Weather Data Analysis Project

This project involves analyzing weather data from a dataset to perform various data cleaning and analysis tasks. The dataset includes information on precipitation, snowfall, temperature, and other weather-related metrics.

Table of Contents

• Weather Data Analysis Project
  • Table of Contents
  • Installation
  • Data Preparation
  • Analysis
    • Temperature Data Analysis
    • Precipitation Data Analysis
    • Snowfall Data Analysis
  • Results

Installation

To run the scripts in this project, you'll need to have Python installed along with the following libraries:

• pandas
• numpy
• matplotlib

You can install these libraries using pip:

pip install pandas numpy matplotlib

Data Preparation

1. Load the dataset dirty_data.csv and convert the date column to DateTime format.
2. Set the date column as the index.
3. Ensure relevant columns are numeric and handle any invalid data:
   • Convert PRCP, SNOW, SNWD, TMAX, TMIN, TOBS, and WESF to numeric, coercing errors.
4. Clean the SNOW column by replacing NaN values with 0.
5. Identify and replace obvious errors in the temperature values (TMAX, TMIN):
   • Replace temperature values outside realistic ranges (e.g., below -50°C or above 50°C) with NaN.
6. Handle missing values in the temperature columns (TMAX, TMIN, TOBS):
   • Replace missing values with the mean of the 4 days before and after the missing value.
7. Remove duplicate rows based on the date index to ensure each date has only one record.

Analysis

Temperature Data Analysis

Data Preparation:

• Load the dataset and prepare the date column.
• Set the date column as the index.

Cleaning Temperature Values:

• Identify and replace obvious errors in temperature values (TMAX, TMIN) with NaN or another appropriate value.
• Replace missing values in the temperature columns (TMAX, TMIN, TOBS) with the mean of the ±4 days before/after.

Monthly Temperature Difference:

• Create a new column Temp_Difference representing the difference between TMAX and TMIN.
• Calculate the average temperature difference for each month and visualize the results in a bar chart.

Highest and Lowest Temperatures:

• Identify the day with the highest and lowest temperature (TMAX and TMIN) in the year 2018.
• Visualize the daily maximum and minimum temperatures for the year 2018 in a line chart.

Precipitation Data Analysis

Monthly Total Precipitation:

• Calculate the monthly total precipitation (PRCP) for the year 2018.
• Visualize the monthly total precipitation in a line chart.

Days Without Precipitation:

• Determine the number of days without precipitation (PRCP = 0) for each year in the dataset.
• Visualize the number of days without precipitation per year in a bar chart.

Snowfall Data Analysis

Winter Analysis:

• Calculate the total snowfall (SNOW) for the winter of early 2018 and late 2018 (December to February).
• Determine the maximum snow depth (SNWD) and the corresponding day.

Results

Monthly Temperature Difference:

Daily Maximum and Minimum Temperatures in 2018:

Monthly Total Precipitation in 2018:

Number of Days Without Precipitation per Year:

Daily Snowfall in Early and Late Winter 2018: