Tag: Python
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Pandas .loc filtering: Replacing lambda with pd.col
Filtering a Python Pandas data frame by the index? Pandas 3 adds pd.col, removing lambda from our .loc expression. Before: Now: Note: You cannot use pd.col with a series, only a data frame.
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Pandas row filtering: Using df.loc with a lambda expression
To filter a Python Pandas data frame by rows, use the same syntax and rules as for series: The lambda still returns a boolean series. The expression can use any column from the data frame. Tomorrow: How Pandas 3 improves on this.
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Filtering a Pandas series with loc and lambda: Three benefits
Why do I use loc+lambda to filter a Python Pandas series? 1. If earlier lines filter common values, later loc/lambda lines have less to filter — so queries run faster.2. It’s easier to build queries, one line at a time.3. No assignment means less to track and clean up.
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Pandas Series filtering: Use loc with lambda to chain filters
Best filter method for a Python Pandas series? Use loc + lambda, thus filtering the series you got, not the global s:
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Pandas Series filtering: Chaining loc for multiple conditions
My favorite way to apply multiple filters to a Python Pandas series? Method chaining calling loc 2x: But: This can break! Tomorrow, a better way.
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Pandas Series filtering: Split long conditions across lines
When applying multiple filters to a Python Pandas series, it’s often best to use multiple lines: This is easier to read, write, and maintain.
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Pandas boolean filtering: Wrap each condition in parentheses
Want to filter a Python Pandas series with two conditions? One option: Combine two boolean series with &. But careful: Put () around each boolean condition.
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Pandas boolean indexing: Selecting series items with a mask
Retrieve selected items from a Python Pandas series by broadcasting a condition, then using it as a boolean (“mask”) index:
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Pandas Series .loc: Selecting with labels and booleans
Retrieve from a Python Pandas series with .loc and indexes: If you pass booleans, True means “return a value,” and False means “ignore it.”
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Pandas Series broadcasting: Arithmetic and comparisons with scalars
Apply an operator to a Python Pandas series, with a scalar value, *broadcasts*: Arithmetic is most obvious: s + 5 # [15, 25, 35]s ** 2 # [100, 400, 900] But comparisons work, too: s >= 20 # [False, True, True]