Python: Add rows into existing dataframe with loop

Question

I am using pandas Python library and I want to add rows to existing DF and also keep the existing one.

My data looks like this:

product price   max_move_%
  1     100      10

I run loops like this:

for i, row in df_merged.iterrows():
for a in range((row['max_move_%']) * (- 1), row['max_move_%']):
    df_merged['price_new'] = df_merged['price'] * (1 - a / 100.00)

I want to get:

product price   max_move_%  true_move     price_new
1       100      10          -10            90
1       100      10          -9             91
 .....
1       100      10          10             110

But nothing happens and the df looks the same like before. What can I do to add new values to columns and on the same time leave the data from existing df?

I tried this:

df_loop = []
for i, row in df_merged.iterrows():
    for a in range((row['max_move_%']) * (- 1), row['max_move_%'] + 1):
    df_loop.append((df_merged['product'], df_merged['price'], f_merged['max_move_%'],a))

pd.DataFrame(df_loop, columns=('product','price','max_move_%','price_new'))

But it doesnt work like I supposed.

Thank you!

Answer 1

I just created a new DataFrame with all 5 desired columns, to add rows into this one:

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 just modified your % change equation for evaluating price_new column values.

Answer 2

If i understand correctly, something like this would work. Use the following to create a larger DataFrame based on your original one (but with 21x as many rows, in this example):

max_move = df_merged['max_move_%'][0]  # 10 in this case
num_rows_needed = max_move * 2 + 1  # 21 in this case
new = pd.concat([df_merged] * num_rows_needed).reset_index(drop=True)

Then add the new columns:

new['true_move'] = [i for i in range(-max_move, max_move + 1)]
new['price_new'] = new['price'] + new['true_move']

This gives the desired results as posted in your question.

Depending on what your larger dataset looks like, this might need to be tweaked slightly. If this doesn't work for your needs, please edit your question with a representative dataset to test on.