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 6 other fieldsHigh correlation
ic_asalud is highly overall correlated with ic_ali_nc and 3 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_sbv and 1 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 7 other fieldsHigh correlation
plp is highly overall correlated with ic_ali and 6 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 19:36:52.750598
Analysis finished2024-02-03 19:37:25.256337
Duration32.51 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
2024-02-03T19:37:25.554124image/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%
michoacĂ¡n 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%
2024-02-03T19:37:26.577552image/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 (%)
Ă¡ 2
40.0%
Ă³ 1
20.0%
Ă© 1
20.0%
Ă­ 1
20.0%

plp_e
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17175026
Minimum0.049076669
Maximum0.43407855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:27.037133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.049076669
5-th percentile0.066945284
Q10.094654507
median0.14733464
Q30.22815473
95-th percentile0.32884387
Maximum0.43407855
Range0.38500188
Interquartile range (IQR)0.13350022

Descriptive statistics

Standard deviation0.094856573
Coefficient of variation (CV)0.55229361
Kurtosis0.3360822
Mean0.17175026
Median Absolute Deviation (MAD)0.064011302
Skewness0.87482599
Sum5.4960084
Variance0.0089977695
MonotonicityNot monotonic
2024-02-03T19:37:27.498955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.07938247012 1
 
3.1%
0.04907666882 1
 
3.1%
0.2313440078 1
 
3.1%
0.2620115311 1
 
3.1%
0.2782096584 1
 
3.1%
0.1379932938 1
 
3.1%
0.22702407 1
 
3.1%
0.1001843884 1
 
3.1%
0.06774383573 1
 
3.1%
0.2087347463 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.04907666882 1
3.1%
0.06596927717 1
3.1%
0.06774383573 1
3.1%
0.07147153168 1
3.1%
0.07938247012 1
3.1%
0.08498040197 1
3.1%
0.08597183099 1
3.1%
0.09217061058 1
3.1%
0.09548247199 1
3.1%
0.09714858862 1
3.1%
ValueCountFrequency (%)
0.4340785498 1
3.1%
0.3546957743 1
3.1%
0.3076923077 1
3.1%
0.2782096584 1
3.1%
0.2730287399 1
3.1%
0.2698791982 1
3.1%
0.2620115311 1
3.1%
0.2313440078 1
3.1%
0.2270916335 1
3.1%
0.22702407 1
3.1%

plp
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51252764
Minimum0.26916721
Maximum0.78199396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:27.950462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.26916721
5-th percentile0.33656804
Q10.3952955
median0.51089963
Q30.60157903
95-th percentile0.71188815
Maximum0.78199396
Range0.51282675
Interquartile range (IQR)0.20628353

Descriptive statistics

Standard deviation0.13289657
Coefficient of variation (CV)0.2592964
Kurtosis-0.97692362
Mean0.51252764
Median Absolute Deviation (MAD)0.11012145
Skewness0.12404241
Sum16.400885
Variance0.017661499
MonotonicityNot monotonic
2024-02-03T19:37:28.441268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.3817729084 1
 
3.1%
0.2691672056 1
 
3.1%
0.6007763222 1
 
3.1%
0.6625026692 1
 
3.1%
0.6885747939 1
 
3.1%
0.5010317256 1
 
3.1%
0.6014770241 1
 
3.1%
0.3970497849 1
 
3.1%
0.3686779433 1
 
3.1%
0.5475272961 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.2691672056 1
3.1%
0.3361605881 1
3.1%
0.3369014085 1
3.1%
0.3650237678 1
3.1%
0.3686779433 1
3.1%
0.3735492191 1
3.1%
0.3817729084 1
3.1%
0.3900326471 1
3.1%
0.3970497849 1
3.1%
0.4006615878 1
3.1%
ValueCountFrequency (%)
0.7819939577 1
3.1%
0.7183738639 1
3.1%
0.7065816492 1
3.1%
0.6885747939 1
3.1%
0.6625026692 1
3.1%
0.6573139975 1
3.1%
0.6209044931 1
3.1%
0.6018850392 1
3.1%
0.6014770241 1
3.1%
0.6007763222 1
3.1%

ic_rezedu
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21164883
Minimum0.12219148
Maximum0.32435045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:28.916151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.12219148
5-th percentile0.1578536
Q10.17867191
median0.19773804
Q30.22578457
95-th percentile0.30939075
Maximum0.32435045
Range0.20215898
Interquartile range (IQR)0.047112653

Descriptive statistics

Standard deviation0.049784452
Coefficient of variation (CV)0.23522196
Kurtosis0.15478572
Mean0.21164883
Median Absolute Deviation (MAD)0.020544735
Skewness0.87104191
Sum6.7727625
Variance0.0024784917
MonotonicityNot monotonic
2024-02-03T19:37:29.406759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1784860558 1
 
3.1%
0.2000431127 1
 
3.1%
0.2555070354 1
 
3.1%
0.3080290412 1
 
3.1%
0.1638398115 1
 
3.1%
0.1742326541 1
 
3.1%
0.170678337 1
 
3.1%
0.178733866 1
 
3.1%
0.1547588993 1
 
3.1%
0.2114108328 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.1221914776 1
3.1%
0.1547588993 1
3.1%
0.1603856266 1
3.1%
0.1638398115 1
3.1%
0.1672112676 1
3.1%
0.170678337 1
3.1%
0.1742326541 1
3.1%
0.1784860558 1
3.1%
0.178733866 1
3.1%
0.1828342169 1
3.1%
ValueCountFrequency (%)
0.3243504532 1
3.1%
0.3110550652 1
3.1%
0.3080290412 1
3.1%
0.302953361 1
3.1%
0.2754782233 1
3.1%
0.2661846309 1
3.1%
0.2555070354 1
3.1%
0.2444116925 1
3.1%
0.2195755249 1
3.1%
0.2136462162 1
3.1%

ic_asalud
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26098734
Minimum0.168
Maximum0.39155419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:29.975837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.168
5-th percentile0.18478867
Q10.2125335
median0.24261615
Q30.31295376
95-th percentile0.36989892
Maximum0.39155419
Range0.22355419
Interquartile range (IQR)0.10042026

Descriptive statistics

Standard deviation0.062079394
Coefficient of variation (CV)0.23786362
Kurtosis-0.71044509
Mean0.26098734
Median Absolute Deviation (MAD)0.038031829
Skewness0.58117627
Sum8.3515949
Variance0.0038538512
MonotonicityNot monotonic
2024-02-03T19:37:30.494571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.2027888446 1
 
3.1%
0.2401379608 1
 
3.1%
0.2481319748 1
 
3.1%
0.3110185778 1
 
3.1%
0.2765606596 1
 
3.1%
0.1946092339 1
 
3.1%
0.2699671772 1
 
3.1%
0.2129071911 1
 
3.1%
0.195741816 1
 
3.1%
0.2063797902 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.168 1
3.1%
0.1746285562 1
3.1%
0.1931014984 1
3.1%
0.1946092339 1
3.1%
0.195741816 1
3.1%
0.2027888446 1
3.1%
0.2063797902 1
3.1%
0.2114124149 1
3.1%
0.2129071911 1
3.1%
0.2229851825 1
3.1%
ValueCountFrequency (%)
0.3915541895 1
3.1%
0.3722054381 1
3.1%
0.3680117697 1
3.1%
0.3592318405 1
3.1%
0.3380525235 1
3.1%
0.3279292557 1
3.1%
0.3265838011 1
3.1%
0.3187592995 1
3.1%
0.3110185778 1
3.1%
0.2902047593 1
3.1%

ic_segsoc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54097813
Minimum0.29666959
Maximum0.78827795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:31.012343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.29666959
5-th percentile0.35884427
Q10.43922268
median0.54428074
Q30.63925378
95-th percentile0.75386267
Maximum0.78827795
Range0.49160836
Interquartile range (IQR)0.2000311

Descriptive statistics

Standard deviation0.13072661
Coefficient of variation (CV)0.24164861
Kurtosis-0.84983309
Mean0.54097813
Median Absolute Deviation (MAD)0.10435576
Skewness0.10128218
Sum17.3113
Variance0.017089448
MonotonicityNot monotonic
2024-02-03T19:37:31.546684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.3920318725 1
 
3.1%
0.4197743767 1
 
3.1%
0.5472100922 1
 
3.1%
0.6939995729 1
 
3.1%
0.6544169611 1
 
3.1%
0.4457054424 1
 
3.1%
0.5880743982 1
 
3.1%
0.3717271051 1
 
3.1%
0.3753260961 1
 
3.1%
0.5692571184 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.2966695881 1
3.1%
0.3430985915 1
3.1%
0.3717271051 1
3.1%
0.3753260961 1
3.1%
0.3920318725 1
3.1%
0.3936285547 1
3.1%
0.41674995 1
3.1%
0.4197743767 1
3.1%
0.4457054424 1
3.1%
0.4759212138 1
3.1%
ValueCountFrequency (%)
0.7882779456 1
3.1%
0.7563051702 1
3.1%
0.7518642602 1
3.1%
0.713706706 1
3.1%
0.6939995729 1
3.1%
0.6765571045 1
3.1%
0.6602966725 1
3.1%
0.6544169611 1
3.1%
0.6341993819 1
3.1%
0.6132082387 1
3.1%

ic_cv
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10344927
Minimum0.031332165
Maximum0.26186102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:32.033570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.031332165
5-th percentile0.038965489
Q10.070805809
median0.089111752
Q30.12001519
95-th percentile0.21645467
Maximum0.26186102
Range0.23052885
Interquartile range (IQR)0.049209376

Descriptive statistics

Standard deviation0.054281548
Coefficient of variation (CV)0.52471661
Kurtosis1.8943774
Mean0.10344927
Median Absolute Deviation (MAD)0.024350526
Skewness1.4011261
Sum3.3103765
Variance0.0029464864
MonotonicityNot monotonic
2024-02-03T19:37:32.605762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.04452191235 1
 
3.1%
0.09068046274 1
 
3.1%
0.1541969918 1
 
3.1%
0.1597266709 1
 
3.1%
0.0789163722 1
 
3.1%
0.06100077379 1
 
3.1%
0.09463894967 1
 
3.1%
0.09305470191 1
 
3.1%
0.06176891357 1
 
3.1%
0.1147505887 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.03133216477 1
3.1%
0.03217430571 1
3.1%
0.04452191235 1
3.1%
0.06100077379 1
3.1%
0.06176891357 1
3.1%
0.06249381984 1
3.1%
0.06604953715 1
3.1%
0.06786287089 1
3.1%
0.07178678894 1
3.1%
0.07238944005 1
3.1%
ValueCountFrequency (%)
0.261861018 1
3.1%
0.2375998319 1
3.1%
0.1991540785 1
3.1%
0.1687242798 1
3.1%
0.1597266709 1
3.1%
0.1541969918 1
3.1%
0.135201131 1
3.1%
0.1276619718 1
3.1%
0.1174662568 1
3.1%
0.1147505887 1
3.1%

ic_sbv
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22508127
Minimum0.033665339
Maximum0.58396385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:33.073380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.033665339
5-th percentile0.059603565
Q10.10801541
median0.16060432
Q30.29769678
95-th percentile0.57128543
Maximum0.58396385
Range0.55029851
Interquartile range (IQR)0.18968138

Descriptive statistics

Standard deviation0.16478982
Coefficient of variation (CV)0.73213473
Kurtosis-0.02078759
Mean0.22508127
Median Absolute Deviation (MAD)0.078608243
Skewness1.0608852
Sum7.2026008
Variance0.027155684
MonotonicityNot monotonic
2024-02-03T19:37:33.591787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.03366533865 1
 
3.1%
0.1534094992 1
 
3.1%
0.4838427948 1
 
3.1%
0.4742686312 1
 
3.1%
0.0777385159 1
 
3.1%
0.1301263864 1
 
3.1%
0.4387308534 1
 
3.1%
0.1439459127 1
 
3.1%
0.08709921737 1
 
3.1%
0.3265895954 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.03366533865 1
3.1%
0.04593923459 1
3.1%
0.07078347257 1
3.1%
0.0777385159 1
3.1%
0.07920498368 1
3.1%
0.08119486768 1
3.1%
0.08709921737 1
3.1%
0.09161169965 1
3.1%
0.1134833071 1
3.1%
0.1188732394 1
3.1%
ValueCountFrequency (%)
0.5839638504 1
3.1%
0.5803523182 1
3.1%
0.5638670695 1
3.1%
0.4838427948 1
3.1%
0.4742686312 1
3.1%
0.4387308534 1
3.1%
0.3387739365 1
3.1%
0.3265895954 1
3.1%
0.2880658436 1
3.1%
0.2775730779 1
3.1%

ic_ali
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21743451
Minimum0.14064894
Maximum0.40905361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:34.144655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.14064894
5-th percentile0.14189087
Q10.17317561
median0.21193045
Q30.24552754
95-th percentile0.30954969
Maximum0.40905361
Range0.26840467
Interquartile range (IQR)0.072351928

Descriptive statistics

Standard deviation0.060169605
Coefficient of variation (CV)0.27672518
Kurtosis1.9950817
Mean0.21743451
Median Absolute Deviation (MAD)0.038835462
Skewness1.101755
Sum6.9579045
Variance0.0036203814
MonotonicityNot monotonic
2024-02-03T19:37:34.659533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1773904382 1
 
3.1%
0.1429905871 1
 
3.1%
0.2337700146 1
 
3.1%
0.2414050822 1
 
3.1%
0.2691401649 1
 
3.1%
0.1412174362 1
 
3.1%
0.4090536105 1
 
3.1%
0.2324523663 1
 
3.1%
0.209374737 1
 
3.1%
0.1818668379 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.140648944 1
3.1%
0.1412174362 1
3.1%
0.1424418605 1
3.1%
0.1429905871 1
3.1%
0.1516971062 1
3.1%
0.1618875593 1
3.1%
0.166739702 1
3.1%
0.172933748 1
3.1%
0.1732562342 1
3.1%
0.1773904382 1
3.1%
ValueCountFrequency (%)
0.4090536105 1
3.1%
0.3348103318 1
3.1%
0.2888818831 1
3.1%
0.2746253992 1
3.1%
0.2739944245 1
3.1%
0.2691401649 1
3.1%
0.2545082491 1
3.1%
0.2514842777 1
3.1%
0.2435419612 1
3.1%
0.2414050822 1
3.1%

ic_ali_nc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23514019
Minimum0.14315192
Maximum0.43435449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-02-03T19:37:35.131698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.14315192
5-th percentile0.14714617
Q10.18522556
median0.23391407
Q30.26674159
95-th percentile0.35283809
Maximum0.43435449
Range0.29120256
Interquartile range (IQR)0.081516028

Descriptive statistics

Standard deviation0.070770218
Coefficient of variation (CV)0.30097032
Kurtosis0.57434644
Mean0.23514019
Median Absolute Deviation (MAD)0.049518732
Skewness0.86426739
Sum7.5244861
Variance0.0050084237
MonotonicityNot monotonic
2024-02-03T19:37:35.538293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1860557769 1
 
3.1%
0.1516849896 1
 
3.1%
0.2538573508 1
 
3.1%
0.2565663037 1
 
3.1%
0.32803298 1
 
3.1%
0.1431519216 1
 
3.1%
0.4343544858 1
 
3.1%
0.2350338045 1
 
3.1%
0.2101321215 1
 
3.1%
0.1917148362 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
0.1431519216 1
3.1%
0.1459124525 1
3.1%
0.1481555734 1
3.1%
0.1516849896 1
3.1%
0.157368026 1
3.1%
0.1673063897 1
3.1%
0.1695150453 1
3.1%
0.1827348977 1
3.1%
0.1860557769 1
3.1%
0.1900252982 1
3.1%
ValueCountFrequency (%)
0.4343544858 1
3.1%
0.3655030801 1
3.1%
0.3424758302 1
3.1%
0.32803298 1
3.1%
0.3095062638 1
3.1%
0.3033057245 1
3.1%
0.2968272932 1
3.1%
0.2877605832 1
3.1%
0.2597352525 1
3.1%
0.2565663037 1
3.1%

Interactions

2024-02-03T19:37:20.321734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:53.186178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:56.841434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:59.897716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:03.664551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:07.015373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:10.144964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:13.580347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:16.934526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:20.692903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:53.956466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:57.169566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:00.334927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:04.085841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:07.394791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:10.514579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:13.960040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:17.340360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:21.034444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:54.305593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:57.438552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:00.755008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:04.474327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:07.757463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:11.094234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:14.319237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:17.720199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:21.416485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:54.697075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:57.764336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:01.176198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:04.879889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:08.154603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:11.478331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:14.713380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:18.134719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:21.768180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:55.057185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:58.039756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:01.586512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:05.224638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:08.501395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:11.826691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:15.097360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:18.519862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:22.137514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:55.427207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:58.332859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:02.012359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:05.581869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:08.872889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:12.190027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:15.451999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:18.901947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:23.180748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:55.775595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:58.692693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:02.392894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:05.935457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:09.167608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:12.535135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:15.830935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:19.303600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:23.532775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:56.131454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:59.091009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:02.804613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:06.299131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:09.450986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:12.881984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:16.188077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:19.621164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:23.889759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:56.493995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:36:59.504783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:03.229620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:06.657261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:09.794781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:13.236545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:16.576755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-03T19:37:19.966296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-03T19:37:35.806404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ic_aliic_ali_ncic_asaludic_cvic_rezeduic_sbvic_segsocplpplp_e
ic_ali1.0000.9900.4990.6410.1300.6320.6380.7160.727
ic_ali_nc0.9901.0000.5360.6610.1590.6570.6720.7460.757
ic_asalud0.4990.5361.0000.4220.4750.4990.7880.6830.597
ic_cv0.6410.6610.4221.0000.4790.8660.5510.5590.653
ic_rezedu0.1300.1590.4750.4791.0000.5190.6300.4070.398
ic_sbv0.6320.6570.4990.8660.5191.0000.6650.6570.701
ic_segsoc0.6380.6720.7880.5510.6300.6651.0000.8690.832
plp0.7160.7460.6830.5590.4070.6570.8691.0000.964
plp_e0.7270.7570.5970.6530.3980.7010.8320.9641.000

Missing values

2024-02-03T19:37:24.225291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-03T19:37:24.949370image/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
0Aguascalientes0.0793820.3817730.1784860.2027890.3920320.0445220.0336650.1773900.186056
1Baja California0.0490770.2691670.2000430.2401380.4197740.0906800.1534090.1429910.151685
2Baja California Sur0.0859720.3369010.1672110.1680000.3430990.1276620.1188730.2304230.237183
3Campeche0.2270920.5819300.1917490.2114120.5546840.1352010.3387740.2435420.259735
4Coahuila0.0921710.4044700.1603860.2245840.2966700.0313320.0459390.1667400.169515
5Colima0.0659690.3361610.2136460.1931010.4759210.0879280.1362740.1729340.182735
6Chiapas0.4340790.7819940.3243500.3722050.7882780.1991540.5638670.2090630.242417
7Chihuahua0.1058030.3900330.2074760.1746290.4167500.0785530.0916120.1406490.145912
8CDMX0.1239620.4575540.1221910.2902050.4763700.0884340.1873820.1783070.199336
9Durango0.1448630.5098390.1921290.2373180.4977750.0624940.0792050.1889650.190843
Estadosplp_eplpic_rezeduic_asaludic_segsocic_cvic_sbvic_aliic_ali_nc
22Quintana Roo0.2698790.6018850.1971330.2393470.5413510.1687240.2880660.2739940.296827
23San Luis PotosĂ­0.2087350.5475270.2114110.2063800.5692570.1147510.3265900.1818670.191715
24Sinaloa0.0677440.3686780.1547590.1957420.3753260.0617690.0870990.2093750.210132
25Sonora0.1001840.3970500.1787340.2129070.3717270.0930550.1439460.2324520.235034
26Tabasco0.2270240.6014770.1706780.2699670.5880740.0946390.4387310.4090540.434354
27Tamaulipas0.1379930.5010320.1742330.1946090.4457050.0610010.1301260.1412170.143152
28Tlaxcala0.2782100.6885750.1638400.2765610.6544170.0789160.0777390.2691400.328033
29Veracruz0.2620120.6625030.3080290.3110190.6940000.1597270.4742690.2414050.256566
30YucatĂ¡n0.2313440.6007760.2555070.2481320.5472100.1541970.4838430.2337700.253857
31Zacatecas0.1701290.5436890.2195760.2402350.6043120.0321740.0707830.1618880.167306