[Python] How to debug why a sum of data in a PivotTable does not match the original dataframe?

I'm trying to create a pivot table in Python, but when I compare the sum of my count on my original dataframe with the sum of the count in the PivotTable it does not add up. I've tried filling NaN to see if this is not maybe the reason, but no luck. Below is my code as well as the output.

Python code:

import pandas as pd
import numpy as np

# Ensure that missing values are populated for the class variables
merged_df_stage['Sale_Indicator'].fillna('Unknown', inplace=True)

# Add additional count columns
merged_df_stage['Count_val'] = 1
merged_df_stage['Count_val'] = merged_df_stage['Count_val'].fillna(1)

# Calculate the sum of the 'Count' column
count_sum = merged_df_stage['Count_val'].sum()
print(f"Sum of Count: {count_sum}")

# Filter out NaN values in 'Sale_Indicator' column
filtered_df = merged_df_stage.dropna(subset=['Sale_Indicator'])

# Assuming 'column_name' is the name of the column in your DataFrame
value_counts = merged_df_stage['Sale_Indicator'].value_counts(dropna=False)

print(value_counts)

# Create pivot table with counts on measure fields and additional counts
pivot_table = pd.pivot_table(merged_df_stage,
values=['Count_val'],
index=['Sale_Indicator'],
aggfunc=np.sum,
fill_value=0,
dropna=False)

# Reset index to convert the pivot table into a regular dataframe
pivot_table.reset_index(inplace=True)

# Rename the measure columns
pivot_table.rename(columns={'Count_val': 'Count'}, inplace=True)

# Testing Counts written to the pivot table to see that they add up to shape attributes:
count_sum = pivot_table['Count'].sum()
print(f"Sum of Pivot Count: {count_sum}")


Output:

Sum of Count: 945947

NaN 890532
Y 55415
Name: Sale_Indicator, dtype: int64

Sum of Pivot Count: 55415


I've tried filling NaN's as I thought this could be the reason. Otherwise not sure what is causing the counts to be different.

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