课程

Intermediate Python for Finance

中级技能水平

更新时间 2022年6月

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

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PythonApplied Finance4 小时15 视频52 练习4,150 经验值33,720成就声明

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课程描述

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.

先决条件

Introduction to Python for Finance

1

Datetimes and Dictionaries

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

50 经验值

Creating datetimes for dates

100 经验值

Datetimes from strings

100 经验值

Converting format with datetimes

100 经验值

Working with datetimes

50 经验值

Accessing datetime attributes

100 经验值

Comparing datetimes

100 经验值

Making relative datetimes

100 经验值

Dictionaries

50 经验值

Creating and accessing dictionaries

100 经验值

Accessing safely and deleting

100 经验值

2

Control Flow and Logic

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

50 经验值

Equality across types

50 经验值

Assignment and equality

100 经验值

Comparing dividends

100 经验值

Boolean operators

50 经验值

Decisions with Boolean operations

100 经验值

Assigning variables with Boolean operators

100 经验值

Negating with Boolean operators

100 经验值

If statements

50 经验值

Control statements

50 经验值

Comparing sales and purchases

100 经验值

Branching with elif and else

100 经验值

For and while loops

50 经验值

Breaking out of a for loop

100 经验值

Controlling loop execution

100 经验值

3

Pandas Dataframe

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

50 经验值

Creating DataFrames

100 经验值

Reading market history

100 经验值

Accessing Data

50 经验值

Accessing using names

100 经验值

Accessing using indexes

100 经验值

Aggregating and summarizing

50 经验值

Mean prices

50 经验值

Median prices

100 经验值

Extending and manipulating data

50 经验值

Creating new columns

100 经验值

Dropping columns from DataFrame

100 经验值

Manipulating data with Pandas

100 经验值

4

Working with NASDAQ Stock Data

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

50 经验值

Why use describe

50 经验值

Peek at top and bottom

100 经验值

Describing data

100 经验值

Filtering data

50 经验值

Why filter data

50 经验值

Filtering stock data.

100 经验值

Selecting data from data range

100 经验值

Plotting data

50 经验值

Identifying plot type

50 经验值

Making a line plot

100 经验值

Choose kind of plot

100 经验值

Wrapping up

50 经验值

Intermediate Python for Finance

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