Purpose

Python script that combines the ‘School’ and ‘Program’ columns into a new column called ‘School_Program’ and assigns the created tag to the ‘School_Program’ column.

To Elaborate

The Python script solves the problem of combining the ‘School’ and ‘Program’ columns in a dataset into a single column called ‘School_Program’. This is useful for tagging purposes, where the combination of these two dimensions provides more specific information about a campaign. The script also checks if either the ‘School’ or ‘Program’ column is empty and skips processing for those rows. The final output is a dataframe with the ‘School_Program’ column populated with the created tags.

Walking Through the Code

1. The script starts by defining the primary data source and the column names for the input dataframe.
2. It also defines the output column names and their initial values.
3. The script then loops through each row in the input dataframe.
4. For each row, it retrieves the values of the ‘School’ and ‘Program’ columns.
5. If either the ‘School’ or ‘Program’ value is empty, the script skips processing for that row.
6. If both values are present, the script creates a tag by concatenating the ‘School’ and ‘Program’ values with an underscore.
7. The created tag is printed for information.
8. The created tag is assigned to the ‘School_Program’ column in the output dataframe.
9. After processing all rows, the script removes rows from the output dataframe where both the ‘School_Program’ and ‘SBA Strategy’ columns are blank.
10. If the output dataframe is not empty, an example of the dataframe is printed.
11. If the output dataframe is empty, a message indicating that it is empty is printed.

Vitals

• Script ID : 691
• Client ID / Customer ID: 1306926629 / 60270083
• Action Type: Bulk Upload
• Item Changed: Campaign
• Output Columns: Account, Campaign, School_Program, SBA Strategy
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
• Created by dwaidhas@marinsoftware.com on 2024-02-09 19:49
• Last Updated by Michael Huang on 2024-03-19 04:11

> See it in Action

Python Code

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##
## name: Campaign Name Dimension Auto Tagging: School_Program
## description:
##  Combine Dimensions columns 'School' and 'Program' into column 'School_Program'
##
## author: Dana Waidhas
## created: 2024-02-09
##


# Primary data source and columns
inputDf = dataSourceDict["1"]

# Output columns and initial values
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_SCHOOL = 'School'
RPT_COL_PROGRAM = 'Program'
RPT_COL_SCHOOL_PROGRAM = 'School_Program'
RPT_COL_SBA_STRATEGY = 'SBA Strategy'
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_SCHOOL = 'School'
BULK_COL_PROGRAM = 'Program'
BULK_COL_SCHOOL_PROGRAM = 'School_Program'
BULK_COL_SBA_STRATEGY = 'SBA Strategy'


# Loop through all rows
for index, row in inputDf.iterrows():
    school = row[RPT_COL_SCHOOL]
    program = row[RPT_COL_PROGRAM]
   
    # Skip processing if either School or Program is empty
    if pd.isna(school) or pd.isna(program):
        continue

    # Create School_Program tag by concatenating School and Program with an underscore
    tag = f"{school}_{program}"

    # Print tag information
    print("Tag [%s]" % tag)

    # Assign the created tag to BULK_COL_SCHOOL_PROGRAM
    outputDf.at[index, BULK_COL_SCHOOL_PROGRAM] = tag

# Don't include in Bulk if both tags are blank
outputDf = outputDf.dropna(subset=[BULK_COL_SCHOOL_PROGRAM, BULK_COL_SBA_STRATEGY], how='all')

if not outputDf.empty:
    print("outputDf example", tableize(outputDf.head()))
else:
    print("Empty outputDf")

Post generated on 2024-05-15 07:44:05 GMT