What is Pandas
pip install pandas
import pandas as pd # pd label can be used to access the pandas library.
Why Use Pandas
- Great Handling of Data :
- Cleaning up Data :
Data cleaning is very important and the Pandas library makes it very easy for us.
- Handling Missing Data :
- Input and Output Tools :
- Multiple File Formats Supported :
- Optimized Performance :
- Perform Mathematical operations on the data :
Best Pandas Courses
Codeacademy is one of the best platforms among e-learning platforms. The course I recommend here is Codeacademy – Learn Data Analysis with Pandas.
In this course :
- In the first part, you are going to use Pandas to create and manipulate tables so that you can process your data faster and get your insights sooner.
- In the second part, you are going to learn the basics of aggregate functions in Pandas, which let us calculate quantities that describe groups of data.
- In the third part, you are going to learn how to combine information from multiple DataFrames.
Also, in this course, you are going to create 3 projects for your portfolio.
Udacity is an e-learning platform that offers high quality content, especially in the field of artificial intelligence and data science.
The course I recommend here is the udacity intro to data analysis course.
In this course :
- In the first part, you are going to learn Data analysis Process :
- In the second part you are going to learn NumPy and Pandas for 1D Data :
- In the third part you are going to learn NumPy and Pandas for 2D Data :
- In the fourth part yo ure going to learn Investigate a Dataset :
Udemy - Data Analysis with Pandas and Python
- Perform a multitude of data operations in Python’s popular “pandas” library including grouping, pivoting, joining and more!
- Learn hundreds of methods and attributes across numerous pandas objects
- Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
- Resolve common issues in broken or incomplete data sets
Best Pandas Books
- Use the IPython shell and Jupyter notebook for exploratory computing
- Learn basic and advanced features in NumPy (Numerical Python)
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examples.
- IPython and Jupyter: provide computational environments for data scientists using Python
- NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
- Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
- Matplotlib: includes capabilities for a flexible range of data visualizations in Python
- Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
What You Will Learn
- Install pandas on Windows, Mac, and Linux using the Anaconda Python distribution
- Learn how pandas builds on NumPy to implement flexible indexed data
- Adopt pandas’ Series and DataFrame objects to represent one- and two-dimensional data constructs
- Index, slice, and transform data to derive meaning from information
- Load data from files, databases, and web services
- Manipulate dates, times, and time series data
- Group, aggregate, and summarize data
- Visualize techniques for pandas and statistical data
About the Author
Table of Content
- A Tour of pandas
- Installing pandas
- Numpy for pandas
- The pandas Series Object
- The pandas Dataframe Object
- Accessing Data
- Tidying up Your Data
- Combining and Reshaping Data
- Grouping and Aggregating Data
- Time-series Data
- Applications to Finance