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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

Using the CSV files in the exercises/ folder and what we have learned so far in this book, complete the following exercises:

  1. With the earthquakes.csv file, select all the earthquakes in Japan with a magType of mb and a magnitude of 4.9 or greater.
  2. Create bins for each full number of magnitude (for example, the first bin is 0-1, the second is 1-2, and so on) with a magType of ml and count how many are in each bin.
  3. Using the faang.csv file, group by the ticker and resample to monthly frequency. Make the following aggregations:
    • Mean of the opening price
    • Maximum of the high price
    • Minimum of the low price
    • Mean of the closing price
    • Sum of the volume traded
  1. Build a crosstab with the earthquake data between the tsunami column and the magType column. Rather than showing the frequency count, show the maximum magnitude that was observed for each combination. Put the magType...
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