Module secfsdstools.e_presenter.presenting
Default Presenter implementations.
Expand source code
"""
Default Presenter implementations.
"""
import pandas as pd
from secfsdstools.d_container.databagmodel import JoinedDataBag
from secfsdstools.d_container.presentation import Presenter
class StandardStatementPresenter(Presenter[JoinedDataBag]):
"""
Reformats the data so that the presentation and order reflects the way how the basic
financial statements are presented.
"""
def __init__(self, flatten_index: bool = True, add_form_column: bool = False,
invert_negating: bool = False):
"""
Args:
flatten_index: if True, the index is reset, so that the columns
adsh', 'coreg', 'tag', 'version', 'stmt',
'report', 'line', 'uom', 'negating', 'inpth'
appear as columns and not in the index of the dataframe.
add_form_column: adds a "form" column to the resulting dataframe which contains
the type of the report ('10-K', '10-Q', ...)
invert_negating: In a report, values are sometimes shown as positive even when
from a calculating perspective they are negative, and vice versa.
Such values are marked with a "1" in the negating column.
When this flag is true, the value of such tags where negating is set
to 1 is inverted. A typical case is "paiddividends" where the value
is shown as positive but from a calculating point of view it should
be negative.
"""
self.flatten_index = flatten_index
self.add_form_column = add_form_column
self.invert_negating = invert_negating
def present(self, databag: JoinedDataBag) -> pd.DataFrame:
"""
creates the standard presentation of financial statements (Balance Sheet, Income Statement,
Cash Flow).
- Index columns are
['adsh', 'coreg', 'tag', 'version', 'stmt', 'report', 'line', 'uom', 'negating', 'inpth']
- Value columns are by ddate
- Order by
['adsh', 'coreg', 'stmt', 'report', 'line', 'inpth']
Args:
databag(JoinedDataBag) : bag to present
Returns:
pd.DataFrame: the dataframe with the final presentation
"""
pre_num_df = databag.pre_num_df
if self.invert_negating:
pre_num_df = pre_num_df.copy()
pre_num_df.loc[pre_num_df.negating == 1, 'value'] = -pre_num_df.value
num_pre_pivot_df = pre_num_df.pivot_table(
index=['adsh', 'coreg', 'tag', 'version', 'stmt',
'report', 'line', 'uom', 'negating', 'inpth'],
columns=['qtrs', 'ddate'], # we need to pivot by qtrs and ddate
values='value'
)
# some cleanup and ordering
num_pre_pivot_df.sort_values(['adsh', 'coreg', 'stmt', 'report', 'line', 'inpth'],
inplace=True)
# ensure column order, so that the latest date is first
col_order = sorted(num_pre_pivot_df.columns.values, reverse=True)
num_pre_pivot_df = num_pre_pivot_df[col_order]
# the columns have a multi index (by qtrs and ddate) so we flatten the names of the columns
num_pre_pivot_df.columns = [f"qrtrs_{qtrs}/{ddate}"
for qtrs, ddate in num_pre_pivot_df.columns.values]
if self.flatten_index:
num_pre_pivot_df.reset_index(drop=False, inplace=True)
if self.add_form_column:
adsh_form_map = \
databag.sub_df[['adsh', 'form']].set_index('adsh').to_dict()['form']
num_pre_pivot_df['form'] = num_pre_pivot_df['adsh'].map(adsh_form_map)
return num_pre_pivot_dfClasses
class StandardStatementPresenter (flatten_index: bool = True, add_form_column: bool = False, invert_negating: bool = False)-
Reformats the data so that the presentation and order reflects the way how the basic financial statements are presented.
Args
flatten_index- if True, the index is reset, so that the columns adsh', 'coreg', 'tag', 'version', 'stmt', 'report', 'line', 'uom', 'negating', 'inpth' appear as columns and not in the index of the dataframe.
add_form_column- adds a "form" column to the resulting dataframe which contains the type of the report ('10-K', '10-Q', …)
invert_negating- In a report, values are sometimes shown as positive even when from a calculating perspective they are negative, and vice versa. Such values are marked with a "1" in the negating column. When this flag is true, the value of such tags where negating is set to 1 is inverted. A typical case is "paiddividends" where the value is shown as positive but from a calculating point of view it should be negative.
Expand source code
class StandardStatementPresenter(Presenter[JoinedDataBag]): """ Reformats the data so that the presentation and order reflects the way how the basic financial statements are presented. """ def __init__(self, flatten_index: bool = True, add_form_column: bool = False, invert_negating: bool = False): """ Args: flatten_index: if True, the index is reset, so that the columns adsh', 'coreg', 'tag', 'version', 'stmt', 'report', 'line', 'uom', 'negating', 'inpth' appear as columns and not in the index of the dataframe. add_form_column: adds a "form" column to the resulting dataframe which contains the type of the report ('10-K', '10-Q', ...) invert_negating: In a report, values are sometimes shown as positive even when from a calculating perspective they are negative, and vice versa. Such values are marked with a "1" in the negating column. When this flag is true, the value of such tags where negating is set to 1 is inverted. A typical case is "paiddividends" where the value is shown as positive but from a calculating point of view it should be negative. """ self.flatten_index = flatten_index self.add_form_column = add_form_column self.invert_negating = invert_negating def present(self, databag: JoinedDataBag) -> pd.DataFrame: """ creates the standard presentation of financial statements (Balance Sheet, Income Statement, Cash Flow). - Index columns are ['adsh', 'coreg', 'tag', 'version', 'stmt', 'report', 'line', 'uom', 'negating', 'inpth'] - Value columns are by ddate - Order by ['adsh', 'coreg', 'stmt', 'report', 'line', 'inpth'] Args: databag(JoinedDataBag) : bag to present Returns: pd.DataFrame: the dataframe with the final presentation """ pre_num_df = databag.pre_num_df if self.invert_negating: pre_num_df = pre_num_df.copy() pre_num_df.loc[pre_num_df.negating == 1, 'value'] = -pre_num_df.value num_pre_pivot_df = pre_num_df.pivot_table( index=['adsh', 'coreg', 'tag', 'version', 'stmt', 'report', 'line', 'uom', 'negating', 'inpth'], columns=['qtrs', 'ddate'], # we need to pivot by qtrs and ddate values='value' ) # some cleanup and ordering num_pre_pivot_df.sort_values(['adsh', 'coreg', 'stmt', 'report', 'line', 'inpth'], inplace=True) # ensure column order, so that the latest date is first col_order = sorted(num_pre_pivot_df.columns.values, reverse=True) num_pre_pivot_df = num_pre_pivot_df[col_order] # the columns have a multi index (by qtrs and ddate) so we flatten the names of the columns num_pre_pivot_df.columns = [f"qrtrs_{qtrs}/{ddate}" for qtrs, ddate in num_pre_pivot_df.columns.values] if self.flatten_index: num_pre_pivot_df.reset_index(drop=False, inplace=True) if self.add_form_column: adsh_form_map = \ databag.sub_df[['adsh', 'form']].set_index('adsh').to_dict()['form'] num_pre_pivot_df['form'] = num_pre_pivot_df['adsh'].map(adsh_form_map) return num_pre_pivot_dfAncestors
- Presenter
- typing.Generic
Methods
def present(self, databag: JoinedDataBag) ‑> pandas.core.frame.DataFrame-
creates the standard presentation of financial statements (Balance Sheet, Income Statement, Cash Flow). - Index columns are ['adsh', 'coreg', 'tag', 'version', 'stmt', 'report', 'line', 'uom', 'negating', 'inpth'] - Value columns are by ddate - Order by ['adsh', 'coreg', 'stmt', 'report', 'line', 'inpth']
Args
databag(JoinedDataBag) : bag to present
Returns
pd.DataFrame- the dataframe with the final presentation
Expand source code
def present(self, databag: JoinedDataBag) -> pd.DataFrame: """ creates the standard presentation of financial statements (Balance Sheet, Income Statement, Cash Flow). - Index columns are ['adsh', 'coreg', 'tag', 'version', 'stmt', 'report', 'line', 'uom', 'negating', 'inpth'] - Value columns are by ddate - Order by ['adsh', 'coreg', 'stmt', 'report', 'line', 'inpth'] Args: databag(JoinedDataBag) : bag to present Returns: pd.DataFrame: the dataframe with the final presentation """ pre_num_df = databag.pre_num_df if self.invert_negating: pre_num_df = pre_num_df.copy() pre_num_df.loc[pre_num_df.negating == 1, 'value'] = -pre_num_df.value num_pre_pivot_df = pre_num_df.pivot_table( index=['adsh', 'coreg', 'tag', 'version', 'stmt', 'report', 'line', 'uom', 'negating', 'inpth'], columns=['qtrs', 'ddate'], # we need to pivot by qtrs and ddate values='value' ) # some cleanup and ordering num_pre_pivot_df.sort_values(['adsh', 'coreg', 'stmt', 'report', 'line', 'inpth'], inplace=True) # ensure column order, so that the latest date is first col_order = sorted(num_pre_pivot_df.columns.values, reverse=True) num_pre_pivot_df = num_pre_pivot_df[col_order] # the columns have a multi index (by qtrs and ddate) so we flatten the names of the columns num_pre_pivot_df.columns = [f"qrtrs_{qtrs}/{ddate}" for qtrs, ddate in num_pre_pivot_df.columns.values] if self.flatten_index: num_pre_pivot_df.reset_index(drop=False, inplace=True) if self.add_form_column: adsh_form_map = \ databag.sub_df[['adsh', 'form']].set_index('adsh').to_dict()['form'] num_pre_pivot_df['form'] = num_pre_pivot_df['adsh'].map(adsh_form_map) return num_pre_pivot_df