# Pandas for Data Science

Learning Path ⋅ **Skills:** Pandas, Data Science, Data Visualization

In this learning path, you’ll get started with Pandas and get to know the ins and outs of how you can use it to analyze data with Python.

Pandas is a game-changer for data science and analytics, particularly if you came to Python because you were searching for something more powerful than Excel and VBA. Pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive.

## Pandas for Data Science

Learning Path ⋅ 11 Resources

**Tutorial**

### Using Pandas and Python to Explore Your Dataset

Learn how to start exploring a dataset with Pandas and Python. You'll learn how to access specific rows and columns to answer questions about your data. You'll also see how to handle missing values and prepare to visualize your dataset in a Jupyter notebook.

**Tutorial**

### The Pandas DataFrame: Make Working With Data Delightful

Get started with Pandas DataFrames, which are powerful and widely used two-dimensional data structures. You'll learn how to perform basic operations with data, handle missing values, work with time-series data, and visualize data from a Pandas DataFrame.

**Tutorial**

### Pythonic Data Cleaning With Pandas and NumPy

Get started with basic data cleaning techniques in Python using Pandas and NumPy.

**Tutorial**

### Pandas: How to Read and Write Files

Learn about the Pandas IO tools API and how you can use it to read and write files. You'll use the Pandas read_csv() function to work with CSV files. You'll also cover similar methods for efficiently working with Excel, CSV, JSON, HTML, SQL, pickle, and big data files.

**Tutorial**

### SettingWithCopyWarning in Pandas: Views vs Copies

Learn about views and copies in NumPy and Pandas. You'll see why the SettingWithCopyWarning occurs in Pandas and how to properly write code that avoids it.

**Tutorial**

### Fast, Flexible, Easy and Intuitive: How to Speed Up Your Pandas Projects

What is it about Pandas that has data scientists, analysts, and engineers raving? This is a guide to using Pandas Pythonically to get the most out of its powerful and easy-to-use built-in features. Additionally, you will learn a couple of practical time-saving tips.

**Tutorial**

### Pandas GroupBy: Your Guide to Grouping Data in Python

Learn how to work adeptly with the Pandas GroupBy facility while mastering ways to manipulate, transform, and summarize data. You'll work with real-world datasets and chain GroupBy methods together to get data in an output that suits your purpose.

**Tutorial**

### Combining Data in Pandas With merge(), .join(), and concat()

Learn three techniques for combining data in Pandas: merge(), .join(), and concat(). Combining Series and DataFrame objects in Pandas is a powerful way to gain new insights into your data.

**Tutorial**

### NumPy, SciPy, and Pandas: Correlation With Python

Learn what correlation is and how you can calculate it with Python. You'll use SciPy, NumPy, and Pandas correlation methods to calculate three different correlation coefficients. You'll also see how to visualize data, regression lines, and correlation matrices with Matplotlib.

**Course**

### Python Pandas: Tricks & Features You May Not Know

See how to use some lesser-used but idiomatic Pandas capabilities that lend your code better readability, versatility, and speed.

**Tutorial**

### Pandas Project: Make a Gradebook With Python & Pandas

Build a script to calculate grades for a class. You'll see examples of loading, merging, and saving data with pandas, as well as plotting some summary statistics.