Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
git branch -m old_branch new_branch # Rename branch locally | |
git push origin :old_branch # Delete the old branch | |
git push --set-upstream origin new_branch # Push the new branch, set local branch to track the new remote |
{ | |
"1":{ | |
"name":"Bulbasaur", | |
"attack":49, | |
"defense":49, | |
"evolveLevel":16, | |
"evolveTo":"2", | |
"type":"grass", | |
"moves":[ | |
"tackle", |
#!/usr/bin/env python | |
import sys | |
import pandas as pd | |
import pymongo | |
import json | |
def import_content(filepath): | |
mng_client = pymongo.MongoClient('localhost', 27017) |
<!DOCTYPE HTML> | |
<html lang="en" ng-app="myApp"> | |
<head> | |
<meta charset="utf-8"> | |
<title>Dynamic Pagination w/ Filtering</title> | |
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
<meta name="description" content=""> | |
<meta name="author" content="Kim Maida"> | |
<!-- JS Libraries --> |
This small subclass of the Pandas sqlalchemy-based SQL support for reading/storing tables uses the Postgres-specific "COPY FROM" method to insert large amounts of data to the database. It is much faster that using INSERT. To acheive this, the table is created in the normal way using sqlalchemy but no data is inserted. Instead the data is saved to a temporary CSV file (using Pandas' mature CSV support) then read back to Postgres using Psychopg2 support for COPY FROM STDIN.