Pandas Dataframe increased or decreased in a certain amount of time

Question

I have a DataFrame with the following structure:

# Import pandas library
import pandas as pd


# initialize list of lists
data = [['R.04T', 1, 2013, 23456, 22, 1 ], ['R.04T', 15, 2014, 
23456, 22, 1], ['F.04T', 9, 2010, 75920, 00, 3], ['F.04T', 4, 
2012,  75920, 00, 3], ['R.04T', 7,  2013, 20054, 13, 1], 
['R.04T',12,  2014, 20058,13, 1]]


# Create the pandas DataFrame
df = pd.DataFrame(data, columns=['product_code', 'sold', 'year', 'city_number', 
'district_number', 'number_of_the_department'])
print(df)

I want to know if the locations ('city_number' + 'district_number' + 'number_of_the_department') have increased or decreased the amount of sales per year, per article. Id thought about joining the columns to one location column like the following:

# join the locations
df['location'] = df['city_number'].astype(str) + ','+ 
df['district_number'].astype(str) + ','+ df['number_of_the_department'].astype(str)

But I'm not sure how to groupby? the df to get my answer of the question.

I want to know if the sales have increased or decreased (per year and item) by a certain percentage per year (p.ex. 2013 to 2014 x% decreased).

Maybe someone can help? :)

Answer 1

Try this:

df = df.assign(
    pct_change_sold=df.sort_values(by="year")
    .groupby(by=["city_number", "district_number", "number_of_the_department"])["sold"]
    .pct_change()
    .fillna(0)
)

---

  product_code  sold  year  city_number  district_number  number_of_the_department  pct_change_sold
0        R.04T     1  2013        23456               22                         1         0.000000
1        R.04T    15  2014        23456               22                         1        14.000000
2        F.04T     9  2010        75920                0                         3         0.000000
3        F.04T     4  2012        75920                0                         3        -0.555556
4        R.04T     7  2006        75920               22                         1         0.000000
5        U.90G    12  2005        75021               34                         3         0.000000