I created a data frame with 5 columns as you desired, so added rows into this one as shown in the below code:
import pandas as pd
df_merged = pd.DataFrame(data=[[1, 100, 10]], columns=['product', 'price', 'max_move_%'])
print(df_merged)
# product price max_move_%
# 0 1 100 10
new_columns = ['product', 'price', 'max_move_%', 'true_move', 'price_new']
df_new = pd.DataFrame(columns=new_columns)
idx = 0
for i, row in df_merged.iterrows():
for true_move in range((row['max_move_%']) * (- 1), row['max_move_%']+1):
price_new = df_merged.iloc[i]['price'] * (1 + true_move / 100.00)
df_new.loc[idx] = row.values.tolist() + [true_move, price_new]
idx += 1
print(df_new)
# product price max_move_% true_move price_new
# 0 1.0 100.0 10.0 -10.0 90.0
# 1 1.0 100.0 10.0 -9.0 91.0
# 2 1.0 100.0 10.0 -8.0 92.0
# 3 1.0 100.0 10.0 -7.0 93.0
# 4 1.0 100.0 10.0 -6.0 94.0
# 5 1.0 100.0 10.0 -5.0 95.0
# 6 1.0 100.0 10.0 -4.0 96.0
# 7 1.0 100.0 10.0 -3.0 97.0
# 8 1.0 100.0 10.0 -2.0 98.0
# 9 1.0 100.0 10.0 -1.0 99.0
# 10 1.0 100.0 10.0 0.0 100.0
# 11 1.0 100.0 10.0 1.0 101.0
# 12 1.0 100.0 10.0 2.0 102.0
# 13 1.0 100.0 10.0 3.0 103.0
# 14 1.0 100.0 10.0 4.0 104.0
# 15 1.0 100.0 10.0 5.0 105.0
# 16 1.0 100.0 10.0 6.0 106.0
# 17 1.0 100.0 10.0 7.0 107.0
# 18 1.0 100.0 10.0 8.0 108.0
# 19 1.0 100.0 10.0 9.0 109.0
# 20 1.0 100.0 10.0 10.0 110.0
I modified your % to an equation for evaluating price_new column values.