cowidev.grapher.files

cowidev.grapher.files.explorer

class cowidev.grapher.files.explorer.Exploriser(location: str = 'location', date: str = 'date', pivot_column: str = None, pivot_values: str = None, function_input: Callable = <function Exploriser.<lambda> at 0x7f05468f4ca0>, function_output: Callable = <function Exploriser.<lambda> at 0x7f05468f4dc0>)

Bases: object

date: str = 'date'

function_input()

function_output()

location: str = 'location'

pipe_nan_to_none(df: DataFrame) → DataFrame

pipe_pivot(df: DataFrame) → DataFrame

pipe_to_dict(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → dict

pivot_column: str = None

pivot_values: str = None

read(input_path: str)

run(input_path: str, output_path: str)

to_json(obj)

cowidev.grapher.files.grapher

class cowidev.grapher.files.grapher.Grapheriser(location: str = 'location', date: str = 'date', date_ref: datetime.datetime = datetime.datetime(2020, 1, 21, 0, 0), fillna: bool = False, fillna_0: bool = True, pivot_column: str = None, pivot_values: str = None, suffixes: list = None, function_input: Callable = <function Grapheriser.<lambda> at 0x7f05468e6b80>, function_output: Callable = <function Grapheriser.<lambda> at 0x7f05468e6c10>, columns_non_fillna_0: list = <factory>)

Bases: object

columns_data(df: DataFrame) → list

property columns_metadata: list

columns_non_fillna_0: list

date: str = 'date'

date_ref: datetime = datetime.datetime(2020, 1, 21, 0, 0)

property do_pivot

fillna: bool = False

fillna_0: bool = True

function_input()

function_output()

location: str = 'location'

property metric2suffix: dict

pipe_fillna(df: DataFrame) → DataFrame

pipe_metadata_columns(df: DataFrame) → DataFrame

Rename columns and convert date to Year grapher metric.

pipe_normalize_columns(df)

Normalize column names.

If columns are multiindex (of length 2), use first and second positions to create new column name.

This only applies if pivot has been done, i.e. pivot_column and pivot_values are not None.

pipe_order_columns(df: DataFrame) → DataFrame

Re-order the columns of the dataframe.

First columns are [Country, Year]

pipe_pivot(df: DataFrame) → DataFrame

Pivot values of columns of interest.

pipeline(df: DataFrame)

pivot_column: str = None

pivot_values: str = None

property pivot_values_list: list

read(input_path: str)

run(input_path: str, output_path: str)

suffixes: list = None

property suffixes_list: list

class cowidev.grapher.files.Exploriser(location: str = 'location', date: str = 'date', pivot_column: str = None, pivot_values: str = None, function_input: Callable = <function Exploriser.<lambda> at 0x7f05468f4ca0>, function_output: Callable = <function Exploriser.<lambda> at 0x7f05468f4dc0>)

Bases: object

date: str = 'date'

function_input()

function_output()

location: str = 'location'

pipe_nan_to_none(df: DataFrame) → DataFrame

pipe_pivot(df: DataFrame) → DataFrame

pipe_to_dict(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → dict

pivot_column: str = None

pivot_values: str = None

read(input_path: str)

run(input_path: str, output_path: str)

to_json(obj)

class cowidev.grapher.files.Grapheriser(location: str = 'location', date: str = 'date', date_ref: datetime.datetime = datetime.datetime(2020, 1, 21, 0, 0), fillna: bool = False, fillna_0: bool = True, pivot_column: str = None, pivot_values: str = None, suffixes: list = None, function_input: Callable = <function Grapheriser.<lambda> at 0x7f05468e6b80>, function_output: Callable = <function Grapheriser.<lambda> at 0x7f05468e6c10>, columns_non_fillna_0: list = <factory>)

Bases: object

columns_data(df: DataFrame) → list

property columns_metadata: list

columns_non_fillna_0: list

date: str = 'date'

date_ref: datetime = datetime.datetime(2020, 1, 21, 0, 0)

property do_pivot

fillna: bool = False

fillna_0: bool = True

function_input()

function_output()

location: str = 'location'

property metric2suffix: dict

pipe_fillna(df: DataFrame) → DataFrame

pipe_metadata_columns(df: DataFrame) → DataFrame

Rename columns and convert date to Year grapher metric.

pipe_normalize_columns(df)

Normalize column names.

If columns are multiindex (of length 2), use first and second positions to create new column name.

This only applies if pivot has been done, i.e. pivot_column and pivot_values are not None.

pipe_order_columns(df: DataFrame) → DataFrame

Re-order the columns of the dataframe.

First columns are [Country, Year]

pipe_pivot(df: DataFrame) → DataFrame

Pivot values of columns of interest.

pipeline(df: DataFrame)

pivot_column: str = None

pivot_values: str = None

property pivot_values_list: list

read(input_path: str)

run(input_path: str, output_path: str)

suffixes: list = None

property suffixes_list: list