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Importing and Managing Financial Data in Python

4.8+
11 reviews
Intermediate

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 Exercises
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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.
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In the following Tracks

Finance Fundamentals in Python

Go To Track
  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 in Python

Go To Track

Datasets

Amex listings .csv fileIncome growth .csv fileListings .xlsx fileNasdaq listings .csv filePer capita income .csv file

Collaborators

Collaborator's avatar
Lore Dirick
Stefan Jansen HeadshotStefan 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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*4.8
from 11 reviews
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Sort by
  • TARIK H.
    about 2 months

    Top!

  • Peter K.
    6 months

    Enjoying the Python Financial track. Learning lots being a financial person aiming to add some technical knowledge.

  • Sue D.
    9 months

    Interesting course and awesome instructor!

  • Ana U.
    10 months

    Excellent course. It provides an overview of the most important financial-data issues that can also be applied to other kind of data stored in Excel spreadsheet for example. I enjoyed and learned a lot.

  • Krishan G.
    about 1 year

    Learnt a few import functions in Pandas which I never thought existed. Recommend this to anyone.

"Top!"

TARIK H.

"Enjoying the Python Financial track. Learning lots being a financial person aiming to add some technical knowledge."

Peter K.

"Interesting course and awesome instructor!"

Sue D.

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