To answer your question, I'll assume that you have three pandas Series or DataFrames: `fund1`, `fund2`, and `etf`, each representing the daily prices of two funds and one ETF. Here's how you can create a DataFrame of daily returns for these three time series:

```python
import pandas as pd

# Assuming fund1, fund2, and etf are your time series data
# Calculate daily returns
fund1_returns = fund1.pct_change()
fund2_returns = fund2.pct_change()
etf_returns = etf.pct_change()

# Combine the three series into one DataFrame
combined = pd.concat([fund1_returns, fund2_returns, etf_returns], axis=1)

# Drop rows with missing values
combined = combined.dropna()

# Rename columns
combined.columns = ['Fund1 Returns', 'Fund2 Returns', 'ETF Returns']

# Display the final DataFrame
print(combined)
```

This code first calculates the daily returns for each time series using the `pct_change()` function. It then combines these three series into one DataFrame using `pd.concat()`. The `dropna()` function is used to remove any rows with missing values. Finally, the columns are renamed appropriately using the `columns` attribute, and the final DataFrame is displayed.

Question

To answer your question, I'll assume that you have three pandas Series or DataFrames: `fund1`, `fund2`, and `etf`, each representing the daily prices of two funds and one ETF. Here's how you can create a DataFrame of daily returns for these three time series:

```python
import pandas as pd

# Assuming fund1, fund2, and etf are your time series data
# Calculate daily returns
fund1_returns = fund1.pct_change()
fund2_returns = fund2.pct_change()
etf_returns = etf.pct_change()

# Combine the three series into one DataFrame
combined = pd.concat([fund1_returns, fund2_returns, etf_returns], axis=1)

# Drop rows with missing values
combined = combined.dropna()

# Rename columns
combined.columns = ['Fund1 Returns', 'Fund2 Returns', 'ETF Returns']

# Display the final DataFrame
print(combined)
```

This code first calculates the daily returns for each time series using the `pct_change()` function. It then combines these three series into one DataFrame using `pd.concat()`. The `dropna()` function is used to remove any rows with missing values. Finally, the columns are renamed appropriately using the `columns` attribute, and the final DataFrame is displayed.

Knowee AI · Accepted Answer

To answer your question, I'll assume that you have three pandas Series or DataFrames: `fund1`, `fund2`, and `etf`, each representing the daily prices of two funds and one ETF. Here's how you can create a DataFrame of daily returns for these three time series:

```python
import pandas as pd

# Assuming fund1, fund2, and etf are your time series data
# Calculate daily returns
fund1_returns = fund1.pct_change()
fund2_returns = fund2.pct_change()
etf_returns = etf.pct_change()

# Combine the three series into one DataFrame
combined = pd.concat([fund1_returns, fund2_returns, etf_returns], axis=1)

# Drop rows with missing values
combined = combined.dropna()

# Rename columns
combined.columns = ['Fund1 Returns', 'Fund2 Returns', 'ETF Returns']

# Display the final DataFrame
print(combined)
```

This code first calculates the daily returns for each time series using the `pct_change()` function. It then combines these three series into one DataFrame using `pd.concat()`. The `dropna()` function is used to remove any rows with missing values. Finally, the columns are renamed appropriately using the `columns` attribute, and the final DataFrame is displayed.

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