Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations3081125
Missing cells67075
Missing cells (%)0.1%
Duplicate rows33486
Duplicate rows (%)1.1%
Total size in memory446.6 MiB
Average record size in memory152.0 B

Variable types

Text6
Categorical6
DateTime2
Numeric5

Alerts

TIPO_TRANSACCION has constant value "Wires Out"Constant
Dataset has 33486 (1.1%) duplicate rowsDuplicates
PEP is highly overall correlated with RIESGOHigh correlation
PERFIL_WIRES_IN_FRECUENCIA is highly overall correlated with PERFIL_WIRES_IN_MONTO and 2 other fieldsHigh correlation
PERFIL_WIRES_IN_MONTO is highly overall correlated with PERFIL_WIRES_IN_FRECUENCIA and 2 other fieldsHigh correlation
PERFIL_WIRES_OUT_FRECUENCIA is highly overall correlated with PERFIL_WIRES_IN_FRECUENCIA and 2 other fieldsHigh correlation
PERFIL_WIRES_OUT_MONTO is highly overall correlated with PERFIL_WIRES_IN_FRECUENCIA and 2 other fieldsHigh correlation
RIESGO is highly overall correlated with PEPHigh correlation
PEP is highly imbalanced (79.1%)Imbalance
RIESGO is highly imbalanced (53.8%)Imbalance
TIPO_CUENTA is highly imbalanced (94.4%)Imbalance
PEP has 65849 (2.1%) missing valuesMissing
MONTO is highly skewed (γ1 = 266.776926)Skewed
PERFIL_WIRES_IN_MONTO is highly skewed (γ1 = 172.1668942)Skewed
PERFIL_WIRES_OUT_MONTO is highly skewed (γ1 = 172.5410598)Skewed
PERFIL_WIRES_IN_FRECUENCIA has 107649 (3.5%) zerosZeros
PERFIL_WIRES_IN_MONTO has 107649 (3.5%) zerosZeros
PERFIL_WIRES_OUT_FRECUENCIA has 109722 (3.6%) zerosZeros
PERFIL_WIRES_OUT_MONTO has 109722 (3.6%) zerosZeros

Reproduction

Analysis started2024-08-15 23:10:56.779751
Analysis finished2024-08-15 23:15:41.803055
Duration4 minutes and 45.02 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct32235
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:41.963518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5779301
Min length2

Characters and Unicode

Total characters17186300
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5598 ?
Unique (%)0.2%

Sample

1st rowC0
2nd rowC0
3rd rowC0
4th rowC0
5th rowC0
ValueCountFrequency (%)
c7400 60999
 
2.0%
c6850 26959
 
0.9%
c11375 23501
 
0.8%
c11026 18984
 
0.6%
c12407 16459
 
0.5%
c1336 15970
 
0.5%
c2923 13907
 
0.5%
c11460 13491
 
0.4%
c17908 13247
 
0.4%
c2255 12639
 
0.4%
Other values (32225) 2864969
93.0%
2024-08-15T18:15:42.220457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 3081125
17.9%
1 2865881
16.7%
2 1717120
10.0%
0 1340478
7.8%
3 1271330
7.4%
4 1261645
7.3%
7 1209845
 
7.0%
6 1147151
 
6.7%
5 1147019
 
6.7%
9 1126702
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17186300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 3081125
17.9%
1 2865881
16.7%
2 1717120
10.0%
0 1340478
7.8%
3 1271330
7.4%
4 1261645
7.3%
7 1209845
 
7.0%
6 1147151
 
6.7%
5 1147019
 
6.7%
9 1126702
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17186300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 3081125
17.9%
1 2865881
16.7%
2 1717120
10.0%
0 1340478
7.8%
3 1271330
7.4%
4 1261645
7.3%
7 1209845
 
7.0%
6 1147151
 
6.7%
5 1147019
 
6.7%
9 1126702
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17186300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 3081125
17.9%
1 2865881
16.7%
2 1717120
10.0%
0 1340478
7.8%
3 1271330
7.4%
4 1261645
7.3%
7 1209845
 
7.0%
6 1147151
 
6.7%
5 1147019
 
6.7%
9 1126702
 
6.6%
Distinct93
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size23.5 MiB
2024-08-15T18:15:42.316052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6162240
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCO
2nd rowCO
3rd rowCO
4th rowCO
5th rowCO
ValueCountFrequency (%)
co 2693932
87.4%
pa 219004
 
7.1%
sv 53986
 
1.8%
vg 16953
 
0.6%
us 14838
 
0.5%
hk 12242
 
0.4%
ve 9439
 
0.3%
cr 7920
 
0.3%
ai 6611
 
0.2%
gt 4995
 
0.2%
Other values (83) 41200
 
1.3%
2024-08-15T18:15:42.446727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2710891
44.0%
O 2695063
43.7%
A 233883
 
3.8%
P 221916
 
3.6%
V 80457
 
1.3%
S 76131
 
1.2%
G 23736
 
0.4%
E 18580
 
0.3%
U 18141
 
0.3%
K 15931
 
0.3%
Other values (16) 67511
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6162240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2710891
44.0%
O 2695063
43.7%
A 233883
 
3.8%
P 221916
 
3.6%
V 80457
 
1.3%
S 76131
 
1.2%
G 23736
 
0.4%
E 18580
 
0.3%
U 18141
 
0.3%
K 15931
 
0.3%
Other values (16) 67511
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6162240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2710891
44.0%
O 2695063
43.7%
A 233883
 
3.8%
P 221916
 
3.6%
V 80457
 
1.3%
S 76131
 
1.2%
G 23736
 
0.4%
E 18580
 
0.3%
U 18141
 
0.3%
K 15931
 
0.3%
Other values (16) 67511
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6162240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2710891
44.0%
O 2695063
43.7%
A 233883
 
3.8%
P 221916
 
3.6%
V 80457
 
1.3%
S 76131
 
1.2%
G 23736
 
0.4%
E 18580
 
0.3%
U 18141
 
0.3%
K 15931
 
0.3%
Other values (16) 67511
 
1.1%

CUENTA
Text

Distinct49321
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:42.609185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.709533
Min length2

Characters and Unicode

Total characters17591785
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20013 ?
Unique (%)0.6%

Sample

1st rowS49343
2nd rowS49343
3rd rowS49343
4th rowS49343
5th rowS49343
ValueCountFrequency (%)
s40707 54258
 
1.8%
s50814 26860
 
0.9%
s1799 23501
 
0.8%
s82743 18984
 
0.6%
s82364 16459
 
0.5%
s1420 13897
 
0.5%
s1908 13482
 
0.4%
s53914 13247
 
0.4%
s41908 13126
 
0.4%
s2341 11265
 
0.4%
Other values (49311) 2876046
93.3%
2024-08-15T18:15:42.837423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3081125
17.5%
5 1795037
10.2%
8 1666699
9.5%
1 1616701
9.2%
4 1600622
9.1%
2 1593347
9.1%
3 1325266
7.5%
0 1253506
7.1%
6 1241410
7.1%
7 1235912
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17591785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 3081125
17.5%
5 1795037
10.2%
8 1666699
9.5%
1 1616701
9.2%
4 1600622
9.1%
2 1593347
9.1%
3 1325266
7.5%
0 1253506
7.1%
6 1241410
7.1%
7 1235912
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17591785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 3081125
17.5%
5 1795037
10.2%
8 1666699
9.5%
1 1616701
9.2%
4 1600622
9.1%
2 1593347
9.1%
3 1325266
7.5%
0 1253506
7.1%
6 1241410
7.1%
7 1235912
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17591785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 3081125
17.5%
5 1795037
10.2%
8 1666699
9.5%
1 1616701
9.2%
4 1600622
9.1%
2 1593347
9.1%
3 1325266
7.5%
0 1253506
7.1%
6 1241410
7.1%
7 1235912
7.0%

ESTADO_CUENTA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
ACTIVO
2154040 
CANCELADO
892194 
BLOQUEADO
 
34891

Length

Max length9
Median length6
Mean length6.9026752
Min length6

Characters and Unicode

Total characters21268005
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ACTIVO 2154040
69.9%
CANCELADO 892194
29.0%
BLOQUEADO 34891
 
1.1%

Length

2024-08-15T18:15:42.915913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-15T18:15:42.971489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
activo 2154040
69.9%
cancelado 892194
29.0%
bloqueado 34891
 
1.1%

Most occurring characters

ValueCountFrequency (%)
A 3973319
18.7%
C 3938428
18.5%
O 3116016
14.7%
T 2154040
10.1%
I 2154040
10.1%
V 2154040
10.1%
E 927085
 
4.4%
L 927085
 
4.4%
D 927085
 
4.4%
N 892194
 
4.2%
Other values (3) 104673
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21268005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3973319
18.7%
C 3938428
18.5%
O 3116016
14.7%
T 2154040
10.1%
I 2154040
10.1%
V 2154040
10.1%
E 927085
 
4.4%
L 927085
 
4.4%
D 927085
 
4.4%
N 892194
 
4.2%
Other values (3) 104673
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21268005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3973319
18.7%
C 3938428
18.5%
O 3116016
14.7%
T 2154040
10.1%
I 2154040
10.1%
V 2154040
10.1%
E 927085
 
4.4%
L 927085
 
4.4%
D 927085
 
4.4%
N 892194
 
4.2%
Other values (3) 104673
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21268005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3973319
18.7%
C 3938428
18.5%
O 3116016
14.7%
T 2154040
10.1%
I 2154040
10.1%
V 2154040
10.1%
E 927085
 
4.4%
L 927085
 
4.4%
D 927085
 
4.4%
N 892194
 
4.2%
Other values (3) 104673
 
0.5%
Distinct5226
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
Minimum2007-01-02 00:00:00
Maximum2024-08-01 00:00:00
2024-08-15T18:15:43.030429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:43.276551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct5226
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
Minimum2007-01-02 00:00:00
Maximum2024-08-01 00:00:00
2024-08-15T18:15:43.344835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:43.412287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MONTO
Real number (ℝ)

SKEWED 

Distinct1221402
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102597.89
Minimum0
Maximum9.6915699 × 108
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:43.488312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile327
Q12729
median11610
Q349000
95-th percentile325000
Maximum9.6915699 × 108
Range9.6915699 × 108
Interquartile range (IQR)46271

Descriptive statistics

Standard deviation1317527.9
Coefficient of variation (CV)12.841667
Kurtosis133750.82
Mean102597.89
Median Absolute Deviation (MAD)10691.28
Skewness266.77693
Sum3.1611692 × 1011
Variance1.7358799 × 1012
MonotonicityNot monotonic
2024-08-15T18:15:43.560941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 38034
 
1.2%
5000 36734
 
1.2%
20000 28135
 
0.9%
1000 27368
 
0.9%
2000 26396
 
0.9%
3000 22991
 
0.7%
50000 22473
 
0.7%
100000 22200
 
0.7%
30000 20098
 
0.7%
15000 18264
 
0.6%
Other values (1221392) 2818432
91.5%
ValueCountFrequency (%)
0 20
< 0.1%
0.01 45
< 0.1%
0.02 8
 
< 0.1%
0.03 5
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 5
 
< 0.1%
0.07 1
 
< 0.1%
0.08 3
 
< 0.1%
0.09 1
 
< 0.1%
ValueCountFrequency (%)
969156988 1
< 0.1%
512187500 1
< 0.1%
498472792 1
< 0.1%
483691957.9 1
< 0.1%
462680000 1
< 0.1%
447422000 1
< 0.1%
362318065.2 1
< 0.1%
308268215 1
< 0.1%
283790000 1
< 0.1%
276890000 1
< 0.1%
Distinct216
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:43.712819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.726324
Min length0

Characters and Unicode

Total characters5319020
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowCO
2nd rowUS
3rd rowCO
4th rowCO
5th rowUS
ValueCountFrequency (%)
us 650218
29.7%
co 416736
19.0%
cn 207077
 
9.5%
pa 87351
 
4.0%
es 55557
 
2.5%
sv 50125
 
2.3%
hk 49190
 
2.2%
mx 46950
 
2.1%
de 43310
 
2.0%
br 43242
 
2.0%
Other values (203) 538426
24.6%
2024-08-15T18:15:43.935214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
858065
16.1%
S 781501
14.7%
C 725951
13.6%
U 666068
12.5%
O 427741
8.0%
N 258369
 
4.9%
E 173926
 
3.3%
P 135943
 
2.6%
A 132870
 
2.5%
R 110397
 
2.1%
Other values (27) 1048189
19.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5319020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
858065
16.1%
S 781501
14.7%
C 725951
13.6%
U 666068
12.5%
O 427741
8.0%
N 258369
 
4.9%
E 173926
 
3.3%
P 135943
 
2.6%
A 132870
 
2.5%
R 110397
 
2.1%
Other values (27) 1048189
19.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5319020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
858065
16.1%
S 781501
14.7%
C 725951
13.6%
U 666068
12.5%
O 427741
8.0%
N 258369
 
4.9%
E 173926
 
3.3%
P 135943
 
2.6%
A 132870
 
2.5%
R 110397
 
2.1%
Other values (27) 1048189
19.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5319020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
858065
16.1%
S 781501
14.7%
C 725951
13.6%
U 666068
12.5%
O 427741
8.0%
N 258369
 
4.9%
E 173926
 
3.3%
P 135943
 
2.6%
A 132870
 
2.5%
R 110397
 
2.1%
Other values (27) 1048189
19.7%
Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:44.033492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.6437908
Min length0

Characters and Unicode

Total characters5064725
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPA
2nd rowPA
3rd rowPA
4th rowPA
5th rowPA
ValueCountFrequency (%)
pa 1431779
68.1%
co 589904
28.0%
ky 44364
 
2.1%
sv 10253
 
0.5%
ve 4412
 
0.2%
vg 4357
 
0.2%
us 4171
 
0.2%
cr 1798
 
0.1%
gt 1605
 
0.1%
hk 1512
 
0.1%
Other values (77) 9173
 
0.4%
2024-08-15T18:15:44.187230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1434321
28.3%
P 1432126
28.3%
858069
16.9%
C 594546
11.7%
O 590133
11.7%
K 45971
 
0.9%
Y 44730
 
0.9%
V 19048
 
0.4%
S 15473
 
0.3%
G 6575
 
0.1%
Other values (18) 23733
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5064725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1434321
28.3%
P 1432126
28.3%
858069
16.9%
C 594546
11.7%
O 590133
11.7%
K 45971
 
0.9%
Y 44730
 
0.9%
V 19048
 
0.4%
S 15473
 
0.3%
G 6575
 
0.1%
Other values (18) 23733
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5064725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1434321
28.3%
P 1432126
28.3%
858069
16.9%
C 594546
11.7%
O 590133
11.7%
K 45971
 
0.9%
Y 44730
 
0.9%
V 19048
 
0.4%
S 15473
 
0.3%
G 6575
 
0.1%
Other values (18) 23733
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5064725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1434321
28.3%
P 1432126
28.3%
858069
16.9%
C 594546
11.7%
O 590133
11.7%
K 45971
 
0.9%
Y 44730
 
0.9%
V 19048
 
0.4%
S 15473
 
0.3%
G 6575
 
0.1%
Other values (18) 23733
 
0.5%

PEP
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing65849
Missing (%)2.1%
Memory size23.5 MiB
NO
2915600 
SI
 
99676

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6030552
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
NO 2915600
94.6%
SI 99676
 
3.2%
(Missing) 65849
 
2.1%

Length

2024-08-15T18:15:44.255514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-15T18:15:44.302620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 2915600
96.7%
si 99676
 
3.3%

Most occurring characters

ValueCountFrequency (%)
N 2915600
48.3%
O 2915600
48.3%
S 99676
 
1.7%
I 99676
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6030552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 2915600
48.3%
O 2915600
48.3%
S 99676
 
1.7%
I 99676
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6030552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 2915600
48.3%
O 2915600
48.3%
S 99676
 
1.7%
I 99676
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6030552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 2915600
48.3%
O 2915600
48.3%
S 99676
 
1.7%
I 99676
 
1.7%

PERFIL_WIRES_IN_FRECUENCIA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1761002.4
Minimum0
Maximum99999999
Zeros107649
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:44.359035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median10
Q325
95-th percentile100
Maximum99999999
Range99999999
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13152830
Coefficient of variation (CV)7.468945
Kurtosis51.804568
Mean1761002.4
Median Absolute Deviation (MAD)8
Skewness7.3351574
Sum5.4258686 × 1012
Variance1.7299694 × 1014
MonotonicityNot monotonic
2024-08-15T18:15:44.431945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 354551
 
11.5%
5 289766
 
9.4%
2 246964
 
8.0%
3 242912
 
7.9%
20 235975
 
7.7%
15 199964
 
6.5%
1 150892
 
4.9%
30 128636
 
4.2%
50 108034
 
3.5%
0 107649
 
3.5%
Other values (64) 1015782
33.0%
ValueCountFrequency (%)
0 107649
 
3.5%
1 150892
4.9%
2 246964
8.0%
3 242912
7.9%
4 95180
 
3.1%
5 289766
9.4%
6 101964
 
3.3%
7 72557
 
2.4%
8 52024
 
1.7%
9 16317
 
0.5%
ValueCountFrequency (%)
99999999 54258
1.8%
40005 1
 
< 0.1%
10000 22
 
< 0.1%
1262 123
 
< 0.1%
1000 8195
 
0.3%
300 13706
 
0.4%
250 1624
 
0.1%
200 25667
0.8%
190 1154
 
< 0.1%
160 918
 
< 0.1%

PERFIL_WIRES_IN_MONTO
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2544
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24074736
Minimum0
Maximum9 × 1010
Zeros107649
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:44.501089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4000
Q180179.22
median500000
Q33000000
95-th percentile30148941
Maximum9 × 1010
Range9 × 1010
Interquartile range (IQR)2919820.8

Descriptive statistics

Standard deviation4.8314264 × 108
Coefficient of variation (CV)20.06845
Kurtosis32016.363
Mean24074736
Median Absolute Deviation (MAD)490000
Skewness172.16689
Sum7.4177271 × 1013
Variance2.3342681 × 1017
MonotonicityNot monotonic
2024-08-15T18:15:44.569063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000000 116492
 
3.8%
500000 114502
 
3.7%
1000000 113058
 
3.7%
0 107649
 
3.5%
300000 92794
 
3.0%
100000 89618
 
2.9%
2000000 87852
 
2.9%
3000000 83991
 
2.7%
200000 71941
 
2.3%
50000 65382
 
2.1%
Other values (2534) 2137846
69.4%
ValueCountFrequency (%)
0 107649
3.5%
1 9
 
< 0.1%
10 5
 
< 0.1%
20 3
 
< 0.1%
21 2
 
< 0.1%
25 1
 
< 0.1%
50 15
 
< 0.1%
69.38 2
 
< 0.1%
70 34
 
< 0.1%
80 3
 
< 0.1%
ValueCountFrequency (%)
9 × 101082
 
< 0.1%
2.8 × 10102
 
< 0.1%
5800000000 4
 
< 0.1%
999999999 54258
1.8%
601424543 968
 
< 0.1%
600000000 321
 
< 0.1%
400000000 1
 
< 0.1%
300000000 178
 
< 0.1%
250000000 79
 
< 0.1%
200000000 1336
 
< 0.1%

PERFIL_WIRES_OUT_FRECUENCIA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1761009.9
Minimum0
Maximum99999999
Zeros109722
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:44.686941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median10
Q330
95-th percentile170
Maximum99999999
Range99999999
Interquartile range (IQR)26

Descriptive statistics

Standard deviation13152829
Coefficient of variation (CV)7.4689128
Kurtosis51.804568
Mean1761009.9
Median Absolute Deviation (MAD)8
Skewness7.3351574
Sum5.4258915 × 1012
Variance1.7299691 × 1014
MonotonicityNot monotonic
2024-08-15T18:15:44.758609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 306631
 
10.0%
2 246619
 
8.0%
5 241596
 
7.8%
20 234536
 
7.6%
3 161231
 
5.2%
15 156833
 
5.1%
4 147082
 
4.8%
1 137552
 
4.5%
30 125157
 
4.1%
0 109722
 
3.6%
Other values (68) 1214166
39.4%
ValueCountFrequency (%)
0 109722
3.6%
1 137552
4.5%
2 246619
8.0%
3 161231
5.2%
4 147082
4.8%
5 241596
7.8%
6 82426
 
2.7%
7 74022
 
2.4%
8 64440
 
2.1%
9 21150
 
0.7%
ValueCountFrequency (%)
99999999 54258
1.8%
10000 193
 
< 0.1%
3000 8
 
< 0.1%
1000 5
 
< 0.1%
731 123
 
< 0.1%
440 198
 
< 0.1%
350 23501
0.8%
300 40566
1.3%
250 18083
 
0.6%
200 15825
 
0.5%

PERFIL_WIRES_OUT_MONTO
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2482
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24008629
Minimum0
Maximum9 × 1010
Zeros109722
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:44.826956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2000
Q170000
median500000
Q33000000
95-th percentile37543409
Maximum9 × 1010
Range9 × 1010
Interquartile range (IQR)2930000

Descriptive statistics

Standard deviation4.827057 × 108
Coefficient of variation (CV)20.105508
Kurtosis32126.461
Mean24008629
Median Absolute Deviation (MAD)494429
Skewness172.54106
Sum7.3973588 × 1013
Variance2.3300479 × 1017
MonotonicityNot monotonic
2024-08-15T18:15:44.895105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000 112895
 
3.7%
0 109722
 
3.6%
5000000 102952
 
3.3%
500000 90597
 
2.9%
2000000 77637
 
2.5%
300000 70498
 
2.3%
100000 69720
 
2.3%
3000000 63740
 
2.1%
50000 60749
 
2.0%
10000000 57574
 
1.9%
Other values (2472) 2265041
73.5%
ValueCountFrequency (%)
0 109722
3.6%
1 7
 
< 0.1%
1.11 2
 
< 0.1%
5 6
 
< 0.1%
10.63 1
 
< 0.1%
24 8
 
< 0.1%
40 5
 
< 0.1%
50 49
 
< 0.1%
70 1
 
< 0.1%
83.33 6
 
< 0.1%
ValueCountFrequency (%)
9 × 101082
 
< 0.1%
1.8 × 10102
 
< 0.1%
5000000000 4
 
< 0.1%
999999999 54258
1.8%
600000000 321
 
< 0.1%
400000000 1
 
< 0.1%
240000000 79
 
< 0.1%
185000000 1
 
< 0.1%
180000000 1336
 
< 0.1%
100000000 2616
 
0.1%
Distinct32235
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
2024-08-15T18:15:45.081292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5779301
Min length2

Characters and Unicode

Total characters17186300
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5598 ?
Unique (%)0.2%

Sample

1st rowC0
2nd rowC0
3rd rowC0
4th rowC0
5th rowC0
ValueCountFrequency (%)
c7400 60999
 
2.0%
c6850 26959
 
0.9%
c11375 23501
 
0.8%
c11026 18984
 
0.6%
c12407 16459
 
0.5%
c1336 15970
 
0.5%
c2923 13907
 
0.5%
c11460 13491
 
0.4%
c17908 13247
 
0.4%
c2255 12639
 
0.4%
Other values (32225) 2864969
93.0%
2024-08-15T18:15:45.354939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 3081125
17.9%
1 2865881
16.7%
2 1717120
10.0%
0 1340478
7.8%
3 1271330
7.4%
4 1261645
7.3%
7 1209845
 
7.0%
6 1147151
 
6.7%
5 1147019
 
6.7%
9 1126702
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17186300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 3081125
17.9%
1 2865881
16.7%
2 1717120
10.0%
0 1340478
7.8%
3 1271330
7.4%
4 1261645
7.3%
7 1209845
 
7.0%
6 1147151
 
6.7%
5 1147019
 
6.7%
9 1126702
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17186300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 3081125
17.9%
1 2865881
16.7%
2 1717120
10.0%
0 1340478
7.8%
3 1271330
7.4%
4 1261645
7.3%
7 1209845
 
7.0%
6 1147151
 
6.7%
5 1147019
 
6.7%
9 1126702
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17186300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 3081125
17.9%
1 2865881
16.7%
2 1717120
10.0%
0 1340478
7.8%
3 1271330
7.4%
4 1261645
7.3%
7 1209845
 
7.0%
6 1147151
 
6.7%
5 1147019
 
6.7%
9 1126702
 
6.6%

RIESGO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing1221
Missing (%)< 0.1%
Memory size23.5 MiB
MEDIO
2450115 
ALTO
331416 
MEDIO_BAJO
 
164852
BAJO
 
75651
MEDIO_ALTO
 
57870

Length

Max length10
Median length5
Mean length5.2294042
Min length4

Characters and Unicode

Total characters16106063
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
MEDIO 2450115
79.5%
ALTO 331416
 
10.8%
MEDIO_BAJO 164852
 
5.4%
BAJO 75651
 
2.5%
MEDIO_ALTO 57870
 
1.9%
(Missing) 1221
 
< 0.1%

Length

2024-08-15T18:15:45.442817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-15T18:15:45.497808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
medio 2450115
79.6%
alto 331416
 
10.8%
medio_bajo 164852
 
5.4%
bajo 75651
 
2.5%
medio_alto 57870
 
1.9%

Most occurring characters

ValueCountFrequency (%)
O 3302626
20.5%
M 2672837
16.6%
E 2672837
16.6%
D 2672837
16.6%
I 2672837
16.6%
A 629789
 
3.9%
L 389286
 
2.4%
T 389286
 
2.4%
B 240503
 
1.5%
J 240503
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16106063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 3302626
20.5%
M 2672837
16.6%
E 2672837
16.6%
D 2672837
16.6%
I 2672837
16.6%
A 629789
 
3.9%
L 389286
 
2.4%
T 389286
 
2.4%
B 240503
 
1.5%
J 240503
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16106063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 3302626
20.5%
M 2672837
16.6%
E 2672837
16.6%
D 2672837
16.6%
I 2672837
16.6%
A 629789
 
3.9%
L 389286
 
2.4%
T 389286
 
2.4%
B 240503
 
1.5%
J 240503
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16106063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 3302626
20.5%
M 2672837
16.6%
E 2672837
16.6%
D 2672837
16.6%
I 2672837
16.6%
A 629789
 
3.9%
L 389286
 
2.4%
T 389286
 
2.4%
B 240503
 
1.5%
J 240503
 
1.5%

TIPO_CLIENTE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
JURIDICO
2737589 
NATURAL
343536 

Length

Max length8
Median length8
Mean length7.8885031
Min length7

Characters and Unicode

Total characters24305464
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
JURIDICO 2737589
88.9%
NATURAL 343536
 
11.1%

Length

2024-08-15T18:15:45.557125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-15T18:15:45.605200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
juridico 2737589
88.9%
natural 343536
 
11.1%

Most occurring characters

ValueCountFrequency (%)
I 5475178
22.5%
U 3081125
12.7%
R 3081125
12.7%
J 2737589
11.3%
D 2737589
11.3%
C 2737589
11.3%
O 2737589
11.3%
A 687072
 
2.8%
N 343536
 
1.4%
T 343536
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24305464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 5475178
22.5%
U 3081125
12.7%
R 3081125
12.7%
J 2737589
11.3%
D 2737589
11.3%
C 2737589
11.3%
O 2737589
11.3%
A 687072
 
2.8%
N 343536
 
1.4%
T 343536
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24305464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 5475178
22.5%
U 3081125
12.7%
R 3081125
12.7%
J 2737589
11.3%
D 2737589
11.3%
C 2737589
11.3%
O 2737589
11.3%
A 687072
 
2.8%
N 343536
 
1.4%
T 343536
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24305464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 5475178
22.5%
U 3081125
12.7%
R 3081125
12.7%
J 2737589
11.3%
D 2737589
11.3%
C 2737589
11.3%
O 2737589
11.3%
A 687072
 
2.8%
N 343536
 
1.4%
T 343536
 
1.4%

TIPO_CUENTA
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
CUENTA
3042012 
CDT
 
24144
PRESTAMO
 
13999
LEASING
 
970

Length

Max length8
Median length6
Mean length5.9858935
Min length3

Characters and Unicode

Total characters18443286
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
CUENTA 3042012
98.7%
CDT 24144
 
0.8%
PRESTAMO 13999
 
0.5%
LEASING 970
 
< 0.1%

Length

2024-08-15T18:15:45.659580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-15T18:15:45.710351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cuenta 3042012
98.7%
cdt 24144
 
0.8%
prestamo 13999
 
0.5%
leasing 970
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 3080155
16.7%
C 3066156
16.6%
E 3056981
16.6%
A 3056981
16.6%
N 3042982
16.5%
U 3042012
16.5%
D 24144
 
0.1%
S 14969
 
0.1%
P 13999
 
0.1%
R 13999
 
0.1%
Other values (5) 30908
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18443286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 3080155
16.7%
C 3066156
16.6%
E 3056981
16.6%
A 3056981
16.6%
N 3042982
16.5%
U 3042012
16.5%
D 24144
 
0.1%
S 14969
 
0.1%
P 13999
 
0.1%
R 13999
 
0.1%
Other values (5) 30908
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18443286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 3080155
16.7%
C 3066156
16.6%
E 3056981
16.6%
A 3056981
16.6%
N 3042982
16.5%
U 3042012
16.5%
D 24144
 
0.1%
S 14969
 
0.1%
P 13999
 
0.1%
R 13999
 
0.1%
Other values (5) 30908
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18443286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 3080155
16.7%
C 3066156
16.6%
E 3056981
16.6%
A 3056981
16.6%
N 3042982
16.5%
U 3042012
16.5%
D 24144
 
0.1%
S 14969
 
0.1%
P 13999
 
0.1%
R 13999
 
0.1%
Other values (5) 30908
 
0.2%

TIPO_TRANSACCION
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.5 MiB
Wires Out
3081125 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters27730125
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWires Out
2nd rowWires Out
3rd rowWires Out
4th rowWires Out
5th rowWires Out

Common Values

ValueCountFrequency (%)
Wires Out 3081125
100.0%

Length

2024-08-15T18:15:45.764905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-15T18:15:45.810073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wires 3081125
50.0%
out 3081125
50.0%

Most occurring characters

ValueCountFrequency (%)
W 3081125
11.1%
i 3081125
11.1%
r 3081125
11.1%
e 3081125
11.1%
s 3081125
11.1%
3081125
11.1%
O 3081125
11.1%
u 3081125
11.1%
t 3081125
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27730125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 3081125
11.1%
i 3081125
11.1%
r 3081125
11.1%
e 3081125
11.1%
s 3081125
11.1%
3081125
11.1%
O 3081125
11.1%
u 3081125
11.1%
t 3081125
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27730125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 3081125
11.1%
i 3081125
11.1%
r 3081125
11.1%
e 3081125
11.1%
s 3081125
11.1%
3081125
11.1%
O 3081125
11.1%
u 3081125
11.1%
t 3081125
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27730125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 3081125
11.1%
i 3081125
11.1%
r 3081125
11.1%
e 3081125
11.1%
s 3081125
11.1%
3081125
11.1%
O 3081125
11.1%
u 3081125
11.1%
t 3081125
11.1%

Interactions

2024-08-15T18:15:20.103542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:16.868821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:17.706932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:18.510552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:19.301685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:20.269423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:17.055101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:17.869658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:18.672040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:19.463959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:20.428484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:17.227328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:18.040310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:18.826958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:19.627862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:20.588200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:17.387435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:18.200500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:18.984805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:19.784020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:20.742793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:17.547517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:18.358328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:19.143705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-15T18:15:19.946118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-15T18:15:45.845578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ESTADO_CUENTAMONTOPEPPERFIL_WIRES_IN_FRECUENCIAPERFIL_WIRES_IN_MONTOPERFIL_WIRES_OUT_FRECUENCIAPERFIL_WIRES_OUT_MONTORIESGOTIPO_CLIENTETIPO_CUENTA
ESTADO_CUENTA1.0000.0000.0570.0880.0020.0880.0020.3560.2020.101
MONTO0.0001.0000.0000.1670.2660.1310.2660.0000.0010.000
PEP0.0570.0001.0000.0250.0010.0250.0010.5080.0350.012
PERFIL_WIRES_IN_FRECUENCIA0.0880.1670.0251.0000.7710.8980.7690.0680.0470.015
PERFIL_WIRES_IN_MONTO0.0020.2660.0010.7711.0000.7820.9530.0100.0020.000
PERFIL_WIRES_OUT_FRECUENCIA0.0880.1310.0250.8980.7821.0000.7960.0680.0470.015
PERFIL_WIRES_OUT_MONTO0.0020.2660.0010.7690.9530.7961.0000.0100.0020.000
RIESGO0.3560.0000.5080.0680.0100.0680.0101.0000.2670.102
TIPO_CLIENTE0.2020.0010.0350.0470.0020.0470.0020.2671.0000.221
TIPO_CUENTA0.1010.0000.0120.0150.0000.0150.0000.1020.2211.000

Missing values

2024-08-15T18:15:23.949428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-15T18:15:28.478752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-15T18:15:36.552363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CLIENTE_CODIGOTIPO_CLIENTEFECHA_TRANSACCIONPEPRIESGOCLIENTE_PAISPRODUCTO_CODIGOCUENTATIPO_CUENTAESTADO_CUENTAPERFIL_WIRES_IN_MONTOPERFIL_WIRES_IN_FRECUENCIAPERFIL_WIRES_OUT_MONTOPERFIL_WIRES_OUT_FRECUENCIAFECHA_TRANSACCION:1TIPO_TRANSACCIONMONTOPAIS_ORIGEN_TRANSACCIONPAIS_DESTINO_TRANSACCION
0C0NATURAL2012-11-14NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022012-11-14 00:00:00Wires Out12000.0PACO
1C0NATURAL2012-11-27NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022012-11-27 00:00:00Wires Out2900.0PAUS
2C0NATURAL2012-11-27NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022012-11-27 00:00:00Wires Out5000.0PACO
3C0NATURAL2012-12-11NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022012-12-11 00:00:00Wires Out6000.0PACO
4C0NATURAL2012-12-26NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022012-12-26 00:00:00Wires Out1100.0PAUS
5C0NATURAL2012-12-26NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022012-12-26 00:00:00Wires Out2500.0PACO
6C0NATURAL2012-12-27NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022012-12-27 00:00:00Wires Out2500.0PACO
7C0NATURAL2013-01-04NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022013-01-04 00:00:00Wires Out2850.0PAUS
8C0NATURAL2013-01-04NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022013-01-04 00:00:00Wires Out15000.0PACO
9C0NATURAL2013-01-08NoneMEDIOCOC0S49343CUENTACANCELADO12000.018000.022013-01-08 00:00:00Wires Out2850.0PAUS
CLIENTE_CODIGOTIPO_CLIENTEFECHA_TRANSACCIONPEPRIESGOCLIENTE_PAISPRODUCTO_CODIGOCUENTATIPO_CUENTAESTADO_CUENTAPERFIL_WIRES_IN_MONTOPERFIL_WIRES_IN_FRECUENCIAPERFIL_WIRES_OUT_MONTOPERFIL_WIRES_OUT_FRECUENCIAFECHA_TRANSACCION:1TIPO_TRANSACCIONMONTOPAIS_ORIGEN_TRANSACCIONPAIS_DESTINO_TRANSACCION
3081115C32293JURIDICO2024-07-24NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-24 00:00:00Wires Out1500.0PADO
3081116C32293JURIDICO2024-07-26NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-26 00:00:00Wires Out1239.0PADO
3081117C32293JURIDICO2024-07-26NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-26 00:00:00Wires Out1534.0PADO
3081118C32293JURIDICO2024-07-26NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-26 00:00:00Wires Out1800.0PADO
3081119C32293JURIDICO2024-07-29NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-29 00:00:00Wires Out2045.0PADO
3081120C32293JURIDICO2024-07-30NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-30 00:00:00Wires Out1768.0PADO
3081121C32293JURIDICO2024-07-31NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-31 00:00:00Wires Out4210.0PADO
3081122C32293JURIDICO2024-07-31NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-31 00:00:00Wires Out5000.0PACO
3081123C32293JURIDICO2024-07-31NoneNoneCOC32293S81718CUENTAACTIVO287488.145270304.5352024-07-31 00:00:00Wires Out15000.0PADO
3081124C32294JURIDICO2024-07-23NoneNoneCOC32294S81767CUENTAACTIVO0.0000.0002024-07-23 00:00:00Wires Out20000.0PAUS

Duplicate rows

Most frequently occurring

CLIENTE_CODIGOCLIENTE_PAISCUENTAESTADO_CUENTAFECHA_TRANSACCIONFECHA_TRANSACCION:1MONTOPAIS_DESTINO_TRANSACCIONPAIS_ORIGEN_TRANSACCIONPEPPERFIL_WIRES_IN_FRECUENCIAPERFIL_WIRES_IN_MONTOPERFIL_WIRES_OUT_FRECUENCIAPERFIL_WIRES_OUT_MONTOPRODUCTO_CODIGORIESGOTIPO_CLIENTETIPO_CUENTATIPO_TRANSACCION# duplicates
22331C6611COS40842CANCELADO2015-04-092015-04-09 00:00:0015000.0NO57500.057500.0C6611MEDIONATURALCUENTAWires Out29
13154C20072COS84000ACTIVO2022-11-302022-11-30 00:00:007900.0USPASI305000000.02005000000.0C20072ALTOJURIDICOCUENTAWires Out25
13173C20072COS84000ACTIVO2022-12-142022-12-14 00:00:003950.0USPASI305000000.02005000000.0C20072ALTOJURIDICOCUENTAWires Out25
12668C20072COS84000ACTIVO2020-02-252020-02-25 00:00:007900.0USPASI305000000.02005000000.0C20072ALTOJURIDICOCUENTAWires Out24
12700C20072COS84000ACTIVO2020-04-202020-04-20 00:00:007900.0USPASI305000000.02005000000.0C20072ALTOJURIDICOCUENTAWires Out24
13007C20072COS84000ACTIVO2022-03-252022-03-25 00:00:007900.0USPASI305000000.02005000000.0C20072ALTOJURIDICOCUENTAWires Out24
13135C20072COS84000ACTIVO2022-10-282022-10-28 00:00:007900.0USPASI305000000.02005000000.0C20072ALTOJURIDICOCUENTAWires Out24
24373C7400COS40707ACTIVO2007-09-132007-09-13 00:00:001250.0631NO99999999999999999.099999999999999999.0C7400MEDIOJURIDICOCUENTAWires Out24
12714C20072COS84000ACTIVO2020-05-262020-05-26 00:00:007900.0USPASI305000000.02005000000.0C20072ALTOJURIDICOCUENTAWires Out23
12727C20072COS84000ACTIVO2020-06-232020-06-23 00:00:007900.0USPASI305000000.02005000000.0C20072ALTOJURIDICOCUENTAWires Out23