el

Includes functions to manipulate adwords datasets

To install: pip install el

Features

This package provides essential tools for analyzing and managing advertising campaigns, particularly focusing on budget pacing and anomaly detection in spending data. Below are the main functionalities provided:

1. Budget Pacing Calculation

The calculate_budget_pacing function calculates how a budget should be spent over a campaign period to ensure even distribution of funds. It adds columns for daily budget, cumulative budget, and pacing to the DataFrame.

Usage

import pandas as pd
from el import calculate_budget_pacing

# Example DataFrame
df = pd.DataFrame({
    'date': pd.to_datetime(['2021-01-01', '2021-01-02']),
    'total_budget': [1000, 1000],
    'current_spend': [100, 300]
})

# Calculate budget pacing
result = calculate_budget_pacing(df, 'total_budget', 'current_spend', 'date', 30)
print(result)

2. Anomaly Detection in Spend

The detect_anomalies_in_spend function identifies anomalies in spending data using a rolling Z-score method. It considers fluctuations in spending over a specified window and marks those that deviate significantly from the norm.

Usage

import pandas as pd
from el import detect_anomalies_in_spend

# Example DataFrame
df = pd.DataFrame({
    'date': pd.to_datetime(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05']),
    'spend': [100, 150, 200, 1200, 180]
})

# Detect anomalies in spending
result = detect_anomalies_in_spend(df, 'spend')
print(result)

Documentation

Functions

calculate_budget_pacing(df, budget_col, spend_col, date_col, total_days)

• Parameters:
  • df (pd.DataFrame): DataFrame containing the campaign data.
  • budget_col (str): Column name for the total budget.
  • spend_col (str): Column name for the current spend.
  • date_col (str): Column name for the date of the spend.
  • total_days (int): Total number of days in the campaign period.
• Returns:
  • pd.DataFrame: DataFrame with additional columns for daily budget, cumulative budget, and pacing.

detect_anomalies_in_spend(df, spend_col, window=7, z_threshold=3)

• Parameters:
  • df (pd.DataFrame): DataFrame containing the spend data.
  • spend_col (str): Column name for the spend data.
  • window (int): Number of days to consider for the rolling mean and standard deviation.
  • z_threshold (float): Z-score threshold to identify an anomaly.
• Returns:
  • pd.DataFrame: DataFrame with an additional column indicating if the spend is an anomaly.

These tools are designed to help advertisers and marketers optimize their campaigns and detect any irregularities in their spending patterns efficiently.