python-3.x

问题 I have a nested list of strings which I would like to extract them the date. The date format is: Two numbers (from 01 to 12 ) hyphen tree letters (a valid month) hyphen two numbers, for example: 08-Jan—07 or 03-Oct—01 I tried to use the following regex: r'\d{2}(—|-)(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)-\d{2,4}' Then I tested it as follows: import pandas as pd df = pd.DataFrame({'blobs':['6-Feb- 1 4 Facebook’s virtual-reality division created a 3-EBÚ7 11 network of 500 free demo

问题 I am using scikit-learn for text processing, but my CountVectorizer isn't giving the output I expect. My CSV file looks like: "Text";"label" "Here is sentence 1";"label1" "I am sentence two";"label2" ... and so on. I want to use Bag-of-Words first in order to understand how SVM in python works: import pandas as pd from sklearn import svm from sklearn.feature_extraction.text import CountVectorizer data = pd.read_csv(open('myfile.csv'),sep=';') target = data["label"] del data["label"] #

问题 I am using scikit-learn for text processing, but my CountVectorizer isn't giving the output I expect. My CSV file looks like: "Text";"label" "Here is sentence 1";"label1" "I am sentence two";"label2" ... and so on. I want to use Bag-of-Words first in order to understand how SVM in python works: import pandas as pd from sklearn import svm from sklearn.feature_extraction.text import CountVectorizer data = pd.read_csv(open('myfile.csv'),sep=';') target = data["label"] del data["label"] #

问题 EDIT to format: This is the original code from __future__ import print_function import socket import sys def socket_accept(): conn, address = s.accept() print("Connection has been established | " + "IP " + address[0] + "| Port " + str(address[1])) send_commands(conn) conn.close() def send_commands(conn): while True: cmd = raw_input() if cmd == 'quit': conn.close() s.close() sys.exit() if len(str.encode(cmd)) > 0: conn.send(str.encode(cmd)) client_response = str(conn.recv(1024), "utf-8") print

问题 EDIT to format: This is the original code from __future__ import print_function import socket import sys def socket_accept(): conn, address = s.accept() print("Connection has been established | " + "IP " + address[0] + "| Port " + str(address[1])) send_commands(conn) conn.close() def send_commands(conn): while True: cmd = raw_input() if cmd == 'quit': conn.close() s.close() sys.exit() if len(str.encode(cmd)) > 0: conn.send(str.encode(cmd)) client_response = str(conn.recv(1024), "utf-8") print

问题 All I'm looking for is a list of possible values of classinfo since the documentation doesn't provide one and I can't seem to find one anywhere else online, let alone SO. 回答1: print([t for t in __builtins__.__dict__.values() if isinstance(t, type)]) Output (line-breaks inserted for readability): [ <class '_frozen_importlib.BuiltinImporter'>, <class 'bool'>, <class 'memoryview'>, <class 'bytearray'>, <class 'bytes'>, <class 'classmethod'>, <class 'complex'>, <class 'dict'>, <class 'enumerate'>

问题 I am trying to do a quick proof of concept for building a data processing pipeline in Python. To do this, I want to build a Google Function which will be triggered when certain .csv files will be dropped into Cloud Storage. I followed along this Google Functions Python tutorial and while the sample code does trigger the Function to create some simple logs when a file is dropped, I am really stuck on what call I have to make to actually read the contents of the data. I tried to search for an

问题 I have a text file with my precomputed word vectors in the following format (example): word -0.0762464299711 0.0128308048976 ... 0.0712385589283\n’ on each line for every word (with 297 extra floats in place of the ... ). I am trying to load these with Gensim as KeyedVectors, because I ultimately would like to compute the cosine similarity, find most similar words, etc. Unfortunately I have not worked with Gensim before and from the documentation it's not quite clear to me how to do this. I

问题 I have a text file with my precomputed word vectors in the following format (example): word -0.0762464299711 0.0128308048976 ... 0.0712385589283\n’ on each line for every word (with 297 extra floats in place of the ... ). I am trying to load these with Gensim as KeyedVectors, because I ultimately would like to compute the cosine similarity, find most similar words, etc. Unfortunately I have not worked with Gensim before and from the documentation it's not quite clear to me how to do this. I

问题 This question already has answers here : dict.fromkeys all point to same list (4 answers) Closed 5 years ago . I have a dictionary of empty lists with all keys declared at the beginning: >>> keys = ["k1", "k2", "k3"] >>> d = dict.fromkeys(keys, []) >>> d {'k2': [], 'k3': [], 'k1': []} When I try to add a coordinate pair (the list ["x1", "y1"] ) to one of the key's lists, it instead adds to all the keys' lists: >>> d["k1"].append(["x1", "y1"]) >>> d {'k1': [['x1', 'y1']], 'k2': [['x1', 'y1']],