signa | Chatops toolkit for Slack | Chat library
kandi X-RAY | signa Summary
kandi X-RAY | signa Summary
Signa is a Slack bot and ChatOps tool written in Go. It offers built-in commands for Kubernetes and a framework to develop ChatOps tools.
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Top functions reviewed by kandi - BETA
- Run starts the RTM API .
- Deploy a container
- parse parses s into a Cmd struct
- Info displays the image version .
- Get parses the command
- extractUser extracts a bot message from Slack message
- initiateDeploymentProcedure is invoked when a deployment is completed .
- extractText extracts text from a slack message
- postMessageToSlackChannel posts a message to a channel
- loadConfig loads a configuration file .
signa Key Features
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QUESTION
I need to extract the text (header and its paragraphs) that match a header level 1 string passed to the python function. Below an example mardown text where I'm working:
...ANSWER
Answered 2021-Mar-21 at 12:38If I understand correctly, you are trying to capture only one # symbol at the beginning of each line.
The regular expression that helps you solve the issue is: r"(?:^|\s)(?:[#]\ )(.*\n+##\ ([^#]*\n)+)"
. The brackets isolate the capturing or non capturing groups. The first group (?:^|\s)
is a non capturing group, because it starts with a question mark. Here you want that your matched string starts with the beginning of a line or a whitespace, then in the second group ([#]\ )
, [#]
will match exactly one # character. \
matches the space between the hash and the h1 tag text content. finally you want to match any possible character until the end of the line so you use the special characther .
, which identifies any character, followed by +
that will match any repetition of the previous matched character.
This is probably the code snippet you are looking for, I tested it with the same sample test you used.
QUESTION
Although many questions have been asked like this at this forum but I've gone through most of them and my problem is a bit different. I have a dataframe:
...ANSWER
Answered 2020-Dec-09 at 14:39## generate a list of feature columns however you like
feature_cols = c("A", "B", "C")
## keep rows where there are fewer NAs (in the feature columns) than feature columns
new_data = old_data[rowSums(is.na(old_data[feature_cols])) < length(feature_cols), ]
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