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py-data-analysis/.venv/lib/python3.12/site-packages/pandas/io/formats/info.py

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from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import sys
from textwrap import dedent
from typing import TYPE_CHECKING
from pandas._config import get_option
from pandas.io.formats import format as fmt
from pandas.io.formats.printing import pprint_thing
if TYPE_CHECKING:
from collections.abc import (
Iterable,
Iterator,
Mapping,
Sequence,
)
from pandas._typing import (
Dtype,
WriteBuffer,
)
from pandas import (
DataFrame,
Index,
Series,
)
frame_max_cols_sub = dedent(
"""\
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."""
)
show_counts_sub = dedent(
"""\
show_counts : bool, optional
Whether to show the non-null counts. By default, this is shown
only if the DataFrame 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."""
)
frame_examples_sub = dedent(
"""\
>>> 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: 165.9 MB"""
)
frame_see_also_sub = dedent(
"""\
DataFrame.describe: Generate descriptive statistics of DataFrame
columns.
DataFrame.memory_usage: Memory usage of DataFrame columns."""
)
frame_sub_kwargs = {
"klass": "DataFrame",
"type_sub": " and columns",
"max_cols_sub": frame_max_cols_sub,
"show_counts_sub": show_counts_sub,
"examples_sub": frame_examples_sub,
"see_also_sub": frame_see_also_sub,
"version_added_sub": "",
}
series_examples_sub = dedent(
"""\
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> s = pd.Series(text_values, index=int_values)
>>> s.info()
<class 'pandas.core.series.Series'>
Index: 5 entries, 1 to 5
Series name: None
Non-Null Count Dtype
-------------- -----
5 non-null object
dtypes: object(1)
memory usage: 80.0+ bytes
Prints a summary excluding information about its values:
>>> s.info(verbose=False)
<class 'pandas.core.series.Series'>
Index: 5 entries, 1 to 5
dtypes: object(1)
memory usage: 80.0+ bytes
Pipe output of Series.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:
>>> import io
>>> buffer = io.StringIO()
>>> s.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 Series and fine-tune memory optimization:
>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> s = pd.Series(np.random.choice(['a', 'b', 'c'], 10 ** 6))
>>> s.info()
<class 'pandas.core.series.Series'>
RangeIndex: 1000000 entries, 0 to 999999
Series name: None
Non-Null Count Dtype
-------------- -----
1000000 non-null object
dtypes: object(1)
memory usage: 7.6+ MB
>>> s.info(memory_usage='deep')
<class 'pandas.core.series.Series'>
RangeIndex: 1000000 entries, 0 to 999999
Series name: None
Non-Null Count Dtype
-------------- -----
1000000 non-null object
dtypes: object(1)
memory usage: 55.3 MB"""
)
series_see_also_sub = dedent(
"""\
Series.describe: Generate descriptive statistics of Series.
Series.memory_usage: Memory usage of Series."""
)
series_sub_kwargs = {
"klass": "Series",
"type_sub": "",
"max_cols_sub": "",
"show_counts_sub": show_counts_sub,
"examples_sub": series_examples_sub,
"see_also_sub": series_see_also_sub,
"version_added_sub": "\n.. versionadded:: 1.4.0\n",
}
INFO_DOCSTRING = dedent(
"""
Print a concise summary of a {klass}.
This method prints information about a {klass} including
the index dtype{type_sub}, non-null values and memory usage.
{version_added_sub}\
Parameters
----------
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}
memory_usage : bool, str, optional
Specifies whether total memory usage of the {klass}
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. See the
:ref:`Frequently Asked Questions <df-memory-usage>` for more
details.
{show_counts_sub}
Returns
-------
None
This method prints a summary of a {klass} and returns None.
See Also
--------
{see_also_sub}
Examples
--------
{examples_sub}
"""
)
def _put_str(s: str | Dtype, space: int) -> str:
"""
Make string of specified length, padding to the right if necessary.
Parameters
----------
s : Union[str, Dtype]
String to be formatted.
space : int
Length to force string to be of.
Returns
-------
str
String coerced to given length.
Examples
--------
>>> pd.io.formats.info._put_str("panda", 6)
'panda '
>>> pd.io.formats.info._put_str("panda", 4)
'pand'
"""
return str(s)[:space].ljust(space)
def _sizeof_fmt(num: float, size_qualifier: str) -> str:
"""
Return size in human readable format.
Parameters
----------
num : int
Size in bytes.
size_qualifier : str
Either empty, or '+' (if lower bound).
Returns
-------
str
Size in human readable format.
Examples
--------
>>> _sizeof_fmt(23028, '')
'22.5 KB'
>>> _sizeof_fmt(23028, '+')
'22.5+ KB'
"""
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"
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
class _BaseInfo(ABC):
"""
Base class for DataFrameInfo and SeriesInfo.
Parameters
----------
data : DataFrame or Series
Either dataframe or series.
memory_usage : bool or str, optional
If "deep", introspect the data deeply by interrogating object dtypes
for system-level memory consumption, and include it in the returned
values.
"""
data: DataFrame | Series
memory_usage: bool | str
@property
@abstractmethod
def dtypes(self) -> Iterable[Dtype]:
"""
Dtypes.
Returns
-------
dtypes : sequence
Dtype of each of the DataFrame's columns (or one series column).
"""
@property
@abstractmethod
def dtype_counts(self) -> Mapping[str, int]:
"""Mapping dtype - number of counts."""
@property
@abstractmethod
def non_null_counts(self) -> Sequence[int]:
"""Sequence of non-null counts for all columns or column (if series)."""
@property
@abstractmethod
def memory_usage_bytes(self) -> int:
"""
Memory usage in bytes.
Returns
-------
memory_usage_bytes : int
Object's total memory usage in bytes.
"""
@property
def memory_usage_string(self) -> str:
"""Memory usage in a form of human readable string."""
return f"{_sizeof_fmt(self.memory_usage_bytes, self.size_qualifier)}\n"
@property
def size_qualifier(self) -> str:
size_qualifier = ""
if self.memory_usage:
if self.memory_usage != "deep":
# size_qualifier is just a best effort; not guaranteed to catch
# all cases (e.g., it misses categorical data even with object
# categories)
if (
"object" in self.dtype_counts
or self.data.index._is_memory_usage_qualified()
):
size_qualifier = "+"
return size_qualifier
@abstractmethod
def render(
self,
*,
buf: WriteBuffer[str] | None,
max_cols: int | None,
verbose: bool | None,
show_counts: bool | None,
) -> None:
pass
class DataFrameInfo(_BaseInfo):
"""
Class storing dataframe-specific info.
"""
def __init__(
self,
data: DataFrame,
memory_usage: bool | str | None = None,
) -> None:
self.data: DataFrame = data
self.memory_usage = _initialize_memory_usage(memory_usage)
@property
def dtype_counts(self) -> Mapping[str, int]:
return _get_dataframe_dtype_counts(self.data)
@property
def dtypes(self) -> Iterable[Dtype]:
"""
Dtypes.
Returns
-------
dtypes
Dtype of each of the DataFrame's columns.
"""
return self.data.dtypes
@property
def ids(self) -> Index:
"""
Column names.
Returns
-------
ids : Index
DataFrame's column names.
"""
return self.data.columns
@property
def col_count(self) -> int:
"""Number of columns to be summarized."""
return len(self.ids)
@property
def non_null_counts(self) -> Sequence[int]:
"""Sequence of non-null counts for all columns or column (if series)."""
return self.data.count()
@property
def memory_usage_bytes(self) -> int:
deep = self.memory_usage == "deep"
return self.data.memory_usage(index=True, deep=deep).sum()
def render(
self,
*,
buf: WriteBuffer[str] | None,
max_cols: int | None,
verbose: bool | None,
show_counts: bool | None,
) -> None:
printer = _DataFrameInfoPrinter(
info=self,
max_cols=max_cols,
verbose=verbose,
show_counts=show_counts,
)
printer.to_buffer(buf)
class SeriesInfo(_BaseInfo):
"""
Class storing series-specific info.
"""
def __init__(
self,
data: Series,
memory_usage: bool | str | None = None,
) -> None:
self.data: Series = data
self.memory_usage = _initialize_memory_usage(memory_usage)
def render(
self,
*,
buf: WriteBuffer[str] | None = None,
max_cols: int | None = None,
verbose: bool | None = None,
show_counts: bool | None = None,
) -> None:
if max_cols is not None:
raise ValueError(
"Argument `max_cols` can only be passed "
"in DataFrame.info, not Series.info"
)
printer = _SeriesInfoPrinter(
info=self,
verbose=verbose,
show_counts=show_counts,
)
printer.to_buffer(buf)
@property
def non_null_counts(self) -> Sequence[int]:
return [self.data.count()]
@property
def dtypes(self) -> Iterable[Dtype]:
return [self.data.dtypes]
@property
def dtype_counts(self) -> Mapping[str, int]:
from pandas.core.frame import DataFrame
return _get_dataframe_dtype_counts(DataFrame(self.data))
@property
def memory_usage_bytes(self) -> int:
"""Memory usage in bytes.
Returns
-------
memory_usage_bytes : int
Object's total memory usage in bytes.
"""
deep = self.memory_usage == "deep"
return self.data.memory_usage(index=True, deep=deep)
class _InfoPrinterAbstract:
"""
Class for printing dataframe or series info.
"""
def to_buffer(self, buf: WriteBuffer[str] | None = None) -> None:
"""Save dataframe info into buffer."""
table_builder = self._create_table_builder()
lines = table_builder.get_lines()
if buf is None: # pragma: no cover
buf = sys.stdout
fmt.buffer_put_lines(buf, lines)
@abstractmethod
def _create_table_builder(self) -> _TableBuilderAbstract:
"""Create instance of table builder."""
class _DataFrameInfoPrinter(_InfoPrinterAbstract):
"""
Class for printing dataframe info.
Parameters
----------
info : DataFrameInfo
Instance of DataFrameInfo.
max_cols : int, optional
When to switch from the verbose to the truncated output.
verbose : bool, optional
Whether to print the full summary.
show_counts : bool, optional
Whether to show the non-null counts.
"""
def __init__(
self,
info: DataFrameInfo,
max_cols: int | None = None,
verbose: bool | None = None,
show_counts: bool | None = None,
) -> None:
self.info = info
self.data = info.data
self.verbose = verbose
self.max_cols = self._initialize_max_cols(max_cols)
self.show_counts = self._initialize_show_counts(show_counts)
@property
def max_rows(self) -> int:
"""Maximum info rows to be displayed."""
return get_option("display.max_info_rows", len(self.data) + 1)
@property
def exceeds_info_cols(self) -> bool:
"""Check if number of columns to be summarized does not exceed maximum."""
return bool(self.col_count > self.max_cols)
@property
def exceeds_info_rows(self) -> bool:
"""Check if number of rows to be summarized does not exceed maximum."""
return bool(len(self.data) > self.max_rows)
@property
def col_count(self) -> int:
"""Number of columns to be summarized."""
return self.info.col_count
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 _initialize_show_counts(self, show_counts: bool | None) -> bool:
if show_counts is None:
return bool(not self.exceeds_info_cols and not self.exceeds_info_rows)
else:
return show_counts
def _create_table_builder(self) -> _DataFrameTableBuilder:
"""
Create instance of table builder based on verbosity and display settings.
"""
if self.verbose:
return _DataFrameTableBuilderVerbose(
info=self.info,
with_counts=self.show_counts,
)
elif self.verbose is False: # specifically set to False, not necessarily None
return _DataFrameTableBuilderNonVerbose(info=self.info)
elif self.exceeds_info_cols:
return _DataFrameTableBuilderNonVerbose(info=self.info)
else:
return _DataFrameTableBuilderVerbose(
info=self.info,
with_counts=self.show_counts,
)
class _SeriesInfoPrinter(_InfoPrinterAbstract):
"""Class for printing series info.
Parameters
----------
info : SeriesInfo
Instance of SeriesInfo.
verbose : bool, optional
Whether to print the full summary.
show_counts : bool, optional
Whether to show the non-null counts.
"""
def __init__(
self,
info: SeriesInfo,
verbose: bool | None = None,
show_counts: bool | None = None,
) -> None:
self.info = info
self.data = info.data
self.verbose = verbose
self.show_counts = self._initialize_show_counts(show_counts)
def _create_table_builder(self) -> _SeriesTableBuilder:
"""
Create instance of table builder based on verbosity.
"""
if self.verbose or self.verbose is None:
return _SeriesTableBuilderVerbose(
info=self.info,
with_counts=self.show_counts,
)
else:
return _SeriesTableBuilderNonVerbose(info=self.info)
def _initialize_show_counts(self, show_counts: bool | None) -> bool:
if show_counts is None:
return True
else:
return show_counts
class _TableBuilderAbstract(ABC):
"""
Abstract builder for info table.
"""
_lines: list[str]
info: _BaseInfo
@abstractmethod
def get_lines(self) -> list[str]:
"""Product in a form of list of lines (strings)."""
@property
def data(self) -> DataFrame | Series:
return self.info.data
@property
def dtypes(self) -> Iterable[Dtype]:
"""Dtypes of each of the DataFrame's columns."""
return self.info.dtypes
@property
def dtype_counts(self) -> Mapping[str, int]:
"""Mapping dtype - number of counts."""
return self.info.dtype_counts
@property
def display_memory_usage(self) -> bool:
"""Whether to display memory usage."""
return bool(self.info.memory_usage)
@property
def memory_usage_string(self) -> str:
"""Memory usage string with proper size qualifier."""
return self.info.memory_usage_string
@property
def non_null_counts(self) -> Sequence[int]:
return self.info.non_null_counts
def add_object_type_line(self) -> None:
"""Add line with string representation of dataframe to the table."""
self._lines.append(str(type(self.data)))
def add_index_range_line(self) -> None:
"""Add line with range of indices to the table."""
self._lines.append(self.data.index._summary())
def add_dtypes_line(self) -> None:
"""Add summary line with dtypes present in dataframe."""
collected_dtypes = [
f"{key}({val:d})" for key, val in sorted(self.dtype_counts.items())
]
self._lines.append(f"dtypes: {', '.join(collected_dtypes)}")
class _DataFrameTableBuilder(_TableBuilderAbstract):
"""
Abstract builder for dataframe info table.
Parameters
----------
info : DataFrameInfo.
Instance of DataFrameInfo.
"""
def __init__(self, *, info: DataFrameInfo) -> None:
self.info: DataFrameInfo = info
def get_lines(self) -> list[str]:
self._lines = []
if self.col_count == 0:
self._fill_empty_info()
else:
self._fill_non_empty_info()
return self._lines
def _fill_empty_info(self) -> None:
"""Add lines to the info table, pertaining to empty dataframe."""
self.add_object_type_line()
self.add_index_range_line()
self._lines.append(f"Empty {type(self.data).__name__}\n")
@abstractmethod
def _fill_non_empty_info(self) -> None:
"""Add lines to the info table, pertaining to non-empty dataframe."""
@property
def data(self) -> DataFrame:
"""DataFrame."""
return self.info.data
@property
def ids(self) -> Index:
"""Dataframe columns."""
return self.info.ids
@property
def col_count(self) -> int:
"""Number of dataframe columns to be summarized."""
return self.info.col_count
def add_memory_usage_line(self) -> None:
"""Add line containing memory usage."""
self._lines.append(f"memory usage: {self.memory_usage_string}")
class _DataFrameTableBuilderNonVerbose(_DataFrameTableBuilder):
"""
Dataframe info table builder for non-verbose output.
"""
def _fill_non_empty_info(self) -> None:
"""Add lines to the info table, pertaining to non-empty dataframe."""
self.add_object_type_line()
self.add_index_range_line()
self.add_columns_summary_line()
self.add_dtypes_line()
if self.display_memory_usage:
self.add_memory_usage_line()
def add_columns_summary_line(self) -> None:
self._lines.append(self.ids._summary(name="Columns"))
class _TableBuilderVerboseMixin(_TableBuilderAbstract):
"""
Mixin for verbose info output.
"""
SPACING: str = " " * 2
strrows: Sequence[Sequence[str]]
gross_column_widths: Sequence[int]
with_counts: bool
@property
@abstractmethod
def headers(self) -> Sequence[str]:
"""Headers names of the columns in verbose table."""
@property
def header_column_widths(self) -> Sequence[int]:
"""Widths of header columns (only titles)."""
return [len(col) for col in self.headers]
def _get_gross_column_widths(self) -> Sequence[int]:
"""Get widths of columns containing both headers and actual content."""
body_column_widths = self._get_body_column_widths()
return [
max(*widths)
for widths in zip(self.header_column_widths, body_column_widths)
]
def _get_body_column_widths(self) -> Sequence[int]:
"""Get widths of table content columns."""
strcols: Sequence[Sequence[str]] = list(zip(*self.strrows))
return [max(len(x) for x in col) for col in strcols]
def _gen_rows(self) -> Iterator[Sequence[str]]:
"""
Generator function yielding rows content.
Each element represents a row comprising a sequence of strings.
"""
if self.with_counts:
return self._gen_rows_with_counts()
else:
return self._gen_rows_without_counts()
@abstractmethod
def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
"""Iterator with string representation of body data with counts."""
@abstractmethod
def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
"""Iterator with string representation of body data without counts."""
def add_header_line(self) -> None:
header_line = self.SPACING.join(
[
_put_str(header, col_width)
for header, col_width in zip(self.headers, self.gross_column_widths)
]
)
self._lines.append(header_line)
def add_separator_line(self) -> None:
separator_line = self.SPACING.join(
[
_put_str("-" * header_colwidth, gross_colwidth)
for header_colwidth, gross_colwidth in zip(
self.header_column_widths, self.gross_column_widths
)
]
)
self._lines.append(separator_line)
def add_body_lines(self) -> None:
for row in self.strrows:
body_line = self.SPACING.join(
[
_put_str(col, gross_colwidth)
for col, gross_colwidth in zip(row, self.gross_column_widths)
]
)
self._lines.append(body_line)
def _gen_non_null_counts(self) -> Iterator[str]:
"""Iterator with string representation of non-null counts."""
for count in self.non_null_counts:
yield f"{count} non-null"
def _gen_dtypes(self) -> Iterator[str]:
"""Iterator with string representation of column dtypes."""
for dtype in self.dtypes:
yield pprint_thing(dtype)
class _DataFrameTableBuilderVerbose(_DataFrameTableBuilder, _TableBuilderVerboseMixin):
"""
Dataframe info table builder for verbose output.
"""
def __init__(
self,
*,
info: DataFrameInfo,
with_counts: bool,
) -> None:
self.info = info
self.with_counts = with_counts
self.strrows: Sequence[Sequence[str]] = list(self._gen_rows())
self.gross_column_widths: Sequence[int] = self._get_gross_column_widths()
def _fill_non_empty_info(self) -> None:
"""Add lines to the info table, pertaining to non-empty dataframe."""
self.add_object_type_line()
self.add_index_range_line()
self.add_columns_summary_line()
self.add_header_line()
self.add_separator_line()
self.add_body_lines()
self.add_dtypes_line()
if self.display_memory_usage:
self.add_memory_usage_line()
@property
def headers(self) -> Sequence[str]:
"""Headers names of the columns in verbose table."""
if self.with_counts:
return [" # ", "Column", "Non-Null Count", "Dtype"]
return [" # ", "Column", "Dtype"]
def add_columns_summary_line(self) -> None:
self._lines.append(f"Data columns (total {self.col_count} columns):")
def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
"""Iterator with string representation of body data without counts."""
yield from zip(
self._gen_line_numbers(),
self._gen_columns(),
self._gen_dtypes(),
)
def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
"""Iterator with string representation of body data with counts."""
yield from zip(
self._gen_line_numbers(),
self._gen_columns(),
self._gen_non_null_counts(),
self._gen_dtypes(),
)
def _gen_line_numbers(self) -> Iterator[str]:
"""Iterator with string representation of column numbers."""
for i, _ in enumerate(self.ids):
yield f" {i}"
def _gen_columns(self) -> Iterator[str]:
"""Iterator with string representation of column names."""
for col in self.ids:
yield pprint_thing(col)
class _SeriesTableBuilder(_TableBuilderAbstract):
"""
Abstract builder for series info table.
Parameters
----------
info : SeriesInfo.
Instance of SeriesInfo.
"""
def __init__(self, *, info: SeriesInfo) -> None:
self.info: SeriesInfo = info
def get_lines(self) -> list[str]:
self._lines = []
self._fill_non_empty_info()
return self._lines
@property
def data(self) -> Series:
"""Series."""
return self.info.data
def add_memory_usage_line(self) -> None:
"""Add line containing memory usage."""
self._lines.append(f"memory usage: {self.memory_usage_string}")
@abstractmethod
def _fill_non_empty_info(self) -> None:
"""Add lines to the info table, pertaining to non-empty series."""
class _SeriesTableBuilderNonVerbose(_SeriesTableBuilder):
"""
Series info table builder for non-verbose output.
"""
def _fill_non_empty_info(self) -> None:
"""Add lines to the info table, pertaining to non-empty series."""
self.add_object_type_line()
self.add_index_range_line()
self.add_dtypes_line()
if self.display_memory_usage:
self.add_memory_usage_line()
class _SeriesTableBuilderVerbose(_SeriesTableBuilder, _TableBuilderVerboseMixin):
"""
Series info table builder for verbose output.
"""
def __init__(
self,
*,
info: SeriesInfo,
with_counts: bool,
) -> None:
self.info = info
self.with_counts = with_counts
self.strrows: Sequence[Sequence[str]] = list(self._gen_rows())
self.gross_column_widths: Sequence[int] = self._get_gross_column_widths()
def _fill_non_empty_info(self) -> None:
"""Add lines to the info table, pertaining to non-empty series."""
self.add_object_type_line()
self.add_index_range_line()
self.add_series_name_line()
self.add_header_line()
self.add_separator_line()
self.add_body_lines()
self.add_dtypes_line()
if self.display_memory_usage:
self.add_memory_usage_line()
def add_series_name_line(self) -> None:
self._lines.append(f"Series name: {self.data.name}")
@property
def headers(self) -> Sequence[str]:
"""Headers names of the columns in verbose table."""
if self.with_counts:
return ["Non-Null Count", "Dtype"]
return ["Dtype"]
def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
"""Iterator with string representation of body data without counts."""
yield from self._gen_dtypes()
def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
"""Iterator with string representation of body data with counts."""
yield from zip(
self._gen_non_null_counts(),
self._gen_dtypes(),
)
def _get_dataframe_dtype_counts(df: DataFrame) -> Mapping[str, int]:
"""
Create mapping between datatypes and their number of occurrences.
"""
# groupby dtype.name to collect e.g. Categorical columns
return df.dtypes.value_counts().groupby(lambda x: x.name).sum()
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