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read_data.py
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read_data.py
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import pandas as pd
import numpy as np
def read_raw_data(file_name):
inputfile = open(file_name, 'rb')
dateParser = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
data = pd.read_csv(inputfile,
usecols=['tpep_pickup_datetime',
'tpep_dropoff_datetime',
'passenger_count',
'trip_distance',
'PULocationID',
'DOLocationID',
'fare_amount'
],
iterator= True,
dtype={'passenger_count':np.int,
'trip_distance':np.float,
'PULocation':np.int,
'DOLocation':np.int,
'fare_amount':np.float
},
parse_dates=['tpep_pickup_datetime', 'tpep_dropoff_datetime'],
date_parser= dateParser,
na_filter=False,
)
loop = True
chunkSize = 10000
chunks = []
# i = 0 ### used in test, control the data size to read
while loop:
try:
# i += 1
# if i > 3:
# break
chunk = data.get_chunk(chunkSize)
chunks.append(chunk)
except StopIteration:
loop = False
data = pd.concat(chunks, ignore_index = True)
data = data[(data['PULocationID'] != data['DOLocationID']) & data['trip_distance']>0]
# data = data[data['trip_distance']>0]
print('here1')
data['hour'] = \
data.apply(lambda x: int(x['tpep_pickup_datetime'].hour), axis=1)
data['pickup_time'] = \
data.apply(lambda x: int(x['tpep_pickup_datetime'].hour * 60 + x['tpep_pickup_datetime'].minute), axis=1)
### original pickup time
data['dropoff_time'] = \
data.apply(lambda x: int(x['tpep_dropoff_datetime'].hour * 60 + x['tpep_dropoff_datetime'].minute), axis=1)
data = data[data['dropoff_time'] > data['pickup_time']]
data['travel_time'] = data['dropoff_time'] - data['pickup_time']
data['day'] = data.apply(lambda x: x['tpep_pickup_datetime'].day, axis=1)
# data['is_Served'] = 0
data.drop(['tpep_pickup_datetime','tpep_dropoff_datetime'], axis=1, inplace=True)
print('here2')
data.to_csv('trip_data/filtered_yellow_tripdata_2018-01.csv', sep = ',', header = True, index = False)
data.to_csv('trip_data/filtered_yellow_tripdata.csv', sep = ',', header = False, index = False, mode='a')
inputfile.close()
'''
data: DataFrame
'day',
'hour',
'pickup_time',
'dropoff_time',
'travel_time',
'passenger_count',
'trip_distance',
'PULocationID',
'DOLocationID',
'fare_amount'
'''
def read_filtered_data(file_name):
inputfile = open(file_name, 'rb')
data = pd.read_csv(inputfile,
usecols=['day',
'hour',
'pickup_time',
'dropoff_time',
'travel_time',
'passenger_count',
'trip_distance',
'PULocationID',
'DOLocationID',
'fare_amount',
],
iterator= True,
dtype={'day':np.int,
'hour':np.int,
'pickup_time':np.int,
'dropoff_time':np.int,
'travel_time':np.int,
'passenger_count':np.int,
'trip_distance':np.float,
'PULocation':np.int,
'DOLocation':np.int,
'fare_amount':np.float
},
na_filter=False,
)
loop = True
chunkSize = 10000
chunks = []
# i = 0 ### used in test, control the data size to read
while loop:
try:
# i += 1
# if i > 2:
# break
chunk = data.get_chunk(chunkSize)
chunks.append(chunk)
except StopIteration:
loop = False
data = pd.concat(chunks, ignore_index = True)
inputfile.close()
return data
'''
data: DataFrame
'day',
'hour',
'pickup_time',
'dropoff_time',
'travel_time',
'passenger_count',
'trip_distance',
'PULocationID',
'DOLocationID',
'fare_amount'
'''
def read_request_data(file_name):
inputfile = open(file_name, 'rb')
data = pd.read_csv(inputfile,
usecols=['day',
'pickup_time',
'passenger_count',
'PULocationID',
'DOLocationID',
],
iterator= True,
dtype={'day':np.int,
'pickup_time':np.int,
'passenger_count':np.int,
'PULocation':np.int,
'DOLocation':np.int,
},
na_filter=False,
)
loop = True
chunkSize = 10000
chunks = []
# i = 0 ### used in test, control the data size to read
while loop:
try:
# i += 1
# if i > 2:
# break
chunk = data.get_chunk(chunkSize)
chunks.append(chunk)
except StopIteration:
loop = False
data = pd.concat(chunks, ignore_index = True)
inputfile.close()
return data
'''
data: DataFrame
'day',
'pickup_time',
'passenger_count',
'PULocationID',
'DOLocationID',
'''
# read_raw_data('trip_data/yellow_tripdata_2018-01.csv')