Load the data set hmeq$\_$small.csv $($Nicapotato$.$ "Vallala, Ajay. "HMEQ$\_$Data." Kaggle, $25$ Mar. $2018,$ www$.$kaggle.com$/$ajay$1735/$hmeq$-$data.$)$ as a data frame.

Create a new data frame with all the rows with missing data deleted.

Create a second data frame with all missing data filled in with the mean value of the column.

Find the means of the columns for both new data frames.

Ex: Using only the first hundred rows, found in hmeq$\_$sample.csv$,$ the output is:

Means for df$1$ are LOAN $3208\mathrm{.}333333$

MORTDUE $67495\mathrm{.}958333$

VALUE $82529\mathrm{.}125000$

YOJ $8\mathrm{.}500000$

CLAGE $144\mathrm{.}749455$

CLNO $16\mathrm{.}583333$

DEBTINC $33\mathrm{.}052122$

dtype: float$64$

Means for df$2$ are LOAN $3045\mathrm{.}918367$

MORTDUE $49386\mathrm{.}494253$

VALUE $64033\mathrm{.}483871$

YOJ $8\mathrm{.}179775$

CLAGE $140\mathrm{.}209320$

CLNO $15\mathrm{.}586957$

DEBTINC $30\mathrm{.}947152$

dtype: float$64$Load the data set hmeq$\_$small.csv $($Nicapotato$.$ "Vallala, Ajay. "HMEQ$\_$Data." Kaggle, $25$ Mar. $2018,$ www$.$kaggle.com$/$ajay$1735/$hmeq$-$data.$)$ as a data frame.

Create a new data frame with all the rows with missing data deleted.

Create a second data frame with all missing data filled in with the mean value of the column.

Find the means of the columns for both new data frames.

Ex: Using only the first hundred rows, found in hmeq$\_$sample.csv$,$ the output is:

Means for df$1$ are LOAN $3208\mathrm{.}333333$

MORTDUE $67495\mathrm{.}958333$

VALUE $82529\mathrm{.}125000$

YOJ $8\mathrm{.}500000$

CLAGE $144\mathrm{.}749455$

CLNO $16\mathrm{.}583333$

DEBTINC $33\mathrm{.}052122$

dtype: float$64$

Means for df$2$ are LOAN $3045\mathrm{.}918367$

MORTDUE $49386\mathrm{.}494253$

VALUE $64033\mathrm{.}483871$

YOJ $8\mathrm{.}179775$

CLAGE $140\mathrm{.}209320$

CLNO $15\mathrm{.}586957$

DEBTINC $30\mathrm{.}947152$

dtype: float$64$