Datatype: SQL DB#

In this example, we are using the gcbm_output.db file.

In order to open a SQL file in a jupyter notebook, you need to import the sqlite3, pandas and sqlalchemy libraries.

Follow the given steps to open a SQL file:

  1. Import sqlite3, pandas and sqlalchemy modules

import sqlite3
import pandas as pd
import sqlalchemy 
  1. Create a read-write connection

conn = sqlite3.connect("gcbm_output.db")
  1. Create a cursor object

 curs = conn.cursor()
  1. To list all the tables in the database, use the cursor’s fetchall() method to get the tables listed in sqlite_master

curs.execute("SELECT name FROM sqlite_master WHERE type = 'table'").fetchall()
[('ClassifierSetDimension',),
 ('DateDimension',),
 ('PoolDimension',),
 ('LandClassDimension',),
 ('ModuleInfoDimension',),
 ('AgeClassDimension',),
 ('LocationDimension',),
 ('DisturbanceTypeDimension',),
 ('DisturbanceDimension',),
 ('Pools',),
 ('Fluxes',),
 ('ErrorDimension',),
 ('LocationErrorDimension',),
 ('AgeArea',),
 ('sqlite_stat1',),
 ('r_change_type_categories',),
 ('sqlite_sequence',),
 ('r_pool_collections',),
 ('r_pool_collection_pools',),
 ('r_flux_indicators',),
 ('r_flux_indicator_collections',),
 ('r_flux_indicator_collection_flux_indicators',),
 ('r_stock_changes',),
 ('r_pool_indicators',),
 ('r_location',),
 ('r_stand_area',),
 ('v_flux_indicators',),
 ('v_flux_indicator_aggregates',),
 ('v_stock_change_indicators',),
 ('v_pool_indicators',),
 ('v_disturbance_indicators',),
 ('v_age_indicators',),
 ('v_error_indicators',),
 ('v_total_disturbed_areas',),
 ('v_disturbance_fluxes',),
 ('v_flux_indicators_density',),
 ('v_flux_indicator_aggregates_density',),
 ('v_stock_change_indicators_density',)]

fetchall() returns a list of tuples, and in this case each tuple has a single element, which is why they each have a trailing comma.

  1. Here is an example of a map that gives out the total biomass from the year 1920 to 2020

query = f"""
        SELECT years.year, COALESCE(SUM(i.pool_tc), 0) / 1e6 AS total_biomass_mt
        FROM (SELECT DISTINCT year FROM v_age_indicators ORDER BY year) AS years
        LEFT JOIN v_pool_indicators i
            ON years.year = i.year
        WHERE i.indicator = 'Total Biomass'
            AND (years.year BETWEEN 1920 AND 2020)
        GROUP BY years.year
        ORDER BY years.year
        """
df = pd.read_sql_query(query, conn)
ax = df.plot.line("year")
../../../_images/ea760d3c625973a3b0b46857b59c03fcad8df5bdccdf82f6e14aae8269d47aae.png