HomePythonImporting and Managing Financial Data in Python

# Importing and Managing Financial Data in Python

4.8+
11 reviews
Intermediate

In this course, you'll learn how to import and manage financial data in Python using various tools and sources.

5 Hours16 Videos53 Exercises

or

Training 2 or more people?Try DataCamp For Business

## Course Description

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.

### .css-1goj2uy{margin-right:8px;}Group.css-gnv7tt{font-size:20px;font-weight:700;white-space:nowrap;}.css-12nwtlk{box-sizing:border-box;margin:0;min-width:0;color:#05192D;font-size:16px;line-height:1.5;font-size:20px;font-weight:700;white-space:nowrap;}Training 2 or more people?

Try DataCamp for BusinessFor a bespoke solution book a demo.

Go To Track
1. 1

### Importing stock listing data from Excel

Free

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.

Play Chapter Now
Reading, inspecting, and cleaning data from CSV
50 xp
Import stock listing info from the NASDAQ
100 xp
How to fix the data import?
50 xp
100 xp
50 xp
Load listing info from a single sheet
100 xp
Load listing data from two sheets
100 xp
Combine data from multiple worksheets
50 xp
Load all listing data and iterate over key-value dictionary pairs
100 xp
How many companies are listed on the NYSE and NASDAQ?
50 xp
100 xp
2. 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. 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. 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.

### In the following Tracks

#### Finance Fundamentals in Python

Go To Track

Datasets

Amex listings .csv fileIncome growth .csv fileListings .xlsx fileNasdaq listings .csv filePer capita income .csv file

Collaborators

Stefan Jansen

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.
See More

## Don’t just take our word for it

*4.8
from 11 reviews
82%
18%
0%
0%
0%
Sort by
• TARIK H.

Top!

• Peter K.
6 months

Enjoying the Python Financial track. Learning lots being a financial person aiming to add some technical knowledge.

• Sue D.
9 months

Interesting course and awesome instructor!

• Ana U.
10 months

Excellent course. It provides an overview of the most important financial-data issues that can also be applied to other kind of data stored in Excel spreadsheet for example. I enjoyed and learned a lot.

• Krishan G.

Learnt a few import functions in Pandas which I never thought existed. Recommend this to anyone.

"Top!"

TARIK H.

"Enjoying the Python Financial track. Learning lots being a financial person aiming to add some technical knowledge."

Peter K.

"Interesting course and awesome instructor!"

Sue D.