diff --git "a/dataset/report.html" "b/dataset/report.html" deleted file mode 100644--- "a/dataset/report.html" +++ /dev/null @@ -1,487 +0,0 @@ -Trimmed CB Dataset Profiling Report

Overview

Dataset statistics
Number of variables23
Number of observations330000
Missing cells235343
Missing cells (%)3.1%
Duplicate rows46
Duplicate rows (%)< 0.1%
Total size in memory57.9 MiB
Average record size in memory184.0 B

Variable types
Categorical12
Numeric11

Alerts

BUY_LUX_YN has constant value "0"Constant
Dataset has 46 (< 0.1%) duplicate rowsDuplicates
AGE is highly overall correlated with LIF_STGHigh correlation
FAM_OWN_HOUS_CNT is highly overall correlated with FAM_OWN_LIV_YNHigh correlation
FAM_OWN_LIV_YN is highly overall correlated with FAM_OWN_HOUS_CNT and 1 other fieldsHigh correlation
LIF_STG is highly overall correlated with AGEHigh correlation
OWN_HOUS_CNT is highly overall correlated with OWN_LIV_YNHigh correlation
OWN_LIV_YN is highly overall correlated with OWN_HOUS_CNTHigh correlation
PYE_FAM_CNT is highly overall correlated with FAM_OWN_LIV_YNHigh correlation
VIP_CARD_YN is highly imbalanced (80.2%)Imbalance
TRAVEL_OS is highly imbalanced (96.8%)Imbalance
TRAVEL_JJ is highly imbalanced (> 99.9%)Imbalance
GOLF_INDOOR is highly imbalanced (91.1%)Imbalance
PREFER_SPORTS is highly imbalanced (94.3%)Imbalance
FST_CAR_ELPS has 235343 (71.3%) missing valuesMissing
HB_1ST has 126124 (38.2%) zerosZeros
PYE_FAM_CNT has 21738 (6.6%) zerosZeros
HOUS_LN_BAL has 282387 (85.6%) zerosZeros
CRDT_LN_BAL has 223385 (67.7%) zerosZeros
CD_USE_AMT has 5287 (1.6%) zerosZeros
PYE_SC0000000 has 8814 (2.7%) zerosZeros

Reproduction
Analysis started2026-04-08 08:57:26.170533
Analysis finished2026-04-08 08:57:45.404412
Duration19.23 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1
165258 
2
164742 

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 row2
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1165258
50.1%
2164742
49.9%

Length

2026-04-08T17:57:45.538673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:45.582802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1165258
50.1%
2164742
49.9%

Most occurring characters

ValueCountFrequency (%)
1165258
50.1%
2164742
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1165258
50.1%
2164742
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1165258
50.1%
2164742
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1165258
50.1%
2164742
49.9%

AGE
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.953955
Minimum20
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:45.619877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum20
5-th percentile20
Q135
median45
Q355
95-th percentile70
Maximum70
Range50
Interquartile range (IQR)20

Descriptive statistics
Standard deviation14.366109
Coefficient of variation (CV)0.31957387
Kurtosis-1.0027367
Mean44.953955
Median Absolute Deviation (MAD)10
Skewness0.020228628
Sum14834805
Variance206.3851
MonotonicityNot monotonic

2026-04-08T17:57:45.665478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5038979
11.8%
4038088
11.5%
4538031
11.5%
5532109
9.7%
2531551
9.6%
3530273
9.2%
6030141
9.1%
3028257
8.6%
7022884
6.9%
6521885
6.6%
ValueCountFrequency (%)
2017802
5.4%
2531551
9.6%
3028257
8.6%
3530273
9.2%
4038088
11.5%
4538031
11.5%
5038979
11.8%
5532109
9.7%
6030141
9.1%
6521885
6.6%
ValueCountFrequency (%)
7022884
6.9%
6521885
6.6%
6030141
9.1%
5532109
9.7%
5038979
11.8%
4538031
11.5%
4038088
11.5%
3530273
9.2%
3028257
8.6%
2531551
9.6%

JB_TP
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581.30815
Minimum410
Maximum910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:45.705584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum410
5-th percentile420
Q1420
median440
Q3910
95-th percentile910
Maximum910
Range500
Interquartile range (IQR)490

Descriptive statistics
Standard deviation215.13077
Coefficient of variation (CV)0.37008043
Kurtosis-1.2272673
Mean581.30815
Median Absolute Deviation (MAD)30
Skewness0.82656923
Sum1.9183169 × 108
Variance46281.249
MonotonicityNot monotonic

2026-04-08T17:57:45.751017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
420151352
45.9%
91097301
29.5%
51060794
18.4%
41010984
 
3.3%
4409554
 
2.9%
52015
 
< 0.1%
ValueCountFrequency (%)
41010984
 
3.3%
420151352
45.9%
4409554
 
2.9%
51060794
18.4%
52015
 
< 0.1%
91097301
29.5%
ValueCountFrequency (%)
91097301
29.5%
52015
 
< 0.1%
51060794
18.4%
4409554
 
2.9%
420151352
45.9%
41010984
 
3.3%

LIF_STG
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4693455
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:45.792847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1
Q11
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics
Standard deviation1.7915907
Coefficient of variation (CV)0.51640597
Kurtosis-1.3208762
Mean3.4693455
Median Absolute Deviation (MAD)1
Skewness-0.25240351
Sum1144884
Variance3.2097973
MonotonicityNot monotonic

2026-04-08T17:57:45.833495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
493528
28.3%
193162
28.2%
562700
19.0%
644769
13.6%
323814
 
7.2%
212027
 
3.6%
ValueCountFrequency (%)
193162
28.2%
212027
 
3.6%
323814
 
7.2%
493528
28.3%
562700
19.0%
644769
13.6%
ValueCountFrequency (%)
644769
13.6%
562700
19.0%
493528
28.3%
323814
 
7.2%
212027
 
3.6%
193162
28.2%

HB_1ST
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6420939
Minimum0
Maximum16
Zeros126124
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:45.873888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median3
Q38
95-th percentile14
Maximum16
Range16
Interquartile range (IQR)8

Descriptive statistics
Standard deviation5.0669747
Coefficient of variation (CV)1.0915278
Kurtosis-0.84365191
Mean4.6420939
Median Absolute Deviation (MAD)3
Skewness0.72619234
Sum1531891
Variance25.674233
MonotonicityNot monotonic

2026-04-08T17:57:45.924670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0126124
38.2%
645184
 
13.7%
1233021
 
10.0%
120088
 
6.1%
318748
 
5.7%
213882
 
4.2%
1311359
 
3.4%
79194
 
2.8%
89141
 
2.8%
147870
 
2.4%
Other values (6)35389
 
10.7%
ValueCountFrequency (%)
0126124
38.2%
120088
 
6.1%
213882
 
4.2%
318748
 
5.7%
43417
 
1.0%
56647
 
2.0%
645184
 
13.7%
79194
 
2.8%
89141
 
2.8%
96828
 
2.1%
ValueCountFrequency (%)
166966
 
2.1%
155997
 
1.8%
147870
 
2.4%
1311359
 
3.4%
1233021
10.0%
105534
 
1.7%
96828
 
2.1%
89141
 
2.8%
79194
 
2.8%
645184
13.7%

BUY_LUX_YN
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
330000 

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
Distinct characters1
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 (%)
0330000
100.0%

Length

2026-04-08T17:57:45.985532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:46.020214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0330000
100.0%

Most occurring characters

ValueCountFrequency (%)
0330000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0330000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0330000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0330000
100.0%

CAR_YN
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
287205 
1
42795 

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 (%)
0287205
87.0%
142795
 
13.0%

Length

2026-04-08T17:57:46.066940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:46.103942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0287205
87.0%
142795
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0287205
87.0%
142795
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0287205
87.0%
142795
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0287205
87.0%
142795
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0287205
87.0%
142795
 
13.0%

VIP_CARD_YN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
319836 
1
 
10164

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 (%)
0319836
96.9%
110164
 
3.1%

Length

2026-04-08T17:57:46.153792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:46.189901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0319836
96.9%
110164
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0319836
96.9%
110164
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0319836
96.9%
110164
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0319836
96.9%
110164
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0319836
96.9%
110164
 
3.1%

TRAVEL_OS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
328894 
1
 
1106

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 (%)
0328894
99.7%
11106
 
0.3%

Length

2026-04-08T17:57:46.233616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:46.271646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0328894
99.7%
11106
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0328894
99.7%
11106
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0328894
99.7%
11106
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0328894
99.7%
11106
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0328894
99.7%
11106
 
0.3%

TRAVEL_JJ
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
329990 
1
 
10

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 (%)
0329990
> 99.9%
110
 
< 0.1%

Length

2026-04-08T17:57:46.315895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:46.352434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0329990
> 99.9%
110
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0329990
> 99.9%
110
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0329990
> 99.9%
110
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0329990
> 99.9%
110
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0329990
> 99.9%
110
 
< 0.1%

GOLF_INDOOR
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
326266 
1
 
3734

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 (%)
0326266
98.9%
13734
 
1.1%

Length

2026-04-08T17:57:46.396464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:46.433817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0326266
98.9%
13734
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0326266
98.9%
13734
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0326266
98.9%
13734
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0326266
98.9%
13734
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0326266
98.9%
13734
 
1.1%

PREFER_SPORTS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
327828 
1
 
2172

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 (%)
0327828
99.3%
12172
 
0.7%

Length

2026-04-08T17:57:46.477863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:46.513810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0327828
99.3%
12172
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0327828
99.3%
12172
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0327828
99.3%
12172
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0327828
99.3%
12172
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0327828
99.3%
12172
 
0.7%

FST_CAR_ELPS
Real number (ℝ)

Missing 

Distinct264
Distinct (%)0.3%
Missing235343
Missing (%)71.3%
Infinite0
Infinite (%)0.0%
Mean126.23218
Minimum3
Maximum295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:46.559248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum3
5-th percentile54
Q189
median126
Q3161
95-th percentile206
Maximum295
Range292
Interquartile range (IQR)72

Descriptive statistics
Standard deviation48.771439
Coefficient of variation (CV)0.38636298
Kurtosis-0.27789737
Mean126.23218
Median Absolute Deviation (MAD)36
Skewness0.17164873
Sum11948759
Variance2378.6533
MonotonicityNot monotonic

2026-04-08T17:57:46.625675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142736
 
0.2%
140724
 
0.2%
131722
 
0.2%
127720
 
0.2%
157720
 
0.2%
132713
 
0.2%
143709
 
0.2%
134709
 
0.2%
146709
 
0.2%
121707
 
0.2%
Other values (254)87488
 
26.5%
(Missing)235343
71.3%
ValueCountFrequency (%)
388
 
< 0.1%
6117
< 0.1%
9114
 
< 0.1%
12182
0.1%
15166
0.1%
18176
0.1%
21201
0.1%
24204
0.1%
27216
0.1%
30289
0.1%
ValueCountFrequency (%)
29587
< 0.1%
29410
 
< 0.1%
2934
 
< 0.1%
2927
 
< 0.1%
2918
 
< 0.1%
2907
 
< 0.1%
2893
 
< 0.1%
2885
 
< 0.1%
2872
 
< 0.1%
2867
 
< 0.1%

TOT_ASST
Real number (ℝ)

Distinct265207
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398401.71
Minimum0
Maximum3318750
Zeros848
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:46.691511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile54972.9
Q1123056.25
median268090.5
Q3547034.5
95-th percentile1197629.5
Maximum3318750
Range3318750
Interquartile range (IQR)423978.25

Descriptive statistics
Standard deviation376277.56
Coefficient of variation (CV)0.94446773
Kurtosis3.1497987
Mean398401.71
Median Absolute Deviation (MAD)172815
Skewness1.6949533
Sum1.3147256 × 1011
Variance1.415848 × 1011
MonotonicityNot monotonic

2026-04-08T17:57:46.760874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0848
 
0.3%
650498
 
< 0.1%
622177
 
< 0.1%
678217
 
< 0.1%
743767
 
< 0.1%
1020787
 
< 0.1%
1217086
 
< 0.1%
846156
 
< 0.1%
1232766
 
< 0.1%
556086
 
< 0.1%
Other values (265197)329092
99.7%
ValueCountFrequency (%)
0848
0.3%
218431
 
< 0.1%
223871
 
< 0.1%
227111
 
< 0.1%
228501
 
< 0.1%
229771
 
< 0.1%
230631
 
< 0.1%
231771
 
< 0.1%
232801
 
< 0.1%
233431
 
< 0.1%
ValueCountFrequency (%)
33187501
< 0.1%
32466401
< 0.1%
30533491
< 0.1%
30072681
< 0.1%
29980151
< 0.1%
29938471
< 0.1%
29852731
< 0.1%
29366061
< 0.1%
29344001
< 0.1%
29212501
< 0.1%

PYE_FAM_CNT
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4633818
Minimum0
Maximum6
Zeros21738
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:46.811678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics
Standard deviation1.3631107
Coefficient of variation (CV)0.55334934
Kurtosis0.048226361
Mean2.4633818
Median Absolute Deviation (MAD)1
Skewness0.45463376
Sum812916
Variance1.8580708
MonotonicityNot monotonic

2026-04-08T17:57:46.854943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2125767
38.1%
359609
18.1%
452215
15.8%
147509
 
14.4%
021738
 
6.6%
512786
 
3.9%
610376
 
3.1%
ValueCountFrequency (%)
021738
 
6.6%
147509
 
14.4%
2125767
38.1%
359609
18.1%
452215
15.8%
512786
 
3.9%
610376
 
3.1%
ValueCountFrequency (%)
610376
 
3.1%
512786
 
3.9%
452215
15.8%
359609
18.1%
2125767
38.1%
147509
 
14.4%
021738
 
6.6%

OWN_HOUS_CNT
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
215794 
1
99513 
2
 
14690
3
 
3

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0215794
65.4%
199513
30.2%
214690
 
4.5%
33
 
< 0.1%

Length

2026-04-08T17:57:46.910397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:46.951807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0215794
65.4%
199513
30.2%
214690
 
4.5%
33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0215794
65.4%
199513
30.2%
214690
 
4.5%
33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0215794
65.4%
199513
30.2%
214690
 
4.5%
33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0215794
65.4%
199513
30.2%
214690
 
4.5%
33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0215794
65.4%
199513
30.2%
214690
 
4.5%
33
 
< 0.1%

OWN_LIV_YN
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
3
253937 
1
74842 
0
 
1221

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3253937
77.0%
174842
 
22.7%
01221
 
0.4%

Length

2026-04-08T17:57:47.002323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:47.042233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3253937
77.0%
174842
 
22.7%
01221
 
0.4%

Most occurring characters

ValueCountFrequency (%)
3253937
77.0%
174842
 
22.7%
01221
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3253937
77.0%
174842
 
22.7%
01221
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3253937
77.0%
174842
 
22.7%
01221
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3253937
77.0%
174842
 
22.7%
01221
 
0.4%

FAM_OWN_HOUS_CNT
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
135887 
1
125884 
2
50916 
3
15225 
4
 
2088

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 row1
2nd row2
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0135887
41.2%
1125884
38.1%
250916
 
15.4%
315225
 
4.6%
42088
 
0.6%

Length

2026-04-08T17:57:47.090743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:47.134587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0135887
41.2%
1125884
38.1%
250916
 
15.4%
315225
 
4.6%
42088
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0135887
41.2%
1125884
38.1%
250916
 
15.4%
315225
 
4.6%
42088
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0135887
41.2%
1125884
38.1%
250916
 
15.4%
315225
 
4.6%
42088
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0135887
41.2%
1125884
38.1%
250916
 
15.4%
315225
 
4.6%
42088
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0135887
41.2%
1125884
38.1%
250916
 
15.4%
315225
 
4.6%
42088
 
0.6%

FAM_OWN_LIV_YN
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
3
162303 
1
148969 
0
18728 

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters330000
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 row1
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3162303
49.2%
1148969
45.1%
018728
 
5.7%

Length

2026-04-08T17:57:47.269790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-04-08T17:57:47.308163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3162303
49.2%
1148969
45.1%
018728
 
5.7%

Most occurring characters

ValueCountFrequency (%)
3162303
49.2%
1148969
45.1%
018728
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3162303
49.2%
1148969
45.1%
018728
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3162303
49.2%
1148969
45.1%
018728
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)330000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3162303
49.2%
1148969
45.1%
018728
 
5.7%

HOUS_LN_BAL
Real number (ℝ)

Zeros 

Distinct38096
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13205.009
Minimum0
Maximum255276
Zeros282387
Zeros (%)85.6%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:47.359632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile99375.05
Maximum255276
Range255276
Interquartile range (IQR)0

Descriptive statistics
Standard deviation34101.332
Coefficient of variation (CV)2.5824542
Kurtosis5.5863303
Mean13205.009
Median Absolute Deviation (MAD)0
Skewness2.5395972
Sum4.3576531 × 109
Variance1.1629009 × 109
MonotonicityNot monotonic

2026-04-08T17:57:47.427752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0282387
85.6%
717706
 
< 0.1%
969086
 
< 0.1%
740225
 
< 0.1%
944655
 
< 0.1%
800785
 
< 0.1%
967185
 
< 0.1%
965125
 
< 0.1%
1035575
 
< 0.1%
814335
 
< 0.1%
Other values (38086)47566
 
14.4%
ValueCountFrequency (%)
0282387
85.6%
240851
 
< 0.1%
262671
 
< 0.1%
274791
 
< 0.1%
277241
 
< 0.1%
277601
 
< 0.1%
283011
 
< 0.1%
283951
 
< 0.1%
285291
 
< 0.1%
287291
 
< 0.1%
ValueCountFrequency (%)
2552761
< 0.1%
2519851
< 0.1%
2438461
< 0.1%
2423301
< 0.1%
2387391
< 0.1%
2381261
< 0.1%
2321931
< 0.1%
2314531
< 0.1%
2302201
< 0.1%
2300391
< 0.1%

CRDT_LN_BAL
Real number (ℝ)

Zeros 

Distinct48237
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8562.5568
Minimum0
Maximum266495
Zeros223385
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:47.494356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q310210
95-th percentile45443.05
Maximum266495
Range266495
Interquartile range (IQR)10210

Descriptive statistics
Standard deviation18173.621
Coefficient of variation (CV)2.1224526
Kurtosis15.496107
Mean8562.5568
Median Absolute Deviation (MAD)0
Skewness3.3583794
Sum2.8256437 × 109
Variance3.3028051 × 108
MonotonicityNot monotonic

2026-04-08T17:57:47.561955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0223385
67.7%
1165714
 
< 0.1%
713413
 
< 0.1%
1018813
 
< 0.1%
839513
 
< 0.1%
683913
 
< 0.1%
907313
 
< 0.1%
812412
 
< 0.1%
899912
 
< 0.1%
1001912
 
< 0.1%
Other values (48227)106500
32.3%
ValueCountFrequency (%)
0223385
67.7%
14801
 
< 0.1%
16961
 
< 0.1%
18171
 
< 0.1%
18191
 
< 0.1%
18211
 
< 0.1%
18371
 
< 0.1%
18891
 
< 0.1%
19491
 
< 0.1%
19551
 
< 0.1%
ValueCountFrequency (%)
2664951
< 0.1%
2495711
< 0.1%
2440741
< 0.1%
2422081
< 0.1%
2394271
< 0.1%
2388181
< 0.1%
2375481
< 0.1%
2369681
< 0.1%
2333591
< 0.1%
2275491
< 0.1%

CD_USE_AMT
Real number (ℝ)

Zeros 

Distinct29383
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5690.2905
Minimum0
Maximum95757
Zeros5287
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:47.628386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile430
Q11794
median3698
Q36822
95-th percentile17857.05
Maximum95757
Range95757
Interquartile range (IQR)5028

Descriptive statistics
Standard deviation6969.4308
Coefficient of variation (CV)1.2247935
Kurtosis16.159853
Mean5690.2905
Median Absolute Deviation (MAD)2260
Skewness3.4572528
Sum1.8777959 × 109
Variance48572965
MonotonicityNot monotonic

2026-04-08T17:57:47.698275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05287
 
1.6%
43274
 
< 0.1%
209474
 
< 0.1%
52673
 
< 0.1%
69972
 
< 0.1%
56070
 
< 0.1%
48370
 
< 0.1%
47969
 
< 0.1%
85869
 
< 0.1%
114568
 
< 0.1%
Other values (29373)324074
98.2%
ValueCountFrequency (%)
05287
1.6%
1441
 
< 0.1%
1452
 
< 0.1%
1461
 
< 0.1%
1532
 
< 0.1%
1541
 
< 0.1%
1561
 
< 0.1%
1573
 
< 0.1%
1582
 
< 0.1%
1592
 
< 0.1%
ValueCountFrequency (%)
957571
< 0.1%
876001
< 0.1%
863651
< 0.1%
854751
< 0.1%
842501
< 0.1%
832901
< 0.1%
812531
< 0.1%
806011
< 0.1%
796441
< 0.1%
795061
< 0.1%

PYE_SC0000000
Real number (ℝ)

Zeros 

Distinct645
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean838.40495
Minimum0
Maximum1000
Zeros8814
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2026-04-08T17:57:47.769177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile477
Q1764
median911
Q3979
95-th percentile1000
Maximum1000
Range1000
Interquartile range (IQR)215

Descriptive statistics
Standard deviation200.77897
Coefficient of variation (CV)0.23947732
Kurtosis5.9686163
Mean838.40495
Median Absolute Deviation (MAD)89
Skewness-2.2108081
Sum2.7667363 × 108
Variance40312.196
MonotonicityNot monotonic

2026-04-08T17:57:47.838030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100054940
 
16.6%
08814
 
2.7%
9791505
 
0.5%
9741484
 
0.4%
9821455
 
0.4%
9671454
 
0.4%
9721452
 
0.4%
9921447
 
0.4%
9771440
 
0.4%
9781439
 
0.4%
Other values (635)254570
77.1%
ValueCountFrequency (%)
08814
2.7%
3561
 
< 0.1%
3581
 
< 0.1%
3594
 
< 0.1%
3603
 
< 0.1%
3615
 
< 0.1%
3622
 
< 0.1%
3639
 
< 0.1%
3647
 
< 0.1%
36514
 
< 0.1%
ValueCountFrequency (%)
100054940
16.6%
9991224
 
0.4%
9981333
 
0.4%
9971358
 
0.4%
9961313
 
0.4%
9951385
 
0.4%
9941372
 
0.4%
9931308
 
0.4%
9921447
 
0.4%
9911417
 
0.4%

Interactions

2026-04-08T17:57:43.367058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:34.816100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.680338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:36.489834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.488730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.326978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:39.023377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:39.945026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.799113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:41.624226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:42.521276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:43.440034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:34.913856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.755338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:36.560819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.570198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.388859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:39.101126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.017089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.905929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:41.696945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:42.596506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:43.514512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:36.644586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:41.770231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:42.675592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:43.588733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.073628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.901562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:36.734356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.722269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.511955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:39.332594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.162352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:41.050245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:41.841773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:42.750133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:43.653061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.139456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.965642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:36.944817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.789558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.576766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:39.398229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.270984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:41.114268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:41.906828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:42.814382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:43.728136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.215784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:36.039777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.027441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.869110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.637979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:39.477865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.345781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:43.804431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:37.108646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.944556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:39.560558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.420761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:42.148120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:35.370297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:36.188486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:43.962667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:38.102169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-04-08T17:57:42.298880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:43.126031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:44.047112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.526660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:36.341966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.340984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.183945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.887103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:39.794786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.646059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:41.478132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:42.373199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:43.208345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:44.125839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:35.603017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:36.415815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:37.415709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.261961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:38.948251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:39.870816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:40.723483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:41.550843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:42.445177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-04-08T17:57:43.289111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-04-08T17:57:47.907140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGECAR_YNCD_USE_AMTCRDT_LN_BALFAM_OWN_HOUS_CNTFAM_OWN_LIV_YNFST_CAR_ELPSGOLF_INDOORHB_1STHOUS_LN_BALJB_TPLIF_STGOWN_HOUS_CNTOWN_LIV_YNPREFER_SPORTSPYE_FAM_CNTPYE_SC0000000SEXTOT_ASSTTRAVEL_JJTRAVEL_OSVIP_CARD_YN
AGE1.0000.120-0.1080.0340.0890.1040.3180.053-0.2000.1480.0680.8950.2150.2110.035-0.0180.2040.0610.0200.0080.0220.104
CAR_YN0.1201.0000.1070.0920.0190.0170.0270.0770.1270.0680.1000.0840.0640.0530.0390.0300.0670.1550.0290.0000.0310.077
CD_USE_AMT-0.1080.1071.0000.3690.0270.023-0.0550.0620.2330.172-0.196-0.1130.0550.0550.085-0.0140.0820.0420.1040.0230.0400.176
CRDT_LN_BAL0.0340.0920.3691.0000.0240.014-0.1200.0590.0740.198-0.1590.0140.0820.0420.028-0.047-0.2250.134-0.0380.0000.0030.072
FAM_OWN_HOUS_CNT0.0890.0190.0270.0241.0000.5390.0470.0180.0510.1280.0230.0850.4000.3070.0140.1990.1090.0210.1740.0030.0040.048
FAM_OWN_LIV_YN0.1040.0170.0230.0140.5391.0000.0610.0110.0470.1280.0240.0980.2840.4250.0030.5700.1860.0070.1450.0000.0030.013
FST_CAR_ELPS0.3180.027-0.055-0.1200.0470.0611.0000.063-0.0480.0610.0440.2710.0840.0670.0430.0370.0690.0930.0680.0100.0450.009
GOLF_INDOOR0.0530.0770.0620.0590.0180.0110.0631.0000.1480.0340.0370.0440.0240.0190.1030.0070.0190.0630.0480.0180.0400.065
HB_1ST-0.2000.1270.2330.0740.0510.047-0.0480.1481.0000.031-0.087-0.1780.0860.0780.064-0.0170.0620.4430.1250.0300.1470.122
HOUS_LN_BAL0.1480.0680.1720.1980.1280.1280.0610.0340.0311.000-0.0480.1380.2550.2570.023-0.0080.0300.0780.1670.0000.0140.061
JB_TP0.0680.100-0.196-0.1590.0230.0240.0440.037-0.087-0.0481.0000.0810.0690.0650.0110.029-0.1470.140-0.0130.0000.0070.050
LIF_STG0.8950.084-0.1130.0140.0850.0980.2710.044-0.1780.1380.0811.0000.1970.2000.036-0.0050.1890.1280.0390.0070.0200.089
OWN_HOUS_CNT0.2150.0640.0550.0820.4000.2840.0840.0240.0860.2550.0690.1971.0000.5260.0000.0490.1460.0710.2080.0020.0040.057
OWN_LIV_YN0.2110.0530.0550.0420.3070.4250.0670.0190.0780.2570.0650.2000.5261.0000.0000.1430.2390.0690.1560.0000.0030.033
PREFER_SPORTS0.0350.0390.0850.0280.0140.0030.0430.1030.0640.0230.0110.0360.0000.0001.0000.0080.0180.0000.0650.0020.0090.046
PYE_FAM_CNT-0.0180.030-0.014-0.0470.1990.5700.0370.007-0.017-0.0080.029-0.0050.0490.1430.0081.0000.0660.0220.1410.0040.0090.021
PYE_SC00000000.2040.0670.082-0.2250.1090.1860.0690.0190.0620.030-0.1470.1890.1460.2390.0180.0661.0000.0480.2070.0000.0180.056
SEX0.0610.1550.0420.1340.0210.0070.0930.0630.4430.0780.1400.1280.0710.0690.0000.0220.0481.0000.0410.0020.0030.015
TOT_ASST0.0200.0290.104-0.0380.1740.1450.0680.0480.1250.167-0.0130.0390.2080.1560.0650.1410.2070.0411.0000.0050.0390.151
TRAVEL_JJ0.0080.0000.0230.0000.0030.0000.0100.0180.0300.0000.0000.0070.0020.0000.0020.0040.0000.0020.0051.0000.0810.003
TRAVEL_OS0.0220.0310.0400.0030.0040.0030.0450.0400.1470.0140.0070.0200.0040.0030.0090.0090.0180.0030.0390.0811.0000.051
VIP_CARD_YN0.1040.0770.1760.0720.0480.0130.0090.0650.1220.0610.0500.0890.0570.0330.0460.0210.0560.0150.1510.0030.0511.000

Missing values

2026-04-08T17:57:44.233983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-04-08T17:57:44.749843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SEXAGEJB_TPLIF_STGHB_1STBUY_LUX_YNCAR_YNVIP_CARD_YNTRAVEL_OSTRAVEL_JJGOLF_INDOORPREFER_SPORTSFST_CAR_ELPSTOT_ASSTPYE_FAM_CNTOWN_HOUS_CNTOWN_LIV_YNFAM_OWN_HOUS_CNTFAM_OWN_LIV_YNHOUS_LN_BALCRDT_LN_BALCD_USE_AMTPYE_SC0000000
023542046000000094.01987592.003110027051000.0
1250910500000000NaN9375004.003210056911000.0
2130420100000000NaN3783806.00303001198845.0
3125910120000000NaN6142264.00321005032915.0
42404203120000000NaN860671.01313004636966.0
5255510510110000NaN4174182.00303009264992.0
6230510112010000079.0684944.00303004567965.0
7160420500000000113.0816560.003000155765651471.0
8150420400000000162.0763713.0030301040014028863.0
9165510630000000NaN10578722.011118179707867720.0
SEXAGEJB_TPLIF_STGHB_1STBUY_LUX_YNCAR_YNVIP_CARD_YNTRAVEL_OSTRAVEL_JJGOLF_INDOORPREFER_SPORTSFST_CAR_ELPSTOT_ASSTPYE_FAM_CNTOWN_HOUS_CNTOWN_LIV_YNFAM_OWN_HOUS_CNTFAM_OWN_LIV_YNHOUS_LN_BALCRDT_LN_BALCD_USE_AMTPYE_SC0000000
3299901304201140000000NaN2308143.00311002734921.0
3299912454204120000000NaN1714832.003110024471000.0
329992165420650000000NaN2334482.01111001273968.0
329993165510660100000NaN762353.00303002320925.0
3299941405103160000000176.0905932.0030307043423508594.0
3299951555105140000000NaN2869282.0030305302039664580.0
329996260910510000000NaN6159962.00303004033999.0
329997220910100000000NaN1313512.0030300781601.0
3299982454204120000000NaN1239414.011210073521000.0
329999155420590000000NaN5422854.0111191916269394930981.0

Duplicate rows

Most frequently occurring

SEXAGEJB_TPLIF_STGHB_1STBUY_LUX_YNCAR_YNVIP_CARD_YNTRAVEL_OSTRAVEL_JJGOLF_INDOORPREFER_SPORTSFST_CAR_ELPSTOT_ASSTPYE_FAM_CNTOWN_HOUS_CNTOWN_LIV_YNFAM_OWN_HOUS_CNTFAM_OWN_LIV_YNHOUS_LN_BALCRDT_LN_BALCD_USE_AMTPYE_SC0000000# duplicates
0125910100000000NaN00.000000000.07
31240910400000000NaN00.000000000.06
4130910120000000NaN00.000000000.05
14165910600000000NaN00.000000000.05
16170910600000000NaN00.000000000.05
21230910160000000NaN00.000000000.05
18225910160000000NaN00.000000000.04
292409103120000000NaN00.000000000.04
1125910160000000NaN00.000000000.03
5130910320100000NaN00.000000000.03
\ No newline at end of file