Book Image

Hands-On Data Analysis with Pandas

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas

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)
Free Chapter
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

Conventions used

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

CodeInText: 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 ...:

>>> import pandas as pd

>>> 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:

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

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.plot(
... x='date',
... y='price',
... kind='line',
... title='Price over Time',
... legend=False,
... 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
Warnings or important notes appear like this.
Tips and tricks appear like this.