This code provides a versatile solution for plotting time-series data with different Y-axis labels. By changing the filename and Y-axis label, you can easily generate three distinct graphs from a single codebase. This is particularly useful when dealing with datasets containing diverse variables.

Instructions:-

1. Prepare Data:

• Ensure that your data is in CSV format.
• Place your data file ('data.csv') in the same directory as the code.

2. Run the Code:

• Execute the code in a Python environment.
• Ensure that you have the required libraries installed.

3. Generate Graphs:

• You can generate three different graphs by making slight modifications to the code.
• Change the filename and Y-axis label as follows:

Graph 1: Air Temperature data = pd.read_csv('data.csv', skiprows=9, names=['Month', 'Air Temperature'])

plt.plot(data['Air Temperature'], label='Air Temperature', color='red')

plt.ylabel('Temperature(°C)')

plt.title('Air Temperature Data', fontsize=20)

Graph 2:

Monthly Precipitation

data = pd.read_csv('data.csv', skiprows=9, names=['Month', 'mm per Day'])

plt.plot(data['mm per Day'], label='mm per Day', color='red')

plt.ylabel('mm per Day')

plt.title('Monthly Precipitation Data', fontsize=20)

Graph 3:

Surface Soil Moisture

data = pd.read_csv('data.csv', skiprows=9, names=['Month', 'kg per metre square'])

plt.plot(data['kg per metre square'], label='kg per metre square', color='red')

plt.ylabel('kg per metre square')

plt.title('Surface Soil Moisture Data', fontsize=20)

5. View Graphs:

• Run the modified code to generate and view the respective graphs.