Built on the Python programming language, pandas provides a flexible and open source tool for data manipulation. In this course, you'll develop the skills you need to get started with this library. You'll begin by installing pandas from a Jupyter notebook using pip.

Next, you'll instantiate a pandas object, including a Series and DataFrame, and practice several ways of instantiating Dataframes - for instance, from lists, dictionaries of lists, and tuples created from lists using the zip method.

You round out this course by performing filter operations on DataFrames using the loc and iloc operations - fundamental techniques used to access specific rows and columns. You'll use loc to identify rows based on labels and iloc to access rows based on the index offset position starting from 0.

You can analyze a myriad of data formats through pandas - all you need to know is how. In this course, you'll bring various data types into pandas and perform several operations on the data.

You'll practice using common file types such as CSV, Excel, JSON, and HTML through pandas. You'll not only learn how to open and read files of different types, but you'll also serialize objects and copy them to the in-memory clipboard.

You'll move on to perform various fundamental operations on DataFrame objects. Lastly, you'll learn to compute basic statistics, access metadata, and modify and sort data in rows.

Not all data is useful. Luckily, there are some powerful filtering operations available in pandas. The course begins with a detailed look at how loc and iloc can be used to access specific data from a DataFrame. You'll move on to filter data using the classic pandas lookup syntax and the pandas filter and query methods. You'll illustrate how the filter function accepts wildcards as well as regular expressions and use various methods such as the .isin method to filter data.

Furthermore, you'll filter data using either two pairs of square brackets - in which case the resulting subset is itself a DataFrame - or a single pair of square brackets, in which case the returned data takes the form of a Series. You'll drop rows and columns from a pandas DataFrame and see how rows can be filtered out of a DataFrame. Lastly, you'll identify a possible gotcha that arises when you drop rows in-place but neglect to reset the index labels in your object.

For data analysis to be useful and accurate, the analyzed data needs to be cleaned and curated. There are copious methods to achieve this in pandas. In this course, you'll learn how to identify and eliminate duplicates in pandas.

You'll start by using the pandas cut method to discretize data into bins, using bins to plot histograms and identify outliers using box-and-whisker plots. You'll parse and work with datetime objects read in from strings and convert string columns to datetime using the dateutils python library.

Moving on, you'll master different pandas methods for aggregating data - including the groupby, pivot, and pivot_table methods. Lastly, you'll perform various joins - inner, left outer, right outer, and full outer - using both the merge and join methods.