leaf_engine.adapt.adapt_utilities

Functions

add_multilevel_columns(→ pandas.DataFrame)
cluster_by_city_state(→ pandas.DataFrame)
ends(→ pandas.DataFrame)
flatten_multilevel_columns(→ pandas.DataFrame)
format_datetime_columns(→ pandas.DataFrame)
get_centroids(geometry_col)
get_rate_types(→ pandas.DataFrame)	Given a dataframe of individual shipments, estimates whether each
get_window_size(period)	Given a Series of dates, calculates the number of days in the date range
group(→ pandas.DataFrame)	Wrapper for groupby() to standardize and streamline group agg syntax.
load_data(→ pandas.DataFrame)	Standard pipeline for loading .csv files.
mode(→ object)	Returns the most common value in a pd.Series or other iterable.
ordinals_to_days(iterable)
pipe(obj[, thru])	Passes an object through a sequence of functions, where the output of one
read_file(→ str)
remove_index(→ pandas.DataFrame)	Removes default index column if it exists.
snakecase_column_names(→ pandas.DataFrame)
upcase_string_values(→ pandas.DataFrame)
wkt_to_coords(→ Dict[str, float])

Module Contents

leaf_engine.adapt.adapt_utilities.add_multilevel_columns(df: pandas.DataFrame, top_level: str) → pandas.DataFrame

Parameters:

• df (pandas.DataFrame) –

• top_level (str) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.cluster_by_city_state(df: pandas.DataFrame, od: str) → pandas.DataFrame

Parameters:

• df (pandas.DataFrame) –

• od (str) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.ends(df: pandas.DataFrame, n: int = 1) → pandas.DataFrame

Parameters:

• df (pandas.DataFrame) –

• n (int) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.flatten_multilevel_columns(df: pandas.DataFrame, delimiter: str = '_') → pandas.DataFrame

Parameters:

• df (pandas.DataFrame) –

• delimiter (str) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.format_datetime_columns(df: pandas.DataFrame, datetime_cols: list) → pandas.DataFrame

Parameters:

• df (pandas.DataFrame) –

• datetime_cols (list) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.get_centroids(geometry_col: pandas.Series)

Parameters:

geometry_col (pandas.Series) –

leaf_engine.adapt.adapt_utilities.get_rate_types(_ships: pandas.DataFrame) → pandas.DataFrame

Given a dataframe of individual shipments, estimates whether each shipment’s rate type is contract or spot, and returns dataframe with rate_type column added.

Parameters:

_ships (pandas.DataFrame) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.get_window_size(period: pandas.Series)

Given a Series of dates, calculates the number of days in the date range and rounds that number down to the nearest multiple of 30 (up to 360 days)

Parameters:

period (pandas.Series) –

leaf_engine.adapt.adapt_utilities.group(df: pandas.DataFrame, by: list, agg: dict, reset_index: bool = True) → pandas.DataFrame

Wrapper for groupby() to standardize and streamline group agg syntax.

Index resets by default.

Parameters:

• df (pandas.DataFrame) –

• by (list) –

• agg (dict) –

• reset_index (bool) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.load_data(csv: str, converters: dict = {'d_zip': str, 'o_zip': str, 'zip': str}, datetime_cols: list = ['period']) → pandas.DataFrame

Standard pipeline for loading .csv files.

Takes file’s relative path, returns formatted dataframe.

Parameters:

• csv (str) –

• converters (dict) –

• datetime_cols (list) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.mode(iterable: pandas.Series) → object

Returns the most common value in a pd.Series or other iterable.

If no mode, returns the first value. If 2+ modes, returns the first mode. Note: This method is 3x faster than native Pandas methods.

Parameters:

iterable (pandas.Series) –

Return type:

object

leaf_engine.adapt.adapt_utilities.ordinals_to_days(iterable: Iterable[Any])

Parameters:

iterable (Iterable[Any]) –

leaf_engine.adapt.adapt_utilities.pipe(obj, thru=[])

Passes an object through a sequence of functions, where the output of one is the input of the next.

Example: pipe(3, thru = [lambda x: x ** 3, str]) # => “27”

leaf_engine.adapt.adapt_utilities.read_file(filename: str) → str

Parameters:

filename (str) –

Return type:

str

leaf_engine.adapt.adapt_utilities.remove_index(df: pandas.DataFrame, index_col: str = 'Unnamed:') → pandas.DataFrame

Removes default index column if it exists.

Parameters:

• df (pandas.DataFrame) –

• index_col (str) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.snakecase_column_names(df: pandas.DataFrame) → pandas.DataFrame

Parameters:

df (pandas.DataFrame) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.upcase_string_values(df: pandas.DataFrame, excl_cols=[]) → pandas.DataFrame

Parameters:

df (pandas.DataFrame) –

Return type:

pandas.DataFrame

leaf_engine.adapt.adapt_utilities.wkt_to_coords(wkt: str) → Dict[str, float]

Parameters:

wkt (str) –

Return type:

Dict[str, float]