Data Analysis with Pandas and Python | Discount Coupon for Udemy Course

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.

Get to know a little about your instructor.

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

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

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.

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

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.

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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.

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

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!

In this section, we'll be playing around with the 2-dimensional DataFrame object. We define what dimensions are and also introduce the nba.csv dataset.

The Series and DataFrame objects share many attributes and methods in common. In this lesson, we'll review attributes like index, values, shape, and dtypes and see how they return different results depending on the object they're invoked on. We also introduce exclusive attributes like columns and hasnans.

Series and DataFrames may share attributes and methods but they are still different objects. In this lesson, we'll see how methods like sum operate differently depending on the object they are called on.

We can use two syntactical options to extract a single column from a  DataFrame: dot syntax and square brackets. I prefer the square bracket approach because it works 100% of the time.

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In this lesson, we'll select two or more columns from a pandas DataFrame. We'll still need bracket syntax to extract but now we'll include a Python list to specify the specific columns we'd like to pull out. The result will be a new DataFrame.

See the answers to the coding challenge in the previous lesson.

We can use square bracket syntax to add a new column on the right end of a DataFrame and populate it with values. In this lesson, we also introduce the insert method to add a column at a specified column index.

In this lesson, we apply mathematical operations to columns in our nba DataFrame and add the new Series as columns to the DataFrame.

Refresh your memory on the value_counts Series method, which counts the number of times each unique value occurs within the Series.

Pandas marks null/absent values with the NaN designation. In this lesson, we'll learn how to delete rows with NaN values with the dropna method. We'll also modify the method arguments to customize which rows will be removed.

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One alternative to dropping null value is populating them with a predefined value. In this lesson, we'll call the .fillna() method to accomplish this. We'll practice the method on both DataFrame and Series objects.

In this lesson, we convert the data types in our nba columns using the astype method. We also review how to overwrite a DataFrame column with a Series of new data values.

In this lesson, we introduce the category data type, which is optimal when there is a small number of unique values in a Series.

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We can use the sort_values method to sort a DataFrame by one or more columns. In this lesson, we explore the method in depth including how to customize the sort order.

In this lesson, we'll explore additional parameters to the sort_values method to sort a DataFrame by values across multiple columns. We'll also cover how to specify different sort orders (ascending vs. descending) for different columns.

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

In this lesson, we review the sort_index method, which sorts the DataFrame by its index positions/labels.

We can rank Series values from lowest to greatest (or vice versa) with the rank method. In this lesson, we use this method to rank the salaries of the players in our nba dataset.

Downloaded the completed Jupyter Notebook for the course section.

In this lesson, we create the Jupyter Notebook for our new section, our second focusing on the 2D DataFrame object. The focus of this module is filtering data or, in other words, how we extract rows based on one or more conditions. We also introduce the employees.csv dataset that we'll be working with.

In this lesson, we'll filter rows from the employees DataFrame based on a single condition. The logic involves creating a Boolean Series of True and False values, then passing it in square brackets after our DataFrame.

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In this lesson, we'll explore more complex row filtering based on multiple conditions. The syntax requires some additional symbols (&) to specify that we want to check the truthiness of multiple conditions.

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In this lesson, we'll continue filtering rows from the DataFrame based on multiple conditions. However, this time we'll use a new symbol ( | ) to specify an OR check. This requires only one of the tested conditions to evaluate to True in order to include the row.

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

One common problem is data analysis is extracting rows whose values fall within a collection of values. Instead of writing multiple OR statements, we can use the isin method and pass in a list of values to match against.

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Call the .isnull() and .notnull() methods to create Boolean Series for extracting rows will null or non-null values. Both methods return a Boolean Series object, which can be passed within square brackets after the DataFrame to filter it.

In this lesson, we'll learn the between method, which extract rows where a column value falls between a range of values. The between method returns a Boolean Series, which can be passed within square brackets after the DataFrame to filter it.

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Call the .duplicated() method to create a Boolean Series and use it to extract rows that have duplicate values. This is another example of a method that returns a Boolean Series object, which can be passed within square brackets after the DataFrame to filter it.

An alternative option to identifying duplicate rows and removing them through filtering is the .drop_duplicates() method. In this lesson, we'll invoke the method to remove rows with duplicate values in a DataFrame. We'll also provide custom arguments to modify how the method operates.

In this lesson, we introduce the unique and nunique methods, which extract the unique values and a count of the unique values in a Series.

In this lesson, we introduce the third DataFrame-focused section of the course. The upcoming lessons cover how to:

• set and reset an index in a DataFrame

• retrieve DataFrame rows by index position or index label

• set new values for one or more cells in the DataFrame

• rename or delete rows or columns

• extract a random sample of rows / columns

and more!

Pandas will default to assigning a data structure a numeric index starting at 0. In this lesson, we'll explore how we can use the set_index and reset_index methods to customize and reset the index labels of a DataFrame object.

In this lesson, we'll use the loc[] accessor to retrieve DataFrame rows based on index label. We also look at providing multiple index labels within a list.

In this lesson, we'll use the .iloc[] accessor to retrieve DataFrame rows based on index position. We also look at providing multiple index positions within a list.

The .loc[] and loc accessors can take second arguments to specify the column(s) that should be extracted. In this lesson, we'll practice extracting movies from our dataset with this syntax.

In this lesson, we'll discuss how to assign a new value to one cell in a DataFrame. We first extract the cell value by using the .ix[] method with a row and column argument, then reset its value with the assignment operator (=).

In this lesson, we explore how we can overwrite multiple values in a DataFrame by passing a Boolean Series to the loc accessor. We also discuss how we can accidentally overwrite values on a slice of data rather than the original DataFrame itself.

In this lesson, we invoke the rename method on a DataFrame to change the names of the index labels or column names. We can either combine the mapper and axis parameters, or target the columns and index parameter exclusively. In either case, we provide an argument of a dictionary where the keys represent the current label names and the values represent the desired label names.

In this lesson, we practice 3 different syntactical options to delete rows or columns from a DataFrame. They include the .drop() method, the .pop() method, and Python's built in del keyword.

In this lesson, we'll call the .sample() method to pull out a random sample of rows or columns from a DataFrame. We'll specify the number of values to include by modifying the n parameter.

There is a shortcut available to pull out the rows with the smallest or largest values in a column. Instead of sorting the rows and using the .head() method, we can call the .nsmallest() and .nlargest() methods. We'll dive into these methods and their parameters in this lesson.

Sometimes, you'll want to retain the structure of the original DataFrame when you extract a subset. In this lesson, we'll call the .where() method to return a modified DataFrame that holds NaN values for all rows that don't match our provided condition.

Our filtration process so far has involved using official pandas syntax. In this lesson, I'll introduce the .query() method, an alternate string-based syntax for extracting a subset from a DataFrame.

In this review of a lesson from our Series Module, we'll call the .apply() method on a Series to apply a Python function on every value within it. This will act as a foundation for the next lesson, where we'll invoke the same method on a DataFrame.

The .apply() method applies a Python function on a row-by-row basis in a DataFrame. In this example, we'll create a custom ranking function for our films, then demonstrate how it can be applied to a DataFrame.

The default bracket syntax extracts a component of the larger DataFrame. Any operations on that component will affect the larger DataFrame. If we want to separate the two objects, we can use the .copy() method, which create an independent copy of a pandas object.

Datasets can arrive with plenty of improperly formatted text data. The Working with Text Data section introduces the methods available in pandas to clean your data. In this introductory lesson, we create a Jupyter Notebook for this sectionand import a CSV file with public data on employees in the city of Chicago. We also optimize the DataFrame for speed and efficiency.

String methods in pandas require a .str prefix to work properly. In this lesson, we introduce 4 popular string methods on Series:

• str.lower to convert a string's characters to lowercase

• str.upper to convert a string's characters to uppercase

• str.title to capitalize the first letter of every word in a string

• str.len to return a count of the number of characters in a string

See the solution to the previous lesson's coding challenge.

The str.replace() method replaces a substring within a string with another value for all Series values. In this lesson, we use it to convert our Employee Annual Salary column to store numeric values instead of text ones.

In this lesson, we'll introduce the .str.contains(), .str.startswith(), and .str.endswith() methods. All three create a Boolean Series, which can be used to extracting rows from a DataFrame. We'll also discuss case normalization to increase the accuracy of our results.

In this lesson, we'll invoke the .str.strip() family of methods to remove leading and trailing whitespace from strings in a Series. The .str.lstrip() method removes whitespace from the left side (beginning) of a string, the .str.strip() method removes whitespace from the right side (end) of a string, and the .str.strip() method does both.

The past few lessons focused on calling string methods on the values in a column of our dataset. In this lesson, we'll familiarize ourselves with calling the same string methods on the index labels and column names of a DataFrame.

Strings can often contain multiple pieces of information that are separated by a common delimiter. In this lesson, we'll introduce the .str.split() method, which can split a string value based on an occurrence of a user-specified value. This is equivalent to the Text to Columns feature in Microsoft Excel.

In this lesson, we'll utilize additional parameters on the .str.split() method to modify its performance. We'll extract the first names of all the employees in our dataset, a slightly more challenging puzzle than the one in the previous lesson.

In this lesson, we'll explore even more parameters on the .str.split() method. The expand parameter allows us to expand the generated Python list into DataFrame columns while the n parameter limits the total number of splits.

A DataFrame or Series can hold multiple levels or layers in its index. The object that stores this index is called a MultiIndex. In this lesson, we create a Jupyter Notebook for this section and explore a new bigmac.csv dataset.

In this lesson, we'll create a multi-layer MultiIndex on a DataFrame with the .set_index() method. The method can be passed a list instead of a string to transfer multiple columns to the index.

See the solution to the previous lesson's coding challenge.

The index attribute returns the underlying object that makes up the index of a DataFrame. In this lesson, we invoke the get_level_values method on the index to extract the values from one of its levels. We show how this can done either by the layer's index position or by its name.

See the solution to the previous lesson's coding challenge.

The levels or layers of a MultiIndex can be changed. In this lesson, we'll call the .set_names() method on a MultiIndex object to rename its levels.

In this lesson, we explore how the sort_index method operates on a MultiIndex DataFrame. We show how to sort all levels in the same order as well as how to vary up the sort order for different levels.

In this lesson, we review the familiar .loc[] and .iloc[] accessors for extracting rows from a MultiIndex DataFrame. We discuss how to package up multiple level values within a tuple to be more precise in communicating what we want to extract.

See the solution to the previous lesson's coding challenge.

In this lesson, we invoke the transpose method on a MultiIndex DataFrame to swap its row and column axes.  We then discuss how to use the loc accessor to attribute a column from a MultiIndex column index.

The swaplevel method swaps two levels within a MultiIndex. In this lesson, we practice moving around levels in the bigmac dataset. If the MultiIndex consists of only two levels, no additional arguments are required.

The .stack() method stacks an index from the column axis to the row axis. It essentially transfers the columns to the row index. In this lesson, we'll see a live example on our bigmac dataset.

The .unstack() method does the exact opposite of the .stack() method. It moves an index level from the rows to the columns. In this lesson, we'll call the method without any arguments.

In this lesson, we'll continue our exploration of the .unstack() method. We'll introduce the numerous argument types we can feed it as arguments including positive integers, negative integers, and index level names.

Multiple levels of the row-based MultiIndex can be shifted with the .unstack() method. In this lesson, we'll explore how to provide a list argument to the level parameter to move multiple layers at a time. We'll also introduce the fill_value parameter to plug in missing values in the resulting DataFrame.

In this lesson, we'll reorganize the unique values in a DataFrame column as the column headers with the pivot method. This can be a particularly effective method for shortening the length of the DataFrame.

In this lesson, we'll emulate Excel's Pivot Table functionality with the pivot_table method. We'll explore the values, index, column, and aggfunc parameters. We'll also discuss the variety of aggregation functions that we can use including sum, count, max, and min.

The pd.melt method effectively perform santi-pivot operations. In this lesson, we'll call the method on a DataFrame to convert its current data structure into a more tabular format. We'll also explore the optional parameters available to modify the resulting column names in the new DataFrame.

See the solution to the previous lesson's coding challenge.

The pandas DataFrameGroupBy object allows us to create groupings of data based on common values in one or more DataFrame columns. In this lesson, we'll setup a new Jupyter Notebook in preparation for this module.

The GroupBy object does not offer us much of substance until we call a method on it. In this lesson, we'll call the .first(), .last(), and .size() methods on a GroupBy object to gain a better understanding of its internal data structure.

The .get_group() method extracts a grouping from a GroupBy object. In this lesson, we'll practice pulling out a few groups from our companies dataset.

Aggregation methods allow us to perform calculations on all groupings within a GroupBy object. In this lesson, we'll call some mathematical methods on the groups, including the .sum(), .mean(), and .max() methods.

A GroupBy object does not have to be made up of values from a single column. In this lesson, we'll create a new GroupBy object based on unique value combinations from two of our DataFame columns.

Certain situations may require different aggregation methods on different columns within our groupings. In this lesson, we'll invoke the .agg() method on our GroupBy object to apply a different aggregation operation to each inner column.

A standard Python for loop can be used to iterate over the groups in a pandas GroupBy object. In this lesson, we'll loop over all of our gropings to extract selected rows from each inner DataFrame. We'll append these rows to a running DataFrame and then view the final result.

Welcome to the Merging, Joining, and Concatenating section! In this module, we'll cover how to combine data from multiple DataFrames into one. In this section, we create a new Jupyter Notebook and introduce the 4 CSV files that we will be using.

The pd.concat method concatenates two or more DataFrames together. The process is simple when the DataFrames have an identical structure (i.e. the same column names). In this lesson, we also explore how to replace the merged index with a newly generated once.

In this lesson, we use the keys parameter on the pd.concat method to label each concatenated DataFrame with a unique identifier. This parameter yields a MultiIndex DataFrame where the outermost layer holds the keys and the innermost layer holds each DataFrame's original index values.

An inner join merges the values in two DataFrames based on common values across one or more columns. In this lesson, we'll explore the concept by merging on identical values in a single column.

This lesson continues our exploration of the .merge() method. This time, we'll merge the values in two DataFrames based on common values in multiple columns. We'll also validate the data with some filtering.

An outer join combines values that exist in either DataFrame into a central DataFrame. In this lesson, we'll invoke the .merge() method with a modified argument to the how parameter to perform an outer join on our weekly sales data sets.

A left join establishes one of the DataFrames as the base dataset for the merge. It attempts to find each value in another DataFrame and drag over that DataFrame's rows when there's a value match. In this lesson, we'll practice executing this join with the .merge() method.

DataFrames may come equipped with different names for columns that represent the same data. In this lesson, we'll talk about how to utilize the left_on and right_on parameters to specify how to match values in differently named columns across two DataFrames.

Our merges so far have involved matches based on common column values. In this lesson, we'll explore how to merge DataFrames based on common index labels.

Call the .join() method, a simple method to concatenate two DataFrames vertically when they share the same index. This is a shortcut to a more explicit .merge() method.

Call the pd.merge() method on the pandas library to merge two DataFrames. This is an alternate syntax to calling the .merge() method directly on a DataFrame.

The Working with Dates and Times section offers a review of Python's built-in datetime objects as well as a comprehensive introduction to similar tools in the pandas library. In this lesson, we setup our Jupyter Notebook and import Python's datetime module.

Python includes built-in date and datetime objects for working with dates and times. This lesson offers a review of how we can create these objects as well as some of the attributes (.year, .month, .day etc) that are available on them.

The pandas library includes its own Timestamp object to represent moments in time. In this lesson, we'll use the pd.Timestamp() constructor method with a variety of inputs (strings, date objects, date objects) to create some Timestamp objects.

A DatetimeIndex is a pandas object for storing multiple Timestamp objects. In this lesson, we'll create a few DatetimeIndex objects from Python lists.

The pd.to_datetime() method is a convenience method to convert various inputs to pandas-focused objects. In this lesson, we'll pass a variety of inputs (date objects, datetime objects, strings, lists) to the constructor method to see what it returns.

Over the course of the next three lessons, we'll call the pd.date_range() method to generate a DatetimeIndex of Timestamp objects. This constructor method includes 3 critical parameters (start, end, and periods); we need to provide 2 of these 3 for it to function. In this lesson, we'll see how the pd.date_range() method operates with arguments for the start and end parameters.

In this lesson, we'll see how the pd.date_range() method operates with arguments for the start and periods parameters. This approach creates a set number of dates beginning from a specific point.

In this lesson, we'll see how the pd.date_range() method operates with arguments for the end and periods parameters. This approach creates a set number of dates, proceeding backwards from a specified date point. We'll also continue our exploration of the freq parameter to vary the durations between each Timestamp.

The .dt accessor on a Series of Timestamp object allows us to access specific datetime properties, much like the .str accessor allows us to call specific methods on a Series of strings. In this lesson, we'll explore popular attributes like .day, .weekday_name, and .month.

Upcoming lessons rely on the pandas-datareader library to fetch financial datasets from Yahoo Finance. In this lesson, we'll  install the pandas-datareader library.

On a Mac system, open the Terminal. On a Windows machine, look for the Anaconda Prompt from the Start Menu.

Once the application is open, run the following commands.

1. conda activate followed by your environment name (for example, conda activate pandas_playground)

2. conda install pandas-datareader

In this lesson, we use our pandas_datareader library to fetch stock data for Microsoft. The result arrives in a DataFrame object with a DatetimeIndex.

Extracting rows from a DataFrame with a DatetimeIndex is no different than in previous sections. In this lesson, we review the familiar .loc and .iloc accessors. As a reminder, these methods use a pair of square brackets to target one or more rows by either index label or index position.

In this lesson, we'll explore some of the attributes and methods on a pandas Timestamp object. We'll also practice extracting similar information from a complete DatetimeIndex of Timestamps.

In this lesson, we'll use the pd.DateOffset object to add hours, days, weeks, months, and years to each value in a DatetimeIndex.

In this lesson, we'll explore how we can use timeseries offsets to arrive at specific datetime values (such as the end of the month or the start of the year).

Over the next two lessons, we'll explore the pandas Timedelta object which represents durations. A Timedelta represents a distance of time while a Timestamp represents a specific moment in time.

In this lesson, we'll create a Series of Timedelta objects by calculating the duration differences between two columns of Timestamps. Time difference operations can be easily performed with the subtraction ( - ) sign.

Create a Jupyter Notebook for the Input and Output module. This collection of lessons focuses on importing and exporting various file formats into Jupyter Notebook and pandas.

In this lesson, we pass URL string argument to the pd.read_csv method to fetch a public dataset of baby names from New York City.

In this lesson, we review how we can convert a Pandas Series to native Python objects including lists, dictionaries and strings.

Export a DataFrame to a .csv file with the .to_csv() method. The method includes several parameters to modify the output, including what columns will be included and whether the index will be attached.

In this lesson, we install the xlrd and openpyxl libraries that are needed to work with Excel Workbooks in Pandas.

To do so, access the Terminal (Mac) or Anaconda Prompt (Windows) and execute the following commands.

1. conda activate pandas_playground

2. conda install xlrd openpyxl

In this lecture, we explore how import an Excel workbook into pandas. We discuss how we can target worksheets by index position or by sheet name, as well as how to import all of the worksheets in a workbook.

In this lesson, we write multiple DataFrames to an Excel workbook by creating an Excel Writer object. The lesson also covers how to target specific columns in the export.

Test your knowledge of the concepts introduced in the Input and Output module in this quiz!

Welcome to the Visualization section! This collection of lessons focuses on the pandas library's integration with the matplotlib library for plotting. Throughou the upcoming lessons, we'll be using matplotlib to render various plots, charts, and graphs.

In this lesson, we use the plot method on DataFrame and Series objects to render a line chart with matplotlib. We also explore what can go wrong when the scale of our stored values differs.

In this lesson, we use matplotlib library's built-in templates to modify the aesthetic look of charts and graphs. The plt.style.available attribute can be used to reveal a list with all available template options.

A bar graph is used to count the occurrences of separate, independent values. In this lesson, we generate a bar graph with plot method and its kind parameter.

In this lesson, we draw a pie chart with matplotlib. Once again, we can invoke the plot method on a pandas data structure and pass a different argument to the kind parameter to modify the type of chart that renders.

Test your knowledge of the visualization concepts we introduced in this module!

Create a new Jupyter Notebook for the module and load the requisite libraries. In this module, we'll be exploring how we can modify the display settings of the pandas library.

Access and modify pandas settings using attributes within pd.options.display. In this lesson, we'll modify the maximum number of rows and columns that appear in an outputted DataFrame.

Use the

• get_option()
• set_option()
• reset_option()
• describe_option()

methods to modify the settings of the pandas library. We explore this method with the familiar max_rows and max_columns settings.

Use the precision option to modify the number of digits that appear after the decimal point in a floating point number. This option does not modify the original values in the DataFrame.

Celebrate the completion of the pandas for Data Analysis course! Congratulations on becoming a better data analyst!

Explore the bonus content for the course!