Overview

Dataset statistics

Number of variables6
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory49.2 B

Variable types

NUM4
BOOL1
CAT1

Reproduction

Analysis started2020-08-24 23:54:50.386858
Analysis finished2020-08-24 23:54:53.305921
Duration2.92 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

NC has 2 (1.9%) zeros Zeros

Variables

seeded
Boolean

Distinct count2
Unique (%)1.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
1
54
0
54
ValueCountFrequency (%) 
15450.0%
 
05450.0%
 

season
Categorical

Distinct count4
Unique (%)3.7%
Missing0
Missing (%)0.0%
Memory size992.0 B
2
32
1
28
3
24
0
24
ValueCountFrequency (%) 
23229.6%
 
12825.9%
 
32422.2%
 
02422.2%
 
2020-08-24T23:54:53.375048image/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 (%) 
23229.6%
 
12825.9%
 
02422.2%
 
32422.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number108100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
23229.6%
 
12825.9%
 
02422.2%
 
32422.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common108100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
23229.6%
 
12825.9%
 
02422.2%
 
32422.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII108100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
23229.6%
 
12825.9%
 
02422.2%
 
32422.2%
 

NC
Real number (ℝ≥0)

ZEROS

Distinct count97
Unique (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5063888889530466
Minimum0.0
Maximum9.420000076293944
Zeros2
Zeros (%)1.9%
Memory size992.0 B
2020-08-24T23:54:53.486779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03049999997
Q10.409999989
median1.284999967
Q32.132500112
95-th percentile4.211999798
Maximum9.420000076
Range9.420000076
Interquartile range (IQR)1.722500123

Descriptive statistics

Standard deviation1.431687097
Coefficient of variation (CV)0.9504100222
Kurtosis8.399550898
Mean1.506388889
Median Absolute Deviation (MAD)0.860000059
Skewness2.199116725
Sum162.69
Variance2.049727945
2020-08-24T23:54:53.587077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.00999999977632.8%
 
1.521.9%
 
1.02999997121.9%
 
2.7599999921.9%
 
1.44000005721.9%
 
0.129999995221.9%
 
2.11999988621.9%
 
0.0500000007521.9%
 
1.37999999521.9%
 
021.9%
 
1.26999998110.9%
 
0.34999999410.9%
 
0.959999978510.9%
 
1.13999998610.9%
 
0.759999990510.9%
 
0.189999997610.9%
 
1.09000003310.9%
 
0.109999999410.9%
 
2.04999995210.9%
 
0.699999988110.9%
 
1.41999995710.9%
 
1.64999997610.9%
 
0.519999980910.9%
 
4.23999977110.9%
 
1.9900000110.9%
 
Other values (72)7266.7%
 
ValueCountFrequency (%) 
021.9%
 
0.00999999977632.8%
 
0.0199999995510.9%
 
0.0500000007521.9%
 
0.070000000310.9%
 
0.0799999982110.9%
 
0.100000001510.9%
 
0.109999999410.9%
 
0.129999995221.9%
 
0.15000000610.9%
 
ValueCountFrequency (%) 
9.42000007610.9%
 
5.94999980910.9%
 
5.07999992410.9%
 
4.44000005710.9%
 
4.34000015310.9%
 
4.23999977110.9%
 
4.15999984710.9%
 
3.66000008610.9%
 
3.26999998110.9%
 
3.0099999910.9%
 

SC
Real number (ℝ≥0)

Distinct count89
Unique (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.202129626819105
Minimum0.019999999552965164
Maximum4.019999980926514
Zeros0
Zeros (%)0.0%
Memory size992.0 B
2020-08-24T23:54:53.701340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.01999999955
5-th percentile0.200499998
Q10.5350000113
median0.9950000048
Q31.662499964
95-th percentile3.075499928
Maximum4.019999981
Range3.999999981
Interquartile range (IQR)1.127499953

Descriptive statistics

Standard deviation0.8917828903
Coefficient of variation (CV)0.7418358806
Kurtosis0.9604905424
Mean1.202129627
Median Absolute Deviation (MAD)0.5550000072
Skewness1.118054527
Sum129.8299997
Variance0.7952767235
2020-08-24T23:54:53.819054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.870000004832.8%
 
0.850000023832.8%
 
0.579999983332.8%
 
1.62000000521.9%
 
1.08000004321.9%
 
0.379999995221.9%
 
0.430000007221.9%
 
0.589999973821.9%
 
0.709999978521.9%
 
1.65999996721.9%
 
0.469999998821.9%
 
0.230000004221.9%
 
121.9%
 
1.12999999521.9%
 
0.439999997621.9%
 
0.639999985721.9%
 
2.10999989510.9%
 
0.239999994610.9%
 
0.0799999982110.9%
 
1.03999996210.9%
 
3.14000010510.9%
 
0.280000001210.9%
 
2.47000002910.9%
 
0.720000028610.9%
 
1.76999998110.9%
 
Other values (64)6459.3%
 
ValueCountFrequency (%) 
0.0199999995510.9%
 
0.0399999991110.9%
 
0.0799999982110.9%
 
0.129999995210.9%
 
0.140000000610.9%
 
0.189999997610.9%
 
0.219999998810.9%
 
0.230000004221.9%
 
0.239999994610.9%
 
0.270000010710.9%
 
ValueCountFrequency (%) 
4.01999998110.9%
 
3.97000002910.9%
 
3.59999990510.9%
 
3.28999996210.9%
 
3.14000010510.9%
 
3.09999990510.9%
 
3.02999997110.9%
 
2.76999998110.9%
 
2.68000006710.9%
 
2.47000002910.9%
 

NWC
Real number (ℝ≥0)

Distinct count101
Unique (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1928703719750047
Minimum0.03999999910593033
Maximum7.8400001525878915
Zeros0
Zeros (%)0.0%
Memory size992.0 B
2020-08-24T23:54:53.940598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.03999999911
5-th percentile0.2749999978
Q10.8824999928
median1.915000021
Q33.049999952
95-th percentile5.366499972
Maximum7.840000153
Range7.800000153
Interquartile range (IQR)2.167499959

Descriptive statistics

Standard deviation1.632941877
Coefficient of variation (CV)0.7446595559
Kurtosis0.7114489552
Mean2.192870372
Median Absolute Deviation (MAD)1.099999964
Skewness0.9822412742
Sum236.8300002
Variance2.666499175
2020-08-24T23:54:54.058534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.569999992821.9%
 
0.910000026221.9%
 
2.18000006721.9%
 
0.699999988121.9%
 
0.899999976221.9%
 
2.32999992421.9%
 
0.939999997621.9%
 
3.01999998110.9%
 
0.860000014310.9%
 
0.980000019110.9%
 
7.84000015310.9%
 
0.340000003610.9%
 
1.27999997110.9%
 
0.660000026210.9%
 
3.59999990510.9%
 
1.82000005210.9%
 
3.67000007610.9%
 
0.509999990510.9%
 
1.53999996210.9%
 
0.579999983310.9%
 
4.19999980910.9%
 
1.22000002910.9%
 
4.44000005710.9%
 
3.2400000110.9%
 
1.73000001910.9%
 
Other values (76)7670.4%
 
ValueCountFrequency (%) 
0.0399999991110.9%
 
0.0599999986610.9%
 
0.0900000035810.9%
 
0.100000001510.9%
 
0.20000000310.9%
 
0.239999994610.9%
 
0.340000003610.9%
 
0.34999999410.9%
 
0.40000000610.9%
 
0.419999986910.9%
 
ValueCountFrequency (%) 
7.84000015310.9%
 
6.40000009510.9%
 
6.38999986610.9%
 
610.9%
 
5.48000001910.9%
 
5.36999988610.9%
 
5.36000013410.9%
 
5.05999994310.9%
 
4.55999994310.9%
 
4.48000001910.9%
 

target
Real number (ℝ≥0)

Distinct count94
Unique (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.232592590843086
Minimum0.0
Maximum6.0
Zeros1
Zeros (%)0.9%
Memory size992.0 B
2020-08-24T23:54:54.179774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1234999966
Q10.5124999881
median0.9099999964
Q31.674999982
95-th percentile3.152500081
Maximum6
Range6
Interquartile range (IQR)1.162499994

Descriptive statistics

Standard deviation1.077179043
Coefficient of variation (CV)0.8739132872
Kurtosis5.033299526
Mean1.232592591
Median Absolute Deviation (MAD)0.5150000006
Skewness1.953467641
Sum133.1199998
Variance1.16031469
2020-08-24T23:54:54.297801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.370000004832.8%
 
0.759999990532.8%
 
0.740000009521.9%
 
1.59000003321.9%
 
1.83000004321.9%
 
0.389999985721.9%
 
1.08000004321.9%
 
1.11000001421.9%
 
0.879999995221.9%
 
0.40000000621.9%
 
0.439999997621.9%
 
0.810000002421.9%
 
1.22000002910.9%
 
0.629999995210.9%
 
1.41999995710.9%
 
0.0199999995510.9%
 
0.959999978510.9%
 
0.239999994610.9%
 
0.569999992810.9%
 
0.189999997610.9%
 
0.129999995210.9%
 
3.43000006710.9%
 
1.40999996710.9%
 
0.310000002410.9%
 
0.419999986910.9%
 
Other values (69)6963.9%
 
ValueCountFrequency (%) 
010.9%
 
0.0199999995510.9%
 
0.0399999991110.9%
 
0.0500000007510.9%
 
0.070000000310.9%
 
0.119999997310.9%
 
0.129999995210.9%
 
0.170000001810.9%
 
0.189999997610.9%
 
0.219999998810.9%
 
ValueCountFrequency (%) 
610.9%
 
5.11999988610.9%
 
4.9699997910.9%
 
4.05999994310.9%
 
3.43000006710.9%
 
3.38000011410.9%
 
2.73000001910.9%
 
2.67000007610.9%
 
2.52999997110.9%
 
2.510.9%
 

Interactions

2020-08-24T23:54:50.629397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:50.735077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:50.869159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:50.999056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:51.112647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:51.244692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:51.387506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:51.528245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:51.670496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:51.810681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:52.144114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:52.286726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:52.419624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:52.533959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:52.672461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:52.813914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-24T23:54:54.419344image/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.
2020-08-24T23:54:54.604915image/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.
2020-08-24T23:54:54.789300image/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.
2020-08-24T23:54:54.973850image/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.

Missing values

2020-08-24T23:54:53.039115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:54:53.230131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

seededseasonNCSCNWCtarget
0001.651.803.331.69
1101.090.791.590.74
2012.390.362.060.81
3112.961.274.051.44
4014.162.166.002.48
5112.760.874.170.84
6111.080.853.450.37
7010.260.470.900.37
8122.531.083.651.33
9022.763.105.063.38

Last rows

seededseasonNCSCNWCtarget
98010.802.252.791.08
99119.423.607.846.00
100022.743.036.392.67
101120.000.190.060.36
102020.960.641.240.58
103121.381.862.911.36
104021.222.281.581.17
105122.462.472.392.37
106020.050.020.090.02
107120.610.871.350.92