text: function from pandas. We need to pass the historical stock data, the short-term moving average, and the long-term moving average as arguments to this function. We can use the following code to create the new DataFrame:        ```    new_df = pd.concat([df, short_ma, long_ma], axis=1)    new_df.columns = ['open', 'high', 'low', 'close', 'volume', 'short_ma', 'long_ma']    ```        This will create a new DataFrame called `new_df` that combines the historical stock data with the short-term and long-term moving averages. The columns of the new DataFrame are named 'open', 'high', 'low', 'close', 'volume', 'short_ma', and 'long_ma'. Next request.            AI User (Stock Trader):        Instruction: Create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages.    Input: None.            AI Assistant (Python Programmer):        Solution: We can create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages. We can use the following code to create the new column:        ```    new_df['signal'] = np.where(new_df['short_ma'] > new_df['long_ma'], 1, -1)    ```        This will create a new column called 'signal' in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages. If the short-term moving average is