. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.1.1 Integration with Apache Parquet file format . . . . . . . . . . . . . . . . . . . . . 8 1.2.1.2 infer_objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 3.5.2 Testing With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 3.5.3 Test-driven development/code number of bug fixes. We recommend that all users upgrade to this version. Highlights include: • Integration with Apache Parquet, including a new top-level read_parquet() function and DataFrame. to_parquet()

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 3.5.2 Testing With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 3.5.3 Test-driven development/code New .agg() API for Series/DataFrame similar to the groupby-rolling-resample API’s, see here • Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() neither' to choose the rolling window-endpoint closedness. See the documentation (GH13965) • Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather()

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 3.5.2 Testing With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 3.5.3 Test-driven development/code New .agg() API for Series/DataFrame similar to the groupby-rolling-resample API’s, see here • Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather() neither' to choose the rolling window-endpoint closedness. See the documentation (GH13965) • Integration with the feather-format, including a new top-level pd.read_feather() and DataFrame.to_feather()

suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Running Google BigQuery Integration Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Running the vbench performance from SparseArray (which instead is the object of the SparseBlock) – Sparse suite now supports integration with non-sparse data. Non-float sparse data is supportable (partially implemented) – Operations conda, simply install pip, and use pip to install these packages: conda install pip pip install django Installing from PyPI pandas can be installed via pip from PyPI. 322 Chapter 2. Installation pandas:

suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Running Google BigQuery Integration Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Running the vbench performance from SparseArray (which instead is the object of the SparseBlock) – Sparse suite now supports integration with non-sparse data. Non-float sparse data is supportable (partially implemented) – Operations conda, simply install pip, and use pip to install these packages: conda install pip pip install django Installing from PyPI pandas can be installed via pip from PyPI. 324 Chapter 2. Installation pandas:

conda, then install pip, and then use pip to install those packages: conda install pip pip install django 44 Chapter 2. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.0 Installing parquet, both of which are supported by pandas’ IO facilities. 3.22.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper) Style Guidelines * Pandas-specific Types * Validating Type Hints – Testing with continuous integration – Test-driven development/code writing * Writing tests * Transitioning to pytest * Using pytest

conda, then install pip, and then use pip to install those packages: conda install pip pip install django 36 Chapter 2. Installation pandas: powerful Python data analysis toolkit, Release 0.25.0 2.3.3 msgpack, both of which are supported by pandas’ IO facilities. 4.20.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper) Python (PEP8 / black) * Import formatting * Backwards compatibility – Testing with continuous integration – Test-driven development/code writing * Writing tests * Transitioning to pytest * Using pytest

conda, then install pip, and then use pip to install those packages: conda install pip pip install django 2.2.3 Installing from PyPI pandas can be installed via pip from PyPI. pip install pandas 2.2 msgpack, both of which are supported by pandas’ IO facilities. 4.20.10 Computation Numerical integration (sample-based) of a time series 4.20. Cookbook 869 pandas: powerful Python data analysis toolkit Python (PEP8 / black) * Import formatting * Backwards compatibility – Testing with continuous integration – Test-driven development/code writing * Writing tests * Transitioning to pytest * Using pytest

DataFrame. To introduction tutorial To user guide Straight to tutorial... Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...). Importing conda, then install pip, and then use pip to install those packages: conda install pip pip install django 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.5 Installing parquet, both of which are supported by pandas’ IO facilities. 2.22.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper)

DataFrame. To introduction tutorial To user guide Straight to tutorial... Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...). Importing conda, then install pip, and then use pip to install those packages: conda install pip pip install django 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.4 Installing parquet, both of which are supported by pandas’ IO facilities. 2.22.10 Computation Numerical integration (sample-based) of a time series Correlation Often it’s useful to obtain the lower (or upper)