Best filter method for a Python Pandas series? Use loc + lambda, thus filtering the series you got, not the global s: The post Pandas Series filtering: Use loc with lambda to chain filters appeared first on LernerPython.

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. The post Pandas Series filtering: Chaining loc for multiple conditions appeared first on LernerPython.

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. The post Pandas Series filtering: Split long conditions across lines appeared first on LernerPython.

Want to filter a Python Pandas series with two conditions? One option: Combine two boolean series with &. But careful: Put () around each boolean condition. The post Pandas boolean filtering: Wrap each condition in parentheses appeared first on LernerPython.

Retrieve selected items from a Python Pandas series by broadcasting a condition, then using it as a boolean (“mask”) index: The post Pandas boolean indexing: Selecting series items with a mask appeared first on LernerPython.

Over my 30+ years teaching Python, I’ve included practice exercises in all of my courses. And when I started to teach Git and Pandas, I made sure to include practice […] The post New: In-browser Python/Pandas/Git exercises, with AI support appeared first on LernerPython.

Retrieve from a Python Pandas series with .loc and indexes: If you pass booleans, True means “return a value,” and False means “ignore it.” The post Pandas Series .loc: Selecting with labels and booleans appeared first on LernerPython.

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] […] The post Pandas Series broadcasting: Arithmetic and comparisons with scalars appeared first on LernerPython.

What’s the fastest way to retrieve the first 2 items from a Python Pandas series? The post Pandas Series selection speed: iloc[:2] beats loc and head appeared first on LernerPython.

Using .loc to retrieve from a Python Pandas series is more convenient. But .iloc is faster: %timeit s.loc[‘a’] # 2.09 μs%timeit s.iloc[0] # 1.5 μs %timeit s.loc[[‘a’, ‘b’]] # 105 […] The post .loc vs .iloc in Pandas: Convenience versus speed appeared first on LernerPython.

Retrieve from a Python Pandas series by position, rather than the index, with iloc: The post Pandas iloc vs loc: Selecting series values by position appeared first on LernerPython.

Pass a list to .loc on a Python Pandas series, and get a series back: The post Pandas .loc with a list returns a series, not a scalar appeared first on LernerPython.

The index in a Python Pandas series feels like a dict keys. But the keys can repeat: The post Pandas Series duplicate index labels: When .loc returns a Series appeared first on LernerPython.

To retrieve from a Python Pandas series, you can use [] to retrieve items. But don’t! In a series, [] uses the index. In a data frame, [] uses the […] The post Pandas Series indexing: Why .loc beats [] for retrieval appeared first on LernerPython.

You can set a Python Pandas series index with set_axis. This returns a new series with the new index applied: s.index # still Index([‘a’, ‘b’, ‘c’], dtype=’str’) The post Pandas set_axis: Replacing a Series index without mutating it appeared first on LernerPython.

Want to change the index of a Python Pandas series? Just assign to it: The post Pandas Series index: Change it by assigning to s.index appeared first on LernerPython.

By default, a Python Pandas series has a RangeIndex, like a string or list: Set an index like this: The post Pandas Series index: Setting labels instead of RangeIndex appeared first on LernerPython.

Is a Python value of a particular type? It’s tempting to say: Far better to say: Why?– Works with subclasses– Second argument can be a tuple of possibilities The post isinstance vs type(): The safer way to check Python types appeared first on LernerPython.

When should you use inheritance in Python? – An existing class mostly does what you want– Inheriting reduces your work — you only define differences– It’s an IS-A relationship, not […] The post Python inheritance: When to use it and when to prefer composition appeared first on LernerPython.

What is a Python metaclass? – A subclass of type– Classes can be instances of a metaclass, rather than type– The metaclass’s __init__ sets attributes and methods Realistically, you should […] The post Python metaclasses: How they work and when to use a decorator instead appeared first on LernerPytho...

A Python class is an instance of type. So type.__new__ and type.__init__ create the class and set attributes. Consider MyType, a subclass of type, setting some attributes in its __init__. […] The post Python metaclasses: How type.__new__ and type.__init__ create classes appeared first on LernerPytho...

Every Python class inherits (directly or not) from object. object is a class. So type(object) is type, the class of all classes. But type is a class, and inherits from […] The post object and type in Python: The circular class hierarchy appeared first on LernerPython.

When you define a class, Python checks it can get an unambiguous MRO. Children must always precede parents. This gives a TypeError when D is defined, because B (child of […] The post Python MRO errors: Why child classes must precede parents appeared first on LernerPython.

In Python, invoke super as: Where’s self? Or the MRO? – At compile time, Python adds a __class__ closure cell.– At runtime, super() grabs self from the calling frame — […] The post super() without arguments: How Python finds self and the MRO appeared first on LernerPython.

Some Python rules for inheritance: – Use super().x() to find the next class with a method “x”– “Next” is defined on the instance, aka type(self).__mro__– Every ancestor class should also […] The post Python cooperative inheritance: How super() walks the MRO appeared first on LernerPython.

Python class C inherits from (A, B), and both A.__init__ and B.__init__ invoke super().__init__(). We run C(). It prints “B, A”. How does B.__init__ run? A doesn’t inherit from B. […] The post Python multiple inheritance: How super() follows the MRO appeared first on LernerPython.

Multiple inheritance in Python? Make sure all parents run super(): The post Python multiple inheritance: Why every class needs super() appeared first on LernerPython.

In a Python child class with two parents, which __init__ runs? The post Python multiple inheritance: Which __init__ actually runs appeared first on LernerPython.

Does Python allow for multiple inheritance? Yes: And so: C.__bases__ returns: (<class ‘__main__.A’>, <class ‘__main__.B’>) The post Python multiple inheritance: Listing parents with __bases__ appeared first on LernerPython.

In Python, super() searches the MRO until it finds a match: The post Python super() and the MRO: How method lookup climbs the hierarchy appeared first on LernerPython.