Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the 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 pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how 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. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, and user input. Here is an example: "Use pip to install the packages in the requirements.txt file."

A block of code is set as follows. The start of the line will be preceded by >>> and continuations of that line will be preceded by ...:

>>> df = pd.read_csv(
...     'data/fb_2018.csv', index_col='date', parse_dates=True
... )
>>> df.head()

Any code without the preceding >>> or ... is not something we will run—it is for reference:

    del df['ones']
except KeyError:
    pass # handle the error here

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

>>> df.price.plot(
...     title='Price over Time', ylim=(0, None)
... )

Results will be shown without anything preceding the lines:

>>> pd.Series(np.random.rand(2), name='random')
0 0.235793
1 0.257935
Name: random, dtype: float64

Any command-line input or output is written as follows:

# Windows:
C:\path\of\your\choosing> mkdir pandas_exercises
# Linux, Mac, and shorthand:
$ mkdir pandas_exercises

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Using the File Browser pane, double-click on the ch_01 folder, which contains the Jupyter Notebook that we will use to validate our setup."

Tips or important notes

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