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https://github.com/nilayhangarge/data-analysis-with-python
This repository provides a practical introduction to data acquisition and analysis using Pandas. It covers loading datasets, exploring data, manipulating data, and gaining insights through statistical summaries. Ideal for beginners, it offers code examples and explanations to enhance your data manipulation skills using Pandas for Python.
https://github.com/nilayhangarge/data-analysis-with-python
data-acquisition data-analysis data-analytics data-binning data-cleaning data-engineering data-fundamentals data-insights data-integration data-preprocessing data-science data-wrangling numpy pandas python
Last synced: about 2 months ago
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This repository provides a practical introduction to data acquisition and analysis using Pandas. It covers loading datasets, exploring data, manipulating data, and gaining insights through statistical summaries. Ideal for beginners, it offers code examples and explanations to enhance your data manipulation skills using Pandas for Python.
- Host: GitHub
- URL: https://github.com/nilayhangarge/data-analysis-with-python
- Owner: nilayhangarge
- Created: 2023-07-02T08:50:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-11T12:35:00.000Z (over 1 year ago)
- Last Synced: 2024-11-09T23:37:28.809Z (3 months ago)
- Topics: data-acquisition, data-analysis, data-analytics, data-binning, data-cleaning, data-engineering, data-fundamentals, data-insights, data-integration, data-preprocessing, data-science, data-wrangling, numpy, pandas, python
- Language: Jupyter Notebook
- Homepage: https://skills.yourlearning.ibm.com/activity/SN-COURSE-V1:COGNITIVECLASS+DA0101EN+V1
- Size: 274 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Data Analysis with Python**
## Table of Contents
* [Lab-1 Introduction](#lab-1-introduction)
* [Lab-2 Data Wrangling](#lab-2-data-wrangling)
## **Lab-1: Introduction**
This notebook provides an introduction to data acquisition and basic insights using the Pandas library. It covers data loading, exploration, and statistical summaries.
### Topics
- Data acquisition: Loading dataset from local or online sources using Pandas.
- Basic insights: Data types, statistical summaries, and dataset information.
### Prerequisites
- Python and Jupyter Notebook.
- Basic understanding of Python programming and data manipulation.
### Dataset
[Automobile Dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data) (CSV Format)
### Libraries Used
- Pandas: Data manipulation and analysis.
- NumPy: Numerical computations.
## **Lab-2: Data Wrangling**
This notebook focuses on data wrangling tasks, which involve preparing and cleaning data for analysis.
### Topics
- Identify & Handle missing values
- Identify & Deal with missing values
- Correct data format
- Standardizing & Normalizing data.
- Binning Numerical Variables.
- Indicator Variable (Dummy Variable).
### Prerequisites
- Lab-1 Introduction
- Familiarity with Pandas library.
### Dataset
[Automobile Dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data) (CSV Format)
### Libraries Used
- Pandas
- NumPy
- Matplotlib: Data visualization.
