from into import discover, into, resource
discover(1)

discover([1, 2, 3])

discover([[1, 2, 3],
          [4, 5, 6]])

discover([{'x': 1, 'y': 1.0},
          {'x': 2, 'y': 2.0},
          {'x': 3, 'y': 3.0}])

import pandas as pd
df = pd.DataFrame([['Alice',   100],
                   ['Bob',     200],
                   ['Charlie', 300]], columns=['name', 'balance'])
discover(df)

# Should be 2 * int64
discover([1, 2])  











discover(df)

import numpy as np
x = into(np.ndarray, df)
discover(x)  # different container, same datashape

t = into('sqlite:///:memory:::mydf', df)
discover(t)  # different container, mostly the same datashape

salaries = resource('sqlite:///data/lahman2013.sqlite::Salaries')
discover(salaries)

db = resource('sqlite:///data/lahman2013.sqlite')
discover(db)

data = [{'name': 'Alice', 'balance': 100},
        {'name': 'Bob',   'balance': 200}]
into(np.ndarray, data)

discover(data)

from into import dshape
ds = dshape("2 * {balance: int64, name: string[10]}") # max length of 10

into(np.ndarray, data, dshape=ds)

ds = dshape("2 * {balance: int64, name: string[10, 'ascii']}") # max length of 10
into(np.ndarray, data, dshape=ds)

csv = resource('data/iris.csv')
discover(csv)







data = [(33.1, -89.2), (37, -141.5), (41, -120.5)]
into(pd.DataFrame, data)

ds = dshape('var * {lat: float64, long: float64}')
into(pd.DataFrame, data, dshape=ds)

ds = dshape('100 * 100 * int64')
dset = resource('myfile.hdf5::/x', dshape=ds)
dset





import sqlalchemy as sa

engine = sa.create_engine('sqlite:///data/my.db')
metadata = sa.MetaData(engine)
transactions = sa.Table('transactions2', metadata,
                        sa.Column('name', sa.String),
                        sa.Column('balance', sa.Integer),
                        sa.Column('timestamp', sa.DateTime))
transactions.create()