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Selecting

Selecting#

Pandas includes a few useful commands for isolating components of our `DataFrame.

Columns#

We can select columns using their index labels or name attribute.

df['MAR'] # returns MAR column

df.mar # returns MAR column

df[['MAR', 'AGEP']] # select multiple columns

When selecting multiple columns, the inner brackets ([]) define the column names to subset or select. The outer brackets select data from a dataframe. In this multi-column example, age_sex is a DataFrame because it is a two-dimensional object.

Rows#

We can select rows using their index.

  • .loc for location attribute

  • .iloc for numerical index

For our sample dataframe, the row indeces are integers, so we could use these commands interchangeably.

df.loc[2] # select third row

df.iloc[2] # select third row

Additional Resources#

Consult the “Different choices for indexing” documentation for more on indexing options.

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