def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, **kwds):
"""
internal. pprinter for iterables. you should probably use pprint_thing()
rather then calling this directly.
"""
fmt = "{{{things}}}"
pairs = []
pfmt = "{key}: {val}"
if max_seq_items is False:
nitems = len(seq)
else:
nitems = max_seq_items or get_option("max_seq_items") or len(seq)
for k, v in list(seq.items())[:nitems]:
pairs.append(
pfmt.format(
key=pprint_thing(k, _nest_lvl + 1,
max_seq_items=max_seq_items, **kwds),
val=pprint_thing(v, _nest_lvl + 1,
max_seq_items=max_seq_items, **kwds)))
if nitems < len(seq):
return fmt.format(things=", ".join(pairs) + ", ...")
else:
return fmt.format(things=", ".join(pairs))
def _write_table(self, indent=0):
_classes = ['dataframe'] # Default class.
use_mathjax = get_option("display.html.use_mathjax")
if not use_mathjax:
_classes.append('tex2jax_ignore')
if self.classes is not None:
if isinstance(self.classes, str):
self.classes = self.classes.split()
if not isinstance(self.classes, (list, tuple)):
raise TypeError('classes must be a string, list, or tuple, '
'not {typ}'.format(typ=type(self.classes)))
_classes.extend(self.classes)
if self.table_id is None:
id_section = ""
else:
id_section = ' id="{table_id}"'.format(table_id=self.table_id)
self.write('<table border="{border}" class="{cls}"{id_section}>'
.format(border=self.border, cls=' '.join(_classes),
id_section=id_section), indent)
if self.fmt.header or self.show_row_idx_names:
self._write_header(indent + self.indent_delta)
self._write_body(indent + self.indent_delta)
self.write('</table>', indent)
def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, **kwds):
"""
internal. pprinter for iterables. you should probably use pprint_thing()
rather then calling this directly.
bounds length of printed sequence, depending on options
"""
if isinstance(seq, set):
fmt = "{{{body}}}"
else:
fmt = "[{body}]" if hasattr(seq, '__setitem__') else "({body})"
if max_seq_items is False:
nitems = len(seq)
else:
nitems = max_seq_items or get_option("max_seq_items") or len(seq)
s = iter(seq)
# handle sets, no slicing
r = [pprint_thing(next(s),
_nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
for i in range(min(nitems, len(seq)))]
body = ", ".join(r)
if nitems < len(seq):
body += ", ..."
elif isinstance(seq, tuple) and len(seq) == 1:
body += ','
return fmt.format(body=body)
def _use_inf_as_na(key):
"""Option change callback for na/inf behaviour
Choose which replacement for numpy.isnan / -numpy.isfinite is used.
Parameters
----------
flag: bool
True means treat None, NaN, INF, -INF as null (old way),
False means None and NaN are null, but INF, -INF are not null
(new way).
Notes
-----
This approach to setting global module values is discussed and
approved here:
* http://stackoverflow.com/questions/4859217/
programmatically-creating-variables-in-python/4859312#4859312
"""
from pandas._config import get_option
flag = get_option(key)
if flag:
globals()['_isna'] = _isna_old
else:
globals()['_isna'] = _isna_new
def _format_data(self):
# TODO: integrate with categorical and make generic
# name argument is unused here; just for compat with base / categorical
n = len(self)
max_seq_items = min((get_option(
'display.max_seq_items') or n) // 10, 10)
formatter = str
if n == 0:
summary = '[]'
elif n == 1:
first = formatter(self[0])
summary = '[{first}]'.format(first=first)
elif n == 2:
first = formatter(self[0])
last = formatter(self[-1])
summary = '[{first}, {last}]'.format(first=first, last=last)
else:
if n > max_seq_items:
n = min(max_seq_items // 2, 10)
head = [formatter(x) for x in self[:n]]
tail = [formatter(x) for x in self[-n:]]
summary = '[{head} ... {tail}]'.format(
head=', '.join(head), tail=', '.join(tail))
else:
tail = [formatter(x) for x in self]
summary = '[{tail}]'.format(tail=', '.join(tail))
return summary
def __bytes__(self):
"""
Return a string representation for a particular object.
"""
from pandas._config import get_option
encoding = get_option("display.encoding")
return self.__unicode__().encode(encoding, 'replace')
def _format_attrs(self):
"""
Return a list of tuples of the (attr,formatted_value)
"""
max_categories = (10 if get_option("display.max_categories") == 0 else
get_option("display.max_categories"))
attrs = [
('categories',
ibase.default_pprint(self.categories,
max_seq_items=max_categories)),
('ordered', self.ordered)]
if self.name is not None:
attrs.append(('name', ibase.default_pprint(self.name)))
attrs.append(('dtype', "'%s'" % self.dtype.name))
max_seq_items = get_option('display.max_seq_items') or len(self)
if len(self) > max_seq_items:
attrs.append(('length', len(self)))
return attrs
def __bytes__(self):
"""
Return a string representation for a particular object.
Invoked by bytes(obj) in py3 only.
Yields a bytestring in both py2/py3.
"""
from pandas._config import get_option
encoding = get_option("display.encoding")
return self.__unicode__().encode(encoding, 'replace')
def __init__(self, formatter, classes=None, border=None):
self.fmt = formatter
self.classes = classes
self.frame = self.fmt.frame
self.columns = self.fmt.tr_frame.columns
self.elements = []
self.bold_rows = self.fmt.kwds.get('bold_rows', False)
self.escape = self.fmt.kwds.get('escape', True)
self.show_dimensions = self.fmt.show_dimensions
if border is None:
border = get_option('display.html.border')
self.border = border
self.table_id = self.fmt.table_id
self.render_links = self.fmt.render_links
def _get_level_lengths(index, hidden_elements=None):
"""
Given an index, find the level length for each element.
Optional argument is a list of index positions which
should not be visible.
Result is a dictionary of (level, inital_position): span
"""
sentinel = object()
levels = index.format(sparsify=sentinel, adjoin=False, names=False)
if hidden_elements is None:
hidden_elements = []
lengths = {}
if index.nlevels == 1:
for i, value in enumerate(levels):
if(i not in hidden_elements):
lengths[(0, i)] = 1
return lengths
for i, lvl in enumerate(levels):
for j, row in enumerate(lvl):
if not get_option('display.multi_sparse'):
lengths[(i, j)] = 1
elif (row != sentinel) and (j not in hidden_elements):
last_label = j
lengths[(i, last_label)] = 1
elif (row != sentinel):
# even if its hidden, keep track of it in case
# length >1 and later elements are visible
last_label = j
lengths[(i, last_label)] = 0
elif(j not in hidden_elements):
lengths[(i, last_label)] += 1
non_zero_lengths = {
element: length for element, length in lengths.items() if length >= 1}
return non_zero_lengths
def __init__(self, data, precision=None, table_styles=None, uuid=None,
caption=None, table_attributes=None, cell_ids=True):
self.ctx = defaultdict(list)
self._todo = []
if not isinstance(data, (pd.Series, pd.DataFrame)):
raise TypeError("``data`` must be a Series or DataFrame")
if data.ndim == 1:
data = data.to_frame()
if not data.index.is_unique or not data.columns.is_unique:
raise ValueError("style is not supported for non-unique indices.")
self.data = data
self.index = data.index
self.columns = data.columns
self.uuid = uuid
self.table_styles = table_styles
self.caption = caption
if precision is None:
precision = get_option('display.precision')
self.precision = precision
self.table_attributes = table_attributes
self.hidden_index = False
self.hidden_columns = []
self.cell_ids = cell_ids
# display_funcs maps (row, col) -> formatting function
def default_display_func(x):
if is_float(x):
return '{:>.{precision}g}'.format(x, precision=self.precision)
else:
return x
self._display_funcs = defaultdict(lambda: default_display_func)
def format_object_attrs(obj):
"""
Return a list of tuples of the (attr, formatted_value)
for common attrs, including dtype, name, length
Parameters
----------
obj : object
must be iterable
Returns
-------
list
"""
attrs = []
if hasattr(obj, 'dtype'):
attrs.append(('dtype', "'{}'".format(obj.dtype)))
if getattr(obj, 'name', None) is not None:
attrs.append(('name', default_pprint(obj.name)))
max_seq_items = get_option('display.max_seq_items') or len(obj)
if len(obj) > max_seq_items:
attrs.append(('length', len(obj)))
return attrs
ABCMultiIndex, ABCPeriodIndex,
ABCSeries)
from pandas.core.dtypes.missing import isna, notna
import pandas.core.common as com
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib import converter
from pandas.plotting._matplotlib.compat import _mpl_ge_3_0_0
from pandas.plotting._matplotlib.style import _get_standard_colors
from pandas.plotting._matplotlib.tools import (_flatten, _get_all_lines,
_get_xlim, _handle_shared_axes,
_subplots, format_date_labels,
table)
if get_option('plotting.matplotlib.register_converters'):
converter.register(explicit=False)
class MPLPlot:
"""
Base class for assembling a pandas plot using matplotlib
Parameters
----------
data :
"""
@property
def _kind(self):
"""Specify kind str. Must be overridden in child class"""
def max_rows(self) -> int:
"""Maximum info rows to be displayed."""
return get_option("display.max_info_rows", len(self.data) + 1)
def pprint_thing(thing,
_nest_lvl=0,
escape_chars=None,
default_escapes=False,
quote_strings=False,
max_seq_items=None):
"""
This function is the sanctioned way of converting objects
to a unicode representation.
properly handles nested sequences containing unicode strings
(unicode(object) does not)
Parameters
----------
thing : anything to be formatted
_nest_lvl : internal use only. pprint_thing() is mutually-recursive
with pprint_sequence, this argument is used to keep track of the
current nesting level, and limit it.
escape_chars : list or dict, optional
Characters to escape. If a dict is passed the values are the
replacements
default_escapes : bool, default False
Whether the input escape characters replaces or adds to the defaults
max_seq_items : False, int, default None
Pass thru to other pretty printers to limit sequence printing
Returns
-------
result - unicode str
"""
def as_escaped_unicode(thing, escape_chars=escape_chars):
# Unicode is fine, else we try to decode using utf-8 and 'replace'
# if that's not it either, we have no way of knowing and the user
# should deal with it himself.
try:
result = str(thing) # we should try this first
except UnicodeDecodeError:
# either utf-8 or we replace errors
result = str(thing).decode('utf-8', "replace")
translate = {
'\t': r'\t',
'\n': r'\n',
'\r': r'\r',
}
if isinstance(escape_chars, dict):
if default_escapes:
translate.update(escape_chars)
else:
translate = escape_chars
escape_chars = list(escape_chars.keys())
else:
escape_chars = escape_chars or tuple()
for c in escape_chars:
result = result.replace(c, translate[c])
return str(result)
if hasattr(thing, '__next__'):
return str(thing)
elif (isinstance(thing, dict)
and _nest_lvl < get_option("display.pprint_nest_depth")):
result = _pprint_dict(thing,
_nest_lvl,
quote_strings=True,
max_seq_items=max_seq_items)
elif (is_sequence(thing)
and _nest_lvl < get_option("display.pprint_nest_depth")):
result = _pprint_seq(thing,
_nest_lvl,
escape_chars=escape_chars,
quote_strings=quote_strings,
max_seq_items=max_seq_items)
elif isinstance(thing, str) and quote_strings:
result = "'{thing}'".format(thing=as_escaped_unicode(thing))
else:
result = as_escaped_unicode(thing)
return str(result) # always unicode
def format_object_summary(obj,
formatter,
is_justify=True,
name=None,
indent_for_name=True):
"""
Return the formatted obj as a unicode string
Parameters
----------
obj : object
must be iterable and support __getitem__
formatter : callable
string formatter for an element
is_justify : boolean
should justify the display
name : name, optional
defaults to the class name of the obj
indent_for_name : bool, default True
Whether subsequent lines should be be indented to
align with the name.
Returns
-------
summary string
"""
from pandas.io.formats.console import get_console_size
from pandas.io.formats.format import _get_adjustment
display_width, _ = get_console_size()
if display_width is None:
display_width = get_option('display.width') or 80
if name is None:
name = obj.__class__.__name__
if indent_for_name:
name_len = len(name)
space1 = "\n%s" % (' ' * (name_len + 1))
space2 = "\n%s" % (' ' * (name_len + 2))
else:
space1 = "\n"
space2 = "\n " # space for the opening '['
n = len(obj)
sep = ','
max_seq_items = get_option('display.max_seq_items') or n
# are we a truncated display
is_truncated = n > max_seq_items
# adj can optionally handle unicode eastern asian width
adj = _get_adjustment()
def _extend_line(s, line, value, display_width, next_line_prefix):
if (adj.len(line.rstrip()) + adj.len(value.rstrip()) >= display_width):
s += line.rstrip()
line = next_line_prefix
line += value
return s, line
def best_len(values):
if values:
return max(adj.len(x) for x in values)
else:
return 0
close = ', '
if n == 0:
summary = '[]{}'.format(close)
elif n == 1:
first = formatter(obj[0])
summary = '[{}]{}'.format(first, close)
elif n == 2:
first = formatter(obj[0])
last = formatter(obj[-1])
summary = '[{}, {}]{}'.format(first, last, close)
else:
if n > max_seq_items:
n = min(max_seq_items // 2, 10)
head = [formatter(x) for x in obj[:n]]
tail = [formatter(x) for x in obj[-n:]]
else:
head = []
tail = [formatter(x) for x in obj]
# adjust all values to max length if needed
if is_justify:
# however, if we are not truncated and we are only a single
# line, then don't justify
if (is_truncated
or not (len(', '.join(head)) < display_width
and len(', '.join(tail)) < display_width)):
max_len = max(best_len(head), best_len(tail))
head = [x.rjust(max_len) for x in head]
tail = [x.rjust(max_len) for x in tail]
summary = ""
line = space2
for i in range(len(head)):
word = head[i] + sep + ' '
summary, line = _extend_line(summary, line, word, display_width,
space2)
if is_truncated:
# remove trailing space of last line
summary += line.rstrip() + space2 + '...'
line = space2
for i in range(len(tail) - 1):
word = tail[i] + sep + ' '
summary, line = _extend_line(summary, line, word, display_width,
space2)
# last value: no sep added + 1 space of width used for trailing ','
summary, line = _extend_line(summary, line, tail[-1],
display_width - 2, space2)
summary += line
# right now close is either '' or ', '
# Now we want to include the ']', but not the maybe space.
close = ']' + close.rstrip(' ')
summary += close
if len(summary) > (display_width):
summary += space1
else: # one row
summary += ' '
# remove initial space
summary = '[' + summary[len(space2):]
return summary
UserWarning)
except ImportError: # pragma: no cover
pass
_USE_BOTTLENECK = False
def set_use_bottleneck(v=True):
# set/unset to use bottleneck
global _USE_BOTTLENECK
if _BOTTLENECK_INSTALLED:
_USE_BOTTLENECK = v
set_use_bottleneck(get_option('compute.use_bottleneck'))
class disallow(object):
def __init__(self, *dtypes):
super(disallow, self).__init__()
self.dtypes = tuple(pandas_dtype(dtype).type for dtype in dtypes)
def check(self, obj):
return hasattr(obj, 'dtype') and issubclass(obj.dtype.type,
self.dtypes)
def __call__(self, f):
@functools.wraps(f)
def _f(*args, **kwargs):
obj_iter = itertools.chain(args, compat.itervalues(kwargs))
def _translate(self,
sparsify_index: bool | None = None,
sparsify_cols: bool | None = None):
"""
Process Styler data and settings into a dict for template rendering.
Convert data and settings from ``Styler`` attributes such as ``self.data``,
``self.tooltips`` including applying any methods in ``self._todo``.
Parameters
----------
sparsify_index : bool, optional
Whether to sparsify the index or print all hierarchical index elements
sparsify_cols : bool, optional
Whether to sparsify the columns or print all hierarchical column elements
Returns
-------
d : dict
The following structure: {uuid, table_styles, caption, head, body,
cellstyle, table_attributes}
"""
if sparsify_index is None:
sparsify_index = get_option("display.multi_sparse")
if sparsify_cols is None:
sparsify_cols = get_option("display.multi_sparse")
ROW_HEADING_CLASS = "row_heading"
COL_HEADING_CLASS = "col_heading"
INDEX_NAME_CLASS = "index_name"
DATA_CLASS = "data"
BLANK_CLASS = "blank"
BLANK_VALUE = " "
# construct render dict
d = {
"uuid": self.uuid,
"table_styles": _format_table_styles(self.table_styles or []),
"caption": self.caption,
}
head = self._translate_header(BLANK_CLASS, BLANK_VALUE,
INDEX_NAME_CLASS, COL_HEADING_CLASS,
sparsify_cols)
d.update({"head": head})
self.cellstyle_map: DefaultDict[tuple[CSSPair, ...],
list[str]] = defaultdict(list)
body = self._translate_body(DATA_CLASS, ROW_HEADING_CLASS,
sparsify_index)
d.update({"body": body})
cellstyle: list[dict[str, CSSList | list[str]]] = [{
"props":
list(props),
"selectors":
selectors
} for props, selectors in self.cellstyle_map.items()]
d.update({"cellstyle": cellstyle})
table_attr = self.table_attributes
use_mathjax = get_option("display.html.use_mathjax")
if not use_mathjax:
table_attr = table_attr or ""
if 'class="' in table_attr:
table_attr = table_attr.replace('class="',
'class="tex2jax_ignore ')
else:
table_attr += ' class="tex2jax_ignore"'
d.update({"table_attributes": table_attr})
if self.tooltips:
d = self.tooltips._translate(self.data, self.uuid, d)
return d
except ImportError:
_HAVE_FASTPARQUET = False
pytestmark = pytest.mark.filterwarnings(
"ignore:RangeIndex.* is deprecated:DeprecationWarning")
# TODO(ArrayManager) fastparquet relies on BlockManager internals
# setup engines & skips
@pytest.fixture(params=[
pytest.param(
"fastparquet",
marks=pytest.mark.skipif(
not _HAVE_FASTPARQUET
or get_option("mode.data_manager") == "array",
reason="fastparquet is not installed or ArrayManager is used",
),
),
pytest.param(
"pyarrow",
marks=pytest.mark.skipif(not _HAVE_PYARROW,
reason="pyarrow is not installed"),
),
])
def engine(request):
return request.param
@pytest.fixture
def pa():
def format_object_summary(
obj,
formatter: Callable,
is_justify: bool = True,
name: Optional[str] = None,
indent_for_name: bool = True,
line_break_each_value: bool = False,
) -> str:
"""
Return the formatted obj as a unicode string
Parameters
----------
obj : object
must be iterable and support __getitem__
formatter : callable
string formatter for an element
is_justify : boolean
should justify the display
name : name, optional
defaults to the class name of the obj
indent_for_name : bool, default True
Whether subsequent lines should be be indented to
align with the name.
line_break_each_value : bool, default False
If True, inserts a line break for each value of ``obj``.
If False, only break lines when the a line of values gets wider
than the display width.
.. versionadded:: 0.25.0
Returns
-------
summary string
"""
from pandas.io.formats.console import get_console_size
from pandas.io.formats.format import _get_adjustment
display_width, _ = get_console_size()
if display_width is None:
display_width = get_option("display.width") or 80
if name is None:
name = type(obj).__name__
if indent_for_name:
name_len = len(name)
space1 = f'\n{(" " * (name_len + 1))}'
space2 = f'\n{(" " * (name_len + 2))}'
else:
space1 = "\n"
space2 = "\n " # space for the opening '['
n = len(obj)
if line_break_each_value:
# If we want to vertically align on each value of obj, we need to
# separate values by a line break and indent the values
sep = ",\n " + " " * len(name)
else:
sep = ","
max_seq_items = get_option("display.max_seq_items") or n
# are we a truncated display
is_truncated = n > max_seq_items
# adj can optionally handle unicode eastern asian width
adj = _get_adjustment()
def _extend_line(
s: str, line: str, value: str, display_width: int, next_line_prefix: str
) -> Tuple[str, str]:
if adj.len(line.rstrip()) + adj.len(value.rstrip()) >= display_width:
s += line.rstrip()
line = next_line_prefix
line += value
return s, line
def best_len(values: List[str]) -> int:
if values:
return max(adj.len(x) for x in values)
else:
return 0
close = ", "
if n == 0:
summary = f"[]{close}"
elif n == 1 and not line_break_each_value:
first = formatter(obj[0])
summary = f"[{first}]{close}"
elif n == 2 and not line_break_each_value:
first = formatter(obj[0])
last = formatter(obj[-1])
summary = f"[{first}, {last}]{close}"
else:
if n > max_seq_items:
n = min(max_seq_items // 2, 10)
head = [formatter(x) for x in obj[:n]]
tail = [formatter(x) for x in obj[-n:]]
else:
head = []
tail = [formatter(x) for x in obj]
# adjust all values to max length if needed
if is_justify:
if line_break_each_value:
# Justify each string in the values of head and tail, so the
# strings will right align when head and tail are stacked
# vertically.
head, tail = _justify(head, tail)
elif is_truncated or not (
len(", ".join(head)) < display_width
and len(", ".join(tail)) < display_width
):
# Each string in head and tail should align with each other
max_length = max(best_len(head), best_len(tail))
head = [x.rjust(max_length) for x in head]
tail = [x.rjust(max_length) for x in tail]
# If we are not truncated and we are only a single
# line, then don't justify
if line_break_each_value:
# Now head and tail are of type List[Tuple[str]]. Below we
# convert them into List[str], so there will be one string per
# value. Also truncate items horizontally if wider than
# max_space
max_space = display_width - len(space2)
value = tail[0]
for max_items in reversed(range(1, len(value) + 1)):
pprinted_seq = _pprint_seq(value, max_seq_items=max_items)
if len(pprinted_seq) < max_space:
break
head = [_pprint_seq(x, max_seq_items=max_items) for x in head]
tail = [_pprint_seq(x, max_seq_items=max_items) for x in tail]
summary = ""
line = space2
for max_items in range(len(head)):
word = head[max_items] + sep + " "
summary, line = _extend_line(summary, line, word, display_width, space2)
if is_truncated:
# remove trailing space of last line
summary += line.rstrip() + space2 + "..."
line = space2
for max_items in range(len(tail) - 1):
word = tail[max_items] + sep + " "
summary, line = _extend_line(summary, line, word, display_width, space2)
# last value: no sep added + 1 space of width used for trailing ','
summary, line = _extend_line(summary, line, tail[-1], display_width - 2, space2)
summary += line
# right now close is either '' or ', '
# Now we want to include the ']', but not the maybe space.
close = "]" + close.rstrip(" ")
summary += close
if len(summary) > (display_width) or line_break_each_value:
summary += space1
else: # one row
summary += " "
# remove initial space
summary = "[" + summary[len(space2) :]
return summary
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib import converter
from pandas.plotting._matplotlib.compat import _mpl_ge_3_0_0
from pandas.plotting._matplotlib.style import _get_standard_colors
from pandas.plotting._matplotlib.tools import (
_flatten,
_get_all_lines,
_get_xlim,
_handle_shared_axes,
_subplots,
format_date_labels,
table,
)
if get_option("plotting.matplotlib.register_converters"):
converter.register(explicit=False)
class MPLPlot:
"""
Base class for assembling a pandas plot using matplotlib
Parameters
----------
data :
"""
@property
def _kind(self):
"""Specify kind str. Must be overridden in child class"""
def pprint_thing(
thing: Any,
_nest_lvl: int = 0,
escape_chars: Optional[EscapeChars] = None,
default_escapes: bool = False,
quote_strings: bool = False,
max_seq_items: Optional[int] = None,
) -> str:
"""
This function is the sanctioned way of converting objects
to a string representation and properly handles nested sequences.
Parameters
----------
thing : anything to be formatted
_nest_lvl : internal use only. pprint_thing() is mutually-recursive
with pprint_sequence, this argument is used to keep track of the
current nesting level, and limit it.
escape_chars : list or dict, optional
Characters to escape. If a dict is passed the values are the
replacements
default_escapes : bool, default False
Whether the input escape characters replaces or adds to the defaults
max_seq_items : int or None, default None
Pass through to other pretty printers to limit sequence printing
Returns
-------
str
"""
def as_escaped_string(
thing: Any, escape_chars: Optional[EscapeChars] = escape_chars
) -> str:
translate = {"\t": r"\t", "\n": r"\n", "\r": r"\r"}
if isinstance(escape_chars, dict):
if default_escapes:
translate.update(escape_chars)
else:
translate = escape_chars
escape_chars = list(escape_chars.keys())
else:
escape_chars = escape_chars or tuple()
result = str(thing)
for c in escape_chars:
result = result.replace(c, translate[c])
return result
if hasattr(thing, "__next__"):
return str(thing)
elif isinstance(thing, dict) and _nest_lvl < get_option(
"display.pprint_nest_depth"
):
result = _pprint_dict(
thing, _nest_lvl, quote_strings=True, max_seq_items=max_seq_items
)
elif is_sequence(thing) and _nest_lvl < get_option("display.pprint_nest_depth"):
result = _pprint_seq(
thing,
_nest_lvl,
escape_chars=escape_chars,
quote_strings=quote_strings,
max_seq_items=max_seq_items,
)
elif isinstance(thing, str) and quote_strings:
result = "'{thing}'".format(thing=as_escaped_string(thing))
else:
result = as_escaped_string(thing)
return result
def _initialize_memory_usage(
memory_usage: bool | str | None = None, ) -> bool | str:
"""Get memory usage based on inputs and display options."""
if memory_usage is None:
memory_usage = get_option("display.memory_usage")
return memory_usage
def _initialize_max_cols(self, max_cols: int | None) -> int:
if max_cols is None:
return get_option("display.max_info_columns", self.col_count + 1)
return max_cols
def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False,
quote_strings=False, max_seq_items=None):
"""
This function is the sanctioned way of converting objects
to a unicode representation.
properly handles nested sequences containing unicode strings
(unicode(object) does not)
Parameters
----------
thing : anything to be formatted
_nest_lvl : internal use only. pprint_thing() is mutually-recursive
with pprint_sequence, this argument is used to keep track of the
current nesting level, and limit it.
escape_chars : list or dict, optional
Characters to escape. If a dict is passed the values are the
replacements
default_escapes : bool, default False
Whether the input escape characters replaces or adds to the defaults
max_seq_items : False, int, default None
Pass thru to other pretty printers to limit sequence printing
Returns
-------
result - unicode object on py2, str on py3. Always Unicode.
"""
def as_escaped_unicode(thing, escape_chars=escape_chars):
# Unicode is fine, else we try to decode using utf-8 and 'replace'
# if that's not it either, we have no way of knowing and the user
# should deal with it himself.
try:
result = str(thing) # we should try this first
except UnicodeDecodeError:
# either utf-8 or we replace errors
result = str(thing).decode('utf-8', "replace")
translate = {'\t': r'\t', '\n': r'\n', '\r': r'\r', }
if isinstance(escape_chars, dict):
if default_escapes:
translate.update(escape_chars)
else:
translate = escape_chars
escape_chars = list(escape_chars.keys())
else:
escape_chars = escape_chars or tuple()
for c in escape_chars:
result = result.replace(c, translate[c])
return str(result)
if hasattr(thing, '__next__'):
return str(thing)
elif (isinstance(thing, dict) and
_nest_lvl < get_option("display.pprint_nest_depth")):
result = _pprint_dict(thing, _nest_lvl, quote_strings=True,
max_seq_items=max_seq_items)
elif (is_sequence(thing) and
_nest_lvl < get_option("display.pprint_nest_depth")):
result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars,
quote_strings=quote_strings,
max_seq_items=max_seq_items)
elif isinstance(thing, str) and quote_strings:
result = "'{thing}'".format(thing=as_escaped_unicode(thing))
else:
result = as_escaped_unicode(thing)
return str(result) # always unicode
def _translate(self):
"""
Convert the DataFrame in `self.data` and the attrs from `_build_styles`
into a dictionary of {head, body, uuid, cellstyle}.
"""
table_styles = self.table_styles or []
caption = self.caption
ctx = self.ctx
precision = self.precision
hidden_index = self.hidden_index
hidden_columns = self.hidden_columns
uuid = self.uuid or str(uuid1()).replace("-", "_")
ROW_HEADING_CLASS = "row_heading"
COL_HEADING_CLASS = "col_heading"
INDEX_NAME_CLASS = "index_name"
DATA_CLASS = "data"
BLANK_CLASS = "blank"
BLANK_VALUE = ""
def format_attr(pair):
return "{key}={value}".format(**pair)
# for sparsifying a MultiIndex
idx_lengths = _get_level_lengths(self.index)
col_lengths = _get_level_lengths(self.columns, hidden_columns)
cell_context = dict()
n_rlvls = self.data.index.nlevels
n_clvls = self.data.columns.nlevels
rlabels = self.data.index.tolist()
clabels = self.data.columns.tolist()
if n_rlvls == 1:
rlabels = [[x] for x in rlabels]
if n_clvls == 1:
clabels = [[x] for x in clabels]
clabels = list(zip(*clabels))
cellstyle = []
head = []
for r in range(n_clvls):
# Blank for Index columns...
row_es = [{"type": "th",
"value": BLANK_VALUE,
"display_value": BLANK_VALUE,
"is_visible": not hidden_index,
"class": " ".join([BLANK_CLASS])}] * (n_rlvls - 1)
# ... except maybe the last for columns.names
name = self.data.columns.names[r]
cs = [BLANK_CLASS if name is None else INDEX_NAME_CLASS,
"level{lvl}".format(lvl=r)]
name = BLANK_VALUE if name is None else name
row_es.append({"type": "th",
"value": name,
"display_value": name,
"class": " ".join(cs),
"is_visible": not hidden_index})
if clabels:
for c, value in enumerate(clabels[r]):
cs = [COL_HEADING_CLASS, "level{lvl}".format(lvl=r),
"col{col}".format(col=c)]
cs.extend(cell_context.get(
"col_headings", {}).get(r, {}).get(c, []))
es = {
"type": "th",
"value": value,
"display_value": value,
"class": " ".join(cs),
"is_visible": _is_visible(c, r, col_lengths),
}
colspan = col_lengths.get((r, c), 0)
if colspan > 1:
es["attributes"] = [
format_attr({"key": "colspan", "value": colspan})
]
row_es.append(es)
head.append(row_es)
if (self.data.index.names and
com._any_not_none(*self.data.index.names) and
not hidden_index):
index_header_row = []
for c, name in enumerate(self.data.index.names):
cs = [INDEX_NAME_CLASS,
"level{lvl}".format(lvl=c)]
name = '' if name is None else name
index_header_row.append({"type": "th", "value": name,
"class": " ".join(cs)})
index_header_row.extend(
[{"type": "th",
"value": BLANK_VALUE,
"class": " ".join([BLANK_CLASS])
}] * (len(clabels[0]) - len(hidden_columns)))
head.append(index_header_row)
body = []
for r, idx in enumerate(self.data.index):
row_es = []
for c, value in enumerate(rlabels[r]):
rid = [ROW_HEADING_CLASS, "level{lvl}".format(lvl=c),
"row{row}".format(row=r)]
es = {
"type": "th",
"is_visible": (_is_visible(r, c, idx_lengths) and
not hidden_index),
"value": value,
"display_value": value,
"id": "_".join(rid[1:]),
"class": " ".join(rid)
}
rowspan = idx_lengths.get((c, r), 0)
if rowspan > 1:
es["attributes"] = [
format_attr({"key": "rowspan", "value": rowspan})
]
row_es.append(es)
for c, col in enumerate(self.data.columns):
cs = [DATA_CLASS, "row{row}".format(row=r),
"col{col}".format(col=c)]
cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
formatter = self._display_funcs[(r, c)]
value = self.data.iloc[r, c]
row_dict = {"type": "td",
"value": value,
"class": " ".join(cs),
"display_value": formatter(value),
"is_visible": (c not in hidden_columns)}
# only add an id if the cell has a style
if (self.cell_ids or
not(len(ctx[r, c]) == 1 and ctx[r, c][0] == '')):
row_dict["id"] = "_".join(cs[1:])
row_es.append(row_dict)
props = []
for x in ctx[r, c]:
# have to handle empty styles like ['']
if x.count(":"):
props.append(x.split(":"))
else:
props.append(['', ''])
cellstyle.append({'props': props,
'selector': "row{row}_col{col}"
.format(row=r, col=c)})
body.append(row_es)
table_attr = self.table_attributes
use_mathjax = get_option("display.html.use_mathjax")
if not use_mathjax:
table_attr = table_attr or ''
if 'class="' in table_attr:
table_attr = table_attr.replace('class="',
'class="tex2jax_ignore ')
else:
table_attr += ' class="tex2jax_ignore"'
return dict(head=head, cellstyle=cellstyle, body=body, uuid=uuid,
precision=precision, table_styles=table_styles,
caption=caption, table_attributes=table_attr)
def format_object_summary(obj, formatter, is_justify=True, name=None,
indent_for_name=True):
"""
Return the formatted obj as a unicode string
Parameters
----------
obj : object
must be iterable and support __getitem__
formatter : callable
string formatter for an element
is_justify : boolean
should justify the display
name : name, optional
defaults to the class name of the obj
indent_for_name : bool, default True
Whether subsequent lines should be be indented to
align with the name.
Returns
-------
summary string
"""
from pandas.io.formats.console import get_console_size
from pandas.io.formats.format import _get_adjustment
display_width, _ = get_console_size()
if display_width is None:
display_width = get_option('display.width') or 80
if name is None:
name = obj.__class__.__name__
if indent_for_name:
name_len = len(name)
space1 = "\n%s" % (' ' * (name_len + 1))
space2 = "\n%s" % (' ' * (name_len + 2))
else:
space1 = "\n"
space2 = "\n " # space for the opening '['
n = len(obj)
sep = ','
max_seq_items = get_option('display.max_seq_items') or n
# are we a truncated display
is_truncated = n > max_seq_items
# adj can optionally handle unicode eastern asian width
adj = _get_adjustment()
def _extend_line(s, line, value, display_width, next_line_prefix):
if (adj.len(line.rstrip()) + adj.len(value.rstrip()) >=
display_width):
s += line.rstrip()
line = next_line_prefix
line += value
return s, line
def best_len(values):
if values:
return max(adj.len(x) for x in values)
else:
return 0
close = ', '
if n == 0:
summary = '[]{}'.format(close)
elif n == 1:
first = formatter(obj[0])
summary = '[{}]{}'.format(first, close)
elif n == 2:
first = formatter(obj[0])
last = formatter(obj[-1])
summary = '[{}, {}]{}'.format(first, last, close)
else:
if n > max_seq_items:
n = min(max_seq_items // 2, 10)
head = [formatter(x) for x in obj[:n]]
tail = [formatter(x) for x in obj[-n:]]
else:
head = []
tail = [formatter(x) for x in obj]
# adjust all values to max length if needed
if is_justify:
# however, if we are not truncated and we are only a single
# line, then don't justify
if (is_truncated or
not (len(', '.join(head)) < display_width and
len(', '.join(tail)) < display_width)):
max_len = max(best_len(head), best_len(tail))
head = [x.rjust(max_len) for x in head]
tail = [x.rjust(max_len) for x in tail]
summary = ""
line = space2
for i in range(len(head)):
word = head[i] + sep + ' '
summary, line = _extend_line(summary, line, word,
display_width, space2)
if is_truncated:
# remove trailing space of last line
summary += line.rstrip() + space2 + '...'
line = space2
for i in range(len(tail) - 1):
word = tail[i] + sep + ' '
summary, line = _extend_line(summary, line, word,
display_width, space2)
# last value: no sep added + 1 space of width used for trailing ','
summary, line = _extend_line(summary, line, tail[-1],
display_width - 2, space2)
summary += line
# right now close is either '' or ', '
# Now we want to include the ']', but not the maybe space.
close = ']' + close.rstrip(' ')
summary += close
if len(summary) > (display_width):
summary += space1
else: # one row
summary += ' '
# remove initial space
summary = '[' + summary[len(space2):]
return summary
def _ensure_decoded(s):
""" if we have bytes, decode them to unicode """
if isinstance(s, (np.bytes_, bytes)):
s = s.decode(get_option("display.encoding"))
return s
def infor(file, verbose=None, buf=None, max_cols=None, null_counts=None):
lines = []
lines.append(str(type(file)))
lines.append(file.index._summary())
if len(file.columns) == 0:
lines.append("Empty {name}".format(name=type(file).__name__))
fmt.buffer_put_lines(buf, lines)
return
cols = file.columns
# hack
if max_cols is None:
max_cols = get_option("display.max_info_columns",
len(file.columns) + 1)
max_rows = get_option("display.max_info_rows", len(file) + 1)
if null_counts is None:
show_counts = (len(file.columns) <= max_cols) and (len(file) <
max_rows)
else:
show_counts = null_counts
exceeds_info_cols = len(file.columns) > max_cols
def _verbose_repr():
lines.append("Data columns (total %d columns):" % len(file.columns))
space = max(len(pprint_thing(k)) for k in file.columns) + 4
counts = None
tmpl = "{count}{dtype}"
if show_counts:
counts = file.count()
if len(cols) != len(counts): # pragma: no cover
raise AssertionError("Columns must equal counts "
"({cols:d} != {counts:d})".format(
cols=len(cols), counts=len(counts)))
tmpl = "{count} non-null {dtype}"
dtypes = file.dtypes
for i, col in enumerate(file.columns):
dtype = dtypes.iloc[i]
col = pprint_thing(col)
count = ""
if show_counts:
count = counts.iloc[i]
lines.append(
_put_str(col, space) + tmpl.format(count=count, dtype=dtype))
def _non_verbose_repr():
lines.append(file.columns._summary(name="Columns"))
if verbose:
_verbose_repr()
elif verbose is False: # specifically set to False, not nesc None
_non_verbose_repr()
else:
if exceeds_info_cols:
_non_verbose_repr()
else:
_verbose_repr()
counts = file._data.get_dtype_counts()
dtypes = [
"{k}({kk:d})".format(k=k[0], kk=k[1]) for k in sorted(counts.items())
]
lines.append("dtypes: {types}".format(types=", ".join(dtypes)))
# fmt.buffer_put_lines(buf, lines)
if any(isinstance(x, str) for x in lines):
lines = [str(x) for x in lines]
return "\n".join(lines)
bn = import_optional_dependency("bottleneck",
raise_on_missing=False,
on_version="warn")
_BOTTLENECK_INSTALLED = bn is not None
_USE_BOTTLENECK = False
def set_use_bottleneck(v: bool = True) -> None:
# set/unset to use bottleneck
global _USE_BOTTLENECK
if _BOTTLENECK_INSTALLED:
_USE_BOTTLENECK = v
set_use_bottleneck(get_option("compute.use_bottleneck"))
class disallow:
def __init__(self, *dtypes):
super().__init__()
self.dtypes = tuple(pandas_dtype(dtype).type for dtype in dtypes)
def check(self, obj) -> bool:
return hasattr(obj, "dtype") and issubclass(obj.dtype.type,
self.dtypes)
def __call__(self, f: F) -> F:
@functools.wraps(f)
def _f(*args, **kwargs):
obj_iter = itertools.chain(args, kwargs.values())
except ImportError: # pragma: no cover
pass
_USE_BOTTLENECK = False
def set_use_bottleneck(v=True):
# set/unset to use bottleneck
global _USE_BOTTLENECK
if _BOTTLENECK_INSTALLED:
_USE_BOTTLENECK = v
set_use_bottleneck(get_option('compute.use_bottleneck'))
class disallow(object):
def __init__(self, *dtypes):
super(disallow, self).__init__()
self.dtypes = tuple(pandas_dtype(dtype).type for dtype in dtypes)
def check(self, obj):
return hasattr(obj, 'dtype') and issubclass(obj.dtype.type,
self.dtypes)
def __call__(self, f):
@functools.wraps(f)
def _f(*args, **kwargs):
pytestmark = pytest.mark.filterwarnings(
"ignore:RangeIndex.* is deprecated:DeprecationWarning"
)
# TODO(ArrayManager) fastparquet relies on BlockManager internals
# setup engines & skips
@pytest.fixture(
params=[
pytest.param(
"fastparquet",
marks=pytest.mark.skipif(
not _HAVE_FASTPARQUET or get_option("mode.data_manager") == "array",
reason="fastparquet is not installed or ArrayManager is used",
),
),
pytest.param(
"pyarrow",
marks=pytest.mark.skipif(
not _HAVE_PYARROW, reason="pyarrow is not installed"
),
),
]
)
def engine(request):
return request.param
'b_value': b_value},
casting='safe')
except ValueError as detail:
if 'unknown type object' in str(detail):
pass
except Exception as detail:
raise TypeError(str(detail))
if result is None:
result = _where_standard(cond, a, b)
return result
# turn myself on
set_use_numexpr(get_option('compute.use_numexpr'))
def _has_bool_dtype(x):
try:
if isinstance(x, ABCDataFrame):
return 'bool' in x.dtypes
else:
return x.dtype == bool
except AttributeError:
return isinstance(x, (bool, np.bool_))
def _bool_arith_check(op_str, a, b, not_allowed=frozenset(('/', '//', '**')),
unsupported=None):
if unsupported is None:
def fp():
if not _HAVE_FASTPARQUET:
pytest.skip("fastparquet is not installed")
elif get_option("mode.data_manager") == "array":
pytest.skip("ArrayManager is not supported with fastparquet")
return "fastparquet"
def _translate(self,
sparse_index: bool,
sparse_cols: bool,
blank: str = " "):
"""
Process Styler data and settings into a dict for template rendering.
Convert data and settings from ``Styler`` attributes such as ``self.data``,
``self.tooltips`` including applying any methods in ``self._todo``.
Parameters
----------
sparse_index : bool
Whether to sparsify the index or print all hierarchical index elements.
Upstream defaults are typically to `pandas.options.styler.sparse.index`.
sparse_cols : bool
Whether to sparsify the columns or print all hierarchical column elements.
Upstream defaults are typically to `pandas.options.styler.sparse.columns`.
Returns
-------
d : dict
The following structure: {uuid, table_styles, caption, head, body,
cellstyle, table_attributes}
"""
ROW_HEADING_CLASS = "row_heading"
COL_HEADING_CLASS = "col_heading"
INDEX_NAME_CLASS = "index_name"
TRIMMED_COL_CLASS = "col_trim"
TRIMMED_ROW_CLASS = "row_trim"
DATA_CLASS = "data"
BLANK_CLASS = "blank"
BLANK_VALUE = blank
# construct render dict
d = {
"uuid": self.uuid,
"table_styles": _format_table_styles(self.table_styles or []),
"caption": self.caption,
}
max_elements = get_option("styler.render.max_elements")
max_rows, max_cols = _get_trimming_maximums(len(self.data.index),
len(self.data.columns),
max_elements)
head = self._translate_header(
BLANK_CLASS,
BLANK_VALUE,
INDEX_NAME_CLASS,
COL_HEADING_CLASS,
sparse_cols,
max_cols,
TRIMMED_COL_CLASS,
)
d.update({"head": head})
self.cellstyle_map: DefaultDict[tuple[CSSPair, ...],
list[str]] = defaultdict(list)
body = self._translate_body(
DATA_CLASS,
ROW_HEADING_CLASS,
sparse_index,
max_rows,
max_cols,
TRIMMED_ROW_CLASS,
TRIMMED_COL_CLASS,
)
d.update({"body": body})
cellstyle: list[dict[str, CSSList | list[str]]] = [{
"props":
list(props),
"selectors":
selectors
} for props, selectors in self.cellstyle_map.items()]
d.update({"cellstyle": cellstyle})
table_attr = self.table_attributes
use_mathjax = get_option("display.html.use_mathjax")
if not use_mathjax:
table_attr = table_attr or ""
if 'class="' in table_attr:
table_attr = table_attr.replace('class="',
'class="tex2jax_ignore ')
else:
table_attr += ' class="tex2jax_ignore"'
d.update({"table_attributes": table_attr})
if self.tooltips:
d = self.tooltips._translate(self.data, self.uuid, d)
return d
def info(
data, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None
) -> None:
"""
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
Parameters
----------
data : DataFrame
DataFrame to print information about.
verbose : bool, optional
Whether to print the full summary. By default, the setting in
``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default, the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
max_cols : int, optional
When to switch from the verbose to the truncated output. If the
DataFrame has more than `max_cols` columns, the truncated output
is used. By default, the setting in
``pandas.options.display.max_info_columns`` is used.
memory_usage : bool, str, optional
Specifies whether total memory usage of the DataFrame
elements (including the index) should be displayed. By default,
this follows the ``pandas.options.display.memory_usage`` setting.
True always show memory usage. False never shows memory usage.
A value of 'deep' is equivalent to "True with deep introspection".
Memory usage is shown in human-readable units (base-2
representation). Without deep introspection a memory estimation is
made based in column dtype and number of rows assuming values
consume the same memory amount for corresponding dtypes. With deep
memory introspection, a real memory usage calculation is performed
at the cost of computational resources.
null_counts : bool, optional
Whether to show the non-null counts. By default, this is shown
only if the frame is smaller than
``pandas.options.display.max_info_rows`` and
``pandas.options.display.max_info_columns``. A value of True always
shows the counts, and False never shows the counts.
Returns
-------
None
This method prints a summary of a DataFrame and returns None.
See Also
--------
DataFrame.describe: Generate descriptive statistics of DataFrame
columns.
DataFrame.memory_usage: Memory usage of DataFrame columns.
Examples
--------
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
... "float_col": float_values})
>>> df
int_col text_col float_col
0 1 alpha 0.00
1 2 beta 0.25
2 3 gamma 0.50
3 4 delta 0.75
4 5 epsilon 1.00
Prints information of all columns:
>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 int_col 5 non-null int64
1 text_col 5 non-null object
2 float_col 5 non-null float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes
Prints a summary of columns count and its dtypes but not per column
information:
>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes
Pipe output of DataFrame.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:
>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
... encoding="utf-8") as f: # doctest: +SKIP
... f.write(s)
260
The `memory_usage` parameter allows deep introspection mode, specially
useful for big DataFrames and fine-tune memory optimization:
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
... 'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 column_1 1000000 non-null object
1 column_2 1000000 non-null object
2 column_3 1000000 non-null object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 column_1 1000000 non-null object
1 column_2 1000000 non-null object
2 column_3 1000000 non-null object
dtypes: object(3)
memory usage: 188.8 MB
"""
if buf is None: # pragma: no cover
buf = sys.stdout
lines = []
lines.append(str(type(data)))
lines.append(data.index._summary())
if len(data.columns) == 0:
lines.append(f"Empty {type(data).__name__}")
fmt.buffer_put_lines(buf, lines)
return
cols = data.columns
col_count = len(data.columns)
# hack
if max_cols is None:
max_cols = get_option("display.max_info_columns", len(data.columns) + 1)
max_rows = get_option("display.max_info_rows", len(data) + 1)
if null_counts is None:
show_counts = (col_count <= max_cols) and (len(data) < max_rows)
else:
show_counts = null_counts
exceeds_info_cols = col_count > max_cols
def _verbose_repr():
lines.append(f"Data columns (total {len(data.columns)} columns):")
id_head = " # "
column_head = "Column"
col_space = 2
max_col = max(len(pprint_thing(k)) for k in cols)
len_column = len(pprint_thing(column_head))
space = max(max_col, len_column) + col_space
max_id = len(pprint_thing(col_count))
len_id = len(pprint_thing(id_head))
space_num = max(max_id, len_id) + col_space
header = _put_str(id_head, space_num) + _put_str(column_head, space)
if show_counts:
counts = data.count()
if len(cols) != len(counts): # pragma: no cover
raise AssertionError(
f"Columns must equal counts ({len(cols)} != {len(counts)})"
)
count_header = "Non-Null Count"
len_count = len(count_header)
non_null = " non-null"
max_count = max(len(pprint_thing(k)) for k in counts) + len(non_null)
space_count = max(len_count, max_count) + col_space
count_temp = "{count}" + non_null
else:
count_header = ""
space_count = len(count_header)
len_count = space_count
count_temp = "{count}"
dtype_header = "Dtype"
len_dtype = len(dtype_header)
max_dtypes = max(len(pprint_thing(k)) for k in data.dtypes)
space_dtype = max(len_dtype, max_dtypes)
header += _put_str(count_header, space_count) + _put_str(
dtype_header, space_dtype
)
lines.append(header)
lines.append(
_put_str("-" * len_id, space_num)
+ _put_str("-" * len_column, space)
+ _put_str("-" * len_count, space_count)
+ _put_str("-" * len_dtype, space_dtype)
)
for i, col in enumerate(data.columns):
dtype = data.dtypes.iloc[i]
col = pprint_thing(col)
line_no = _put_str(f" {i}", space_num)
count = ""
if show_counts:
count = counts.iloc[i]
lines.append(
line_no
+ _put_str(col, space)
+ _put_str(count_temp.format(count=count), space_count)
+ _put_str(dtype, space_dtype)
)
def _non_verbose_repr():
lines.append(data.columns._summary(name="Columns"))
def _sizeof_fmt(num, size_qualifier):
# returns size in human readable format
for x in ["bytes", "KB", "MB", "GB", "TB"]:
if num < 1024.0:
return f"{num:3.1f}{size_qualifier} {x}"
num /= 1024.0
return f"{num:3.1f}{size_qualifier} PB"
if verbose:
_verbose_repr()
elif verbose is False: # specifically set to False, not nesc None
_non_verbose_repr()
else:
if exceeds_info_cols:
_non_verbose_repr()
else:
_verbose_repr()
# groupby dtype.name to collect e.g. Categorical columns
counts = data.dtypes.value_counts().groupby(lambda x: x.name).sum()
dtypes = [f"{k[0]}({k[1]:d})" for k in sorted(counts.items())]
lines.append(f"dtypes: {', '.join(dtypes)}")
if memory_usage is None:
memory_usage = get_option("display.memory_usage")
if memory_usage:
# append memory usage of df to display
size_qualifier = ""
if memory_usage == "deep":
deep = True
else:
# size_qualifier is just a best effort; not guaranteed to catch
# all cases (e.g., it misses categorical data even with object
# categories)
deep = False
if "object" in counts or data.index._is_memory_usage_qualified():
size_qualifier = "+"
mem_usage = data.memory_usage(index=True, deep=deep).sum()
lines.append(f"memory usage: {_sizeof_fmt(mem_usage, size_qualifier)}\n")
fmt.buffer_put_lines(buf, lines)
if _can_use_numexpr(None, "where", a, b, "where"):
result = ne.evaluate(
"where(cond_value, a_value, b_value)",
local_dict={"cond_value": cond, "a_value": a, "b_value": b},
casting="safe",
)
if result is None:
result = _where_standard(cond, a, b)
return result
# turn myself on
set_use_numexpr(get_option("compute.use_numexpr"))
def _has_bool_dtype(x):
try:
return x.dtype == bool
except AttributeError:
return isinstance(x, (bool, np.bool_))
def _bool_arith_check(
op_str, a, b, not_allowed=frozenset(("/", "//", "**")), unsupported=None
):
if unsupported is None:
unsupported = {"+": "|", "*": "&", "-": "^"}
def info(self) -> None:
"""
Print a concise summary of a %(klass)s.
This method prints information about a %(klass)s including
the index dtype%(type_sub)s, non-null values and memory usage.
Parameters
----------
data : %(klass)s
%(klass)s to print information about.
verbose : bool, optional
Whether to print the full summary. By default, the setting in
``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default, the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
%(max_cols_sub)s
memory_usage : bool, str, optional
Specifies whether total memory usage of the %(klass)s
elements (including the index) should be displayed. By default,
this follows the ``pandas.options.display.memory_usage`` setting.
True always show memory usage. False never shows memory usage.
A value of 'deep' is equivalent to "True with deep introspection".
Memory usage is shown in human-readable units (base-2
representation). Without deep introspection a memory estimation is
made based in column dtype and number of rows assuming values
consume the same memory amount for corresponding dtypes. With deep
memory introspection, a real memory usage calculation is performed
at the cost of computational resources.
null_counts : bool, optional
Whether to show the non-null counts. By default, this is shown
only if the %(klass)s is smaller than
``pandas.options.display.max_info_rows`` and
``pandas.options.display.max_info_columns``. A value of True always
shows the counts, and False never shows the counts.
Returns
-------
None
This method prints a summary of a %(klass)s and returns None.
See Also
--------
%(see_also_sub)s
Examples
--------
%(examples_sub)s
"""
lines = []
lines.append(str(type(self.data)))
lines.append(self.data.index._summary())
ids, dtypes = self._get_ids_and_dtypes()
col_count = len(ids)
if col_count == 0:
lines.append(f"Empty {type(self.data).__name__}")
fmt.buffer_put_lines(self.buf, lines)
return
# hack
max_cols = self.max_cols
if max_cols is None:
max_cols = get_option("display.max_info_columns", col_count + 1)
max_rows = get_option("display.max_info_rows", len(self.data) + 1)
if self.null_counts is None:
show_counts = (col_count <= max_cols) and (len(self.data) <
max_rows)
else:
show_counts = self.null_counts
exceeds_info_cols = col_count > max_cols
if self.verbose:
self._verbose_repr(lines, ids, dtypes, show_counts)
elif self.verbose is False: # specifically set to False, not necessarily None
self._non_verbose_repr(lines, ids)
else:
if exceeds_info_cols:
self._non_verbose_repr(lines, ids)
else:
self._verbose_repr(lines, ids, dtypes, show_counts)
# groupby dtype.name to collect e.g. Categorical columns
counts = dtypes.value_counts().groupby(lambda x: x.name).sum()
collected_dtypes = [
f"{k[0]}({k[1]:d})" for k in sorted(counts.items())
]
lines.append(f"dtypes: {', '.join(collected_dtypes)}")
if self.memory_usage:
# append memory usage of df to display
size_qualifier = ""
if self.memory_usage == "deep":
deep = True
else:
# size_qualifier is just a best effort; not guaranteed to catch
# all cases (e.g., it misses categorical data even with object
# categories)
deep = False
if "object" in counts or self.data.index._is_memory_usage_qualified(
):
size_qualifier = "+"
mem_usage = self._get_mem_usage(deep=deep)
lines.append(
f"memory usage: {_sizeof_fmt(mem_usage, size_qualifier)}\n")
fmt.buffer_put_lines(self.buf, lines)
def _translate(self):
"""
Convert the DataFrame in `self.data` and the attrs from `_build_styles`
into a dictionary of {head, body, uuid, cellstyle}.
"""
table_styles = self.table_styles or []
caption = self.caption
ctx = self.ctx
precision = self.precision
hidden_index = self.hidden_index
hidden_columns = self.hidden_columns
uuid = self.uuid or str(uuid1()).replace("-", "_")
ROW_HEADING_CLASS = "row_heading"
COL_HEADING_CLASS = "col_heading"
INDEX_NAME_CLASS = "index_name"
DATA_CLASS = "data"
BLANK_CLASS = "blank"
BLANK_VALUE = ""
def format_attr(pair):
return "{key}={value}".format(**pair)
# for sparsifying a MultiIndex
idx_lengths = _get_level_lengths(self.index)
col_lengths = _get_level_lengths(self.columns, hidden_columns)
cell_context = dict()
n_rlvls = self.data.index.nlevels
n_clvls = self.data.columns.nlevels
rlabels = self.data.index.tolist()
clabels = self.data.columns.tolist()
if n_rlvls == 1:
rlabels = [[x] for x in rlabels]
if n_clvls == 1:
clabels = [[x] for x in clabels]
clabels = list(zip(*clabels))
cellstyle = []
head = []
for r in range(n_clvls):
# Blank for Index columns...
row_es = [
{
"type": "th",
"value": BLANK_VALUE,
"display_value": BLANK_VALUE,
"is_visible": not hidden_index,
"class": " ".join([BLANK_CLASS]),
}
] * (n_rlvls - 1)
# ... except maybe the last for columns.names
name = self.data.columns.names[r]
cs = [
BLANK_CLASS if name is None else INDEX_NAME_CLASS,
"level{lvl}".format(lvl=r),
]
name = BLANK_VALUE if name is None else name
row_es.append(
{
"type": "th",
"value": name,
"display_value": name,
"class": " ".join(cs),
"is_visible": not hidden_index,
}
)
if clabels:
for c, value in enumerate(clabels[r]):
cs = [
COL_HEADING_CLASS,
"level{lvl}".format(lvl=r),
"col{col}".format(col=c),
]
cs.extend(
cell_context.get("col_headings", {}).get(r, {}).get(c, [])
)
es = {
"type": "th",
"value": value,
"display_value": value,
"class": " ".join(cs),
"is_visible": _is_visible(c, r, col_lengths),
}
colspan = col_lengths.get((r, c), 0)
if colspan > 1:
es["attributes"] = [
format_attr({"key": "colspan", "value": colspan})
]
row_es.append(es)
head.append(row_es)
if (
self.data.index.names
and com.any_not_none(*self.data.index.names)
and not hidden_index
):
index_header_row = []
for c, name in enumerate(self.data.index.names):
cs = [INDEX_NAME_CLASS, "level{lvl}".format(lvl=c)]
name = "" if name is None else name
index_header_row.append(
{"type": "th", "value": name, "class": " ".join(cs)}
)
index_header_row.extend(
[{"type": "th", "value": BLANK_VALUE, "class": " ".join([BLANK_CLASS])}]
* (len(clabels[0]) - len(hidden_columns))
)
head.append(index_header_row)
body = []
for r, idx in enumerate(self.data.index):
row_es = []
for c, value in enumerate(rlabels[r]):
rid = [
ROW_HEADING_CLASS,
"level{lvl}".format(lvl=c),
"row{row}".format(row=r),
]
es = {
"type": "th",
"is_visible": (_is_visible(r, c, idx_lengths) and not hidden_index),
"value": value,
"display_value": value,
"id": "_".join(rid[1:]),
"class": " ".join(rid),
}
rowspan = idx_lengths.get((c, r), 0)
if rowspan > 1:
es["attributes"] = [
format_attr({"key": "rowspan", "value": rowspan})
]
row_es.append(es)
for c, col in enumerate(self.data.columns):
cs = [DATA_CLASS, "row{row}".format(row=r), "col{col}".format(col=c)]
cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
formatter = self._display_funcs[(r, c)]
value = self.data.iloc[r, c]
row_dict = {
"type": "td",
"value": value,
"class": " ".join(cs),
"display_value": formatter(value),
"is_visible": (c not in hidden_columns),
}
# only add an id if the cell has a style
if self.cell_ids or not (len(ctx[r, c]) == 1 and ctx[r, c][0] == ""):
row_dict["id"] = "_".join(cs[1:])
row_es.append(row_dict)
props = []
for x in ctx[r, c]:
# have to handle empty styles like ['']
if x.count(":"):
props.append(x.split(":"))
else:
props.append(["", ""])
cellstyle.append(
{
"props": props,
"selector": "row{row}_col{col}".format(row=r, col=c),
}
)
body.append(row_es)
table_attr = self.table_attributes
use_mathjax = get_option("display.html.use_mathjax")
if not use_mathjax:
table_attr = table_attr or ""
if 'class="' in table_attr:
table_attr = table_attr.replace('class="', 'class="tex2jax_ignore ')
else:
table_attr += ' class="tex2jax_ignore"'
return dict(
head=head,
cellstyle=cellstyle,
body=body,
uuid=uuid,
precision=precision,
table_styles=table_styles,
caption=caption,
table_attributes=table_attr,
)
def _initialize_memory_usage(
memory_usage: Optional[Union[bool, str]] = None, ) -> Union[bool, str]:
"""Get memory usage based on inputs and display options."""
if memory_usage is None:
memory_usage = get_option("display.memory_usage")
return memory_usage
