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Read in BART ETD data from files and write to SQL.
import sqlite3
import numpy as np
import pandas as pd
DATABASE = 'bart.db'
FILES = ['plza.csv', 'mont.csv']
def define_weekday(dt_index):
"""Return an array with 0's for elements of dt_series that are on a
weekday, 1 if a Saturday, 2 if a Sunday.
:param obs_time: DatetimeIndex
result = np.zeros(len(dt_index))
weekdays = dt_index.weekday
result[weekdays == 5] = 1
result[weekdays == 6] = 2
return result
def parse_data(file_name, chunksize=1E5):
"""Return a dataframe from csv file, with times parsed.
:param file_name: csv file
:return: DataFrame from csv file
return pd.read_csv(file_name, names=['time','dest','dir','len','etd'],
header=None, chunksize=chunksize)
def time2minute_of_day(dt_index):
"""Return the minute of day (12:00 midnight = 0) for each time in
:param obs_time: DatetimeIndex
return dt_index.hour * 60 + dt_index.minute
def csv2sql(conn, files):
"""Read in BART ETD data from files and write that data to the SQL database
accessed by conn.
:param conn: SQL database connection
:param files: the files to read data from
output_cols = ['dest', 'dir', 'etd', 'station', 'minute_of_day',
conn.execute("DROP TABLE IF EXISTS etd")
for sta_file in files:
for i, df in enumerate(parse_data(sta_file)):
print 'Working on chunk {} of {}'.format(i, sta_file)
dt_index = (pd.DatetimeIndex(pd.to_datetime(df['time'], unit='s'))
df['station'] = sta_file.split('.')[0]
df['day_of_week'] = define_weekday(dt_index)
df['etd'] = df['etd'].replace('Leaving', 0).dropna().astype(
df['minute_of_day'] = time2minute_of_day(dt_index)
df[output_cols].to_sql('etd', conn, index=False, if_exists='append')
ON etd(station, dest, minute_of_day, day_of_week)
if __name__ == '__main__':
conn = sqlite3.connect(DATABASE)
csv2sql(conn, FILES)