Like I want this type of dictionary by reading file: or this will be enough. I have a file lets name file.txt which has data like I am trying but i dint get the result this following is my try: it gives me output {‘A’: ‘7’, ‘B’: ‘8’, ‘C’: ‘9’} I know its obvious it will not give me 3d
Tag: parsing
How to pass arguments to ros launch file from bash script and how to create ros launch file running python script with those parser arguments
I have a python script which runs as follows: I have launch file are follows: Bash script as: I want to pass this foldername in bash to roslaunch file (cam_calibrator.launch) as above, then get that folder-name as argument and send that to my python script “cameracalibrator.py” just like –size, –square and image:=/topic_name arguments as well to the image_pub_sub c++ script.
How to parse the log data which is in form of nested [key=value] format using python pandas
I have huge Sensor log data which is in form of [key=value] pair I need to parse the data column wise i found this code for my problem the above code is suitable when the data is in form of “Priority=0, X=776517049” but my data is something like this [Priority=0][X=776517049] and there is no separator in between two columns how
Parse text with uncertain number of fields
I have a file (~50,000 lines) text.txt as below, which contains some gene info from five individuals (AB, BB, CA, DD, GG). The t in the file is a tab seperator. There are also a lot of info that are not useful in the file, and I would like to clean it up. So What I need is to extract
capture pattern_X repeatedly, then capture pattern_Y once, then repeat until EOS
[update:] Accepted answer suggests, this can not be done with the python re library in one step. If you know otherwise, please comment. I’m reverse-engineering a massive ETL pipeline, I’d like to extract the full data lineage from stored procedures and views. I’m struggling with the following regexp. TLDR: I’d like to capture from a string like where a,b,e,f,h match
Proper way to handle ambiguous tokens in PLY
I am implementing an existing scripting language in part as a toy project and in part so that I can write my own implementation of the program that uses the language. One of the issues I’m running into is that I have a few constructs that overlap in terms of specification but are more clear when used: This mostly works,
Is there a way to use python argparse with nargs=’*’, choices, AND default?
My use case is multiple optional positional arguments, taken from a constrained set of choices, with a default value that is a list containing two of those choices. I can’t change the interface, due to backwards compatibility issues. I also have to maintain compatibility with Python 3.4. Here is my code. You can see that I want my default to
Pandas Reading csv file with ” in the data
I want to parse CSV file but the data look like in the below. While using separator as ,” it does not distribute file correctly to the columns. Is there any way to ignore ” or escaping with regex? 3,”Gunnar Nielsen Aaby”,”M”,24,NA,NA,”Denmark”,”DEN” 4,”Edgar Lindenau Aabye”,”M”,34,NA,NA,”Denmark/Sweden” 5,”Christine Jacoba Aaftink”,”F”,21,185,82,”Netherlands” 5,”Christine Jacoba Aaftink”,”F”,21,185,82,”Netherlands” 6,”Per Knut Aaland”,”M”,31,188,75,”United States”,”USA” Thanks ins advance Answer Reading
How to extract multiple specific string lines from another string?
I’m using a FEM software (Code_Aster) which is developed in Python, but its file extension is .comm. However, Python snippets can be inserted in .comm files. I have a function which returns a very long string containing nodes, elements, elements group, and so on in the below form: My goal is to add each row to a list/dictionary with its
I cannot parse this xml file in python
I am trying to create an API connection and response is looking like below. I need to parse this data and turn it into a pd dataframe and/or create loop to find specific information belong to tags. Below is the code i try to run but it returns with empty list, and it looks not iterable. Also it is not