package

Submodules

package.DashBoardsTemplates module

Dashboared templates for visualising data

Returns:

ploat: figure

package.DashBoardsTemplates.build_dogged_from(data, title)

_summary_

Args:

data (_type_): _description_ title (_type_): _description_

Returns:

_type_: _description_

package.DashBoardsTemplates.data_verience_whole_table(chuncks)

_summary_

Args:

chuncks (_type_): _description_

Returns:

_type_: _description_

package.DashBoardsTemplates.export_graphs_hist(chuncks)

_summary_

Args:

chuncks (_type_): _description_

Returns:

_type_: _description_

package.DashBoardsTemplates.export_graphs_line(chuncks)

_summary_

Args:

chuncks (_type_): _description_

Returns:

_type_: _description_

package.DashBoardsTemplates.export_graphs_nested_color_mapped(chuncks)

_summary_

Args:

chuncks (_type_): _description_

Returns:

_type_: _description_

package.DashBoardsTemplates.export_graphs_pie_charts(chuncks)

_summary_

Args:

chuncks (_type_): _description_

Returns:

_type_: _description_

package.dashboardutil module

Dashboared Elements builder

Raises:

ValueError: data validation

Returns:

List: list

Yields:

List: list

class package.dashboardutil.DashboardElementsBuilder(data, DataClassifier)

Bases: object

DashboardElementsBuilder build data visualisation elements on behalf of DataFrame

build_ploats(algorithm, columns)

generate ploat data for user provided algorithm,columns

get_columns_groups(columns)

Builds Groups on Behalf of all possible Catagories of Data

prepare_sections(columns)

selects required cols

package.dataclassifier module

dataclassifier

Raises:

NotImplementedError: implementation pending

Yields:

List: returns list

class package.dataclassifier.DataClassifier(algorithm=None)

Bases: object

DataClassifier classification Data in Defined algorithm

and and their classification format

– We can define of algorithum

– build data classification Process for build data groups

build_ploats(algorithm, groups)

build data classification Process for build data groups

package.formatcalculator module

calculates formats of data

Returns:

List|str: formatclaculator class

Yields:

List: list

class package.formatcalculator.FormatCalculator

Bases: object

FormatCalculator class fetches formets from any type of data input data should be in df or records format and helps us to do

analysis over it to fetch format and itration format and seeds from data then we must able to regenerate that data by having following receipies

— data formats

—itration format

— itration format seeds

—data format seeds

Format Calculator class have following features

– regex_formattor (groups regex string and generate optimised

regex generated by FormatCalculator inputargs:[cls,str])

—get_unique_hashes_from_data ( gets uniques hashes from data columns wise inputargs:[cls,LIST[ANY]])

– split_all_labels_to_words_with_new_cols ( splits string and create new cols in dataframe inputargs:[cls,Dataframe,exer=[” “]])

—hash_df_single_df_column ( hashes single df col inputargs:[cls,pd.Series])

– hash_df_formats ( hashes all data in dataframe )

inputargs:[cls,pd.Dataframe]

—get_unique_hashes_from_df_columnwise ( column wise hashes generater in records format inputargs:[cls,pd.DataFrame])

classmethod dict_mapped_tup(dictionary)

_summary_

Args:

dictionary (Dict): convert dict to tuple

Yields:

Any: List of tuples

classmethod find_max_length(lst)

finds max length in list of lists

Args:

lst (LIST): list for find maxlength

Returns:

Int: max length

classmethod format_regex_list(regex)

_summary_

Args:

regex (_type_): _description_

Returns:

_type_: _description_

classmethod generate_datamiter(_df)

generates data miter from df

Args:

df (dataframe): dataframe of data

Returns:

dataframe: returns multiindex dataframe

classmethod generate_regex_from_list_of_str(datalist)

_summary_

Args:

datalist (_type_): _description_

Yields:

_type_: _description_

classmethod get_unique_hashes_from_data(chuncks: List[Any])

( gets uniques hashes from data columns wise inputargs:[cls,LIST[ANY]])

classmethod get_unique_hashes_from_df_columnwise(_df)

( column wise hashes generater in records format inputargs:[cls,pd.DataFrame] )

classmethod hash_df_formats(_df)

( hashes all data in dataframe ) inputargs:[cls,pd.Dataframe]

classmethod hash_df_single_df_column(_series)

( hashes single df col inputargs:[cls,pd.Series])

classmethod regex_filter(val, regex)

match regex function

Args:

val (str): value for pattern match regex (str): keyboard pattern

Returns:

str|bool: pattern match state

classmethod split_all_labels_to_words_with_new_cols(_df, exer: List[str] = None)

(splits string and create new cols in dataframe inputargs:[cls,Dataframe,exer=[” “]])

class package.formatcalculator.Mitter(_df, dataset, colorder: Generator)

Bases: object

returns Mitter object for validation hasattr df for dataframe transformation

column_wise_format_ordring()

_summary_

Yields:

tuple: indexes

columnwise_data_pattern_ordring_seq()

_summary_

Returns:

_type_: _description_

forecast_row_values(length: int, alorithum: object)

_summary_

Args:

length (int): _description_ alorithum (object): _description_

Raises:

NotImplementedError: _description_

formatted_rows(df, iterlen, part)

_summary_

Args:

df (_type_): _description_ iterlen (_type_): _description_ part (_type_): _description_

Returns:

_type_: _description_

formatwise_mitter()

Groups data formatwise

Returns:

DataFrame: Formatwise Data

generate_itemwise_data(df, iterlen, var)

_summary_

Args:

df (_type_): _description_ iterlen (_type_): _description_ var (_type_): _description_

Returns:

_type_: _description_

get_ordring_seq_tuple(row_ordring_seq)

_summary_

Args:

row_ordring_seq (_type_): _description_

Returns:

_type_: _description_

get_row_optimised(df, iterlen, part)

_summary_

Args:

df (_type_): _description_ iterlen (_type_): _description_ part (_type_): _description_

Returns:

_type_: _description_

get_row_ordring_seq_from_dataset(dataset, iterlen, part=2)

generate row pattern indexes acording to table pattern

Args:

dataset (Dataframe): pd.read_csv(“path/to/tabularfile”)

Returns:

Dataframe: row patterns

classmethod get_vertical_str_slices(seqlist, iterlen)

_summary_

Args:

seqlist (_type_): _description_ iterlen (_type_): _description_

Returns: _ type_: _description_

classmethod hash_str_patterns(mitter)

_summary_

Args:

mitter (str): _description_.

Returns: _ type_: _description_.

merge(dicts)

_summary_

Args:

dicts (_type_): _description_

Returns:

_type_: _description_

normalize_seq_patterns(seqlist)

generate unique data and ordring from seq

Args:

seqlist (List): seq list of keyboard sequences

Returns:

List[str]: _description_

regenerate_seq_normalized(normlist)

regenerate all data from ordring and seq

Args:

normlist (List[str]): normalized lists

Returns:

List[str]: returns actual sequence of keyboard sequences

row_patterns(df)

_summary_

Args:

df (_type_): _description_

Returns:

_type_: _description_

search_pat(pat, mitter)

_summary_

Args:

pat (_type_): _description_ mitter (_type_): _description_

Returns:

_type_: _description_

classmethod update_mitter(val)

_summary_

Args:

val (_type_): _description_

Returns: _ type_: _description_

class package.formatcalculator.Ordring(values: List[str], ordring: List[str])

Bases: object

ordring seed values and ordring seq

package.keyborddata module

keyboard format variables

package.variationcalculator module

class package.variationcalculator.VERIATIONS(columnsTable: DataFrame, RowTable: DataFrame, keyboard: List | None = None, Mitter: object = None)

Bases: object

_summary_

add_data_slices(datalist)

_summary_

Args:

datalist (_type_): _description_

Returns:

_type_: _description_

add_destructured_slices(splitdata)

_summary_

Args:

splitdata (_type_): _description_

Returns:

_type_: _description_

clssifiy_column_mitterdata()

_summary_

divide_chunks(l, n)

_summary_

Args:

l (_type_): _description_ n (_type_): _description_

Yields:

_type_: _description_

format_keyboard_values()

_summary_

Returns:

_type_: _description_.

format_regenerated_data(columns: List[str])

formats our data

Args:

columns (List[str]): columns seqs need to be same as when we loaded data

formats_and_no_of_patterns()

_summary_

Returns:

_type_: _description_

generate_row_patterns()

_summary_

Returns:

_type_: _description_

get_column_row_pattern(formatpattern, patternseq)

_summary_

Args:

formatpattern (_type_): _description_ patternseq (_type_): _description_

Returns:

_type_: _description_

get_columnswise_rowpatterns()

_summary_

Returns:

_type_: _description_.

get_destructured_slices(z)

_summary_

Args:

z (_type_): _description_

Returns:

_type_: _description_

get_format_hash_str(z, datadict)

_summary_

Args:

z (_type_): _description_ datadict (_type_): _description_

Returns:

_type_: _description_

merge(list1, list2)

_summary_

Args:

list1 (_type_): _description_ list2 (_type_): _description_

Returns:

_type_: _description_

regenerate_data_from_optimised_mitter()

regenerates data from optimised mitter

Returns:

dataframe: Pd.DataFrame

replace_empty_vals(rows)

_summary_

Args:

rows (_type_): _description_

Yields:

_type_: _description_

row_sequance_veriations()

_summary_

Returns:

columns: list of columns seq veriations

solve_iteration(string, filter_params=[])

_summary_

Args:

string (str): _description_. filter_params (list, optional): _description_. Defaults to [].

Returns:

_type_: _description_.

transform_keybord_seq_to_data()

_summary_

Returns:

_type_: _description_

transform_sequences_to_keyboard_values()

_summary_

Yields:

_type_: _description_

Module contents

module for dashboared and formets