""" This module is imported from the pandas package __init__.py file in order to ensure that the core.config options registered here will be available as soon as the user loads the package. if register_option is invoked inside specific modules, they will not be registered until that module is imported, which may or may not be a problem. If you need to make sure options are available even before a certain module is imported, register them here rather than in the module. """ import os from typing import Callable import warnings import pandas._config.config as cf from pandas._config.config import ( is_bool, is_callable, is_instance_factory, is_int, is_nonnegative_int, is_one_of_factory, is_str, is_text, ) from pandas.util._exceptions import find_stack_level # compute use_bottleneck_doc = """ : bool Use the bottleneck library to accelerate if it is installed, the default is True Valid values: False,True """ def use_bottleneck_cb(key): from pandas.core import nanops nanops.set_use_bottleneck(cf.get_option(key)) use_numexpr_doc = """ : bool Use the numexpr library to accelerate computation if it is installed, the default is True Valid values: False,True """ def use_numexpr_cb(key): from pandas.core.computation import expressions expressions.set_use_numexpr(cf.get_option(key)) use_numba_doc = """ : bool Use the numba engine option for select operations if it is installed, the default is False Valid values: False,True """ def use_numba_cb(key): from pandas.core.util import numba_ numba_.set_use_numba(cf.get_option(key)) with cf.config_prefix("compute"): cf.register_option( "use_bottleneck", True, use_bottleneck_doc, validator=is_bool, cb=use_bottleneck_cb, ) cf.register_option( "use_numexpr", True, use_numexpr_doc, validator=is_bool, cb=use_numexpr_cb ) cf.register_option( "use_numba", False, use_numba_doc, validator=is_bool, cb=use_numba_cb ) # # options from the "display" namespace pc_precision_doc = """ : int Floating point output precision in terms of number of places after the decimal, for regular formatting as well as scientific notation. Similar to ``precision`` in :meth:`numpy.set_printoptions`. """ pc_colspace_doc = """ : int Default space for DataFrame columns. """ pc_max_rows_doc = """ : int If max_rows is exceeded, switch to truncate view. Depending on `large_repr`, objects are either centrally truncated or printed as a summary view. 'None' value means unlimited. In case python/IPython is running in a terminal and `large_repr` equals 'truncate' this can be set to 0 and pandas will auto-detect the height of the terminal and print a truncated object which fits the screen height. The IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to do correct auto-detection. """ pc_min_rows_doc = """ : int The numbers of rows to show in a truncated view (when `max_rows` is exceeded). Ignored when `max_rows` is set to None or 0. When set to None, follows the value of `max_rows`. """ pc_max_cols_doc = """ : int If max_cols is exceeded, switch to truncate view. Depending on `large_repr`, objects are either centrally truncated or printed as a summary view. 'None' value means unlimited. In case python/IPython is running in a terminal and `large_repr` equals 'truncate' this can be set to 0 and pandas will auto-detect the width of the terminal and print a truncated object which fits the screen width. The IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to do correct auto-detection. """ pc_max_categories_doc = """ : int This sets the maximum number of categories pandas should output when printing out a `Categorical` or a Series of dtype "category". """ pc_max_info_cols_doc = """ : int max_info_columns is used in DataFrame.info method to decide if per column information will be printed. """ pc_nb_repr_h_doc = """ : boolean When True, IPython notebook will use html representation for pandas objects (if it is available). """ pc_pprint_nest_depth = """ : int Controls the number of nested levels to process when pretty-printing """ pc_multi_sparse_doc = """ : boolean "sparsify" MultiIndex display (don't display repeated elements in outer levels within groups) """ float_format_doc = """ : callable The callable should accept a floating point number and return a string with the desired format of the number. This is used in some places like SeriesFormatter. See formats.format.EngFormatter for an example. """ max_colwidth_doc = """ : int or None The maximum width in characters of a column in the repr of a pandas data structure. When the column overflows, a "..." placeholder is embedded in the output. A 'None' value means unlimited. """ colheader_justify_doc = """ : 'left'/'right' Controls the justification of column headers. used by DataFrameFormatter. """ pc_expand_repr_doc = """ : boolean Whether to print out the full DataFrame repr for wide DataFrames across multiple lines, `max_columns` is still respected, but the output will wrap-around across multiple "pages" if its width exceeds `display.width`. """ pc_show_dimensions_doc = """ : boolean or 'truncate' Whether to print out dimensions at the end of DataFrame repr. If 'truncate' is specified, only print out the dimensions if the frame is truncated (e.g. not display all rows and/or columns) """ pc_east_asian_width_doc = """ : boolean Whether to use the Unicode East Asian Width to calculate the display text width. Enabling this may affect to the performance (default: False) """ pc_ambiguous_as_wide_doc = """ : boolean Whether to handle Unicode characters belong to Ambiguous as Wide (width=2) (default: False) """ pc_latex_repr_doc = """ : boolean Whether to produce a latex DataFrame representation for jupyter environments that support it. (default: False) """ pc_table_schema_doc = """ : boolean Whether to publish a Table Schema representation for frontends that support it. (default: False) """ pc_html_border_doc = """ : int A ``border=value`` attribute is inserted in the ```` tag for the DataFrame HTML repr. """ pc_html_use_mathjax_doc = """\ : boolean When True, Jupyter notebook will process table contents using MathJax, rendering mathematical expressions enclosed by the dollar symbol. (default: True) """ pc_max_dir_items = """\ : int The number of items that will be added to `dir(...)`. 'None' value means unlimited. Because dir is cached, changing this option will not immediately affect already existing dataframes until a column is deleted or added. This is for instance used to suggest columns from a dataframe to tab completion. """ pc_width_doc = """ : int Width of the display in characters. In case python/IPython is running in a terminal this can be set to None and pandas will correctly auto-detect the width. Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to correctly detect the width. """ pc_chop_threshold_doc = """ : float or None if set to a float value, all float values smaller then the given threshold will be displayed as exactly 0 by repr and friends. """ pc_max_seq_items = """ : int or None When pretty-printing a long sequence, no more then `max_seq_items` will be printed. If items are omitted, they will be denoted by the addition of "..." to the resulting string. If set to None, the number of items to be printed is unlimited. """ pc_max_info_rows_doc = """ : int or None df.info() will usually show null-counts for each column. For large frames this can be quite slow. max_info_rows and max_info_cols limit this null check only to frames with smaller dimensions than specified. """ pc_large_repr_doc = """ : 'truncate'/'info' For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can show a truncated table (the default from 0.13), or switch to the view from df.info() (the behaviour in earlier versions of pandas). """ pc_memory_usage_doc = """ : bool, string or None This specifies if the memory usage of a DataFrame should be displayed when df.info() is called. Valid values True,False,'deep' """ pc_latex_escape = """ : bool This specifies if the to_latex method of a Dataframe uses escapes special characters. Valid values: False,True """ pc_latex_longtable = """ :bool This specifies if the to_latex method of a Dataframe uses the longtable format. Valid values: False,True """ pc_latex_multicolumn = """ : bool This specifies if the to_latex method of a Dataframe uses multicolumns to pretty-print MultiIndex columns. Valid values: False,True """ pc_latex_multicolumn_format = """ : string This specifies the format for multicolumn headers. Can be surrounded with '|'. Valid values: 'l', 'c', 'r', 'p{}' """ pc_latex_multirow = """ : bool This specifies if the to_latex method of a Dataframe uses multirows to pretty-print MultiIndex rows. Valid values: False,True """ def table_schema_cb(key): from pandas.io.formats.printing import enable_data_resource_formatter enable_data_resource_formatter(cf.get_option(key)) def is_terminal() -> bool: """ Detect if Python is running in a terminal. Returns True if Python is running in a terminal or False if not. """ try: # error: Name 'get_ipython' is not defined ip = get_ipython() # type: ignore[name-defined] except NameError: # assume standard Python interpreter in a terminal return True else: if hasattr(ip, "kernel"): # IPython as a Jupyter kernel return False else: # IPython in a terminal return True with cf.config_prefix("display"): cf.register_option("precision", 6, pc_precision_doc, validator=is_nonnegative_int) cf.register_option( "float_format", None, float_format_doc, validator=is_one_of_factory([None, is_callable]), ) cf.register_option("column_space", 12, validator=is_int) cf.register_option( "max_info_rows", 1690785, pc_max_info_rows_doc, validator=is_instance_factory((int, type(None))), ) cf.register_option("max_rows", 60, pc_max_rows_doc, validator=is_nonnegative_int) cf.register_option( "min_rows", 10, pc_min_rows_doc, validator=is_instance_factory([type(None), int]), ) cf.register_option("max_categories", 8, pc_max_categories_doc, validator=is_int) def _deprecate_negative_int_max_colwidth(key): value = cf.get_option(key) if value is not None and value < 0: warnings.warn( "Passing a negative integer is deprecated in version 1.0 and " "will not be supported in future version. Instead, use None " "to not limit the column width.", FutureWarning, stacklevel=find_stack_level(), ) cf.register_option( # TODO(2.0): change `validator=is_nonnegative_int` see GH#31569 "max_colwidth", 50, max_colwidth_doc, validator=is_instance_factory([type(None), int]), cb=_deprecate_negative_int_max_colwidth, ) if is_terminal(): max_cols = 0 # automatically determine optimal number of columns else: max_cols = 20 # cannot determine optimal number of columns cf.register_option( "max_columns", max_cols, pc_max_cols_doc, validator=is_nonnegative_int ) cf.register_option( "large_repr", "truncate", pc_large_repr_doc, validator=is_one_of_factory(["truncate", "info"]), ) cf.register_option("max_info_columns", 100, pc_max_info_cols_doc, validator=is_int) cf.register_option( "colheader_justify", "right", colheader_justify_doc, validator=is_text ) cf.register_option("notebook_repr_html", True, pc_nb_repr_h_doc, validator=is_bool) cf.register_option("pprint_nest_depth", 3, pc_pprint_nest_depth, validator=is_int) cf.register_option("multi_sparse", True, pc_multi_sparse_doc, validator=is_bool) cf.register_option("expand_frame_repr", True, pc_expand_repr_doc) cf.register_option( "show_dimensions", "truncate", pc_show_dimensions_doc, validator=is_one_of_factory([True, False, "truncate"]), ) cf.register_option("chop_threshold", None, pc_chop_threshold_doc) cf.register_option("max_seq_items", 100, pc_max_seq_items) cf.register_option( "width", 80, pc_width_doc, validator=is_instance_factory([type(None), int]) ) cf.register_option( "memory_usage", True, pc_memory_usage_doc, validator=is_one_of_factory([None, True, False, "deep"]), ) cf.register_option( "unicode.east_asian_width", False, pc_east_asian_width_doc, validator=is_bool ) cf.register_option( "unicode.ambiguous_as_wide", False, pc_east_asian_width_doc, validator=is_bool ) cf.register_option("latex.repr", False, pc_latex_repr_doc, validator=is_bool) cf.register_option("latex.escape", True, pc_latex_escape, validator=is_bool) cf.register_option("latex.longtable", False, pc_latex_longtable, validator=is_bool) cf.register_option( "latex.multicolumn", True, pc_latex_multicolumn, validator=is_bool ) cf.register_option( "latex.multicolumn_format", "l", pc_latex_multicolumn, validator=is_text ) cf.register_option("latex.multirow", False, pc_latex_multirow, validator=is_bool) cf.register_option( "html.table_schema", False, pc_table_schema_doc, validator=is_bool, cb=table_schema_cb, ) cf.register_option("html.border", 1, pc_html_border_doc, validator=is_int) cf.register_option( "html.use_mathjax", True, pc_html_use_mathjax_doc, validator=is_bool ) cf.register_option( "max_dir_items", 100, pc_max_dir_items, validator=is_nonnegative_int ) tc_sim_interactive_doc = """ : boolean Whether to simulate interactive mode for purposes of testing """ with cf.config_prefix("mode"): cf.register_option("sim_interactive", False, tc_sim_interactive_doc) use_inf_as_null_doc = """ : boolean use_inf_as_null had been deprecated and will be removed in a future version. Use `use_inf_as_na` instead. """ use_inf_as_na_doc = """ : boolean True means treat None, NaN, INF, -INF as NA (old way), False means None and NaN are null, but INF, -INF are not NA (new way). """ # We don't want to start importing everything at the global context level # or we'll hit circular deps. def use_inf_as_na_cb(key): from pandas.core.dtypes.missing import _use_inf_as_na _use_inf_as_na(key) with cf.config_prefix("mode"): cf.register_option("use_inf_as_na", False, use_inf_as_na_doc, cb=use_inf_as_na_cb) cf.register_option( "use_inf_as_null", False, use_inf_as_null_doc, cb=use_inf_as_na_cb ) cf.deprecate_option( "mode.use_inf_as_null", msg=use_inf_as_null_doc, rkey="mode.use_inf_as_na" ) data_manager_doc = """ : string Internal data manager type; can be "block" or "array". Defaults to "block", unless overridden by the 'PANDAS_DATA_MANAGER' environment variable (needs to be set before pandas is imported). """ with cf.config_prefix("mode"): cf.register_option( "data_manager", # Get the default from an environment variable, if set, otherwise defaults # to "block". This environment variable can be set for testing. os.environ.get("PANDAS_DATA_MANAGER", "block"), data_manager_doc, validator=is_one_of_factory(["block", "array"]), ) # user warnings chained_assignment = """ : string Raise an exception, warn, or no action if trying to use chained assignment, The default is warn """ with cf.config_prefix("mode"): cf.register_option( "chained_assignment", "warn", chained_assignment, validator=is_one_of_factory([None, "warn", "raise"]), ) string_storage_doc = """ : string The default storage for StringDtype. """ with cf.config_prefix("mode"): cf.register_option( "string_storage", "python", string_storage_doc, validator=is_one_of_factory(["python", "pyarrow"]), ) # Set up the io.excel specific reader configuration. reader_engine_doc = """ : string The default Excel reader engine for '{ext}' files. Available options: auto, {others}. """ _xls_options = ["xlrd"] _xlsm_options = ["xlrd", "openpyxl"] _xlsx_options = ["xlrd", "openpyxl"] _ods_options = ["odf"] _xlsb_options = ["pyxlsb"] with cf.config_prefix("io.excel.xls"): cf.register_option( "reader", "auto", reader_engine_doc.format(ext="xls", others=", ".join(_xls_options)), validator=is_one_of_factory(_xls_options + ["auto"]), ) with cf.config_prefix("io.excel.xlsm"): cf.register_option( "reader", "auto", reader_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)), validator=is_one_of_factory(_xlsm_options + ["auto"]), ) with cf.config_prefix("io.excel.xlsx"): cf.register_option( "reader", "auto", reader_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)), validator=is_one_of_factory(_xlsx_options + ["auto"]), ) with cf.config_prefix("io.excel.ods"): cf.register_option( "reader", "auto", reader_engine_doc.format(ext="ods", others=", ".join(_ods_options)), validator=is_one_of_factory(_ods_options + ["auto"]), ) with cf.config_prefix("io.excel.xlsb"): cf.register_option( "reader", "auto", reader_engine_doc.format(ext="xlsb", others=", ".join(_xlsb_options)), validator=is_one_of_factory(_xlsb_options + ["auto"]), ) # Set up the io.excel specific writer configuration. writer_engine_doc = """ : string The default Excel writer engine for '{ext}' files. Available options: auto, {others}. """ _xls_options = ["xlwt"] _xlsm_options = ["openpyxl"] _xlsx_options = ["openpyxl", "xlsxwriter"] _ods_options = ["odf"] with cf.config_prefix("io.excel.xls"): cf.register_option( "writer", "auto", writer_engine_doc.format(ext="xls", others=", ".join(_xls_options)), validator=str, ) cf.deprecate_option( "io.excel.xls.writer", msg="As the xlwt package is no longer maintained, the xlwt engine will be " "removed in a future version of pandas. This is the only engine in pandas that " "supports writing in the xls format. Install openpyxl and write to an " "xlsx file instead.", ) with cf.config_prefix("io.excel.xlsm"): cf.register_option( "writer", "auto", writer_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)), validator=str, ) with cf.config_prefix("io.excel.xlsx"): cf.register_option( "writer", "auto", writer_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)), validator=str, ) with cf.config_prefix("io.excel.ods"): cf.register_option( "writer", "auto", writer_engine_doc.format(ext="ods", others=", ".join(_ods_options)), validator=str, ) # Set up the io.parquet specific configuration. parquet_engine_doc = """ : string The default parquet reader/writer engine. Available options: 'auto', 'pyarrow', 'fastparquet', the default is 'auto' """ with cf.config_prefix("io.parquet"): cf.register_option( "engine", "auto", parquet_engine_doc, validator=is_one_of_factory(["auto", "pyarrow", "fastparquet"]), ) # Set up the io.sql specific configuration. sql_engine_doc = """ : string The default sql reader/writer engine. Available options: 'auto', 'sqlalchemy', the default is 'auto' """ with cf.config_prefix("io.sql"): cf.register_option( "engine", "auto", sql_engine_doc, validator=is_one_of_factory(["auto", "sqlalchemy"]), ) # -------- # Plotting # --------- plotting_backend_doc = """ : str The plotting backend to use. The default value is "matplotlib", the backend provided with pandas. Other backends can be specified by providing the name of the module that implements the backend. """ def register_plotting_backend_cb(key): if key == "matplotlib": # We defer matplotlib validation, since it's the default return from pandas.plotting._core import _get_plot_backend _get_plot_backend(key) with cf.config_prefix("plotting"): cf.register_option( "backend", defval="matplotlib", doc=plotting_backend_doc, validator=register_plotting_backend_cb, ) register_converter_doc = """ : bool or 'auto'. Whether to register converters with matplotlib's units registry for dates, times, datetimes, and Periods. Toggling to False will remove the converters, restoring any converters that pandas overwrote. """ def register_converter_cb(key): from pandas.plotting import ( deregister_matplotlib_converters, register_matplotlib_converters, ) if cf.get_option(key): register_matplotlib_converters() else: deregister_matplotlib_converters() with cf.config_prefix("plotting.matplotlib"): cf.register_option( "register_converters", "auto", register_converter_doc, validator=is_one_of_factory(["auto", True, False]), cb=register_converter_cb, ) # ------ # Styler # ------ styler_sparse_index_doc = """ : bool Whether to sparsify the display of a hierarchical index. Setting to False will display each explicit level element in a hierarchical key for each row. """ styler_sparse_columns_doc = """ : bool Whether to sparsify the display of hierarchical columns. Setting to False will display each explicit level element in a hierarchical key for each column. """ styler_render_repr = """ : str Determine which output to use in Jupyter Notebook in {"html", "latex"}. """ styler_max_elements = """ : int The maximum number of data-cell (
) elements that will be rendered before trimming will occur over columns, rows or both if needed. """ styler_max_rows = """ : int, optional The maximum number of rows that will be rendered. May still be reduced to satsify ``max_elements``, which takes precedence. """ styler_max_columns = """ : int, optional The maximum number of columns that will be rendered. May still be reduced to satsify ``max_elements``, which takes precedence. """ styler_precision = """ : int The precision for floats and complex numbers. """ styler_decimal = """ : str The character representation for the decimal separator for floats and complex. """ styler_thousands = """ : str, optional The character representation for thousands separator for floats, int and complex. """ styler_na_rep = """ : str, optional The string representation for values identified as missing. """ styler_escape = """ : str, optional Whether to escape certain characters according to the given context; html or latex. """ styler_formatter = """ : str, callable, dict, optional A formatter object to be used as default within ``Styler.format``. """ styler_multirow_align = """ : {"c", "t", "b"} The specifier for vertical alignment of sparsified LaTeX multirows. """ styler_multicol_align = r""" : {"r", "c", "l", "naive-l", "naive-r"} The specifier for horizontal alignment of sparsified LaTeX multicolumns. Pipe decorators can also be added to non-naive values to draw vertical rules, e.g. "\|r" will draw a rule on the left side of right aligned merged cells. """ styler_hrules = """ : bool Whether to add horizontal rules on top and bottom and below the headers. """ styler_environment = """ : str The environment to replace ``\\begin{table}``. If "longtable" is used results in a specific longtable environment format. """ styler_encoding = """ : str The encoding used for output HTML and LaTeX files. """ styler_mathjax = """ : bool If False will render special CSS classes to table attributes that indicate Mathjax will not be used in Jupyter Notebook. """ with cf.config_prefix("styler"): cf.register_option("sparse.index", True, styler_sparse_index_doc, validator=is_bool) cf.register_option( "sparse.columns", True, styler_sparse_columns_doc, validator=is_bool ) cf.register_option( "render.repr", "html", styler_render_repr, validator=is_one_of_factory(["html", "latex"]), ) cf.register_option( "render.max_elements", 2**18, styler_max_elements, validator=is_nonnegative_int, ) cf.register_option( "render.max_rows", None, styler_max_rows, validator=is_nonnegative_int, ) cf.register_option( "render.max_columns", None, styler_max_columns, validator=is_nonnegative_int, ) cf.register_option("render.encoding", "utf-8", styler_encoding, validator=is_str) cf.register_option("format.decimal", ".", styler_decimal, validator=is_str) cf.register_option( "format.precision", 6, styler_precision, validator=is_nonnegative_int ) cf.register_option( "format.thousands", None, styler_thousands, validator=is_instance_factory([type(None), str]), ) cf.register_option( "format.na_rep", None, styler_na_rep, validator=is_instance_factory([type(None), str]), ) cf.register_option( "format.escape", None, styler_escape, validator=is_one_of_factory([None, "html", "latex"]), ) cf.register_option( "format.formatter", None, styler_formatter, validator=is_instance_factory([type(None), dict, Callable, str]), ) cf.register_option("html.mathjax", True, styler_mathjax, validator=is_bool) cf.register_option( "latex.multirow_align", "c", styler_multirow_align, validator=is_one_of_factory(["c", "t", "b", "naive"]), ) val_mca = ["r", "|r|", "|r", "r|", "c", "|c|", "|c", "c|", "l", "|l|", "|l", "l|"] val_mca += ["naive-l", "naive-r"] cf.register_option( "latex.multicol_align", "r", styler_multicol_align, validator=is_one_of_factory(val_mca), ) cf.register_option("latex.hrules", False, styler_hrules, validator=is_bool) cf.register_option( "latex.environment", None, styler_environment, validator=is_instance_factory([type(None), str]), )