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# Create an in-memory table
table = kx.Table(data = {
    'sym': kx.random.random(1000, ['AAPL', 'GOOG']),
    'price': kx.random.random(1000, 10.0),
    'volume': kx.random.random(1000, 1000)})

# Query this data
table.select(
    columns = kx.Column('price').max().name('max_price') &
              kx.Column('price').min().name('min_price') &
              kx.Column('price').wavg(kx.Column('volume')).name('vwap'),
    by = kx.Column('sym'))

# Add a date column 
table.update(kx.Column('date', value = kx.DateAtom('today')), inplace=True)

# Reorder columns setting date as the first column
table.reorder_columns('date', inplace=True)

# Generate a table with multiple days of data
table = kx.Table(data = {
    'date': kx.DateAtom('today') - kx.random.random(10000, 5),
    'sym': kx.random.random(10000, ['AAPL', 'GOOG']),
    'price': kx.random.random(10000, 10.0),
    'volume': kx.random.random(10000, 1000)})

# Create your on-disk database
db = kx.DB('database')
db.create(table, 'trades', 'date')

# Preview the table
db.trades

# Rename the 'sym' column as 'symbol'
db.rename_column('trades', 'sym', 'symbol')

# Multiply the value of the column 'volume' by 1000
db.apply_function('trades', 'volume', lambda x: 1000*x)

# Generate a table with multiple days of data
table = kx.Table(data = {
    'date': kx.DateAtom('today') - kx.random.random(10000, 5),
    'sym': kx.random.random(10000, ['AAPL', 'GOOG']),
    'price': kx.random.random(10000, 10.0),
    'volume': kx.random.random(10000, 1000)})

# Access the first 10 rows
table[:10]
table.head(10)

# Sort the columns of the table in ascending order by price
table = table.sort_values('price')

# Calculate the max price by date and sym multiple ways
table.groupby(['date', 'sym'])['price'].max()
table.select(kx.Column('price').max(), by= kx.Column('date') & kx.Column('sym'))

# Calculate the correlation between the content of two columns
table.exec(kx.Column('price').correlation(kx.Column('volume')))
kx.q.cor(table['price'], table['volume'])