fastPath | optimized jsonpath parser | JSON Processing library

I parse some files with logs, and I get specific values using capture groups in sed. It works, but the alignment is not proper when I have some big values

This is an example of a log:

== 2022-02-01 18:30:36.301 +0100 == Packet received at ingress stage, tag 0, type ORDERED Packet info: len 131 port 18 interface 18 vsys 1 wqe index 31527 packet 0x0xc0046e99c0, HA: 0, IC: 0 Packet decoded dump: L2: 00:50:56:b8:17:29->00:50:56:b8:6f:d7, type 0x0800 IP: 172.16.10.20->172.16.0.10, protocol 6 version 4, ihl 5, tos 0x00, len 117, id 27949, frag_off 0x4000, ttl 128, checksum 5931(0x2b17) TCP: sport 21, dport 62040, seq 3281740227, ack 4101579101, reserved 0, offset 5, window 256, checksum 21106, flags 0x18 ( ACK PSH), urgent data 0, l4 data len 77 TCP option:

And here is the output of my command:

I wanted to extract the number of specific values from the columns - (I s a x) etc, so as I extracted I got stack that my chart doesn't want to read that.

I am trying to convert a 3D numpy array to a data array however I am getting an error that I cannot figure out.

I have a 3D numpy array (lat, lon, and time), and I am hoping to convert it into an xarray data array with the dimensions being lat, lon, and time.

The np.random.rand is just to make a reproducible example of a 3D array:

I have the following python dictionary:

sdata ={'Ohio': 35000, 'Oregon': 16000, 'Texas': 71000, 'Utah': 5000}

Suppose I want to create pandas Series from this dictionary. For some reasons, I want to construct the Series with additional columns:

I'd like to perform a calculation over an X, Y data to produce a calculated Z. My code is below:

Example Data set for injection_wells.csv

I'm trying to setup a Pandas Dataframe for processing my data in Databricks. My data is imported from a file on my local machine as seen below: Snip of the data

I am trying to create time-frequency representation of my brain signal. I would like to create the data for frequencies from 0hz - 120hz (so it can cover, delta, theta, alpha, beta, low-gamma and high-gamma frequency bands).

Here is my code:

First, I visualize my brain signal:

There is a dataset with columns ID and Feature_1. Feature_1 could be understood as a specific duration of session in secs. There is also a custom function, which calculates moving average with addition of simple average in the begining according to number of NaN's caused by window width. Here it is:

TLDR: I need to add the length or the number of items in each group to a new column. How can that be achieved?

I am dealing with experimental results containing the results of several repetitions (repeating the same experiment with same settings to improve data's statistical resilience). Each experiment has an identifier, whereas each single repetition/run has an index 'inside' each experiment (see code snippet for clarification).

Due to upcoming data processing and to show the run number in relation to the total number of runs per experiment (e.g. run 1/3, run 2/3, run 3/3) I need to add two columns containing

• a 'human-readable' run number (basically a one-based run index) and
• the total number of runs per experiment.

The first can be easily achieved by incrementing the run_id given for each run: