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Import from datetime module instead.� )� stacklevel)ry �npzuThe pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead> �SparseDataFrame�SparseSerieszThe zq class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version� �SparseArrayz�The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.)r� z"module 'pandas' has no attribute '�') �warnings�warn� FutureWarningry r �type�pandas.core.arrays.sparser� �AttributeError)�namer� �dtr| Z_SparseArrayr r �E/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/__init__.py�__getattr__� s: r� a� pandas - a powerful data analysis and manipulation library for Python ===================================================================== **pandas** is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python. Additionally, it has the broader goal of becoming **the most powerful and flexible open source data analysis / manipulation tool available in any language**. It is already well on its way toward this goal. Main Features ------------- Here are just a few of the things that pandas does well: - Easy handling of missing data in floating point as well as non-floating point data. - Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects - Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let `Series`, `DataFrame`, etc. automatically align the data for you in computations. - Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data. - Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects. - Intelligent label-based slicing, fancy indexing, and subsetting of large data sets. - Intuitive merging and joining data sets. - Flexible reshaping and pivoting of data sets. - Hierarchical labeling of axes (possible to have multiple labels per tick). - Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format. - Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. )�� __docformat__Zhard_dependenciesZmissing_dependencies� dependency� __import__�ImportError�e�append�joinZ pandas.compatr Z_np_version_under1p18r Z _is_numpy_devZpandas._libsr Z _hashtabler Z_libr Z_tslib�str�replace�moduleZpandas._configr r r r r r Zpandas.core.config_initZpandasZpandas.core.apir r r r r r r r r r r r r r r r r! r"