CODEWITHSUNDEEP
pythonpandasdataframeperformancelarge-data
Harold
Harold

Reputation: 31

How can I efficiently filter and aggregate data in a Pandas DataFrame with multiple conditions?

I have a large Pandas DataFrame with multiple columns, including Category, SubCategory, Value, and Date. I need to filter this DataFrame based on multiple conditions and then aggregate the filtered results. Specifically, I want to:

Filter rows where Category is either "A" or "B".
Further filter these rows to include only those where Value is greater than 10.
Group the filtered data by SubCategory and calculate the sum of Value for each SubCategory.
Sort the results by the summed Value in descending order.

Here is a simplified version of my DataFrame:

data = {
    'Category': ['A', 'B', 'A', 'C', 'B', 'A'],
    'SubCategory': ['X', 'Y', 'X', 'Z', 'X', 'Y'],
    'Value': [5, 15, 20, 25, 10, 30],
    'Date': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05', '2023-01-06'])
}
df = pd.DataFrame(data)

I have attempted to chain several Pandas methods to achieve this, but I am unsure if this is the most efficient way to do it. Here is my current approach:

filtered_df = df[(df['Category'].isin(['A', 'B'])) & (df['Value'] > 10)]
grouped_df = filtered_df.groupby('SubCategory')['Value'].sum().reset_index()
sorted_df = grouped_df.sort_values(by='Value', ascending=False)
print(sorted_df)

This code seems to work, but I am concerned about its efficiency and readability, especially with a much larger dataset.

Upvotes: 1

Views: 109

Answers (2)

Andrej Kesely
Andrej Kesely

Reputation: 195573

I suggest to do comparison if Value > 10 after the filtering dataframe for categories (to not do check Value>10 for every value of original dataframe), e.g.:

out = (
    df[df.Category.isin(["A", "B"])]
    .query("Value > 10")
    .groupby("SubCategory", as_index=False)["Value"]
    .sum()
    .sort_values(by="Value", ascending=False)
)
print(out)

Prints:

  SubCategory  Value
1           Y     45
0           X     20

Upvotes: 3

BeRT2me
BeRT2me

Reputation: 13241

What you have is good, using masks is generally best practice for filtering as they are very efficient, even on large datasets.

I suppose you could preemptively select the necessary columns:

output = (
    df.loc[
        df.Category.isin(['A', 'B'])  # Filter rows where Category is either "A" or "B".
        & df.Value.gt(10),  # Further filter these rows to include only those where Value is greater than 10.
        ["SubCategory", "Value"],
    ]
    .groupby("SubCategory", as_index=False).sum()  # Group the filtered data by SubCategory and calculate the sum of Value for each SubCategory.
    .sort_values(by="Value", ascending=False)  # Sort the results by the summed Value in descending order.
)

Formatting like this also allows for comments at every step~

Upvotes: 1

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