Cleaning and Transforming Columns

The pandas DataFrame method .info() displays a table of information for each column.

parks.info()

• # indicates the column index number
• Column refers to the column name
• Non-Null Count is the number of non-missing values in the column
• DType is the column’s data type

The pandas .drop() method is used to remove irrelevant columns from a DataFrame. This method has two keywords:

• labels takes a list of column names to drop
• axis=1 tells pandas we want to drop columns (not rows)

The code snippet drops the index column to produce

The pandas .rename() method renames columns in a DataFrame. There are two particularly important keywords for .rename():

• mapper takes a dictionary mapping the old column names (as keys) to the new column names (as values)
• axis=1 tells pandas to rename the columns axis

Python has built-in arithmetic operators for performing calculations, including

• Addition (+),
• Subtraction (-),
• Multiplication (*)
• Division (/)

Like mathematics, Python uses parentheses to control the order of operations in a calculation.

The round() function in Python rounds a number to a certain number of decimals using the following syntax:

round(numeric_variable, number_of_decimals)

In pandas, arithmetic operators like +, -, /, and * can be applied to all the rows of a column at once.

Here’s a sample DataFrame parks.

The code snippet produces the following DataFrame:

The pandas method .str.split(pat='x', expand=True) will split the information in a text column into multiple columns using 'x' as a delimiter. Common delimiters include commas (,), colons (:), and dashes (-).

The keyword argument expand=True creates a DataFrame containing the split information that can be accessed through pandas indexing.

The Series method .str.cat() combines text from two columns into a single string:

df['Combined'] = df['Column1'].str.cat(
     df['Column2'], 
     sep=',')

• .cat() places the text in Column2 after the text in Column1
• sep=',' places a comma ',' after the text from Column1 and before the text from Column2

The code snippet produces the following Location column:

Pandas can alter text case using

• .str.lower() - converts all text to lowercase
• .str.upper() - converts all text to uppercase
• .str.title() - converts all text to titles

Convert Park to lowercase and uppercase

The pandas method .str.replace() performs a find-and-replace on each row of a series. Every section of text that matches the string passed to pat will be replaced by the string passed to repl.

df['Column'] = df['Column'].str.replace(
                   pat='old_pattern',
                   repl='new_pattern',
                   regex=False)

The pandas method .astype() converts the type of a column from one type to another. The new type is specified within the parentheses:

• float64 for decimals
• int64 for integers
• object for text/objects
• category for categorical data