<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
Station = Base.classes.station
Measurement = Base.classes.measurement
# Create our session (link) from Python to the DB
session = Session(engine)
#Check to see
mea=session.query(Measurement).first()
mea.__dict__
#Check to see
sta = session.query(Station).first()
sta.__dict__<jupyter_output><empty_output><jupyter_text>Exploratory Precipitation Analysis<jupyter_code># Find the most recent date in the data set.
recent_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()
recent_date
# 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.
# Perform a query to retrieve the data and precipitation scores
pre_12_months = session.query(Measurement.date, Measurement.prcp).\
filter(Measurement.date >= '2016-08-24').filter(Measurement.date <= '2017-08-23').order_by(Measurement.date).all()

# Save the query results as a Pandas DataFrame and set the index to the date column
pre_12_months_df = pd.DataFrame(pre_12_months)
pre_12_months_df.set_index('date')

# Sort the dataframe by date
pre_12_months_df.sort_values(by=['date'])

# Use Pandas Plotting with Matplotlib to plot the data
pre_12_months_df.plot(x='date', y='prcp')
plt.xlabel("Date")
plt.ylabel("Inches")
plt.xticks(rotation='60')
plt.show()
# Use Pandas to calcualte the summary statistics for the precipitation data
pre_12_months_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
total_stations = session.query(Station.id).count()
total_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.
most_active_stations = session.query(Measurement.station, func.count(Measurement.station)).\
                        group_by(Measurement.station).order_by(func.count(Measurement.station).desc()).all()
most_active_stations[0]

# Using the most active station id from the previous query, calculate the lowest, highest, and average temperature.
lowest_temp = session.query(func.min(Measurement.tobs)).filter(Measurement.station == 'USC00519281').all()
highest_temp = session.query(func.max(Measurement.tobs)).filter(Measurement.station == 'USC00519281').all()
avg_temp = session.query(func.avg(Measurement.tobs)).filter(Measurement.station == 'USC00519281').all()

print(f'The lowest tempt is {lowest_temp}, highest is {highest_temp}, and average is {avg_temp}')
# 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
tempt_obs = pd.DataFrame(session.query(Measurement.tobs).filter(Measurement.station == 'USC00519281').filter(Measurement.date >= '2016-08-23').all(), columns=['tempt'])
tempt_obs.plot.hist(bins=12)
plt.xlabel('Temperature')<jupyter_output><empty_output><jupyter_text>Close session<jupyter_code># Close Session
session.close()<jupyter_output><empty_output>
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