import pandas as pd
[docs]def create_costs_dict(df, id_cols):
"""
Note:
This function creates a dictionary from the passed DataFrame, specifically for cost-related DataFrames.
Parameters:
df: A DataFrame to be converted to a dictionary.\n
id_cols: The identifying columns (args) from the DataFrame to use as keys for the returned dictionary.
Return:
A dictionary based on the passed DataFrame with keys consisting of id_cols plus Disc-Rate (added) and with id_cols removed.
"""
dict_df = df.copy()
# create some keys for the dict; 0 is added to the end of the key for the discount rate if discount rate is not already there
if 'Disc-Rate' in dict_df.columns:
if len(id_cols) == 3:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], dict_df['Disc-Rate']))
if len(id_cols) == 4:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], dict_df[id_cols[3]], dict_df['Disc-Rate']))
if len(id_cols) == 5:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], dict_df[id_cols[3]],
dict_df[id_cols[4]], dict_df['Disc-Rate']))
dict_df.drop(columns=id_cols + ['Disc-Rate'], inplace=True) # these can be put back in after converting back to df
else:
if len(id_cols) == 3:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], [0] * len(dict_df)))
if len(id_cols) == 4:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], dict_df[id_cols[3]], [0] * len(dict_df)))
if len(id_cols) == 5:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], dict_df[id_cols[3]],
dict_df[id_cols[4]], [0] * len(dict_df)))
if len(id_cols) == 6:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], dict_df[id_cols[3]],
dict_df[id_cols[4]], dict_df[id_cols[5]], [0] * len(dict_df)))
dict_df.drop(columns=id_cols, inplace=True) # these can be put back in after converting back to df
dict_df.insert(0, 'key', keys)
dict_df.set_index('key', inplace=True)
return dict_df.to_dict('index')
[docs]def create_effects_dict(df, id_cols):
"""
Note:
This function creates a dictionary from the passed DataFrame, specifically for effects-related DataFrames.
Parameters:
df: A DataFrame to be converted to a dictionary.\n
id_cols: The identifying columns (args) from the DataFrame to use as keys for the returned dictionary.
Return:
A dictionary based on the passed DataFrame with keys consisting of id_cols and with id_cols removed.
"""
dict_df = df.copy()
# create some keys for the dict
if len(id_cols) == 4:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], dict_df[id_cols[3]]))
if len(id_cols) == 6:
keys = pd.Series(zip(dict_df[id_cols[0]], dict_df[id_cols[1]], dict_df[id_cols[2]], dict_df[id_cols[3]],
dict_df[id_cols[4]], dict_df[id_cols[5]]))
dict_df.drop(columns=id_cols, inplace=True) # these can be put back in after converting back to df
dict_df.insert(0, 'key', keys)
dict_df.set_index('key', inplace=True)
return dict_df.to_dict('index')
[docs]def convert_dict_to_df(dict_to_convert, *args):
"""
Note:
This function converts a dictionary to a DataFrame.
Parameters:
dict_to_convert: A dictionary meant for conversion to a DataFrame.\n
args: The arguments to use as column names for the separated keys.
Return:
A DataFrame containing the passed dictionary of data with additional columns based on args.
"""
# print('Converting dictionary to DataFrame.')
df = pd.DataFrame(dict_to_convert).transpose()
df.reset_index(inplace=True)
for idx, arg in enumerate(args):
df.rename(columns={f'level_{idx}': arg}, inplace=True)
df.reset_index(drop=True, inplace=True)
return df
if __name__ == '__main__':
print('This module does not run as a script.')