hunterowens

hunterowens

hunterowens

hunterowens

import pandas as pd

import altair as alt

plot_series = df.groupby('Fiscal Year')['cost'].sum()

alt.Chart(plot_series).mark_line().encode(
    x='??',
    y='??'
)

hunterowens

hunterowens

hunterowens

def catagorize(row):
    """
    apply function to catagorize a set of traing data values into 
    catagories. 
    """
    sentiment = 0
    focus = 0 
    energy_level = 0 ## TODO replace with real
    if sentiment > 0:
        if focus > 0: # focus outward

hunterowens

type DMEvent struct {
	XMLName     xml.Name    `xml:"event_message"`
	Category    string      `xml:"category,attr"`
	MsgType     string      `xml:"message_type,attr"`
	OrigSys     string      `xml:"orig_sys,attr"`
	Version     string      `xml:"version,attr"`
	Description string      `xml:",innerxml"`
	Core        []DMCore    `xml:"core_info"`
	ElarmsMsg   []ElarmsMsg `xml:"elarms_info"`
	OnsiteMsg   []OnsiteMsg `xml:"onsite_info"`

hunterowens

library(tidyverse)

dot_data <- read_csv('./path/to/dot.csv')
311_data <- read_csv('./path/to/311.csv') 

# Shared headers, based on dot list you gave me

headers <- c(Columns by SR # / CD # / Request Type / Address / Description / Submit Date / Closed Date / Current Status / Owner) 

dot_data %>% 

hunterowens

import pandas as pd 
df = pd.read_csv('path/to/csv')
df.groupby(['CD','RequestType']).count().to_csv('2016_311_counts.csv')