How Do I Efficiently Select Columns in Pandas DataFrames?

Selecting Columns in Pandas Dataframes

When dealing with data manipulation tasks, selecting specific columns becomes necessary. In Pandas, there are various options for selecting columns.

Option 1: Using Column Names

To select columns by their names, simply pass a list of column names as follows:

df1 = df[['a', 'b']]

Option 2: Using Numerical Indices

If the column indices are known, use the iloc function to select them. Note that Python indexing is zero-based.

df1 = df.iloc[:, 0:2]  # Select columns with indices 0 and 1

Alternative Option: Indexing Using Dictionary

For cases where column indices may change, use the following approach:

column_dict = {df.columns.get_loc(c): c for idx, c in enumerate(df.columns)}
df1 = df.iloc[:, list(column_dict.keys())]

Unrecommended Approaches

The following approaches are not recommended as they can lead to errors:

df1 = df['a':'b']  # Slicing column names does not work
df1 = df.ix[:, 'a':'b']  # Deprecated indexing method

Preserving Original Data

Note that selecting columns only creates a view or reference to the original dataframe. If you need an independent copy of the selected columns, use the copy() method:

df1 = df.iloc[:, 0:2].copy()

The above is the detailed content of How Do I Efficiently Select Columns in Pandas DataFrames?. For more information, please follow other related articles on the PHP Chinese website!