pandas
Python library for data manipulation and analysis, providing data structures and operations for working with tabular data and time series
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| + | Data Manipulation | Simplifies data manipulation tasks, such as filtering, sorting, and merging. |
|---|---|---|
| + | Efficient Memory Usage | DataFrames and Series provide efficient memory usage and lightning-fast operations for handling tabular data |
| + | Data Cleaning | Clean messy data, handle missing values (NaN), filter, sort, and transform data |
| + | Flexible Indexing | access and manipulate data subsets using label-based indexing, Boolean masking, and slicing, enabling granular control over data |
| + | Time Series Handling | Specialized tools for working with time series data, including date/time manipulation, resampling, and time zone handling, cater to time-based analysis needs |
| + | Data Aggregation | Perform calculations (like sum, mean, count) within groups |
| + | Group By Functionality | Group-by functionality for split-apply-combine operations on data sets. |
| + | Column Insertion and Deletion | Can easily insert and delete columns from DataFrames and other higher-dimensional objects. |
| + | Data Visualization | Offers basic plotting capabilities for quick data exploration and visualization |
| + | Format Support | Read and write data from various file formats (CSV, Excel, JSON, SQL databases) and facilitate data exchange with other tools. |
| + | Missing Data Handling | Handles missing data (represented as NaN), in both floating-point and non-floating-point data, by identifying it, imputing missing values, or dropping rows/columns with missing data depending on the analysis requirements. |
| + | Merge and Join | Combine data from multiple sources using merge and join operations. |
| + | Efficient Computation | Optimized computation using vectorized operations for large datasets |
| + | Multi-level Indexing | Hierarchical indexing for complex data structures enabling efficient querying and manipulation of data with multiple levels of labels. |
| + | Data Reshaping | Pivot, melt, and stack data for different views and perspectives. |
| + | Data Transformation | Apply functions to data using apply, map, and transform. |
| - | Learning Curve | While user-friendly, mastering its complex operations can be challenging |
| - | Performance Issues with Massive Data | Can deal with big data, but massive datasets might not give you the best performance |
| - | Data Type Limitations | Optimized for tabular data and may not perform well with other data types like images or audio |
| - | API inconsistencies | API inconsistencies might manifest in different naming conventions, parameter names, or behavior across similar functions which can lead to user confusion |
System Requirements
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