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.Start Course for Free
5 Hours16 Videos53 Exercises36,763 Learners4350 XPFinance Fundamentals in Python Track
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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.
Importing stock listing data from ExcelFree
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.Reading, inspecting, and cleaning data from CSV50 xpImport stock listing info from the NASDAQ100 xpHow to fix the data import?50 xpRead data using .read_csv() with adequate parsing arguments100 xpRead data from Excel worksheets50 xpLoad listing info from a single sheet100 xpLoad listing data from two sheets100 xpCombine data from multiple worksheets50 xpLoad all listing data and iterate over key-value dictionary pairs100 xpHow many companies are listed on the NYSE and NASDAQ?50 xpAutomate the loading and combining of data from multiple Excel worksheets100 xp
Importing financial data from the web
This chapter introduces online data access to Google Finance and the Federal Reserve Data Service through the `pandas` `DataReader`. You will pull data, perform basic manipulations, combine data series, and visualize the results.The DataReader: Access financial data online50 xpGet stock data for a single company100 xpVisualize a stock price trend100 xpEconomic data from the Federal Reserve50 xpVisualize the long-term oil price trend100 xpCompare labor market participation and unemployment rates100 xpCompare bond and stock performance100 xpSelect stocks and get data from Yahoo! Finance50 xpSelect the top 5 listed consumer companies100 xpGet the ticker of the largest consumer services company100 xpGet the largest consumer company listed after 1998100 xpGet several stocks & manage a MultiIndex50 xpGet data for the 3 largest financial companies100 xp
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?Summarize your data with descriptive stats50 xpList the poorest and richest countries worldwide100 xpGlobal incomes: Central tendency100 xpDescribe the distribution of your data with quantiles50 xpGlobal incomes: Dispersion100 xpDeciles of the global income distribution100 xpGetting all the statistics50 xpVisualize the distribution of your data50 xpVisualizing international income distribution100 xpGrowth rates in Brazil, China, and the US100 xpHighlighting values in the distribution100 xpSummarize categorical variables50 xpCompanies by sector on all exchanges100 xpTechnology IPOs by year on all exchanges100 xp
Aggregating and describing your data by category
This chapter introduces the ability to group data by one or more categorical variables, and to calculate and visualize summary statistics for each caategory. In the process, you will learn to compare company statistics for different sectors and IPO vintages, analyze the global income distribution over time, and learn how to create various statistical charts from the seaborn library.Aggregate your data by category50 xpMedian market capitalization by sector100 xpMedian market capitalization by IPO year100 xpAll summary statistics by sector100 xpMore ways to aggregate your data50 xpCompany value by exchange and sector100 xpCalculate several metrics by sector and exchange100 xpSummary statistics by category with seaborn50 xpPlot IPO timeline for all exchanges using countplot()100 xpGlobal median per capita income over time100 xpCalculate several metrics by sector and IPO year100 xpDistributions by category with seaborn50 xpInflation trends in China, India, and the US100 xpDistribution of inflation rates in China, India, and the US100 xpCongratulations!50 xp
In the following tracksFinance Fundamentals in Python
DatasetsAmex listings .csv fileIncome growth .csv fileListings .xlsx fileNasdaq listings .csv filePer capita income .csv file
PrerequisitesData Manipulation with pandas
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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