Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory46.3 B

Variable types

Numeric4
Categorical1

Alerts

tco_btc_u_ct is highly overall correlated with tco_btc_u_amHigh correlation
tco_btc_u_am is highly overall correlated with tco_btc_u_ctHigh correlation
tco_btc_u_am has unique valuesUnique
agegrp_dc has 12 (12.0%) zerosZeros

Reproduction

Analysis started2023-12-11 22:41:40.161506
Analysis finished2023-12-11 22:41:41.789451
Duration1.63 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

crym
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201998.42
Minimum201901
Maximum202109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T07:41:41.841343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201901
5-th percentile201902
Q1201908
median202007.5
Q3202103
95-th percentile202108
Maximum202109
Range208
Interquartile range (IQR)195

Descriptive statistics

Standard deviation83.497315
Coefficient of variation (CV)0.00041335628
Kurtosis-1.5645484
Mean201998.42
Median Absolute Deviation (MAD)97.5
Skewness0.1299651
Sum20199842
Variance6971.8016
MonotonicityNot monotonic
2023-12-12T07:41:41.932699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
201902 6
 
6.0%
202105 6
 
6.0%
202107 6
 
6.0%
202011 6
 
6.0%
202009 5
 
5.0%
201901 4
 
4.0%
201907 4
 
4.0%
201912 4
 
4.0%
202109 4
 
4.0%
202004 4
 
4.0%
Other values (21) 51
51.0%
ValueCountFrequency (%)
201901 4
4.0%
201902 6
6.0%
201903 3
3.0%
201904 1
 
1.0%
201905 3
3.0%
201906 3
3.0%
201907 4
4.0%
201908 3
3.0%
201909 3
3.0%
201910 2
 
2.0%
ValueCountFrequency (%)
202109 4
4.0%
202108 3
3.0%
202107 6
6.0%
202106 1
 
1.0%
202105 6
6.0%
202104 3
3.0%
202103 4
4.0%
202102 2
 
2.0%
202101 2
 
2.0%
202012 3
3.0%

ma_fem_dc
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
60 
2
40 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 60
60.0%
2 40
40.0%

Length

2023-12-12T07:41:42.025637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:41:42.093518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 60
60.0%
2 40
40.0%

agegrp_dc
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.7
Minimum0
Maximum90
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T07:41:42.156898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median40
Q370
95-th percentile90
Maximum90
Range90
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.760769
Coefficient of variation (CV)0.64341765
Kurtosis-1.0980177
Mean44.7
Median Absolute Deviation (MAD)20
Skewness0.017310278
Sum4470
Variance827.18182
MonotonicityNot monotonic
2023-12-12T07:41:42.228721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
40 13
13.0%
60 13
13.0%
90 12
12.0%
0 12
12.0%
20 10
10.0%
50 10
10.0%
30 9
9.0%
10 7
7.0%
70 7
7.0%
80 7
7.0%
ValueCountFrequency (%)
0 12
12.0%
10 7
7.0%
20 10
10.0%
30 9
9.0%
40 13
13.0%
50 10
10.0%
60 13
13.0%
70 7
7.0%
80 7
7.0%
90 12
12.0%
ValueCountFrequency (%)
90 12
12.0%
80 7
7.0%
70 7
7.0%
60 13
13.0%
50 10
10.0%
40 13
13.0%
30 9
9.0%
20 10
10.0%
10 7
7.0%
0 12
12.0%

tco_btc_u_ct
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1050407.6
Minimum2
Maximum6824744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T07:41:42.317562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile35.8
Q12536.5
median362676.5
Q31341416
95-th percentile4385880.5
Maximum6824744
Range6824742
Interquartile range (IQR)1338879.5

Descriptive statistics

Standard deviation1591688.7
Coefficient of variation (CV)1.5153058
Kurtosis3.1891536
Mean1050407.6
Median Absolute Deviation (MAD)362472.5
Skewness1.8947252
Sum1.0504076 × 108
Variance2.5334729 × 1012
MonotonicityNot monotonic
2023-12-12T07:41:42.420567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 2
 
2.0%
1950 1
 
1.0%
3288635 1
 
1.0%
399947 1
 
1.0%
495862 1
 
1.0%
10 1
 
1.0%
2881 1
 
1.0%
7630 1
 
1.0%
514444 1
 
1.0%
77017 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
2 1
1.0%
5 1
1.0%
7 1
1.0%
10 1
1.0%
32 1
1.0%
36 1
1.0%
39 1
1.0%
52 1
1.0%
56 1
1.0%
75 2
2.0%
ValueCountFrequency (%)
6824744 1
1.0%
6515961 1
1.0%
6413439 1
1.0%
4823882 1
1.0%
4629147 1
1.0%
4373077 1
1.0%
4324272 1
1.0%
3977839 1
1.0%
3741927 1
1.0%
3436817 1
1.0%

tco_btc_u_am
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.391152 × 1010
Minimum134420
Maximum3.0658159 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T07:41:42.524548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum134420
5-th percentile858614.8
Q199966451
median1.7154884 × 1010
Q38.070696 × 1010
95-th percentile1.8670306 × 1011
Maximum3.0658159 × 1011
Range3.0658145 × 1011
Interquartile range (IQR)8.0606994 × 1010

Descriptive statistics

Standard deviation7.5096471 × 1010
Coefficient of variation (CV)1.3929578
Kurtosis1.8836034
Mean5.391152 × 1010
Median Absolute Deviation (MAD)1.7152305 × 1010
Skewness1.5778068
Sum5.391152 × 1012
Variance5.63948 × 1021
MonotonicityNot monotonic
2023-12-12T07:41:42.623030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85255299 1
 
1.0%
55418298661 1
 
1.0%
2424308 1
 
1.0%
26037727618 1
 
1.0%
38907814418 1
 
1.0%
653687 1
 
1.0%
122172678 1
 
1.0%
684575123 1
 
1.0%
36809748521 1
 
1.0%
6913349946 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
134420 1
1.0%
202300 1
1.0%
260714 1
1.0%
653687 1
1.0%
672715 1
1.0%
868399 1
1.0%
1744250 1
1.0%
1841611 1
1.0%
2391543 1
1.0%
2424308 1
1.0%
ValueCountFrequency (%)
306581586287 1
1.0%
296217479689 1
1.0%
293329937600 1
1.0%
226246742522 1
1.0%
191103848741 1
1.0%
186471436938 1
1.0%
183035285353 1
1.0%
182160963990 1
1.0%
181360535814 1
1.0%
179767736210 1
1.0%

Interactions

2023-12-12T07:41:41.424442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:40.523871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:40.760416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.165349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.484517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:40.578135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:40.832533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.224136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.543192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:40.631033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.040052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.285649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.608269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:40.696079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.101736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:41:41.355139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:41:42.691524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
crymma_fem_dcagegrp_dctco_btc_u_cttco_btc_u_am
crym1.0000.0000.1280.3430.090
ma_fem_dc0.0001.0000.0870.4960.497
agegrp_dc0.1280.0871.0000.6730.628
tco_btc_u_ct0.3430.4960.6731.0000.982
tco_btc_u_am0.0900.4970.6280.9821.000
2023-12-12T07:41:42.764767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
crymagegrp_dctco_btc_u_cttco_btc_u_amma_fem_dc
crym1.000-0.1050.1290.1360.000
agegrp_dc-0.1051.000-0.0110.0130.056
tco_btc_u_ct0.129-0.0111.0000.9970.479
tco_btc_u_am0.1360.0130.9971.0000.482
ma_fem_dc0.0000.0560.4790.4821.000

Missing values

2023-12-12T07:41:41.689664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:41:41.759763image/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.

Sample

crymma_fem_dcagegrp_dctco_btc_u_cttco_btc_u_am
0201902110195085255299
12020042404823882226246742522
220210712066745728904188009
3201904140146704393846566877
4202105150129292474427492918
520210729065439648586
62021052406515961296217479689
720190919031327807810
82020112304373077182160963990
920190519023720436506
crymma_fem_dcagegrp_dctco_btc_u_cttco_btc_u_am
90202104180124061062629111
91202107150133120476327091119
922021052103125137687550
9320190915069825145503274038
94201903202202300
9520210816042012427813345411
962021071302289600118766762952
9720201110361744250
98201905270812706766456864
9920200219033428809510