eshangj/stackoverflow_q_and_a_sample huggingface dataset:
Question: General idea: I want to take a slice of a 3D surface plot of a function of two variables f(x,y) at a given x = some value. The problem is that I have to know the index where x assumes this value after creating x as a vector with np.linspace, for instance. Finding this index turns out to be doable thanks to another post in SO. What I can't do is use this index as is returned to subset a different vector Z, because of the index is returned as a 1-element list (or tuple), and I need an integer. When I use int() I get a warning: import numpy as np lim = 10 x = np.linspace(-lim,lim,2000) y = np.linspace(-lim,lim,2000) X, Y = np.meshgrid(x, y) Z = X**2 + Y**2 def find_nearest(array, value): array = np.asarray(array) idx = (np.abs(array - value)).argmin() return array[idx] idx = int(np.where(x == find_nearest(x,0))[0]) print(idx) print(Z[idx,:].shape)  Output with warning: <ipython-input-282-c2403341abda>:16: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) idx = int(np.where(x == find_nearest(x,0))[0]) 1000 (2000,)  The shape of the ultimate sub-setting of Z is (2000,), which allows me to plot it against x. However, if instead I just extract with [0] the value return by np.where(), the shape of the final sub-set Z, i.e. (1,2000) is not going to allow me to plot it against x: idx1 = np.where(x == find_nearest(x,0))[0] print(idx1) print(Z[idx1,:].shape)  How can I extract the index corresponding to the value of x is want as an integer, and thus avoid the warning (see below)? DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) idx = int(np.where(x == find_nearest(x,0))[0])

Answer: Break down the resulting value you get from np.where: >>> np.where(x == find_nearest(x,0)) (array([1000]),)  Then dereference the first element: >>> np.where(x == find_nearest(x,0))[0] array([1000])  Ok, same thing. Get first element: >>> np.where(x == find_nearest(x,0))[0][0] 1000
Question: I have a dataframe after concatenting several dataframes df1 = instance scan comp sort 0 A 23:15:12 NaN NaN 1 B 23:17:12 NaN NaN 2 C 23:16:12 NaN NaN 0 A NaN 23:19:32 NaN 1 B NaN 23:19:32 NaN 2 C NaN 23:43:23 NaN 0 A NaN NaN 23:45:32 1 B NaN NaN 23:45:26 2 C NaN NaN 23:45:12  I need to simplify above and have all the columns for the same instance to be updated df2 = instance scan comp sort A 23:15:12 23:19:32 23:45:32 B 23:17:12 23:19:32 23:45:26 C 23:16:12 23:43:23 23:45:12  There can 100's of such instances and many columns for each instance such as scan, comp,sort etc. I have tried groupby(['instance'), but it doesnt seem to work and resulting in object errors. <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f72f595f650>

Answer: You can use groupby_first: df2 = df1.groupby('instance', as_index=False).first() print(df2) # Output instance scan comp sort 0 A 23:15:12 23:19:32 23:45:32 1 B 23:17:12 23:19:32 23:45:26 2 C 23:16:12 23:43:23 23:45:12  From the documentation, the function returns:  First non-null of values within each group.  Maybe I'm wrong but IIUC, your dataframes (before pd.concat) should look like: >>> df1 instance scan 0 A 23:15:12 1 B 23:17:12 2 C 23:16:12 >>> df2 instance comp 0 A 23:19:32 1 B 23:19:32 2 C 23:43:23 >>> df3 instance sort 0 A 23:45:32 1 B 23:45:26 2 C 23:45:12  So you can use pd.merge instead of pd.concat with reduce function from functools module: from functools import reduce out = reduce(lambda left, right: left.merge(right, on='instance', how='outer'), [df1, df2, df3]) print(out) # Output instance scan comp sort 0 A 23:15:12 23:19:32 23:45:32 1 B 23:17:12 23:19:32 23:45:26 2 C 23:16:12 23:43:23 23:45:12
Question: I have some messy data that requires me to split it up as a combination of values. This is sort of what it looks like: import pandas as pd data = {"Name":["name1", "name2"], "A": [[1, 2, 3], [1]], "B": [["a", "b"], ["a"]]} df = pd.DataFrame(data)  And I would like to permute the two columns with list data so that the resulting dataframe carries each combination of pairs in individual rows like so: {"Name": ["name1", "name1", "name1", "name1", "name1", "name1", "name2"], "A": [1, 1, 2, 2, 3, 3, 1], "B": ["a", "b", "a", "b", "a", "b", "a"]}  How do I do this while keeping non combined columns along side them?

Answer: You can explode the two columns successively: df.explode('A').explode('B', ignore_index=True)  Output:  Name A B 0 name1 1 a 1 name1 1 b 2 name1 2 a 3 name1 2 b 4 name1 3 a 5 name1 3 b 6 name2 1 a  If you have more than two columns, you could use functools.reduce: from functools import reduce df = pd.DataFrame({'Name': ['name1', 'name2'], 'A': [[1, 2, 3], [1]], 'B': [['a', 'b'], ['a']], 'C': [['w', 'x'], ['y', 'z']] }) out = reduce(lambda a, b: a.explode(b, ignore_index=True), ['A', 'B', 'C'], df)  Or a python loop: out = df for col in ['A', 'B', 'C']: out = out.explode(col, ignore_index=True)  Output:  Name A B C 0 name1 1 a w 1 name1 1 a x 2 name1 1 b w 3 name1 1 b x 4 name1 2 a w 5 name1 2 a x 6 name1 2 b w 7 name1 2 b x 8 name1 3 a w 9 name1 3 a x 10 name1 3 b w 11 name1 3 b x 12 name2 1 a y 13 name2 1 a z
Question: I'm building a program that needs to have and input from the user where the user introduces a sequence of numbers separated with commas, example: 1,2,3,4. The sequence can be any amount of numbers, have any number in it and have spaces in between, the only thing that has to be followed is that there's a number then a coma and maybe in between somewhere a space I've done this until now `sec=input("Please insert a sequence of numbers separated by commas:") com = 0 num = 1 numlist=[] for x in sec: if x == "," or x==" ": None else: try: numlist.append(int(x)) except: print("Please just insert numbers and commas") if x == ",": com+=1 elif x == " ": None else: num+=1 if com <= num and (com>(num-3)): print(numlist) print(tuple(numlist)) else: print("Please enter a comma between the numbers")`  The only thing I'm missing is to check the that the sequence is number comma number comma etc... Because with the code I have actually, you can introduce Number comma comma Number and it would work 1, 2, ,3 5 I was expecting this: Please enter a comma between the numbers Got this Please insert a sequence of numbers separated by commas:1, 2, ,3 5 [1, 2, 3, 5] (1, 2, 3, 5)

Answer: You can use regular expression re to match the string whether it follows your pattern or not. For example, if your requirement is: Any string where there is a single comma , between digits, can have multiple spaces in between and the string should end with a number or space (I am assuming you don't want to end with a comma), you can use below expression : ^(\s*\d+\s*\,)*(\s*\d+\s*)$  If you are new to regular expressions, see below explanation: In above expression,  (\s*\d+\s*\,), means multiple spaces \s* (even 0) Followed by one or more digit \d+ Followed by again multiple spaces \s* Ending with a comma \,. The * after this group means above pattern can repeat zero or more time The last group (\s*\d+\s*) means a digit with leading and trailing spaces. Your string should end with this pattern.  Below is the full code that may help you: import re sec=input("Please insert a sequence of numbers separated by commas:") # Check if your string matches your pattern if re.match(r'^(\s*\d+\s*\,)*(\s*\d+\s*)$', sec): # Split the string from comma # str.split("pattern") converts your string into a string of list # "1, 2, 4".split(",") -> ["1 ", "2 ", "4"] # "1@@ 2@@ abcd".split("@@") -> ["1 ", "2 ", "abcd"] arr = sec.split(",") # For each element in arr, use int() function # map(function, iterable): applies given function on each element # of the iterator. Here, we want to apply int() on each element numlist = list(map(int, arr)) print(numlist) print(tuple(numlist)) # Otherwise ask user to input proper string else: print("Please enter a comma between the numbers")  Hope it fulfills your requirement.
Question: I have a function f(x,y) that I know for certain values of X and Y. Then, I have a new function x,y= g(t) that produces a new series of points X and Y. for those points, using the pre-calculated data I need interpolate. import numpy as np import timeit import matplotlib.pyplot as plt def f(X,Y): return np.sin(X)+np.cos(Y) def g(t): return 3*np.cos(t)-0.5, np.sin(5*t)-1 x= np.linspace(-4,4,100) y= np.linspace(-4,4,100) X, Y= np.meshgrid(x,y) Z= f(X,Y) t= np.linspace(0,1,50) x_t, y_t= g(t) plt.figure() plt.imshow(Z, extent=(-4, 4, -4, 4), origin='lower') plt.scatter(x_t,y_t) plt.show()   In other words, I need to obtain the shown curve with the previously calculated Z values, doing interpolation since in real life I dont have access to the actual function Many thanks! EDIT: I found a function that does exactly what I want, but it produces the wrongs samples. import numpy as np import timeit import matplotlib.pyplot as plt from scipy.interpolate import interpn from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D def f(X,Y): return np.sin(X)+np.cos(Y) def g(t): return -np.cos(t), np.sin(2*t) x= np.linspace(-4,4,1001) y= np.linspace(-4,4,1001) X, Y= np.meshgrid(x,y) Z= f(X,Y) grid= (x,y) t= np.linspace(0,2*np.pi, 50) x_t, y_t= g(t) xy_t= np.stack([x_t, y_t], axis=-1) Z_real= f(x_t, y_t) Z_inter= interpn(grid, Z, xy_t) fig= plt.figure() gs= fig.add_gridspec(1,2, hspace=0.1, wspace=0.1) ax1= fig.add_subplot(gs[0,0],projection="3d") surf= ax1.plot_surface(X,Y,Z, cmap=cm.jet, alpha=0.3) ax1.scatter(x_t, y_t, Z_real) fig.colorbar(surf, shrink=0.5, aspect=5) ax2= fig.add_subplot(gs[0,1]) ax2.plot(t, Z_real) ax2.plot(t, Z_inter, '+') plt.show()   Can anyone tell me what I am doing wrong?

Answer: I recently shared a solution to a comparable question that is applicable to your case as well. In brief, scipy.interpolate provides a range of 2D interpolation options. In this instance, I utilized RegularGridInterpolator or CloughTocher2DInterpolator, but you also have the option to employ another methods within scipy.interpolate. RegularGridInterpolator outperforms CloughTocher2DInterpolator in terms of speed. However, the latter does not necessitate data to be arranged on a grid. import scipy.interpolate # Option 1: RegularGridInterpolator z_reg_interpolator = RegularGridInterpolator( (x, y), f(*np.meshgrid(x, y, indexing='ij', sparse=True)) ) Z_reg_inter= z_reg_interpolator(np.array(g(t)).T) # Option 2: CloughTocher2DInterpolator with less datapoints to speed it up n = 101 x_n= np.linspace(-4, 4, n) y_n= np.linspace(-4, 4, n) XY_n = np.meshgrid(x_n,y_n) z_interpolator = scipy.interpolate.CloughTocher2DInterpolator( np.dstack(XY_n).reshape(-1,2), f(*XY_n).reshape(-1,1)) Z_clo_inter = z_interpolator(np.array(g(t)).T)
Question: I need to update the values of a column (X) in correspondance with certain values ('v') of another column Y with values from another column (Z) times some numbers and then cast the obtained column to int: In pandas the code is as follow: df.loc[df["Y"] == "v", "X"] = ( df.loc[df["Y"] == "v", "Z"] * 1.341 * 15).astype(int)  In polars i'm able to select values (e.g. nulls) from a column and replace them with values from another column: df=df.with_columns( pl.when(df['col'].is_null()) .then(df['other_col']) .otherwise(df['col']) .alias('alias_col') )  but i'm stuck at nesting the call to the (modified) third column. Any help would be very much appreciated!

Answer: First, we create a dataframe to operate on. I inferred the context and types from your pandas example. import polars as pl df = pl.DataFrame({ "X": [1, 1, 1, 1, 1], "Y": ["w", "w", "v", "w", "v"], "Z": [1, 2, 3, 4, 5], })  Output. shape: (5, 3) ┌─────┬─────┬─────┐ │ X ┆ Y ┆ Z │ │ --- ┆ --- ┆ --- │ │ i64 ┆ str ┆ i64 │ ╞═════╪═════╪═════╡ │ 1 ┆ w ┆ 1 │ │ 1 ┆ w ┆ 2 │ │ 1 ┆ v ┆ 3 │ │ 1 ┆ w ┆ 4 │ │ 1 ┆ v ┆ 5 │ └─────┴─────┴─────┘  Then, it looks like you want to do the following  When column Y is equal to "v", then take the integer value of column Z times 3.41 x 15. Otherwise, take the value of columns X. Store the result again in column X.  This can be achieved with polars' when-then-otherwise expression. df.with_columns( pl.when( pl.col("Y") == "v" ).then( (pl.col("Z") * 1.341 * 15).cast(pl.Int64) ).otherwise( pl.col("X") ).alias("X") )  Output. shape: (5, 3) ┌─────┬─────┬─────┐ │ X ┆ Y ┆ Z │ │ --- ┆ --- ┆ --- │ │ i64 ┆ str ┆ i64 │ ╞═════╪═════╪═════╡ │ 1 ┆ w ┆ 1 │ │ 1 ┆ w ┆ 2 │ │ 60 ┆ v ┆ 3 │ │ 1 ┆ w ┆ 4 │ │ 100 ┆ v ┆ 5 │ └─────┴─────┴─────┘
Question: have a python dataframe. df1 Load Instance_name counter_name counter_value 0 A bytes_read 0 0 A bytes_written 90 0 B bytes_read 100 0 B bytes_Written 90 1 A bytes_read 10 1 A bytes_written 940 1 B bytes_read 1100 1 B bytes_written 910  To simplify view, I need something like below ex. transform counter_name column fields to columns and rearrange data.*** df2 = Load Instance_name bytes_read bytes_written 0 A 0 90 0 B 100 90 1 A 10 940 1 B 1100 910  I am new to python dataframe libraries and not sure the right way to achieve this.

Answer: Try this: df.set_index(['Load', 'Instance_name', 'counter_name'])['counter_value'].unstack()  Output: counter_name Load Instance_name bytes_Written bytes_read bytes_written 0 0 A NaN 0.0 90.0 1 0 B 90.0 100.0 NaN 2 1 A NaN 10.0 940.0 3 1 B NaN 1100.0 910.0  Note typo in input dataframe bytes_Written and bytes_written.
Question: Is it possible to provide values for complex types generated by polyfactory? I use pydantic for models and pydantic ModelFactory. I noticed that build method supports kwargs that can provide values for constructed model, but I didn't figure if it's possible to provide values for nested fields. For example, if I have model A which is also the type for field a in model B, is ti possible to construct B via polyfactory and provide some values for field 'a'? I tried to call build with MyFactory.build(**{"a": {"nested_value": "b"}})  but it does not work. Is it possible to override nested values?

Answer:  just add another Factory for 'b' example code:  from pydantic_factories import ModelFactory from datetime import date, datetime from typing import List, Union, Dict from pydantic import BaseModel, UUID4 class B(BaseModel): k1: int k2: int class Person(BaseModel): id: UUID4 name: str hobbies: List[str] age: Union[float, int] birthday: Union[datetime, date] nested_model: B class PersonFactory(ModelFactory): __model__ = Person class KFactory(ModelFactory): __model__ = B result = PersonFactory.build(**{"name" :"test","hobbies" : [1,2],"nested_model" : KFactory.build(k1=1,k2=2)}) print(result) # same result result = PersonFactory.build(**{"name" :"test","hobbies" : [1,2],"nested_model" : KFactory.build(**{"k1":1,"k2":2})}) print(result)   result:  id=UUID('1ff7c9ed-223a-4f98-a95e-e3307426f54e') name='test' hobbies=['1', '2'] age=488202245.889748 birthday=datetime.date(2023, 3, 2) nested_model=B(k1=1, k2=2)
Question: I have a child class named USA and it has two parent classes, A and B. Both parents have a method named change_to_4 and in B.__init__ I call the method, but instead of using the method that I defined in B it uses the A definition of the change_to_4 method. class A: def __init__(self) -> None: self.a = 1 super().__init__() def change_to_4(self): self.x = 4 class B: def __init__(self) -> None: self.b = 2 self.change_to_4() super().__init__() def change_to_4(self): self.b = 4 class USA(A, B): def __init__(self) -> None: super().__init__() print(f"A vars = {vars(A())}") print(f"B vars = {vars(B())}") print(f"USA vars = {vars(USA())}") print(f"USA mro -> {USA.__mro__}")  I expect something like this: A vars = {'a': 1} B vars = {'b': 4} USA vars = {'a': 1, 'b': 4} USA mro -> (<class '__main__.USA'>, <class '__main__.A'>, <class '__main__.B'>, <class 'object'>)  But the output is A vars = {'a': 1} B vars = {'b': 4} USA vars = {'a': 1, 'b': 2, 'x': 4} USA mro -> (<class '__main__.USA'>, <class '__main__.A'>, <class '__main__.B'>, <class 'object'>) 

Answer: When the interpreter looks up an attribute of an instance, in this case self.change_to_4 on an instance of USA, it first tries to find 'change_to_4' in self.__dict__, and failing to find it there, the interpreter then follows the method resolution order of USA (USA, A, B, object as shown in the output) to find the change_to_4 attribute in the first base class that has it defined. Excerpt from the documentation of Custom classes:  Class attribute references are translated to lookups in this dictionary, e.g., C.x is translated to C.__dict__["x"] (although there are a number of hooks which allow for other means of locating attributes). When the attribute name is not found there, the attribute search continues in the base classes. This search of the base classes uses the C3 method resolution order which behaves correctly even in the presence of ‘diamond’ inheritance structures where there are multiple inheritance paths leading back to a common ancestor.  In this case, A is the first base class of USA that defines change_to_4, so self.change_to_4() gets translated to A.change_to_4(self), resulting in self.x = 4 getting executed. That the call self.change_to_4() is made from a method in B does not change the fact that A comes before B in the method resolution order of a USA instance.
Question: Let's say I make a generic class whose objects only contain one value (of type T). T = TypeVar('T') class Contains(Generic[T]): val: T def __init__(self, val: T): self.val = val  Note that self.val can itself be a Contains object, so a recursive structure is possible. I want to define a function that will reduce such a structure to a single non-Contains object. def flatten(x): while isinstance(x, Contains): x = x.val return x  What should the type signature of 'flatten' be? I tried to make a recursive Nested type Nested = T | Contains['Nested[T]']  but it confuses the type checker as T can also mean a Contains object. def flatten(x: Nested[T]) -> T: while isinstance(x, Contains): reveal_type(x) # reveals Contains[Unknown] | Contains[Nested] x = x.val reveal_type(x) # reveals object* | Unknown return x  Another approach was to make a separate class class Base(Generic[T]): self.val: T def __init__(self, val): self.val = val Nested = Base[T] | Contains['Nested[T]'] def flatten(x: Nested[T]) -> T: while isinstance(x, Contains): x = x.val return x.val  This works, but you have to wrap the argument in a Base object every time, which is cumbersome. Furthermore, Base has the same behaviour as Contains, it's the same thing written twice! I tried to use NewType instead, but it isn't subscriptable. Is there any nice (or, at least not too ugly) way to do it?

Answer: The problem is that static type checkers are typically unable to infer dynamic type changes from assignments such as: x = x.val  As a workaround you can make flatten a recursive function instead to avoid an assignment to x: from typing import TypeVar, Generic, TypeAlias T = TypeVar('T') class Contains(Generic[T]): val: T def __init__(self, val: T): self.val = val Nested: TypeAlias = T | Contains['Nested[T]'] def flatten(x: Nested[T]) -> T: if isinstance(x, Contains): return flatten(x.val) reveal_type(x) # Type of "x" is "object*" return x  Demo with PyRight here Demo with mypy here
Question: I am attempting to add together specific elements of two numpy arrays. For example consider these two arrays: f1 = np.array([ [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]] ]) f2 = np.array([ [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], [[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]] ])  I would like to create a new array that looks like this: [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]  The operation I would like to perform would look something like (f1[r][c][0]+f2[r][c][0])/2 for each array of three values in f1 and f2. I know that np.add(f1,f2)/2 would result in something close to what I'm looking for, except that it would perform the add and scale operations on every element of the array, as opposed to just the first element in each length 3 subarray. Is the best way to do this just breaking up each original array into three separate arrays, thus allowing me to use np.add(f1,f2)/2? Apologies if this is a duplicate, I couldn't seem to find anything about performing an operation like this.

Answer: You can index the desired position before computing the sum: (f1[...,0]+f2[...,0])/2  Output: array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]])
Question: I've upgraded from Python 3.8 32 bit to 3.12 64 bit because of memory limitations. However, the module PIL isn't being recognised, with this error: Traceback (most recent call last): File "C:\Users\rhann\Desktop\Scripts\langton's ant.py", line 2, in <module> from PIL import Image ModuleNotFoundError: No module named 'PIL' The version of Pillow is 10.2.0, and I installed it using the command pip3 install Pillow on the command line. The line causing the error is from PIL import Image. The line before it, import math, does not cause problems. I've looked up questions on this website, and some others, but none of them have helped. I've tried uninstalling and reinstalling Pillow. Let me know if you need any more details.

Answer: Each version of Python (and each virtualenv) has a separate set of installed packages -- so just running pip3 will install a package for one Python 3.x environment (usually, but not always, the same one that python3 will start an interpreter for), not every Python 3.x environment. As an alternative to invoking pip3 via its command wrapper, you can invoke pip as a module within the specific interpreter you want it to install a package for; that way it doesn't matter which interpreter's copy of pip3 comes first in your PATH.  Installing Pillow for Python 3.12 on Windows this way might look like: py -3.12 -m pip install Pillow  On more UNIX-y systems, this might instead be python3.12 -m pip install Pillow.
Question: Is is possible to invoke a function in a KeyboardInterrupt after pressing CTRL+C? I have following code: try: #other code except KeyboardInterrupt: GPIO.cleanup() finally: print(summary.getSummary())  I have also tried putting the print(summary.getSummary) before the GPIO.cleanup() inside the exception, but the program exits before executing the print command. Thanks in advance

Answer: Catching KeyboardInterrupt should work as expected: import time try: while True: print("Sleeping") time.sleep(1) except KeyboardInterrupt as ki: print("\nCaught KeyboardInterrupt") finally: print("Finally here")  prints: ^C Sleeping Caught KeyboardInterrupt Finally here  It is possible that the code in the try block is catching KeyboardInterrupt or BaseException, so the outer block cannot catch it. Also note, from the documentation:  Catching a KeyboardInterrupt requires special consideration. Because it can be raised at unpredictable points, it may, in some circumstances, leave the running program in an inconsistent state. It is generally best to allow KeyboardInterrupt to end the program as quickly as possible or avoid raising it entirely. (See Note on Signal Handlers and Exceptions.)