Data slicing in Pandas

We provide a detailed guide on how to slice and dice data using Pandas, enabling you to handle even the most complex data sets with ease.

Getting Started with Data Slicing in Pandas

To start slicing data with Pandas, you must first have the library installed and your data loaded into a DataFrame or Series. Ensure you’re familiar with these basic concepts before proceeding.

Basic Data Slicing Techniques

Selecting Columns: Use column names to extract specific columns.

subset = df['column_name']

Selecting Rows by Index: Use index numbers to slice specific rows.

subset = df[10:20] # slices rows from 11th to 20th

Conditional Selection: Use conditions to filter rows.

condition = df[df['column'] > 50]

Using loc and iloc:

loc: Slices by labels/names of the index.
iloc: Slices by the positions of the index (integer-based).

# Using loc
rows_with_loc = df.loc[10:20, ['column1', 'column2']]

# Using iloc
rows_with_iloc = df.iloc[10:20, 0:2]

Advanced Slicing Techniques

Slicing Time Series: If working with time series data, use time-based indexing to slice data.

time_slice = df['2020-01-01':'2020-12-31']

MultiIndex Slicing: For DataFrames with multiple index levels, use tuples to slice.

multi_slice = df.loc[('index1', 'index2'), :]

Conditional Slicing with query(): A more readable way to perform conditional slicing.

query_slice = df.query('column1 > 20 and column2 < 50')

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