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()

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")