I am using gurobipy to read LP files. The command
model=gurobipy.read("name.lp", env=env) gives me the number of rows, columns, and non-zeroes. However, I need to retrieve the number of non-zeroes. I don't believe there is a function that does this automatically (i.e. model.getnonzeros() )
Is there a way to obtain the non-zeros? How would I write python code to be able to do this if there isn't a built in function?
Consulted resources
Get constraints in matrix format from gurobipy
Ok I figured it out - perusing the Gurobi reference manual, in chapter 6, Python API overview, I see there is an attribute called "NumNZs" that can be called as:
print(model.getAttr("NumNZs"))
This will give the non-zeroes
Related
I'm studying how to work with data right now and so I'm following along with a tutorial for working with Time Series data. Among the first things he does is read_csv on the file path and then use squeeze=True to read it as a Series. Unfortunately (and as you probably know), squeeze has been depricated from read_csv.
I've been reading documentation to figure out how to read a csv as a series, and everything I try fails. The documentation itself says to use pd.read_csv('filename').squeeze('columns') , but, when I check the type afterward, it is always still a Dataframe.
I've looked up various other methods online, but none of them seem to work. I'm doing this on a Jupyter Notebook using Python3 (which the tutorial uses as well).
If anyone has any insights into why I cannot change the type in this way, I would appreciate it. I'm not sure if I've misunderstood the tutorial altogether or if I'm not understanding the documentation.
I do literally type .squeeze("columns") when I write this out because when I write a column name or index, it fails completely. Am I doing that correctly? Is this the correct method or am I missing a better method?
Thanks for the help!
shampoo = pd.read_csv('shampoo_with_exog.csv',index_col= [0], parse_dates=True).squeeze("columns")
I would start with this...
#Change the the stuff between the '' to the entire file path of where your csv is located.
df = pd.read_csv(r'c:\user\documents\shampoo_with_exog.csv')
To start this will name your dataframe as df which is kind of the unspoken industry standard the same as pd for pandas.
Additionally, this will allow you to use a "raw" (the r) string which makes it easier to insert directories into your python code.
Once you are are able to successfully run this you can simply put df in a separate cell in jupyter. This will show you what your data looks like from your CSV. Once you have done all that you can start manipulating your data. While you can use the fancy stuff in pd.read_csv() I mostly just try to get the data and manipulate it from the code itself. Obviously there are reasons not to only do a pd.read_csv but as you progress you can start adding things here and there. I almost never use squeeze although I'm sure there will be those here to comment stating how "essential" it is for whatever the specific case might be.
I'm using Concerto post processing app from AVL and I'm having hard time with scripting language we use here. It looks like MS visual basic but not that much and I want to know more about what version of Basic is this, so that I can find more documentation on the web.
When I try code from MS Visual Basic documentation site, like generating int array and puting some elements to initialize it (as you can see below), I got syntax error on Concerto Scripting editor
' Declare a single-dimension array and set its 4 values.
Dim numbers = New Integer() {1, 2, 4, 8}
This is from Concerto's own documentation and I cannot find how to create a simple array but Dataset istead, which is something similar I beleive
NewDSArray
This function generates a new array.
Syntax:
A = NewDSArray([Rows], [Columns])
Parameters:
Rows (optional, 1 = default): Numeric initial number of lines
Columns (optional, 0 = default): Numeric initial number of columns
Function:
A is now a new array with which the Dataset Array class commands can be used.
thisDSMatrix=newdsarray(1,2)
FirstCol={1,2,3}
FirstCol.name="FirstCol" //.name will pass the name into the matrix
thisDSMatrix.PutCell(FirstCol,1,1)
SecondCol={4,5,6}
SecondCol.name="SecondCol"
thisDSMatrix.PutCell(SecondCol,1,2)
Mcols=thisDSMatrix.ColCount //returns 2
Mrows=thisDSMatrix.RowCount //returns 1
thisDSMatrix.AddColumn("MyNewCol") //a column is added and thisDSMatrix.ColCount will now show 3
thisDSMatrix.PutCell({7,8,9},1,thisDSMatrix.ColCount)
return thisDSMatrix
I appreciate if you can help me to spot exact version of Basic Concerto uses. Thank you.
We had basically the same issue and because I didn't want to learn a proprietary language I dicided to go with Python. AVL Concerto provides from version V5.x onwards a Python-API. Despite that the API is not perfect from my point of view, this might be an alternative to the Concerto scripting language. And learning Python will thertenly the better solution.
I am referring to the following code posted on GitHub which creates a wrapper interface around the blpapi package to replicate Excel functionality of the popular functions: BDP, BDH, and BDS.
https://github.com/691175002/BLPInterface/blob/master/blp.py
for those familiar, I am interested in replicating the result of calling in Excel
=BDS("NHSLTOT Index","ECO_FUTURE_RELEASE_DATE_LIST","START_DT","20140901","END_DT","20170923")
The wrapper equivalent of this function is referred to as bulkRequest - and it works well without the START_DT and END_DT arguments, i.e.
blp.bulkRequest("NHSLTOT Index", "ECO_FUTURE_RELEASE_DATE_LIST")
returns a DataFrame, but I would like to include the date range as above, which i believe the module provides for via the *kwargs, but I am unable to get this to work correctly. it would seem to me that these should be input as a dictionary, i.e.
blp.bulkRequest("NHSLTOT Index", "ECO_FUTURE_RELEASE_DATE_LIST", elements={"START_DT":"20140901","END_DT":"20170923"})
but this returns an error. any guidance appreciated
I am writing detailed information for each function in a class, and hope when others use my python code, they could check the info for each function, by using something like help(function_name)
For example, I have created a python file called text_preprocess.py, in this file I have created a class which includes functions.
class Preprocess():
def str_process(self, row_string):
'''
This Standford CoreNLP package requires the text input as 1 single string.
The input annotators are in you command line input.
:param row_string: The string format input for Standford CoreNLP
:return: Json format output
'''
parsed_json = self.nlp.annotate(row_string, properties={
'annotators': self.standford_annotators,
'outputFormat': 'json'
})
return parsed_json
In this function, as you can see the info is within triple quotes.
But I don't know how could other users see this info without looking into my code.
I have checked many solutions online, people use help(function_name), but it seems that they wrote the code through terminal, and then type help(function_name), many of those examples do not have class either. Using --help through command line only gives them the parameter descriptions I added through argparse....
So, if I hope others could check the info of my function without looking into the code, where and how could they do that?
Either be in the same directory as your script, or make sure your script is in one of the directories listed in sys.path (usually, the first option is easier for simple things, but if you want to do the second, use a virtualenv rather than trying to install the module system-wide).
Run pydoc3 text_preprocess to get documentation for the whole module. This recursively includes all of the items below the module, such as classes, their members, and functions.
Run pydoc3 text_preprocess.Preprocess if you just want the class.
Run pydoc3 text_preprocess.Preprocess.str_process if you just want the method.
Use Sphinx if you want nicely-formatted HTML or other formats such as PDF.
You may also want to remove that empty line at the beginning of your docstring; some docstring-parsing code may misinterpret it.
Putting triple quoted strings just below the declaration of a class or method is called documentation. You can read this using Preprocess.str_process.__doc__.
I need to open a .xlsx-file (without writing to it) in python, to change some fields and get the output after the formulas in some fields were calculated; I only know the input fields, the output field and the name of the sheet.
To write some code: Here is how it would look like if I would have created the library
file = excel.open("some_file.xlsx")
sheet = file[sheet_name]
for k, v in input_fields.items():
sheet[k] = v
file.do_calculations()
print(sheet[output_field])
Is there an easy way to do this? Wich library should I use to get the result of the formulas after providing new values for some fields?
Is there a better way than using something like pyoo, maybe something that doesn't require another application (a python library is clearly better) to be installed?
I'll just thank you in advance.
I now came up with a (very ugly) solution.
I am now reading the xml within the xlsx-file, and I am now using eval and some regular expressions to find out wich fields are needed; and I have defined some functions to run the calculations.
It works, but it would be great if there were a better solution.
If the resulting library is ready, and I don't forget to do this; I'll add a link to the library (that'll be hosted on Github) to this answer to my own question.