def shuffle_set(ray_df):
# Shuffle and convert
ray_df = ray_df.set_index('tpep_pickup_datetime')
ray_df.index = pd.to_datetime(ray_df.index)
# Accesses
result = ray_df.head()
result = ray_df.tail()
def timeseries_set(ray_df):
# Shuffle and convert
ray_df = ray_df.set_index('tpep_pickup_datetime')
ray_df.index = pd.to_datetime(ray_df.index)
# Resample
ray_resamp = ray_df.passenger_count.resample('1d')
ray_mn = ray_resamp.mean()
# Rolling aggregation
ray_roll = ray_df.passenger_count.rolling(10)
result = ray_roll.mean()
def aggregation_set(ray_df): # Convert and len ray_df.tpep_pickup_datetime = pd.to_datetime(ray_df.tpep_pickup_datetime) result = len(ray_df) # Groupby ray_groupby = ray_df.groupby(ray_df.passenger_count) # Ray does not support getting column from groupby below # result = ray_groupby.trip_distance.mean() # Filter, new column ray_df2 = ray_df[(ray_df.tip_amount > 0) & (ray_df.fare_amount > 0)] ray_df2["tip_fraction"] = ray_df2.tip_amount / ray_df2.fare_amount
import ray.dataframe as pd
#import pandas as pd
print('############ 12: Test Ray TimeSeries #############')
ray_df = pd.read_csv("yellow_tripdata_2015-01-01.csv")
print(' Read_CSV finished. Result:')
print(ray_df.head(3))
ray_df = ray_df.set_index('tpep_pickup_datetime')
print(' set_index finished. Result:')
print(ray_df.head(3))
ray_df.index = pd.to_datetime(ray_df.index)
print(' to_datetime finished. Result:')
print(ray_df.head(3))
ray_resamp = ray_df.passenger_count.resample('1d')
print(' resample finished')
ray_mn = ray_resamp.mean()
print(' mean of resample finished. Result:')
print(ray_mn)
ray_roll = ray_df.passenger_count.rolling(10)
print(' rolling aggregation finished. Result:')
print(ray_roll)
result = ray_roll.mean()
import ray.dataframe as pd
#import pandas as pd
print('############ 3: Test Ray Convert [Column] toDateTime #############')
ray_df = pd.read_csv("yellow_1of3.csv")
print(' Read_CSV finished. Result:')
print(ray_df.head(3))
ray_df['tpep_pickup_datetime'] = pd.to_datetime(ray_df['tpep_pickup_datetime'])
print(ray_df.head(3))
print(' to_datetime(df[column]) finished. Result above.')
"!!! to_datetime(df.index): Ray may yield ValueErrors in later parts !!!"
)
# If skip this, may also need to skip day_of_week/hour calculation step 09 later. Remember TimeSeries section requires DateTime conversion
if runToCompletion or True: # Always skip this
print(
"!!! Skipping this for now to guarantee run to completion of this script !!!"
)
printTimer(total_t, True, True)
else:
start_t = datetime.datetime.now()
# Always hangs?
# ray_df['tpep_pickup_datetime'] = pd.to_datetime(ray_df['tpep_pickup_datetime'])
# Alternative code 1 - Dot syntax for column seems to work here. But takes 17 seconds!
ray_df.tpep_pickup_datetime = pd.to_datetime(
ray_df.tpep_pickup_datetime) #
# Alternative code 2 - Changes index. May yield ValueErrors in later parts
# ray_df = ray_df.set_index('tpep_pickup_datetime')
# ray_df.index = pd.to_datetime(ray_df.index)
total_t = datetime.datetime.now() - start_t
result = ray_df.head(topN)
printRes(result, True)
printTimer(total_t, True)
if runPandas:
start_t = datetime.datetime.now()
pd_df['tpep_pickup_datetime'] = old_pd.to_datetime(
pd_df['tpep_pickup_datetime'])
total_t = datetime.datetime.now() - start_t