Overview

Dataset statistics

Number of variables20
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory156.4 KiB
Average record size in memory160.1 B

Variable types

CAT11
NUM6
BOOL3

Reproduction

Analysis started2020-08-25 01:23:22.185833
Analysis finished2020-08-25 01:23:30.454080
Duration8.27 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Savings-account has 63 (6.3%) zeros Zeros
Purpose has 97 (9.7%) zeros Zeros
Credit-history has 40 (4.0%) zeros Zeros

Variables

Status
Categorical

Distinct count4
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
394
0
274
1
269
3
 
63
ValueCountFrequency (%) 
239439.4%
 
027427.4%
 
126926.9%
 
3636.3%
 
2020-08-25T01:23:30.523913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
239439.4%
 
027427.4%
 
126926.9%
 
3636.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
239439.4%
 
027427.4%
 
126926.9%
 
3636.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
239439.4%
 
027427.4%
 
126926.9%
 
3636.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
239439.4%
 
027427.4%
 
126926.9%
 
3636.3%
 

Liable-people
Categorical

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
845
2
 
155
ValueCountFrequency (%) 
184584.5%
 
215515.5%
 
2020-08-25T01:23:30.653421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
184528.2%
 
21555.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number200066.7%
 
Other Punctuation100033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0100050.0%
 
184542.2%
 
21557.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
184528.2%
 
21555.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
184528.2%
 
21555.2%
 

Existing-credits
Categorical

Distinct count4
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
633
2
333
3
 
28
4
 
6
ValueCountFrequency (%) 
163363.3%
 
233333.3%
 
3282.8%
 
460.6%
 
2020-08-25T01:23:30.780979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
163321.1%
 
233311.1%
 
3280.9%
 
460.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number200066.7%
 
Other Punctuation100033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0100050.0%
 
163331.6%
 
233316.7%
 
3281.4%
 
460.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
163321.1%
 
233311.1%
 
3280.9%
 
460.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
163321.1%
 
233311.1%
 
3280.9%
 
460.2%
 

Duration
Real number (ℝ≥0)

Distinct count33
Unique (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.903
Minimum4.0
Maximum72.0
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T01:23:30.891932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q112
median18
Q324
95-th percentile48
Maximum72
Range68
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.05881445
Coefficient of variation (CV)0.5768939603
Kurtosis0.9197813601
Mean20.903
Median Absolute Deviation (MAD)6
Skewness1.094184172
Sum20903
Variance145.415006
2020-08-25T01:23:31.000526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2418418.4%
 
1217917.9%
 
1811311.3%
 
36838.3%
 
6757.5%
 
15646.4%
 
9494.9%
 
48484.8%
 
30404.0%
 
21303.0%
 
10282.8%
 
27131.3%
 
60131.3%
 
42111.1%
 
1190.9%
 
2080.8%
 
870.7%
 
460.6%
 
3950.5%
 
750.5%
 
4550.5%
 
1340.4%
 
1440.4%
 
3330.3%
 
2830.3%
 
Other values (8)111.1%
 
ValueCountFrequency (%) 
460.6%
 
510.1%
 
6757.5%
 
750.5%
 
870.7%
 
9494.9%
 
10282.8%
 
1190.9%
 
1217917.9%
 
1340.4%
 
ValueCountFrequency (%) 
7210.1%
 
60131.3%
 
5420.2%
 
48484.8%
 
4710.1%
 
4550.5%
 
42111.1%
 
4010.1%
 
3950.5%
 
36838.3%
 

Personal-status
Categorical

Distinct count4
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
548
0
310
2
 
92
1
 
50
ValueCountFrequency (%) 
354854.8%
 
031031.0%
 
2929.2%
 
1505.0%
 
2020-08-25T01:23:31.142670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
354854.8%
 
031031.0%
 
2929.2%
 
1505.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
354854.8%
 
031031.0%
 
2929.2%
 
1505.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
354854.8%
 
031031.0%
 
2929.2%
 
1505.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
354854.8%
 
031031.0%
 
2929.2%
 
1505.0%
 

Savings-account
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.685
Minimum0
Maximum4
Zeros63
Zeros (%)6.3%
Memory size7.9 KiB
2020-08-25T01:23:31.250068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.239883991
Coefficient of variation (CV)0.7358361967
Kurtosis-0.434849282
Mean1.685
Median Absolute Deviation (MAD)0
Skewness0.9932585806
Sum1685
Variance1.537312312
2020-08-25T01:23:31.356656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
160360.3%
 
418318.3%
 
210310.3%
 
0636.3%
 
3484.8%
 
ValueCountFrequency (%) 
0636.3%
 
160360.3%
 
210310.3%
 
3484.8%
 
418318.3%
 
ValueCountFrequency (%) 
418318.3%
 
3484.8%
 
210310.3%
 
160360.3%
 
0636.3%
 

Property
Categorical

Distinct count4
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
332
3
282
0
232
2
154
ValueCountFrequency (%) 
133233.2%
 
328228.2%
 
023223.2%
 
215415.4%
 
2020-08-25T01:23:31.496164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
133233.2%
 
328228.2%
 
023223.2%
 
215415.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
133233.2%
 
328228.2%
 
023223.2%
 
215415.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
133233.2%
 
328228.2%
 
023223.2%
 
215415.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
133233.2%
 
328228.2%
 
023223.2%
 
215415.4%
 

Purpose
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.484
Minimum0
Maximum9
Zeros97
Zeros (%)9.7%
Memory size7.9 KiB
2020-08-25T01:23:31.604666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q36
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.421075299
Coefficient of variation (CV)0.5399365075
Kurtosis-0.3608249899
Mean4.484
Median Absolute Deviation (MAD)2
Skewness0.04015026653
Sum4484
Variance5.861605606
2020-08-25T01:23:31.892868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
628028.0%
 
423423.4%
 
318118.1%
 
910310.3%
 
0979.7%
 
2505.0%
 
7222.2%
 
5121.2%
 
1121.2%
 
890.9%
 
ValueCountFrequency (%) 
0979.7%
 
1121.2%
 
2505.0%
 
318118.1%
 
423423.4%
 
5121.2%
 
628028.0%
 
7222.2%
 
890.9%
 
910310.3%
 
ValueCountFrequency (%) 
910310.3%
 
890.9%
 
7222.2%
 
628028.0%
 
5121.2%
 
423423.4%
 
318118.1%
 
2505.0%
 
1121.2%
 
0979.7%
 

Telephone
Boolean

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
596
0
404
ValueCountFrequency (%) 
159659.6%
 
040440.4%
 

Job
Categorical

Distinct count4
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
630
3
200
0
148
2
 
22
ValueCountFrequency (%) 
163063.0%
 
320020.0%
 
014814.8%
 
2222.2%
 
2020-08-25T01:23:32.021417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
163063.0%
 
320020.0%
 
014814.8%
 
2222.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
163063.0%
 
320020.0%
 
014814.8%
 
2222.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
163063.0%
 
320020.0%
 
014814.8%
 
2222.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
163063.0%
 
320020.0%
 
014814.8%
 
2222.2%
 

Installments
Categorical

Distinct count3
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
814
0
 
139
2
 
47
ValueCountFrequency (%) 
181481.4%
 
013913.9%
 
2474.7%
 
2020-08-25T01:23:32.153377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
181481.4%
 
013913.9%
 
2474.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
181481.4%
 
013913.9%
 
2474.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
181481.4%
 
013913.9%
 
2474.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
181481.4%
 
013913.9%
 
2474.7%
 

Credit-history
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.019
Minimum0
Maximum4
Zeros40
Zeros (%)4.0%
Memory size7.9 KiB
2020-08-25T01:23:32.260193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.174742107
Coefficient of variation (CV)0.3891162993
Kurtosis-0.3948366223
Mean3.019
Median Absolute Deviation (MAD)0
Skewness-0.8127106653
Sum3019
Variance1.380019019
2020-08-25T01:23:32.368828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
453053.0%
 
229329.3%
 
3888.8%
 
1494.9%
 
0404.0%
 
ValueCountFrequency (%) 
0404.0%
 
1494.9%
 
229329.3%
 
3888.8%
 
453053.0%
 
ValueCountFrequency (%) 
453053.0%
 
3888.8%
 
229329.3%
 
1494.9%
 
0404.0%
 

Debtors
Categorical

Distinct count3
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
907
1
 
52
0
 
41
ValueCountFrequency (%) 
290790.7%
 
1525.2%
 
0414.1%
 
2020-08-25T01:23:32.513918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
290790.7%
 
1525.2%
 
0414.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
290790.7%
 
1525.2%
 
0414.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
290790.7%
 
1525.2%
 
0414.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
290790.7%
 
1525.2%
 
0414.1%
 

Foreign
Boolean

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
963
0
 
37
ValueCountFrequency (%) 
196396.3%
 
0373.7%
 

Credit
Real number (ℝ≥0)

Distinct count921
Unique (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3271.258
Minimum250.0
Maximum18424.0
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T01:23:32.619411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile708.95
Q11365.5
median2319.5
Q33972.25
95-th percentile9162.7
Maximum18424
Range18174
Interquartile range (IQR)2606.75

Descriptive statistics

Standard deviation2822.736876
Coefficient of variation (CV)0.8628903241
Kurtosis4.292590308
Mean3271.258
Median Absolute Deviation (MAD)1097.5
Skewness1.94962768
Sum3271258
Variance7967843.471
2020-08-25T01:23:32.727834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
139330.3%
 
126230.3%
 
127530.3%
 
125830.3%
 
147830.3%
 
112620.2%
 
203920.2%
 
43320.2%
 
159720.2%
 
297820.2%
 
395920.2%
 
137420.2%
 
138220.2%
 
257820.2%
 
194020.2%
 
115420.2%
 
153320.2%
 
154620.2%
 
123120.2%
 
238420.2%
 
141020.2%
 
60920.2%
 
263120.2%
 
383220.2%
 
595420.2%
 
Other values (896)94594.5%
 
ValueCountFrequency (%) 
25010.1%
 
27610.1%
 
33810.1%
 
33910.1%
 
34310.1%
 
36210.1%
 
36810.1%
 
38510.1%
 
39210.1%
 
40910.1%
 
ValueCountFrequency (%) 
1842410.1%
 
1594510.1%
 
1585710.1%
 
1567210.1%
 
1565310.1%
 
1489610.1%
 
1478210.1%
 
1455510.1%
 
1442110.1%
 
1431810.1%
 

Age
Real number (ℝ≥0)

Distinct count53
Unique (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.546
Minimum19.0
Maximum75.0
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T01:23:32.855056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q127
median33
Q342
95-th percentile60
Maximum75
Range56
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.37546857
Coefficient of variation (CV)0.3200210593
Kurtosis0.5957795671
Mean35.546
Median Absolute Deviation (MAD)7
Skewness1.020739269
Sum35546
Variance129.4012853
2020-08-25T01:23:32.960093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
27515.1%
 
26505.0%
 
23484.8%
 
24444.4%
 
28434.3%
 
25414.1%
 
35404.0%
 
30404.0%
 
36393.9%
 
31383.8%
 
29373.7%
 
32343.4%
 
33333.3%
 
34323.2%
 
37292.9%
 
22272.7%
 
40252.5%
 
38242.4%
 
42222.2%
 
39212.1%
 
46181.8%
 
41171.7%
 
43171.7%
 
44171.7%
 
47171.7%
 
Other values (28)19619.6%
 
ValueCountFrequency (%) 
1920.2%
 
20141.4%
 
21141.4%
 
22272.7%
 
23484.8%
 
24444.4%
 
25414.1%
 
26505.0%
 
27515.1%
 
28434.3%
 
ValueCountFrequency (%) 
7520.2%
 
7440.4%
 
7010.1%
 
6830.3%
 
6730.3%
 
6650.5%
 
6550.5%
 
6450.5%
 
6380.8%
 
6220.2%
 

Installment-rate
Categorical

Distinct count4
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
4
476
2
231
3
157
1
136
ValueCountFrequency (%) 
447647.6%
 
223123.1%
 
315715.7%
 
113613.6%
 
2020-08-25T01:23:33.094360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
447615.9%
 
22317.7%
 
31575.2%
 
11364.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number200066.7%
 
Other Punctuation100033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0100050.0%
 
447623.8%
 
223111.6%
 
31577.8%
 
11366.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
447615.9%
 
22317.7%
 
31575.2%
 
11364.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
447615.9%
 
22317.7%
 
31575.2%
 
11364.5%
 

Residence-time
Categorical

Distinct count4
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
4
413
2
308
3
149
1
130
ValueCountFrequency (%) 
441341.3%
 
230830.8%
 
314914.9%
 
113013.0%
 
2020-08-25T01:23:33.234563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
441313.8%
 
230810.3%
 
31495.0%
 
11304.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number200066.7%
 
Other Punctuation100033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0100050.0%
 
441320.6%
 
230815.4%
 
31497.4%
 
11306.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
441313.8%
 
230810.3%
 
31495.0%
 
11304.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.100033.3%
 
0100033.3%
 
441313.8%
 
230810.3%
 
31495.0%
 
11304.3%
 

Housing
Categorical

Distinct count3
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
713
2
179
0
 
108
ValueCountFrequency (%) 
171371.3%
 
217917.9%
 
010810.8%
 
2020-08-25T01:23:33.369022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
171371.3%
 
217917.9%
 
010810.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1000100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
171371.3%
 
217917.9%
 
010810.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
171371.3%
 
217917.9%
 
010810.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
171371.3%
 
217917.9%
 
010810.8%
 

target
Boolean

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
700
0
300
ValueCountFrequency (%) 
170070.0%
 
030030.0%
 

Interactions

2020-08-25T01:23:23.657439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:23.813345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:23.996741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:24.145004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:24.305696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:24.470104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:24.624172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:24.788966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:24.966273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:25.126142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:25.302546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:25.478570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:25.639991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:25.782208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:25.932963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:26.074405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:26.235138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:26.384077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:26.530549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:26.697685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:26.869837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:27.032493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:27.387385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:27.560292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:27.734381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:27.896713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:28.070463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:28.221899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:28.389036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:28.559075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:28.713452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:28.873457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:29.043576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:29.191797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:29.349331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:29.504664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:23:33.511353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:23:33.837867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:23:34.171380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:23:34.502583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-25T01:23:34.800199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-25T01:23:29.789083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:23:30.277142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

StatusLiable-peopleExisting-creditsDurationPersonal-statusSavings-accountPropertyPurposeTelephoneJobInstallmentsCredit-historyDebtorsForeignCreditAgeInstallment-rateResidence-timeHousingtarget
021.01.042.024060114217166.029.02.04.021
121.01.018.004360114211126.021.04.02.021
211.01.024.004030314214351.048.01.04.011
301.01.012.004020104211200.023.04.04.021
422.02.012.031160014211963.031.04.02.021
521.02.012.001061112211291.035.04.02.011
601.01.014.031241114213973.022.01.04.001
721.02.018.001161212211098.065.04.04.011
821.01.09.001310114211236.023.01.04.021
921.01.012.004121112212012.061.04.02.011

Last rows

StatusLiable-peopleExisting-creditsDurationPersonal-statusSavings-accountPropertyPurposeTelephoneJobInstallmentsCredit-historyDebtorsForeignCreditAgeInstallment-rateResidence-timeHousingtarget
99001.01.036.031031114215179.029.04.02.010
99101.01.018.021361104211345.026.04.03.010
99201.02.012.03114010121697.046.04.02.010
99321.01.048.031100004214844.033.03.02.020
99411.02.036.031240103212225.057.04.04.000
99511.01.024.001040314212718.020.03.04.020
99611.01.036.002261114012671.050.04.04.000
99701.01.036.001140114211842.034.04.04.010
99811.01.024.011130114214057.043.03.03.010
99901.01.024.001360114212439.035.04.04.010