I would like to simulate individual changes in growth and mortality for a variable number of days. My dataframe is formatted as follows...
import pandas as pd
data = {'unique_id': ['2', '4', '5', '13'],
'length': ['27.7', '30.2', '25.4', '29.1'],
'no_fish': ['3195', '1894', '8', '2774'],
'days_left': ['253', '253', '254', '256'],
'growth': ['0.3898', '0.3414', '0.4080', '0.3839']
}
df = pd.DataFrame(data)
print(df)
unique_id length no_fish days_left growth
0 2 27.7 3195 253 0.3898
1 4 30.2 1894 253 0.3414
2 5 25.4 8 254 0.4080
3 13 29.1 2774 256 0.3839
Ideally, I would like the initial length (i.e., length) to increase by the daily growth rate (i.e., growth) for each of the days remaining in the year (i.e., days_left).
df['final'] = df['length'] (df['days_left'] * df['growth']
However, I would also like to update the number of fish that each individual represents (i.e., no_fish) on a daily basis using a size-specific equation. I'm fairly new to python so I initially thought to use a for-loop (I'm not sure if there is another, more efficient way). My code is as follows:
# keep track of run time - START
start_time = time.perf_counter()
df['z'] = 0.0
for indx in range(len(df)):
count = 1
while count <= int(df.days_to_forecast[indx]):
# (1) update individual length
df.lgth[indx] = df.lgth[indx] df.linearGR[indx]
# (2) estimate daily size-specific mortality
if df.lgth[indx] > 50.0:
df.z[indx] = 0.01
else:
if df.lgth[indx] <= 50.0:
df.z[indx] = 0.052857-((0.03/35)*df.lgth[indx])
elif df.lgth[indx] < 15.0:
df.z[indx] = 0.728*math.exp(-0.1892*df.lgth[indx])
df['no_fish'].round(decimals = 0)
if df.no_fish[indx] < 1.0:
df.no_fish[indx] = 0.0
elif df.no_fish[indx] >= 1.0:
df.no_fish[indx] = df.no_fish[indx]*math.exp(-(df.z[indx]))
# (3) reduce no. of days left in forecast by 1
count = count 1
# keep track of run time - END
total_elapsed_time = round(time.perf_counter() - start_time, 2)
print("Forecast iteration completed in {} seconds".format(total_elapsed_time))
The above code now works correctly, but it is still far to inefficient to run for 40,000 individuals each for 200 days.
I would really appreciate any advice on how to modify the following code to make it pythonic.
Thanks
CodePudding user response：
Another option that was suggested to me is to use the pd.dataframe.apply function. This dramatically reduced the overall the run time and could be useful to someone else in the future.
### === RUN SIMULATION === ###
start_time = time.perf_counter() # keep track of run time -- START
#-------------------------------------------------------------------------#
def function_to_apply( df ):
df['z_instantMort'] = ''
for indx in range(int(df['days_left'])):
# (1) update individual length
df['length'] = df['length'] df['growth']
# (2) estimate daily size-specific mortality
if df['length'] > 50.0:
df['z_instantMort'] = 0.01
else:
if df['length'] <= 50.0:
df['z_instantMort'] = 0.052857-((0.03/35)*df['length'])
elif df['length'] < 15.0:
df['z_instantMort'] = 0.728*np.exp(-0.1892*df['length'])
whole_fish = round(df['no_fish'], 0)
if whole_fish < 1.0:
df['no_fish'] = 0.0
elif whole_fish >= 1.0:
df['no_fish'] = df['no_fish']*np.exp(-(df['z_instantMort']))
return df
#-------------------------------------------------------------------------#
sim_results = df.apply(function_to_apply, axis=1)
total_elapsed_time = round(time.perf_counter() - start_time, 2) # END
print("Forecast iteration completed in {} seconds".format(total_elapsed_time))
print(sim_results)
### ====================== ###
output being...
Forecast iteration completed in 0.05 seconds
unique_id length no_fish days_left growth z_instantMort
0 2.0 126.3194 148.729190 253.0 0.3898 0.01
1 4.0 116.5742 93.018465 253.0 0.3414 0.01
2 5.0 129.0320 0.000000 254.0 0.4080 0.01
3 13.0 127.3784 132.864757 256.0 0.3839 0.01
CodePudding user response：
As I said in my comment, a preferable alternative to for loops in this setting is using vector operations. For instance, running your code:
import pandas as pd
import time
import math
import numpy as np
data = {'unique_id': [2, 4, 5, 13],
'length': [27.7, 30.2, 25.4, 29.1],
'no_fish': [3195, 1894, 8, 2774],
'days_left': [253, 253, 254, 256],
'growth': [0.3898, 0.3414, 0.4080, 0.3839]
}
df = pd.DataFrame(data)
print(df)
# keep track of run time - START
start_time = time.perf_counter()
df['z'] = 0.0
for indx in range(len(df)):
count = 1
while count <= int(df.days_left[indx]):
# (1) update individual length
df.length[indx] = df.length[indx] df.growth[indx]
# (2) estimate daily size-specific mortality
if df.length[indx] > 50.0:
df.z[indx] = 0.01
else:
if df.length[indx] <= 50.0:
df.z[indx] = 0.052857-((0.03/35)*df.length[indx])
elif df.length[indx] < 15.0:
df.z[indx] = 0.728*math.exp(-0.1892*df.length[indx])
df['no_fish'].round(decimals = 0)
if df.no_fish[indx] < 1.0:
df.no_fish[indx] = 0.0
elif df.no_fish[indx] >= 1.0:
df.no_fish[indx] = df.no_fish[indx]*math.exp(-(df.z[indx]))
# (3) reduce no. of days left in forecast by 1
count = count 1
# keep track of run time - END
total_elapsed_time = round(time.perf_counter() - start_time, 2)
print("Forecast iteration completed in {} seconds".format(total_elapsed_time))
print(df)
with output:
unique_id length no_fish days_left growth
0 2 27.7 3195 253 0.3898
1 4 30.2 1894 253 0.3414
2 5 25.4 8 254 0.4080
3 13 29.1 2774 256 0.3839
Forecast iteration completed in 31.75 seconds
unique_id length no_fish days_left growth z
0 2 126.3194 148.729190 253 0.3898 0.01
1 4 116.5742 93.018465 253 0.3414 0.01
2 5 129.0320 0.000000 254 0.4080 0.01
3 13 127.3784 132.864757 256 0.3839 0.01
Now with vector operations, you could do something like:
# keep track of run time - START
start_time = time.perf_counter()
df['z'] = 0.0
for day in range(1, df.days_left.max() 1):
update = day <= df['days_left']
# (1) update individual length
df[update]['length'] = df[update]['length'] df[update]['growth']
# (2) estimate daily size-specific mortality
df[update]['z'] = np.where( df[update]['length'] > 50.0, 0.01, 0.052857-( ( 0.03 / 35)*df[update]['length'] ) )
df[update]['z'] = np.where( df[update]['length'] < 15.0, 0.728 * np.exp(-0.1892*df[update]['length'] ), df[update]['z'] )
df[update]['no_fish'].round(decimals = 0)
df[update]['no_fish'] = np.where(df[update]['no_fish'] < 1.0, 0.0, df[update]['no_fish'] * np.exp(-(df[update]['z'])))
# keep track of run time - END
total_elapsed_time = round(time.perf_counter() - start_time, 2)
print("Forecast iteration completed in {} seconds".format(total_elapsed_time))
print(df)
with output
Forecast iteration completed in 1.32 seconds
unique_id length no_fish days_left growth z
0 2 126.3194 148.729190 253 0.3898 0.0
1 4 116.5742 93.018465 253 0.3414 0.0
2 5 129.0320 0.000000 254 0.4080 0.0
3 13 127.3784 132.864757 256 0.3839 0.0