From 456789381dc6650bba58bcaf2ece00061e002025 Mon Sep 17 00:00:00 2001
From: HaleStorme <159720426+HaleStorme@users.noreply.github.com>
Date: Wed, 21 Feb 2024 15:01:49 -0600
Subject: [PATCH] Create corrected_wrong

This is the exact error message I am receiving in my terminal when running the code "/home/project/banks_project.py:3: DeprecationWarning:
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466

  import pandas as pd
Traceback (most recent call last):
  File "/home/project/banks_project.py", line 55, in <module>
    df = extract(url, table_attribs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/project/banks_project.py", line 30, in extract
    bank_name = col[1].find_all('a')[1]['title']
                ~~~^^^
IndexError: list index out of range"
---
 corrected_wrong | 70 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 corrected_wrong

diff --git a/corrected_wrong b/corrected_wrong
new file mode 100644
index 0000000..4611ad9
--- /dev/null
+++ b/corrected_wrong
@@ -0,0 +1,70 @@
+from bs4 import BeautifulSoup
+import requests
+import pandas as pd
+import numpy as np
+import sqlite3
+from datetime import datetime 
+
+url = 'https://web.archive.org/web/20230908091635/https://en.wikipedia.org/wiki/List_of_largest_banks'
+table_attribs = ["Name", "MC_USD_Billion"]
+db_name = 'Banks.db'
+table_name = 'Largest_banks'
+csv_path = './Largest_banks_data.csv'
+
+def log_progress(message):
+    timestamp_format = '%Y-%h-%d-%H:%M:%S' # Year-Monthname-Day-Hour-Minute-Second 
+    now = datetime.now() # get current timestamp 
+    timestamp = now.strftime(timestamp_format) 
+    with open("./code_log.txt","a") as f:
+        f.write(timestamp + ' : ' + message + '\n') 
+
+def extract(url, table_attribs):
+    page = requests.get(url).text
+    soup = BeautifulSoup(page,'html.parser')
+    df = pd.DataFrame(columns=table_attribs)
+    tables = soup.find_all('tbody')
+    rows = tables[0].find_all('tr')
+    for row in rows:
+        if row.find_all('td') is not None:
+            col = row.find_all('td')
+            bank_name = col[1].find_all('a')[1]['title']
+            market_cap = col[2].contents[0][:-1]
+            data_dict = {"Name": bank_name, "MC_USD_Billion": float(market_cap)}
+            df1 = pd.DataFrame(data_dict, index =[0])
+            df = pd.concat([df,df1], ignore_index = True)
+    return df
+
+def transform(df, csv_path):
+    exchange_rate = pd.read_csv(csv_path)
+    exchange_rate = exchange_rate.set_index('Currency').to_dict()['Rate']
+    df['MC_GBP_Billion']= [np.round(x*exchange_rate['GBP'],2) for x in df['MC_USD_Billion']]
+    df['MC_EUR_Billion']= [np.round(x*exchange_rate['EUR'],2) for x in df['MC_USD_Billion']]
+    df['MC_INR_Billion']= [np.round(x*exchange_rate['INR'],2) for x in df['MC_USD_Billion']]
+    return df
+
+def load_to_csv(df, output_path):
+    df.to_csv(csv_path)
+
+def load_to_db(df, sql_connection, table_name):
+    df.to_sql(table_name, sql_connection, if_exists='replace', index=False)
+
+def run_query(query_statement, sql_connection):
+    print(query_statement)
+    query_output = pd.read_sql(query_statement, sql_connection)
+    print(query_output)
+
+log_progress('Preliminaries complete. Initiating ETL process')
+df = extract(url, table_attribs)
+log_progress('Data extraction complete. Initiating Transformation process')
+df = transform(df,'exchange_rate.csv')
+log_progress('Data transformation complete. Initiating loading process')
+load_to_csv(df, csv_path)
+log_progress('Data saved to CSV file')
+sql_connection = sqlite3.connect('World_Economies.db')
+log_progress('SQL Connection initiated.')
+load_to_db(df, sql_connection, table_name)
+log_progress('Data loaded to Database as table. Running the query')
+query_statement = f"SELECT * from {table_name} WHERE MC_USD_Billion >= 100"
+run_query(query_statement, sql_connection)
+log_progress('Process Complete.')
+sql_connection.close()