solar_flare

Dataset statistics

Number of variables	13
Number of observations	1066
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	701
Duplicate rows (%)	65.8%
Total size in memory	108.4 KiB
Average record size in memory	104.1 B

Variable types

CAT	8
NUM	4
BOOL	1

Reproduction

Analysis started	2020-08-25 01:52:54.654972
Analysis finished	2020-08-25 01:52:58.542140
Duration	3.89 seconds
Version	pandas-profiling v2.8.0
Command line	`pandas_profiling --config_file config.yaml [YOUR_FILE.csv]`
Download configuration	config.yaml

Warnings

`Area_of_the_largest_spot` has constant value "1"	Constant
Dataset has 701 (65.8%) duplicate rows	Duplicates
`largest_spot_size` has 216 (20.3%) zeros	Zeros
`C-class_flares_production_by_this_region` has 884 (82.9%) zeros	Zeros
`M-class_flares_production_by_this_region` has 1030 (96.6%) zeros	Zeros
`target` has 147 (13.8%) zeros	Zeros

largest_spot_size
Real number (ℝ_≥0)

ZEROS

Distinct count	6
Unique (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	2.958724202626642
Minimum	0
Maximum	5
Zeros	216
Zeros (%)	20.3%
Memory size	8.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	4
Q3	4
95-th percentile	5
Maximum	5
Range	5
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.703402798
Coefficient of variation (CV)	0.5757220619
Kurtosis	-0.7660704256
Mean	2.958724203
Median Absolute Deviation (MAD)	1
Skewness	-0.7874597319
Sum	3154
Variance	2.901581094

Histogram

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
4	414	38.8%
3	218	20.5%
0	216	20.3%
5	145	13.6%
2	46	4.3%
1	27	2.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	216	20.3%
1	27	2.5%
2	46	4.3%
3	218	20.5%
4	414	38.8%
5	145	13.6%

Value	Count	Frequency (%)
5	145	13.6%
4	414	38.8%
3	218	20.5%
2	46	4.3%
1	27	2.5%
0	216	20.3%

spot_distribution
Categorical

Distinct count	4
Unique (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

2	477
3	331
1	223
0	35

Value	Count	Frequency (%)
2	477	44.7%
3	331	31.1%
1	223	20.9%
0	35	3.3%

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Overview of Unicode Properties

Unique unicode characters	4
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

Value	Count	Frequency (%)
2	477	44.7%
3	331	31.1%
1	223	20.9%
0	35	3.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1066	100.0%

Most frequent Decimal Number characters

Value	Count	Frequency (%)
2	477	44.7%
3	331	31.1%
1	223	20.9%
0	35	3.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	1066	100.0%

Most frequent Common characters

Value	Count	Frequency (%)
2	477	44.7%
3	331	31.1%
1	223	20.9%
0	35	3.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1066	100.0%

Most frequent ASCII characters

Value	Count	Frequency (%)
2	477	44.7%
3	331	31.1%
1	223	20.9%
0	35	3.3%

Activity
Categorical

Distinct count	2
Unique (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

1	902
2	164

Value	Count	Frequency (%)
1	902	84.6%
2	164	15.4%

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Overview of Unicode Properties

Unique unicode characters	2
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

Value	Count	Frequency (%)
1	902	84.6%
2	164	15.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1066	100.0%

Most frequent Decimal Number characters

Value	Count	Frequency (%)
1	902	84.6%
2	164	15.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	1066	100.0%

Most frequent Common characters

Value	Count	Frequency (%)
1	902	84.6%
2	164	15.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1066	100.0%

Most frequent ASCII characters

Value	Count	Frequency (%)
1	902	84.6%
2	164	15.4%

Evolution
Categorical

Distinct count	3
Unique (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

3	505
2	484
1	77

Value	Count	Frequency (%)
3	505	47.4%
2	484	45.4%
1	77	7.2%

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Overview of Unicode Properties

Unique unicode characters	3
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

Value	Count	Frequency (%)
3	505	47.4%
2	484	45.4%
1	77	7.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1066	100.0%

Most frequent Decimal Number characters

Value	Count	Frequency (%)
3	505	47.4%
2	484	45.4%
1	77	7.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	1066	100.0%

Most frequent Common characters

Value	Count	Frequency (%)
3	505	47.4%
2	484	45.4%
1	77	7.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1066	100.0%

Most frequent ASCII characters

Value	Count	Frequency (%)
3	505	47.4%
2	484	45.4%
1	77	7.2%

Previous_24_hour_flare_activity_code
Categorical

Distinct count	3
Unique (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

1	1028
3	25
2	13

Value	Count	Frequency (%)
1	1028	96.4%
3	25	2.3%
2	13	1.2%

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Overview of Unicode Properties

Unique unicode characters	3
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

Value	Count	Frequency (%)
1	1028	96.4%
3	25	2.3%
2	13	1.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1066	100.0%

Most frequent Decimal Number characters

Value	Count	Frequency (%)
1	1028	96.4%
3	25	2.3%
2	13	1.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	1066	100.0%

Most frequent Common characters

Value	Count	Frequency (%)
1	1028	96.4%
3	25	2.3%
2	13	1.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1066	100.0%

Most frequent ASCII characters

Value	Count	Frequency (%)
1	1028	96.4%
3	25	2.3%
2	13	1.2%

Historically-complex
Categorical

Distinct count	2
Unique (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

1	635
2	431

Value	Count	Frequency (%)
1	635	59.6%
2	431	40.4%

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Overview of Unicode Properties

Unique unicode characters	2
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

Value	Count	Frequency (%)
1	635	59.6%
2	431	40.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1066	100.0%

Most frequent Decimal Number characters

Value	Count	Frequency (%)
1	635	59.6%
2	431	40.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	1066	100.0%

Most frequent Common characters

Value	Count	Frequency (%)
1	635	59.6%
2	431	40.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1066	100.0%

Most frequent ASCII characters

Value	Count	Frequency (%)
1	635	59.6%
2	431	40.4%

Did_region_become_historically_complex
Categorical

Distinct count	2
Unique (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

2	933
1	133

Value	Count	Frequency (%)
2	933	87.5%
1	133	12.5%

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Overview of Unicode Properties

Unique unicode characters	2
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

Value	Count	Frequency (%)
2	933	87.5%
1	133	12.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1066	100.0%

Most frequent Decimal Number characters

Value	Count	Frequency (%)
2	933	87.5%
1	133	12.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	1066	100.0%

Most frequent Common characters

Value	Count	Frequency (%)
2	933	87.5%
1	133	12.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1066	100.0%

Most frequent ASCII characters

Value	Count	Frequency (%)
2	933	87.5%
1	133	12.5%

Area
Categorical

Distinct count	2
Unique (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

1	1039
2	27

Value	Count	Frequency (%)
1	1039	97.5%
2	27	2.5%

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Overview of Unicode Properties

Unique unicode characters	2
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

Value	Count	Frequency (%)
1	1039	97.5%
2	27	2.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1066	100.0%

Most frequent Decimal Number characters

Value	Count	Frequency (%)
1	1039	97.5%
2	27	2.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	1066	100.0%

Most frequent Common characters

Value	Count	Frequency (%)
1	1039	97.5%
2	27	2.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1066	100.0%

Most frequent ASCII characters

Value	Count	Frequency (%)
1	1039	97.5%
2	27	2.5%

Area_of_the_largest_spot
Boolean

CONSTANT
REJECTED

Distinct count	1
Unique (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

1	1066

Frequency Table

Value	Count	Frequency (%)
1	1066	100.0%

C-class_flares_production_by_this_region
Real number (ℝ_≥0)

ZEROS

Distinct count	8
Unique (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	0.300187617260788
Minimum	0
Maximum	8
Zeros	884
Zeros (%)	82.9%
Memory size	8.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	8
Range	8
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.8357841683
Coefficient of variation (CV)	2.784206011
Kurtosis	19.6165169
Mean	0.3001876173
Median Absolute Deviation (MAD)	0
Skewness	3.952904981
Sum	320
Variance	0.698535176

Histogram

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
0	884	82.9%
1	112	10.5%
2	33	3.1%
3	20	1.9%
4	9	0.8%
5	4	0.4%
6	3	0.3%
8	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	884	82.9%
1	112	10.5%
2	33	3.1%
3	20	1.9%
4	9	0.8%
5	4	0.4%
6	3	0.3%
8	1	0.1%

Value	Count	Frequency (%)
8	1	0.1%
6	3	0.3%
5	4	0.4%
4	9	0.8%
3	20	1.9%
2	33	3.1%
1	112	10.5%
0	884	82.9%

M-class_flares_production_by_this_region
Real number (ℝ_≥0)

ZEROS

Distinct count	6
Unique (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	0.04690431519699812
Minimum	0
Maximum	5
Zeros	1030
Zeros (%)	96.6%
Memory size	8.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	5
Range	5
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.3028112169
Coefficient of variation (CV)	6.455935145
Kurtosis	116.685621
Mean	0.0469043152
Median Absolute Deviation (MAD)	0
Skewness	9.559345482
Sum	50
Variance	0.09169463309

Histogram

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
0	1030	96.6%
1	29	2.7%
2	3	0.3%
3	2	0.2%
5	1	0.1%
4	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1030	96.6%
1	29	2.7%
2	3	0.3%
3	2	0.2%
4	1	0.1%
5	1	0.1%

Value	Count	Frequency (%)
5	1	0.1%
4	1	0.1%
3	2	0.2%
2	3	0.3%
1	29	2.7%
0	1030	96.6%

X-class_flares_production_by_this_region
Categorical

Distinct count	3
Unique (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	8.5 KiB

0	1061
1	4
2	1

Value	Count	Frequency (%)
0	1061	99.5%
1	4	0.4%
2	1	0.1%

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Overview of Unicode Properties

Unique unicode characters	3
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

Value	Count	Frequency (%)
0	1061	99.5%
1	4	0.4%
2	1	0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1066	100.0%

Most frequent Decimal Number characters

Value	Count	Frequency (%)
0	1061	99.5%
1	4	0.4%
2	1	0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	1066	100.0%

Most frequent Common characters

Value	Count	Frequency (%)
0	1061	99.5%
1	4	0.4%
2	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1066	100.0%

Most frequent ASCII characters

Value	Count	Frequency (%)
0	1061	99.5%
1	4	0.4%
2	1	0.1%

target
Real number (ℝ_≥0)

ZEROS

Distinct count	6
Unique (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	2.627579737335835
Minimum	0
Maximum	5
Zeros	147
Zeros (%)	13.8%
Memory size	8.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	2
Q3	5
95-th percentile	5
Maximum	5
Range	5
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	1.844965019
Coefficient of variation (CV)	0.7021537701
Kurtosis	-1.44657903
Mean	2.627579737
Median Absolute Deviation (MAD)	1
Skewness	0.134861704
Sum	2801
Variance	3.403895921

Histogram

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
5	331	31.1%
2	239	22.4%
1	211	19.8%
0	147	13.8%
3	95	8.9%
4	43	4.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	147	13.8%
1	211	19.8%
2	239	22.4%
3	95	8.9%
4	43	4.0%
5	331	31.1%

Value	Count	Frequency (%)
5	331	31.1%
4	43	4.0%
3	95	8.9%
2	239	22.4%
1	211	19.8%
0	147	13.8%

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.

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.

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.

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.

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.

Count
Matrix

First rows

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	C-class_flares_production_by_this_region	target
0	0	3	1	3	1	1	1	1	1	0	5
1	3	2	1	3	1	1	2	1	1	0	2
2	4	2	1	3	1	1	2	1	1	0	1
3	3	3	1	2	1	1	1	1	1	0	5
4	4	3	1	1	1	1	2	1	1	0	5
5	0	2	1	2	1	1	2	1	1	0	1
6	5	2	1	3	1	1	2	1	1	0	0
7	0	2	1	3	1	1	2	1	1	0	1
8	0	2	1	2	1	1	2	1	1	1	1
9	5	2	1	3	1	1	2	1	1	0	0

Last rows

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	C-class_flares_production_by_this_region	target
1056	4	3	1	2	1	1	2	1	1	1	5
1057	4	2	1	2	1	2	2	1	1	0	2
1058	4	3	1	2	2	2	2	1	1	0	5
1059	4	3	2	2	1	1	2	1	1	0	5
1060	3	2	1	3	1	1	2	1	1	0	2
1061	4	3	1	2	1	1	1	1	1	0	5
1062	4	3	2	2	1	1	2	1	1	0	5
1063	4	2	1	2	1	2	2	1	1	0	1
1064	3	3	1	2	1	1	2	1	1	0	5
1065	5	2	1	1	1	1	2	1	1	0	0

Most frequent

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	target	count
130	5	2	1	3	1	1	2	1	1	0	67
113	4	3	1	2	1	1	2	1	1	5	44
63	3	2	1	3	1	1	2	1	1	1	39
111	4	3	1	2	1	1	1	1	1	5	34
128	5	2	1	2	1	1	2	1	1	0	26
117	4	3	1	3	1	1	1	1	1	5	25
58	3	2	1	2	1	1	2	1	1	1	23
115	4	3	1	2	1	2	1	1	1	5	23
97	4	2	1	3	1	1	2	1	1	1	21
64	3	2	1	3	1	1	2	1	1	2	20

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	C-class_flares_production_by_this_region	target
0	0	3	1	3	1	1	1	1	1	0	5
1	3	2	1	3	1	1	2	1	1	0	2
2	4	2	1	3	1	1	2	1	1	0	1
3	3	3	1	2	1	1	1	1	1	0	5
4	4	3	1	1	1	1	2	1	1	0	5
5	0	2	1	2	1	1	2	1	1	0	1
6	5	2	1	3	1	1	2	1	1	0	0
7	0	2	1	3	1	1	2	1	1	0	1
8	0	2	1	2	1	1	2	1	1	1	1
9	5	2	1	3	1	1	2	1	1	0	0

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	target	count
130	5	2	1	3	1	1	2	1	1	0	67
113	4	3	1	2	1	1	2	1	1	5	44
63	3	2	1	3	1	1	2	1	1	1	39
111	4	3	1	2	1	1	1	1	1	5	34
128	5	2	1	2	1	1	2	1	1	0	26
117	4	3	1	3	1	1	1	1	1	5	25
58	3	2	1	2	1	1	2	1	1	1	23
115	4	3	1	2	1	2	1	1	1	5	23
97	4	2	1	3	1	1	2	1	1	1	21
64	3	2	1	3	1	1	2	1	1	2	20

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	C-class_flares_production_by_this_region	target
0	0	3	1	3	1	1	1	1	1	0	5
1	3	2	1	3	1	1	2	1	1	0	2
2	4	2	1	3	1	1	2	1	1	0	1
3	3	3	1	2	1	1	1	1	1	0	5
4	4	3	1	1	1	1	2	1	1	0	5
5	0	2	1	2	1	1	2	1	1	0	1
6	5	2	1	3	1	1	2	1	1	0	0
7	0	2	1	3	1	1	2	1	1	0	1
8	0	2	1	2	1	1	2	1	1	1	1
9	5	2	1	3	1	1	2	1	1	0	0

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	target	count
130	5	2	1	3	1	1	2	1	1	0	67
113	4	3	1	2	1	1	2	1	1	5	44
63	3	2	1	3	1	1	2	1	1	1	39
111	4	3	1	2	1	1	1	1	1	5	34
128	5	2	1	2	1	1	2	1	1	0	26
117	4	3	1	3	1	1	1	1	1	5	25
58	3	2	1	2	1	1	2	1	1	1	23
115	4	3	1	2	1	2	1	1	1	5	23
97	4	2	1	3	1	1	2	1	1	1	21
64	3	2	1	3	1	1	2	1	1	2	20

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent Decimal Number characters

Most occurring scripts

Most frequent Common characters

Most occurring blocks

Most frequent ASCII characters

Most occurring characters

Most occurring categories

Most frequent Decimal Number characters

Most occurring scripts

Most frequent Common characters

Most occurring blocks

Most frequent ASCII characters

Most occurring characters

Most occurring categories

Most frequent Decimal Number characters

Most occurring scripts

Most frequent Common characters

Most occurring blocks

Most frequent ASCII characters

Most occurring characters

Most occurring categories

Most frequent Decimal Number characters

Most occurring scripts

Most frequent Common characters

Most occurring blocks

Most frequent ASCII characters

Most occurring characters

Most occurring categories

Most frequent Decimal Number characters

Most occurring scripts

Most frequent Common characters

Most occurring blocks

Most frequent ASCII characters

Most occurring characters

Most occurring categories

Most frequent Decimal Number characters

Most occurring scripts

Most frequent Common characters

Most occurring blocks

Most frequent ASCII characters

Most occurring characters

Most occurring categories

Most frequent Decimal Number characters

Most occurring scripts

Most frequent Common characters

Most occurring blocks

Most frequent ASCII characters

Most occurring characters

Most occurring categories

Most frequent Decimal Number characters

Most occurring scripts

Most frequent Common characters

Most occurring blocks

Most frequent ASCII characters

Interactions

Correlations

Pearson's r

Spearman's ρ

Kendall's τ

Phik (φk)

Cramér's V (φc)

Missing values

Sample

First rows

Last rows

Duplicate rows

Most frequent

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	C-class_flares_production_by_this_region	target
0	0	3	1	3	1	1	1	1	1	0	5
1	3	2	1	3	1	1	2	1	1	0	2
2	4	2	1	3	1	1	2	1	1	0	1
3	3	3	1	2	1	1	1	1	1	0	5
4	4	3	1	1	1	1	2	1	1	0	5
5	0	2	1	2	1	1	2	1	1	0	1
6	5	2	1	3	1	1	2	1	1	0	0
7	0	2	1	3	1	1	2	1	1	0	1
8	0	2	1	2	1	1	2	1	1	1	1
9	5	2	1	3	1	1	2	1	1	0	0

	largest_spot_size	spot_distribution	Activity	Evolution	Previous_24_hour_flare_activity_code	Historically-complex	Did_region_become_historically_complex	Area	Area_of_the_largest_spot	target	count
130	5	2	1	3	1	1	2	1	1	0	67
113	4	3	1	2	1	1	2	1	1	5	44
63	3	2	1	3	1	1	2	1	1	1	39
111	4	3	1	2	1	1	1	1	1	5	34
128	5	2	1	2	1	1	2	1	1	0	26
117	4	3	1	3	1	1	1	1	1	5	25
58	3	2	1	2	1	1	2	1	1	1	23
115	4	3	1	2	1	2	1	1	1	5	23
97	4	2	1	3	1	1	2	1	1	1	21
64	3	2	1	3	1	1	2	1	1	2	20