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MIS $6335$ Python for Analytics

Homework $6$ Working with Dates

$$ HERE IS WHAT I WANT YOU TO DO:

Your assigned task is to complete the data operations described below using pandas, Python, and the Jupyter Notebook. The data file for this assignment is named $$IBM$.$csv$$ and is available on Blackboard with the other course data. Submit your completed notebook at the appropriate link in Blackboard. Do not submit the data file.

$$ HERE IS WHY I WANT YOU TO DO IT:

In addition to the skills we have practiced to this point, this assignment will give you more experience with the following:

$$ reading csv files containing date data

$$ using resampling

$$ using the plot$()$ function built into the dataframe object

$$ HERE IS HOW TO DO IT:

Use your template notebook as a basis to create the well$-$documented code to show the results of the analysis described below.

This dataset contains all daily stock data for IBM $($stock trading symbol is $...$ wait for it $...$ IBM$)$ since January $1962.$

$1.$ Read the data from the csv file and store it in a data frame object named $$ibm$.$ Make the date column the index and be sure to parse the dates, just to be safe.

$2.$ Use resampling to get the data representing the quarterly means of each column and store it in a new object called $$ibm$\_$qtr$.$

$3.$ Plot the closing price $($y$-$axis$)$ versus the quarter designation $($x$-$axis$)$ using the plot$()$ method built into the ibm$\_$qtr data frame object. Because the quarter designation is the index of ibm$\_$qtr$,$ plot$()$ will default to using it as the x$-$axis, so you don$$t have to worry about it$.$ Just specify the y$-$value $($i$.$e$.,$ the closing price$).$

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$4.$ Use resampling to get another set of data representing the weekly totals of each column and save it in a new object called $$ibm$\_$wk$.$

$5.$ Inside print$()$ statements, use the dataframe methods $$nsmallest$$ and $$nlargest$$ to display the $3$ smallest and $3$ largest volume weeks in ibm$\_$wk$.$

$$ HERE IS WHAT NOT TO WORRY ABOUT:

There is nothing obvious to add here. Ask if you have a question.

Solve the problem$($s$)$ before you start writing code. $-$ HERE IS HOW TO DO IT:

Use your template notebook as a basis to create the well$-$documented code to show the results of the analysis described below.

This dataset contains all daily stock data for IBM $($stock trading symbol is $...$ wait for it $...$ IBM$)$ since January $1962.$

$1.$ Read the data from the csv file and store it in a data frame object named 'ibm.$\text{'}$ Make the date column the index and be sure to parse the dates, just to be safe.

$2.$ Use resampling to get the data representing the quarterly means of each column and store it in a new object called "ibm$\_$qtr$."$

$3.$ Plot the closing price $(\backslash ($ y $\backslash )-$axis$)$ versus the quarter designation $(\backslash ($ x $\backslash )-$axis$)$ using the plot$()$ method built into the ibm$\_$qtr data frame object. Because the quarter designation is the index of ibm$\_$qtr$,$ plot$()$ will default to using it as the $\backslash ($ x $\backslash )-$axis, so you don't have to worry about it$.$ Just specify the $\backslash ($ y $\backslash )-$value $($i$.$e$.,$ the closing price$).4.$ Use resampling to get another set of data representing the weekly totals of each column and save it in a new object called "ibm$\_$wk$."$

$5.$ Inside print$()$ statements, use the dataframe methods "nsmallest" and "nlargest" to display the $3$ smallest and $3$ largest volume weeks in ibm$\_$wk$.$

$-$ HERE IS WHAT NOT TO WORRY ABOUT:

There is nothing obvious to add here. Ask if you have a question.