Now we’re ready to create a DataFrame with three columns. This is because the DataFrame constructor expects all its columns to have the same length. Before we can add these columns to a DataFrame though, we need to append three values to our dateTimes column. Both the column types can take a length parameter in their contructors and are filled with null values initially. A StringDataFrameColumn is a specialized column that holds string values. PrimitiveDataFrameColumn is a generic column that can hold primitive types such as int, float, decimal etc. StringDataFrameColumn strings = new StringDataFrameColumn("Strings", 3) // Makes a column of length 3.
PrimitiveDataFrameColumn ints = new PrimitiveDataFrameColumn("Ints", 3) // Makes a column of length 3. PrimitiveDataFrameColumn dateTimes = new PrimitiveDataFrameColumn("DateTimes") // Default length is 0. Let’s make three columns to hold values of types DateTime, int and string. NET Jupyter Notebook (make sure you’re using the C# or F# kernel): To get started, let’s import the package and namespace into our. To follow along in your browser, click here and navigate to csharp/Samples/DataFrame-Getting Started.ipynb(or fsharp/Samples/DataFrame-Getting Started.ipynb). The full sample can be found on Github( C# and F#). Let’s populate a DataFrame with some sample data and go over the major features.
How to use DataFrame?ĭataFrame stores data as a collection of columns. In this blog post, I’m going to give an overview of this new type and how you can use it from Jupyter notebooks. At a high level, it is an in-memory representation of structured data. If you’ve used Python to manipulate data in notebooks, you’ll already be familiar with the concept of a DataFrame. Today, we’re announcing the preview of a DataFrame type for. NET support for Jupyter notebooks, and showed how to use them to work with.