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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas

By : Stefanie Molin
4.7 (11)
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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas

4.7 (11)
By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Table of Contents (21 chapters)
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1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications - Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Exercises

Solve the following exercises using what we have learned so far in this book and the stock data in the exercises/ directory:

  1. We want to look at data for the Facebook, Apple, Amazon, Netflix, and Google (FAANG) stocks, but we were given each as a separate CSV file (obtained using the stock_analysis package we will build in Chapter 7, Financial Analysis – Bitcoin and the Stock Market). Combine them into a single file and store the dataframe of the FAANG data as faang for the rest of the exercises:
    1. Read each file in.
    2. Add a column to each dataframe, called ticker, indicating the ticker symbol it is for (Apple's is AAPL, for example). This is how you look up a stock. Each file's name is also the ticker symbol, so be sure to capitalize it.
    3. Append them together into a single dataframe.
    4. Save the result in a CSV file called faang.csv.
  1. With faang, use type...
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