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 0x7f90a956af70>, function_output: Callable = <function Exploriser.<lambda> at 0x7f90a94f20d0>)[source]

Bases: object

date: str = 'date'
function_input()
function_output()
location: str = 'location'
pipe_nan_to_none(df: DataFrame) DataFrame[source]
pipe_pivot(df: DataFrame) DataFrame[source]
pipe_to_dict(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) dict[source]
pivot_column: str = None
pivot_values: str = None
read(input_path: str)[source]
run(input_path: str, output_path: str)[source]
to_json(obj)[source]

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 0x7f90a955ce50>, function_output: Callable = <function Grapheriser.<lambda> at 0x7f90a955cee0>, columns_non_fillna_0: list = <factory>)[source]

Bases: object

columns_data(df: DataFrame) list[source]
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[source]
pipe_metadata_columns(df: DataFrame) DataFrame[source]

Rename columns and convert date to Year grapher metric.

pipe_normalize_columns(df)[source]

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[source]

Re-order the columns of the dataframe.

First columns are [Country, Year]

pipe_pivot(df: DataFrame) DataFrame[source]

Pivot values of columns of interest.

pipeline(df: DataFrame)[source]
pivot_column: str = None
pivot_values: str = None
property pivot_values_list: list
read(input_path: str)[source]
run(input_path: str, output_path: str)[source]
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 0x7f90a956af70>, function_output: Callable = <function Exploriser.<lambda> at 0x7f90a94f20d0>)[source]

Bases: object

date: str = 'date'
function_input()
function_output()
location: str = 'location'
pipe_nan_to_none(df: DataFrame) DataFrame[source]
pipe_pivot(df: DataFrame) DataFrame[source]
pipe_to_dict(df: DataFrame) DataFrame[source]
pipeline(df: DataFrame) dict[source]
pivot_column: str = None
pivot_values: str = None
read(input_path: str)[source]
run(input_path: str, output_path: str)[source]
to_json(obj)[source]
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 0x7f90a955ce50>, function_output: Callable = <function Grapheriser.<lambda> at 0x7f90a955cee0>, columns_non_fillna_0: list = <factory>)[source]

Bases: object

columns_data(df: DataFrame) list[source]
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[source]
pipe_metadata_columns(df: DataFrame) DataFrame[source]

Rename columns and convert date to Year grapher metric.

pipe_normalize_columns(df)[source]

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[source]

Re-order the columns of the dataframe.

First columns are [Country, Year]

pipe_pivot(df: DataFrame) DataFrame[source]

Pivot values of columns of interest.

pipeline(df: DataFrame)[source]
pivot_column: str = None
pivot_values: str = None
property pivot_values_list: list
read(input_path: str)[source]
run(input_path: str, output_path: str)[source]
suffixes: list = None
property suffixes_list: list