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Pandas is a Python data analysis library.
1
vote
Accepted
Convert sum from monthly to quarterly values
You could use panda's resample to group your data into quarterly blocks. I think the key thing to note is that your dates start at the end of the month, so you need to set it to resample from the star …
9
votes
Accepted
Groupby and moving average function in pandas works but is slow
So, it's best to keep as much as possible within Pandas to take advantage of its C implementation and avoid Python. … You can use apply on groupby objects to apply a function over every group in Pandas instead of iterating over them individually in Python. …
1
vote
Accepted
Filter if any value in group is null
One way to avoid this would be to count the number of non-nan values and the number of total values per ID in pandas, then mask your data like that. This keeps everything vectorized in Pandas. …
3
votes
Accepted
Function that fills a time series row-by-row by using the values in the row before
EDIT
I can't find where in your function you are setting the value to the next row as mentioned in your title (additional reason to write code into functions for readability), but you can do this in pandas …
3
votes
Accepted
Python Pandas Dataframe code takes too long to finish
You could do the entire thing in pandas and numpy as their Cython implementation will be much faster than iterating over objects in Python. … The main bottleneck is in comparing the sets of user ids, so doing that in either numpy or pandas instead of iterating over Python objects will improve performance. …
5
votes
Accepted
String split for street addresses
Basically, you should look for any opportunity to take advantage of panda's vectorized optimizations (string operations, datetime operations, masks, etc.) as described in the docs: https://pandas.pydata.org/pandas-docs …
1
vote
Accepted
Extracting specific words from PANDAS dataframe
Using Pandas' str methods for pre-processing will be much faster than looping over each sentence and processing them individually, as Pandas utilizes a vectorized implementation in C. … Pandas' str.split function takes a parameter, expand, that splits the str into columns in the dataframe. …
1
vote
Accepted
Monte Carlo Simulation of P-Value
As you mentioned, by calling np.vectorize on your monte_carlo function and applying it to your dataset b, you are essentially running a for loop over each element individually.
np.random.noncentral_c …