| import pandas as pd | |
| trans = pd.read_csv("2014-05-06-exported_transactions.csv", parse_dates=[0]) | |
| trans = trans[trans.Pending == False] | |
| trans = trans[["Date","Amount","Description","Memo"]] | |
| trans = trans.copy(deep=True) | |
| trans["Inflow"] = trans.Amount.map(lambda x: x if x > 0 else 0) | |
| trans["Outflow"] = trans.Amount.map(lambda x: -x if x <= 0 else 0) | |
| del trans["Amount"] | |
| trans["Payee"] = trans["Description"] | |
| del trans["Description"] |
| import pandas as pd | |
| trans = pd.read_csv("2014-04-02-exported_transactions.csv", parse_dates=[0]) | |
| trans = trans[trans.Pending == False] | |
| trans = trans[["Date","Amount","Description","Memo"]] | |
| trans = trans.copy(deep=True) | |
| trans["Inflow"] = trans.Amount.map(lambda x: x if x > 0 else 0) | |
| trans["Outflow"] = trans.Amount.map(lambda x: -x if x <= 0 else 0) | |
| del trans["Amount"] | |
| trans["Payee"] = trans["Description"] | |
| del trans["Description"] |
| // Use Gists to store code you would like to remember later on | |
| console.log(window); // log the "window" object to the console |
| { | |
| "metadata": { | |
| "name": "" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |
| import re, string, sys, pandas | |
| stops = set(open("../stop_words.txt").read().split(",") + list(string.ascii_lowercase)) | |
| words = [x.lower() for x in re.split("[^a-zA-Z]+", open("../pride-and-prejudice.txt").read()) if len(x) > 0 and x.lower() not in stops] | |
| print pandas.Series(words).value_counts().head(25) |
| --------------------------------------------------------------------------- | |
| AttributeError Traceback (most recent call last) | |
| <ipython-input-7-01896d0e98b9> in <module>() | |
| 5 df = pd.DataFrame({"content":["tim hopper", "this data tim"]}) | |
| 6 mapper = DataFrameMapper([("content", CountVectorizer())]) | |
| ----> 7 mapper.fit_transform(df) | |
| C:\Anaconda\lib\site-packages\sklearn\base.pyc in fit_transform(self, X, y, **fit_params) | |
| 406 if y is None: | |
| 407 # fit method of arity 1 (unsupervised transformation) |
| # Used for unshortening list of urls as wel as checking the HTTP status code of a webpage. | |
| # When called from the command line, requires either a filename or STDIN as input | |
| # Filename or standard input should contain one URL per line | |
| # Output is printed to the standard out and saved to a JSON file using PickleDB. | |
| __author__ = "Tim Hopper" | |
| __email__ = "[email protected]" | |
| import fileinput | |
| import urllib2 |
| { | |
| "metadata": { | |
| "name": "OTW hollow slopes" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |
My scripts for building stiglerdiet.com with Pelican and uploading to Amazon S3 bucket.
Requires:
json files should go into templates folder of ofexport. Modify the ofexport commands to fit your needs. In particular, _Nonurgent is the folder my projects are in, and "Posts to Write" and "To Read" are the projects I'm uploading.
| import clipboard | |
| import urllib2 | |
| import webbrowser | |
| clipString = clipboard.get() | |
| marky = 'http://heckyesmarkdown.com/go/?read=1&u=' | |
| queryString = marky + clipString |
