c = Csv2Frame() #DataFrame create object
#Two files available: male and female names, only one can be active at once
#df = c.ReadCSV('imiona_żeńskie.csv')
df = c.ReadCSV('imiona_meskie.csv')
df, woj = c.CreateDF(df)
dfs = c.SetOfDataFrames(df, woj)
plot = Plots() #BarPlots object
plotBar = plot.bars(dfs)
"""Creating polish provinces map with top 1 name"""
#Reading JSON with polish provinces boundaries
pol_url = "https://raw.githubusercontent.com/deldersveld/topojson/master/countries/poland/poland-provinces.json"
#map instance
map = Map()
m = map.readMap(pol_url)
stats = c.DataFramesMax(dfs)
m_stats = c.MergingData(m, stats)
mapWithLabels = map.mapLabels(m_stats)
"""creating WORD CLOUD with names"""
#sorting and summing occurences of each name
dfSum = c.sumByName(df)
#creating word cloud for top 35 names
wc = plot.wc(dfSum.head(35))
"""creating PIE CHART with a percentage distribution"""
dfPie = c.percByName(df, 15)
pie = plot.pie(dfPie)
"""creating PDF RAPORT"""
s = saveToPDF()
s.saving(plotBar, wc, pie, mapWithLabels)
PartieProcessed, KandydaciProcessed = d.TweetsPerDay(db, Partie)
#line plot of tweets per users
tmpPlots['LinePlot1'] = d.DFs2LinePlot(PartieProcessed, 'Partie')
tmpPlots['LinePlot2'] = d.DFs2LinePlot(KandydaciProcessed, 'Kandydaci na prezydenta')
###average stats of tweets
tweetsStats = d.AvgTweetsPerDay(db,Partie)
tmpPlots['Stats'] = p.Table(tweetsStats)
###bar plot of most frequently used words
tmpWords = d.TopWordsOverall(db)
tmpPlots['Bar'] = p.Bar(tmpWords)
###top 5 words by party
tmpDFs = d.Top5WordsPerUser(db, Partie)
#mapping DFs to Dicts for WordCloud format
tmpDicts ={}
for key, value in tmpDFs.items():
tmpDicts[key] = d.DFtoDict(value)
#generating wc plots and saving into dictionary of wc plots
tmpWC = {}
for key, value in tmpDicts.items():
#tmpWC[key] = p.wc(value,key)
tmpPlots[key]= p.wc(value,key)
###creating PDF RAPORT
d.saving(tmpPlots)