Exploratory Data Analysis in Python | pandas, numpy, matplotlib, seaborn
In this video, we dive into Exploratory Data Analysis (EDA) using powerful Python libraries like pandas, numpy, matplotlib, and seaborn. Whether you're a beginner or brushing up your data science skills, this step-by-step guide will help you understand your dataset better and prepare it for modeling.
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Topics Covered:
1. Data Inspection: Get a first look at your dataset
2. Data Validation: Identify and resolve inconsistencies
3. Data Summarization: Use descriptive statistics to understand distributions
4. Handling Missing Data: Clean, remove, impute missing data effectively
5. Exploring Categorical Data: Analyze and visualize categorical features
6. Exploring Numeric Data: Dig into numeric trends and patterns
7. Handling Outliers: Detect and manage extreme values
Python libraries Used: pandas, numpy, matplotlib, seaborn
Chapters:
0:00 Introduction
1:52 Data Inspection
5:43 Data Validation
9:11 Data Summarization
12:15 Handling missing data
15:22 Imputing missing data
16:00 Exploring categorical data
20:00 Exploring numerical data
21:53 Handling Outliers
Datasets:
Penguins data: https://github.com/dsfeorg/EDA_python...
Modified penguins data: https://github.com/dsfeorg/EDA_python...
Salaries data: https://github.com/dsfeorg/EDA_python...
By the end of this tutorial, you’ll have a solid foundation in EDA and be ready to extract insights from any dataset.
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