Pandas: filter rows of DataFrame with operator chaining

User-friendly filters with custom masks

To improve readability and breathe fresh life into your code, sprinkle some custom mask methods onto your DataFrame:

pd.DataFrame.mask = lambda self, cond: self.loc[cond] # DataFrame-level facelift
result = df.mask(df['A'] > 1).mask(df['B'] < 6) # Voila! Stylish, isn't it?
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This method essentially glues custom query functionality onto your DataFrame, escalating your code's elegance levels to infinity and beyond.

Custom filtering with lambda expressions & .loc

Meet lambda (λ) functions, your new best friend for dynamic, on-the-go filtering:

# Lambda to the rescue! Clark Kent, who?
result = df.loc[lambda x: (x['A'] > 1) & (x['B'] < 6)]
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This agile method of inline filtering comes into its own when your filtering involves complex criteria or requires dexterity with multiple operations in one fell swoop.

Chaining filters like a boss with .pipe()

Just when you thought .pipe() was all about smoking, here's how it can smoke out your Python problems:

def filter_A(x):
    return x[x['A'] > 1] # Sherlock A

def filter_B(x):
    return x[x['B'] < 6] # Sherlock B

result = df.pipe(filter_A).pipe(filter_B) # Elementary, my dear Watson!
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With .pipe(), each filtering inquisition can be conducted separately, simplifying debugging and maintaining sanity levels of your code.

The good ol' boolean indexing

Sometimes, it's good to order the classic from the menu:

result = df[(df['A'] > 1) & (df['B'] < 6)] # Dad's favorite
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A timeless algorithm that doesn't shy away from complex expressions and sticks to basic pandas syntax.

Unleashing callable arguments with .loc

Callable arguments in .loc. Sounds complicated? Nah, it's just direct data filtering:

result = df.loc[lambda df: (df['A'] > 1) & (df['B'] < 6)] # Cleaning up. One row at a time!
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This pattern ensures concise and efficient filtering without any pretentious frills.

Living large with DataFrame.query()

Maximize your query power with multiple conditions:

result = df.query('A > 1 & B < 6 & C != 7')  # Triple treat!
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Enjoy the conciseness of query strings and watch how DataFrame.query() makes complex filtering a walk in the park.