Script 1063: Budget Staging for Strategies via GSheets #2

Purpose

Python script that copies program budgets from staging GSheets to update strategy spend targets.

To Elaborate

The Python script solves the problem of updating strategy spend targets by copying program budgets from staging GSheets. It uses the ‘Abbreviation’ column in the GSheets to match with the ‘Strategy’ column. The script loads the current month budgets from GSheets, cleans up the data, and merges it with the input data to update the strategy spend targets. The script also handles cases where the target is cleared or set to zero.

Walking Through the Code

The script starts by checking if it is running on a server or locally.
If running on a server, it skips the initialization step. If running locally, it loads the dataSourceDict from a pickled file and sets up the necessary dependencies.
The script defines the primary data source and columns, as well as the output columns and initial values.
It loads the current month budgets from GSheets and renames the columns.
The script cleans up the budget values from GSheets by removing empty strategy rows and ensuring the strategy is in the correct format.
It cleans up the input data by converting the strategy target to numeric and removing blank or non-numeric rows.
The script merges the input data with the current month budgets and fills in missing targets as 0.
It selects the changed rows from the merged data and renames the columns for the output.
The script handles cases where the target is cleared or set to zero.
Finally, the script outputs the updated strategy spend targets and debug information if running locally.

Vitals

Script ID : 1063
Client ID / Customer ID: 1306926629 / 60270083
Action Type: Bulk Upload
Item Changed: Strategy
Output Columns: Strategy, Goal, Spend Target
Linked Datasource: M1 Report
Reference Datasource: Google Sheets
Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
Created by dwaidhas@marinsoftware.com on 2024-05-10 15:02
Last Updated by dwaidhas@marinsoftware.com on 2024-05-13 16:02

> See it in Action

Python Code

##
## name: Strategy Target Staging - GSheets - All Campus
## description:
##  Copy Program Budgets from staging GSheets to update Strategy Spend Targets
##  Use GSheets 'Abbrevation' column to match with 'Strategy'
## 
## author: Michael S. Huang, Dana Waidhas
## created: 2024-03-15
## 

########### START - Local Mode Config ###########
# Step 1: Uncomment download_preview_input flag and run Preview successfully with the Datasources you want
download_preview_input=False
# Step 2: In MarinOne, go to Scripts -> Preview -> Logs, download 'dataSourceDict' pickle file, and update pickle_path below
# pickle_path = ''
pickle_path = '/Users/mhuang/Downloads/pickle/allcampus_budget_staging_20240507_with_goal.pkl'
# Step 3: Copy this script into local IDE with Python virtual env loaded with pandas and numpy.
# Step 4: Run locally with below code to init dataSourceDict

# determine if code is running on server or locally
def is_executing_on_server():
    try:
        # Attempt to access a known restricted builtin
        dict_items = dataSourceDict.items()
        return True
    except NameError:
        # NameError: dataSourceDict object is missing (indicating not on server)
        return False

local_dev = False

if is_executing_on_server():
    print("Code is executing on server. Skip init.")
elif len(pickle_path) > 3:
    print("Code is NOT executing on server. Doing init.")
    local_dev = True
    # load dataSourceDict via pickled file
    import pickle
    dataSourceDict = pickle.load(open(pickle_path, 'rb'))

    # print shape and first 5 rows for each entry in dataSourceDict
    for key, value in dataSourceDict.items():
        print(f"Shape of dataSourceDict[{key}]: {value.shape}")
        print(f"First 5 rows of dataSourceDict[{key}]:\n{value.head(5)}")

    # set outputDf same as inputDf
    outputDf = dataSourceDict["1"].copy()

    # setup timezone
    import datetime
    CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=+8))

    # import pandas
    import pandas as pd
    import numpy as np

    # import Marin util functions
    from marin_scripts_utils import tableize, select_changed
else:
    print("Running locally but no pickle path defined. dataSourceDict not loaded.")
    exit(1)
########### END - Local Mode Setup ###########

# dial forward to preview next month budgets
# CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=+8))

# today in client timezone
today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_CONSTRAINT = 'Constraint'
RPT_COL_GOAL = 'Goal'
RPT_COL_STRATEGY_TARGET = 'Target'

# reference data source and columns
gSheetsDf = dataSourceDict["2_1"]  # gsheets dataframe (first sheet)
# To access 10th row of Column C, use gSheetsDf.loc[10, 'C']

# output columns and initial values
BULK_COL_GOAL = 'Goal'
BULK_COL_SPEND_TARGET = 'Spend Target'
outputDf[BULK_COL_GOAL] = "<<YOUR VALUE>>"
outputDf[BULK_COL_SPEND_TARGET] = "<<YOUR VALUE>>"

########### User Code Starts Here ###########

### Load Current Month Budgets from GSheets and rename

### Load Current Month Budgets from GSheets and rename

# For debugging, set arbitrary date
# today = datetime.date(2024, 4, 2)

# Construct column key by mapping current month to canonical column name
# Assume column W is January, X is February, Y is March, AH is Dec, etc. In terms of integer months, W=1, X=2, Y=3,.., AH=12.
month_to_column_mapping = {
    1: 'W', 2: 'X', 3: 'Y', 4: 'Z',
    5: 'AA', 6: 'AB', 7: 'AC', 8: 'AD',
    9: 'AE', 10: 'AF', 11: 'AG', 12: 'AH'
}
column_key = month_to_column_mapping[today.month]
print(f"GSheets column key for current month: {today.strftime('%B')} => {column_key}")

# load current month
# - skip first 3 rows via .loc[2:]
# - 'Abbreviation' is column V
# - budgets from January to December are on columns W to AH
current_month_budgets = dataSourceDict['2_1'] \
                        .loc[2:, ['V', column_key]] \
                        .rename(columns={ \
                            'V': RPT_COL_STRATEGY, \
                            column_key: RPT_COL_STRATEGY_TARGET \
                        })
                        

print("current_month_budgets.shape", current_month_budgets.shape)
print("current_month_budgets.info", current_month_budgets.info())
print("current_month_budgets first 10 rows", current_month_budgets.head(10))

### cleanup Budget values from GSheets
# remove empty strategy rows
current_month_budgets = current_month_budgets.loc[current_month_budgets[RPT_COL_STRATEGY].notnull()]
# make sure Strategy is STR
current_month_budgets[RPT_COL_STRATEGY] = current_month_budgets[RPT_COL_STRATEGY].astype(str)
# make sure target is float; remove prefix if not
# note: can't check for `object` since not imported, so use `0` instead
if current_month_budgets[RPT_COL_STRATEGY_TARGET].dtype == 'O':
    current_month_budgets[RPT_COL_STRATEGY_TARGET] = current_month_budgets[RPT_COL_STRATEGY_TARGET] \
                                    .str.replace('US$', '', case=False, regex=False) \
                                    .str.replace('$', '', case=False, regex=False) \
                                    .str.replace(',', '') \
                                    .str.strip() \
                                    .astype(float)

# remove empty target rows
has_strategy = current_month_budgets[RPT_COL_STRATEGY].notnull() & \
                (current_month_budgets[RPT_COL_STRATEGY].str.len() > 3)
has_strategy_target = current_month_budgets[RPT_COL_STRATEGY_TARGET].notnull() & \
                (current_month_budgets[RPT_COL_STRATEGY_TARGET] > 0.5)
current_month_budgets = current_month_budgets.loc[has_strategy & has_strategy_target]

print("after cleanup gsheets")
print("current_month_budgets.shape", current_month_budgets.shape)
print("current_month_budgets.info", current_month_budgets.info())
print(current_month_budgets.head().to_string())

### Cleanup input

# Convert RPT_COL_STRATEGY_TARGET to numeric, coercing errors to NaN
inputDf[RPT_COL_STRATEGY_TARGET] = pd.to_numeric(inputDf[RPT_COL_STRATEGY_TARGET], errors='coerce')
# Replace NaN values with 0.0 if that's the desired behavior
inputDf[RPT_COL_STRATEGY_TARGET].fillna(0.0, inplace=True)

# convert Strategy column into string and remove blank or non-numeric rows
inputDf[RPT_COL_STRATEGY] = inputDf[RPT_COL_STRATEGY].astype(str)
valid_abbrev = inputDf[RPT_COL_STRATEGY].str.len() > 3

# convert Constraint column into string
# NB: Bulk needs to include Goal column in order to change Spend Target, hence, 
#  remove non-Budget strategies just in case.
inputDf[RPT_COL_CONSTRAINT] = inputDf[RPT_COL_CONSTRAINT].astype(str)
budget_constraint = inputDf[RPT_COL_CONSTRAINT] == 'Budget'

# apply cleanup filters
inputDf = inputDf.loc[valid_abbrev & budget_constraint]

print("after cleanup inputDf")
print("inputDf.shape", inputDf.shape)
print("inputDf.info", inputDf.info())

# fill in missing target as 0 for comparison later
inputDf = inputDf.fillna(value={RPT_COL_STRATEGY_TARGET: 0})

# make copy of input for use with select_changed
originalDf = inputDf.copy()

### Merge inputDf with current_month_budgets, and fill in missing target as 0
mergedDf = inputDf.merge(current_month_budgets, on=RPT_COL_STRATEGY, how='left', suffixes=('_old', '')) \
                  .fillna(value={RPT_COL_STRATEGY_TARGET: 0})

print("mergedDf shape", mergedDf.shape)
print("mergedDf", mergedDf.tail(5).to_string())

outputDf, debugDf = select_changed(mergedDf, 
                          originalDf,
                          diff_cols=[RPT_COL_STRATEGY_TARGET],
                          select_cols=[RPT_COL_STRATEGY, RPT_COL_GOAL, RPT_COL_STRATEGY_TARGET],
                          merged_cols=[RPT_COL_STRATEGY]
                          )

# Bulk column is 'Spend Target', whereas report is 'Target'
outputDf.rename(columns={RPT_COL_STRATEGY_TARGET: BULK_COL_SPEND_TARGET}, inplace=True)


change_to_zero_target = (debugDf[RPT_COL_STRATEGY_TARGET + '_new'] < 1) & \
                        (debugDf[RPT_COL_STRATEGY_TARGET + '_orig'] > 1)
print("count of campaigns with target cleared", sum(change_to_zero_target))
print("campaigns with target cleared", tableize(debugDf.loc[change_to_zero_target].head()))

print("outputDf.shape", outputDf.shape)
print("outputDf sample")
print(tableize(outputDf.tail(10)))

## local debug
if local_dev:
    output_filename = 'outputDf.csv'
    outputDf.to_csv(output_filename, index=False)
    print(f"Local Dev: Output written to: {output_filename}")

    debug_filename = 'debugDf.csv'
    debugDf.to_csv(debug_filename, index=False)
    print(f"Local Dev: Debug written to: {debug_filename}")

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

10 May 2024

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