Overview

Dataset statistics

Number of variables20
Number of observations9822
Missing cells0
Missing cells (%)0.0%
Duplicate rows6331
Duplicate rows (%)64.5%
Total size in memory1.5 MiB
Average record size in memory160.0 B

Variable types

NUM15
BOOL3
CAT2

Reproduction

Analysis started2020-08-25 01:17:26.070564
Analysis finished2020-08-25 01:18:00.265528
Duration34.19 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 6331 (64.5%) duplicate rows Duplicates
MOSHOOFD is highly correlated with MOSTYPEHigh correlation
MOSTYPE is highly correlated with MOSHOOFDHigh correlation
ABESAUT has 9730 (99.1%) zeros Zeros
MBERARBG has 1995 (20.3%) zeros Zeros
MFALLEEN has 2916 (29.7%) zeros Zeros
AFIETS has 9573 (97.5%) zeros Zeros
MRELSA has 4185 (42.6%) zeros Zeros
MBERHOOG has 2576 (26.2%) zeros Zeros
MINK123M has 8253 (84.0%) zeros Zeros
MZPART has 1436 (14.6%) zeros Zeros
ATRACTOR has 9576 (97.5%) zeros Zeros
PPERSONG has 9777 (99.5%) zeros Zeros
PLEVEN has 9308 (94.8%) zeros Zeros
MAUT0 has 2475 (25.2%) zeros Zeros

Variables

AWAOREG
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.9 KiB
0
9784
1
 
34
2
 
4
ValueCountFrequency (%) 
0978499.6%
 
1340.3%
 
24< 0.1%
 
2020-08-25T01:18:00.367945image/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 (%) 
0978499.6%
 
1340.3%
 
24< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9822100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0978499.6%
 
1340.3%
 
24< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common9822100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0978499.6%
 
1340.3%
 
24< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9822100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0978499.6%
 
1340.3%
 
24< 0.1%
 

MOSTYPE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count40
Unique (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.25320708613317
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Memory size76.9 KiB
2020-08-25T01:18:00.475023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median30
Q335
95-th percentile39.95
Maximum41
Range40
Interquartile range (IQR)25

Descriptive statistics

Standard deviation12.91805758
Coefficient of variation (CV)0.532632964
Kurtosis-1.3599262
Mean24.25320709
Median Absolute Deviation (MAD)8
Skewness-0.4353623774
Sum238215
Variance166.8762116
2020-08-25T01:18:00.582557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
33140114.3%
 
385695.8%
 
85465.6%
 
395425.5%
 
94604.7%
 
34334.4%
 
233763.8%
 
363733.8%
 
353623.7%
 
413553.6%
 
343253.3%
 
243243.3%
 
313183.2%
 
133023.1%
 
112862.9%
 
102712.8%
 
322342.4%
 
372332.4%
 
12182.2%
 
62092.1%
 
121942.0%
 
301901.9%
 
221691.7%
 
21481.5%
 
291391.4%
 
Other values (15)8458.6%
 
ValueCountFrequency (%) 
12182.2%
 
21481.5%
 
34334.4%
 
4900.9%
 
5700.7%
 
62092.1%
 
7720.7%
 
85465.6%
 
94604.7%
 
102712.8%
 
ValueCountFrequency (%) 
413553.6%
 
401371.4%
 
395425.5%
 
385695.8%
 
372332.4%
 
363733.8%
 
353623.7%
 
343253.3%
 
33140114.3%
 
322342.4%
 

ABESAUT
Real number (ℝ≥0)

ZEROS

Distinct count6
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01109753614335166
Minimum0
Maximum5
Zeros9730
Zeros (%)99.1%
Memory size76.9 KiB
2020-08-25T01:18:00.876582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.1299277127
Coefficient of variation (CV)11.70779811
Kurtosis445.3430412
Mean0.01109753614
Median Absolute Deviation (MAD)0
Skewness17.61981505
Sum109
Variance0.01688121052
2020-08-25T01:18:00.974789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0973099.1%
 
1830.8%
 
24< 0.1%
 
33< 0.1%
 
51< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
0973099.1%
 
1830.8%
 
24< 0.1%
 
33< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
51< 0.1%
 
41< 0.1%
 
33< 0.1%
 
24< 0.1%
 
1830.8%
 
0973099.1%
 

MBERARBG
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.226532274485848
Minimum0
Maximum9
Zeros1995
Zeros (%)20.3%
Memory size76.9 KiB
2020-08-25T01:18:01.079649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.748025074
Coefficient of variation (CV)0.7850885856
Kurtosis0.3741731461
Mean2.226532274
Median Absolute Deviation (MAD)1
Skewness0.698299234
Sum21869
Variance3.05559166
2020-08-25T01:18:01.182035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2232723.7%
 
0199520.3%
 
3196720.0%
 
1152315.5%
 
4100610.2%
 
55245.3%
 
62993.0%
 
71191.2%
 
8380.4%
 
9240.2%
 
ValueCountFrequency (%) 
0199520.3%
 
1152315.5%
 
2232723.7%
 
3196720.0%
 
4100610.2%
 
55245.3%
 
62993.0%
 
71191.2%
 
8380.4%
 
9240.2%
 
ValueCountFrequency (%) 
9240.2%
 
8380.4%
 
71191.2%
 
62993.0%
 
55245.3%
 
4100610.2%
 
3196720.0%
 
2232723.7%
 
1152315.5%
 
0199520.3%
 

MFALLEEN
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8872938301771534
Minimum0
Maximum9
Zeros2916
Zeros (%)29.7%
Memory size76.9 KiB
2020-08-25T01:18:01.280378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.779238395
Coefficient of variation (CV)0.9427458333
Kurtosis0.7698022149
Mean1.88729383
Median Absolute Deviation (MAD)1
Skewness0.947303254
Sum18537
Variance3.165689265
2020-08-25T01:18:01.384789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0291629.7%
 
2214321.8%
 
1161916.5%
 
3143914.7%
 
48909.1%
 
54164.2%
 
62222.3%
 
71071.1%
 
8360.4%
 
9340.3%
 
ValueCountFrequency (%) 
0291629.7%
 
1161916.5%
 
2214321.8%
 
3143914.7%
 
48909.1%
 
54164.2%
 
62222.3%
 
71071.1%
 
8360.4%
 
9340.3%
 
ValueCountFrequency (%) 
9340.3%
 
8360.4%
 
71071.1%
 
62222.3%
 
54164.2%
 
48909.1%
 
3143914.7%
 
2214321.8%
 
1161916.5%
 
0291629.7%
 

AFIETS
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.031459987782529016
Minimum0
Maximum4
Zeros9573
Zeros (%)97.5%
Memory size76.9 KiB
2020-08-25T01:18:01.491888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2090700858
Coefficient of variation (CV)6.645586998
Kurtosis70.00745584
Mean0.03145998778
Median Absolute Deviation (MAD)0
Skewness7.736212958
Sum309
Variance0.04371030076
2020-08-25T01:18:01.600185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0957397.5%
 
11932.0%
 
2530.5%
 
32< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
0957397.5%
 
11932.0%
 
2530.5%
 
32< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
41< 0.1%
 
32< 0.1%
 
2530.5%
 
11932.0%
 
0957397.5%
 

MRELSA
Real number (ℝ≥0)

ZEROS

Distinct count8
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8731419262879251
Minimum0
Maximum7
Zeros4185
Zeros (%)42.6%
Memory size76.9 KiB
2020-08-25T01:18:01.714893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9619549787
Coefficient of variation (CV)1.101716628
Kurtosis2.697212014
Mean0.8731419263
Median Absolute Deviation (MAD)1
Skewness1.313976506
Sum8576
Variance0.9253573811
2020-08-25T01:18:01.831190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0418542.6%
 
1340234.6%
 
2179018.2%
 
32602.6%
 
41331.4%
 
5320.3%
 
6180.2%
 
72< 0.1%
 
ValueCountFrequency (%) 
0418542.6%
 
1340234.6%
 
2179018.2%
 
32602.6%
 
41331.4%
 
5320.3%
 
6180.2%
 
72< 0.1%
 
ValueCountFrequency (%) 
72< 0.1%
 
6180.2%
 
5320.3%
 
41331.4%
 
32602.6%
 
2179018.2%
 
1340234.6%
 
0418542.6%
 

MBERHOOG
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8987986153532885
Minimum0
Maximum9
Zeros2576
Zeros (%)26.2%
Memory size76.9 KiB
2020-08-25T01:18:01.950215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.814405856
Coefficient of variation (CV)0.9555546551
Kurtosis1.469565549
Mean1.898798615
Median Absolute Deviation (MAD)1
Skewness1.196312264
Sum18650
Variance3.29206861
2020-08-25T01:18:02.072021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0257626.2%
 
2227823.2%
 
1211921.6%
 
3128213.1%
 
46416.5%
 
54154.2%
 
62482.5%
 
71591.6%
 
9570.6%
 
8470.5%
 
ValueCountFrequency (%) 
0257626.2%
 
1211921.6%
 
2227823.2%
 
3128213.1%
 
46416.5%
 
54154.2%
 
62482.5%
 
71591.6%
 
8470.5%
 
9570.6%
 
ValueCountFrequency (%) 
9570.6%
 
8470.5%
 
71591.6%
 
62482.5%
 
54154.2%
 
46416.5%
 
3128213.1%
 
2227823.2%
 
1211921.6%
 
0257626.2%
 

MOSHOOFD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.779067399714926
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size76.9 KiB
2020-08-25T01:18:02.175981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q38
95-th percentile9.95
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.874147629
Coefficient of variation (CV)0.4973376205
Kurtosis-1.353248399
Mean5.7790674
Median Absolute Deviation (MAD)2
Skewness-0.3321361204
Sum56762
Variance8.260724596
2020-08-25T01:18:02.280205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8269427.4%
 
3151315.4%
 
9111111.3%
 
19599.8%
 
59409.6%
 
78819.0%
 
28278.4%
 
104925.0%
 
63263.3%
 
4790.8%
 
ValueCountFrequency (%) 
19599.8%
 
28278.4%
 
3151315.4%
 
4790.8%
 
59409.6%
 
63263.3%
 
78819.0%
 
8269427.4%
 
9111111.3%
 
104925.0%
 
ValueCountFrequency (%) 
104925.0%
 
9111111.3%
 
8269427.4%
 
78819.0%
 
63263.3%
 
59409.6%
 
4790.8%
 
3151315.4%
 
28278.4%
 
19599.8%
 

AWALAND
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.9 KiB
0
9613
1
 
209
ValueCountFrequency (%) 
0961397.9%
 
12092.1%
 

MINK123M
Real number (ℝ≥0)

ZEROS

Distinct count9
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2080024434941967
Minimum0
Maximum9
Zeros8253
Zeros (%)84.0%
Memory size76.9 KiB
2020-08-25T01:18:02.382173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5618321201
Coefficient of variation (CV)2.701084231
Kurtosis24.38729288
Mean0.2080024435
Median Absolute Deviation (MAD)0
Skewness4.001573899
Sum2043
Variance0.3156553312
2020-08-25T01:18:02.492223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0825384.0%
 
1126912.9%
 
21881.9%
 
3660.7%
 
4380.4%
 
54< 0.1%
 
62< 0.1%
 
71< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
0825384.0%
 
1126912.9%
 
21881.9%
 
3660.7%
 
4380.4%
 
54< 0.1%
 
62< 0.1%
 
71< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
91< 0.1%
 
71< 0.1%
 
62< 0.1%
 
54< 0.1%
 
4380.4%
 
3660.7%
 
21881.9%
 
1126912.9%
 
0825384.0%
 

PBESAUT
Categorical

Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.9 KiB
0
9730
6
 
71
5
 
17
7
 
4
ValueCountFrequency (%) 
0973099.1%
 
6710.7%
 
5170.2%
 
74< 0.1%
 
2020-08-25T01:18:02.666726image/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 (%) 
0973099.1%
 
6710.7%
 
5170.2%
 
74< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9822100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0973099.1%
 
6710.7%
 
5170.2%
 
74< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common9822100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0973099.1%
 
6710.7%
 
5170.2%
 
74< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9822100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0973099.1%
 
6710.7%
 
5170.2%
 
74< 0.1%
 

MZPART
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.750661779678273
Minimum0
Maximum9
Zeros1436
Zeros (%)14.6%
Memory size76.9 KiB
2020-08-25T01:18:02.772401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.002960102
Coefficient of variation (CV)0.728173895
Kurtosis0.1789732088
Mean2.75066178
Median Absolute Deviation (MAD)1
Skewness0.6817543877
Sum27017
Variance4.01184917
2020-08-25T01:18:02.875044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2252025.7%
 
4166817.0%
 
0143614.6%
 
3141514.4%
 
1117812.0%
 
56396.5%
 
75185.3%
 
63163.2%
 
91091.1%
 
8230.2%
 
ValueCountFrequency (%) 
0143614.6%
 
1117812.0%
 
2252025.7%
 
3141514.4%
 
4166817.0%
 
56396.5%
 
63163.2%
 
75185.3%
 
8230.2%
 
91091.1%
 
ValueCountFrequency (%) 
91091.1%
 
8230.2%
 
75185.3%
 
63163.2%
 
56396.5%
 
4166817.0%
 
3141514.4%
 
2252025.7%
 
1117812.0%
 
0143614.6%
 

ATRACTOR
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03441254327020973
Minimum0
Maximum6
Zeros9576
Zeros (%)97.5%
Memory size76.9 KiB
2020-08-25T01:18:02.979051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2497057754
Coefficient of variation (CV)7.256242977
Kurtosis154.5702339
Mean0.03441254327
Median Absolute Deviation (MAD)0
Skewness10.58589398
Sum338
Variance0.06235297428
2020-08-25T01:18:03.088863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0957697.5%
 
11841.9%
 
2460.5%
 
370.1%
 
460.1%
 
62< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
0957697.5%
 
11841.9%
 
2460.5%
 
370.1%
 
460.1%
 
51< 0.1%
 
62< 0.1%
 
ValueCountFrequency (%) 
62< 0.1%
 
51< 0.1%
 
460.1%
 
370.1%
 
2460.5%
 
11841.9%
 
0957697.5%
 

PPERSONG
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011504785176135207
Minimum0
Maximum6
Zeros9777
Zeros (%)99.5%
Memory size76.9 KiB
2020-08-25T01:18:03.200570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1886991463
Coefficient of variation (CV)16.40179659
Kurtosis460.047022
Mean0.01150478518
Median Absolute Deviation (MAD)0
Skewness19.90030163
Sum113
Variance0.03560736781
2020-08-25T01:18:03.310670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0977799.5%
 
2240.2%
 
370.1%
 
160.1%
 
44< 0.1%
 
62< 0.1%
 
52< 0.1%
 
ValueCountFrequency (%) 
0977799.5%
 
160.1%
 
2240.2%
 
370.1%
 
44< 0.1%
 
52< 0.1%
 
62< 0.1%
 
ValueCountFrequency (%) 
62< 0.1%
 
52< 0.1%
 
44< 0.1%
 
370.1%
 
2240.2%
 
160.1%
 
0977799.5%
 

PLEVEN
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20230095703522705
Minimum0
Maximum9
Zeros9308
Zeros (%)94.8%
Memory size76.9 KiB
2020-08-25T01:18:03.417230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9105737126
Coefficient of variation (CV)4.501084552
Kurtosis22.35284303
Mean0.202300957
Median Absolute Deviation (MAD)0
Skewness4.720107307
Sum1987
Variance0.8291444861
2020-08-25T01:18:03.518625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0930894.8%
 
41721.8%
 
31411.4%
 
6650.7%
 
5650.7%
 
2510.5%
 
1140.1%
 
74< 0.1%
 
91< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
0930894.8%
 
1140.1%
 
2510.5%
 
31411.4%
 
41721.8%
 
5650.7%
 
6650.7%
 
74< 0.1%
 
81< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
91< 0.1%
 
81< 0.1%
 
74< 0.1%
 
6650.7%
 
5650.7%
 
41721.8%
 
31411.4%
 
2510.5%
 
1140.1%
 
0930894.8%
 

MAUT0
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9567297902667482
Minimum0
Maximum9
Zeros2475
Zeros (%)25.2%
Memory size76.9 KiB
2020-08-25T01:18:03.621269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.596841978
Coefficient of variation (CV)0.8160768983
Kurtosis0.7155581591
Mean1.95672979
Median Absolute Deviation (MAD)1
Skewness0.6843407627
Sum19219
Variance2.549904303
2020-08-25T01:18:03.725729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2261126.6%
 
0247525.2%
 
3187119.0%
 
1132713.5%
 
4102010.4%
 
52882.9%
 
61451.5%
 
7400.4%
 
9270.3%
 
8180.2%
 
ValueCountFrequency (%) 
0247525.2%
 
1132713.5%
 
2261126.6%
 
3187119.0%
 
4102010.4%
 
52882.9%
 
61451.5%
 
7400.4%
 
8180.2%
 
9270.3%
 
ValueCountFrequency (%) 
9270.3%
 
8180.2%
 
7400.4%
 
61451.5%
 
52882.9%
 
4102010.4%
 
3187119.0%
 
2261126.6%
 
1132713.5%
 
0247525.2%
 

AGEZONG
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.9 KiB
0
9744
1
 
78
ValueCountFrequency (%) 
0974499.2%
 
1780.8%
 

MKOOPKLA
Real number (ℝ≥0)

Distinct count8
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.260333944206883
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size76.9 KiB
2020-08-25T01:18:04.014214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.998913413
Coefficient of variation (CV)0.4691917204
Kurtosis-0.8764761978
Mean4.260333944
Median Absolute Deviation (MAD)1
Skewness0.2143240098
Sum41845
Variance3.995654832
2020-08-25T01:18:04.127765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3255626.0%
 
6158716.2%
 
4153915.7%
 
59649.8%
 
19389.5%
 
77777.9%
 
27317.4%
 
87307.4%
 
ValueCountFrequency (%) 
19389.5%
 
27317.4%
 
3255626.0%
 
4153915.7%
 
59649.8%
 
6158716.2%
 
77777.9%
 
87307.4%
 
ValueCountFrequency (%) 
87307.4%
 
77777.9%
 
6158716.2%
 
59649.8%
 
4153915.7%
 
3255626.0%
 
27317.4%
 
19389.5%
 

target
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.9 KiB
0
9236
1
 
586
ValueCountFrequency (%) 
0923694.0%
 
15866.0%
 

Interactions

2020-08-25T01:17:27.558088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:27.724511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:27.882934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:28.033290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:28.183669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:28.351327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:28.516586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:28.667358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:28.822571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:28.971560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:29.123716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:29.280405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:29.437668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:29.586646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:29.934456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:30.094668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:30.236524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:30.371723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:30.500617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:30.630570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:30.776708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:30.925302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:31.058943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:31.186658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:31.313881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:31.439340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:31.572927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:31.706615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:31.835190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:31.960779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:32.101351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:32.236453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:32.360261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:32.480800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:32.606319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:32.745225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:32.879749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:32.999631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:33.137168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:33.266876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:33.389334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:33.517336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:33.646822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:33.767067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:33.907481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:34.039308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:34.363054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:34.490506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:34.613047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:34.740235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:34.877883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:35.008028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:35.131208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:35.255562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:35.379940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:35.503783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:35.633342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:35.768627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:35.904401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:36.030207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:36.167093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:36.326241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:36.475834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:36.620733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:36.770457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:36.929854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:37.089767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:37.235310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:37.379971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:37.524197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:37.666779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:37.821274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:37.975134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:38.120755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:38.264342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:38.419675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:38.570493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:38.904021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:39.038432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:39.183577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:39.335993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:39.482190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:39.620908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:39.760084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:39.894457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:40.029369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:40.174736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:40.319172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:40.456370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:40.592155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:40.741184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:40.878343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:41.004788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:41.125822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:41.254577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:41.397445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:41.530540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:41.656727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:41.786029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:41.909947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:42.038536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:42.166129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:42.295572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:42.416248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:42.537650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:42.671404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:42.811980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:42.947840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:43.263410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:43.396589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:43.540353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:43.673591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:43.794445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:43.927308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:44.049596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:44.170690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:44.306116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:44.433860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:44.557851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:44.679216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:44.815850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:44.950162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:45.075597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:45.194942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:45.316691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:45.460369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:45.590683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:45.710097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:45.831785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:45.950790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:46.073626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:46.205659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:46.342624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:46.477131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:46.603750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:46.743065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:46.883142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:47.012298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:47.146619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:47.270106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:47.582623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:47.716284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:47.843656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:47.972995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:48.097456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:48.219771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:48.350355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:48.481292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:48.606093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:48.726847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:48.871428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:49.019751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:49.156781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:49.292537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:49.429292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:49.579659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:49.725860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:49.856808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:49.995897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:50.131587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:50.265563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:50.405131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:50.543084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:50.673770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:50.804087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:50.948104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:51.094244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:51.231426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:51.363286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:51.496522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:51.646015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:51.963247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:52.094323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:52.242556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:52.378365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:52.513932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:52.652260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:52.788316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:52.918701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:53.051125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:53.196508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:53.335177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:53.464541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:53.591120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:53.716258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:53.858998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:53.992635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:54.116227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:54.239862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:54.359911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:54.482510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:54.610091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:54.737038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:54.860841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:54.983878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:55.116348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:55.252404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:55.376750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:55.500093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:55.622918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:55.761221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:55.902394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:56.030575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:56.332774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:56.450857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:56.635484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:56.784429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:56.915967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:57.037379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:57.162308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:57.295442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:57.449026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:57.594303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:57.732210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:57.869233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:58.025790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:58.170739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:58.307021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:58.444707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:58.582391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:58.718045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:58.869107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:59.012325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:59.152380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:17:59.290296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:18:04.278892image/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.
2020-08-25T01:18:04.593509image/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.
2020-08-25T01:18:04.902145image/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.
2020-08-25T01:18:05.218713image/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.
2020-08-25T01:18:05.478118image/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

2020-08-25T01:17:59.593265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:18:00.078474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

AWAOREGMOSTYPEABESAUTMBERARBGMFALLEENAFIETSMRELSAMBERHOOGMOSHOOFDAWALANDMINK123MPBESAUTMZPARTATRACTORPPERSONGPLEVENMAUT0AGEZONGMKOOPKLAtarget
0033051001800010001030
1037000020800030002040
2037004020800000002040
309012024300020000040
40400020101000040001030
5023023062500000003030
6039010020900000001050
7033050022800030002030
8033083001800020003030
9011032002300030002070

Last rows

AWAOREGMOSTYPEABESAUTMBERARBGMFALLEENAFIETSMRELSAMBERHOOGMOSHOOFDAWALANDMINK123MPBESAUTMZPARTATRACTORPPERSONGPLEVENMAUT0AGEZONGMKOOPKLAtarget
9812033062000800030004030
9813022013021500040003020
9814022003003500020003020
9815033031012800010002030
9816010021014300040001080
9817033002025800060002030
9818024034022500020063021
9819036042002800030002030
9820033032011800020002030
982108000028200070000080

Duplicate rows

Most frequent

AWAOREGMOSTYPEABESAUTMBERARBGMFALLEENAFIETSMRELSAMBERHOOGMOSHOOFDAWALANDMINK123MPBESAUTMZPARTATRACTORPPERSONGPLEVENMAUT0AGEZONGMKOOPKLAtargetcount
92703404002080000000006091
80103302200180002000203035
80503302201380104000103035
470200201010004000106033
64903000001070002000302031
87303305002280003000203030
85603304101280102000203029
86403305000080000000003028
83503303202180101000203026
740300603010002000406024