Data Analysis with Pandas and Python

Welcome to Data Analysis with Pandas and Python. In this lesson, we

• introduces the pandas library including its history and purpose

• introduce Jupyter Notebook, the environment in which we'll be writing our code

• explore sample Jupyter Notebooks to showcase some of the technology's features

The datasets for this course are available in a single pandas.zip file. Download and unpack the pandas.zip file in the directory of your choice.

This lesson includes the completed Jupyter Notebooks that were created during the recording of the course.

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The next batch of lessons focuses on installing and configuring the Anaconda distribution on a MacOS machine. When downloading the distribution, choose the latest version of the language. It will have the greatest version number. In this lesson, we also discuss the differences between Python 2 and 3.

In this lesson, we install the Anaconda distribution on a MacOS machine. The setup install Python and over 100 of the most popular libraries for data science in a central directory on your computer. We also explore the Anaconda Navigator program, a visual application for interacting with Anaconda.

The Terminal is an application for issuing text-based commands to your MacOS operating system. In this lesson, you'll learn two ways to access the Terminal. We also verify that Anaconda has been successfully installed and update the version of the conda environment manager.

In this lesson, we use the Terminal to create a new Anaconda environment and install Pandas (and some other libraries) within it. We also learn how to activate and deactivate conda environments, and update packages.

The course materials, a collection of datasets in .csv and .xlsx file formats, is available for download in a single zip file attached to this lesson. I strongly recommend following along with my tutorials by practicing the syntax on your end. In this lesson, we walk through the startup and shutdown process for a Jupyter Notebook session. We also execute our first line of Python code!

Discover if your Windows operating system is running a 32-bit version or a 64-bit version of the OS. You'll need to memorize this number for the next lesson.

In this lesson, we'll download and install the Anaconda distribution for our Windows computers. Anaconda is a software bundle that includes Python and the conda environment manager.

Access the Command Prompt on a Windows machine. The prompt (also known as the command line) is used to interact with the computer with text-based commands. We'll use it to download additional Python libraries for the course and update all installed Anaconda libraries.

In this lesson, we extract our .csv and .xlsx datasets, which are available in a single .zip file attached to this lesson.. We also walk through the startup and shutdown process for a study session, which includes

• activating the correct Anaconda environment

• launching the Jupyter Notebooks application

• opening and closing a Jupyter Notebook

• shutting down the Jupyter server

This lecture walks you through fixing a possible issue with Autocomplete in Jupyter Notebook.

In this lesson, we explore the Jupyter Notebook interface including the toolbars and menus. We also dive into the ways we can restart the Notebook in case of slowness or unresponsiveness.

In this lesson, we learn about the two different modes (Edit Mode and Command Mode) within a Jupyter Notebook. Edit Mode modifies the contents of a single cell while Command Mode enables keyboard shortcuts that work on the Notebook as a whole.

Learn the multiple keyboard shortcuts to execute code cells and Markdown cells. We'll also learn how Jupyter Notebook chooses what to output below a cell that has multiple commands.

In this lesson, we practice using keyboard shortcuts to add and delete cells from the Jupyter Notebook. We also see how to access a helpful cheatsheet of available commands.

In this lesson, we discuss how to use the import keyword to import libraries like panda and numpy into a Jupyter Notebook. We also talk about the as keyword to assign an alias to an import, as well as the popular community aliases for pandas and numpy.

Test your knowledge of the concepts introduced in this course section.

In this lesson, we introduce the Python crash course, a quick bootcamp on the fundamentals on the Python programming language. This section is COMPLETELY OPTIONAL and is designed for students who have limited / no experience in coding.

A comment is a line of code that is ignored by the Python interpreter. You can use it to disable code, to leave documentation, or to write explanations.

This lesson introduces Python's basic data types including integers, floats, strings, Booleans, and the None object. All of these data structures are objects. An object is just a digital container for data.

In this lesson, we go back to grade school and practice using common mathematical operators for addition, subtraction, division, and multiplication. We also explore some special operators like modulo that you may not have used before.

A variable is a name assigned to an object in the program. It is subject to change; a variable can be reassigned to a different value throughout a program's execution.

See the solutions to the coding exercise in the previous lesson.

A function is a recipe, a set of repeatable instructions for achieving some outcome. Functions accept inputs called arguments and produce an output called a return value. In this lesson, we practice invoking Python's built-in functions including len, str, float, int, and more.

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In this lesson, we define our own custom function to convert a temperature from Celsius to Fahrenheit. A custom function allows us to encapsulate a piece of repeatable business logic.

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In this lesson, we introduce string methods like upper, lower, title, replace, and more.  A string is an immutable object so it is incapable of change. These methods return new string objects.

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A list is a mutable data structure for storing elements in order. In this lesson, we declare some sample lists of various data types and explore some common methods for list manipulation.

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Indexing or slicing refers to extracting one or more elements from a data structure like a string or a list. In this lesson, we introduce the square bracket syntax for indexing.

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A dictionary is a mutable, unordered collection of key-value pairs. Dictionaries are ideal for creating associations or relationships between objects. In this lesson, we build up a dictionary representing a restaurant menu, then demonstrate how to add, remove, and modify key-value pairs within it.

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Downloaded the completed Jupyter Notebook for the course section.

Test your knowledge of the concepts introduced in this course section.

In this lesson, we create a new Jupyter Notebook for the Series section of the course. The pandas Series object is a one-dimensional labelled array that combines the best features of a Python list and a Python dictionary.

A pandas Series can be created with the pd.Series() constructor method. In this lesson, we'll practice creating a few sample Series by feeding in Python lists as inputs to the constructor method.

The pd.Series constructor method accepts a variety of inputs, including native Python object. In this lesson, we'll create a Series from a Python dictionary. We'll also explore the differences between the Series and Python's built-in objects, and understand how the index operates in a Series.

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In this lesson, we continue our exploration of methods on the Series object. We practice with the sum, mean, and product methods.

Objects in pandas have attributes and methods. Methods actively interact with and modify the object while attributes return information about the object's state. In this lesson, we'll use the values, index, and dtype attributes on a Series object.

Explore the solution for the previous coding challenge.

Parameters are names for the inputs that a function/method will receive when invoked. Arguments are the values we provide for those parameters when the function/method is invoked. In this lesson, we'll learn the syntax of supplying arguments to parameters on pandas methods.

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The time has come to import our first datasets into our Jupyter Notebook ! In this lesson, we use the pd.read_csv method to import a dataset of Pokemon and Google stock prices. We also explore the squeeze method, which coerces an imported one-column DataFrame into a Series object.

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In this lesson, we learn the head and tail methods for returning a specified number of rows from the beginning and end of a Series.

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See how the Series interacts with Python's built-in functions including len, type, sorted, list, dict, max, and min. pandas works seamlessly with all of them.

In this lesson, we invoke the sort_values method to sort a Series in ascending or descending order.

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Call the .sort_index() method on a pandas Series to sort it by the index instead of its values.

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We can use Python's in keyword to check if a value exists in either the values or index of a Series. By default, pandas will search for the value in the Series index.

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In this lesson, we walk through how to use square bracket notation to extract one or more Series values by their index position. The index position represents the order of the row within the Series.

In this lesson, we explore how to use use bracket notation to extract one or more values from a Series by their index labels.

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In this lesson, we explore an alternative approach to extracting one or more values from a Series by index position or index label. The get method accepts the key to search for in the index as well as a fallback to value in return if the key is not found.

In this lesson, we use square bracket syntax to overwrite a Series value at a given index position/index label.

In this lesson, we discuss the differences between copies and views. We also learn how to use the copy method to create a distinct, independent clone of an existing pandas object.

In this lesson, we learn about the inplace parameter, which "modifies" an object inplace. As we find out, this is a bit of an illusion as pandas still creates a copy of the original object behind the scenes.

In this lesson, we practice invoking common mathematical methods including count, sum, mean, and std on Series objects.

In this lesson, we introduce the syntax for broadcasting, which allows us to apply the same mathematical operation to each Series value.

In this lesson, we learn the value_counts method. It returns a Series that counts the number of the times each unique value occurs in a Series.

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In this lesson, we use the apply method to invoke a function on every Series value. The method returns a new Series with the resulting calculations (i.e. return values).

In this lesson, we use the map method to associate Series values to other values based on mapping object. We practice with a Python dictionary and another Series object.

Downloaded the completed Jupyter Notebook for the course section.

Review the pandas Series concepts you explored in this module with this action-packed quiz!