Calculating new rows in a Pandas Dataframe on two different columns

Question

So I'm a beginner at Python and I have a dataframe with Country, avgTemp and year. What I want to do is calculate new rows on each country where the year adds 20 and avgTemp is multiplied by a variable called tempChange. I don't want to remove the previous values though, I just want to append the new values.

This is how the dataframe looks:

Preferably I would also want to create a loop that runs the code a certain number of times Super grateful for any help!

If you need to copy the values from the dataframe as an example you can have it here:

Country        avgTemp        year

0 Afghanistan    14.481583    2012

1 Africa         24.725917    2012

2 Albania        13.768250    2012

3 Algeria        23.954833    2012

4 American Samoa 27.201417    2012

243 rows × 3 columns

Answer 1

If you want to repeat the rows, I'd create a new dataframe, perform any operation in the new dataframe (sum 20 years, multiply the temperature by a constant or an array, etc...) and use then use concat() to append it to the original dataframe:

import pandas as pd
tempChange=1.15
data = {'Country':['Afghanistan','Africa','Albania','Algeria','American Samoa'],'avgTemp':[14,24,13,23,27],'Year':[2012,2012,2012,2012,2012]}
df = pd.DataFrame(data)
df_2 = df.copy()
df_2['avgTemp'] = df['avgTemp']*tempChange
df_2['Year'] = df['Year']+20
df = pd.concat([df,df_2]) #ignore_index=True if you wish to not repeat the index value
print(df)

Output:

          Country  avgTemp  Year
0     Afghanistan    14.00  2012
1          Africa    24.00  2012
2         Albania    13.00  2012
3         Algeria    23.00  2012
4  American Samoa    27.00  2012
0     Afghanistan    16.10  2032
1          Africa    27.60  2032
2         Albania    14.95  2032
3         Algeria    26.45  2032
4  American Samoa    31.05  2032

Answer 2

where df is your data frame name:

 df['tempChange'] = df['year']+ 20  * df['avgTemp']

This will add a new column to your df with the logic above. I'm not sure if I understood your logic correct so the math may need some work

Answer 3

I believe that what you're looking for is

dfName['newYear'] =  dfName.apply(lambda x: x['year'] + 20,axis=1)
dfName['tempDiff'] =  dfName.apply(lambda x: x['avgTemp']*tempChange,axis=1)

This is how you apply to each row.