def map_election_results(filename):
with open(filename) as fin:
df = pd.read_csv(fin, delimiter='\t', header=0)
colors = ['#0000FF', '#ff0000']
states = {}
for i in xrange(df.shape[0]):
states[df['State'][i]] = 0 if df['Winner'][i]=='Obama' else 1
smp.make_us_state_map(data=states, colors=colors)
def main():
stateCenterDict = {} #Key: state abbrev Value: 2-tuple of (lat,long) of state center
tweetList = [] #list of two items, first is 2-tuple (lat,long), second is tweet
stateCountDict = {} #Key: state abbrev Value: count for word
stateColor = {} #Key, state abbrev Value: frequency count
readStateCenterFile(stateCenterDict)
readTweetFile(tweetList)
for key in stateCenterDict:
stateCountDict[key] = 0
stateColor[key] = 0
wordOfInterest = input("what word are you looking for? ")
wordOfInterest = wordOfInterest.lower()
# find location of tweet and assign word count
for item in tweetList:
state= None
minDist = 400
pos = item[0]
tweet = item[1]
lat = pos[0]
long = pos[1]
if lat > 55:
state = 'AK'
elif lat < 26:
state = 'HI'
else:
for s in stateCenterDict.keys():
sLat = stateCenterDict[s][0]
sLong = stateCenterDict[s][1]
d = distance(sLat, sLong, lat, long)
if d < minDist:
state = s
minDist = d
# assign value and sentiment score to each state
if wordOfInterest in tweet.lower():
if state in stateCenterDict.keys():
stateCountDict[state]+=1
assignStateColor(stateCountDict, stateColor)
print(stateCountDict)
print(stateColor)
COLORS = ["#FFFFFF", "#EBAD99", "#E08566", "#D65C33", "#CC3300", "#A32900", "#7A1F00"]
make_us_state_map(stateColor, COLORS)
stateSentimentScoreDict[
st] = stateSentimentScoreDict[st] + fullDict[sent]
########## Average Sentiment Score ###########
for State in stateCountDict:
if stateCountDict[State] != 0:
stateSentimentScoreDict[State] = (stateSentimentScoreDict[State] /
stateCountDict[State])
#print(State, stateCountDict[State], stateSentimentScoreDict[State])
for state in stateAbbrevList:
print(state, "\n"
"state score: ", stateSentimentScoreDict[state], "\n"
"state count: ", stateCountDict[state])
########### Color Assignment ##############
minimum = findMin(stateSentimentScoreDict)
maximum = findMax(stateSentimentScoreDict)
for ST in stateSentimentScoreDict:
stateSentimentScoreDict[ST] = assignColor(ST, stateSentimentScoreDict,
maximum, minimum)
make_us_state_map(stateSentimentScoreDict, SENTIMENTCOLORS)
def main():
#initilizing dictionaries and lists
stateCenterDict = {} #Key: state abbrev Value: 2-tuple of (lat,long) of state center
tweetList = [] #list of two items, first is 2-tuple (lat,long), second is tweet
sentimentDict = {} #Key: words/phrases Value: sentiment score from -1 .. 1 for each word
#Initilizing these two dictionaries
stateSentimentScore = {}
stateCountDict = {}
#opening the sentimentDictionary using the readSentimentFile function
readSentimentFile(sentimentDict)
#reading the stateCenterDict file with the function readStateCenterFile
readStateCenterFile(stateCenterDict)
#reading and opening the tweetList file using the readTweetFile function
readTweetFile(tweetList)
#Setting the values of the stateCountDict and stateSentimentScore to 0 at each index for future use
for state in stateCenterDict.keys():
stateCountDict[state] = 0
stateSentimentScore[state] = 0
#Getting user input
wordOfInterest=getWordOfInterest()
#creating the InterestList of tweets that have the word of Interest in it.
#Using the findInterestList function
InterestList=findInterestList(tweetList,wordOfInterest)
#creating the interestState list, from findInterestState function. which is states that the tweets of interest are in.
InterestState=findInterestState(InterestList,stateCenterDict)
#if there is no data in index i, add 'addToDict' to the dictionary at that index position.
for i in InterestState:
if i != None:
g=InterestState.count(i)
addToDict={i:g}
stateCountDict.update(addToDict)
#updating the sentimentScore, and the statSentimentScore by calling the updateScores function
sentimentScore, stateSentimentScore=updateScores(InterestList,sentimentDict,InterestState,stateSentimentScore)
#sorts the stateCountDict keys by alphabetical order. for each state,divides the score by how many tweets in that state, creating the average
#print out the data
for state in sorted(stateCountDict.keys()):
if stateCountDict[state] != 0:
stateSentimentScore[state] = stateSentimentScore[state]/stateCountDict[state]
print(state, stateCountDict[state], stateSentimentScore[state])
else:
print(state, stateCountDict[state], stateSentimentScore[state])
#calling the findMaximum and findMinimum functions to determine the min and max scores of the list.
values=stateSentimentScore.values()
minimum=findMinimum(values)
maximum=findMaximum(values)
#This calls the colorDictionary to create the final stateSentimentScore to be used in the coloring of the US map
stateSentimentScore=colorDictionary(minimum,maximum,stateSentimentScore)
##################
#I used color brewer to help me pick the colors.
#Red represents positive sentiment, shading to orange and yellow for less positive senitment.
#Light gray means no data.
#Dark blue represents negative sentiment, shading to light blue for less negative sentiment.
####INDEX#########[ 0 1 2 3 4 5 6 7 8 ]
SENTIMENTCOLORS = ["#4575b4", "#74add1", "#abd9e9", "#e0f3f8", "#bababa", "#fee090", "#fdae61", "#f46d43", "#d73027"]
####COLOR#########["darkblue", "blue", "lightblue", "lightcyan", "lightgray", "yellow", "orange", "darkorange", "red"]
make_us_state_map(stateSentimentScore, SENTIMENTCOLORS)
def main():
#initilizing dictionaries and lists
stateCenterDict = {
} #Key: state abbrev Value: 2-tuple of (lat,long) of state center
tweetList = [
] #list of two items, first is 2-tuple (lat,long), second is tweet
sentimentDict = {
} #Key: words/phrases Value: sentiment score from -1 .. 1 for each word
#Initilizing these two dictionaries
stateSentimentScore = {}
stateCountDict = {}
#opening the sentimentDictionary using the readSentimentFile function
readSentimentFile(sentimentDict)
#reading the stateCenterDict file with the function readStateCenterFile
readStateCenterFile(stateCenterDict)
#reading and opening the tweetList file using the readTweetFile function
readTweetFile(tweetList)
#Setting the values of the stateCountDict and stateSentimentScore to 0 at each index for future use
for state in stateCenterDict.keys():
stateCountDict[state] = 0
stateSentimentScore[state] = 0
#Getting user input
wordOfInterest = getWordOfInterest()
#creating the InterestList of tweets that have the word of Interest in it.
#Using the findInterestList function
InterestList = findInterestList(tweetList, wordOfInterest)
#creating the interestState list, from findInterestState function. which is states that the tweets of interest are in.
InterestState = findInterestState(InterestList, stateCenterDict)
#if there is no data in index i, add 'addToDict' to the dictionary at that index position.
for i in InterestState:
if i != None:
g = InterestState.count(i)
addToDict = {i: g}
stateCountDict.update(addToDict)
#updating the sentimentScore, and the statSentimentScore by calling the updateScores function
sentimentScore, stateSentimentScore = updateScores(InterestList,
sentimentDict,
InterestState,
stateSentimentScore)
#sorts the stateCountDict keys by alphabetical order. for each state,divides the score by how many tweets in that state, creating the average
#print out the data
for state in sorted(stateCountDict.keys()):
if stateCountDict[state] != 0:
stateSentimentScore[
state] = stateSentimentScore[state] / stateCountDict[state]
print(state, stateCountDict[state], stateSentimentScore[state])
else:
print(state, stateCountDict[state], stateSentimentScore[state])
#calling the findMaximum and findMinimum functions to determine the min and max scores of the list.
values = stateSentimentScore.values()
minimum = findMinimum(values)
maximum = findMaximum(values)
#This calls the colorDictionary to create the final stateSentimentScore to be used in the coloring of the US map
stateSentimentScore = colorDictionary(minimum, maximum,
stateSentimentScore)
##################
#I used color brewer to help me pick the colors.
#Red represents positive sentiment, shading to orange and yellow for less positive senitment.
#Light gray means no data.
#Dark blue represents negative sentiment, shading to light blue for less negative sentiment.
####INDEX#########[ 0 1 2 3 4 5 6 7 8 ]
SENTIMENTCOLORS = [
"#4575b4", "#74add1", "#abd9e9", "#e0f3f8", "#bababa", "#fee090",
"#fdae61", "#f46d43", "#d73027"
]
####COLOR#########["darkblue", "blue", "lightblue", "lightcyan", "lightgray", "yellow", "orange", "darkorange", "red"]
make_us_state_map(stateSentimentScore, SENTIMENTCOLORS)
def simplemap():
colors = ["#FF0000", "#0000FF", "#800080"] # 0 = Red, 1 = Blue, 2 = Purple
states = {}
blue_states = {}
red_states = {}
purple_states = {}
states["AL"] = 0
states["AK"] = 0
states["AZ"] = 0
states["AR"] = 0
states["CA"] = 1
states["CO"] = 1
states["CT"] = 1
states["DE"] = 1
states["FL"] = 2
states["GA"] = 0
states["HI"] = 1
states["ID"] = 0
states["IL"] = 1
states["IN"] = 0
states["IA"] = 0
states["KS"] = 0
states["KY"] = 0
states["LA"] = 0
states["ME"] = 1
states["MD"] = 1
states["MA"] = 1
states["MI"] = 1
states["MN"] = 1
states["MS"] = 0
states["MO"] = 0
states["MT"] = 0
states["NE"] = 0
states["NV"] = 2
states["NH"] = 1
states["NJ"] = 1
states["NM"] = 1
states["NY"] = 1
states["NC"] = 2
states["ND"] = 0
states["OH"] = 2
states["OK"] = 0
states["OR"] = 1
states["PA"] = 1
states["RI"] = 1
states["SC"] = 0
states["SD"] = 0
states["TN"] = 0
states["TX"] = 0
states["UT"] = 0
states["VT"] = 1
states["VA"] = 1
states["WA"] = 1
states["WV"] = 0
states["WI"] = 1
states["WY"] = 0
simplemapplot.make_us_state_map(data=states, colors=colors)
def main():
i = 0
stateCenterDict = {
} #Key: state abbrev Value: 2-tuple of (lat,long) of state center
tweetList = [
] #list of two items, first is 2-tuple (lat,long), second is tweet
sentimentDict = {
} #Key: words/phrases Value: sentiment score from -1 .. 1 for each word
stateSentimentScore = {
} #Key: state abbrev Value: sentiment score for word
stateCountDict = {} #Key: state abbrev Value: count for word
readStateCenterFile(stateCenterDict)
readTweetFile(tweetList)
for key in stateCenterDict:
stateCountDict[key] = 0
stateSentimentScore[key] = 0
wordOfInterest = input("what word are you looking for? ")
wordOfInterest = wordOfInterest.lower()
tweet_list_sandwich = display_tweetlist(
tweetList) #list of text from all tweets
found_Tweets = find_tweet(
wordOfInterest, tweet_list_sandwich
) #list of positions of tweets that had the wordOfInterest
states_tuple = display_stateDict(stateCenterDict, found_Tweets, tweetList)
states_list = states_tuple[0] # list of every state used
find_state = states_tuple[
1] # a dictionary where key is position of tweet, and a value of state
updated_Found_Tweets = states_tuple[
2] #updated list of positions of tweets that have the word of interest and were assigned states
counting_states(states_list, stateCenterDict,
stateCountDict) # builds stateCountDict
readSentimentFile(sentimentDict) #builds sentimentDict
assign_sentiment_state(updated_Found_Tweets, tweet_list_sandwich,
sentimentDict, stateSentimentScore, find_state,
stateCountDict)
state_sentiment_scores = stateSentimentScore #creating new variable
SORTED = sorted(stateSentimentScore.keys()
) #organizing by keys aka state abbreviations
for organize in SORTED:
print(organize, stateCountDict[organize],
state_sentiment_scores[organize])
Colors = assignSentimentColors(
stateSentimentScore
) #dictionary how states and their index for color of map
###########################################################################
# FILL IN YOUR MAIN CODE HERE - Add 3-5 functions outside of main to help.
###########################################################################
##################
#I used color brewer to help me pick the colors.
#Red represents positive sentiment, shading to orange and yellow for less positive senitment.
#Light gray means no data.
#Dark blue represents negative sentiment, shading to light blue for less negative sentiment.
####INDEX#########[ 0 1 2 3 4 5 6 7 8 ]
SENTIMENTCOLORS = [
"#4575b4", "#74add1", "#abd9e9", "#e0f3f8", "#bababa", "#fee090",
"#fdae61", "#f46d43", "#d73027"
]
####COLOR#########["darkblue", "blue", "lightblue", "lightcyan", "lightgray", "yellow", "orange", "darkorange", "red"]
make_us_state_map(stateSentimentScore, SENTIMENTCOLORS)
