Update app.py

app.py

@@ -1,5 +1,4 @@
 
-
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -17,17 +16,18 @@ acontainer3 = st.empty()
 def asubmit(aparam):
 	adf2 = aparam["adataframe"]
 	aselecteditem = adf2.loc[adf2["atitle"]==aparam["aselected"], "astationcode"]
+	aselectedrow = adf2[adf2["atitle"]==aparam["aselected"]]
 
 	adf3 = pd.DataFrame(
 		[
 			[
-				adf2.loc[adf2["atitle"]==aparam["aselected"], "alatitude"][0],
-				adf2.loc[adf2["atitle"]==aparam["aselected"], "alongitude"][0],
+				aselectedrow["alatitude"][0],
+				aselectedrow["alongitude"][0],
 			]
 		],
 		columns=['lat', 'lon']
 	)
-	aparam["acontainer"].dataframe(adf3)
+	aparam["acontainer"].dataframe(aselectedrow)
 
 	return
 
@@ -50,14 +50,26 @@ def asubmit(aparam):
 			if (row[2]!=amonth) or (row[3]!=amonthday):
 				continue
 
-			adate = ("{:02d}.{:02d}.{} 00:00:00").format(row[3], row[2], row[1])
+			adate = ("{:02d}.{:02d}.{}").format(row[3], row[2], row[1])
 			csvString += '\n'+(",").join([adate,str(row[4]),""])
 			st.write(row[0],adate)
-			adf3 = adf3.append({"Date Time":adate,"T (degC)":(row[4]),}, ignore_index=True)
+			adf3 = adf3.append({"Date":adate,"Celsius":(row[4]),}, ignore_index=True)
 			break
 	adf3 = pd.read_csv(StringIO(csvString), sep=",")
 	aparam["acontainer"].dataframe(adf3)
 
+	adf3.plot.scatter(x='Date', y='Celsius', alpha=.1)
+
+	from sklearn.linear_model import LinearRegression
+# Creating a Linear Regression model on our data
+	lin = LinearRegression()
+	lin.fit(adf3[['Date']], adf3['Celsius'])
+# Creating a plot
+	ax = adf3.plot.scatter(x='Date', y='Celsius', alpha=.1)
+	ax.plot(adf3['Date'], lin.predict(adf3[['Date']]), c='r')
+
+	lin.score(adf3[['Date']], adf3['Celsius'])
+
 adf = pd.DataFrame(
 	astations,
 	columns=astationcolumns