python-3.x

问题 I am very new in programming. Please help me in this regards. Thanks in advance. Here is my code: from PyQt5 import QtCore, QtGui, QtWidgets class Ui_MainWindow(object): def clear_table(self): self.tableWidget_2.clearContents() def setupUi(self, MainWindow): MainWindow.setObjectName("MainWindow") MainWindow.resize(1920, 1080) self.centralwidget = QtWidgets.QWidget(MainWindow) self.centralwidget.setObjectName("centralwidget") self.tableWidget_2 = QtWidgets.QTableWidget(self.centralwidget) self

问题 In according with the TTK documentation, in my code, I tried to change the background color only for the TTK labes placed in readonly mode, but unfortunately it didn't work. I replicated the issue below: from tkinter import * from tkinter import ttk class MainWindow: def __init__(self): self.parent=Tk() self.parent.geometry("350x250") self.parent.title("Test") self.parent.configure(background="#f0f0f0") style=ttk.Style() # only the "foreground" option works, but the "background" one not. why?

问题 In reading and experimenting with numpy.random, I can't seem to find or create what I need; a 10 parameter Python pseudo-random value generator including count, min, max, mean, sd, 25th%ile, 50th%ile (median), 75th%ile, skew, and kurtosis. From https://docs.python.org/3/library/random.html I see these distributions uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions, though I need to generate values directly to a distribution defined only by my 10

问题 I can change color of all text in label, but I want to change color of one letter. Is that possible? I use tkiner and python 3.3. 回答1: No, it is not possible to change the color of one letter in a label. However, you can use a text widget instead of a label to color just a single character. You could also use a canvas widget. 来源： https://stackoverflow.com/questions/22071505/change-color-of-one-letter-in-label-python

问题 I notice that my netmiko code can't run sh run | i host which is a legitimate Cisco command. When I replace sh run with other command such as sh clo , or show ip interface brief , it works perfectly. from netmiko import ConnectHandler R1 = { 'device_type': 'cisco_ios', 'ip': 'Router1', 'username': 'u', 'password': 'p' } R2 = { 'device_type': 'cisco_ios', 'ip': 'Router2', 'username': 'u', 'password': 'p' } all_devices = [R1, R2] for device in all_devices: connect = ConnectHandler(**device)

问题 Suppose I'm generating a random number of arrays that I need to serialize def generator(): num = 0 while num < random.randint(0, 10): yield np.array(range(2)) num += 1 with open('out.npy', 'wb') as f: for item in generator(): np.save(f, item) Now how do I know exactly how many times I have to np.load() to get all the arrays back? np.load() will eventually throw an exception so I came up with with open('out.npy', 'rb') as f: try: while 1: item = np.load(f) print(item) except: print("EoF") but

问题 { "key1": { "subfield1": 4, "subfield2": "hello" }, "key2": 325, ... } No idea about how deeply nested it would be (max upto 4 - 5 levels). The only guarantee is that each key will be a string type. What I want to do is to convert the above JSON into the following format: { "field1.subfield1": 4, "field1.subfield2": "hello", "field2" : 325, ..., "fieldN.subfieldM.subsubfieldK. ...": "blah blah" } How can I do this? 回答1: I am currently assuming that your dictionary is only containing other

问题 I have two related models with one of the fields of a model having ManyToManyField relationship with the other model like shown here: Models class Processes(models.Model): x_process = models.CharField(primary_key=True, ...) x_process_text = models.CharField(max_length=35, verbose_name='Short Desc') class RelStatuses(models.Model): rel_status = models.CharField(primary_key=True, ...) rel_status_text = models.CharField(max_length=55, verbose_name='Short Desc') app_process_compo = models

问题 Basically I only reused code from iris utils and iris pipeline with minor change on serving input: def _get_serve_tf_examples_fn(model, tf_transform_output): model.tft_layer = tf_transform_output.transform_features_layer() feature_spec = tf_transform_output.raw_feature_spec() print(feature_spec) feature_spec.pop(_LABEL_KEY) @tf.function def serve_tf_examples_fn(*args): parsed_features = {} for arg in args: parsed_features[arg.name.split(":")[0]] = arg print(parsed_features) transformed

问题 -- I'm loading test data into my SQL Server Database using python and was able to successfully take images and break them down into bytes and store them in the database, but when trying to fetch back the bytes and decode it to save it as a new file type, all i get is a blank image file. Not sure what i am doing wrong here... -- I've tried several iterations using base64 from other tutorials and similar questions, but cant seem to find one that will solve my problem. SQLCommand = ("SELECT