Unveiling Insights: A Comprehensive Guide to Data Analysis and EDA

Introduction:

As data scientists, our journey begins when we are handed a dataset. Before we dive into complex models and algorithms, it's crucial to understand the nature of the data. This preliminary step allows us to gain valuable insights. In this blog, we will walk through the process of data analysis and exploratory data analysis (EDA), which are fundamental in extracting knowledge from data.

Data Analysis: The Basics

1. Size of the Data

   The first step in understanding your data is to grasp its size. You can use the dataframe.size attribute to find out how many data points are present. Knowing the size of your dataset sets the stage for further analysis.

2. Initial Data Preview

   Take a quick look at your data to get a sense of what it contains. The dataframe.head method allows you to view the first few records. This initial glance helps you get a feel for the structure of the data.

3. Data Types and Memory Usage

   It's essential to know the data types of each column and the memory they consume. Use the dataframe.dtypes and memory consumption to optimize data storage.

4. Finding Missing Values

   Data quality matters. Detect and address missing values using df.isnull().sum(). Understanding the frequency of null values for each feature is vital for data preparation.

5. Statistical Summary

   A mathematical view of your data can provide valuable insights. Utilize df.describe() to gain statistical measures like mean, standard deviation, and quartiles.

6. Duplicate Values

   Duplicate data can skew your analysis. Use df.duplicated().sum() to find and handle duplicate values.

7. Correlation Between Columns

   Understanding the relationships between columns can be a goldmine for insights. Employ df.corr() to calculate correlations between numerical features.

Exploratory Data Analysis (EDA)

a) Univariate Analysis: The analysis that we perform on a single feature of the dataset is called univariant analysis

1. Categorical Features

   • Count Plot: Visualize the frequency of each category in a particular feature.

   • Pie Chart: Represent category frequencies in the form of a pie with percentages.

2. Numerical Features

   • Histogram: Examine the distribution of numerical data.

   • Dist Plot: Combines a histogram with a kernel density function, useful for understanding skewness.

   • Box Plot: Reveals the five-number summary, aiding in identifying outliers.

b) Multivariate Analysis

1. Scatter Plot

   • For bivariate analysis on two numerical features, use scatter plots.

   • Add categorical or numerical features as a hue attribute for multivariate analysis.

2. Bar Plot

   • Useful for bivariate analysis of one numerical and one categorical feature.

   • Provides the average numerical value for each category.

   • Extend to multivariate analysis by adding another categorical feature as a hue feature.

3. Box Plot

   • Ideal for bivariate analysis of one numerical and one categorical feature.

   • Efficient for outlier detection.

   • Add a categorical feature for multivariate analysis.

4. Dist Plot

   • Compare one numerical feature with one categorical feature.

   • Display the probability distribution curve.

5. Heat Map

   • Analyze two categorical features.

   • Identify feature frequency using color shading.

6. Cluster Map

   • Offers insights into the proximity of categories in two categorical features. It is heatmap with suggestions regarding to closeness of the features.

7. Pair Plot

   • Simplifies the creation of scatter plots for multiple numerical features.

   • Add categorical features for multivariate analysis.

8. Line Plot

   • Ideal for comparing two numerical features, especially when one is time-based and is generally represented on the X-axis.

Conclusion:

Effective data analysis and EDA are crucial for deriving meaningful insights from your datasets. By following these steps and visualization techniques, you can make informed decisions and drive data-driven solutions.