Why does concatenation of DataFrames get exponentially slower?
I have a function which processes a DataFrame, largely to process data into buckets create a binary matrix of features in a particular column using pd.get_dummies(df[col]).
performance
Why is “1000000000000000 in range(1000000000000001)” so fast in Python 3?
It is my understanding that the range() function, which is actually an object type in Python 3, generates its contents on the fly, similar to a generator.
Which Python memory profiler is recommended?
Gives most details.
Speed up millions of regex replacements in Python 3
I have two lists:
Are list-comprehensions and functional functions faster than “for loops”?
In terms of performance in Python, is a list-comprehension, or functions like map(), filter() and reduce() faster than a for loop? Why, technically, they run in a C speed, while the for loop runs in the python virtual machine speed?.
What is the most efficient way to loop through dataframes with pandas?
I want to perform my own complex operations on financial data in dataframes in a sequential manner.
Python: List vs Dict for look up table
I have about 10million values that I need to put in some type of look up table, so I was wondering which would be more efficient a list or dict?
Why does Python code run faster in a function?
Some opcodes tend to come in pairs thus making it possible to
predict the second code when the first is run. For example,
GET_ITER is often followed by FOR_ITER. And FOR_ITER is often
followed by STORE_FAST or UNPACK_SEQUENCE.
Replace values in a pandas series via dictionary efficiently
How to replace values in a Pandas series s via a dictionary d has been asked and re-asked many times.
pandas loc vs. iloc vs. at vs. iat?
Recently began branching out from my safe place (R) into Python and and am a bit confused by the cell localization/selection in Pandas. I’ve read the documentation but I’m struggling to understand the practical implications of the various localization/selection options.