Category: Python
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Pandas nlargest: Finding top rows faster than sort_values
Retrieve rows of a Python Pandas data frame with the 6 largest values of column x: Even better: Why is it better? On a 3m row, 680 MB data frame, sort/tail took 622 ms. nlargest? Only 84 ms — 7x faster!
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Pandas column arguments: Where one name works, a list does too
A Python Pandas rule of thumb: Wherever you can use a column name (string), you can use multiple column names (a list of strings):
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Pandas sort_values: Sorting a data frame by multiple columns
To sort a Python Pandas data frame by column x: By x and y (ascending): By x and y, both descending: Ascending x, descending y:
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Pandas select_dtypes: Selecting columns by their dtype
Need specific column types in Python Pandas? Use select_dtypes:
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Python line continuation: How parentheses split long lines
Use parentheses to split long Python code across lines: But with parentheses, Python sees it as one line: Or in comprehensions…
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Jupyter memory usage: Why del doesn’t free your DataFrame
Python Jupyter memory quirk: Deleting variables doesn’t always free memory! Use df.head() instead of df to keep big values out of Jupyter’s Out dict.
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Python set subset checks with <=: Spelling Bee word validation
Playing NYT Spelling Bee? Use Python to see if your word only uses allowed letters (ignoring length + center): With sets, <= checks “subset or equal”:
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dict.fromkeys: Why a mutable default value gets shared
Create a Python dict with keys a/b/c and values 0: But don’t do this, since the value will be shared and mutable: {‘a’: [‘x’, ‘y’], ‘b’: [‘x’, ‘y’], ‘c’: [‘x’, ‘y’]} 🤯
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Python round and banker’s rounding: Why round(2.5) returns 2
“round” rounds Python numbers: Remember, .5 goes to the nearest *even* value: