Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.
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
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
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
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
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
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
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
Statistics from Stock Data In this lab we will load stock data into a Pandas Dataframe and calculate some statistics on it. We will be working with stock data from Google, Apple, and Amazon. All the stock data was downloaded from yahoo finance in CSV format. In your workspace you should have a file named … Read more
Getting Set Up for the Mini-Project There are two ways to code for the current Mini-Project: Workspace – You can use the Workspace provided on the very next page. The workspace already has the project instructions available in a Jupyter notebook to follow. Your Personal Computer – If you choose this option to work on the project, you may … Read more
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
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
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
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
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
dictionary of pandas series –> dataframe dictionary of lists(arrays) –> dataframe (lists(arrays) must be of the same length) 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 … Read more
sun_planets.py solution.py
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
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
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
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
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