task_180222

.gitignore

@@ -4,3 +4,4 @@
 **.log
 /december/task_081221/files
 /december/task_081221/*.log
+**.plk

february/task_180222/pd_pandas_k_f_cagulis.py

@@ -2,10 +2,16 @@
 # Date - 17.02.2022.
 # Title - Patstāvīgais darbs - pandas
 
-from pathlib import Path
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
+import requests
+from pathlib import Path
+from random import randint
+from fpdf import FPDF
+from statistics import mode
+from PIL import Image
+from io import BytesIO
 from ss_scraper import SS
 
 # flats_few = SS("https://www.ss.com/lv/real-estate/flats/riga-region/all/sell/")
@@ -13,105 +19,202 @@ from ss_scraper import SS
 output_path = "output/graphs"
 all_df = []
 
+QUADRATURE = "Kvadratūra"
+FLOOR = "Stāvs"
+PRICE = "Cena"
+SERIES = "Sērija"
+ROOM_AMOUNT = "Istabu skaits"
+PUB_DATE = "Izvietošanas datums"
 
-def read(path):
-	df = pd.read_excel(path)
-	all_df.append(df)
+series_photos = {
+    "103.": "https://i.ss.com/gallery/5/902/225301/45060087.th2.jpg",
+    "104.": "https://i.ss.com/gallery/5/888/221910/44381841.th2.jpg",
+    "119.": "https://i.ss.com/gallery/5/902/225443/45088567.th2.jpg",
+    "467.": "https://i.ss.com/gallery/5/892/222881/44576186.th2.jpg",
+    "602.": "https://i.ss.com/gallery/5/896/223820/44763891.th2.jpg",
+    "Čehu pr.": "https://i.ss.com/gallery/5/902/225358/45071499.th2.jpg",
+    "Hrušč.": "https://i.ss.com/gallery/5/896/223961/44792152.th2.jpg",
+    "LT proj.": "https://i.ss.com/gallery/5/873/218203/43640498.th2.jpg",
+    "M. ģim.": "https://i.ss.com/gallery/5/871/217506/43501012.th2.jpg",
+    "P. kara": "https://i.ss.com/gallery/5/902/225490/45097851.th2.jpg",
+    "Priv. m.": "https://i.ss.com/gallery/5/895/223697/44739240.th2.jpg",
+    "Renov.": "https://i.ss.com/gallery/5/902/225442/45088303.th2.jpg",
+    "Specpr.": "https://i.ss.com/gallery/5/902/225492/45098378.th2.jpg",
+    "Staļina": "https://i.ss.com/gallery/5/902/225440/45087952.th2.jpg",
+    "Jaun.": "https://i.ss.com/gallery/5/902/225456/45091154.th2.jpg"
+}
 
 
-def get_data():
+class priceGraphs:
+
+	def __init__(self, data, pos, x_value, title, xlabel, y_value=PRICE, ylabel="Price"):
+		self.pos = pos
+		self.x_value = data[x_value]
+		self.y_value = data[y_value]
+		self.title = title
+		self.xlabel = xlabel
+		self.ylabel = ylabel
+
+	def _graph_price(self):
+		plot = plt.subplot2grid((3, 2), self.pos)
+		plot.scatter(self.x_value, self.y_value)
+		plot.set_title(self.title)
+		plot.set_xlabel(self.xlabel)
+		plot.set_ylabel(self.ylabel)
+
+
+def read():
 	files = list(Path(Path(__file__).parent.absolute()).glob("**/*.xlsx"))
 
-	for file in files:
-		read(file)
-	df_out = pd.concat(all_df).reset_index(drop=True)
-	# df_out.to_excel("output/excel/combined.xlsx", index=False)
+	for file_path in files:
+		all_df.append(pd.read_excel(file_path))
+	df_combined = pd.concat(all_df).reset_index(drop=True)
+	df_combined.sort_values(by=[PRICE, PUB_DATE], inplace=True)
+	df_combined.drop_duplicates(subset="Pilns sludinājuma teksts", keep=False, inplace=True)
 
 	# replaces floor value to intiger
-	for value in df_out["Stāvs"]:
-		df_out = df_out.replace(value, int(value[:value.find("/")]))
+	for value in df_combined[FLOOR]:
+		df_combined = df_combined.replace(value, int(float(value[:value.find("/")])))
 
 	# replaces price value to intiger
-	for value in df_out["Cena"]:
-		df_out = df_out.replace(value, replace_value(value))
-	return df_out.sort_values(by="Cena")
+	for value in df_combined[PRICE]:
+		df_combined = df_combined.replace(value, replace_value(value, " ", ",", ""))
+
+	for _ in df_combined[ROOM_AMOUNT]:
+		df_combined = df_combined.replace(["citi", "Citi"], "2")
+	try:
+		for value in df_combined[ROOM_AMOUNT]:
+			df_combined = df_combined.replace(value, int(value))
+	except:
+		pass
+	# converts to datetime
+	df_combined[PUB_DATE] = pd.to_datetime(df_combined[PUB_DATE], format="%d.%m.%Y")
+
+	# df_combined.to_excel("output/excel/combined.xlsx", index=False)
+	return df_combined.sort_values(by=[PRICE, PUB_DATE])
 
 
-def replace_value(value):
-	new_value = value[:value.find(" ")]
-	new_value = new_value.replace(",", "")
-	return int(new_value)
-
-
-def graph_plot():
-	data = get_data()
-	graph_corr(data)
-	graph_price(data)
+# replace value
+replace_value = lambda value, find, replace, replace_to: int(value[:value.find(find)].replace(replace, replace_to))
 
 
 def graph_corr(data):
 	data_corr = data.copy()
-
+	plt.rc("font", size=8)
+	# gets all series
 	series = []
-	for i in data_corr["Sērija"]:
+	for i in data_corr[SERIES]:
 		if i not in series:
 			series.append(i)
-	j = 0
-	for s in series:
-		data_corr = list(map(lambda x: x.replace(s, j), data_corr))
-		j += 1
+	# change series names to numbers
+	data_corr[SERIES] = data_corr[SERIES].replace(series, range(len(series)))
 
-	print(data_corr["Sērija"])
 	sns.heatmap(data_corr.corr())
 	plt.savefig(f"{output_path}/korelacija.png")
+	calc_average(data_corr)
 
 
 def graph_price(data):
-	# plot settings
 	plt.figure(figsize=(50, 30))
 	plt.rc("font", size=15)
-	# plt.rc("font", titlesize=24)
 
-	# placing the plots in the plane
-	plot1 = plt.subplot2grid((3, 2), (0, 0))
-	plot2 = plt.subplot2grid((3, 2), (0, 1))
-	plot3 = plt.subplot2grid((3, 2), (1, 0))
-	plot4 = plt.subplot2grid((3, 2), (1, 1))
-	plot5 = plt.subplot2grid((3, 2), (2, 0))
+	plot1 = priceGraphs(data, (0, 0), FLOOR, "Price to floor", "Floor")
+	plot2 = priceGraphs(data, (0, 1), ROOM_AMOUNT, "Price to room amount", "Room amount")
+	plot3 = priceGraphs(data, (1, 0), QUADRATURE, "Price to quadrature", "Quadrature")
+	plot4 = priceGraphs(data, (1, 1), SERIES, "Price to series", "Series")
+	plot5 = priceGraphs(data, (2, 0), PUB_DATE, "Price to date", "Date")
 
-	# floor to price
-	plot1.scatter(data["Cena"], data["Stāvs"])
-	plot1.set_title("Floor to price")
-	plot1.set_xlabel("Price")
-	plot1.set_ylabel("Floor")
-
-	# room amount to price
-	plot2.scatter(data["Cena"], data["Istabu skaits"])
-	plot2.set_title("Room amount to price")
-	plot2.set_xlabel("Price")
-	plot2.set_ylabel("Room amount")
-
-	# quadrature to price
-	plot3.scatter(data["Cena"], data["Kvadratūra"])
-	plot3.set_title("Quadrature to price")
-	plot3.set_xlabel("Price")
-	plot3.set_ylabel("Quadrature")
-
-	# series to price
-	plot4.scatter(data["Cena"], data["Sērija"])
-	plot4.set_title("Series to price")
-	plot4.set_xlabel("Price")
-	plot4.set_ylabel("Series")
-
-	# date to price
-	plot5.scatter(data["Cena"], data["Izvietošanas datums"])
-	plot5.set_title("Date to price")
-	plot5.set_xlabel("Price")
-	plot5.set_ylabel("Date")
+	plot1._graph_price()
+	plot2._graph_price()
+	plot3._graph_price()
+	plot4._graph_price()
+	plot5._graph_price()
 
 	plt.savefig(f"{output_path}/cenu_grafiki.png")
 
 
+def create_pdf(data):
+	pdf = FPDF("P", "mm", "A4")
+	pdf.add_page()
+	pdf.add_font("Roboto", fname="fonts/Roboto-Regular.ttf", uni=True)
+	pdf.set_font("Roboto", size=12)
+
+	usable_w = pdf.w - 2 * pdf.l_margin
+	width = usable_w / 7
+	hight = pdf.font_size * 2
+	LINE_HIGHT = 5
+
+	columns = [PRICE, FLOOR, ROOM_AMOUNT, SERIES, QUADRATURE, PUB_DATE]
+
+	for column in columns:
+		if column == PUB_DATE:
+			col_width = width * 2
+		else:
+			col_width = width
+		pdf.cell(col_width, hight, column, border=1)
+
+	pdf.ln(hight)
+	pdf.set_font()
+	for _ in range(5):
+		rand_num = randint(2, len(data) - 10)
+		for column in columns:
+			if column == PUB_DATE:
+				col_width = width * 2
+			else:
+				col_width = width
+			pdf.cell(col_width, hight, str(data[column][rand_num]), border=1)
+		pdf.ln(hight)
+
+	text = """
+	"Price to floor" grafiks - lielākā daļa pārdodamo dzīvokļu ir līdz 5. stāvam.
+	"Price to room amount" grafiks - jo mazāk istabu, jo lētāks dzīvoklis.
+	"Price to quadrature" grafiks - jo lielāka dzīvokļa platība, jo dārgāks dzīvoklis.
+	"Price to series" grafiks - dārgākie dzīvokļi ir jaunie.
+	"Price to date" grafiks - nesen pārdošanā ielikto dzīvokļu ir vairāk.
+	"""
+	pdf.ln(hight)
+	pdf.image(f"{output_path}/korelacija.png", w=usable_w)
+	# pdf.write(LINE_HIGHT, "Starp istabu skaitu un cenu, kvadratūru un cenu ir liela korelācija.")
+	pdf.image(f"{output_path}/cenu_grafiki.png", w=usable_w)
+
+	for txt in text.split("\n"):
+		pdf.write(LINE_HIGHT, txt.strip())
+		pdf.ln(LINE_HIGHT)
+
+	average = calc_average(data)
+	for key, value in average.items():
+		if not isinstance(value, str):
+			value = str(round(value))
+		pdf.write(LINE_HIGHT, f"{key} - {value}")
+		pdf.ln(LINE_HIGHT)
+
+	response = requests.get(series_photos[average[SERIES]])
+	img = Image.open(BytesIO(response.content))
+	pdf.image(img)
+	pdf.output("output/pdf.pdf")
+
+
+def calc_average(data):
+	columns = [FLOOR, ROOM_AMOUNT, SERIES, QUADRATURE]
+	mean_price_columns = {FLOOR: None, ROOM_AMOUNT: None, SERIES: None, QUADRATURE: None}
+	for column in columns:
+		if column == SERIES:
+			# print(data[column])
+			# print(f"{column} = {mode(data[column])}")
+			mean_price_columns[column] = (mode(data[SERIES]))
+		else:
+			# print(f"{column} = {mode(data[column])}")
+			mean_price_columns[column] = mode(data[PRICE]) / mode(data[column])
+	return mean_price_columns
+
+
+def graph_plot():
+	data = read()
+	graph_corr(data)
+	graph_price(data)
+	create_pdf(data)
+
+
 def main():
 	graph_plot()
 

february/task_180222/ss_scraper.py

@@ -14,6 +14,7 @@ HEADERS = {
 
 
 class SS:
+
 	def __init__(self, url, name):
 		self.url = url
 		self.name = name
@@ -77,24 +78,20 @@ class SS:
 		chunked_items_list = [items[i:i + chunk_size] for i in range(0, len(items), chunk_size)]  # combines each 'chunk_size' elements into array
 		columns = ["Atrašanās vieta", "Istabu skaits", "Kvadratūra", "Stāvs", "Sērija", "Cena", "Pilns sludinājuma teksts", "Izvietošanas datums"]
 		df = pd.DataFrame(chunked_items_list, columns=columns)
-		time = datetime.now().strftime("%d%m%Y%H%M%S")
-		df.to_excel(excel_writer=f"output/excel/output_{self.name}_{time}.xlsx", index=False)
+		time = datetime.now().strftime("%d%m%y%H%M%S")  # current time
+		df.to_excel(excel_writer=f"output/excel/ss_{self.name}_{time}.xlsx", index=False)
 		print("Done")
 
 
-flats_many = SS("https://www.ss.com/lv/real-estate/flats/riga/all/sell/", "many")
-flats_few = SS("https://www.ss.com/lv/real-estate/flats/riga-region/all/sell/", "few")
+flats_riga = SS("https://www.ss.com/lv/real-estate/flats/riga/all/sell/", "riga")
+flats_rigareg = SS("https://www.ss.com/lv/real-estate/flats/riga-region/all/sell/", "rigareg")
 flats_aizkraukle = SS("https://www.ss.com/lv/real-estate/flats/aizkraukle-and-reg/sell/", "aizkraukle")
 flats_tukums = SS("https://www.ss.com/lv/real-estate/flats/tukums-and-reg/sell/", "tukums")
 flats_ogre = SS("https://www.ss.com/lv/real-estate/flats/ogre-and-reg/sell/", "ogre")
 
 
 def main():
-	# flats_aizkraukle.get_data()
-	# flats_tukums.get_data()
-	# flats_ogre.get_data()
-	# flats_few.get_data()
-	flats_many.get_data()
+	flats_riga.get_data()
 
 
 if __name__ == '__main__':

february/task_180222/test.py

@@ -1,5 +1,2 @@
-from datetime import datetime
-
-time = datetime.now().strftime("%d%m%Y%H%M%S")
-
-print(time)
+val = "5.00"
+print(int((float(val))))