def get_txn_vol(transactions):
txn_norm = transactions.copy()<EOL>txn_norm.index = txn_norm.index.normalize()<EOL>amounts = txn_norm.amount.abs()<EOL>prices = txn_norm.price<EOL>values = amounts * prices<EOL>daily_amounts = amounts.groupby(amounts.index).sum()<EOL>daily_values = values.groupby(values.index).sum()<EOL>daily_amounts.name = "<STR_LIT>"<EOL>daily_values.name = "<STR_LIT>"<EOL>return pd.concat([daily_values, daily_amounts], axis=<NUM_LIT:1>)<EOL>
Extract daily transaction data from set of transaction objects.

Parameters
----------
transactions : pd.DataFrame
    Time series containing one row per symbol (and potentially
    duplicate datetime indices) and columns for amount and
    price.

Returns
-------
pd.DataFrame
    Daily transaction volume and number of shares.
     - See full explanation in tears.create_full_tear_sheet.