Python Tutorial : Exploring the data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/case-study-school-budgeting-with-machine-learning-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- We left off the last video by showing that we'd be predicting probabilities for each budget line item. Let's be clear what this looks like in practice. For example, we'll say we are predicting the hair type and eye color of people. We have the categories "curly," "straight," and "wavy" for hair and "brown" and "blue" for eyes. If we are predicting probabilities, we need a value for each possible value in each column. In this case, the target would have the columns for each hair type and for each eye color. Later in the course, we'll talk more about how to perform this transformation. We'll begin by loading our data. As an example, in this video we'll work with a small sample dataset. For the exercises, you'll be loading the actual school budget dataset. You've seen pandas functions for loading data before, and in this case we'll be working with a file of comma-separated values: a CSV. First, we'll import pandas giving it the alias pd so we don't have to type pandas every time we want to use a function. We'll use the function read_csv and pass the filename to our dataset. Then, the function head shows us the first 5 rows of the DataFrame and the column names. We can see this sample dataset has both numeric data and text data. The function info tells us--yep, you guessed it--a bit more information about our dataset. Importantly, it tells us the datatype of each column. Columns that can be recognized as a numeric type--integers and floats--will be recognized as such. Columns that can't will get the generic type of object. Additionally, info tells us if any of the columns have missing values. As we can see, the column with_missing has 95 non-null entries, which means that there are 5 rows that are missing a value in