Pandas is a popular Python library for data analysis, and it provides a wide range of functions and methods for manipulating and analyzing data. Here are some common Pandas operations that are frequently used in data analysis:
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Reading and writing data: Pandas provides functions for reading and writing data in various formats, such as CSV, Excel, SQL databases, and more.
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Data selection and indexing: Pandas allows you to select and index data using various methods, such as label-based indexing, position-based indexing, and boolean indexing.
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Data filtering: Pandas provides a variety of functions for filtering data based on certain conditions, such as
df[df['column'] > 5]. -
Data aggregation: Pandas allows you to group data based on one or more variables and perform aggregation functions on each group, such as
df.groupby('column').mean(). -
Data cleaning and preprocessing: Pandas provides many functions for cleaning and preprocessing data, such as filling missing values, removing duplicates, and transforming data types.
