Keep the labels from your original file like this: df = read.table(‘data.txt’, header = T) If you have columns named x and y, you can address them like this: df$x df$y If you’d like to actually delete the first row from a data.frame, you can use negative indices like this: df = df[-1,] If you’d … Read more
all of them are LINQ – Language Integrated Query – so they all share a lot of commonality. All these “dialects” basically allow you to do a query-style select of data, from various sources. Linq-to-SQL is Microsoft’s first attempt at an ORM – Object-Relational Mapper. It supports SQL Server only. It’s a mapping technology to … Read more
You can use order on the names, and use that to order the columns when subsetting: test[ , order(names(test))] A B C 1 4 1 0 2 2 3 2 3 4 8 4 4 7 3 7 5 8 2 8 For your own defined order, you will need to define your own mapping … Read more
Since I originally wrote this answer, I have updated it with the many ways that are now available for accessing sample data sets in Python. Personally, I tend to stick with whatever package I am already using (usually seaborn or pandas). If you need offline access, installing the data set with Quilt seems to be … Read more
It really depends on the sort of data you’re bringing back. Since a DataSet is (in effect) just a collection of DataTable objects, you can return multiple distinct sets of data into a single, and therefore more manageable, object. Performance-wise, you’re more likely to get inefficiency from unoptimized queries than from the “wrong” choice of … Read more
Manually, you can use pd.DataFrame constructor, giving a numpy array (data) and a list of the names of the columns (columns). To have everything in one DataFrame, you can concatenate the features and the target into one numpy array with np.c_[…] (note the []): import numpy as np import pandas as pd from sklearn.datasets import … Read more
Here are a couple of discussions explaining why Dispose is not necessary for a DataSet. To Dispose or Not to Dispose ?: The Dispose method in DataSet exists ONLY because of side effect of inheritance– in other words, it doesn’t actually do anything useful in the finalization. Should Dispose be called on DataTable and DataSet … Read more
To modify the float output do this: df= pd.DataFrame(range(5), columns=[‘a’]) df.a = df.a.astype(float) df Out[33]: a 0 0.0000000 1 1.0000000 2 2.0000000 3 3.0000000 4 4.0000000 pd.options.display.float_format=”{:,.0f}”.format df Out[35]: a 0 0 1 1 2 2 3 3 4 4
The accepted answer has many elements of k-NN (k-nearest neighbors), a different algorithm. Both k-NN and NaiveBayes are classification algorithms. Conceptually, k-NN uses the idea of “nearness” to classify new entities. In k-NN ‘nearness’ is modeled with ideas such as Euclidean Distance or Cosine Distance. By contrast, in NaiveBayes, the concept of ‘probability’ is used … Read more