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Welcome to the Dataframes wiki! Dataframes Pandas DataFrame is a two-dimensional data structure with labeled rows and columns that can be created from various sources.

Operations such as selecting, deleting, adding, and renaming rows and columns can be performed on a Pandas DataFrame.

Missing Data can be handled in a Pandas DataFrame using functions such as isnull(), notnull(), fillna(), replace(), and interpolate().

A DataFrame is a tabular data structure provided by libraries like pandas in Python and R. It is a two-dimensional labeled data structure, similar to a table or spreadsheet, where data is organized in rows and columns.

Key features of a DataFrame: Rows and Columns A DataFrame consists of rows and columns. Each row represents a record or observation, while each column represents a variable or feature.

Column Names Each column in a DataFrame has a unique name or label, which helps in accessing and referencing specific columns.

Heterogeneous Data A DataFrame can contain different types of data, such as numerical, categorical, or textual. It allows for mixed data types within a single data structure.

Size Mutable The size of a DataFrame can be dynamically changed. Rows and columns can be added, removed, or modified.

Handling Missing Data DataFrames provide built-in functionality to handle missing data. It allows for missing values, represented as NaN (Not a Number) or NULL, and provides methods to detect, remove, or replace missing data.

Data Alignment DataFrame aligns data automatically, making it easy to perform operations on multiple columns or aligning data based on index values.

Indexing and Selection DataFrame allows for easy indexing and selection of data based on row and column labels or positions. It provides various methods and operators for data selection, slicing, and filtering.

Data Manipulation DataFrames offer a wide range of functions and methods for data manipulation, such as merging, joining, grouping, sorting, reshaping, and aggregating data.

Statistical Operations DataFrames support various statistical operations, including summary statistics (mean, median, min, max, etc.), correlation, covariance, and more.

Integration with Other Libraries DataFrames can seamlessly integrate with other libraries and tools for data analysis, visualization, and machine learning. They can be easily converted to and from other data structures, such as arrays, matrices, or series.

These features make DataFrames a powerful tool for data analysis, manipulation, and exploration, allowing for efficient handling and processing of tabular data.

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