Welcome to the Winter Temperature Analysis for Jaipur project! 🎉 This project involves analyzing temperature variations (in Celsius) from November to February, based on daily data provided for the city of Jaipur. You'll be using NumPy for efficient data handling and performing a variety of data analysis tasks.
We have a dataset in the form of an Excel sheet that records daily temperature variations for November, December, January, and February. Your task is to transform this data into a NumPy array and perform various actions, such as extracting temperature information for specific weeks or days, detecting outliers, and calculating statistics such as average temperatures and temperature differences. Additionally, you'll convert the dataset to Fahrenheit for an international audience.
Here’s a step-by-step breakdown of the tasks you'll be performing:
1. Data Representation: Load the Excel data and represent it as a NumPy array for easier manipulation.
2. Array Dimensions & Shape: Print the dimensions and shape of the NumPy array.
3. First Week Temperatures: Print the daily temperatures for the first week of each month.
4. Tuesday Temperatures: Extract and print the temperatures for Tuesdays of each month.
5. Max Temps in Dec & Feb: Print only the maximum temperatures for all weekdays in December and February.
6. Min Temps in November: Find and print all days in November where the minimum temperature was less than 8°C, along with the week number.
7. Max Temp Threshold in Dec & Jan: Print all weeks in December and January where the maximum temperature crossed 20°C.
8. Outlier Detection: Check for any absurd values (e.g., values that don’t seem realistic for temperature data).
9. Outlier Handling: Propose a strategy to handle outliers and replace any unusual values with appropriate estimates.
10. Outlier Indexes: Find and print the indexes of all outliers present in the dataset.
11. Outlier Replacement: Replace the outliers in the dataset with suitable values.
12. Average Max Temperature: Calculate the average maximum temperature for all winter months.
13. Weekly Min Average (Dec): Find the weekly minimum average temperature for December.
14. Overall Avg Temp (Dec & Jan): Calculate the overall average temperature for December and January.
15. Lowest Temp in Dec & Jan: Identify the lowest temperature in December and January, along with the exact date (Day/Week/Month).
16. Max Temp in Feb: Find the maximum temperature in February and return its corresponding date.
17. Max Temp Drops Below Avg (Nov): Find the days in November where the max temp dropped below the monthly average.
18. Data Reshaping: Reshape the data so that each month's weeks are in rows, with different months placed either below or above each other.
19. Convert to Fahrenheit: Create a new array that converts the data from Celsius to Fahrenheit.
20. Sort by Weekly Average (Dec): Sort the data by weekly average temperatures for December in descending order.
21. Sort First 3 Days (Winter): Sort the temperatures of the first three days of each month in descending order based on the overall winter average.
22. Daily Temp Range: Create an array that stores the difference between the min and max temperatures for each day in all winter months.
23. Max Temp Difference (Consecutive Days): Find the difference in max temperatures between two consecutive days for each month of winter.
24. Min Temp Difference (Consecutive Days): Calculate the difference in minimum temperatures between two consecutive days for each winter month.
25. Combined Temp Difference Array: Combine the results from Tasks 23 and 24 to create a single array that stores the difference between the min and max temperatures for each day of all winter months.
- Python: Core language for data analysis.
- NumPy: For array manipulation and performing efficient calculations.
- Pandas: To load and preprocess the Excel data (if needed).
- November Findings: Days where minimum temperatures dipped below 8°C and week-wise insights.
- Temperature Thresholds: Weeks in Dec & Jan where temperatures crossed 20°C.
- Outlier Detection: Identified and handled any absurd values in the dataset.
- Max Temp Difference: Differences in max temperatures for consecutive days in the winter months.
- Converted Data: Data converted from Celsius to Fahrenheit for international audiences.
Contributions are welcome! Feel free to open an issue or submit a pull request to improve the analysis or add new functionality.
If you have any questions or suggestions, feel free to reach out!
Poorvi Gupta
poorviguptacom@gmail.com
Linkedin: https://www.linkedin.com/in/poorvi-gupta-a817032a0
Thank you for checking out this project! Let’s dive deep into the temperature trends of Jaipur’s winter season! ❄️🌡️