What is this?
pandas is a Python library providing fast, flexible data structures (Series and DataFrame) for working with labeled/relational data, aiming to be the fundamental building block for practical data analysis in Python.
pandas is a Python library providing fast, flexible data structures (Series and DataFrame) for working with labeled/relational data, aiming to be the fundamental building block for practical data analysis in Python.Is it a framework or just a library I import?It's a library: you import pandas into your Python code and use its DataFrame/Series objects, not a standalone application or framework you build around.
It's a library: you import pandas into your Python code and use its DataFrame/Series objects, not a standalone application or framework you build around.How do I add it to a project?Install the binary release from PyPI with pip or from Conda; alternatively build from source using the pandas repo, which requires Cython in addition to the normal dependencies.
Install the binary release from PyPI with pip or from Conda; alternatively build from source using the pandas repo, which requires Cython in addition to the normal dependencies.What does pandas need to run?It requires NumPy for array operations, python-dateutil for datetime extensions, and tzdata for timezone data (tzdata is only mandatory on Windows/Emscripten).
It requires NumPy for array operations, python-dateutil for datetime extensions, and tzdata for timezone data (tzdata is only mandatory on Windows/Emscripten).Does it handle missing or messy data well?Yes, pandas has built-in handling for missing data (NaN, NA, or NaT) across floating point and non-floating point data, plus data alignment so mismatched indices are reconciled automatically.
Yes, pandas has built-in handling for missing data (NaN, NA, or NaT) across floating point and non-floating point data, plus data alignment so mismatched indices are reconciled automatically.What kinds of data operations does pandas make easy?It supports group-by split-apply-combine operations, intuitive merging and joining of datasets, flexible reshaping and pivoting, hierarchical (multi-level) indexing, and robust I/O for CSV, Excel, databases, and the ultrafast HDF5 format.
It supports group-by split-apply-combine operations, intuitive merging and joining of datasets, flexible reshaping and pivoting, hierarchical (multi-level) indexing, and robust I/O for CSV, Excel, databases, and the ultrafast HDF5 format.Does pandas handle time series data?Yes, it includes time-series-specific functionality such as date range generation, frequency conversion, moving window statistics, and date shifting/lagging.
Yes, it includes time-series-specific functionality such as date range generation, frequency conversion, moving window statistics, and date shifting/lagging.Where did pandas come from?Work on pandas started at AQR, a quantitative hedge fund, in 2008 and has been under active development since.
Work on pandas started at AQR, a quantitative hedge fund, in 2008 and has been under active development since.Can I resize a DataFrame after creating it?Yes, pandas supports size mutability, letting you insert and delete columns from a DataFrame or higher-dimensional objects after creation.
Yes, pandas supports size mutability, letting you insert and delete columns from a DataFrame or higher-dimensional objects after creation.What are similar projects?pandas-ai, modin, pygwalker, polars, fg-data-profiling, visidata, pydata-book, spark
pandas-ai, modin, pygwalker, polars, fg-data-profiling, visidata, pydata-book, sparkWhich topics is it in?dataframe, data-processing, csv, pandas, data-analysis, data-visualization, sql, data-science, bsd, sqlite, database, library
dataframe, data-processing, csv, pandas, data-analysis, data-visualization, sql, data-science, bsd, sqlite, database, library