very_fast_eda Tutorial

Welcome to very_fast_eda package documentation! This package offers a suite of functions designed to smplify your EDA. below is an illustration of the usage of the functions with real life examples, featuring, Bill, a student at UON, who is undertaking his final year project on data analysis.

Bill’s Journey

For the last four years in college Bill has been used to perform exploratory data analysis using his computer and an excel spreadsheet. He found it to be very time consuming and frustrating at time if he has very large data sets. His friend, Mark, came across this package and recommended him to use it. After pondering for sometime, he decided to give it a try and he was not dissapointed. Infact, he went ahead to introduce it to his professors and fellow classmates.

Putting the fast_eda package into use

To start he had to import all the necessary libraries and the module from our packages as an alias eda

Import

import seaborn as sns
import pandas as pd
import fast_eda.fast_eda as eda

Load a sample dataset

Bill then went ahead to load the data set he will be using for his analysis. He choose the famous Iris dataset from the Seaborn library (Iris dataset). He could use any data set if he wanted but he has to ensure that is the format of a pandas dataframe.

iris = sns.load_dataset('iris')
iris.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

Bill wanted to get more information about the dataset and therefore he uses iris.info() to get a summary of the dataframe but this is not enough for analysis. He wants to know about the distriubution, correlation, null counts, and some summary statistics.

iris.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   sepal_length  150 non-null    float64
 1   sepal_width   150 non-null    float64
 2   petal_length  150 non-null    float64
 3   petal_width   150 non-null    float64
 4   species       150 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB

Therefore, Bill then went ahead to use the functions in the package to undertake his analysis of the dataset.

Exploratory Data Analysis

Here he could see the distribution plot of all the columns and he derived some insights from it.

dist_plots = eda.distribution_plots(iris, 2, 3)

Here Bill was able to know how many values are missing in any given column that might affect his analysis.

nulls_values = eda.count_nulls(iris)

nulls_values

sepal_length    0
sepal_width     0
petal_length    0
petal_width     0
species         0
dtype: int64

Bill was also able to see the correlation of the different features in the dataset.

correlation_matrix_plot = eda.correlation_matrix_viz(iris).properties(
    width=400,  # Adjust width as needed
    height=200  # Adjust height as needed
)
correlation_matrix_plot

Finally, bill was able to see summary statistics about the different columns in the dataset.

Summary Statistics Calculation: mean, median, and stdv

df = sns.load_dataset('iris')

# Get all numerical columns
numerical_columns = df.select_dtypes(include=['number']).columns.tolist()

print("\nNumerical columns in the Iris dataset:")
print(numerical_columns)

df = iris[numerical_columns]

eda.describe_function(df)

Numerical columns in the Iris dataset:
['sepal_length', 'sepal_width', 'petal_length', 'petal_width']

	sepal_length	sepal_width	petal_length	petal_width
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.057333	3.758000	1.199333
std	0.828066	0.435866	1.765298	0.762238
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

Final Remarks

We hope you found these examples informative and learned through Bills experience. If anything remains unclear, we suggest reviewing the function documentation or creating an issue in our repository and we will get back to you.