babilonia.accounting#

{Short module description (1-3 sentences)} todo docstring

Features#

todo docstring

  • {feature 1}

  • {feature 2}

  • {feature 3}

  • {etc}

Overview#

todo docstring {Overview description}

Examples#

todo docstring {Examples in rST}

Print a message

# print message
print("Hello world!")
# [Output] >> 'Hello world!'

Classes

BBCDB([name, alias])

Budget([name, alias])

CashFlow([name, alias])

A primitive class for handling Cash flow analysis

CashFlowBBCC([name, alias])

A class for handling CSV data from Banco do Brasil Conta Corrente.

CashFlowBBCCPJ([name, alias])

Class for handling BB-CC for PJ accoung CSV data.

CashFlowBBPP([name, alias])

Class for handling BB-PP CSV data.

NFSe([name, alias])

Class for handling NFSe XML data.

NFSeColl([base_object, name, alias])

class babilonia.accounting.Budget(name='MyBudget', alias='Bud')[source]#

Bases: RecordTable

update()[source]#

Refresh all mutable attributes based on data (including paths).

set_data(input_df)[source]#

Set RecordTable data from incoming dataframe. Expected to be incremented downstream.

Parameters:

input_df (dataframe) – incoming dataframe

Returns:

None

Return type:

None

get_summary_by_type()[source]#
get_summary_by_status(filter_df=True)[source]#
get_summary_by_contract(filter_df=True)[source]#
get_summary_by_tags(filter_df=True)[source]#
static parse_annual_budget(year, budget_df, freq_field='Freq')[source]#
class babilonia.accounting.CashFlow(name='CashFlow', alias='CF')[source]#

Bases: DataSet

A primitive class for handling Cash flow analysis

Cashflow Analysis Example
from babilonia.accounting import CashFlow

# create an empty class
cf = CashFlow()

# set the file for CSV
file_csv = "path/to/file.csv" # [change this]

# load data
cf.load_data(file_csv)

# call method
dc = cf.cashflow_analysis(df=cf.data, category="Custeio")

# print data
print(dc["monthly"])
print(dc["yearly"])
load_data(file_data)[source]#

Load data from file.

Parameters:

file_data (str) – file path to data.

static get_cashflow_analysis(df, category=None)[source]#

Perform cash flow analysis with monthly and yearly aggregation.

This method classifies cash flows into inputs and outputs, aggregates values on a monthly and yearly basis, and computes cumulative balances. The analysis is fully independent from class state and inheritance behavior.

Parameters:

  • df (pandas.DataFrame) – Input cash flow data containing at least the columns Data, Categoria and Valor.

  • category (str or None) – Optional category filter. If None, all categories are grouped under "Geral".

Returns:

Dictionary with monthly and yearly cash flow summaries.

Return type:

dict

static compute_oir(df, input_field='Entradas', output_field='Saidas')[source]#
static enrich_time_index(df)[source]#

Add year and year-month time indices to the cash flow data.

This method extracts the calendar year and a YYYY-MM monthly identifier from the Data column.

Parameters:

df (pandas.DataFrame) – Input cash flow data.

Returns:

Copy of the input data with additional Ano and Mes columns.

Return type:

pandas.DataFrame

static classify_flows(df)[source]#

Classify cash flows as inputs or outputs.

Positive or zero values are classified as "In" and negative values as "Out".

Parameters:

df (pandas.DataFrame) – Cash flow data containing a Valor column.

Returns:

Copy of the input data with an additional Flow column.

Return type:

pandas.DataFrame

static filter_category(df, category)[source]#

Filter cash flow data by category.

If no category is provided, all records are grouped under the default category "Geral".

Parameters:

  • df (pandas.DataFrame) – Cash flow data.

  • category (str or None) – Category name used to filter the data.

Returns:

Tuple containing the filtered data and the resolved category name.

Return type:

tuple

static get_monthly_summary(df, category)[source]#

Compute monthly cash flow summaries for each year.

This method aggregates cash flow inputs and outputs on a monthly basis, ensures that all calendar months are present, and computes annual cumulative balances.

Parameters:

  • df (pandas.DataFrame) – Cash flow data enriched with time indices and flow classification.

  • category (str) – Category name associated with the analysis.

Returns:

Monthly cash flow summary table.

Return type:

pandas.DataFrame

static get_yearly_summary(df_monthly, category)[source]#

Compute yearly cash flow summaries.

This method aggregates monthly cash flow data into yearly totals and computes cumulative balances across years.

Parameters:

  • df_monthly (pandas.DataFrame) – Monthly cash flow summary table.

  • category (str) – Category name associated with the analysis.

Returns:

Yearly cash flow summary table.

Return type:

pandas.DataFrame

static get_cashflow_report(df, year=None, initial_cash=None)[source]#

Build a yearly cash flow panel and summary by category.

Parameters:

  • df (pandas.DataFrame) – Cash flow DataFrame containing transactional data with date, category, and value information compatible with the enrich_time_index, classify_flows, and cashflow_analysis methods.

  • year (int, optional) – Year to be analyzed. If None, the current calendar year (local time) is used.

  • initial_cash (float, optional) – Initial account balance at the beginning of the selected year. If None, defaults to 0.0.

Returns:

Dictionary containing: "Pannel": monthly cash flow panel with totals, per-category flows, and running balance. "Summary": yearly summary by category with total, mean, and percentage contribution to total inflows.

Return type:

dict

static format_currency(x: float)[source]#
static format_currency_columns(df: pandas.DataFrame, columns: list[str]) → pandas.DataFrame[source]#

Return a copy of df with selected numeric columns formatted as strings.

class babilonia.accounting.CashFlowBBCC(name='CashFlowBBCC', alias='CFBBCC')[source]#

Bases: CashFlow

A class for handling CSV data from Banco do Brasil Conta Corrente.

Script example
from babilonia.accounting import CashFlowBBCC

# create an empty class
cf = CashFlowBBCC()

# set the file for CSV
file_csv = "path/to/file.csv" # [change this]

# load data
cf.load_data(file_csv)

# standardize data
cf.standardize()

# print data
print(cf.data.head(10))

# save data
file_out = "path/to/output.csv" # [change this]
cf.data.to_csv(file_out, sep=";", index=False)
load_data(file_data)[source]#

Load raw data from bank CSV statement

Parameters:

file_data (str or Path) – Bank statement CSV file path

Returns:

None

Return type:

None

standardize(force=False)[source]#

Standardize data into canonical format.

Parameters:

force – Rebuild parsed data even if it exists

parse_data(df=None)[source]#

Parse data to canonical format

Parameters:

df (pandas.DataFrame) – Optional input data

Returns:

Formated data

Return type:

pandas.DataFrame

parse_date(series)[source]#

Parse BB date field from DD/MM/YYYY to datetime.

Parameters:

series (pandas.Series) – String series

Returns:

Datetime series

Return type:

pandas.Series

parse_valor(series)[source]#

Convert Valor field to float.

Examples

Input

Output

5.000,00

5000.00

-403,00

-403.00
Parameters:

series (pandas.Series) – String series

Returns:

Value series

Return type:

pandas.Series

apply_drops(df)[source]#

Filter dataframe for parsing

Parameters:

df (pandas.DataFrame) – Input data

Returns:

Output data

Return type:

pandas.DataFrame

normalize_columns(df)[source]#

Normalize legacy / alternative column names to the current schema.

Parameters:

df (pandas.DataFrame) – Input data

Returns:

Output data

Return type:

pandas.DataFrame

class babilonia.accounting.CashFlowBBCCPJ(name='CashFlowBBCCPJ', alias='CFBBCCPJ')[source]#

Bases: CashFlowBBCC

Class for handling BB-CC for PJ accoung CSV data.

parse_valor(series)[source]#

Convert Valor field to float.

Examples

Input

Output

5.000,00 C

5000.00

-403,00 D

-403.00
Parameters:

series (pandas.Series) – String series

Returns:

Value series

Return type:

pandas.Series

apply_drops(df)[source]#

Filter dataframe for parsing

Parameters:

df (pandas.DataFrame) – Input data

Returns:

Output data

Return type:

pandas.DataFrame

class babilonia.accounting.CashFlowBBPP(name='CashFlowBBPP', alias='CFBBPP')[source]#

Bases: CashFlowBBCC

Class for handling BB-PP CSV data.

parse_data(df=None)[source]#

Parse data to canonical format

Parameters:

df (pandas.DataFrame) – Optional input data

Returns:

Formated data

Return type:

pandas.DataFrame

parse_valor(series: pandas.Series) → pandas.Series[source]#

Convert Valor field to float.

Examples

Input

Output

5.000,00 C

5000.00

-403,00 D

-403.00
Parameters:

series (pandas.Series) – String series

Returns:

Value series

Return type:

pandas.Series

apply_drops(df)[source]#

Filter dataframe for parsing

Parameters:

df (pandas.DataFrame) – Input data

Returns:

Output data

Return type:

pandas.DataFrame

class babilonia.accounting.BBCDB(name='BBCDB', alias='BBCDB')[source]#

Bases: DataSet

load_data(file_data)[source]#

Load data from file.

Parameters:

file_data (str) – file path to data.

static read_txt(file_txt, encoding='cp1252')[source]#
static drop_blank_lines(lines)[source]#
static drop_lines(lines, contains='-')[source]#
static replace_lines(lines, contains='\xa0', relacer=' ')[source]#
static split_accounts(lines)[source]#
static split_sections(dc_data)[source]#
static get_year(lines)[source]#
class babilonia.accounting.NFSe(name='NFSeDataSet', alias='NFSe')[source]#

Bases: DataSet

Class for handling NFSe XML data.

get_metadata()[source]#

Get a dictionary with object metadata.

Returns:

dictionary with all metadata

Return type:

dict

Warning

Metadata does not necessarily include all object attributes.

load_data(file_data)[source]#

Load and parse XML data from the provided file.

Parameters:

file_data (str) – file path to the NFSe XML data.

Returns:

None

class babilonia.accounting.NFSeColl(base_object=<class 'babilonia.accounting.NFSe'>, name='MyNFeCollection', alias='NFeCol0')[source]#

Bases: Collection

load_folder(folder)[source]#

Load NFSe files from a folder

Parameters:

folder (str) – path to folder

Returns:

None

Return type:

None

load_files(lst_files)[source]#

Load NFSe files from a list of files

Parameters:

lst_files (list) – list of paths to files

Returns:

None

Return type:

None