def get_low_liquidity_transactions(transactions, market_data,<EOL>last_n_days=None):
txn_daily_w_bar = daily_txns_with_bar_data(transactions, market_data)<EOL>txn_daily_w_bar.index.name = '<STR_LIT:date>'<EOL>txn_daily_w_bar = txn_daily_w_bar.reset_index()<EOL>if last_n_days is not None:<EOL><INDENT>md = txn_daily_w_bar.date.max() - pd.Timedelta(days=last_n_days)<EOL>txn_daily_w_bar = txn_daily_w_bar[txn_daily_w_bar.date > md]<EOL><DEDENT>bar_consumption = txn_daily_w_bar.assign(<EOL>max_pct_bar_consumed=(<EOL>txn_daily_w_bar.amount/txn_daily_w_bar.volume)*<NUM_LIT:100><EOL>).sort_values('<STR_LIT>', ascending=False)<EOL>max_bar_consumption = bar_consumption.groupby('<STR_LIT>').first()<EOL>return max_bar_consumption[['<STR_LIT:date>', '<STR_LIT>']]<EOL>
For each traded name, find the daily transaction total that consumed
the greatest proportion of available daily bar volume.

Parameters
----------
transactions : pd.DataFrame
    Prices and amounts of executed trades. One row per trade.
     - See full explanation in create_full_tear_sheet.
market_data : pd.Panel
    Panel with items axis of 'price' and 'volume' DataFrames.
    The major and minor axes should match those of the
    the passed positions DataFrame (same dates and symbols).
last_n_days : integer
    Compute for only the last n days of the passed backtest data.