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Importing and Managing Financial Data in Python

The DataReader: Access financial data online

Name: Importing financial data from the web - The DataReader: Access financial data online
Uploaded: 2023-02-10T00:00:00.000Z
Description: 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.

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Importing and Managing Financial Data in Python

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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.

Exercise 1: Reading, inspecting, and cleaning data from CSV Exercise 2: Import stock listing info from the NASDAQ Exercise 3: How to fix the data import?Exercise 4: Read data using .read_csv() with adequate parsing arguments Exercise 5: Read data from Excel worksheets Exercise 6: Load listing info from a single sheet Exercise 7: Load listing data from two sheets Exercise 8: Combine data from multiple worksheets Exercise 9: Load all listing data and iterate over key-value dictionary pairs Exercise 10: How many companies are listed on the NYSE and NASDAQ?Exercise 11: Automate the loading and combining of data from multiple Excel worksheets

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.

Exercise 1: The DataReader: Access financial data online

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Exercise 2: Get stock data for a single company Exercise 3: Visualize a stock price trend Exercise 4: Economic data from the Federal Reserve Exercise 5: Visualize the long-term oil price trend Exercise 6: Compare labor market participation and unemployment rates Exercise 7: Compare bond and stock performance Exercise 8: Select stocks and get data from Yahoo! Finance Exercise 9: Select the top 5 listed consumer companies Exercise 10: Get the ticker of the largest consumer services company Exercise 11: Get the largest consumer company listed after 1998 Exercise 12: Get several stocks & manage a MultiIndex Exercise 13: Get data for the 3 largest financial companies

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?

Exercise 1: Summarize your data with descriptive stats Exercise 2: List the poorest and richest countries worldwide Exercise 3: Global incomes: Central tendency Exercise 4: Describe the distribution of your data with quantiles Exercise 5: Global incomes: Dispersion Exercise 6: Deciles of the global income distribution Exercise 7: Getting all the statistics Exercise 8: Visualize the distribution of your data Exercise 9: Visualizing international income distribution Exercise 10: Growth rates in Brazil, China, and the US Exercise 11: Highlighting values in the distribution Exercise 12: Summarize categorical variables Exercise 13: Companies by sector on all exchanges Exercise 14: Technology IPOs by year on all exchanges

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.

Exercise 1: Aggregate your data by category Exercise 2: Median market capitalization by sector Exercise 3: Median market capitalization by IPO year Exercise 4: All summary statistics by sector Exercise 5: More ways to aggregate your data Exercise 6: Company value by exchange and sector Exercise 7: Calculate several metrics by sector and exchange Exercise 8: Summary statistics by category with seaborn Exercise 9: Plot IPO timeline for all exchanges using countplot()Exercise 10: Global median per capita income over time Exercise 11: Calculate several metrics by sector and IPO year Exercise 12: Distributions by category with seaborn Exercise 13: Inflation trends in China, India, and the US Exercise 14: Distribution of inflation rates in China, India, and the US Exercise 15: Congratulations!