update solutions

Author: beyondsimulations

part-06/solution/1-scrambled.py

@@ -12,13 +12,11 @@
 # Hint: Use the random.choice() function to select a word from the list and try to come up with the rest of the code yourself!
 
 import random
-import string
 
 # List of words to choose from for the game
 words = ["python", "programming", "computer", "algorithm", "database"]
 
 def scramble_word(word):
-    # Convert the word to a list of characters, shuffle them, and join back into a string
     chars = list(word)
     random.shuffle(chars)
     return ''.join(chars)

part-06/solution/3-random-package.py

@@ -7,7 +7,6 @@
 # - It should also ask the user if they want to play again
 # Your code here
 
-#| eval: false
 import random
 
 def play_game():

part-06/solution/5-password.py

@@ -14,21 +14,21 @@
 import re
 
 def check_password_strength(password):
-    if len(password) < 8:
-        return "Weak"
-    
-    criteria = [
-        r'[A-Z]',  # Uppercase letter
-        r'[a-z]',  # Lowercase letter
-        r'\d',     # Digit
-        r'[!@#$%^&*]'  # Special character
-    ]
-    
-    strength = sum(bool(re.search(pattern, password)) for pattern in criteria)
-    
-    if strength == 4:
+    criteria = 0
+    if len(password) >= 8:
+        criteria += 1
+    if re.search(r'[A-Z]', password):
+        criteria += 1
+    if re.search(r'[a-z]', password):
+        criteria += 1
+    if re.search(r'\d', password):
+        criteria += 1
+    if re.search(r'[!@#$%^&*]', password):
+        criteria += 1
+
+    if criteria == 5:
         return "Strong"
-    elif strength >= 2:
+    elif criteria >= 3:
         return "Medium"
     else:
         return "Weak"

part-06/solution/__pycache__/calculator.cpython-312.pyc

@@ -0,0 +1,45 @@
+#Imagine you're a climate scientist working on a project to analyze temperature data from weather stations across the country. You've been given a large dataset, and you need to use NumPy to process and analyze this data efficiently.
+
+import numpy as np
+
+# First, we simulate loading data from 100 weather stations over 365 days. Each row represents a station, each column a day.
+temp_data = np.random.randint(0, 40, size=(100, 365))
+
+# a) Calculate the average temperature for each station and the overall average temperature.
+station_averages = np.mean(temp_data, axis=1)
+overall_average = np.mean(temp_data)
+print(f"Average temperature for each station:\n{station_averages}")
+print(f"Overall average temperature: {overall_average:.2f}°C")
+
+# b) Find the highest and lowest temperature recorded and print a message with the corresponding stations.
+highest_temp = np.max(temp_data)
+lowest_temp = np.min(temp_data)
+highest_station, highest_day = np.where(temp_data == highest_temp)
+lowest_station, lowest_day = np.where(temp_data == lowest_temp)
+print(f"Highest temperature: {highest_temp}°C at station {highest_station[0]} on day {highest_day[0]}")
+print(f"Lowest temperature: {lowest_temp}°C at station {lowest_station[0]} on day {lowest_day[0]}")
+
+# c) Identify heat waves. A heat wave is defined as 5 consecutive days with temperatures above 30°C. Print a message counting the number of heat waves.
+heat_waves = 0
+for station in temp_data:
+    consecutive_hot_days = 0
+    for temp in station:
+        if temp > 30:
+            consecutive_hot_days += 1
+            if consecutive_hot_days == 5:
+                heat_waves += 1
+                consecutive_hot_days = 0  # Reset to avoid double-counting
+        else:
+            consecutive_hot_days = 0
+print(f"Total number of heat waves across all stations: {heat_waves}")
+
+
+# d) Calculate the temperature anomaly for each day (difference from each individual station's average temperature).
+temp_anomaly = temp_data - station_averages[:, None]
+print("Temperature anomaly shape:", temp_anomaly.shape)
+
+# e) Find the hottest and coldest stations and determine the index of the station with the highest average temperature and the station with the lowest average temperature.
+hottest_station = np.argmax(station_averages)
+coldest_station = np.argmin(station_averages)
+print(f"Hottest station index: {hottest_station}, with average temperature: {station_averages[hottest_station]:.2f}°C")
+print(f"Coldest station index: {coldest_station}, with average temperature: {station_averages[coldest_station]:.2f}°C")

part-07/solution/01-climate-data.py

@@ -0,0 +1,53 @@
+# In this exercise, you'll use Pandas to analyze real global temperature anomaly data from NASA, helping to understand trends in climate change over time.
+
+# The dataset is provided by the GISS Team, 2024: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies. Dataset at https://data.giss.nasa.gov/gistemp/.
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# First, we load the NASA GISTEMP dataset for global temperature anomalies.
+url = "https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv"
+temp_anomaly_data = pd.read_csv(url, skiprows=1) # skiprows=1 ensures that the first column is not read as a row index
+# Convert 'Anomaly' column to float
+melted_data['Anomaly'] = melted_data['Anomaly'].astype(float)
+
+
+# a) Display the first 5 rows and keep only 'Year' and month columns
+temp_anomaly_data = temp_anomaly_data.drop(columns=['J-D', 'D-N','DJF','MAM','JJA','SON'])
+print(temp_anomaly_data.head())
+
+# b) Calculate and print the average temperature anomaly for each year
+melted_data = pd.melt(temp_anomaly_data, id_vars=['Year'], var_name='Month')
+melted_data = melted_data.drop(columns=['Month'])
+print(melted_data.head())
+melted_data.groupby(['Year']).mean()
+print(melted_data.head())
+print("\nAverage temperature anomaly for each year:")
+print(melted_data.head())
+
+# c) Find the year with the highest and lowest temperature anomaly
+max_year = yearly_avg.idxmax()
+min_year = yearly_avg.idxmin()
+print(f"\nYear with highest anomaly: {max_year} ({yearly_avg[max_year]:.2f})")
+print(f"Year with lowest anomaly: {min_year} ({yearly_avg[min_year]:.2f})")
+
+# d) Create 'Anomaly_Category' column
+def categorize_anomaly(value):
+    if value < -0.2:
+        return 'Cool'
+    elif value > 0.2:
+        return 'Warm'
+    else:
+        return 'Neutral'
+
+melted_data['Anomaly_Category'] = melted_data['Anomaly'].apply(categorize_anomaly)
+
+# e) Calculate the percentage of 'Warm' months for each decade
+melted_data['Decade'] = (melted_data['Year'] // 10) * 10
+warm_percentage = melted_data.groupby('Decade')['Anomaly_Category'].apply(lambda x: (x == 'Warm').mean() * 100)
+print("\nPercentage of 'Warm' months for each decade:")
+print(warm_percentage)
+
+# f) Save the DataFrame to Excel
+melted_data.to_excel('temp_anomaly_data.xlsx', index=False)
+print("\nData saved to 'temp_anomaly_data.xlsx'")

part-07/solution/02-gistemp.py

@@ -37,7 +37,7 @@ temp_data = np.random.randint(0, 40, size=(100, 365))
 # TODO: b) Find the highest and lowest temperature recorded and print an message with the corresponding stations.
 # Your code here
 
-# TODO: c) Identify heat waves. A heat wave is defined as 5 consecutive days with temperatures above 35°C. Print a message counting the number of heat waves.
+# TODO: c) Identify heat waves. A heat wave is defined as 5 consecutive days with temperatures above 30°C. Print a message counting the number of heat waves.
 # Your code here
 
 # TODO: d) Calculate the temperature anomaly for each day (difference from each indidvual station's average temperature).