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<blockquote data-quote="Hachiko15" data-source="post: 29617819" data-attributes="member: 568034">Yaluwane help ekak dennako meka karagnnamata poddak meka karana vidiya kiya dunnaam ethiPython walinA file namedweather_2023-02.csvcontains hourly weather observations<table style='width: 100%'><tr><th>Abbreviation</th><th>Meaning</th></tr><tr><td>TA</td><td>Temperature Average</td></tr><tr><td>RH</td><td>Relative Humidity</td></tr><tr><td>WS</td><td>Wind Speed</td></tr><tr><td>WD</td><td>Wind Direction</td></tr><tr><td>PRA</td><td>Precipitation Amount</td></tr><tr><td>PRI</td><td>Precipitation Intensity</td></tr><tr><td>PA</td><td>Pressure Average</td></tr><tr><td>WAWA</td><td>Most Significant Weather Code</td></tr></table>Some of the data has been encrypted with your GPG public key. Ignoring any NaN values, find out what percentage of the hourly temperature observations satisfy the following:mama try kara hari giye na[CODE]import pandas as pdfrom cryptography.fernet import Fernet, InvalidToken# Function to generate or load Fernet keydef load_or_generate_key():&nbsp; &nbsp; try:&nbsp; &nbsp; &nbsp; &nbsp; # Load the key if exists&nbsp; &nbsp; &nbsp; &nbsp; with open(&quot;fernet_key.key&quot;, &quot;rb&quot;) as key_file:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; key = key_file.read()&nbsp; &nbsp; except FileNotFoundError:&nbsp; &nbsp; &nbsp; &nbsp; # Generate a new key if not exists&nbsp; &nbsp; &nbsp; &nbsp; key = Fernet.generate_key()&nbsp; &nbsp; &nbsp; &nbsp; with open(&quot;fernet_key.key&quot;, &quot;wb&quot;) as key_file:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; key_file.write(key)&nbsp; &nbsp; return key# Decrypt the column headers and relevant datadef decrypt_weather_data(file_path, key):&nbsp; &nbsp; f = Fernet(key)&nbsp; &nbsp; try:&nbsp; &nbsp; &nbsp; &nbsp; with open(file_path, &quot;rb&quot;) as encrypted_file:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; encrypted_data = encrypted_file.read()&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; decrypted_data = f.decrypt(encrypted_data)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; # Decode the decrypted data as UTF-8 and split it into lines&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; decrypted_lines = decrypted_data.decode(&quot;utf-8&quot;).split(&quot;\n&quot;)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; # Extract column headers&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; column_headers = decrypted_lines[0].split(&quot;,&quot;)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; # Extract relevant data&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; data = []&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; for line in decrypted_lines[1:]:&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; if line.strip():&nbsp; # Skip empty lines&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; row = line.split(&quot;,&quot;)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; if len(row) == len(column_headers):&nbsp; # Ensure the row has the correct number of columns&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; data.append(row)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; return column_headers, data&nbsp; &nbsp; except InvalidToken:&nbsp; &nbsp; &nbsp; &nbsp; print(&quot;Error: Invalid decryption key or corrupt file.&quot;)&nbsp; &nbsp; &nbsp; &nbsp; return None, None# Main functiondef main():&nbsp; &nbsp; # Load or generate Fernet key&nbsp; &nbsp; key = load_or_generate_key()&nbsp; &nbsp; # Decrypt the weather data&nbsp; &nbsp; file_to_decrypt = 'D:Assignment\\weather_2023-02.csv.encrypted'&nbsp; &nbsp; column_headers, data = decrypt_weather_data(file_to_decrypt, key)&nbsp; &nbsp; if column_headers and data:&nbsp; &nbsp; &nbsp; &nbsp; # Create DataFrame from decrypted data&nbsp; &nbsp; &nbsp; &nbsp; df = pd.DataFrame(data, columns=column_headers)&nbsp; &nbsp; &nbsp; &nbsp; # Check if expected columns exist&nbsp; &nbsp; &nbsp; &nbsp; expected_cols = ['TA_PT1H_MAX', 'TA_PT1H_MIN']&nbsp; &nbsp; &nbsp; &nbsp; if not all(col in df.columns for col in expected_cols):&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; missing_cols = [col for col in expected_cols if col not in df.columns]&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; print(f&quot;Error: Expected columns {', '.join(missing_cols)} not found in data.&quot;)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; return&nbsp; &nbsp; &nbsp; &nbsp; # Extract relevant columns&nbsp; &nbsp; &nbsp; &nbsp; df_filtered = df[expected_cols]&nbsp; &nbsp; &nbsp; &nbsp; # Calculate temperature differences&nbsp; &nbsp; &nbsp; &nbsp; df_filtered['temp_diff'] = df_filtered['TA_PT1H_MAX'] - df_filtered['TA_PT1H_MIN']&nbsp; &nbsp; &nbsp; &nbsp; # Handle NaN values&nbsp; &nbsp; &nbsp; &nbsp; df_filtered = df_filtered.dropna()&nbsp; &nbsp; &nbsp; &nbsp; # Calculate mean and standard deviation&nbsp; &nbsp; &nbsp; &nbsp; mean_diff = df_filtered['temp_diff'].mean()&nbsp; &nbsp; &nbsp; &nbsp; std_diff = df_filtered['temp_diff'].std()&nbsp; &nbsp; &nbsp; &nbsp; # Identify observations within the specified range&nbsp; &nbsp; &nbsp; &nbsp; filtered_df = df_filtered[(df_filtered['temp_diff'] &gt;= mean_diff - 0.6 * std_diff) &amp; (df_filtered['temp_diff'] &lt;= mean_diff + 0.6 * std_diff)]&nbsp; &nbsp; &nbsp; &nbsp; # Calculate the percentage&nbsp; &nbsp; &nbsp; &nbsp; percentage = (filtered_df.shape[0] / df_filtered.shape[0]) * 100&nbsp; &nbsp; &nbsp; &nbsp; print(&quot;Column Headers:&quot;)&nbsp; &nbsp; &nbsp; &nbsp; print(column_headers)&nbsp; &nbsp; &nbsp; &nbsp; print(&quot;\nFirst Few Rows of Decrypted Data:&quot;)&nbsp; &nbsp; &nbsp; &nbsp; print(df.head())&nbsp; # Display the first few rows of the DataFrame&nbsp; &nbsp; &nbsp; &nbsp; print(&quot;\nPercentage of observations satisfying the condition:&quot;, percentage)&nbsp; &nbsp; else:&nbsp; &nbsp; &nbsp; &nbsp; print(&quot;Decryption failed or no data found.&quot;)if __name__ == &quot;__main__&quot;:&nbsp; &nbsp; main()[/CODE]OutPutError: Expected columns TA_PT1H_MAX, TA_PT1H_MIN not found in data.</blockquote>

[QUOTE="Hachiko15, post: 29617819, member: 568034"] Yaluwane help ekak dennako meka karagnna mata poddak meka karana vidiya kiya dunnaam ethi Python walin A file named [B]weather_2023-02.csv[/B] contains hourly weather observations [TABLE] [TR] [TH]Abbreviation[/TH] [TH]Meaning[/TH] [/TR] [TR] [TD]TA[/TD] [TD]Temperature Average[/TD] [/TR] [TR] [TD]RH[/TD] [TD]Relative Humidity[/TD] [/TR] [TR] [TD]WS[/TD] [TD]Wind Speed[/TD] [/TR] [TR] [TD]WD[/TD] [TD]Wind Direction[/TD] [/TR] [TR] [TD]PRA[/TD] [TD]Precipitation Amount[/TD] [/TR] [TR] [TD]PRI[/TD] [TD]Precipitation Intensity[/TD] [/TR] [TR] [TD]PA[/TD] [TD]Pressure Average[/TD] [/TR] [TR] [TD]WAWA[/TD] [TD]Most Significant Weather Code[/TD] [/TR] [/TABLE] Some of the data has been encrypted with your GPG public key. Ignoring any NaN values, find out what percentage of the hourly temperature observations satisfy the following: mama try kara hari giye na [CODE]import pandas as pd from cryptography.fernet import Fernet, InvalidToken # Function to generate or load Fernet key def load_or_generate_key(): try: # Load the key if exists with open("fernet_key.key", "rb") as key_file: key = key_file.read() except FileNotFoundError: # Generate a new key if not exists key = Fernet.generate_key() with open("fernet_key.key", "wb") as key_file: key_file.write(key) return key # Decrypt the column headers and relevant data def decrypt_weather_data(file_path, key): f = Fernet(key) try: with open(file_path, "rb") as encrypted_file: encrypted_data = encrypted_file.read() decrypted_data = f.decrypt(encrypted_data) # Decode the decrypted data as UTF-8 and split it into lines decrypted_lines = decrypted_data.decode("utf-8").split("\n") # Extract column headers column_headers = decrypted_lines[0].split(",") # Extract relevant data data = [] for line in decrypted_lines[1:]: if line.strip(): # Skip empty lines row = line.split(",") if len(row) == len(column_headers): # Ensure the row has the correct number of columns data.append(row) return column_headers, data except InvalidToken: print("Error: Invalid decryption key or corrupt file.") return None, None # Main function def main(): # Load or generate Fernet key key = load_or_generate_key() # Decrypt the weather data file_to_decrypt = 'D:Assignment\\weather_2023-02.csv.encrypted' column_headers, data = decrypt_weather_data(file_to_decrypt, key) if column_headers and data: # Create DataFrame from decrypted data df = pd.DataFrame(data, columns=column_headers) # Check if expected columns exist expected_cols = ['TA_PT1H_MAX', 'TA_PT1H_MIN'] if not all(col in df.columns for col in expected_cols): missing_cols = [col for col in expected_cols if col not in df.columns] print(f"Error: Expected columns {', '.join(missing_cols)} not found in data.") return # Extract relevant columns df_filtered = df[expected_cols] # Calculate temperature differences df_filtered['temp_diff'] = df_filtered['TA_PT1H_MAX'] - df_filtered['TA_PT1H_MIN'] # Handle NaN values df_filtered = df_filtered.dropna() # Calculate mean and standard deviation mean_diff = df_filtered['temp_diff'].mean() std_diff = df_filtered['temp_diff'].std() # Identify observations within the specified range filtered_df = df_filtered[(df_filtered['temp_diff'] >= mean_diff - 0.6 * std_diff) & (df_filtered['temp_diff'] <= mean_diff + 0.6 * std_diff)] # Calculate the percentage percentage = (filtered_df.shape[0] / df_filtered.shape[0]) * 100 print("Column Headers:") print(column_headers) print("\nFirst Few Rows of Decrypted Data:") print(df.head()) # Display the first few rows of the DataFrame print("\nPercentage of observations satisfying the condition:", percentage) else: print("Decryption failed or no data found.") if __name__ == "__main__": main() [/CODE] OutPut [COLOR=rgb(184, 49, 47)]Error: Expected columns TA_PT1H_MAX, TA_PT1H_MIN not found in data.[/COLOR] [/QUOTE]

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