eshangj/stackoverflow_q_and_a_sample huggingface dataset:
Question: I'm confused about the differences of the following codes: class car: def __init__(self, weight): self.weight = weight class car: def __init__(self, weight): self.weight = 0 class car: def __init__(self, weight=0): self.weight = weight class car: def __init__(self, weight=0): self.weight = 0 class car: def __init__(self, weight=0): self.weight = 1 class car: def __init__(self): self.weight = 0  My understanding it's that if I specify weight=0 in def __init__(self, weight=0), then the default attributes is 0. But it seems that I can also do the similar if I don't specify def __init__(self, weight) but add self.weight = 0 afterwards. What's the difference? What if we give two different values?

Answer: You get confused by concepts of function arg, default value for the arg, and member variable, you are mixing them so badly.  Function argument  For def __init__(self,weight) you are expecting a variable passed in when calling the constructor, which is given the name "weight". It is a function argument.  Default value for function argument  For def __init__(self,weight=0), you are still expecting a variable passed in, but you are fine if it's absent, in which case the default value 0 will be simply assigned to symbol "weight". This is a default value for a function argument.  Member attribute  Now for the code below: class car: def __init__(self, weight=0): self.weight = weight  It's effectively saying, pass me a variable, I'll assign it to a symbol "weight", otherwise I'll assign 0 to the symbol. And I'll assign the value of symbol "weight" to "self.weight", they locate at totally different scopes and hence are different symbols. It's not like C++ when you do memberwise initialization. "self.weight" is an attribute of instances of the class.  weight vs self.weight  Hopefully that, now for the one last example, the logic will be clear enough: class car: def __init__(self, weight=0): self.weight = 1  Here you are getting a symbol named "weight", no matter by default or passed-in, it gets simply ignored, i.e., not referred anywhere, instead, you assign a 1 to member variable self.weight.
Question: I have a polars DataFrame with two list columns. However one column contains empty lists and the other contains nulls. I would like consistency and convert empty lists to nulls.  In [306]: df[["spcLink", "proprietors"]] Out[306]: shape: (254_654, 2) ┌───────────┬─────────────────────────────────┐ │ spcLink ┆ proprietors │ │ --- ┆ --- │ │ list[str] ┆ list[str] │ ╞═══════════╪═════════════════════════════════╡ │ [] ┆ null │ │ [] ┆ null │ │ [] ┆ null │ │ [] ┆ null │ │ [] ┆ null │ │ … ┆ … │ │ [] ┆ ["The Steel Company of Canada … │ │ [] ┆ ["Philips' Gloeilampenfabrieke… │ │ [] ┆ ["AEG-Telefunken"] │ │ [] ┆ ["xxxx… │ │ [] ┆ ["yyyy… │ └───────────┴─────────────────────────────────┘  I have attempted this: # Convert empty lists to None for col, dtype in df.schema.items(): if isinstance(dtype, pl.datatypes.List): print(col, dtype) df = df.with_columns( pl.when(pl.col(col).list.len() == 0).then(None).otherwise(pl.col(col)) )  But no change happens in the output; the empty lists remain as such and are not converted.

Answer:  selectors.by_dtype to select all columns of type pl.List(pl.String). list.len() to determine if list is empty.  df = pl.DataFrame({ "spcLink": [[],[]], "proprietors": [None,["xxx"]] }, schema={"spcLink": pl.List(pl.String), "proprietors": pl.List(pl.String)}) ┌───────────┬─────────────┐ │ spcLink ┆ proprietors │ │ --- ┆ --- │ │ list[str] ┆ list[str] │ ╞═══════════╪═════════════╡ │ [] ┆ null │ │ [] ┆ ["xxx"] │ └───────────┴─────────────┘  import polars.selectors as cs df.with_columns( pl.when( cs.by_dtype(pl.List(pl.String)).list.len() > 0 ).then( cs.by_dtype(pl.List(pl.String)) ) ) ┌───────────┬─────────────┐ │ spcLink ┆ proprietors │ │ --- ┆ --- │ │ list[str] ┆ list[str] │ ╞═══════════╪═════════════╡ │ null ┆ null │ │ null ┆ ["xxx"] │ └───────────┴─────────────┘
Question: When I try to assert a dataframe using the PySpark API, if a dataframe is none, I do not get the assertion error, but instead, the method returns false. Is it a bug, or should I handle my test verification differently? from pyspark.testing.utils import assertDataFrameEqual assertDataFrameEqual(spark.createDataFrame([("v1", "v3")]), None) # False from pyspark.testing.utils import assertDataFrameEqual assertDataFrameEqual(spark.createDataFrame([("v1", "v3")]), spark.createDataFrame([("v1", "v2")])) # PySparkAssertionError 

Answer: When running the code you run, I do get an error for your code: [INVALID_TYPE_DF_EQUALITY_ARG] Expected type Union[DataFrame, ps.DataFrame, List[Row]] for `expected` but got type None.  I do not see why this could be different for you: all versions containing this function start with the same check for None values, as found in the code:  if actual is None and expected is None: return True elif actual is None: raise PySparkAssertionError( error_class="INVALID_TYPE_DF_EQUALITY_ARG", message_parameters={ "expected_type": "Union[DataFrame, ps.DataFrame, List[Row]]", "arg_name": "actual", "actual_type": None, }, ) elif expected is None: raise PySparkAssertionError( error_class="INVALID_TYPE_DF_EQUALITY_ARG", message_parameters={ "expected_type": "Union[DataFrame, ps.DataFrame, List[Row]]", "arg_name": "expected", "actual_type": None, }, )  This means that in your case it should return the error I got, getting raised by the last elif. Despite it being unclear what the reason is for your different behaviour, a valid comparison based on the types would be wrapping the None in a list. Edit: Note that the code was run on Azure Databricks DBR 14.3LTS, pyspark 3.5.0.
Question: In a number of aggregation function, such as rolling_mean, rolling_max, rolling_min, etc, the input argument window_size is supposed to be of type int I am wondering how to efficiently compute results when having a list of window_size. Consider the following dataframe: import polars as pl pl.Config(tbl_rows=-1) df = pl.DataFrame( { "symbol": ["A", "A", "A", "A", "A", "B", "B", "B", "B"], "price": [100, 110, 105, 103, 107, 200, 190, 180, 185], } ) shape: (9, 2) ┌────────┬───────┐ │ symbol ┆ price │ │ --- ┆ --- │ │ str ┆ i64 │ ╞════════╪═══════╡ │ A ┆ 100 │ │ A ┆ 110 │ │ A ┆ 105 │ │ A ┆ 103 │ │ A ┆ 107 │ │ B ┆ 200 │ │ B ┆ 190 │ │ B ┆ 180 │ │ B ┆ 185 │ └────────┴───────┘  Let's say I have a list with n elements, such as periods = [2, 3]. I am looking for a solution to compute the rolling means for all periods grouped by symbol in parallel. Speed and memory efficiency is of the essence. The result should be a tidy/long dataframe like this: shape: (18, 4) ┌────────┬───────┬─────────────┬──────────────┐ │ symbol ┆ price ┆ mean_period ┆ rolling_mean │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ u8 ┆ f64 │ ╞════════╪═══════╪═════════════╪══════════════╡ │ A ┆ 100 ┆ 2 ┆ null │ │ A ┆ 110 ┆ 2 ┆ 105.0 │ │ A ┆ 105 ┆ 2 ┆ 107.5 │ │ A ┆ 103 ┆ 2 ┆ 104.0 │ │ A ┆ 107 ┆ 2 ┆ 105.0 │ │ B ┆ 200 ┆ 2 ┆ null │ │ B ┆ 190 ┆ 2 ┆ 195.0 │ │ B ┆ 180 ┆ 2 ┆ 185.0 │ │ B ┆ 185 ┆ 2 ┆ 182.5 │ │ A ┆ 100 ┆ 3 ┆ null │ │ A ┆ 110 ┆ 3 ┆ null │ │ A ┆ 105 ┆ 3 ┆ 105.0 │ │ A ┆ 103 ┆ 3 ┆ 106.0 │ │ A ┆ 107 ┆ 3 ┆ 105.0 │ │ B ┆ 200 ┆ 3 ┆ null │ │ B ┆ 190 ┆ 3 ┆ null │ │ B ┆ 180 ┆ 3 ┆ 190.0 │ │ B ┆ 185 ┆ 3 ┆ 185.0 │ └────────┴───────┴─────────────┴──────────────┘ 

Answer: You can use comprehension to generate a DataFrame for each value in periods list and then concat() DataFrames into single long DataFrame: periods = [2, 3] pl.concat( df.with_columns( mean_period = pl.lit(p), rolling_mean = pl.col.price.rolling_mean(p).over("symbol") ) for p in periods ) ┌────────┬───────┬─────────────┬──────────────┐ │ symbol ┆ price ┆ mean_period ┆ rolling_mean │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i32 ┆ f64 │ ╞════════╪═══════╪═════════════╪══════════════╡ │ A ┆ 100 ┆ 2 ┆ null │ │ A ┆ 110 ┆ 2 ┆ 105.0 │ │ A ┆ 105 ┆ 2 ┆ 107.5 │ │ A ┆ 103 ┆ 2 ┆ 104.0 │ │ A ┆ 107 ┆ 2 ┆ 105.0 │ │ B ┆ 200 ┆ 2 ┆ null │ │ B ┆ 190 ┆ 2 ┆ 195.0 │ │ B ┆ 180 ┆ 2 ┆ 185.0 │ │ B ┆ 185 ┆ 2 ┆ 182.5 │ │ A ┆ 100 ┆ 3 ┆ null │ │ A ┆ 110 ┆ 3 ┆ null │ │ A ┆ 105 ┆ 3 ┆ 105.0 │ │ A ┆ 103 ┆ 3 ┆ 106.0 │ │ A ┆ 107 ┆ 3 ┆ 105.0 │ │ B ┆ 200 ┆ 3 ┆ null │ │ B ┆ 190 ┆ 3 ┆ null │ │ B ┆ 180 ┆ 3 ┆ 190.0 │ │ B ┆ 185 ┆ 3 ┆ 185.0 │ └────────┴───────┴─────────────┴──────────────┘
Question: Inputs: arr1 = ["A","B"] arr2 = [[1,2],[3,4,5]]  Expected output:     short_list long_list     0 A 1   1 A 2   2 B 3   3 B 4   4 B 5    Current output:     short_list long_list     0 A [1, 2]   1 A [3, 4, 5]   2 B [1, 2]   3 B [3, 4, 5]    Current Code (using itertools): import pandas as pd from itertools import product def custom_product(arr1, arr2): expand_short_list = [[a1]*len(a2) for a1, a2 in zip(arr1,arr2)] return [[a1,a2] for a1, a2 in zip(sum(expand_short_list,[]),sum(arr2,[]))] arr1 = ["A","B"] arr2 = [[1,2],[3,4,5]] df2 = pd.DataFrame(data = product(arr1,arr2),columns=["short_list", "long_list"])  Alternative code using nested list comprehensions to get the desired output: import pandas as pd def custom_product(arr1, arr2): expand_short_list = [[a1]*len(a2) for a1, a2 in zip(arr1,arr2)] return [[a1,a2] for a1, a2 in zip(sum(expand_short_list,[]),sum(arr2,[]))] arr1 = ["A","B"] arr2 = [[1,2],[3,4,5]] df1 = pd.DataFrame(data = custom_product(arr1, arr2),columns=["short_list", "long_list"])  Question: I'm wondering how could I achieve the desired output using itertools?

Answer: IIUC use DataFrame contructor with DataFrame.explode: arr1 = ["A","B"] arr2 = [[1,2],[3,4,5]] df = (pd.DataFrame({'short_list':arr1, 'long_list':arr2}) .explode('long_list') .reset_index(drop=True)) print (df) short_list long_list 0 A 1 1 A 2 2 B 3 3 B 4 4 B 5  Another idea is use flattening zipped arrays to list of tuples and pass to DataFrame constructor: df = pd.DataFrame([(a, x) for a, b in zip(arr1, arr2) for x in b], columns=['short_list','long_list']) print (df) short_list long_list 0 A 1 1 A 2 2 B 3 3 B 4 4 B 5
Question: In the polars documentation regarding selectors, there are many examples for selecting columns based on their dtypes. I am missing pl.List How can I quickly select all columns of type pl.List within a pl.DataFrame?

Answer: At the moment it's not possible to select all lists with selectors, but if you have lists of specific type, you can do it: df.select(cs.by_dtype(pl.List(pl.Int64)))
Question: Consider the following toy example: import polars as pl pl.Config(tbl_rows=-1) df = pl.DataFrame({"group": ["A", "A", "A", "B", "B"], "value": [1, 2, 3, 4, 5]}) print(df) shape: (5, 2) ┌───────┬───────┐ │ group ┆ value │ │ --- ┆ --- │ │ str ┆ i64 │ ╞═══════╪═══════╡ │ A ┆ 1 │ │ A ┆ 2 │ │ A ┆ 3 │ │ B ┆ 4 │ │ B ┆ 5 │ └───────┴───────┘  Further, I have a list of indicator values, such as vals=[10, 20, 30]. I am looking for an efficient way to insert each of these values in a new column called ìndicator using pl.lit() while expanding the dataframe vertically in a way all existing rows will be repeated for every new element in vals. My current solution is to insert a new column to df, append it to a list and subsequently do a pl.concat. lit_vals = [10, 20, 30] print(pl.concat([df.with_columns(indicator=pl.lit(val)) for val in lit_vals])) shape: (15, 3) ┌───────┬───────┬───────────┐ │ group ┆ value ┆ indicator │ │ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i32 │ ╞═══════╪═══════╪═══════════╡ │ A ┆ 1 ┆ 10 │ │ A ┆ 2 ┆ 10 │ │ A ┆ 3 ┆ 10 │ │ B ┆ 4 ┆ 10 │ │ B ┆ 5 ┆ 10 │ │ A ┆ 1 ┆ 20 │ │ A ┆ 2 ┆ 20 │ │ A ┆ 3 ┆ 20 │ │ B ┆ 4 ┆ 20 │ │ B ┆ 5 ┆ 20 │ │ A ┆ 1 ┆ 30 │ │ A ┆ 2 ┆ 30 │ │ A ┆ 3 ┆ 30 │ │ B ┆ 4 ┆ 30 │ │ B ┆ 5 ┆ 30 │ └───────┴───────┴───────────┘  As df could potentially have quite a lot of rows and columns, I am wondering if my solution is efficient in terms of speed as well as memory allocation? Just for my understanding, if I append a new pl.DataFrame to the list, will this dataframe use additional memory or will just some new pointers be created that link to the chunks in memory which hold the data of the original df?

Answer: You could assign it as a column and .explode() df.with_columns(indicator=vals).explode("indicator")  shape: (15, 3) ┌───────┬───────┬───────────┐ │ group ┆ value ┆ indicator │ │ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ i64 │ ╞═══════╪═══════╪═══════════╡ │ A ┆ 1 ┆ 10 │ │ A ┆ 1 ┆ 20 │ │ A ┆ 1 ┆ 30 │ │ A ┆ 2 ┆ 10 │ │ A ┆ 2 ┆ 20 │ │ … ┆ … ┆ … │ │ B ┆ 4 ┆ 20 │ │ B ┆ 4 ┆ 30 │ │ B ┆ 5 ┆ 10 │ │ B ┆ 5 ┆ 20 │ │ B ┆ 5 ┆ 30 │ └───────┴───────┴───────────┘  To specify a dtype, you can use pl.lit() (df.with_columns(indicator=pl.lit(vals, dtype=pl.List(pl.UInt8))) .explode("indicator") )  shape: (15, 3) ┌───────┬───────┬───────────┐ │ group ┆ value ┆ indicator │ │ --- ┆ --- ┆ --- │ │ str ┆ i64 ┆ u8 │ ╞═══════╪═══════╪═══════════╡ │ A ┆ 1 ┆ 10 │ │ A ┆ 1 ┆ 20 │ │ A ┆ 1 ┆ 30 │ │ A ┆ 2 ┆ 10 │ │ A ┆ 2 ┆ 20 │ │ … ┆ … ┆ … │ │ B ┆ 4 ┆ 20 │ │ B ┆ 4 ┆ 30 │ │ B ┆ 5 ┆ 10 │ │ B ┆ 5 ┆ 20 │ │ B ┆ 5 ┆ 30 │ └───────┴───────┴───────────┘
Question: I'm trying to use the Neo4jGraph class from the langchain_community.graphs module in my Python project to interact with a Neo4j database. My script here: from langchain.chains import GraphCypherQAChain from langchain_community.graphs import Neo4jGraph from langchain_openai import ChatOpenAI enhanced_graph = Neo4jGraph( url="bolt://localhost:7687", username="neo4j", password="password", enhanced_schema=True, ) print(enhanced_graph.schema) chain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=enhanced_graph, verbose=True ) chain.invoke({"query": "Who is Bob?"})  Error here: ValueError: Could not use APOC procedures. Please ensure the APOC plugin is installed in Neo4j and that 'apoc.meta.data()' is allowed in Neo4j configuration neo4j.exceptions.ClientError: {code: Neo.ClientError.Procedure.ProcedureNotFound} {message: There is no procedure with the name `apoc.meta.data` registered for this database instance. Please ensure you've spelled the procedure name correctly and that the procedure is properly deployed.}  How to solve the problem?

Answer: This is a known error:  copy this file 'apoc-5.14.0-core.jar' from /var/lib/neo4j/labs/ to /var/lib/neo4j/plugins  update this file /var/lib/neo4j/conf/neo4j.conf dbms.security.procedures.unrestricted=apoc.* dbms.security.procedures.allowlist=apoc.*   Git link with solutions : https://github.com/langchain-ai/langchain/issues/12901
Question: Why does the size of a struct change if endianness is specified, notably even when the endianness matches the native endianness of the platform? Example: >>> import struct >>> struct.calcsize("BI") 8 >>> struct.calcsize(">BI") 5 >>> struct.calcsize("<BI") 5 >>>  Why is the extra padding added?

Answer: If byte ordering is not specified then it's implicitly "@" in which case both the size and alignment are native. See the "Format Strings" section of this document. You will note that "@" is the only format string prefix that forces alignment. The format character "I" denotes an unsigned integer which has a size of 32 bits. The format character "B" denotes an unsigned char (8 bits). Thus, the integer will be aligned on a 32-bit boundary which leads to the padding. When alignment does not occur then the packed bytes object will just be the size of the sum of its constituent parts which, in this case will be 1+4==5
Question: I would like to install python 3.13.0rc1 with conda on an M1 Mac. However, conda create fails with error message "python 3.13.0rc1** is not installable because it requires _python_rc, which does not exist (perhaps a missing channel)": % conda search python ... python 3.12.4 h99e199e_1 pkgs/main python 3.12.5 h30c5eda_0_cpython conda-forge python 3.13.0rc1 h17d3ab0_0_cp313t conda-forge python 3.13.0rc1 h17d3ab0_1_cp313t conda-forge python 3.13.0rc1 h17d3ab0_2_cp313t conda-forge python 3.13.0rc1 h8754ccd_100_cp313 conda-forge python 3.13.0rc1 h8754ccd_101_cp313 conda-forge python 3.13.0rc1 h8754ccd_102_cp313 conda-forge  % conda create --name py python=3.13.0rc1 --channel conda-forge --override-channels Channels: - conda-forge Platform: osx-arm64 Collecting package metadata (repodata.json): done Solving environment: failed LibMambaUnsatisfiableError: Encountered problems while solving: - nothing provides _python_rc needed by python-3.13.0rc1-h17d3ab0_0_cp313t Could not solve for environment specs The following package could not be installed ââ python 3.13.0rc1** is not installable because it requires ââ _python_rc, which does not exist (perhaps a missing channel).  Note that installing the latest python without specifying the version (conda create --name py python) installs python 3.12.5. See also:  How to install the latest development version of Python with conda? Install python 3.12 using mamba on mac 

Answer: python_rc is under the python_rc label, seehere, you can specify it directly in your command line like this: conda create --name py python=3.13.0rc1 conda-forge/label/python_rc::_python_rc --channel conda-forge --override-channels
Question: I was trying to get a good understanding of numpy apply along axis. Below is the code from the numpy documentation (https://numpy.org/doc/stable/reference/generated/numpy.apply_along_axis.html) import numpy as np def my_func(a): """Average first and last element of a 1-D array""" return (a[0] + a[-1]) * 0.5 b = np.array([[1,2,3], [4,5,6], [7,8,9]]) print(np.apply_along_axis(my_func, 0, b)) #array([4., 5., 6.]) print(np.apply_along_axis(my_func, 1, b)) #array([2., 5., 8.])  According to webpage, the above code has a similar functionality to the code below which I took from the webpage and modified it (played around with it) to understand it: arr = np.array([[1,2,3], [4,5,6], [7,8,9]]) axis = 0 def my_func(a): """Average first and last element of a 1-D array""" print(a, a[0], a[-1]) return (a[0] + a[-1]) * 0.5 out = np.empty(arr.shape[axis+1:]) Ni, Nk = arr.shape[:axis], arr.shape[axis+1:] print(Ni) for ii in np.ndindex(Ni): for kk in np.ndindex(Nk): f = my_func(arr[ii + np.s_[:,] + kk]) Nj = f.shape for jj in np.ndindex(Nj): out[ii + jj + kk] = f[jj] #The code below may help in understanding what I was trying to figure out. #print(np.shape(np.asarray(1))) #x = np.int32(1) #print(x, type(x), x.shape)  I understand from the numpy documentation that scalars and arrays in numpy have the same attributes and methods. I am trying to understand the difference between '()' and 0. I understand that () is a tuple. See below. Example: In the code below, the first for-loop does not iterate but the second for-loop iterates once. I am trying to understand why. import numpy as np for i in np.ndindex(0): print(i) #does not run. for i in np.ndindex(()): print(i) #runs once  In summary: Given the above context, what is the difference between () and 0?

Answer:  In summary: Given the above context, what is the difference between () and 0?  The first one represents a zero dimensional array with one element. The second one represents a one dimensional array with zero elements. A zero dimensional array always has a single element. Example: >>> array = np.array(42) >>> array array(42)  Zero dimensional arrays have a shape of (). >>> array.shape ()  Indexing into a zero dimensional array produces a scalar. >>> array[()] 42  Zero dimensional arrays are kind of like scalars, in that both of them can only have a single element. However, they act differently in a few subtle ways. The differences between zero-dimensional arrays and scalars are out of scope of this post. One dimensional arrays, unlike zero dimensional arrays, can contain any number of elements. For example, this one dimensional array contains zero elements: >>> array = np.array([]) >>> array array([], dtype=float64)  It has a shape of (0,). >>> array.shape (0,)  (When you provide a shape of 0 to np.nditer(), this is implicitly converted to (0,). This is the same shape as your example.) If you loop over each array with for i in np.nditer(array.shape):, the loop over the array with one element will run once. The loop over the array with zero elements will run zero times.
Question: I'm having trouble being able to get my code to be executable and testable. Here's my project's file structure: project/ ├── .pytest_cache/ │ ├── src/ │ ├── __init__.py | ├── .pytest_cache/ │ ├── module1.py │ └── module2.py │ ├── tests/ | ├── .pytest_cache/ | ├── __init__.py | └── test_module.py | ├── venv/  where both __init__.py files are empty I'm using pytest and calling test_module.py by calling pytest in the cli at the root level of the project. This works when I use absolute imports in all of my modules in src: from src.module2.py import some_func, some_other_func  but then executing module1.py from the cli no longer works: Traceback (most recent call last): File "C:\project\src\module1.py", line 3, in <module> from src.module2 import some_func, some_other_func, ModuleNotFoundError: No module named 'src'  However, when I remove the src. from the import statements, I can execute the scripts just fine, but no longer test them. I've tried appending my src directory to the project path in the testfile: test_module.py: import sys import os sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../src')))  Also tried this: test_module.py: import os, sys sys.path.append(os.path.join(os.getcwd(), os.path.pardir))  then calling my test in the root of my project: python -m tests.test_module and nothing happens. What can I do so executing tests & my scripts are possible? Are there any libs/packages to make this easier?

Answer: Before diving into testing, ensure your project is structured properly. Project Structure Your src/ directory should contain a subdirectory with the name of your module: super-cool-module/ ├── src/ │ └── super_cool_module/ │ ├── __init__.py │ ├── submodule1.py │ └── submodule2.py ├── tests/ │ ├── __init__.py │ ├── test_submodule1.py │ └── test_submodule2.py ├── venv/ ├── setup.py # setuptools ├── README.md # Documentation └── .gitignore # Exclude venv, etc.  Alternatively, if you prefer not to use a src/ directory: super-cool-module/ ├── super_cool_module/ │ ├── __init__.py │ ├── submodule1.py │ └── submodule2.py ├── tests/ │ ├── __init__.py │ ├── test_submodule1.py │ └── test_submodule2.py ├── venv/ ├── setup.py # setuptools ├── README.md # Documentation └── .gitignore # Exclude venv, etc.  Testing Setup To run tests, you have a few options:  Using PYTHONPATH: Run pytest with the path to your src directory: PYTHONPATH=src pytest   Pytest Configuration: Alternatively, you can set up a pytest.ini configuration file: # pytest.ini [pytest] pythonpath = src   Editable Mode Installation: If you’ve activated your virtual environment, install your module in editable mode: source venv/bin/activate pip install -e .  Now you can run tests by simply calling: pytest  This works because your module is installed in your local environment.   Note: Remember to exclude the venv/ directory from version control by adding it to your .gitignore file.