Overview

Dataset statistics

Number of variables23
Number of observations67463
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 MiB
Average record size in memory184.0 B

Variable types

Numeric19
Categorical4

Alerts

Loan Status is highly imbalanced (55.5%)Imbalance
Delinquency - two years has 52054 (77.2%) zerosZeros
Inquires - six months has 60486 (89.7%) zerosZeros

Reproduction

Analysis started2023-01-07 18:32:27.019462
Analysis finished2023-01-07 18:34:03.381527
Duration1 minute and 36.36 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Loan Amount
Real number (ℝ)

Distinct27525
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16848.903
Minimum1014
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:03.539261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1014
5-th percentile4485.1
Q110012
median16073
Q322106
95-th percentile31741
Maximum35000
Range33986
Interquartile range (IQR)12094

Descriptive statistics

Standard deviation8367.8657
Coefficient of variation (CV)0.49664158
Kurtosis-0.79813668
Mean16848.903
Median Absolute Deviation (MAD)6048
Skewness0.28808301
Sum1.1366775 × 109
Variance70021177
MonotonicityNot monotonic
2023-01-08T00:04:03.845359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15932 13
 
< 0.1%
14424 12
 
< 0.1%
15800 11
 
< 0.1%
15639 11
 
< 0.1%
15118 11
 
< 0.1%
14556 11
 
< 0.1%
14689 11
 
< 0.1%
15962 11
 
< 0.1%
15348 11
 
< 0.1%
15811 10
 
< 0.1%
Other values (27515) 67351
99.8%
ValueCountFrequency (%)
1014 1
< 0.1%
1020 1
< 0.1%
1024 1
< 0.1%
1025 1
< 0.1%
1030 1
< 0.1%
1031 1
< 0.1%
1036 1
< 0.1%
1038 1
< 0.1%
1045 1
< 0.1%
1046 1
< 0.1%
ValueCountFrequency (%)
35000 1
< 0.1%
34999 1
< 0.1%
34997 1
< 0.1%
34996 1
< 0.1%
34995 1
< 0.1%
34993 1
< 0.1%
34991 1
< 0.1%
34990 1
< 0.1%
34988 2
< 0.1%
34987 1
< 0.1%

Funded Amount
Real number (ℝ)

Distinct24548
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15770.599
Minimum1014
Maximum34999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:04.123567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1014
5-th percentile5894.3
Q19266.5
median13042
Q321793
95-th percentile32112.8
Maximum34999
Range33985
Interquartile range (IQR)12526.5

Descriptive statistics

Standard deviation8150.9927
Coefficient of variation (CV)0.51684737
Kurtosis-0.61713242
Mean15770.599
Median Absolute Deviation (MAD)5097
Skewness0.67263298
Sum1.0639319 × 109
Variance66438681
MonotonicityNot monotonic
2023-01-08T00:04:04.360890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10835 16
 
< 0.1%
11034 15
 
< 0.1%
11451 14
 
< 0.1%
10728 14
 
< 0.1%
7691 14
 
< 0.1%
11187 14
 
< 0.1%
8860 13
 
< 0.1%
11080 13
 
< 0.1%
8795 13
 
< 0.1%
10946 13
 
< 0.1%
Other values (24538) 67324
99.8%
ValueCountFrequency (%)
1014 1
< 0.1%
1032 1
< 0.1%
1080 1
< 0.1%
1087 1
< 0.1%
1098 1
< 0.1%
1153 1
< 0.1%
1154 1
< 0.1%
1163 1
< 0.1%
1179 1
< 0.1%
1236 1
< 0.1%
ValueCountFrequency (%)
34999 2
< 0.1%
34998 2
< 0.1%
34995 1
< 0.1%
34994 1
< 0.1%
34993 1
< 0.1%
34988 1
< 0.1%
34986 2
< 0.1%
34983 1
< 0.1%
34982 2
< 0.1%
34977 1
< 0.1%

Funded Amount Investor
Real number (ℝ)

Distinct67441
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14621.799
Minimum1114.5902
Maximum34999.746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:04.648494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1114.5902
5-th percentile6327.9798
Q19831.685
median12793.682
Q317807.594
95-th percentile28884.736
Maximum34999.746
Range33885.156
Interquartile range (IQR)7975.9091

Descriptive statistics

Standard deviation6785.3452
Coefficient of variation (CV)0.46405678
Kurtosis0.46186792
Mean14621.799
Median Absolute Deviation (MAD)3564.6612
Skewness0.99013878
Sum9.8643045 × 108
Variance46040909
MonotonicityNot monotonic
2023-01-08T00:04:04.917178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12099.7183 2
 
< 0.1%
7890.447955 2
 
< 0.1%
13910.43024 2
 
< 0.1%
8879.914835 2
 
< 0.1%
12367.56806 2
 
< 0.1%
31818.45799 2
 
< 0.1%
10417.21663 2
 
< 0.1%
14861.31507 2
 
< 0.1%
14238.25035 2
 
< 0.1%
10936.53653 2
 
< 0.1%
Other values (67431) 67443
> 99.9%
ValueCountFrequency (%)
1114.590204 1
< 0.1%
1127.754818 1
< 0.1%
1129.708853 1
< 0.1%
1242.527961 1
< 0.1%
1246.547591 1
< 0.1%
1250.787941 1
< 0.1%
1372.686804 1
< 0.1%
1441.583282 1
< 0.1%
1525.567016 1
< 0.1%
1537.528946 1
< 0.1%
ValueCountFrequency (%)
34999.74643 1
< 0.1%
34999.43383 1
< 0.1%
34997.89175 1
< 0.1%
34996.88747 1
< 0.1%
34995.26246 1
< 0.1%
34993.60145 1
< 0.1%
34993.49979 1
< 0.1%
34990.5952 1
< 0.1%
34988.98401 1
< 0.1%
34987.513 1
< 0.1%

Term
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
59
43780 
58
22226 
36
 
1457

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters134926
Distinct characters5
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 row59
2nd row59
3rd row59
4th row59
5th row59

Common Values

ValueCountFrequency (%)
59 43780
64.9%
58 22226
32.9%
36 1457
 
2.2%

Length

2023-01-08T00:04:05.193897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-08T00:04:05.507939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
59 43780
64.9%
58 22226
32.9%
36 1457
 
2.2%

Most occurring characters

ValueCountFrequency (%)
5 66006
48.9%
9 43780
32.4%
8 22226
 
16.5%
3 1457
 
1.1%
6 1457
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134926
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 66006
48.9%
9 43780
32.4%
8 22226
 
16.5%
3 1457
 
1.1%
6 1457
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 134926
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 66006
48.9%
9 43780
32.4%
8 22226
 
16.5%
3 1457
 
1.1%
6 1457
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 66006
48.9%
9 43780
32.4%
8 22226
 
16.5%
3 1457
 
1.1%
6 1457
 
1.1%

Interest Rate
Real number (ℝ)

Distinct67448
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.846258
Minimum5.3200058
Maximum27.182348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:05.727770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.3200058
5-th percentile6.1348234
Q19.2971472
median11.377696
Q314.193533
95-th percentile18.600114
Maximum27.182348
Range21.862342
Interquartile range (IQR)4.8963859

Descriptive statistics

Standard deviation3.7186287
Coefficient of variation (CV)0.31390746
Kurtosis0.1490136
Mean11.846258
Median Absolute Deviation (MAD)2.3790711
Skewness0.56338284
Sum799184.1
Variance13.8282
MonotonicityNot monotonic
2023-01-08T00:04:05.988641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.191126068 2
 
< 0.1%
8.637499241 2
 
< 0.1%
9.996610601 2
 
< 0.1%
9.53034331 2
 
< 0.1%
11.97836961 2
 
< 0.1%
9.28661329 2
 
< 0.1%
9.031164731 2
 
< 0.1%
11.2653123 2
 
< 0.1%
17.97529763 2
 
< 0.1%
15.78960848 2
 
< 0.1%
Other values (67438) 67443
> 99.9%
ValueCountFrequency (%)
5.320005799 1
< 0.1%
5.320159165 1
< 0.1%
5.320433439 1
< 0.1%
5.320547017 1
< 0.1%
5.321130759 1
< 0.1%
5.321256189 1
< 0.1%
5.322212834 1
< 0.1%
5.322458098 1
< 0.1%
5.322651425 1
< 0.1%
5.322937103 1
< 0.1%
ValueCountFrequency (%)
27.18234758 1
< 0.1%
27.07000405 1
< 0.1%
27.01820329 1
< 0.1%
26.9329474 1
< 0.1%
26.92044891 1
< 0.1%
26.83306079 1
< 0.1%
26.54588757 1
< 0.1%
26.51588192 1
< 0.1%
26.32641724 1
< 0.1%
26.31597117 1
< 0.1%

Home Ownership
Real number (ℝ)

Distinct67454
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80541.503
Minimum14573.537
Maximum406561.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:06.298395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14573.537
5-th percentile33448.726
Q151689.843
median69335.833
Q394623.323
95-th percentile168296.72
Maximum406561.54
Range391988
Interquartile range (IQR)42933.479

Descriptive statistics

Standard deviation45029.12
Coefficient of variation (CV)0.55907972
Kurtosis7.0277345
Mean80541.503
Median Absolute Deviation (MAD)20118.159
Skewness2.1304881
Sum5.4335714 × 109
Variance2.0276217 × 109
MonotonicityNot monotonic
2023-01-08T00:04:06.613177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39753.81982 2
 
< 0.1%
71159.7124 2
 
< 0.1%
61831.12988 2
 
< 0.1%
27139.67231 2
 
< 0.1%
35858.04083 2
 
< 0.1%
91745.81071 2
 
< 0.1%
37623.24185 2
 
< 0.1%
28714.13609 2
 
< 0.1%
58867.57595 2
 
< 0.1%
30224.43666 1
 
< 0.1%
Other values (67444) 67444
> 99.9%
ValueCountFrequency (%)
14573.53717 1
< 0.1%
14652.37968 1
< 0.1%
14678.63863 1
< 0.1%
14788.61394 1
< 0.1%
14836.55226 1
< 0.1%
14859.64954 1
< 0.1%
14901.41773 1
< 0.1%
14938.0786 1
< 0.1%
14996.99281 1
< 0.1%
15013.52595 1
< 0.1%
ValueCountFrequency (%)
406561.5364 1
< 0.1%
405697.0616 1
< 0.1%
404550.444 1
< 0.1%
401352.3764 1
< 0.1%
400877.5635 1
< 0.1%
400676.3457 1
< 0.1%
400348.8196 1
< 0.1%
399925.7864 1
< 0.1%
399103.7444 1
< 0.1%
398416.3107 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
2
33036 
1
18078 
0
16349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters3
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 row0
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 33036
49.0%
1 18078
26.8%
0 16349
24.2%

Length

2023-01-08T00:04:06.922196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-08T00:04:07.170492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2 33036
49.0%
1 18078
26.8%
0 16349
24.2%

Most occurring characters

ValueCountFrequency (%)
2 33036
49.0%
1 18078
26.8%
0 16349
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67463
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 33036
49.0%
1 18078
26.8%
0 16349
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 67463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 33036
49.0%
1 18078
26.8%
0 16349
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 33036
49.0%
1 18078
26.8%
0 16349
24.2%

Debit to Income
Real number (ℝ)

Distinct67454
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.299241
Minimum0.67529909
Maximum39.629862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:07.367808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.67529909
5-th percentile10.205682
Q116.756416
median22.656658
Q330.0484
95-th percentile37.396621
Maximum39.629862
Range38.954563
Interquartile range (IQR)13.291983

Descriptive statistics

Standard deviation8.4518237
Coefficient of variation (CV)0.36275104
Kurtosis-0.90502119
Mean23.299241
Median Absolute Deviation (MAD)6.5435829
Skewness0.080966764
Sum1571836.7
Variance71.433324
MonotonicityNot monotonic
2023-01-08T00:04:07.639511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.97736061 2
 
< 0.1%
22.36852703 2
 
< 0.1%
18.79251904 2
 
< 0.1%
35.46709898 2
 
< 0.1%
24.50545264 2
 
< 0.1%
17.62506899 2
 
< 0.1%
27.34419347 2
 
< 0.1%
38.97468269 2
 
< 0.1%
24.41063595 2
 
< 0.1%
37.12728123 1
 
< 0.1%
Other values (67444) 67444
> 99.9%
ValueCountFrequency (%)
0.675299086 1
< 0.1%
0.763630198 1
< 0.1%
0.961457001 1
< 0.1%
1.117458713 1
< 0.1%
1.237228585 1
< 0.1%
1.300557774 1
< 0.1%
1.329297599 1
< 0.1%
1.372873946 1
< 0.1%
1.391419093 1
< 0.1%
1.397374381 1
< 0.1%
ValueCountFrequency (%)
39.62986189 1
< 0.1%
39.62964383 1
< 0.1%
39.6278639 1
< 0.1%
39.62757603 1
< 0.1%
39.62741588 1
< 0.1%
39.62732201 1
< 0.1%
39.62731 1
< 0.1%
39.62664966 1
< 0.1%
39.62590781 1
< 0.1%
39.62567144 1
< 0.1%

Delinquency - two years
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32712746
Minimum0
Maximum8
Zeros52054
Zeros (%)77.2%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:07.896687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80088838
Coefficient of variation (CV)2.4482456
Kurtosis30.676297
Mean0.32712746
Median Absolute Deviation (MAD)0
Skewness4.6350213
Sum22069
Variance0.64142219
MonotonicityNot monotonic
2023-01-08T00:04:08.155048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 52054
77.2%
1 11736
 
17.4%
2 2651
 
3.9%
3 445
 
0.7%
7 252
 
0.4%
6 191
 
0.3%
5 74
 
0.1%
8 44
 
0.1%
4 16
 
< 0.1%
ValueCountFrequency (%)
0 52054
77.2%
1 11736
 
17.4%
2 2651
 
3.9%
3 445
 
0.7%
4 16
 
< 0.1%
5 74
 
0.1%
6 191
 
0.3%
7 252
 
0.4%
8 44
 
0.1%
ValueCountFrequency (%)
8 44
 
0.1%
7 252
 
0.4%
6 191
 
0.3%
5 74
 
0.1%
4 16
 
< 0.1%
3 445
 
0.7%
2 2651
 
3.9%
1 11736
 
17.4%
0 52054
77.2%

Inquires - six months
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14575397
Minimum0
Maximum5
Zeros60486
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:08.385932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47329129
Coefficient of variation (CV)3.2471931
Kurtosis15.143928
Mean0.14575397
Median Absolute Deviation (MAD)0
Skewness3.711972
Sum9833
Variance0.22400464
MonotonicityNot monotonic
2023-01-08T00:04:08.609566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 60486
89.7%
1 4558
 
6.8%
2 2042
 
3.0%
3 320
 
0.5%
4 54
 
0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
0 60486
89.7%
1 4558
 
6.8%
2 2042
 
3.0%
3 320
 
0.5%
4 54
 
0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 54
 
0.1%
3 320
 
0.5%
2 2042
 
3.0%
1 4558
 
6.8%
0 60486
89.7%

Open Account
Real number (ℝ)

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.266561
Minimum2
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:08.880482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q110
median13
Q316
95-th percentile29
Maximum37
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.2250604
Coefficient of variation (CV)0.43633925
Kurtosis1.821184
Mean14.266561
Median Absolute Deviation (MAD)3
Skewness1.4651073
Sum962465
Variance38.751378
MonotonicityNot monotonic
2023-01-08T00:04:09.129767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
12 8480
12.6%
13 7907
11.7%
11 7323
10.9%
14 6056
 
9.0%
10 5804
 
8.6%
9 4658
 
6.9%
15 3350
 
5.0%
8 3141
 
4.7%
16 2089
 
3.1%
7 1895
 
2.8%
Other values (26) 16760
24.8%
ValueCountFrequency (%)
2 6
 
< 0.1%
3 44
 
0.1%
4 197
 
0.3%
5 472
 
0.7%
6 1016
 
1.5%
7 1895
 
2.8%
8 3141
4.7%
9 4658
6.9%
10 5804
8.6%
11 7323
10.9%
ValueCountFrequency (%)
37 94
 
0.1%
36 152
 
0.2%
35 231
 
0.3%
34 346
0.5%
33 499
0.7%
32 513
0.8%
31 564
0.8%
30 631
0.9%
29 590
0.9%
28 668
1.0%

Revolving Balance
Real number (ℝ)

Distinct20582
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7699.3424
Minimum0
Maximum116933
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:09.417442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile528
Q12557
median5516
Q310184.5
95-th percentile22447.5
Maximum116933
Range116933
Interquartile range (IQR)7627.5

Descriptive statistics

Standard deviation7836.1482
Coefficient of variation (CV)1.0177685
Kurtosis16.903173
Mean7699.3424
Median Absolute Deviation (MAD)3469
Skewness2.9511352
Sum5.1942074 × 108
Variance61405218
MonotonicityNot monotonic
2023-01-08T00:04:09.741943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1394 18
 
< 0.1%
3997 16
 
< 0.1%
311 16
 
< 0.1%
1202 15
 
< 0.1%
869 15
 
< 0.1%
360 15
 
< 0.1%
829 15
 
< 0.1%
1252 15
 
< 0.1%
574 14
 
< 0.1%
1428 14
 
< 0.1%
Other values (20572) 67310
99.8%
ValueCountFrequency (%)
0 7
< 0.1%
1 11
< 0.1%
2 6
< 0.1%
3 7
< 0.1%
4 10
< 0.1%
5 7
< 0.1%
6 6
< 0.1%
7 7
< 0.1%
8 6
< 0.1%
9 8
< 0.1%
ValueCountFrequency (%)
116933 1
< 0.1%
114621 1
< 0.1%
111223 1
< 0.1%
108050 1
< 0.1%
105820 1
< 0.1%
104159 1
< 0.1%
104133 1
< 0.1%
103901 1
< 0.1%
99484 1
< 0.1%
97895 1
< 0.1%

Revolving Utilities
Real number (ℝ)

Distinct67458
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.889443
Minimum0.00517236
Maximum100.88005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:10.049245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.00517236
5-th percentile11.714745
Q138.658825
median54.082334
Q369.177117
95-th percentile88.513405
Maximum100.88005
Range100.87488
Interquartile range (IQR)30.518292

Descriptive statistics

Standard deviation22.53945
Coefficient of variation (CV)0.42616162
Kurtosis-0.54489853
Mean52.889443
Median Absolute Deviation (MAD)15.239263
Skewness-0.23724537
Sum3568080.5
Variance508.02682
MonotonicityNot monotonic
2023-01-08T00:04:10.323403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.61185859 2
 
< 0.1%
40.55837053 2
 
< 0.1%
91.31748448 2
 
< 0.1%
9.409246945 2
 
< 0.1%
66.65954626 2
 
< 0.1%
75.32411353 1
 
< 0.1%
70.09958645 1
 
< 0.1%
21.77597781 1
 
< 0.1%
92.26758399 1
 
< 0.1%
51.29358823 1
 
< 0.1%
Other values (67448) 67448
> 99.9%
ValueCountFrequency (%)
0.00517236 1
< 0.1%
0.021283828 1
< 0.1%
0.02999706 1
< 0.1%
0.035816294 1
< 0.1%
0.044888253 1
< 0.1%
0.051037999 1
< 0.1%
0.051458193 1
< 0.1%
0.054950741 1
< 0.1%
0.058213542 1
< 0.1%
0.05911439 1
< 0.1%
ValueCountFrequency (%)
100.8800498 1
< 0.1%
100.8668139 1
< 0.1%
100.8586132 1
< 0.1%
100.8553354 1
< 0.1%
100.8549743 1
< 0.1%
100.8356926 1
< 0.1%
100.8355207 1
< 0.1%
100.8265117 1
< 0.1%
100.8231983 1
< 0.1%
100.798556 1
< 0.1%

Total Accounts
Real number (ℝ)

Distinct69
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.627929
Minimum4
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:10.584854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q113
median18
Q323
95-th percentile33
Maximum72
Range68
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3192464
Coefficient of variation (CV)0.44660071
Kurtosis1.3267531
Mean18.627929
Median Absolute Deviation (MAD)5
Skewness0.73412152
Sum1256696
Variance69.209861
MonotonicityNot monotonic
2023-01-08T00:04:10.839204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 3770
 
5.6%
17 3727
 
5.5%
19 3700
 
5.5%
20 3596
 
5.3%
16 3404
 
5.0%
21 3292
 
4.9%
22 3001
 
4.4%
15 2993
 
4.4%
23 2630
 
3.9%
14 2553
 
3.8%
Other values (59) 34797
51.6%
ValueCountFrequency (%)
4 1160
1.7%
5 1329
2.0%
6 1415
2.1%
7 1768
2.6%
8 1958
2.9%
9 2083
3.1%
10 2219
3.3%
11 2155
3.2%
12 2125
3.1%
13 2238
3.3%
ValueCountFrequency (%)
72 2
 
< 0.1%
71 2
 
< 0.1%
70 1
 
< 0.1%
69 1
 
< 0.1%
68 3
 
< 0.1%
67 2
 
< 0.1%
66 1
 
< 0.1%
65 8
< 0.1%
64 3
 
< 0.1%
63 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
0
36299 
1
31164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters2
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 row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36299
53.8%
1 31164
46.2%

Length

2023-01-08T00:04:11.144882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-08T00:04:11.394199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 36299
53.8%
1 31164
46.2%

Most occurring characters

ValueCountFrequency (%)
0 36299
53.8%
1 31164
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67463
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36299
53.8%
1 31164
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common 67463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36299
53.8%
1 31164
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36299
53.8%
1 31164
46.2%

Total Received Interest
Real number (ℝ)

Distinct67451
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2068.9925
Minimum4.7367463
Maximum14301.368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:11.590199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.7367463
5-th percentile160.09622
Q1570.90381
median1330.8428
Q32656.9568
95-th percentile6921.6826
Maximum14301.368
Range14296.632
Interquartile range (IQR)2086.053

Descriptive statistics

Standard deviation2221.9187
Coefficient of variation (CV)1.0739134
Kurtosis5.1874918
Mean2068.9925
Median Absolute Deviation (MAD)906.32473
Skewness2.1352431
Sum1.3958044 × 108
Variance4936922.9
MonotonicityNot monotonic
2023-01-08T00:04:11.831396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437.9250212 2
 
< 0.1%
2476.701276 2
 
< 0.1%
941.274347 2
 
< 0.1%
9061.050032 2
 
< 0.1%
607.2602052 2
 
< 0.1%
525.9471228 2
 
< 0.1%
3174.594809 2
 
< 0.1%
672.3328503 2
 
< 0.1%
453.6216698 2
 
< 0.1%
658.0427657 2
 
< 0.1%
Other values (67441) 67443
> 99.9%
ValueCountFrequency (%)
4.736746327 1
< 0.1%
4.740085405 1
< 0.1%
5.029317935 1
< 0.1%
5.037685745 1
< 0.1%
5.121524759 1
< 0.1%
5.167160376 1
< 0.1%
5.307849325 1
< 0.1%
5.397308646 1
< 0.1%
5.486680943 1
< 0.1%
5.521208352 1
< 0.1%
ValueCountFrequency (%)
14301.36831 1
< 0.1%
14290.59148 1
< 0.1%
14281.46799 1
< 0.1%
14258.3024 1
< 0.1%
14256.74704 1
< 0.1%
14255.15602 1
< 0.1%
14236.69012 1
< 0.1%
14227.81424 1
< 0.1%
14195.13756 1
< 0.1%
14172.13655 1
< 0.1%

Total Received Late Fee
Real number (ℝ)

Distinct67380
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1439686
Minimum3.06 × 10-6
Maximum42.618882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:12.092202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.06 × 10-6
5-th percentile0.0042217567
Q10.021113871
median0.043397546
Q30.071883977
95-th percentile0.14803093
Maximum42.618882
Range42.618879
Interquartile range (IQR)0.050770106

Descriptive statistics

Standard deviation5.2443651
Coefficient of variation (CV)4.584361
Kurtosis25.992569
Mean1.1439686
Median Absolute Deviation (MAD)0.024643616
Skewness5.0845106
Sum77175.556
Variance27.503365
MonotonicityNot monotonic
2023-01-08T00:04:12.384792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037407587 2
 
< 0.1%
0.08816139 2
 
< 0.1%
0.058945258 2
 
< 0.1%
0.08077534 2
 
< 0.1%
0.030372569 2
 
< 0.1%
0.111829744 2
 
< 0.1%
9.98 × 10-52
 
< 0.1%
0.036133219 2
 
< 0.1%
0.083389349 2
 
< 0.1%
0.050149162 2
 
< 0.1%
Other values (67370) 67443
> 99.9%
ValueCountFrequency (%)
3.06 × 10-61
< 0.1%
3.84 × 10-61
< 0.1%
5.7 × 10-61
< 0.1%
1.27 × 10-51
< 0.1%
1.79 × 10-51
< 0.1%
1.9 × 10-51
< 0.1%
2 × 10-51
< 0.1%
2.07 × 10-51
< 0.1%
2.3 × 10-51
< 0.1%
2.38 × 10-52
< 0.1%
ValueCountFrequency (%)
42.6188823 1
< 0.1%
42.5951275 1
< 0.1%
42.58806301 1
< 0.1%
42.44903972 1
< 0.1%
42.41656912 1
< 0.1%
42.41545401 1
< 0.1%
42.38591859 1
< 0.1%
42.33250232 1
< 0.1%
42.30642544 1
< 0.1%
42.29851714 1
< 0.1%

Recoveries
Real number (ℝ)

Distinct67387
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.691578
Minimum3.56 × 10-5
Maximum4354.4674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:12.676369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.56 × 10-5
5-th percentile0.33131175
Q11.6298176
median3.3445241
Q35.4537268
95-th percentile9.1838589
Maximum4354.4674
Range4354.4674
Interquartile range (IQR)3.8239093

Descriptive statistics

Standard deviation357.02635
Coefficient of variation (CV)5.9811846
Kurtosis58.183685
Mean59.691578
Median Absolute Deviation (MAD)1.8681336
Skewness7.3717873
Sum4026972.9
Variance127467.81
MonotonicityNot monotonic
2023-01-08T00:04:12.954238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.816960301 2
 
< 0.1%
4.764209111 2
 
< 0.1%
5.151216155 2
 
< 0.1%
6.983539658 2
 
< 0.1%
3.225564581 2
 
< 0.1%
3.772796302 2
 
< 0.1%
3.950778781 2
 
< 0.1%
2.641522989 2
 
< 0.1%
2.753067871 2
 
< 0.1%
1.013758103 2
 
< 0.1%
Other values (67377) 67443
> 99.9%
ValueCountFrequency (%)
3.56 × 10-51
< 0.1%
9.02 × 10-51
< 0.1%
0.000220811 1
< 0.1%
0.000350407 1
< 0.1%
0.000372444 1
< 0.1%
0.00042457 1
< 0.1%
0.000595908 1
< 0.1%
0.000696606 1
< 0.1%
0.000865389 1
< 0.1%
0.000882372 1
< 0.1%
ValueCountFrequency (%)
4354.467419 1
< 0.1%
4339.261318 1
< 0.1%
4330.782063 1
< 0.1%
4325.079801 1
< 0.1%
4313.548899 1
< 0.1%
4299.375307 1
< 0.1%
4262.898182 1
< 0.1%
4254.856665 1
< 0.1%
4241.493689 1
< 0.1%
4220.932555 1
< 0.1%

Collection Recovery Fee
Real number (ℝ)

Distinct67313
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1251409
Minimum3.62 × 10-5
Maximum166.833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:13.251679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.62 × 10-5
5-th percentile0.13024329
Q10.47625936
median0.78014063
Q31.0705655
95-th percentile1.426481
Maximum166.833
Range166.83296
Interquartile range (IQR)0.59430618

Descriptive statistics

Standard deviation3.4898845
Coefficient of variation (CV)3.101731
Kurtosis173.3263
Mean1.1251409
Median Absolute Deviation (MAD)0.29662508
Skewness11.102131
Sum75905.383
Variance12.179294
MonotonicityNot monotonic
2023-01-08T00:04:13.512737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.125373051 3
 
< 0.1%
0.53373764 2
 
< 0.1%
0.792153159 2
 
< 0.1%
1.139062259 2
 
< 0.1%
0.345641963 2
 
< 0.1%
1.17911419 2
 
< 0.1%
0.577143464 2
 
< 0.1%
0.564290849 2
 
< 0.1%
1.081363446 2
 
< 0.1%
1.196215319 2
 
< 0.1%
Other values (67303) 67442
> 99.9%
ValueCountFrequency (%)
3.62 × 10-51
< 0.1%
4.5 × 10-51
< 0.1%
7.34 × 10-51
< 0.1%
8.09 × 10-51
< 0.1%
0.000144092 1
< 0.1%
0.000184412 1
< 0.1%
0.000236446 1
< 0.1%
0.000261 1
< 0.1%
0.000359239 1
< 0.1%
0.000390586 1
< 0.1%
ValueCountFrequency (%)
166.833 1
< 0.1%
54.22278838 1
< 0.1%
53.46508418 1
< 0.1%
51.42711665 1
< 0.1%
51.04841185 1
< 0.1%
50.84705334 1
< 0.1%
50.66485507 1
< 0.1%
49.94395918 1
< 0.1%
49.85959833 1
< 0.1%
49.52562248 1
< 0.1%

Total Collection Amount
Real number (ℝ)

Distinct2193
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.46799
Minimum1
Maximum16421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:13.820411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q124
median36
Q346
95-th percentile496
Maximum16421
Range16420
Interquartile range (IQR)22

Descriptive statistics

Standard deviation744.38223
Coefficient of variation (CV)5.0822179
Kurtosis207.01677
Mean146.46799
Median Absolute Deviation (MAD)11
Skewness12.910972
Sum9881170
Variance554104.91
MonotonicityNot monotonic
2023-01-08T00:04:14.085709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 1665
 
2.5%
37 1656
 
2.5%
36 1637
 
2.4%
41 1623
 
2.4%
40 1622
 
2.4%
35 1616
 
2.4%
34 1597
 
2.4%
33 1590
 
2.4%
42 1566
 
2.3%
32 1565
 
2.3%
Other values (2183) 51326
76.1%
ValueCountFrequency (%)
1 310
0.5%
2 320
0.5%
3 344
0.5%
4 390
0.6%
5 413
0.6%
6 452
0.7%
7 477
0.7%
8 533
0.8%
9 563
0.8%
10 618
0.9%
ValueCountFrequency (%)
16421 1
< 0.1%
16385 1
< 0.1%
16086 1
< 0.1%
16013 1
< 0.1%
15956 1
< 0.1%
15916 1
< 0.1%
15895 1
< 0.1%
15663 1
< 0.1%
15460 1
< 0.1%
15459 1
< 0.1%

Total Current Balance
Real number (ℝ)

Distinct60901
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159573.93
Minimum617
Maximum1177412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:14.369857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum617
5-th percentile18215.3
Q150379
median118369
Q3228375
95-th percentile432485.9
Maximum1177412
Range1176795
Interquartile range (IQR)177996

Descriptive statistics

Standard deviation139033.25
Coefficient of variation (CV)0.87127792
Kurtosis3.1257696
Mean159573.93
Median Absolute Deviation (MAD)78486
Skewness1.5115779
Sum1.0765336 × 1010
Variance1.9330243 × 1010
MonotonicityNot monotonic
2023-01-08T00:04:14.630000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51737 5
 
< 0.1%
36268 5
 
< 0.1%
69865 4
 
< 0.1%
51723 4
 
< 0.1%
44225 4
 
< 0.1%
82583 4
 
< 0.1%
85459 4
 
< 0.1%
34633 4
 
< 0.1%
40640 4
 
< 0.1%
40288 4
 
< 0.1%
Other values (60891) 67421
99.9%
ValueCountFrequency (%)
617 1
< 0.1%
623 1
< 0.1%
628 1
< 0.1%
630 1
< 0.1%
667 1
< 0.1%
681 1
< 0.1%
691 1
< 0.1%
707 1
< 0.1%
710 1
< 0.1%
798 1
< 0.1%
ValueCountFrequency (%)
1177412 1
< 0.1%
1165601 1
< 0.1%
1157944 1
< 0.1%
1150619 1
< 0.1%
1145991 1
< 0.1%
1140709 1
< 0.1%
1128432 1
< 0.1%
1114351 1
< 0.1%
1091714 1
< 0.1%
1071342 1
< 0.1%
Distinct37708
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23123.006
Minimum1000
Maximum201169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size527.2 KiB
2023-01-08T00:04:14.942807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2723.2
Q18155.5
median16733
Q332146.5
95-th percentile63298.9
Maximum201169
Range200169
Interquartile range (IQR)23991

Descriptive statistics

Standard deviation20916.7
Coefficient of variation (CV)0.90458396
Kurtosis5.9800859
Mean23123.006
Median Absolute Deviation (MAD)10394
Skewness1.9771503
Sum1.5599473 × 109
Variance4.3750834 × 108
MonotonicityNot monotonic
2023-01-08T00:04:15.200782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5310 10
 
< 0.1%
7026 10
 
< 0.1%
6083 9
 
< 0.1%
4754 9
 
< 0.1%
5413 9
 
< 0.1%
5642 9
 
< 0.1%
7058 9
 
< 0.1%
12364 9
 
< 0.1%
4895 8
 
< 0.1%
11614 8
 
< 0.1%
Other values (37698) 67373
99.9%
ValueCountFrequency (%)
1000 2
 
< 0.1%
1001 5
< 0.1%
1003 1
 
< 0.1%
1005 3
< 0.1%
1007 1
 
< 0.1%
1008 5
< 0.1%
1009 1
 
< 0.1%
1010 3
< 0.1%
1011 4
< 0.1%
1013 1
 
< 0.1%
ValueCountFrequency (%)
201169 1
< 0.1%
197112 1
< 0.1%
193312 1
< 0.1%
192276 1
< 0.1%
190060 1
< 0.1%
189087 1
< 0.1%
188063 1
< 0.1%
185719 1
< 0.1%
185594 1
< 0.1%
184987 1
< 0.1%

Loan Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size527.2 KiB
0
61222 
1
6241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67463
Distinct characters2
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 row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Length

2023-01-08T00:04:15.501361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-08T00:04:15.791626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67463
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 67463
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61222
90.7%
1 6241
 
9.3%

Interactions

2023-01-08T00:03:57.370226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:33.496050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:37.950505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:42.819336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:47.442399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:51.890353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:56.692251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:01.312645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:05.867126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:10.847679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:15.396700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:20.030733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:24.714843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:29.456825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:34.183867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:38.672015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:43.127087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:47.825794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:52.655467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:57.607091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:33.752475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:38.217483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:43.080502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:47.700784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:52.177492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:56.925876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:01.544432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:06.101200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:11.131077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:15.621319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:20.261143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:24.950725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:29.694706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:34.411569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:38.896055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:43.348390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:48.083254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:52.893711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:57.850734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:33.972578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:38.513837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:43.324341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:47.930954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:52.430805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:57.171773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:01.794545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:06.343590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:11.394342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:15.895031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:20.498461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:25.210922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:29.950942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:34.638158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:39.120096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:43.624099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:48.343611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:53.116097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:58.101832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:34.197948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:38.807732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:43.548134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:48.155902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:52.665227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:57.409853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:02.032212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:06.592419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:11.614670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:16.143444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:20.779410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:25.481169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:30.193639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:34.901609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:39.383535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:43.860215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:48.611310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:53.374165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:58.401131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:34.444930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:39.064051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:43.763246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:48.369870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:52.938514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:57.681502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:02.245492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:06.862803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:11.827678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:16.367752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:20.986242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:25.739299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:30.441271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:35.106802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:39.601036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:44.120895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:48.912241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:53.617522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:58.707762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:34.690836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:39.338189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:44.023803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:48.603085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:53.206911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:57.921366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:02.481087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:07.152784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:12.058707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:16.594072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:21.252378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:26.003244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:30.682421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:35.392638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:39.833941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:44.358054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:49.211105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:53.868506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:58.946148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:34.927122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:39.590814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:44.312998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:48.845930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:53.441505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:58.178618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:02.710061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:07.437028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:12.315913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:16.827678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:21.491707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:26.255828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:30.908614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:35.609328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:40.089689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:44.618736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:49.470834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:54.154267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:59.218102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:35.155433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:39.820863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:44.585393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:49.096747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:53.693817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:58.431487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:02.971905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:07.709720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:12.560569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:17.114992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:21.754364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:26.513858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:31.164360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:35.881835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:40.319495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:44.880504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:49.697814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:54.406052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:59.484362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:35.382061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:40.066346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:02:44.802802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-08T00:03:42.864632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:47.568853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:52.400482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-08T00:03:57.073984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-08T00:04:15.981555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Loan AmountFunded AmountFunded Amount InvestorInterest RateHome OwnershipDebit to IncomeDelinquency - two yearsInquires - six monthsOpen AccountRevolving BalanceRevolving UtilitiesTotal AccountsTotal Received InterestTotal Received Late FeeRecoveriesCollection Recovery FeeTotal Collection AmountTotal Current BalanceTotal Revolving Credit LimitTermVerification StatusInitial List StatusLoan Status
Loan Amount1.000-0.0010.002-0.0060.0180.009-0.0000.0090.013-0.0040.017-0.002-0.002-0.0010.0090.002-0.022-0.0110.0010.0000.0250.0000.006
Funded Amount-0.0011.0000.0160.004-0.0120.0020.005-0.0010.004-0.0020.0070.0080.0060.009-0.0030.019-0.023-0.0000.0070.0000.0370.0100.000
Funded Amount Investor0.0020.0161.000-0.006-0.001-0.0010.005-0.005-0.008-0.015-0.0030.0020.005-0.0020.004-0.055-0.0120.0040.0030.0370.0280.0290.007
Interest Rate-0.0060.004-0.0061.0000.009-0.0110.0040.015-0.0030.0230.0070.0080.0060.011-0.0340.016-0.009-0.0020.0160.0260.0080.0070.008
Home Ownership0.018-0.012-0.0010.0091.0000.024-0.0060.0060.0090.018-0.0050.023-0.009-0.010-0.033-0.025-0.0250.0120.0090.0610.0130.0230.000
Debit to Income0.0090.002-0.001-0.0110.0241.000-0.0080.0030.002-0.0080.005-0.0050.0070.0120.006-0.0020.042-0.009-0.0090.0050.0100.0260.000
Delinquency - two years-0.0000.0050.0050.004-0.006-0.0081.0000.0110.0060.0050.000-0.005-0.012-0.000-0.001-0.001-0.005-0.0010.0070.0220.0050.0000.008
Inquires - six months0.009-0.001-0.0050.0150.0060.0030.0111.000-0.0010.0020.0050.0090.0110.003-0.0090.012-0.0080.0020.0040.0210.0080.0080.000
Open Account0.0130.004-0.008-0.0030.0090.0020.006-0.0011.0000.0160.0120.0070.015-0.009-0.0040.008-0.025-0.009-0.0000.0350.0260.0630.012
Revolving Balance-0.004-0.002-0.0150.0230.018-0.0080.0050.0020.0161.000-0.0030.003-0.0060.005-0.021-0.012-0.058-0.0090.0260.0220.0140.0200.000
Revolving Utilities0.0170.007-0.0030.007-0.0050.0050.0000.0050.012-0.0031.000-0.0050.0080.0190.0120.001-0.021-0.022-0.0110.0180.0120.0320.004
Total Accounts-0.0020.0080.0020.0080.023-0.005-0.0050.0090.0070.003-0.0051.0000.019-0.006-0.0220.0260.001-0.0030.0310.0070.0150.0250.005
Total Received Interest-0.0020.0060.0050.006-0.0090.007-0.0120.0110.015-0.0060.0080.0191.0000.025-0.0230.0100.0040.0020.0130.0250.0110.0090.008
Total Received Late Fee-0.0010.009-0.0020.011-0.0100.012-0.0000.003-0.0090.0050.019-0.0060.0251.0000.044-0.0320.0090.0300.0160.0150.0110.0070.000
Recoveries0.009-0.0030.004-0.034-0.0330.006-0.001-0.009-0.004-0.0210.012-0.022-0.0230.0441.000-0.031-0.0330.0180.0100.0070.0090.0070.000
Collection Recovery Fee0.0020.019-0.0550.016-0.025-0.002-0.0010.0120.008-0.0120.0010.0260.010-0.032-0.0311.000-0.013-0.0200.0210.0220.0000.0000.000
Total Collection Amount-0.022-0.023-0.012-0.009-0.0250.042-0.005-0.008-0.025-0.058-0.0210.0010.0040.009-0.033-0.0131.0000.029-0.0350.0110.0000.0060.012
Total Current Balance-0.011-0.0000.004-0.0020.012-0.009-0.0010.002-0.009-0.009-0.022-0.0030.0020.0300.018-0.0200.0291.0000.0040.0070.0290.0160.000
Total Revolving Credit Limit0.0010.0070.0030.0160.009-0.0090.0070.004-0.0000.026-0.0110.0310.0130.0160.0100.021-0.0350.0041.0000.0140.0000.0160.006
Term0.0000.0000.0370.0260.0610.0050.0220.0210.0350.0220.0180.0070.0250.0150.0070.0220.0110.0070.0141.0000.0800.0600.024
Verification Status0.0250.0370.0280.0080.0130.0100.0050.0080.0260.0140.0120.0150.0110.0110.0090.0000.0000.0290.0000.0801.0000.0970.000
Initial List Status0.0000.0100.0290.0070.0230.0260.0000.0080.0630.0200.0320.0250.0090.0070.0070.0000.0060.0160.0160.0600.0971.0000.013
Loan Status0.0060.0000.0070.0080.0000.0000.0080.0000.0120.0000.0040.0050.0080.0000.0000.0000.0120.0000.0060.0240.0000.0131.000

Missing values

2023-01-08T00:04:02.382920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-08T00:04:02.962223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Loan AmountFunded AmountFunded Amount InvestorTermInterest RateHome OwnershipVerification StatusDebit to IncomeDelinquency - two yearsInquires - six monthsOpen AccountRevolving BalanceRevolving UtilitiesTotal AccountsInitial List StatusTotal Received InterestTotal Received Late FeeRecoveriesCollection Recovery FeeTotal Collection AmountTotal Current BalanceTotal Revolving Credit LimitLoan Status
0100003223612329.3628605911.135007176346.62670016.28475810132424674.932551702929.6463150.1020552.4982910.7937243131130166190
136091194012191.9969205912.23756339833.92100215.412409001281278.297186131772.7693850.0361812.3772150.97482153182610208850
228276931121603.2245505912.54588491506.69105228.137619001418432.073040200863.32439618.7786604.3162771.0200753489801261550
311170695417877.1558505916.731201108286.57590218.0437301071381967.467951120288.1731960.0441310.1070200.749971409189602140
4168901322613539.9266705915.00830044234.82545217.2098861313154485.250761220129.23955319.3066461294.8187510.368953430126029225790
534631302038635.9316133617.24698698957.4756107.9143333216227751.564476200464.8181240.0885845.0435750.5816884251252274800
6308441977315777.5118305910.731432102391.82430115.08391100111450146.808804370525.7381090.0835283.1679370.553076338842069310680
720744106097645.0148025813.99368861723.52014029.82971500141306723.9366243301350.2452120.0449650.0984480.04758948184909433030
892991123813429.4566105911.17845763205.09072126.24471000654915.9473861704140.1989780.0171060.5302140.216985266812674820
91923289627004.097481585.52041342015.46586210.0485491011136135.0733453012149.6669630.0083382.9122150.8868643571650148710
Loan AmountFunded AmountFunded Amount InvestorTermInterest RateHome OwnershipVerification StatusDebit to IncomeDelinquency - two yearsInquires - six monthsOpen AccountRevolving BalanceRevolving UtilitiesTotal AccountsInitial List StatusTotal Received InterestTotal Received Late FeeRecoveriesCollection Recovery FeeTotal Collection AmountTotal Current BalanceTotal Revolving Credit LimitLoan Status
6745331161160008386.746929596.52464681220.63670234.38774000950551.6133142211057.8260020.0068611.1090810.942539399204397050
674549712258968740.5898415814.72981139889.60578029.475335001126325.1137621912934.5402770.0287370.2556951.08309318311173467240
6745551271695613917.4852205919.38868399748.53668233.6222940010129649.160800181265.4799680.0931461.8051521.0854863943981162190
6745611703197369972.2026965911.43075750548.01172232.637618001477877.360718241380.9073940.0678631.2332400.778051317445397710
67457114401767222965.7629005915.02526076128.78634121.929698008526012.080662702258.0387120.0107220.0610960.32556438859647214680
6745813601684813175.285830599.40885883961.15003128.1051271013411297.7793891901978.9459600.023478564.6148520.86523048181775343011
6745983231104615637.463010599.97210465491.12817217.6942790012973715.6907031403100.8031250.0270952.0154941.403368372269287140
67460158973292112329.4577505919.65094334813.96985110.29577400721951.500090902691.9955320.0282125.6730921.60709317176857423300
6746116567497521353.6846505913.16909596938.8356407.6146240014117268.4818821513659.3342020.0745081.1574540.20760861361339390750
67462153532987514207.4486005916.034631105123.15580116.0521120030876281.6923281611324.2559220.0006711.8564800.36638647196960660600