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Kurs

Importing and Managing Financial Data in Python

MittelSchwierigkeitsgrad

Aktualisiert 02/2023

In diesem Kurs lernst du, wie du Finanzdaten mit verschiedenen Tools und Quellen in Python importierst und verwaltest.

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PythonApplied Finance

5 Std.

16 Videos

53 Übungen

4,350 XP

45,029

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Kursbeschreibung

Wenn du deine neuen „Python for Data Science“-Skills auf reale Finanzdaten anwenden willst, bekommst du in diesem Kurs wertvolle Werkzeuge an die Hand. Zuerst lernst du, wie du Daten aus Excel in pandas importierst und wieder zurück exportierst. Danach erfährst du, wie du Aktienkurse über verschiedene Online-APIs wie Google oder Yahoo! Finance abrufst, Makrodaten von der Federal Reserve sowie Wechselkurse von OANDA. Abschließend lernst du, wie du Renditen über verschiedene Zeithorizonte berechnest, Aktienentwicklungen nach Sektoren für IPOs analysierst und Korrelationen berechnest und zusammenfasst.

Voraussetzungen

Data Manipulation with pandas

1

Importing stock listing data from Excel

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 CSV

Import stock listing info from the NASDAQ

How to fix the data import?

Read data using .read_csv() with adequate parsing arguments

Read data from Excel worksheets

Load listing info from a single sheet

Load listing data from two sheets

Combine data from multiple worksheets

Load all listing data and iterate over key-value dictionary pairs

How many companies are listed on the NYSE and NASDAQ?

Automate the loading and combining of data from multiple Excel worksheets

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2

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 online

Get stock data for a single company

Visualize a stock price trend

Economic data from the Federal Reserve

Visualize the long-term oil price trend

Compare labor market participation and unemployment rates

Compare bond and stock performance

Select stocks and get data from Yahoo! Finance

Select the top 5 listed consumer companies

Get the ticker of the largest consumer services company

Get the largest consumer company listed after 1998

Get several stocks & manage a MultiIndex

Get data for the 3 largest financial companies

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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?

Summarize your data with descriptive stats

List the poorest and richest countries worldwide

Global incomes: Central tendency

Describe the distribution of your data with quantiles

Global incomes: Dispersion

Deciles of the global income distribution

Getting all the statistics

Visualize the distribution of your data

Visualizing international income distribution

Growth rates in Brazil, China, and the US

Highlighting values in the distribution

Summarize categorical variables

Companies by sector on all exchanges

Technology IPOs by year on all exchanges

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4

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 category

Median market capitalization by sector

Median market capitalization by IPO year

All summary statistics by sector

More ways to aggregate your data

Company value by exchange and sector

Calculate several metrics by sector and exchange

Summary statistics by category with seaborn

Plot IPO timeline for all exchanges using countplot()

Global median per capita income over time

Calculate several metrics by sector and IPO year

Distributions by category with seaborn

Inflation trends in China, India, and the US

Distribution of inflation rates in China, India, and the US

Congratulations!

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Importing and Managing Financial Data in Python

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