Introduction about 'pandas' library.

Python pandas is a popular open-source data analysis and manipulation library designed to work with tabular, labeled data. It provides data structures for efficiently storing and manipulating large datasets in a variety of ways.

 

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• DataFrame: A two-dimensional table like data structure with labeled axes (rows and columns), which is similar to a spreadsheet or SQL table. It allows for data manipulation operations such as merging, filtering, grouping, and reshaping.
• Series: A one-dimensional labeled array capable of holding any data type, such as integers, floating point numbers, and strings.
• Data Input/Output: pandas provides easy-to use functions for reading and writing data from various sources such as CSV, Excel, SQL databases, and more.
• Handling missing data: pandas provides a number of ways to handle missing or NaN (Not a Number) data, such as filling in missing values with a mean or median, or simply removing rows or columns with missing data.
• Time series analysis: pandas includes tools for working with time series data, including date range generation, frequency conversion, and moving window statistics.

 

Pandas is a powerful tool for working with data, and it is widely used in a variety of fields, including finance, economics, social sciences, and more.