.568600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.05445270045 1
 
3.1%
0.1143432927 1
 
3.1%
0.1776516805 1
 
3.1%
0.1772567124 1
 
3.1%
0.08895066018 1
 
3.1%
0.08820303932 1
 
3.1%
0.1237845347 1
 
3.1%
0.1053215078 1
 
3.1%
0.08094632906 1
 
3.1%
0.1095873333 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.04593334485 1
3.1%
0.04691943128 1
3.1%
0.05417295123 1
3.1%
0.05445270045 1
3.1%
0.06078881869 1
3.1%
0.07448453608 1
3.1%
0.08094632906 1
3.1%
0.08820303932 1
3.1%
0.08895066018 1
3.1%
0.09059678837 1
3.1%
ValueCountFrequency (%)
0.3020245145 1
3.1%
0.2589998771 1
3.1%
0.2357430276 1
3.1%
0.2240725474 1
3.1%
0.1776516805 1
3.1%
0.1772567124 1
3.1%
0.1629651419 1
3.1%
0.1508509541 1
3.1%
0.1296130952 1
3.1%
0.1240190583 1
3.1%

ic_sbv
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24436572
Minimum0.033492292
Maximum0.62580912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:18.085777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.033492292
5-th percentile0.066886852
Q10.12336206
median0.18280409
Q30.29369196
95-th percentile0.59278828
Maximum0.62580912
Range0.59231682
Interquartile range (IQR)0.17032991

Descriptive statistics

Standard deviation0.1696497
Coefficient of variation (CV)0.69424506
Kurtosis0.09777773
Mean0.24436572
Median Absolute Deviation (MAD)0.075679032
Skewness1.0958751
Sum7.8197031
Variance0.02878102
MonotonicityNot monotonic
2024-02-03T19:38:18.584177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.03349229234 1
 
3.1%
0.2140541488 1
 
3.1%
0.511348756 1
 
3.1%
0.5148827603 1
 
3.1%
0.1039610841 1
 
3.1%
0.1533761554 1
 
3.1%
0.4647322118 1
 
3.1%
0.1816796009 1
 
3.1%
0.1223971063 1
 
3.1%
0.3444707642 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.03349229234 1
3.1%
0.0546537731 1
3.1%
0.0768957346 1
3.1%
0.09741075918 1
3.1%
0.1024207078 1
3.1%
0.1039610841 1
3.1%
0.112628866 1
3.1%
0.1223971063 1
3.1%
0.1236837067 1
3.1%
0.1269984528 1
3.1%
ValueCountFrequency (%)
0.6258091172 1
3.1%
0.6204693451 1
3.1%
0.5701401415 1
3.1%
0.5148827603 1
3.1%
0.511348756 1
3.1%
0.4647322118 1
3.1%
0.3771142815 1
3.1%
0.3444707642 1
3.1%
0.2767656951 1
3.1%
0.271063479 1
3.1%

ic_ali
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21325041
Minimum0.12751292
Maximum0.46982559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:19.146470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.12751292
5-th percentile0.14420053
Q10.17708791
median0.2011548
Q30.23094084
95-th percentile0.31879006
Maximum0.46982559
Range0.34231267
Interquartile range (IQR)0.053852938

Descriptive statistics

Standard deviation0.067303486
Coefficient of variation (CV)0.31560777
Kurtosis6.2645614
Mean0.21325041
Median Absolute Deviation (MAD)0.029932982
Skewness2.1065158
Sum6.8240131
Variance0.0045297592
MonotonicityNot monotonic
2024-02-03T19:38:19.642332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1320838416 1
 
3.1%
0.1614825199 1
 
3.1%
0.2094063728 1
 
3.1%
0.2660673065 1
 
3.1%
0.1994440584 1
 
3.1%
0.1701394329 1
 
3.1%
0.4698255904 1
 
3.1%
0.2441796009 1
 
3.1%
0.2337471894 1
 
3.1%
0.1794040643 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.1275129236 1
3.1%
0.1320838416 1
3.1%
0.1541141785 1
3.1%
0.1547680412 1
3.1%
0.1550779243 1
3.1%
0.1614825199 1
3.1%
0.1640271493 1
3.1%
0.1701394329 1
3.1%
0.1794040643 1
3.1%
0.1818720379 1
3.1%
ValueCountFrequency (%)
0.4698255904 1
3.1%
0.3613827297 1
3.1%
0.2839415162 1
3.1%
0.2728342403 1
3.1%
0.2660673065 1
3.1%
0.2532871972 1
3.1%
0.2441796009 1
3.1%
0.2337471894 1
3.1%
0.2300053967 1
3.1%
0.2246403205 1
3.1%

ic_ali_nc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23162173
Minimum0.13048057
Maximum0.50378145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:38:20.167617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.13048057
5-th percentile0.15298092
Q10.18262014
median0.22520498
Q30.25649547
95-th percentile0.35934884
Maximum0.50378145
Range0.37330088
Interquartile range (IQR)0.073875331

Descriptive statistics

Standard deviation0.075060585
Coefficient of variation (CV)0.32406539
Kurtosis4.8449413
Mean0.23162173
Median Absolute Deviation (MAD)0.039426811
Skewness1.8285166
Sum7.4118953
Variance0.0056340915
MonotonicityNot monotonic
2024-02-03T19:38:20.668069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1404014639 1
 
3.1%
0.1696311224 1
 
3.1%
0.2334133566 1
 
3.1%
0.2824687158 1
 
3.1%
0.2412786657 1
 
3.1%
0.1756227479 1
 
3.1%
0.5037814478 1
 
3.1%
0.2495842572 1
 
3.1%
0.2370710724 1
 
3.1%
0.1897519013 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.1304805667 1
3.1%
0.1404014639 1
3.1%
0.1632731959 1
3.1%
0.1696311224 1
3.1%
0.1711337443 1
3.1%
0.1713478335 1
3.1%
0.1714429361 1
3.1%
0.1756227479 1
3.1%
0.1849526066 1
3.1%
0.1883039067 1
3.1%
ValueCountFrequency (%)
0.5037814478 1
3.1%
0.3959509709 1
3.1%
0.3294016464 1
3.1%
0.2950433887 1
3.1%
0.2824687158 1
3.1%
0.2741759242 1
3.1%
0.2667757774 1
3.1%
0.2638062284 1
3.1%
0.2540585542 1
3.1%
0.2495842572 1
3.1%

Interactions

2024-02-03T19:38:05.952377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:38.647141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:41.725514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:45.014405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:48.629887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:51.909055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:55.157365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:58.222812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:01.578015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:06.320051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:38.984082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:42.072758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:45.364001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:49.050142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:52.246866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:55.518565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:58.573989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:01.941814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:06.685962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:39.315712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:42.418646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:45.724421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:49.433750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:52.611111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:55.782727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:58.930455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:03.373478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:07.064243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:39.697868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:42.783316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:46.140403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:49.808117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:52.983443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:56.095942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:59.310262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:03.808771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:07.439117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:40.042542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:43.147298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:46.534466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:50.146755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:53.360371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:56.421339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:59.712402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:04.232287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:07.776302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:40.357788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:43.492409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:46.905315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:50.469331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:53.740050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:56.784961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:00.096701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:04.613106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:08.100044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:40.692318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:43.839062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:47.330344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:50.812222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:54.089887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:57.139925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:00.476266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:04.861680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:08.436838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:41.032083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:44.199453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:47.753250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:51.172169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:54.442110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:57.491783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:00.828466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:05.220491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:08.796852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:41.376210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:44.609608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:48.185073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:51.539845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:54.810774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:57.852254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:01.207171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:38:05.574271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-03T19:38:21.074103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ic_aliic_ali_ncic_asaludic_cvic_rezeduic_sbvic_segsocplpplp_e
ic_ali1.0000.9710.1910.6150.2760.6300.5780.6150.611
ic_ali_nc0.9711.0000.2370.6600.2980.6990.6800.7140.705
ic_asalud0.1910.2371.0000.2870.2270.2750.4550.3080.237
ic_cv0.6150.6600.2871.0000.5440.8830.5710.4450.511
ic_rezedu0.2760.2980.2270.5441.0000.6010.6800.4560.533
ic_sbv0.6300.6990.2750.8830.6011.0000.6790.5790.651
ic_segsoc0.5780.6800.4550.5710.6800.6791.0000.8740.867
plp0.6150.7140.3080.4450.4560.5790.8741.0000.954
plp_e0.6110.7050.2370.5110.5330.6510.8670.9541.000

Missing values

2024-02-03T19:38:09.320865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-03T19:38:10.084410image/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
0Aguascalientes0.0672060.3676390.1700120.1147830.3754020.0544530.0334920.1320840.140401
1Baja California0.0364500.2982560.2049420.1652500.4249540.1143430.2140540.1614830.169631
2Baja California Sur0.0403560.2541260.1627130.1075300.3372870.1508510.1269980.1823100.192496
3Campeche0.1609060.5412560.1895870.1164880.5711130.1629650.3771140.2728340.295043
4Coahuila0.0737350.4086510.1572270.1344330.2685200.0459330.0546540.1895180.192626
5Colima0.0804100.3762550.2174850.1040470.5114950.1117110.1695630.2300050.241554
6Chiapas0.4886920.8112950.3121960.1749690.8321080.2357430.5701400.2208960.254059
7Chihuahua0.1092910.4079250.2174640.1127270.4568990.0905970.1024210.1819130.188304
8CDMX0.0649960.3823370.1177970.1961940.4766820.0924690.1515680.1541140.171134
9Durango0.1318720.5146920.1818720.1310430.4926540.0469190.0768960.1818720.184953
Estadosplp_eplpic_rezeduic_asaludic_segsocic_cvic_sbvic_aliic_ali_nc
22Quintana Roo0.0966200.4016490.1915910.1492170.4862320.2240730.2710630.1831820.207255
23San Luis Potosí0.1781570.5399580.2189250.0826580.5925700.1095870.3444710.1794040.189752
24Sinaloa0.0514220.3831260.1680520.1346170.4054160.0809460.1223970.2337470.237071
25Sonora0.0715080.3783260.1572890.1255540.3970340.1053220.1816800.2441800.249584
26Tabasco0.2003400.6068840.1839790.1264080.6375980.1237850.4647320.4698260.503781
27Tamaulipas0.1301900.5049350.1801660.1151500.4449320.0882030.1533760.1701390.175623
28Tlaxcala0.1273110.6107020.1619180.1359280.6450310.0889510.1039610.1994440.241279
29Veracruz0.2966830.6967560.3000850.1572110.7205690.1772570.5148830.2660670.282469
30Yucatán0.1256000.5259710.2469450.1301830.5573990.1776520.5113490.2094060.233413
31Zacatecas0.1464300.5833330.2063850.1182760.6113620.0541730.0974110.1640270.171443