Understanding the Problem and Requirements

The problem presented involves a pandas DataFrame containing sales data with a TXN_YM column representing the transaction year and month. The task is to create a new column, LY, which contains the value of SALES_AMOUNT from the previous year for months where there are missing values in the original TXN_YM column.

Splitting TXN_YM into Years and Months

To tackle this problem, we first need to split the TXN_YM column into two separate columns: TXN_YEAR and TXN_MONTH. This can be achieved using the str.split() function provided by pandas.

# Split TXN_YM into years and months
df["TXN_YEAR"] = df["TXN_YM"].str[:4).astype(int]
df["TXN_MONTH"] = df["TXN_YM"].str[1:5).astype(int)

Creating a Function to Get Previous Year Values

Next, we need to create a function that can retrieve the value from the previous year for each month. We’ll use the shift() function to shift the values up by one row, and then use the diff() function to check if the difference between consecutive years is 1.

# Create a function to get previous year values
def get_value_ly(x):
    y = x["SALES_AMOUNT"].shift() * x["TXN_YEAR"].diff().eq(1)
    return y

Grouping by Store and Month, and Applying the Function

We then group the DataFrame by STORE and TXN_MONTH, and apply this function to each group.

# Group by store and month, and apply the function
df["LY"] = df.groupby(["STORE", "TXN_MONTH"]).apply(lambda x: get_value_ly(x))

However, this approach has a flaw. The use of shift() followed by multiplication with diff().eq(1) is inefficient and may not give the correct result as expected.

Alternative Approach Using Merge

Another way to solve this problem is to merge the DataFrame with itself shifted up by one row using outer join on the year columns, then select only rows where the difference between the year of current row and previous year’s is 1.

# Calculate LY column for each row in original dataframe
df_ly = df.merge(df.shift(1).rename(columns={'TXN_YM': 'old_TXN_YM', 'SALES_AMOUNT': 'old_SALES_AMOUNT'}), on=['STORE', 'TXN_YEAR'], how='outer')
df['LY'] = df.apply(lambda x: df_ly.loc[(x.STORE == x.STORE) & (df_ly['old_TXN_YM'].str[:4).astype(int) - x.TXN_YEAR == 1)], axis=1)

# Fill NaN values with NA
df['LY'] = df['LY'].fillna('NA')

Issues and Limitations

The problem doesn’t specify how the missing year should be treated. The above approach assumes that if the difference between years is not equal to 1, then it’s a new month for that store.

However, there might be cases where the year difference can be more than one (for example, February of 2014 being the same as January of 2015).

Conclusion

The solution provided here uses a combination of merging and applying lambda functions to achieve the desired result. However, it’s essential to consider edge cases such as handling missing years accurately.

In conclusion, while this approach solves the specific problem presented, there are potential issues that should be addressed in future revisions.

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Last modified on 2023-10-05