Skip to main content
Search type Search syntax
Tags [tag]
Exact "words here"
Author user:1234
user:me (yours)
Score score:3 (3+)
score:0 (none)
Answers answers:3 (3+)
answers:0 (none)
isaccepted:yes
hasaccepted:no
inquestion:1234
Views views:250
Code code:"if (foo != bar)"
Sections title:apples
body:"apples oranges"
URL url:"*.example.com"
Saves in:saves
Status closed:yes
duplicate:no
migrated:no
wiki:no
Types is:question
is:answer
Exclude -[tag]
-apples
For more details on advanced search visit our help page
Results tagged with
Search options answers only not deleted user 147747

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 …
mochi's user avatar
  • 1,124
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. …
mochi's user avatar
  • 1,124
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. …
mochi's user avatar
  • 1,124
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
mochi's user avatar
  • 1,124
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. …
mochi's user avatar
  • 1,124
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 …
mochi's user avatar
  • 1,124
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. …
mochi's user avatar
  • 1,124
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 …
mochi's user avatar
  • 1,124