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Machine Learning with NumPy, pandas, scikit-learn, and More

Indexing

Explore indexing methods in pandas to retrieve and manipulate DataFrame rows and columns effectively. Understand direct indexing, loc, and iloc properties to access data using labels or integer positions. Learn to modify DataFrame entries and write code that processes data for analysis and visualization.

We'll cover the following...

Chapter Goals:

  • Learn how to index a DataFrame to retrieve rows and columns
  • Write code for indexing a DataFrame

A. Direct indexing

When indexing into a DataFrame, we can treat the DataFrame as a dictionary of Series objects, where each column represents a Series. Each column label then becomes a key, allowing us to directly retrieve columns using dictionary-like bracket notation.

The code below shows how to directly index into a DataFrame's columns.

Python 3.5
df = pd.DataFrame({'c1': [1, 2], 'c2': [3, 4],
                   'c3': [5, 6]}, index=['r1', 'r2'])
col1 = df['c1']
# Newline for separating print statements
print('{}\n'.format(col1))
col1_df = df[['c1']]
print('{}\n'.format(col1_df))
col23 = df[['c2', 'c3']]
print('{}\n'.format(col23))

Note that when we use a single column label inside the bracket (as was the case for col1 in the code example), the output is a Series representing the corresponding column. When we use a list of column labels (as was the case for col1_df and col23), the ...