3 – Pandas 3 V1

Just like we did with NumPy arrays, we can perform element-wise arithmetic operations on Pandas series. In this video, we will look at arithmetic operations between Pandas series and single numbers. Let’s create a new series that holds a grocery list of fruits. The first argument we pass in is the data, and the second … Read more

7 – Pandas 7 V1

When working with data you’ll most likely use databases from many sources. Pandas allows us to load databases of different formats into DataFrames. One of the most popular formats used to store data is CSV or comma separated values. We can load CSV files into DataFrames using the read CSV function. Let’s load Google stock … Read more

6 – Pandas 6 V1

Before we can begin analyzing data or using it to train our learning algorithms, we need to clean it. This means we need a way to detect and correct errors in our data. While any given dataset can have many types of bad data such as outliers or incorrect values, the type of bad data … Read more

5 – Pandas 5 V1

We can access elements in a DataFrame in different ways. In general, we can access rows, columns, or individual elements by using the row and column labels. Let’s see some examples. Here’s a DataFrame we created in the last video. We can access the bikes column using the column label, like this, and use a … Read more

4 – Pandas 4 V1

The second main data structure in Pandas is a DataFrame, which is a two-dimensional object with labeled rows and columns and can also hold multiple data types. If you’re familiar with Excel, you can think of a DataFrame as a really powerful spreadsheet. We can create Pandas DataFrames manually or by loading data from a … Read more

2 – Pandas 2 V1

In the last video, we created this Panda series of a grocery list. Now, how do we access or modify its elements? One great advantage of the series object, is that it allows us to access data in multiple ways. One way is accessing elements with their index labels. This accesses the quantity of eggs … Read more

1 – Pandas 1 V1

Pandas is a powerful tool for data analysis and manipulation. If you remember from the last lesson, this package is built on top of NumPy which makes it very fast and efficient. In this lesson, we will go over the two main data structures in Pandas. The Pandas series and the Panda’s dataframe. Let’s start … Read more

3-3-3-13. Glossary

Glossary Below is the summary of all the functions and methods that you learned in this lesson: Category: Initialization and Utility Function/Method Description pandas.read_csv(relative_path_to_file) Reads a comma-separated values (csv) file present at relative_path_to_file and loads it as a DataFrame pandas.DataFrame(data) Returns a 2-D heterogeneous tabular data. Note: There are other optional arguments as well that you can … Read more

3-3-3-11. Manipulate a DataFrame

book_r.py solution.py Level Up From the DataFrame above can you now pick all the books that had a rating of 5?Hint – You can do this in just one line of code, by using pandas.DataFrame.any. Try to do it yourself first, you’ll find the answer below: pandas.DataFrame.any DataFrame.any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)Return whether any element is True, potentially over an … Read more

3-3-3-5. Accessing and Deleting Elements in Pandas Series

In the last video, we created this Panda series of a grocery list. Now, how do we access or modify its elements? One great advantage of the series object, is that it allows us to access data in multiple ways. One way is accessing elements with their index labels. This accesses the quantity of eggs … Read more

3-3-3-4. Creating pandas Series

Pandas is a powerful tool for data analysis and manipulation. If you remember from the last lesson, this package is built on top of NumPy which makes it very fast and efficient. In this lesson, we will go over the two main data structures in Pandas. The Pandas series and the Panda’s dataframe. Let’s start … Read more

3-3-3-3. Why Use pandas?

Why Use Pandas? The recent success of machine learning algorithms is partly due to the huge amounts of data that we have available to train our algorithms on. However, when it comes to data, quantity is not the only thing that matters, the quality of your data is just as important. It often happens that … Read more