I want to create a DataFrame which includes the hourly heatdemand of n consumers (here n=5) for 10 hours. --> DataFrame called "Village", n columns (each representing a consumer) and 10 rows (10 hours) All consumers follow the same demand-profile with the only difference that it is shiftet within a random amount of hours. The random number follows a normal distribution.
I managed to create a list of discrete numbers that follow a normal distribution and i managed to create a DataFrame with n rows where the same demand-profile gets shiftet by that random number.
The problem i cant solve is, that NaN appears instead of filling up the shifted hours with the values that where cut of because of the shift.
Example: if a the demand-profile gets shiftet by 1 hour (like consumer 5 for example). Now there appears "NaN" as the demand in the first hour. Instead of "NaN" i would like the value of the 10th hour of the original demand-profile to appear (4755.005240). So instead of shifting the values of the demand-profile i kind of want it more to "rotate".
heat_demand
0 1896.107462
1 1964.878199
2 2072.946499
3 2397.151402
4 3340.292937
5 4912.195496
6 6159.893152
7 5649.024821
8 5157.805271
9 4755.005240
Consumer 1 Consumer 2 Consumer 3 Consumer 4 Consumer 5
0 1896.107462 NaN 1964.878199 NaN NaN
1 1964.878199 NaN 2072.946499 NaN 1896.107462
2 2072.946499 NaN 2397.151402 NaN 1964.878199
3 2397.151402 1896.107462 3340.292937 1896.107462 2072.946499
4 3340.292937 1964.878199 4912.195496 1964.878199 2397.151402
5 4912.195496 2072.946499 6159.893152 2072.946499 3340.292937
6 6159.893152 2397.151402 5649.024821 2397.151402 4912.195496
7 5649.024821 3340.292937 5157.805271 3340.292937 6159.893152
8 5157.805271 4912.195496 4755.005240 4912.195496 5649.024821
9 4755.005240 6159.893152 NaN 6159.893152 5157.805271
Could someone maybe give me a hint how to solve that problem? Thanks a lot already in advance and kind regards
Luise
import numpy as np
import pandas as pd
import os
path= os.path.dirname(os.path.abspath(os.path.join(file)))
#Create a list with discrete numbers following normal distribution
n = 5
timeshift_1h = np.random.normal(loc=0.1085, scale=1.43825, size=n)
timeshift_1h = np.round(timeshift_1h).astype(int)
print ("Time Shift in h:", timeshift_1h)
#Read the Standard Load Profile
cols = ["heat_demand"]
df_StandardLoadProfile = pd.read_excel(os.path.join(path, '10_h_example.xlsx'),usecols=cols)
print(df_StandardLoadProfile)
#Create a df for n consumers, whose demand equals a shifted StandardLoadProfile.
#It is shifted by a random amount of hours, that is taken from the list timeshift_1h
list_consumers = list(range(1,n+1))
Village=pd.DataFrame()
for i in list_consumers:
a=timeshift_1h[i-1]
name = "Consumer {}".format(i)
Village[name] = df_StandardLoadProfile.shift(a)
print(Village)
There's a very nice numpy function for that use-case, namely np.roll (see here for the documentation). It takes an array and shifts it by the steps specified withshift.
For your example, this could look like the following:
import pandas as pd
import numpy as np
df = pd.read_csv("demand.csv")
df['Consumer 1'] = np.roll(df["heat_demand"], shift=1)