To answer this question, I would need to have access to the specific dataframe you're referring to. However, I can guide you on how you might do this with a hypothetical dataframe.

Let's assume you have a dataframe named `df` and it contains historical data of CBA's stock prices. The dataframe has a column named `Close` which represents the closing price of the stock each day.

Here's a step-by-step guide on how you might calculate the total return:

1. First, filter the dataframe to only include data from the last 5 years. You can do this by using the `datetime` library to get the date 5 years ago and then using this date to filter the dataframe.

```python
import datetime
import pandas as pd

# Assuming df is your dataframe and 'Date' is your date column
five_years_ago = datetime.datetime.now() - pd.DateOffset(years=5)
df_five_years = df[df['Date'] five_years_ago]
```

2. Next, get the closing price of the stock 5 years ago and the latest closing price. You can do this by selecting the first and last row of the `Close` column in the filtered dataframe.

```python
price_five_years_ago = df_five_years.iloc[0]['Close']
latest_price = df_five_years.iloc[-1]['Close']
```

3. Calculate the total return. The formula for total return is `(latest_price - price_five_years_ago) / price_five_years_ago * 100`.

```python
total_return = (latest_price - price_five_years_ago) / price_five_years_ago * 100
```

4. Finally, round the total return to 2 decimal places and print the output.

```python
total_return = round(total_return, 2)
print(f'The total return for investing in CBA 5 years ago is {total_return}%')
```

Please replace `df` and column names with your actual dataframe and column names. Also, please note that this code assumes that the dataframe is sorted by date in ascending order. If it's not, you would need to sort it first.

Question

To answer this question, I would need to have access to the specific dataframe you're referring to. However, I can guide you on how you might do this with a hypothetical dataframe.

Let's assume you have a dataframe named `df` and it contains historical data of CBA's stock prices. The dataframe has a column named `Close` which represents the closing price of the stock each day.

Here's a step-by-step guide on how you might calculate the total return:

1. First, filter the dataframe to only include data from the last 5 years. You can do this by using the `datetime` library to get the date 5 years ago and then using this date to filter the dataframe.

```python
import datetime
import pandas as pd

# Assuming df is your dataframe and 'Date' is your date column
five_years_ago = datetime.datetime.now() - pd.DateOffset(years=5)
df_five_years = df[df['Date'] > five_years_ago]
```

2. Next, get the closing price of the stock 5 years ago and the latest closing price. You can do this by selecting the first and last row of the `Close` column in the filtered dataframe.

```python
price_five_years_ago = df_five_years.iloc[0]['Close']
latest_price = df_five_years.iloc[-1]['Close']
```

3. Calculate the total return. The formula for total return is `(latest_price - price_five_years_ago) / price_five_years_ago * 100`.

```python
total_return = (latest_price - price_five_years_ago) / price_five_years_ago * 100
```

4. Finally, round the total return to 2 decimal places and print the output.

```python
total_return = round(total_return, 2)
print(f'The total return for investing in CBA 5 years ago is {total_return}%')
```

Please replace `df` and column names with your actual dataframe and column names. Also, please note that this code assumes that the dataframe is sorted by date in ascending order. If it's not, you would need to sort it first.

Knowee AI · Accepted Answer