1. Aprender
    2. /
  2. Cursos
    3. /
  3. Importing and Managing Financial Data in Python
  • 1

    Importing stock listing data from Excel

    Gratuito

    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.

  • 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.

  • 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?

  • 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.

Initializing

Exercicio

Inflation trends in China, India, and the US

Finally, the seaborn package includes functions that allow you to visualize the distribution of levels of categorical variables.

In the next two exercises, you will examine the historical inflation data in China, India, and the US over the past 50+ years in data from FRED. Before jumping into using the functions you have just learned, you should first familiarize yourself with the raw data. pandas as pd, matplotlib.pyplot as plt, and seaborn as sns have been imported for you. The FRED inflation data is in your workspace as inflation.

Instruções

100 XP
  • Inspect inflation using .info().
  • Group inflation by 'Country' and assign to inflation_by_country.
  • In a for loop, iterate over country, data pairs returned by inflation_by_country. In each iteration, use .plot() on data with title set to country to show the historical time series.