Automated Exploratory Data Analysis

PACKAGE              VERSION
Python               3.8.5
numpy                1.19.2
pandas               1.1.3
matplotlib           3.3.2
seaborn              0.11.0
astropy              4.0.2

Table of Contents

• 1. Dataset overview
  • 1.1 Numerical variables distributions
  • 1.2 Categorical variables distributions
• 2. Numerical variables relationships
• 3. Numerical-categorical relationships
• 4. Categorical variables relationships
• 5. Time series

1. Dataset overview

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female
...	...	...	...	...	...	...	...
339	Gentoo	Biscoe	NaN	NaN	NaN	NaN	NaN
340	Gentoo	Biscoe	46.8	14.3	215.0	4850.0	Female
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

344 rows × 7 columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 7 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   species            344 non-null    object 
 1   island             344 non-null    object 
 2   bill_length_mm     342 non-null    float64
 3   bill_depth_mm      342 non-null    float64
 4   flipper_length_mm  342 non-null    float64
 5   body_mass_g        342 non-null    float64
 6   sex                333 non-null    object 
dtypes: float64(4), object(3)
memory usage: 18.9+ KB

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
count	344	344	344	344	344	344	344
unique	3	3	165	81	56	95	3
top	Adelie	Biscoe	41.1	17.0	190.0	3800.0	Male
freq	152	168	7	12	22	12	168

VARIABLE             TYPE
species              categorical
island               categorical
bill_length_mm       numerical continuous
bill_depth_mm        numerical continuous
flipper_length_mm    numerical discrete (integers only)
body_mass_g          numerical discrete (integers only)
sex                  binary

Maximum number of unique values for plotting categorical/discrete variables: 8 

OVERVIEW OF VARIABLES GROUPED ACCORDING TO PLOTS

Histograms
 ['bill_length_mm' 'bill_depth_mm' 'flipper_length_mm' 'body_mass_g'] 

Scatter plots
 ['bill_length_mm' 'bill_depth_mm' 'flipper_length_mm' 'body_mass_g'] 

Numerical discrete variable to visualize like categorical variables
 [] 

Bar charts and hue in scatter plots
 ['species' 'island' 'sex'] 

Categorical variable with too many unique values to be plotted
 [] 

Datetime variable to use as index for time series
 []

1.1 Numerical variables distributions

	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g
count	342.00	342.00	342.00	342.00
mean	43.92	17.15	200.92	4201.75
std	5.46	1.97	14.06	801.95
min	32.10	13.10	172.00	2700.00
25%	39.23	15.60	190.00	3550.00
50%	44.45	17.30	197.00	4050.00
75%	48.50	18.70	213.00	4750.00
max	59.60	21.50	231.00	6300.00

Automatically selected histogram bin widths

VARIABLE             BIN WIDTH
bill_length_mm             2.5
bill_depth_mm              1.0
flipper_length_mm            5
body_mass_g              250.0

1.2 Categorical variables distributions

Description of categorical/numerical discrete variables with few enough
unique values to be visualized

	species	island	sex
count	344	344	333
unique	3	3	2
top	Adelie	Biscoe	Male
freq	152	168	168

2. Numerical variables relationships

Variables to plot as hue in scatterplot matrix
 ['species' 'island' 'sex']

3. Numerical-categorical relationships

4. Categorical variables relationships

5. Time series

There is no time series in this dataset