Here are a few title options, each highlighting a different aspect of the solution: Focusing on the Problem: * How to Process Large Pandas DataFrames Without Memory Errors? * Memory Error in Pandas:

Slicing Large Pandas Dataframes

Problem:

Attempts to pass a large dataframe through a function result in Memory Error, suggesting the dataframe size is excessive. The goal is to:

1. Chunk the dataframe into smaller segments.
2. Iterate through smaller chunks within the function.
3. Consolidate the processed segments into a single dataframe.

Solution:

Slicing by Row Count

Splitting by a fixed row count can be done using list comprehension or array_split from numpy:

<code class="python">n = 200000  # Chunk row size
list_df = [df[i:i + n] for i in range(0, df.shape[0], n)]</code>

<code class="python">list_df = np.array_split(df, math.ceil(len(df) / n))</code>

Slicing by AcctName

To slice by a specific column value, such as AcctName:

<code class="python">list_df = []

for n, g in df.groupby('AcctName'):
    list_df.append(g)</code>

Consolidation

Once the large dataframe has been sliced, it can be reassembled using pd.concat:

<code class="python">consolidated_df = pd.concat(list_df)</code>

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