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## Proportion vs. binary response with pglm

I’m working with panel data comprising several years of observations of schools. My DV is a proportion of exam passers but is not normally distributed, and many observations of the DV are > 0.8. A panel linear model using plm() is therefore inappropriate, so I am trying to treat the DV as a binary response […]

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## Fast way to remove array of specific row values from 2D numpy array

I have a 2D array like this: a = np.array([[25, 83, 18, 71], [75, 7, 0, 85], [25, 83, 18, 71], [25, 83, 18, 71], [75, 48, 8, 43], [ 7, 47, 96, 94], [ 7, 47, 96, 94], [56, 75, 50, 0], [19, 49, 92, 57], [52, 93, 58, 9]]) and I want to […]

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## Seaborn lineplot and barplot don’t align in the X axis

Okay so I’ve been stuck here for the past 5 hours but I can’t seem to do this combo graph correctly. import pandas as pd from matplotlib import pyplot as plt import seaborn as sns data = pd.read_csv(‘rating_conversion.csv’) df = pd.DataFrame(data) overall_conversion_rate = df[‘overall_conversion_rate’] page_view_conversion = df[‘page_view_conversion’] Avg_Rating = df[‘avg_rating’] Total_Hired = df[‘total_hires’] df[:12] fig, […]

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## How would someone in 1917 describe elements 43, 61, 72, 75, 84, 85, 87, 89, and 91?

https://books.google.com/books?id=aaELAAAAYAAJ&printsec=frontcover#v=onepage&q&f=false Notice the list of elements on pages 62â€“63. It includes a short description of each element’s "Nature under Ordinary Conditions." I am curious to know what the author might have written for elements 43, 61, 72, 75, 84, 85, 87, 89, and 91 (based on 1917 knowledge; for those which hadn’t yet been discovered, […]

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## How to do Constrained Linear Regression – scikit learn?

I am trying to carry out linear regression subject using some constraints to get a certain prediction. I want to make the model predicting half of the linear prediction, and the last half linear prediction near the last value in the first half using a very narrow range (using constraints) similar to a green line […]

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## Drag & Drop Widget Implementation Error- RuntimeWarning: MetaObjectBuilder::addMethod: Invalid method signature provided for “dropped”

This is the custom widget I created for this: from PySide2.QtCore import * from PySide2.QtWidgets import * from PySide2.QtGui import * import os class DragDropWidget(QWidget): def __init__(self, parent=None): super(DragDropWidget, self).__init__(parent) self.setAcceptDrops(True) def dragEnterEvent(self, event): if event.mimeData().hasUrls: event.accept() else: event.ignore() def dragMoveEvent(self, event): if event.mimeData().hasUrls: if len(event.mimeData().urls()) != 1: event.ignore() else: event.setDropAction(Qt.CopyAction) event.accept() else: event.ignore() def […]

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## Object.keys(myObject) returns an arbitrary list of numbers, not expected value

This is a bit of a head-scratcher – and has been a source of pain for the past 3 hours. Here’s my code. const payloadBody = JSON.parse(gatewayEvent.body); console.log(payloadBody); console.log(gatewayEvent.body); console.log(payloadBody.paymentMethodId); console.log(payloadBody[‘paymentMethodId’]); console.log(Object.keys(payloadBody)); If I look at the CloudWatch logs for this (I am executing in Lambda), the line console.log(payloadBody) logs: { "customerId": "cus_REDACTED", "paymentMethodId": "pm_REDACTED", […]

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## Filter cells based on multiple data in 2 columns

Hi, I need to create a conditional filter where at least 3 of the numbers in either cell is greater than 60. I have highlighted the rows in bold that qualify. The first one (lets call them a1 and b1 ) do not qualify as only 1 number is over 60 The second row (a2 […]

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## Efficient neighbourhood search in numpy ndarray instead of nested conditional for loops

Although there are many instances of the question: "What is the numpy alternative to nested for loops", I was unable to fins a suitable answer for my case. Here it goes: I have a 3D numpy array with "0" background and other integers as foreground. I would like to find and store the foreground voxels […]