In Jupyter notebook or another similar alternative, run this code first:

import pandas as pd

import numpy as np

data $=$ pd$.$read$\_$csv$(\text{'}$NVDA$.$csv$\text{'},$index$\_$col$=0,$ parse$\_$dates$=$True$)$

#data $=$ pd$.$read$\_$csv$(\text{'}$NVDA$.$csv$\text{'},$index$\_$col$=0)$

#data.index$=$data$1.$index.astype$(\text{'}$datetime$64[$ns$]\text{'})$

stock $=$ data$[\text{'}$Close$\text{'}]$

Next line:

# print all the outputs in a cell

from IPython.core.interactiveshell import InteractiveShell

InteractiveShell.ast$\_$node$\_$interactivity $=$ "all"

Next line:

#Set to $2$ decimal places, and display all columns $($and up to $300$ rows$)$

pd$.$set$\_$option$(\text{'}$display$.$float$\_$format', lambda x: $\text{'}\%\mathrm{.}2$f$\text{'}\%$ x$)$

pd$.$set$\_$option$(\text{'}$display$.$max$\_$columns', None$)$

#pd$.$set$\_$option$(\text{'}$display$.$max$\_$rows', $300)$

Next line:

$\%$pylab inline

stock.plot$()$

Next line:

stock.head$()$

stock.tail$()$

Based on this information, answer the following quesiton: After stock price hit $330$ dollar, how many trading days and how many calendar days it took to reach the same value?