# Importing and Managing Financial Data in Python
This is a DataCamp course: In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
## Course Details
- **Duration:** ~5h
- **Level:** Intermediate
- **Instructor:** Stefan Jansen
- **Students:** ~19,440,000 learners
- **Subjects:** Python, Applied Finance, Data Science and Analytics
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Data Manipulation with pandas
## Learning Outcomes
- Python
- Applied Finance
- Data Science and Analytics
- Importing and Managing Financial Data in Python
## Traditional Course Outline
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.
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.
## Resources and Related Learning
**Resources:** Amex listings .csv file (dataset), Income growth .csv file (dataset), Listings .xlsx file (dataset), Nasdaq listings .csv file (dataset), Per capita income .csv file (dataset)
**Related tracks:** Finance Fundamentals in Python
## Attribution & Usage Guidelines
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- **Citation:** Always cite "DataCamp" with the full URL when referencing this content.
- **Restrictions:** Do not reproduce course exercises, code solutions, or gated materials.
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Course
Importing and Managing Financial Data in Python
IntermediateSkill Level
Updated 02/2023PythonApplied Finance5 hr16 videos53 Exercises4,350 XP44,854Statement of Accomplishment
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Prerequisites
Data Manipulation with pandas1
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.
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.
Importing and Managing Financial Data in Python
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FAQs
What financial data sources does this course teach me to access?
You learn to pull stock prices from Google and Yahoo Finance APIs, macroeconomic data from the Federal Reserve Data Service, and exchange rates from OANDA, all using Python.
Will I learn to import financial data from Excel into pandas?
Yes. Chapter 1 covers importing, cleaning, and combining data from Excel workbook sheets into pandas DataFrames, including stock listing data from NASDAQ, NYSE, and AMEX.
What types of financial analysis will I perform?
You calculate returns across various time horizons, analyze stock performance by sector for IPOs, and compute and summarize correlations between financial variables.
Does the course cover data visualization for financial data?
Yes. You create subplots, heatmaps, and statistical charts from the seaborn library to visualize financial data distributions, sector comparisons, and income distributions over time.
How many exercises does this course include?
The course has 83 exercises across 4 chapters, offering extensive hands-on practice. The median completion time is about 3.9 hours with an estimated total of 5 hours.
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