A$.$ As promised, please see the instructions below for the submission of Assignments $2$ and $3.$

$1.$ For both Assignments, you will continue to extract your datasheet allotment from the MS Excel Workbook used in Assignment$\_1.$

$2.$ The first part of these instructions is similar to Assignment$\_1.$

However, in addition to your Indicating the dtype of each column, you will further supply the following information:

$1.$ In a second worksheet of your work book you will state the shape of your dataframe, which translates to the number of rows and columns taken up by the data in your worksheet.

$2.$ Specifically, the row component of the shape of your dataframe omits the header row which has the names of data columns. The remainder of the $$effective rectangle$$ of filled cells constitutes the range of rows and columns.

$3.$ For example, if your $$effective rectangle$$ of data spans a range of $237$ rows and $23$ columns, you will write your shape as: Shape: $($r$,$ c$)=(237,23).$

$4.$ Next, in this very second worksheet, you will make a summary for each of the several dtypes in your dataframe. For example, let us take the datasheet template in the attached file $<$ Demo Heart Datasheet$\_$Tutorial $1.$xlsx$>$ for which the dtypes are assigned $($not necessarily all correct$),$ and we have:

Shape: $($r$,$ c$)=(918,16)$

DTypeSN Dtype Counts

$1$ Index $1$

$2$ DateTime $0$

$3$ Not classified $0$

$4$ Num float $1$

$5$ Num integer $4$

$6$ Cat binary $5$

$7$ Cat ordinal $2$

$8$ Cat nominal $3$

Total columns $16$

Note:

$1.$ You can copy and paste the above table and use as a template for returning your Assignments.

$2.$ You MUST be careful when determining the number of rows. For example, with double$-$header rows for this instance with our template, we need to subtract $2$ from the last worksheet row index value: $9202=918.$ You should not rely on the sequence of index columns because not all index columns have sequential integer values.

$3.$ You will notice that the number of columns in the shape statement has the exact same value as the sum$-$counts of the dtypes. This exactness WILL ALWAYS be the case when you have accounted for all columns in your dataframe.

PSN AccDesig PDR WtMTpP ExRtPed SC$\_$C

$70$ NBC$3820\mathrm{.}8749686051\mathrm{.}861607143$ S SC$0$

$71$ KS $120\mathrm{.}195305932\mathrm{.}8734$ Pd SC$0$

$72$ NDC $880\mathrm{.}6054483842\mathrm{.}161290323$ Pd SC$0$

$73$ SBS$2560\mathrm{.}4687331812\mathrm{.}2014$ S SC$0$

$74$ ACS $730\mathrm{.}6640401633\mathrm{.}058823529$ S SC$0$

$75$ NDC $880\mathrm{.}7812237211\mathrm{.}477925$ Pd SC$0$

$76$ LGL $700\mathrm{.}4062354245\mathrm{.}869230769$ S SC$0$

$77$ SBS$2560\mathrm{.}6835707561\mathrm{.}546628571$ Pd$\_$S SC$0$

$78$ NRS $860\mathrm{.}8788766862\mathrm{.}225355556$ S SC$0$

$79$ NFS $800\mathrm{.}9374663631\mathrm{.}1507$ S SC$1$

$80$ KS $120\mathrm{.}2148365232\mathrm{.}474909091$ Pd SC$1$

$81$ ABL $160\mathrm{.}8593460932\mathrm{.}386$ S SC$0$

$82$ LPL $670\mathrm{.}2812399095\mathrm{.}691666667$ S SC$0$

$83$ AGT $720\mathrm{.}9179378721\mathrm{.}276425532$ Pd SC$0$

$84$ PBL $50\mathrm{.}1757753375\mathrm{.}950818182$ S SC$0$

$85$ CHR$2580\mathrm{.}3124887883\mathrm{.}7451$ S SC$1$

$86$ KSC $410\mathrm{.}3124894882\mathrm{.}075875$ S SC$1$

$87$ TPP $10\mathrm{.}6640401633\mathrm{.}870764706$ S SC$0$

$88$ GT $20\mathrm{.}5077954191\mathrm{.}390615385$ Pd$\_$S SC$1$

$89$ NBC$2810\mathrm{.}4296730471\mathrm{.}869227273$ Pd$\_$S SC$0$

$90$ THM$4460\mathrm{.}4374843035\mathrm{.}950818182$ S SC$1$

$91$ NWS $870\mathrm{.}6835707563\mathrm{.}960542857$ S SC$0$

$92$ TAB$1470\mathrm{.}7616931281\mathrm{.}441282051$ Pd SC$0$

$93$ ABS $190\mathrm{.}249991035\mathrm{.}950818182$ Pd SC$0$

$94$ MBSBL $210\mathrm{.}6054483844\mathrm{.}170096774$ S SC$1$

$95$ WS $130\mathrm{.}2734283023\mathrm{.}662285714$ S SC$1$

$96$ THM$3450\mathrm{.}3437376661\mathrm{.}310272727$ Pd SC$0$

$97$ GT $20\mathrm{.}4296730471\mathrm{.}187545455$ Pd$\_$S SC$1$

$98$ ADBS $320\mathrm{.}7421625353\mathrm{.}052315789$ S SC$1$