data-science

问题 I have a 87288-point dataset that I need to filter. The filtering fields for the dataset are a X position and a Y position, as latitude and longitude. Plotted the data looks like this: The problem is , I only need data along a certain path, which is known in advance. Something like this: I already know how to filter data in a Pandas DF, but given the path is not linear, I need an effective strategy to clear out all the noisy data with a certain degree of precision (since the dataset is so

问题 I am confused on the order of feature selection, sampling and cross validation, My dataset has 468 rows and 23000 columns, out of which 269 belong to class I and 199 belong to class II , The data when split to train and test has [215 class I and 159 class II in train ][54 class I and 40 class II in test].Due to less number of samples I had to apply SMOTE oversampling on the train data to reduce bias. Or should I apply Under Sampling here which leads to data loss resulting in much smaller

问题 Let’s say I have two NumPy arrays, a and b : a = np.array([ [1, 2, 3], [2, 3, 4] ]) b = np.array([8,9]) And I would like to append the same array b to every row (ie. adding multiple columns) to get an array, c : b = np.array([ [1, 2, 3, 8, 9], [2, 3, 4, 8, 9] ]) How can I do this easily and efficiently in NumPy? I am especially concerned about its behaviour with big datasets (where a is much bigger than b ), is there any way around creating many copies (ie. a.shape[0] ) of b ? Related to this

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 months ago . Improve this question Is new macbook m1 suitable for Data Science? Do Data Science python libraries such as pandas, numpy, sklearn etc work on the macbook m1 (Apple Silicon) chip and how fast compared to the previous generation intel based macbooks? 回答1: This GitHub repository has

问题 My task: For the next set of questions, we will be using census data from the United States Census Bureau. Counties are political and geographic subdivisions of states in the United States. This dataset contains population data for counties and states in the US from 2010 to 2015. See this document for a description of the variable names. The census dataset (census.csv) should be loaded as census_df. Answer questions using this as appropriate. Question 5 Which state has the most counties in it

问题 My task: For the next set of questions, we will be using census data from the United States Census Bureau. Counties are political and geographic subdivisions of states in the United States. This dataset contains population data for counties and states in the US from 2010 to 2015. See this document for a description of the variable names. The census dataset (census.csv) should be loaded as census_df. Answer questions using this as appropriate. Question 5 Which state has the most counties in it

问题 I'm trying to train a Linear Regression Model with Python via using Google Stock Prices that can be found here: https://www.kaggle.com/medharawat/google-stock-price And trying to predict future stocks by given features. After that I'm planning to plot it with the values in current dataset. First, I read dataframes with date values with date parser and concatted these 2 dataframes into one in order to split it myself: parser = lambda date: pd.datetime.strptime(date, '%m/%d/%Y') df_test=pd.read

问题 Here is the code I'm trying to execute to encode the values of the first column of my data set using dummy values. import numpy as py import matplotlib.pyplot as plt import pandas as pd DataSet = pd.read_csv('Data.csv') x=DataSet.iloc[:, :-1].values y=DataSet.iloc[:,3].values from sklearn.impute import SimpleImputer imputer=SimpleImputer(missing_values=py.nan,strategy='mean') imputer=imputer.fit(x[:, 1:3]) x[:, 1:3]=imputer.transform(x[:, 1:3]) from sklearn.preprocessing import OneHotEncoder

问题 When shell commands are run within a Jupyter Notebook Python Cell , like this: output = ! some-shell-command each line emitted to the standard output ( stdout ) is captured in a list like IPython data-structure called a SList . For instance: output = !echo -e 'line1\nline2\nline3' print(output) # A IPython SList data-structure. ['line1', 'line2', 'line3'] Sometimes, however, you want to preserve the original string output format, without tokenization into a list, like this: print(output)

问题 I'm trying to pull tweets from a user's timeline in real-time. I then want to do some analysis on those tweets. Having read the docs it looks like I will need to use tweepy.Stream for this use case. I've done the following: stream.filter(follow='25073877') But Twitter's filter API states the following: Tweets created by the user. Tweets which are retweeted by the user. Replies to any Tweet created by the user. Retweets of any Tweet created by the user. Manual replies, created without pressing