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:-Python Tutorial-php.cn

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:

Patricia Arquette

Release： 2024-10-27 06:19:29

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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: Efficiently Handling Large Dataframes?

Focusing on t

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:

Chunk the dataframe into smaller segments.
Iterate through smaller chunks within the function.
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:

1 2	`<code` `class="python">n = 200000 # Chunk row size` `list_df = [df[i:i + n]` `for` `i in range(0, df.shape[0], n)]</code>`

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

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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>

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Consolidation

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

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

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