Pandas DataFrame: Difference between revisions
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==<tt>[]</tt>== | ==<tt>[]</tt>== | ||
==<tt>iloc[]</tt>== | ==<tt>iloc[]</tt>== | ||
A property that allows integer-based access (indexing). | |||
==<tt>loc[]</tt>== | |||
A property that allows label-based access (indexing). | |||
==<tt>squeeze()</tt>== | ==<tt>squeeze()</tt>== | ||
=Operations on DataFrames= | =Operations on DataFrames= | ||
Revision as of 18:29, 8 October 2023
External
- https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe
- https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame
Internal
Overview
A DataFrame is a two-dimensional data structure with columns of potentially different types. Can be thought of as a dict-like container for Series objects, where each column is a Series. The dimensionality of the DataFrame is given by its shape property.
Shape
shape is a property of the DataFrame, containing a tuple that returns the dimensionality of the DataFrame: rows, columns.
Create a DataFrame
Create a DataFrame from a CSV File
Accessing Elements of a DataFrame
[]
iloc[]
A property that allows integer-based access (indexing).
loc[]
A property that allows label-based access (indexing).