Purpose:

The script tags the “Current Organic Average Position” dimension with the previous day’s Organic Average Position from Google Search Console data.

To Elaborate

The Python script is designed to update a dataset by tagging the “Current Organic Average Position” dimension with the Organic Average Position from the previous day, sourced from Google Search Console data. This process involves taking the existing data, converting the relevant column to a numeric type, and handling any missing values by filling them with zero. The script ensures that the data is in a consistent format, which is crucial for accurate reporting and analysis. This tagging process is essential for businesses to track and analyze their search engine performance over time, allowing them to make informed decisions based on historical data trends.

Walking Through the Code

1. Initialization and Setup
   • The script begins by defining a dictionary MATCH_TYPE to map match types to their string representations.
   • It retrieves the primary data source into inputDf from a dictionary dataSourceDict.
2. Data Preparation
   • The script initializes an output DataFrame outputDf by copying the input DataFrame inputDf.
   • It coerces the “Organic Average Position Conv.” column to a numeric type, filling any missing values with zero to ensure data integrity.
3. Data Conversion
   • The script converts the “Current Organic Average Position” column to a float type, ensuring that all data entries are in a consistent numeric format.
4. Output
   • Finally, the script prints the resulting DataFrame outputDf and displays the first few rows of the input DataFrame using a tableize function for a quick preview.

Vitals

• Script ID : 1431
• Client ID / Customer ID: 1306928241 / 60269279
• Action Type: Bulk Upload
• Item Changed: Keyword
• Output Columns: Account, Campaign, Group, Keyword, Match Type, Current Organic Average Position
• Linked Datasource: M1 Report
• Reference Datasource: None
• Owner: Jeff Sands (jsands@marinsoftware.com)
• Created by Jeff Sands on 2024-10-10 15:22
• Last Updated by Jeff Sands on 2024-10-10 15:43

> See it in Action

Python Code

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##
## name: Current OAP Dimension Tagging
## description:
##  Tag "Current Organic Average Position" dimension with yesterday's
##  Organic Average Position from Google search console data.
## author: Jeff Sands
## created: 2024-10-10
## 

MATCH_TYPE = {
  'EXACT': 'exact',
  'PHRASE': 'phrase',
  'BROAD': 'broad',
}
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_KEYWORD = 'Keyword'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_GROUP = 'Group'
RPT_COL_STATUS = 'Status'
RPT_COL_MATCH_TYPE = 'Match Type'
RPT_COL_ORGANIC_AVERAGE_POSITION_CONV = 'Organic Average Position Conv.'
RPT_COL_CURRENT_ORGANIC_AVERAGE_POSITION = 'Current Organic Average Position'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_CURRENT_ORGANIC_AVERAGE_POSITION = 'Current Organic Average Position'

# Output DataFrame initialization
outputDf = inputDf.copy()

# Coerce the Organic Average Position to a float type explicitly, filling NaNs with a placeholder if needed
outputDf[RPT_COL_CURRENT_ORGANIC_AVERAGE_POSITION] = pd.to_numeric(
    inputDf[RPT_COL_ORGANIC_AVERAGE_POSITION_CONV], errors='coerce'
).fillna(0)  # Fill missing values with 0

# Convert to integer if you expect all positions to be integers
outputDf[RPT_COL_CURRENT_ORGANIC_AVERAGE_POSITION] = outputDf[RPT_COL_CURRENT_ORGANIC_AVERAGE_POSITION].astype(float)

# Display the result
print(outputDf)
# user code start here
print(tableize(inputDf.head()))

Post generated on 2025-03-11 01:25:51 GMT