Python break_time_col Examples

Example #1

if __name__ == '__main__':
    with open('./makefiles/year_list.pkl') as f:
        year_list = [2012, 2013, 2014, 2015]
    with open('./makefiles/columns_list.pkl') as f:
        columns_list = pickle.load(f)
    with open('./makefiles/columns_dict.pkl') as f:
        columns_dict = pickle.load(f)

    dfs_list = []
    for year in year_list:
        df_path = '../data/csvs/fires_weather_' + str(year) + '.csv'
        df = pd.read_csv(df_path)
        dfs_list.append(df)

    df = combine_dfs(dfs_list)
    df = break_time_col(df, 'date_fire')

    nearby_fires_df_filepath = './modeling/nearby_done_df_' + str(
        columns_dict['add_nearby_fires']['dist_measure']) + '.pkl'
    # This process takes awhile, and I won't need to do it each time.
    if 'nearby_fires_done' not in columns_dict.keys():
        dist_measure = columns_dict['add_nearby_fires']['dist_measure']
        time_measures = columns_dict['add_nearby_fires']['time_measures']
        df = gen_nearby_fires_count(
            df, dist_measure, time_measures,
            columns_dict['add_nearby_fires']['rate_measures'])
        with open(nearby_fires_df_filepath, 'w') as f:
            pickle.dump(df, f)
    else:
        with open(nearby_fires_df_filepath) as f:
            df = pickle.load(f)

Example #2

File: create_inputs.py Project: koverholt/forest-fires

import pandas as pd
import sys
import pickle

from data_manip.general_featurization import combine_dfs, grab_columns, return_all_dummies, boolean_fire
from data_manip.time_featurization import break_time_col


if __name__ == '__main__': 
	with open('../makefiles/year_list.pkl') as f: 
		year_list = pickle.load(f)
	with open('../makefiles/columns_list.pkl') as f: 
		columns_list = pickle.load(f)

	dfs_list = []
	for year in year_list: 
		df_path = '../../data/csvs/fires_' + str(year) + '.csv'
		df = pd.read_csv(df_path)
		dfs_list.append(df)

	df = combine_dfs(dfs_list)
	df = grab_columns(df, columns_list)
	df = break_time_col(df, 'date')
	df = boolean_fire(df)

	dummy_cols = ['year', 'month']
	df = return_all_dummies(df, dummy_cols)

	with open('./input_df.pkl', 'w+') as f: 
		pickle.dump(df, f)

Example #3

File: create_inputs.py Project: satpreetsingh/forest-fires

	with open('./makefiles/year_list.pkl') as f: 
		year_list = [2012, 2013, 2014, 2015]
	with open('./makefiles/columns_list.pkl') as f: 
		columns_list = pickle.load(f)
	with open('./makefiles/columns_dict.pkl') as f: 
		columns_dict = pickle.load(f)

	dfs_list = []
	for year in year_list: 
		df_path = '../data/csvs/fires_weather_' + str(year) + '.csv'
		df = pd.read_csv(df_path)
		dfs_list.append(df)


	df = combine_dfs(dfs_list)
	df = break_time_col(df, 'date_fire')

	nearby_fires_df_filepath = './modeling/nearby_done_df_' + str(columns_dict['add_nearby_fires']['dist_measure']) + '.pkl'
	# This process takes awhile, and I won't need to do it each time. 
	if 'nearby_fires_done' not in columns_dict.keys(): 
		dist_measure = columns_dict['add_nearby_fires']['dist_measure']
		time_measures = columns_dict['add_nearby_fires']['time_measures']
		df = gen_nearby_fires_count(df, dist_measure, time_measures, columns_dict['add_nearby_fires']['rate_measures'])
		with open(nearby_fires_df_filepath, 'w') as f: 
			pickle.dump(df, f)
	else: 
		with open(nearby_fires_df_filepath) as f:
			df = pickle.load(f)

	df = grab_columns(df, columns_list)
	featurization_dict = {'all_dummies': return_all_dummies, 'bool_col': boolean_col, 'return_top_n': return_top_n,