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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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#df is the original dataframe with a list of names you want to prevail
#dfF is the dataframe with Names that can be matched only fuzzily
#For each Name in df the code finds the most likely match from the dfF and saves that name
#We then merge on that new key 'Name_r'
#some code is to cover the event of no match (perhaps b/c df has names not in dfF) 
# From http://stackoverflow.com/questions/13636848/is-it-possible-to-do-fuzzy-match-merge-with-python-pandas 
# http://stackoverflow.com/questions/36557722/python-pandas-difflib-throws-list-index-out-of-range-error

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping largest cities\n",
    "## Experiments with folium, zipfile and pandas"
   ]
  },

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def drop_cols(df):
    '''drop columns that don't vary'''
    df.drop([ col for col in df.columns if (len(df[col].unique()) == 1)], 
        axis=1, inplace=True)

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# to create a multinindex

df.sort_values(by = ['Year', 'UnitDesc','bin'], inplace = True)
df = df.set_index(['Year', 'UnitDesc','bin'],drop = False)

# To turn one level of the index into a column
df = df.unstack('UnitDesc')['Value']
df.head()

# to pull out all data in level='Year' 

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from sympy import init_printing; init_printing(use_latex='mathjax')

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import pandas as pd
import pymysql

mysql_connection = pymysql.connect(host='localhost',
                    user='root',
                    password='',
                    db='tutorfall2016',
                    charset='utf8',
                    cursorclass=pymysql.cursors.DictCursor)