From 41e84310ea3374a2134af4c5c573b8f76c5e495b Mon Sep 17 00:00:00 2001
From: Daniel Vargas Herrera <daniel.vhya@gmail.com>
Date: Tue, 17 Jul 2018 10:38:18 +0200
Subject: [PATCH] #12 Update bild_scraping.py fixed bugs. fixes #12. worked
 with @mayarali @mavischer

---
 Wetter_de_scraping.py |  2 ++
 bild_scraping.py      | 23 +++++++++++++++--------
 db_info.py            |  6 +++---
 3 files changed, 20 insertions(+), 11 deletions(-)

diff --git a/Wetter_de_scraping.py b/Wetter_de_scraping.py
index bf21e21..76e5282 100644
--- a/Wetter_de_scraping.py
+++ b/Wetter_de_scraping.py
@@ -98,6 +98,8 @@
 hourly_dict['uvi'] = [None]*number_of_predictions
 
 df = pd.DataFrame(data=hourly_dict)
+df.date_of_acquisition = df.date_of_acquisition.apply(lambda x: datetime.datetime.strptime(x, '%Y%m%d%H').date())
+df.date_for_which_weather_is_predicted = df.date_for_which_weather_is_predicted.apply(lambda x: datetime.datetime.strptime(x, '%Y%m%d%H%M').date())
 try:
     db_manager.insert_df("HourlyPrediction", df)
 finally:
diff --git a/bild_scraping.py b/bild_scraping.py
index 3066099..0b8ccd5 100644
--- a/bild_scraping.py
+++ b/bild_scraping.py
@@ -13,7 +13,7 @@
 import warnings
 import os
 import datetime
-# import db_manager
+import db_manager
 
 #FIRST PART: ONCE-A-DAY PREDICTIONS
 #These are the urls referring directly to high, low temperature
@@ -102,9 +102,9 @@
           "Köln":"Cologne", "München":"Munich"}
 
 daily_dict = {'website':[], 'date_for_which_weather_is_predicted':[], 'city':[],
-              'date_of_aquisition':[], 'temperature_max':[], 'temperature_min':[],
+              'date_of_acquisition':[], 'temperature_max':[], 'temperature_min':[],
               'wind_speed':[], 'humidity':[], 'precipitation_per':[],
-              'precipitation_l':[], 'wind_direction':[], 'condition':[], 'snow':[], 'UVI':[]}
+              'precipitation_l':[], 'wind_direction':[], 'condition':[], 'snow':[], 'uvi':[]}
 
 
 for i,city in enumerate(cities):
@@ -113,13 +113,14 @@
         daily_dict['date_for_which_weather_is_predicted'].append(
                 datetime.datetime.now().strftime('%Y%m%d%H'))
         daily_dict['city'].append(cities[city])
-        daily_dict['date_of_aquisition'].append(
+        print((date_of_acquisition+datetime.timedelta(days)))
+        daily_dict['date_of_acquisition'].append(
                 (date_of_acquisition+datetime.timedelta(days)).strftime('%Y%m%d%H'))
+
         daily_dict['temperature_max'].append(temp_dicts[days][city][0])
         daily_dict['temperature_min'].append(temp_dicts[days][city][1])
         daily_dict['wind_speed'].append(wind_dicts[days][city][0])
         daily_dict['wind_direction'].append(wind_dicts[days][city][1])
-        daily_dict['wind_speed'].append(None)
         daily_dict['humidity'].append(None)
 
         #bild has precipitation forecasts only for the next 2 days
@@ -129,20 +130,23 @@
             daily_dict['precipitation_per'].append(None)
 
         daily_dict['precipitation_l'].append(None)
-        daily_dict['wind_direction'].append(None)
         daily_dict['condition'].append(None)
         daily_dict['snow'].append(None)
-        daily_dict['UVI'].append(None)
+        daily_dict['uvi'].append(None)
 
 #convert to dataframe and save to file
 df_daily = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in daily_dict.items() ]))
+print(df_daily)
+df_daily.date_of_acquisition = df_daily.date_of_acquisition.apply(lambda x: datetime.datetime.strptime(str(x), '%Y%m%d%H').date())
+print(df_daily.date_for_which_weather_is_predicted)
+df_daily.date_for_which_weather_is_predicted = df_daily.date_for_which_weather_is_predicted.apply(lambda x: datetime.datetime.strptime(str(x), '%Y%m%d%H').date())
 try:
     db_manager.insert_df("DailyPrediction", df_daily)
 finally:
     filename = os.path.expanduser('~/Documents/webscraping_2018/data_bild/daily/daily_')
     timestamp = datetime.datetime.now().strftime('%Y%m%d%H')
     filename += timestamp + ".pkl"
-    DailyPrediction.to_pickle(filename)
+    df_daily.to_pickle(filename)
 
 #SECOND PART: FOUR-TIMES-A-DAY PREDICTIONS
 #scrape specified cities for morning, noon, afternoon, night, extract temperature,
@@ -215,6 +219,9 @@
 
 #convert to dataframe and save to file
 df = pd.DataFrame(daily_periods_dict)
+df.date_of_acquisition = df.date_of_acquisition.apply(lambda x: datetime.datetime.strptime(x, '%Y%m%d%H').date())
+df.date_for_which_weather_is_predicted = df.date_for_which_weather_is_predicted.apply(lambda x: datetime.datetime.strptime(x, '%Y%m%d%H').date())
+
 try:
     pass
     db_manager.insert_df("DailyPeriodPrediction", df)
diff --git a/db_info.py b/db_info.py
index 60ed251..a83cb98 100644
--- a/db_info.py
+++ b/db_info.py
@@ -1,5 +1,5 @@
 #Credentials to log into the database
 
-db_name = "db_name"
-db_user = "username"
-db_password = "p@$$w0rd"
+db_name = "db_webscraping"
+db_user = "webscrapers"
+db_password = "bCCnw3b"