Slice an unsorted datetime index in Python Pandas, and you’ll likely get: KeyError: ‘Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is not allowed.’ Why? If the dates […] The post Pandas DatetimeIndex slicing: Fix KeyError with sort_index appeared first on Lerner...

Want to select a chunk of time from a Python Pandas data frame? Make the datetime column the index, then use .loc and a slice to choose dates: The post Pandas date range selection: Slicing a datetime index with .loc appeared first on LernerPython.

Got NaNs when using Grouper on a Python Pandas data frame? groupby+Grouper chops up the datetime values, from the earliest to the latest, by the specified granularity. If even one […] The post Pandas Grouper NaNs: Why outlier dates create empty groups appeared first on LernerPython.

Want to change the granularity when grouping on a Python Pandas data frame’s datetime column? Use Grouper. For example: Here we get the mean trip_distance per 14-day period: The post Pandas Grouper: Group datetime columns by custom time periods appeared first on LernerPython.

If your Python Pandas data frame has a datetime column, you can group on a portion of that via the “dt” accessor. For example, let’s get mean trip_distance for each […] The post Pandas groupby on datetime parts with the dt accessor appeared first on LernerPython.

Does Python Pandas allow multiple data columns and multiple methods? Yes! The above returns a data frame. Its 4 columns are a 2-level multi-index with mean/std in the inner level. The post Pandas groupby: Aggregating multiple columns with multiple functions appeared first on LernerPython.

Does Python Pandas allow multiple categorical columns and multiple methods? Yes! The above returns: – A 2-column data frame (mean + std)– A 2-level index (passenger_count + VendorID) The post Pandas groupby: Multiple columns and multiple aggregation methods appeared first on LernerPython.

groupby on a single column in Python Pandas returns a series. Want a single-column data frame? Instead of pass a one-element list of data columns: The post Pandas groupby: Get a data frame instead of a series appeared first on LernerPython.

Invoke “groupby” with two categorical columns in Python Pandas, and get a two-part multi-index: Turn into a data frame with unstack: The post Pandas groupby with two columns: Reshape results with unstack appeared first on LernerPython.

A “groupby” call in Python Pandas is normally sorted by index. But if you’re grouping by month name, April will come before January. Pass “sort=False”, and the index will reflect […] The post Pandas groupby with sort=False: Keep groups in order of appearance appeared first on LernerPython.

The simplest grouping in Python Pandas is groupby: For example: Returns a series whose index is the unique values from passenger_count. The post Pandas groupby basics: Aggregating a numeric column by category appeared first on LernerPython.

You can remove NaN from a Python Pandas data frame with dropna, but be careful: It removes rows with even one NaN, which can be overkill. Pass “thresh” to allow […] The post Pandas dropna with thresh: Drop only rows with too many NaN values appeared first on LernerPython.

Coming to Python Pandas from NumPy? You’ll reach for np.isnan: Unfortunately, this works. Better, use s.isna (or s.isnull). But the best way to drop NaN? Use dropna: The post Pandas dropna: The best way to remove NaN values from a series appeared first on LernerPython.

I’m a big fan of exercises, which is why the LernerPython platform includes hundreds of them — all using my in-browser practice system, which handles Python, Pandas, and Git, along […] The post New on the LernerPython practice system: A visual debugger appeared first on LernerPython.

PyArrow dtypes in Python Pandas are nullable (with pd.NA): s is: 0 101 <NA>2 30dtype: int64[pyarrow] The dtype is int64, but allows nulls. (Use np.nan? It’s turned into pd.NA.) The post PyArrow dtypes in pandas: Nullable integers with pd.NA appeared first on LernerPython.

You have a Python Pandas series with ints + NaN. You don’t want float forced on you. Solution: Use the “extension” type Int64 (note Initial Caps) and pd.NA: s is: […] The post Pandas nullable integers: Keeping ints with Int64 and pd.NA appeared first on LernerPython.

Some of the most satisfying work I do happens one on one. You arrive with a real problem from your real job — code that will not behave, an architecture […] The post Get Python help, one on one: my coaching sessions appeared first on LernerPython.

Missing data? NumPy calls it nan. Python Pandas displays it as NaN. But: Pandas doesn’t define pd.nan or pd.NaN. NumPy removed np.NaN in version 2.0. So you have to refer […] The post NaN in Pandas: Why you need np.nan, not pd.NaN appeared first on LernerPython.

Missing data in Python Pandas? We use nan (“not a number”), which comes from NumPy. np.nan is a float, but not a normal one: The post np.nan in Pandas: Why missing values break comparisons appeared first on LernerPython.

What is a “callable” in Python? Typically, a function or class. But really, it’s anything with __call__ defined: The “callable” builtin basically returns True if it finds __call__. The post Python callables: Any object that defines __call__ appeared first on LernerPython.

If you invoke +=, Python can use __add__. MyClass implements __add__ (calling print for debugging): m1 now refers to a new object, and its repr is: MyClass instance, vars(self)={‘x’: 30} The post Python += calls __add__ and rebinds to a new object appeared first on LernerPython.

How does the “in” operator work in Python? – If an object defines __contains__, then its (boolean) result is returned (coerced to bool).– If not, then Python iterates over it […] The post Python in operator: How __contains__ speeds up membership tests appeared first on LernerPython.

I really enjoy reading e-mail newsletters. They’re clever, informative, and funny, and provide me with lots of food for thought — as well as professional information that is crucial to […] The post Newsprint: Turning e-mail newsletters into a personal PDF appeared first on LernerPython.

In Python, we use “or” for conditions. | is bitwise (not boolean) “or”: x | y # 15, or 0b1111 | runs __or__. On dicts, | combines. The post Python | operator: Bitwise or and dict merging with __or__ appeared first on LernerPython.

The % in Python, on numbers, is modulo: But str uses __mod__ for interpolation: Same operator, same magic method — but totally different. The post Python `__mod__`: Modulo for numbers, interpolation for str appeared first on LernerPython.

Just as + in Python invokes __add__, other operators invoke other magic methods: • – is __sub__• * is __mul__• / is __truediv__• // is __floordiv__• % is __mod__• ** is __pow__ Your methods can […] The post Python operator overloading: The arithmetic magic methods appeared first on LernerPython.

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If you tell Python it’ll run x.__add__(5), which handles the int 5. But what about int.__add__ doesn’t know how to handle x. So it returns NotImplemented — and Python turns […] The post Python’s __radd__: How reflected operators handle 5 + x appeared first on LernerPython.

If your Python class implements __add__, it should usually return a new instance of your class, not an int: The post Python `__add__`: Return a new instance, not an int appeared first on LernerPython.

How can your Python object support +? Implement __add__: The post Python operator overloading: Implementing __add__ for + appeared first on LernerPython.