Analyse Sqlite file with climate data using SQLAlchemy ORM queries, Pandas, and Matplotlib.

• Design a query to retrieve 12 months of precipitation data.
• Load the query results into a Pandas DataFrame and set the index to the date column.
• Sort the DataFrame values by date.
• Plot the results using the DataFrame plot method.
• Use Pandas to print the summary statistics for the precipitation data.

• Design a query to calculate the total number of stations.
• Design a query to find the most active stations.
  • List the stations and observation counts in descending order.
  • List the station that has the highest number of observations.
• Design a query to retrieve 12 months of temperature observation data.
  • Filter by the station with the highest number of observations.
  • Plot the results as a histogram.

• Calculate and plot the min, avg, and max temperature from chosen date range as a bar chart.
  • Use the average temperature as the bar height.
  • Use the peak-to-peak value as the y error bar.