Python Projects for Beginners a ten-Week Bootcamp Approach to Python Programming

describe( )

The describe method will give you a base analysis for all numerical data. You’ll be able to view min, max, 25%, 50%, mean, etc., on all columns just by calling this method on the DataFrame. This information is helpful to start your analysis but generally won’t answer those questions you’re looking for. Instead, we can use this method as a guideline of where to start:

# checking the general statistics of the DataFrame using .describe( ), only works on numerical columns df.describe( )

Go ahead and run the cell. Remember that it’ll only give back information on numerical column types, which is why we only see an output for the ages column.

sort_values( )

When you need to sort a DataFrame based on column information, you use this method. You can pass in one or multiple columns to be sorted by. When passing multiple, you must pass them in as a list of column names, in which the first name will take precedence:

# sort based on a given column, but keep the DataFrame in tact using sort_values( ) df = df.sort_values("ages") df.head(5)
Go ahead and run the cell. In this cell, we’ve re-declared the value of our df variable to our newly sorted DataFrame. This way we can view all the people sorted by age. You may also pass in an argument to sort in descending order. Filtration
Let’s look at how to filter DataFrames for information that meets a specific condition.

Conditionals

Rather than filtering out information, we can create a boolean data type column that represents the condition we’re checking. Let’s take our current DataFrame and write a condition that shows those who are 21 or older and can drink:

# using a conditional to create a true/false column to work with can_drink = df["ages"] > 21 print(can_drink)

Go ahead and run the cell. When you want to create a column based on a boolean data type, you need to write out the condition based on the entire column. Here, we created a can_drink variable that is storing the entire ages column values. They are true- false values because of our condition that we created. We could potentially use this to create another column to work with.