Exercise

# Visualize monthly mean, median and standard deviation of S&P500 returns

You have also learned how to calculate several aggregate statistics from upsampled data.

Let's use this to explore how the monthly mean, median and standard deviation of daily S&P500 returns have trended over the last 10 years.

Instructions

**100 XP**

As usual, we have imported `pandas`

as `pd`

and `matplotlib.pyplot`

as `plt`

for you.

- Use
`pd.read_csv()`

to import`'sp500.csv'`

, set a`DateTimeIndex`

based on the`'date'`

column using`parse_dates`

and`index_col`

, assign the results to`sp500`

, and inspect using`.info()`

. - Convert
`sp500`

to a`pd.Series()`

using`.squeeze()`

, and apply`.pct_change()`

to calculate`daily_returns`

. `.resample()`

`daily_returns`

to month-end frequency (alias:`'M'`

), and apply`.agg()`

to calculate`'mean'`

,`'median'`

, and`'std'`

. Assign the result to`stats.`

`.plot()`

`stats`

.