How can I effectively handle large DataFrames in Pandas to avoid memory errors?

Pandas - Slice large DataFrame into chunks

Large DataFrames can be challenging to work with, especially when passing them through functions. Memory errors can occur when working with large DataFrames, and slicing them into smaller chunks can help alleviate this issue.

To slice a DataFrame into smaller chunks:

1. List Comprehension: Utilize list comprehension to create a list of smaller DataFrames.

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

2. Numpy array_split: Leverage numpy's array_split function to split the DataFrame.

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

To access the chunks, simply index the list:

<code class="python">list_df[0]
list_df[1]
etc...</code>

By splitting the DataFrame by AcctName:

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

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

Once the DataFrame is split into chunks, it can be passed through a function and then reassembled into a single DataFrame using pd.concat.

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