"""
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 then in the module.
"""
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_text,
)
# 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))
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
)
#
# options from the "display" namespace
pc_precision_doc = """
: int
Floating point output precision (number of significant digits). This is
only a suggestion
"""
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_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
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=4,
)
cf.register_option(
# FIXME: change `validator=is_nonnegative_int`
# in version 1.2
"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
)
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"
)
# 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"]),
)
# 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=str,
)
with cf.config_prefix("io.excel.xlsm"):
cf.register_option(
"reader",
"auto",
reader_engine_doc.format(ext="xlsm", others=", ".join(_xlsm_options)),
validator=str,
)
with cf.config_prefix("io.excel.xlsx"):
cf.register_option(
"reader",
"auto",
reader_engine_doc.format(ext="xlsx", others=", ".join(_xlsx_options)),
validator=str,
)
with cf.config_prefix("io.excel.ods"):
cf.register_option(
"reader",
"auto",
reader_engine_doc.format(ext="ods", others=", ".join(_ods_options)),
validator=str,
)
with cf.config_prefix("io.excel.xlsb"):
cf.register_option(
"reader",
"auto",
reader_engine_doc.format(ext="xlsb", others=", ".join(_xlsb_options)),
validator=str,
)
# 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"]
with cf.config_prefix("io.excel.xls"):
cf.register_option(
"writer",
"auto",
writer_engine_doc.format(ext="xls", others=", ".join(_xls_options)),
validator=str,
)
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,
)
# 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"]),
)
# --------
# 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
prodiving 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 register_matplotlib_converters
from pandas.plotting import deregister_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,
)