<jupyter_start><jupyter_text>Reflect Tables into SQLAlchemy ORM<jupyter_code># Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
# create engine to hawaii.sqlite
engine = create_engine("sqlite:///Resources/hawaii.sqlite")
# reflect an existing database into a new model
Base = automap_base()
# reflect the tables
Base.prepare(engine, reflect=True)
# View all of the classes that automap found
Base.classes.keys()
# Save references to each table
Measurement = Base.classes.measurement
Station = Base.classes.station
# Create our session (link) from Python to the DB
session = Session(engine)<jupyter_output><empty_output><jupyter_text>Exploratory Precipitation Analysis<jupyter_code># Find the most recent date in the data set.
most_recent_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()
print(f"The most recent date: {most_recent_date[0]}")
# Design a query to retrieve the last 12 months of precipitation data and plot the results. 
# Starting from the most recent data point in the database. 

# Calculate the date one year from the last date in data set.
last_year_date = (dt.datetime.strptime(most_recent_date[0],'%Y-%m-%d') - dt.timedelta(days=365)).strftime('%Y-%m-%d')
print(f"One year before the most recent date: {last_year_date}")

# Perform a query to retrieve the data and precipitation scores
measurement_cols = (Measurement.date, Measurement.prcp)

precipitation = session.query(*measurement_cols).filter(Measurement.date >= last_year_date).all()
precipitation[:15]

# Save the query results as a Pandas DataFrame and set the index to the date column
precipitation_df = pd.DataFrame(precipitation,columns=['date','precipitation'])
precipitation_df = precipitation_df.set_index('date')


# Sort the dataframe by date
precipitation_df = precipitation_df.sort_index(ascending=True)
precipitation_df

# Use Pandas Plotting with Matplotlib to plot the data
barChart = precipitation_df.plot(rot=90)
barChart.set_title("Precipitation per Day over the Last Year");
barChart.set_ylabel("Precipitation");



# Use Pandas to calcualte the summary statistics for the precipitation data
precipitation_df.describe()<jupyter_output><empty_output><jupyter_text>Exploratory Station Analysis<jupyter_code># Design a query to calculate the total number stations in the dataset
stations = session.query(func.count(Station.id)).all()
stations
# Design a query to find the most active stations (i.e. what stations have the most rows?)
# List the stations and the counts in descending order.
stationCounts =  session.query(Measurement.station, func.count(Measurement.station)).\
                    group_by(Measurement.station).\
                    order_by(func.count(Measurement.station).desc()).all()
stationCounts
# Using the most active station id from the previous query, calculate the lowest, highest, and average temperature.
session.query(func.min(Measurement.tobs),func.max(Measurement.tobs),func.avg(Measurement.tobs)).\
                    filter(Measurement.station == 'USC00519281').all()
# Using the most active station id
# Query the last 12 months of temperature observation data for this station and plot the results as a histogram
station281 =  session.query(Measurement.date,Measurement.tobs).filter(Measurement.station == 'USC00519281').\
                            filter(Measurement.date > last_year_date).all()

# Using station281 to make the df, which we can plot
station281_df= pd.DataFrame(station281)
station281_df.plot(kind="hist")<jupyter_output><empty_output><jupyter_text>Close session<jupyter_code># Close Session
session.close()<jupyter_output><empty_output>
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