python pandas documentation
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pandas is a powerful data manipulation and analysis library for python. it provides data structures like dataframes and series, which make working with structured data seamless and efficient. this tutorial will guide you through the essential aspects of pandas, using the official documentation as a reference.
if you haven't installed pandas yet, you can do so using pip:
before using pandas, you need to import it into your python script or jupyter notebook:
here, pd is an alias commonly used for pandas.
a dataframe is a 2-dimensional labeled data structure with columns that can be of different types. you can create a dataframe using various methods. let's create one from a dictionary:
this will output:
pandas supports reading data from various sources like csv, excel, sql databases, and more. let's read a csv file:
pandas provides several functions for exploring and understanding your data. here are a few examples:
you can select specific columns or filter rows based on conditions. examples:
pandas allows for various operations to manipulate data. examples include:
you can group data based on a column and perform aggregate functions. example:
this tutorial covers the basics of pandas using code examples based on the official documentation. pandas is a versatile library that provides powerful tools for data manipulation and analysis. to explore more features and details, refer to the official pandas documentation.
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