Exploratory Data Analysis (EDA) Techniques

Introduction

Exploratory Data Analysis (EDA) is a crucial phase in the data cleaning and preprocessing process. Its primary goal is to understand the underlying structure of the data, identify potential issues, and formulate hypotheses for further analysis. EDA helps to discover patterns, spot anomalies, and check assumptions through visual and quantitative methods.

Key EDA Techniques

1. Descriptive Statistics

Descriptive statistics provide a summary of the data, offering a quick overview of its central tendency, dispersion, and shape. Common metrics include: - Mean: The average value. - Median: The middle value, which is less affected by outliers. - Mode: The most frequently occurring value. - Standard Deviation: A measure of the amount of variation or dispersion.

Example

`python import pandas as pd

df = pd.DataFrame({ 'Age': [22, 25, 29, 35, 29, 30, 22], 'Salary': [50000, 54000, 58000, 62000, 59000, 57000, 50000] })

descriptive_stats = df.describe() print(descriptive_stats)

Displays count, mean, std, min, 25%, 50%, 75%, max

2. Data Visualization

Visual representations of data can reveal insights that are not immediately obvious from descriptive statistics. - Histograms: Useful for understanding the distribution of a numerical variable. - Box Plots: Great for visualizing the range and identifying outliers. - Scatter Plots: Help in identifying relationships between two numerical variables.

Example

`python import matplotlib.pyplot as plt import seaborn as sns

Histogram

plt.figure(figsize=(10,6)) sns.histplot(df['Salary'], bins=10, kde=True) plt.title('Salary Distribution') plt.xlabel('Salary') plt.ylabel('Frequency') plt.show() `

3. Correlation Analysis

Correlation analysis measures the relationship between variables. The correlation coefficient ranges from -1 to +1, indicating the strength and direction of the linear relationship.

Example

`python correlation = df.corr() print(correlation)

Displays correlation matrix

sns.heatmap(correlation, annot=True, cmap='coolwarm') plt.title('Correlation Matrix') plt.show() `

4. Handling Missing Values

Identifying and visualizing missing data is an essential aspect of EDA. Techniques include: - Missing Value Heatmap: Visualizing missing data in a matrix form. - Imputation Strategies: Filling missing values using mean, median, or mode, or even more complex methods like KNN.

Example

`python import numpy as np

Creating missing values

df.loc[2, 'Salary'] = np.nan

Heatmap of missing values

sns.heatmap(df.isnull(), cbar=False, cmap='viridis') plt.title('Missing Values Heatmap') plt.show() `

Conclusion

Exploratory Data Analysis is an essential step in data preprocessing that provides insights into the data's structure and quality. By employing various techniques such as descriptive statistics, data visualization, correlation analysis, and handling missing values, data scientists can effectively identify data issues and prepare the dataset for further analysis.