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Corpus linguistics: method, analysis, interpretation - A Seasonal, Corpus Linguistics, Gift!
FutureLearn [email protected]
Dec 24 (6 days ago)
Well, in the UK it is Christmas Eve and the giving of presents will begin in the morning. So I thought I would start a little early and send you a note to say what the Corpus Linguistics course has in store for you in 2014! OK – a very academic present, I know, but I think it is one you will appreciate. Each week will be split into four. There will be an introductory lecture on a topic, one or more advanced lectures covering similar topics (or just plain interesting topics), a ‘how to’ session where you can develop corpus searching and building skills and, last but not least, one or more ‘in conversation’ videos where I talk to a friend about their use of corpus linguistics. Some of this you know if you have been following my emails and tweets so far (for those of you who are not but would like to follow the tweets follow me @TonyMcEn
| { | |
| "metadata": { | |
| "name": "exploring_a_single_data_file" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |
| from sklearn.metrics import confusion_matrix | |
| def print_cm(cm, labels, hide_zeroes=False, hide_diagonal=False, hide_threshold=None): | |
| """pretty print for confusion matrixes""" | |
| columnwidth = max([len(x) for x in labels]+[5]) # 5 is value length | |
| empty_cell = " " * columnwidth | |
| # Print header | |
| print " " + empty_cell, | |
| for label in labels: | |
| print "%{0}s".format(columnwidth) % label, |
| import os | |
| import sys | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from pandas import DataFrame | |
| from pandas.util.testing import set_trace | |
| dirs = [] |
| library(igraph) # to work with graphs | |
| library(RColorBrewer) # to use a color palette | |
| library(plotrix) # to rescale variables | |
| # Read the data | |
| raw_data <- read.csv("network_data.csv") | |
| names(raw_data) <- c("Source", "Target", "Count", "Money") | |
| # reformat data for igraph library |
The notebooks in this Gist compare the following operations and demonstrate their equivalent outputs:
For pandas 1.3.0:
input_data.groupby(group_by_column).mean()[[expression_column]]