In this chapter, you’ll learn how to create and manipulate Python datetime objects to help you identify key financial events, such as Black Friday. You’ll also learn how to store and efficiently look up items using Python dictionaries. 

Representing time with datetimes

Creating datetimes for dates

Datetimes from strings

Converting format with datetimes

Working with datetimes

Accessing datetime attributes

Comparing datetimes

Making relative datetimes

Dictionaries

Creating and accessing dictionaries

Accessing safely and deleting

Datetimes and Dictionaries 

Through hands-on activities, you’ll discover how to use Boolean logic to determine truth and use comparison and equality operators to control execution in if-statements and loops.

Comparison operators

Equality across types

Assignment and equality

Comparing dividends

Boolean operators

Decisions with Boolean operations

Assigning variables with Boolean operators

Negating with Boolean operators

If statements

Control statements

Comparing sales and purchases

Branching with elif and else

For and while loops

Breaking out of a for loop

Controlling loop execution

Control Flow and Logic

Discover how to create and access DataFrames with pandas using financial data from other data structures, including Dicts, lists, and CSV files. You’ll then uncover additional insights, as you aggregate data across rows or columns, calculate averages, and extend your data using functions.

Creating a DataFrame

Creating DataFrames

Reading market history

Accessing Data

Accessing using names

Accessing using indexes

Aggregating and summarizing

Mean prices

Median prices

Extending and manipulating data

Creating new columns

Dropping columns from DataFrame

Manipulating data with Pandas

Pandas Dataframe

In this final chapter, you’ll try your hand at working with real-world NASDAQ stock data as you learn how to interpret new data, create masks to filter data, and visualize your findings with plots.

Peeking at data with head, tail, and describe

Why use describe

Peek at top and bottom

Describing data

Filtering data

Why filter data

Filtering stock data.

Selecting data from data range

Plotting data

Identifying plot type

Making a line plot

Choose kind of plot

Wrapping up

Working with NASDAQ Stock Data

Alphabet stock

Are you a financial or business analyst, or simply looking for an easier way to manage your stock portfolio? If so learning Python can automate financial tasks such as calculating risk, mapping market health, and visualizing a stock's price trends, saving you time and money.

In this course, you’ll learn how to use Python data structures, execution control statements, and DataFrames to manipulate financial data. You will then work with pandas, using data from the Federal Reserve Bank, to explore national economic trends—an essential part of understanding investment strategies. You will also calculate risk based on stock price data, and display this data in easy to read plots. By the end of this course, you’ll be the new Python of Wall Street.

Introduction to Python for Finance

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

Intermediate Python for Finance

Finance Fundamentals in Python

Finance Fundamentals with CFI

	Symbol	Purchased	Quantity	Price
0	AAPL	2016-01-08	23	96.96
1	AAPL	2018-09-07	50	221.30
2	AMZN	2020-02-14	14	324.95

Accessing using indexes

Intermediate Python for Finance

Hands-on interactive exercise