Introduction to Time Series with Pandas
A free video tutorial from Jose Portilla
Head of Data Science, Pierian Data Inc.
4.6 instructor rating • 34 courses • 2,478,624 students
Learn more from the full coursePython for Financial Analysis and Algorithmic Trading
Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!
16:35:31 of on-demand video • Updated December 2020
- Use NumPy to quickly work with Numerical Data
- Use Pandas for Analyze and Visualize Data
- Use Matplotlib to create custom plots
- Learn how to use statsmodels for Time Series Analysis
- Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..
- Use Exponentially Weighted Moving Averages
- Use ARIMA models on Time Series Data
- Calculate the Sharpe Ratio
- Optimize Portfolio Allocations
- Understand the Capital Asset Pricing Model
- Learn about the Efficient Market Hypothesis
- Conduct algorithmic Trading on Quantopian
English [Auto] Welcome, everyone, to the section on working with Pandas with Time series data. Now that we have an understanding of how to work with pandas for general data, let's go over a few key points of Working of Time series data with the PANDAS Library. The majority of our data sets will be in the form of a time series, that is, it has some sort of date time index and some sort of corresponding value per that date time index, for example, some stock price for a particular date on the index. Well, we're going to be doing in this section is learning how to use Pandas special time series features to work this sort of data. This section will cover the following, we'll talk about daytime indices, talk about time sampling, how to work with time shifts, and then we'll also discuss pandas built in rolling and expanding methods. OK, let's get started. I'll see you at the next lecture.