To download the daily data for Amazon (AMZN), Netflix (NFLX) and Bank of America (BA) from 2/1/2011 to the present day and retain Adj Close only, you can use the `pandas_datareader` library in Python. Here's how you can do it:

Step 1: Import necessary libraries
```python
import pandas as pd
import pandas_datareader as web
from datetime import datetime
```

Step 2: Define the start and end dates
```python
start = datetime(2011, 2, 1)
end = datetime.now()
```

Step 3: Download the data
```python
amzn = web.DataReader('AMZN', 'yahoo', start, end)
nflx = web.DataReader('NFLX', 'yahoo', start, end)
ba = web.DataReader('BA', 'yahoo', start, end)
```

Step 4: Retain only the 'Adj Close' column
```python
amzn = amzn[['Adj Close']]
nflx = nflx[['Adj Close']]
ba = ba[['Adj Close']]
```

Step 5: Rename the 'Adj Close' column to the respective stock symbol
```python
amzn.rename(columns={'Adj Close': 'AMZN'}, inplace=True)
nflx.rename(columns={'Adj Close': 'NFLX'}, inplace=True)
ba.rename(columns={'Adj Close': 'BA'}, inplace=True)
```

Step 6: Merge the dataframes
```python
xdata = pd.concat([amzn, nflx, ba], axis=1)
```

Now, `xdata` is the dataframe that contains the 'Adj Close' data for Amazon, Netflix, and Bank of America from 2/1/2011 to the present day.

Please note that you need to have the `pandas_datareader` library installed. If you haven't installed it yet, you can do so by running `!pip install pandas_datareader` in your Jupyter notebook or `pip install pandas_datareader` in your terminal.

Question

To download the daily data for Amazon (AMZN), Netflix (NFLX) and Bank of America (BA) from 2/1/2011 to the present day and retain Adj Close only, you can use the `pandas_datareader` library in Python. Here's how you can do it:

Step 1: Import necessary libraries
```python
import pandas as pd
import pandas_datareader as web
from datetime import datetime
```

Step 2: Define the start and end dates
```python
start = datetime(2011, 2, 1)
end = datetime.now()
```

Step 3: Download the data
```python
amzn = web.DataReader('AMZN', 'yahoo', start, end)
nflx = web.DataReader('NFLX', 'yahoo', start, end)
ba = web.DataReader('BA', 'yahoo', start, end)
```

Step 4: Retain only the 'Adj Close' column
```python
amzn = amzn[['Adj Close']]
nflx = nflx[['Adj Close']]
ba = ba[['Adj Close']]
```

Step 5: Rename the 'Adj Close' column to the respective stock symbol
```python
amzn.rename(columns={'Adj Close': 'AMZN'}, inplace=True)
nflx.rename(columns={'Adj Close': 'NFLX'}, inplace=True)
ba.rename(columns={'Adj Close': 'BA'}, inplace=True)
```

Step 6: Merge the dataframes
```python
xdata = pd.concat([amzn, nflx, ba], axis=1)
```

Now, `xdata` is the dataframe that contains the 'Adj Close' data for Amazon, Netflix, and Bank of America from 2/1/2011 to the present day.

Please note that you need to have the `pandas_datareader` library installed. If you haven't installed it yet, you can do so by running `!pip install pandas_datareader` in your Jupyter notebook or `pip install pandas_datareader` in your terminal.

Knowee AI · Accepted Answer