EXPLORATORY DATA ANALYSIS ULTIMATE GUIDE
Tags: eventdriven, python, beginners
Published: 2023-02-28 12:47:43

Exploratory data analysis (EDA) is an iterative process that involves visualizing and summarizing data to gain insights and inform further analysis, which makes it a crucial step in any data analysis process.
EDA helps to look at data before making any assumptions. It can help identify errors as well as identify and better understand patterns within the data, detect outliers and find interesting relations.
EDA can aid in answering questions about standard deviations, categorical variables and confidence intervals. Once EDA is complete and insights are drawn, its features can be used for more sophisticated data analysis on modelling.

## **Exploratory Data Analysis Tools**

1. **Python:**: This is a programming language used for data analysis and visualization. It provides libraries for EDA such as NumPy, Pandas, MatplotLib and Seaborn among others.

2.**Tableau**: This is a visualization tool that allows the analyst to create interactive visualizations and dashboards. It is user friendly and has a wide range of visualization options.

3.**Excel**: This is a popular spreadsheet tool that can be used for data analysis and visualization. It provides several in-built functions for data summarization and visualization such as pivot tables and charts.

4.**R**: This is a popular programming language used for statistical analysis and data visualization. It provides a wide range of packages for data exploration and visualization such as ggplot2, dplyr and tidyr.

## Exploratory Data Analysis Techniques

1. **Data Collection and Preparation**: This is usually the first step in EDA. It involves identifying the data sources, gathering data and cleaning and transforming the data. Data cleaning involves removing or correcting any inconsistent, erroneous or missing data, while data transformation involves converting the data into a suitable format for analysis.

2. **Data Summarization**:This enables a data scientist to gain a better understanding of the data's main characteristics. It involves calculating summary statistics such as mean, median, mode, variance and standard deviation for each variable. In addition, frequency tables and histograms can be used to visualize the distribution of the data.

3.**Data Visualization**: This allows a data analyst to visually explore the data and identify patterns and trends. Various graphical techniques can be used to visualize the relationships between variables and detect any anomalies or outliers.

4.**Data Exploration**: This involves conducting further analysis on the data to identify patterns and relationships. This can be achieved by performing correlation analysis, regression analysis and factor analysis to identify the relationship between variables. In addition, clustering analysis can be used to group similar data points together and identify any underlying patterns.

5. **Data Interpretation**: Data interpretation involves making sense of the results obtained from data exploration and visualization. It involves interpreting the statistical significance of the results and identifying any meaningful relationships between variables.
**The Process of Exploratory Data Analysis**

1. **Data Collection and Preparation**:This is usually the first step. It may involve cleaning and transforming the data, handling missing values and outliers and selecting relevant variables for analysis.

2. **Univariate Analysis**: This involves examining individual variables in the data. This can be done by calculating summary statistics such as mean, median and standard deviation, then visualizing the distribution of the data using histograms, box plots and density plots. It is divided into two; non-graphical and graphical.

3.**Bivariate Analysis**: Involves examining the relationship between the variables in the data. This can be done by creating scatterplots, correlation matrices and heatmaps.

4.**Multivariate Analysis**: Involves examining the relationship between multiple variables in the data. This can be done by creating scatterplot matrices, principal component analysis and cluster analysis.

5.**Data Visualization**: This allows for the exploration of complex relationships and patterns in the data. This can be done using various graphical tools such as scatterplots, box plots, histograms and heatmaps.
**EXAMPLE** 
  In this example, we will be analyzing a dataset containing information about diamonds, including their carat weight, cut, color, clarity, and price.
This code loads the diamonds dataset, performs various data cleaning and exploration tasks, and creates several visualizations using MatplotLib and Seaborn. These visualizations include a histogram of carat weight, a boxplot of price by cut, a scatterplot of carat weight and price colored by cut, a correlation matrix of numerical variables, and a principal component analysis. 

_Import libraries_
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
```
_Load the dataset_

```
diamonds = pd.read_csv('diamonds.csv')
```
_View the first five rows of the dataset_

```
print(diamonds.head())
```
_Check for missing values_ 
```
print(diamonds.isnull().sum())
```
_Check for duplicates_ 
```
print(diamonds.duplicated().sum())
```
_Summary statistics of the dataset_

```
print(diamonds.describe())
```
_Histogram of the carat weight variable_

```
plt.hist(diamonds['carat'], bins=30)
plt.xlabel('Carat Weight')
plt.ylabel('Frequency')
plt.title('Distribution of Carat Weight')
plt.show()
```
_Boxplot of the price variable by cut_

```
sns.boxplot(x='cut', y='price', data=diamonds)
plt.xlabel('Cut')
plt.ylabel('Price')
plt.title('Price by Cut')
plt.show()
```
_ Scatterplot of carat weight and price, colored by cut_ 

```
sns.scatterplot(x='carat', y='price', hue='cut', data=diamonds)
plt.xlabel('Carat Weight')
plt.ylabel('Price')
plt.title('Price vs. Carat Weight')
plt.show()
```
_Correlation matrix of numerical variables_

```
corr = diamonds[['carat', 'depth', 'table', 'price']].corr()
sns.heatmap(corr, cmap='coolwarm', annot=True)
plt.title('Correlation Matrix')
plt.show()
```
_ Principal component analysis_

```
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X = diamonds[['carat', 'depth', 'table', 'price']]
pca.fit(X)
X_pca = pca.transform(X)
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=diamonds['cut'])
plt.xlabel('PCA 1')
plt.ylabel('PCA 2')
plt.title('PCA of Diamonds Dataset')
plt.show()
```