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Importing and Managing Financial Data in Python

In this course, you'll learn how to import and manage financial data in Python using various tools and sources.

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5 Hours16 Videos53 Exercises35,860 Learners4350 XPFinance Fundamentals Track

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Course Description

If you want to apply your new 'Python for Data Science' skills to real-world financial data, then this course will give you some very valuable tools. First, you will learn how to get data out of Excel into pandas and back. Then, you will learn how to pull stock prices from various online APIs like Google or Yahoo! Finance, macro data from the Federal Reserve, and exchange rates from OANDA. Finally, you will learn how to calculate returns for various time horizons, analyze stock performance by sector for IPOs, and calculate and summarize correlations.
  1. 1

    Importing stock listing data from Excel

    Free

    In this chapter, you will learn how to import, clean and combine data from Excel workbook sheets into a pandas DataFrame. You will also practice grouping data, summarizing information for categories, and visualizing the result using subplots and heatmaps. You will use data on companies listed on the stock exchanges NASDAQ, NYSE, and AMEX with information on company name, stock symbol, last market capitalization and price, sector or industry group, and IPO year. In Chapter 2, you will build on this data to download and analyze stock price history for some of these companies.

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    Reading, inspecting, and cleaning data from CSV
    50 xp
    Import stock listing info from the NASDAQ
    100 xp
    How to fix the data import?
    50 xp
    Read data using .read_csv() with adequate parsing arguments
    100 xp
    Read data from Excel worksheets
    50 xp
    Load listing info from a single sheet
    100 xp
    Load listing data from two sheets
    100 xp
    Combine data from multiple worksheets
    50 xp
    Load all listing data and iterate over key-value dictionary pairs
    100 xp
    How many companies are listed on the NYSE and NASDAQ?
    50 xp
    Automate the loading and combining of data from multiple Excel worksheets
    100 xp
  2. 3

    Summarizing your data and visualizing the result

    In this chapter, you will learn how to capture key characteristics of individual variables in simple metrics. As a result, it will be easier to understand the distribution of the variables in your data set: Which values are central to, or typical of your data? Is your data widely dispersed, or rather narrowly distributed around some mid point? Are there outliers? What does the overall distribution look like?

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In the following tracks

Finance Fundamentals

Collaborators

lore
Lore Dirick
Stefan Jansen Headshot

Stefan Jansen

Founder & Lead Data Scientist at Applied Artificial Intelligence

Stefan is the Founder & Lead Data Scientist at Applied Artificial Intelligence. He has 15 years of experience in finance and investments, with a big focus on emerging markets.
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