2.1 Introduction

The Pandas DataFrameDataFrame" instructions="A two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns)." chat-enabled="true" pageid="ow0f8" response="glossary" id="yu4u1">is a powerful, two-dimensional data structure designed for organizing and manipulating tabular data (rows and columns). Often likened to an in-memory spreadsheet, the DataFrame provides a wealth of features for analyzing, editing, and extracting data, making it a cornerstone of Python’s dominance in data science.

The table below is an example of how DataFrames are printed in .ipynb format:

A DataFrame is a size-mutable two-dimensional labeled data structure with columns of potentially different types. Think of it as an in-memory spreadsheet. Review the constructor for DataFrames below:

Pandas DataFrame(): documentation

DataFrame([data, index, columns, dtype, copy])

  • data: the actual data to be stored in a tabular format (i.e., rows and columns); can be a dictionary, list, pandas series object, or many other list-like objects

  • index: the index of each row; can be a number or a name; can be specified in a separate list (list n must equal the number of rows in the data) or as one of the existing columns; default to RangeIndex if no indexing information part of input data and no index provided

  • columns: the label names of each column; default to RangeIndex if no indexing information part of input data and no index provided

  • dtype: the intended data type of each column; if set, then it must be the same dtype for all columns; otherwise, will be inferred from the data individually for each column; if set, must be appropriate for the data (i.e., a runtime error will occur if a column is set to be an int when there are non-numeric characters in the column)

  • copy: defaulted to False; if set to True, then the new DataFrame will be a copy of the original; updates to one will not affect the other

A constructor initializes an object with specific parameters, similar to creating a variable of a complex data type. For example, creating a DataFrame involves specifying parameters like data, index, and columns. However, you can omit some or all of these parameters, and default values will be applied.

Why Learn DataFrames?

DataFrames enable efficient and scalable data manipulation, a skill critical for data projects. By mastering their construction and features, you’ll unlock powerful capabilities for data analysis, from cleaning and visualization to advanced modeling and deployment.

This chapter provides an introduction to creating and working with DataFrames, preparing you to tackle more complex data tasks later in the book.

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