as-big | AssemblyScript library for arbitrary-precision decimal | Apps library

I am working on a big excel file similar to following blog.

https://www.dataquest.io/blog/pandas-big-data/

The post is about converting object type columns to category type. This reduces the memory usage of the dataframe.

I am working on a excel file of 88 MB. After following the steps mentioned in the code, I am able to reduce the size of df to 28 MB. The problem is when I write it to csv file, the file size 193 MB, which is more than the original csv file.

Question: What is causing the memory mismatch and how to overcome? Thanks in advance.

I'm trying to achieve a flexbox, where the row will have the titles all lined up. Requirements:

1. The images won't always be the same height
2. The description won't always be the same height
3. The title could be 1 row, or 3 (depending on the length)

Here is a simple fiddle:

https://jsfiddle.net/youradds/r56j4uLe/6/

As you can see this is what you get:

This is more what I'm after:

My SCSS is:

This question is about boosting Pandas' performance during stacking and unstacking operation.

The issue is that I have a large dataframe (~2GB). I followed this blog to compress it to ~150MB successfully. However, my stacking and unstacking operation take infinite amount of time such that I have to kill the kernel and restart everything.

I have also used R's data.table package, and it just flies, meaning it completes the operation in <1 second.

I researched this on SO. It seems that some people have pointed to map-reduce on Dataframe unstack performance - pandas thread, but I am not sure about it for two reasons:

1. stack and unstack on uncompressed runs fine in pandas, but I can't do this on my original dataset because of memory problems.
2. R's data.table easily (<1 second) converts from long to wide format.

I managed to cut a small feed (5MB) for representation purpose for SO. The feed has been uploaded to http://www.filedropper.com/ddataredact. This file should be able to reproduce the problem.

Here's my pandas code:

I am relatively new to the world of Python and trying to use it as a back-up platform to do data analysis. I generally use data.table for my data analysis needs.

The issue is that when I run group-aggregate operation on big CSV file (randomized, zipped, uploaded at http://www.filedropper.com/ddataredact_1), Python throws:

grouping pandas return getattr(obj, method)(*args, **kwds) ValueError: negative dimensions are not allowed

OR (I have even encountered...)

File "C:\Anaconda3\lib\site-packages\pandas\core\reshape\util.py", line 65, in cartesian_product for i, x in enumerate(X)] File "C:\Anaconda3\lib\site-packages\pandas\core\reshape\util.py", line 65, in for i, x in enumerate(X)] File "C:\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py", line 445, in repeat return _wrapfunc(a, 'repeat', repeats, axis=axis) File "C:\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py", line 51, in _wrapfunc return getattr(obj, method)(*args, **kwds) MemoryError

I have spent three days trying to reduce the file size (I was able to reduce the size by 89%), adding breakpoints, debugging it, but I was not able to make any progress.

Surprisingly, I thought of running the same group/aggregate operation in data.table in R, and it hardly took 1 second. Moreover, I didn't have to do any data type conversion etc., suggested at https://www.dataquest.io/blog/pandas-big-data/.

I also researched other threads: Avoiding Memory Issues For GroupBy on Large Pandas DataFrame, Pandas: df.groupby() is too slow for big data set. Any alternatives methods?, and pandas groupby with sum() on large csv file?. It seems these threads are more about matrix multiplication. I'd appreciate if you wouldn't tag this as duplicate.

Here's my Python code:

In a React scenario where a generic App container holds a presentational component called Row, how do we instruct the latter to be twice-as-big as its sibling View using flexbox?

I have two bitfields: (1) one to handle the frame (header), (2) the other to handle a subframe within a frame (identityFieldO2M).

I'm following advice of this article to reduce Pandas DataFrame memory usage, I'm using .astype('|S') on an object column like so: