Learn Data Analysis With Pandas In 2025

An introduction to Series in Pandas.

Let's learn about the most common attributes that we can use with Series.

Let's learn about the most common methods that we can use with Series.

Learn how to open and save CSV files using Pandas.

Let's learn what head() & tail() does in Pandas!

Let's learn how we can use sort_values() with Series to get them sorted.

Counting the amount of values in your Series can be very useful for analysing data. Let's take a look at how that works.

Sometimes you might want to access elements via their position to retrieve certain information, this is how you can do that.

Since we covered how to access elements via their position, the next logical step is accessing elements via their index.

Practise makes perfect!

Here are the solutions for the homework.

An introduction to DataFrames.

We already covered how methods work on Series, but is it similar to how they work with DataFrames?

Let's learn how we can use describe() & info() in Pandas.

Learn how you can use nlargest() & nsmallest() in Pandas.

Let's learn how we can use set_index() & reset_index() to modify the index of our DataFrame.

Learn how to remove columns & rows from your DataFrames using drop().

Let's take a quick look at how we can add new columns to our DataFrames.

Let's learn how we can use dropna() in Pandas.

Let's learn how we can use fillna() in Pandas.

Here's the homework assignment for this section of the course.

Here are the solutions for the homework.

Welcome to part 2 of the DataFrames section! We will be using a new dataset based on the Titanic here.

Learn how you can select specific rows from a DataFrame using .loc[]

Let's continue exploring the power of loc[]!

Learn how you can select rows via their index from a DataFrame using .iloc[]

Let's continue exploring the power of iloc[]!

Let's learn the important concept of broadcasting and element-wise operations.

Learn how you can select data based on certain conditions.

Learn how you can select data based on multiple conditions.

Let's learn what between() does in Pandas.

Let's learn some more methods that are very useful for filtering.

Let's learn how we can rename any column or index in Pandas.

Here are the solutions for the homework.

Practise makes perfect!

New section means new dataset!

Let's learn how we can use apply() in Pandas.

Let's take a quick look at what map() & applymap() does in Pandas.

Learn how you can convert data types into other data types using astype().

Learn how you can replace any value in your DataFrames using replace().

Let's take a look at how we can us where() to select certain rows that match a criteria.

Let's learn what agg() does in Pandas.

Let's learn what copy() does in Pandas.

We know how to handle a single index, but now it's time we start learning how to handle multiple indices.

We know how to handle a single index, but now it's time we start learning how to handle multiple indices (continued).

Here's the homework assignment for this section of the course.

Here are the solutions for the homework.

Learn how you can perform string operations in Pandas using .str.

Let's learn a few more methods that can be used with strings in Pandas.

You can also use .str on columns and indices .

In this section we're going to be looking at how we can reshape our datasets, but first let's take a look at a new dataset!

Let's learn how we can transpose a dataset, and what that means.

Let's learn what stack() and unstack() does in Pandas.

Let's learn what melt() does in Pandas.

Let's learn what pivot() does in Pandas.

Let's learn what pivot_table() does and how it's different from pivot().

Let's learn how we can use groupby() in Pandas!

Let's learn how we can use groupby() in Pandas!

Let's learn how we can use groupby() in Pandas!

Introduction to combining datasets.

Learn how to combine datasets using concat.

An introduction to merge() in Pandas.

Let's learn how we can merge datasets using outer join.

Let's learn how we can merge datasets using inner join.

Let's learn how we can merge datasets using left & right join.

Learn how to merge datasets using left & right join (Minus).

Learn how to merge datasets using outer join (Minus).

Learn how to merge datasets that have different column names.

An introduction to this section of the course.

Let's learn how we can use timestamps and the DatetimeIndex in Pandas.

Let's learn how we can use date_range() in Pandas.

Let's learn how we can use Period & PeriodIndex in Pandas.

Let's learn how we can use Timedelta & TimedeltaIndex in Pandas.

Just like with strings, if you want to access datetime objects from columns, you're going to have to refer to the .df accessor.

Let's learn more about the methods and attributes that belong to Timestamp.

When you have a DatetimeIndex, .loc and .iloc work slightly differently.

Let's take a quick look at how we can use reindex in Pandas.

Learn how you can group dates by using resample().