import pandas as pd import numpy as np # model from lmfit import Minimizer, Parameters, report_fit #plot import chart_studio.plotly as py import ipywidgets as widgets from ipywidgets import interact, interact_manual import cufflinks as cf theCountry=’Canada’ threshhold=10 theData=confirmed_series_21[confirmed_series_21[theCountry]>threshhold] data=theData[theCountry] start_date= data.index[0] end_date= data.index[-1] dateData=pd.date_range(start=start_date,end=end_date) forecastDays=60 dateForecast= pd.date_range(start=end_date,periods=forecastDays+1)[1:] dateObsForecast=dateData.append(dateForecast) #dateObsForecast # define objective function: returns the array ..
Category : AI
import pandas as pd import folium stations_fn=”LatitudeLongitude.csv” df=pd.read_csv(stations_fn) world_map = folium.Map(location=[10,0], tiles=”cartodbpositron”, zoom_start=2,max_zoom=25,min_zoom=2) for i in range(0,len(df)-100000): folium.Circle( location=[df.iloc[i][‘Latitude’],df.iloc[i][‘Longitude’]], radius=0.5, color=’#0066ff’, fill_color=’#3385ff’, fill=True).add_to(world_map)..
Rename columns x1 to x3, x2 to x4 from pyspark.sql import SparkSession spark=SparkSession.builder.appName(‘rename columns’).getOrCreate() data = spark.createDataFrame([(1,2), (3,4)], [‘x1’, ‘x2’]) data.show() data = data.withColumnRenamed(‘x1′,’x3’) \ .withColumnRenamed(‘x2’, ‘x4’) d..
import cv2 input_imgfn=”tobrighten.jpg” output_imgfn=”brightened.jpg” def change_brightness(img, value=30): hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h, s, v = cv2.split(hsv) v = cv2.add(v,value) v[v > 255] = 255 v[v < 0] = 0 final_hsv = cv2.merge((h, s, v)) img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR) return img img = cv2.imread(input_imgfn) #load rgb image img = change_brightness(img, value=90) #increases #img = change_brightness(img, value=-30) ..
output = pd.DataFrame({‘date’ : [],’Forecast’:[],’Cases’: [],’Fitting’:[],’Increase’:[]}) output[‘date’]=dateObsForecast output[‘Forecast’]=y1*last output[‘Cases’].iloc[:dataLen]=data.values*last output[‘Fitting’].iloc[:dataLen]=final.values*last output[‘Increase’].iloc[1:]=(y1[1:]-y1[:-1])*last output=output.set_ind..
Examples for pivot_table of Pandas and crosstab of Pyspark from my work directory:pyWorkDir/Bigdata/Pyspark/DataForYuanPei.ipynb pivot_table casepandas=indcases.toPandas() casetable1=pd.pivot_table(casepandas, values=’VALUE’, index=[“Case identifier number”], columns=[“Case information”], aggfunc=np.sum) crosstab casetable=casedf.crosstab(‘case_Date’,’province’) casetable=casetable.toPandas() casetable=casetable.sort_values(‘case_Date_province’) cumsum_casetable=casetable.set_index(‘case_Date_province’).cumsum() cumsum_casetable[‘CA’]=cumsum_casetable.sum(axis=1) casedftable=casedf.crosstab(‘case_Date’,’health_region’) health_region_table=casedftable.select([‘case_Date_health_region’,’Toronto’,’Montréal’,’Vancouver Coastal’,..
rename columns name: df = df.rename(columns={“oldcol1″:”newcol1″,”oldcol2”: “newcol2”}) change value of a column under a condition: df_confirmed.loc[df_confirmed[‘country’] == “US”, “country”] = “USA” replace NaN with some value: df_confirmed = df_confirmed.replace(np.nan, ”, regex=True) drop several columns(Lat and Long): df = df.drop([‘Lat’,’Long’..
From: https://resources-covid19canada.hub.arcgis.com/pages/media Map source:https://covid19canada.maps.arcgis.com/ Time sereis source:https://covid19canada.maps.a..
Based on my code: Canada_COVID19_cases_information.ipynb I like this way to convert string to date: from pyspark.sql.types import * #data types func = udf (lambda x: datetime.strptime(x, ‘%d/%m/%Y’), DateType()) df = df.withColumn(‘newDate’, func(col(‘Date’))) calculate difference days between two date: Some good examples from pyspark.sql import functions as F df = df.withColumn(‘startDay’,F.lit(‘2020-01-01’).cast(“Date”)) df = df.withColumn(‘Days_from_01_Jan’,F.datediff(F.col(‘newDate’),F.col(‘startDay’))) convert pandas ..
JHU: Coronavirus COVID-19 Global Cases by the Center for CSSE at Johns Hopkins University ViriHealth Canada COVID-19 cases details COVID-19 Canada: COVID-19 Canada Outbreak Tracker WHO Coronavirus disease (COVID-2019) situation reports Status of cases in Canada Status of cases in Ontario Statements from Toronto’s Medical Officer of Health WHO database of COVID-19 research SDSN: Data ..