Unit 2 - Subsetting DataFrames and Series

On df DataFrame you can find data about Airbnb rooms in Lisbon.

Go ahead and preview it!

Row selection

Selecting rows by index position - iloc

• With function iloc you can select specific rows from a DataFrame.
• In order to specify the rows you want to select you can use, for instance, the row position (integer starting from 0) or a list of row positions.

Here, we are selecting the first row and getting it as a pandas Series:

But you can also get a single row as a DataFrame:

Now try to get rows 0, 1 and 3, using iloc:

Array slices also work, so try to select all the rows from position number 2 to position number 7:

Selecting rows by index name - loc

• With function loc you can select specific rows from a DataFrame, like with iloc.
• The difference here is that you specify the rows to select using the rows' indexes.

Here, we're selecting the row that corresponds to room 17031 (with index 17031):

Now, if you select a row for an index that doesn't exist you'll get a KeyError:

Try to select the rows that correspond to rooms 17031 and 25659:

Now check what happens when you pass a boolean array to loc!

Column selection

Selecting columns by name - dot, brackets

You can select columns using dot notation, like this:

Or using brackets, like this:

Now, try to select column room_type from df, but getting the result as a DataFrame instead of a Series:

And now try to select columns room_type and neighborhood:

If you're feeling confident, here's a little challenge: select column room_type using loc!

Mask function

Mask function can be used to "hide" the rows that verify a certain condition:

Where function

And with where function, you can hide the rows that don't verify a certain condition:

Filter data

• You can also use conditions inside brackets to filter data from the DataFrame
• When you do it you get a DataFrame that has a different shape from the initial one
• While with mask/filter, the DataFame shape is the same, and you just get NaNs in the cells that verify/don't verify the conditions

Here we're selecting only the rows of rooms in the Alvalade neighborhood:

Selection based on data types

Check DataFrame's data types by using the class attribute dtypes:

In order to select columns based on their data type, use the function select_dtypes:

Writing data to files

To output your processed data to a file, pandas has many function to help you with that:

• to_csv
• to_json
• to_excel

Check more here !

Here, we're exporting df to a csv file called airbnb_output.csv on the data directory of this unit: