Exploratory Data Analysis (05)

In Statistics since we have covered several topics, Let’s understand some new topics which is, the analysis of data.

How to analyze data, popularly known as Exploratory Data Analysis.

EDA (Exploratory Data Analysis) is the process of exploring and understanding a dataset to find interesting patterns, relationships, and insights.

Here's a brief example of exploratory data analysis (EDA) using Python:

import pandas as pd
import seaborn as sns

# Load the dataset
data = pd.read_csv('data.csv')

# Print the first few rows of the data
print(data.head())

# Check for missing values
print(data.isna().sum())

# Calculate summary statistics
print(data.describe())

# Create a correlation matrix
corr_matrix = data.corr()
sns.heatmap(corr_matrix, annot=True)

# Create histograms of the data
sns.histplot(data['Age'], kde=False)
sns.histplot(data['Income'], kde=False)

# Create a scatter plot of two variables
sns.scatterplot(x='Age', y='Income', data=data)

# Create a box plot of a variable
sns.boxplot(y='Income', data=data)

In this example, we load a dataset into a pandas DataFrame and perform some basic EDA tasks:

1. Print the first few rows of the data to get a sense of what it looks like

2. Check for missing values using the isna() and sum() methods

3. Calculate summary statistics using the describe() method

4. Create a correlation matrix using the corr() method and visualize it using seaborn's heatmap function

5. Create histograms of two variables using seaborn's histplot() function

6. Create a scatter plot of two variables using seaborn's scatterplot() function

7. Create a box plot of a variable using seaborn's boxplot() function

Exploratory Data Analysis (EDA) is a critical step in any data analysis project. In this brief example, we demonstrated a few basic EDA tasks that you might perform on a dataset using Python.

However, it's important to keep in mind that EDA is an iterative process and that you may need to perform many more analyses depending on the nature of your data and the questions you are trying to answer.

By examining your data and visualizing it in various ways, you can gain insights into its distribution, identify outliers, and detect potential issues that may need to be addressed.

With the help of Python and powerful libraries such as pandas and seaborn, you can easily perform EDA tasks and gain a deeper understanding of your data.

Under EDA we do several things to understand and get a hold of data. However, the primary analysis which we perform is through these three basic kinds of analyses techniques which are;

1. Univariate Analysis

2. Bivariate Analysis

3. Multi-Variate Analysis

We'll be understanding all these three Analysis Techniques in depth with creative examples further.

That's the end of the article readers!

Will be explaining more in my following blogs!

"Statistics are no substitute for judgment." - Henry Clay

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