Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells112
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory151.3 B

Variable types

Numeric2
Text1
DateTime5
Categorical10

Alerts

lib_code is highly overall correlated with rec_key and 8 other fieldsHigh correlation
manage_code is highly overall correlated with rec_key and 8 other fieldsHigh correlation
return_type_code is highly overall correlated with rec_key and 8 other fieldsHigh correlation
status is highly overall correlated with rec_key and 6 other fieldsHigh correlation
master_lib_code is highly overall correlated with rec_key and 8 other fieldsHigh correlation
l_device is highly overall correlated with rec_key and 5 other fieldsHigh correlation
loan_manage_code is highly overall correlated with book_key and 4 other fieldsHigh correlation
r_device is highly overall correlated with rec_key and 8 other fieldsHigh correlation
return_manage_code is highly overall correlated with rec_key and 7 other fieldsHigh correlation
rec_key is highly overall correlated with lib_code and 7 other fieldsHigh correlation
book_key is highly overall correlated with lib_code and 7 other fieldsHigh correlation
loan_type_code is highly overall correlated with l_deviceHigh correlation
lib_code is highly imbalanced (80.6%)Imbalance
loan_type_code is highly imbalanced (69.6%)Imbalance
l_device is highly imbalanced (72.6%)Imbalance
manage_code is highly imbalanced (80.6%)Imbalance
loan_manage_code is highly imbalanced (78.9%)Imbalance
return_manage_code is highly imbalanced (54.8%)Imbalance
master_lib_code is highly imbalanced (80.6%)Imbalance
return_date has 8 (8.0%) missing valuesMissing
reservation_date has 52 (52.0%) missing valuesMissing
reservation_expire_date has 52 (52.0%) missing valuesMissing
rec_key has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:47:49.668757
Analysis finished2023-12-10 09:47:53.391502
Duration3.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

rec_key
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5004151 × 1012
Minimum4.1458187 × 108
Maximum2.5000042 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:47:53.609514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1458187 × 108
5-th percentile4.1479861 × 108
Q14.1507678 × 108
median4.1527778 × 108
Q34.1529398 × 108
95-th percentile4.1530939 × 108
Maximum2.5000042 × 1014
Range2.5 × 1014
Interquartile range (IQR)217205.75

Descriptive statistics

Standard deviation4.2861652 × 1013
Coefficient of variation (CV)5.7145706
Kurtosis29.897775
Mean7.5004151 × 1012
Median Absolute Deviation (MAD)30078
Skewness5.5946495
Sum7.5004151 × 1014
Variance1.8371212 × 1027
MonotonicityNot monotonic
2023-12-10T18:47:53.956001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
414581869 1
 
1.0%
415284257 1
 
1.0%
415292671 1
 
1.0%
415291719 1
 
1.0%
415291384 1
 
1.0%
415290916 1
 
1.0%
415290903 1
 
1.0%
415290873 1
 
1.0%
415287097 1
 
1.0%
415284262 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
414581869 1
1.0%
414582314 1
1.0%
414652231 1
1.0%
414731359 1
1.0%
414767948 1
1.0%
414800224 1
1.0%
414800935 1
1.0%
414807860 1
1.0%
414831216 1
1.0%
414911934 1
1.0%
ValueCountFrequency (%)
250000415032938 1
1.0%
250000412497522 1
1.0%
250000411848408 1
1.0%
415309391 1
1.0%
415309388 1
1.0%
415309386 1
1.0%
415309305 1
1.0%
415308287 1
1.0%
415307422 1
1.0%
415305566 1
1.0%

book_key
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1002985 × 1012
Minimum71921117
Maximum2.500004 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:47:54.256340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71921117
5-th percentile97395685
Q11.0796593 × 108
median4.0637592 × 108
Q34.1327996 × 108
95-th percentile4.1468544 × 108
Maximum2.500004 × 1014
Range2.5000033 × 1014
Interquartile range (IQR)3.0531403 × 108

Descriptive statistics

Standard deviation4.0708247 × 1013
Coefficient of variation (CV)5.7333149
Kurtosis30.739493
Mean7.1002985 × 1012
Median Absolute Deviation (MAD)8036140
Skewness5.6502443
Sum7.1002985 × 1014
Variance1.6571614 × 1027
MonotonicityNot monotonic
2023-12-10T18:47:54.641417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
413906431 2
 
2.0%
414501105 2
 
2.0%
413279961 2
 
2.0%
413279892 2
 
2.0%
414685371 2
 
2.0%
414501061 2
 
2.0%
413906497 1
 
1.0%
411538984 1
 
1.0%
112073544 1
 
1.0%
404877206 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
71921117 1
1.0%
86439510 1
1.0%
95373210 1
1.0%
97393175 1
1.0%
97394169 1
1.0%
97395765 1
1.0%
97533711 1
1.0%
97565463 1
1.0%
98296375 1
1.0%
98484370 1
1.0%
ValueCountFrequency (%)
250000399504111 1
1.0%
250000311815431 1
1.0%
210000108667794 1
1.0%
414685451 1
1.0%
414685448 1
1.0%
414685436 1
1.0%
414685412 1
1.0%
414685371 2
2.0%
414685361 1
1.0%
414501105 2
2.0%

user_key
Text

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:47:55.169833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length40
Mean length40.72
Min length40

Characters and Unicode

Total characters4072
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)48.0%

Sample

1st row278BEACEAA2026BE252F6F7F9AE30674309AA0CC
2nd row949c2202dac855d5850593db2e74e0e6f9358ded430b35cd4c547039cf98484d
3rd row298F6C42632AEC50BE405CF744DAB635DAF7B8AE
4th row5859CB0F892F5CB941EBA3E333E79338CE754281
5th row07554F23DD380E8271469610F9ADABCE1C2FACA0
ValueCountFrequency (%)
876850a0d7a3afbab73b1a46c6b0ccc5e0e7bae4 5
 
5.0%
849fcec30c5f5021f72bb2d323977c26ff59d2d0 4
 
4.0%
f1007da5ef287edc22223e39eb8749e192e47052 4
 
4.0%
a1dd55fbbfd69975d35590198eb7c5ad86f5d0e1 4
 
4.0%
e028575738e630e24cab05c5d4e60137199c39c9 3
 
3.0%
56a470cc534c1a0dc5f2c4ccef67b791abadbb29 3
 
3.0%
14cea412698f9eff50ad5743d9edb43dcb5c01bb 3
 
3.0%
859927448aa726d44c6351af8193fa00796b58b0 3
 
3.0%
a1a0b6fe88793a9e623cba29651ebc691fc6e94a 3
 
3.0%
d99c8625c0fd192d4b7e7938d33e1c4cb3bc9436 2
 
2.0%
Other values (57) 66
66.0%
2023-12-10T18:47:56.068264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 279
 
6.9%
9 278
 
6.8%
C 268
 
6.6%
0 264
 
6.5%
7 259
 
6.4%
5 257
 
6.3%
B 254
 
6.2%
F 253
 
6.2%
E 246
 
6.0%
1 240
 
5.9%
Other values (12) 1474
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2458
60.4%
Uppercase Letter 1539
37.8%
Lowercase Letter 75
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 278
11.3%
0 264
10.7%
7 259
10.5%
5 257
10.5%
1 240
9.8%
2 238
9.7%
4 236
9.6%
3 236
9.6%
8 235
9.6%
6 215
8.7%
Uppercase Letter
ValueCountFrequency (%)
A 279
18.1%
C 268
17.4%
B 254
16.5%
F 253
16.4%
E 246
16.0%
D 239
15.5%
Lowercase Letter
ValueCountFrequency (%)
e 16
21.3%
c 15
20.0%
d 14
18.7%
b 13
17.3%
a 10
13.3%
f 7
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2458
60.4%
Latin 1614
39.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 279
17.3%
C 268
16.6%
B 254
15.7%
F 253
15.7%
E 246
15.2%
D 239
14.8%
e 16
 
1.0%
c 15
 
0.9%
d 14
 
0.9%
b 13
 
0.8%
Other values (2) 17
 
1.1%
Common
ValueCountFrequency (%)
9 278
11.3%
0 264
10.7%
7 259
10.5%
5 257
10.5%
1 240
9.8%
2 238
9.7%
4 236
9.6%
3 236
9.6%
8 235
9.6%
6 215
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 279
 
6.9%
9 278
 
6.8%
C 268
 
6.6%
0 264
 
6.5%
7 259
 
6.4%
5 257
 
6.3%
B 254
 
6.2%
F 253
 
6.2%
E 246
 
6.0%
1 240
 
5.9%
Other values (12) 1474
36.2%

loan_date
Date

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2021-09-26 12:00:00
Maximum2021-11-29 12:00:00
2023-12-10T18:47:56.350786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:56.605316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

return_date
Date

MISSING 

Distinct20
Distinct (%)21.7%
Missing8
Missing (%)8.0%
Memory size932.0 B
Minimum2021-10-31 12:00:00
Maximum2021-11-28 12:00:00
2023-12-10T18:47:56.835690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:57.108164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

lib_code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
103
97 
29977
 
3

Length

Max length5
Median length3
Mean length3.06
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row103
2nd row29977
3rd row103
4th row103
5th row103

Common Values

ValueCountFrequency (%)
103 97
97.0%
29977 3
 
3.0%

Length

2023-12-10T18:47:57.461939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:47:57.732888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
103 97
97.0%
29977 3
 
3.0%

loan_type_code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
92 
3
 
4
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92
92.0%
3 4
 
4.0%
1 4
 
4.0%

Length

2023-12-10T18:47:58.073721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:47:58.330406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 92
92.0%
3 4
 
4.0%
1 4
 
4.0%

return_type_code
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
77 
3
15 
<NA>
8 

Length

Max length4
Median length1
Mean length1.24
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row<NA>
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77
77.0%
3 15
 
15.0%
<NA> 8
 
8.0%

Length

2023-12-10T18:47:58.571671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:47:58.772026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 77
77.0%
3 15
 
15.0%
na 8
 
8.0%

return_plan_date
Date

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2021-10-10 12:00:00
Maximum2021-12-13 12:00:00
2023-12-10T18:47:58.984582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:59.238354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

reservation_date
Date

MISSING 

Distinct22
Distinct (%)45.8%
Missing52
Missing (%)52.0%
Memory size932.0 B
Minimum2021-08-31 12:00:00
Maximum2021-10-13 12:00:00
2023-12-10T18:47:59.504992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:59.740145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

reservation_expire_date
Date

MISSING 

Distinct26
Distinct (%)54.2%
Missing52
Missing (%)52.0%
Memory size932.0 B
Minimum2021-10-05 12:00:00
Maximum2021-11-19 12:00:00
2023-12-10T18:48:00.005808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:00.346095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

status
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
45 
R
33 
5
14 
0
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 45
45.0%
R 33
33.0%
5 14
 
14.0%
0 8
 
8.0%

Length

2023-12-10T18:48:00.607142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:00.779649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 45
45.0%
r 33
33.0%
5 14
 
14.0%
0 8
 
8.0%

l_device
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KOLASIII
93 
SMARTID
 
4
KLAS
 
3

Length

Max length8
Median length8
Mean length7.84
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKOLASIII
2nd rowKLAS
3rd rowKOLASIII
4th rowKOLASIII
5th rowKOLASIII

Common Values

ValueCountFrequency (%)
KOLASIII 93
93.0%
SMARTID 4
 
4.0%
KLAS 3
 
3.0%

Length

2023-12-10T18:48:00.991235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:01.178306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kolasiii 93
93.0%
smartid 4
 
4.0%
klas 3
 
3.0%

r_device
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KOLASIII
77 
SMARTID
15 
<NA>
8 

Length

Max length8
Median length8
Mean length7.53
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKOLASIII
2nd row<NA>
3rd rowKOLASIII
4th rowKOLASIII
5th rowKOLASIII

Common Values

ValueCountFrequency (%)
KOLASIII 77
77.0%
SMARTID 15
 
15.0%
<NA> 8
 
8.0%

Length

2023-12-10T18:48:01.441477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:01.719773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kolasiii 77
77.0%
smartid 15
 
15.0%
na 8
 
8.0%

manage_code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
ME
97 
GV
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowME
2nd rowGV
3rd rowME
4th rowME
5th rowME

Common Values

ValueCountFrequency (%)
ME 97
97.0%
GV 3
 
3.0%

Length

2023-12-10T18:48:01.992676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:02.248443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
me 97
97.0%
gv 3
 
3.0%

loan_manage_code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
95 
BR
 
3
GV
 
2

Length

Max length4
Median length4
Mean length3.9
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd rowGV
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 95
95.0%
BR 3
 
3.0%
GV 2
 
2.0%

Length

2023-12-10T18:48:02.458712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:03.037857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 95
95.0%
br 3
 
3.0%
gv 2
 
2.0%

return_manage_code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
86 
BR
9 
MN
 
5

Length

Max length4
Median length4
Mean length3.72
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 86
86.0%
BR 9
 
9.0%
MN 5
 
5.0%

Length

2023-12-10T18:48:03.213255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:03.392304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 86
86.0%
br 9
 
9.0%
mn 5
 
5.0%

master_lib_code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
100
97 
29900
 
3

Length

Max length5
Median length3
Mean length3.06
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row29900
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 97
97.0%
29900 3
 
3.0%

Length

2023-12-10T18:48:03.637106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:03.819115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 97
97.0%
29900 3
 
3.0%

Interactions

2023-12-10T18:47:52.015855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:51.575867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:52.163862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:51.836823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:48:03.967999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
rec_keybook_keyuser_keyloan_datereturn_datelib_codeloan_type_codereturn_type_codereturn_plan_datereservation_datereservation_expire_datestatusl_devicer_devicemanage_codeloan_manage_codereturn_manage_codemaster_lib_code
rec_key1.0001.0001.0001.000NaN0.9630.000NaN1.000NaNNaN0.7871.000NaN0.9630.611NaN0.963
book_key1.0001.0001.0001.000NaN1.0000.000NaN1.000NaNNaN0.3990.940NaN1.0001.000NaN1.000
user_key1.0001.0001.0000.9860.9941.0001.0001.0000.9820.9880.9160.9911.0001.0001.0001.0001.0001.000
loan_date1.0001.0000.9861.0000.9671.0000.6620.3120.9940.8110.9780.7910.9600.3121.0001.0001.0001.000
return_dateNaNNaN0.9940.9671.000NaN0.0000.4000.9590.6610.8970.5520.0000.400NaNNaN0.793NaN
lib_code0.9631.0001.0001.000NaN1.0000.000NaN1.000NaNNaN0.7881.000NaN0.9630.611NaN0.963
loan_type_code0.0000.0001.0000.6620.0000.0001.0000.2910.0000.7490.5810.2240.9400.2910.0000.6110.0000.000
return_type_codeNaNNaN1.0000.3120.400NaN0.2911.0000.4430.0000.0000.7200.5870.998NaNNaNNaNNaN
return_plan_date1.0001.0000.9820.9940.9591.0000.0000.4431.0000.9200.9540.7800.8460.4431.0001.0000.9111.000
reservation_dateNaNNaN0.9880.8110.661NaN0.7490.0000.9201.0000.8760.0000.0000.000NaNNaN0.000NaN
reservation_expire_dateNaNNaN0.9160.9780.897NaN0.5810.0000.9540.8761.0000.5370.7140.000NaNNaN1.000NaN
status0.7870.3990.9910.7910.5520.7880.2240.7200.7800.0000.5371.0000.4720.7200.7881.0000.0000.788
l_device1.0000.9401.0000.9600.0001.0000.9400.5870.8460.0000.7140.4721.0000.5871.0000.6110.0001.000
r_deviceNaNNaN1.0000.3120.400NaN0.2910.9980.4430.0000.0000.7200.5871.000NaNNaNNaNNaN
manage_code0.9631.0001.0001.000NaN0.9630.000NaN1.000NaNNaN0.7881.000NaN1.0000.611NaN0.963
loan_manage_code0.6111.0001.0001.000NaN0.6110.611NaN1.000NaNNaN1.0000.611NaN0.6111.000NaN0.611
return_manage_codeNaNNaN1.0001.0000.793NaN0.000NaN0.9110.0001.0000.0000.000NaNNaNNaN1.000NaN
master_lib_code0.9631.0001.0001.000NaN0.9630.000NaN1.000NaNNaN0.7881.000NaN0.9630.611NaN1.000
2023-12-10T18:48:04.302634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lib_codemanage_codereturn_type_codestatusmaster_lib_codel_deviceloan_type_codeloan_manage_coder_devicereturn_manage_code
lib_code1.0000.8261.0000.5730.8260.9950.0000.3471.0001.000
manage_code0.8261.0001.0000.5730.8260.9950.0000.3471.0001.000
return_type_code1.0001.0001.0000.9541.0000.4000.4681.0000.9601.000
status0.5730.5730.9541.0000.5730.4660.2110.8160.9540.000
master_lib_code0.8260.8261.0000.5731.0000.9950.0000.3471.0001.000
l_device0.9950.9950.4000.4660.9951.0000.7000.3470.4000.000
loan_type_code0.0000.0000.4680.2110.0000.7001.0000.3470.4680.000
loan_manage_code0.3470.3471.0000.8160.3470.3470.3471.0001.0001.000
r_device1.0001.0000.9600.9541.0000.4000.4681.0001.0001.000
return_manage_code1.0001.0001.0000.0001.0000.0000.0001.0001.0001.000
2023-12-10T18:48:04.563102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
rec_keybook_keylib_codeloan_type_codereturn_type_codestatusl_devicer_devicemanage_codeloan_manage_codereturn_manage_codemaster_lib_code
rec_key1.000-0.2470.8260.0001.0000.5730.9951.0000.8260.3471.0000.826
book_key-0.2471.0000.9950.0001.0000.3880.7001.0000.9950.8161.0000.995
lib_code0.8260.9951.0000.0001.0000.5730.9951.0000.8260.3471.0000.826
loan_type_code0.0000.0000.0001.0000.4680.2110.7000.4680.0000.3470.0000.000
return_type_code1.0001.0001.0000.4681.0000.9540.4000.9601.0001.0001.0001.000
status0.5730.3880.5730.2110.9541.0000.4660.9540.5730.8160.0000.573
l_device0.9950.7000.9950.7000.4000.4661.0000.4000.9950.3470.0000.995
r_device1.0001.0001.0000.4680.9600.9540.4001.0001.0001.0001.0001.000
manage_code0.8260.9950.8260.0001.0000.5730.9951.0001.0000.3471.0000.826
loan_manage_code0.3470.8160.3470.3471.0000.8160.3471.0000.3471.0001.0000.347
return_manage_code1.0001.0001.0000.0001.0000.0000.0001.0001.0001.0001.0001.000
master_lib_code0.8260.9950.8260.0001.0000.5730.9951.0000.8260.3471.0001.000

Missing values

2023-12-10T18:47:52.404032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:47:52.845070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T18:47:53.194594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

rec_keybook_keyuser_keyloan_datereturn_datelib_codeloan_type_codereturn_type_codereturn_plan_datereservation_datereservation_expire_datestatusl_devicer_devicemanage_codeloan_manage_codereturn_manage_codemaster_lib_code
0414581869413906497278BEACEAA2026BE252F6F7F9AE30674309AA0CC2021-10-02 12:00:00.02021-10-31 12:00:00.0103002021-10-16 12:00:00.02021-08-31 12:00:00.02021-10-07 12:00:00.0RKOLASIIIKOLASIIIME<NA><NA>100
1250000411848408250000311815431949c2202dac855d5850593db2e74e0e6f9358ded430b35cd4c547039cf98484d2021-11-26 12:00:00.0<NA>299770<NA>2021-12-10 12:00:00.0<NA><NA>0KLAS<NA>GVGV<NA>29900
2414582314413906431298F6C42632AEC50BE405CF744DAB635DAF7B8AE2021-10-22 12:00:00.02021-11-05 12:00:00.0103002021-11-05 12:00:00.02021-09-01 12:00:00.02021-10-23 12:00:00.01KOLASIIIKOLASIIIME<NA><NA>100
34146522314145011055859CB0F892F5CB941EBA3E333E79338CE7542812021-10-24 12:00:00.02021-11-05 12:00:00.0103002021-11-07 12:00:00.02021-09-04 12:00:00.02021-10-28 12:00:00.01KOLASIIIKOLASIIIME<NA><NA>100
441473135940882387907554F23DD380E8271469610F9ADABCE1C2FACA02021-10-31 12:00:00.02021-11-07 12:00:00.0103002021-11-14 12:00:00.02021-09-10 12:00:00.02021-10-31 12:00:00.01KOLASIIIKOLASIIIME<NA><NA>100
5414767948414323162C4767B308AD663FD8F1E8DC2DB9F7A88032E09D52021-10-28 12:00:00.02021-11-09 12:00:00.0103002021-11-11 12:00:00.02021-09-13 12:00:00.02021-10-30 12:00:00.01KOLASIIIKOLASIIIME<NA><NA>100
64148002244132799611656CA3F4773A84FFD4D9DF84AD952AEB100AB8E2021-10-29 12:00:00.02021-11-11 12:00:00.0103002021-11-12 12:00:00.02021-09-15 12:00:00.02021-10-29 12:00:00.01KOLASIIIKOLASIIIME<NA><NA>100
725000041249752225000039950411100cb864bdef31fd0b3ee3e20a810deb5c6194ed0439d8fe2ea67fa0350cb0b482021-11-26 12:00:00.0<NA>299770<NA>2021-12-10 12:00:00.0<NA><NA>0KLAS<NA>GV<NA><NA>29900
84148009354132798925451EFFE9613AF4B1F54C90E82129E5C03C7EE932021-10-27 12:00:00.02021-11-10 12:00:00.0103002021-11-10 12:00:00.02021-09-16 12:00:00.02021-10-28 12:00:00.01KOLASIIIKOLASIIIME<NA><NA>100
9414807860413906478E17CA3C6908580CF86DA6836BAAA7BF1481835EB2021-10-31 12:00:00.0<NA>1030<NA>2021-11-14 12:00:00.02021-09-16 12:00:00.02021-10-31 12:00:00.00KOLASIII<NA>ME<NA><NA>100
rec_keybook_keyuser_keyloan_datereturn_datelib_codeloan_type_codereturn_type_codereturn_plan_datereservation_datereservation_expire_datestatusl_devicer_devicemanage_codeloan_manage_codereturn_manage_codemaster_lib_code
90415305401106412005FFD34177F97B17C6D28607048EFA31573DBD14082021-10-13 12:00:00.02021-11-03 12:00:00.0103002021-11-03 12:00:00.0<NA><NA>RKOLASIIIKOLASIIIME<NA><NA>100
91415305402113891423FFD34177F97B17C6D28607048EFA31573DBD14082021-10-13 12:00:00.02021-11-03 12:00:00.0103002021-11-03 12:00:00.0<NA><NA>RKOLASIIIKOLASIIIME<NA><NA>100
924153055654052862547E27361C345452C9778B428AB08BCB790DA715CA2021-10-13 12:00:00.02021-11-02 12:00:00.0103002021-11-03 12:00:00.0<NA><NA>RKOLASIIIKOLASIIIME<NA><NA>100
934153055664090130447E27361C345452C9778B428AB08BCB790DA715CA2021-10-13 12:00:00.02021-11-02 12:00:00.0103002021-11-03 12:00:00.0<NA><NA>RKOLASIIIKOLASIIIME<NA><NA>100
94415307422989217586921233367DEF5828B96C849DA2C59D9E0AB5C702021-10-13 12:00:00.02021-11-03 12:00:00.0103002021-11-03 12:00:00.0<NA><NA>1KOLASIIIKOLASIIIME<NA><NA>100
95415308287409228848F1D15CFF6850C2FBA208DF1AAA8EBAA640CEF9FB2021-10-13 12:00:00.02021-11-03 12:00:00.0103002021-11-03 12:00:00.0<NA><NA>RKOLASIIIKOLASIIIME<NA><NA>100
964153093054054991511A7271615027EBA2C936D80AF16D8388BF0E07062021-10-13 12:00:00.02021-11-02 12:00:00.0103002021-11-03 12:00:00.0<NA><NA>RKOLASIIIKOLASIIIME<NA><NA>100
97415309386102252209E028575738E630E24CAB05C5D4E60137199C39C92021-10-13 12:00:00.02021-11-04 12:00:00.0103032021-11-03 12:00:00.0<NA><NA>5KOLASIIISMARTIDME<NA>BR100
98415309388102253320E028575738E630E24CAB05C5D4E60137199C39C92021-10-13 12:00:00.02021-11-04 12:00:00.0103032021-11-03 12:00:00.0<NA><NA>5KOLASIIISMARTIDME<NA>BR100
99415309391413457619E028575738E630E24CAB05C5D4E60137199C39C92021-10-13 12:00:00.02021-11-04 12:00:00.0103032021-11-03 12:00:00.0<NA><NA>5KOLASIIISMARTIDME<NA>BR100