Load the hmeq$\_$small.csv data set as a data frame.

Standardize the data set as a new data frame.

Normalize the data set as a new data frame.

Print the means and standard deviations of both the standardized and normalized data.

Ex: Using the first $100$ rows, found in hmeq$\_$sample.csv$,$ the output is:

The means of df$1$ are LOAN $1\mathrm{.}631348$e$-16$

MORTDUE $1\mathrm{.}276118$e$-18$

VALUE $-2\mathrm{.}447266$e$-17$

YOJ $-8\mathrm{.}732091$e$-17$

CLAGE $-1\mathrm{.}036208$e$-16$

CLNO $-5\mathrm{.}068409$e$-17$

DEBTINC $9\mathrm{.}188053$e$-17$

dtype: float$64$

The standard deviations of df$1$ are LOAN $1\mathrm{.}005141$

MORTDUE $1\mathrm{.}005797$

VALUE $1\mathrm{.}005420$

YOJ $1\mathrm{.}005666$

CLAGE $1\mathrm{.}005602$

CLNO $1\mathrm{.}005479$

DEBTINC $1\mathrm{.}017700$

dtype: float$64$

The means of df$2$ are LOAN $0\mathrm{.}671006$

MORTDUE $0\mathrm{.}358735$

VALUE $0\mathrm{.}299044$

YOJ $0\mathrm{.}292135$

CLAGE $0\mathrm{.}448986$

CLNO $0\mathrm{.}346377$

DEBTINC $0\mathrm{.}624927$

dtype: float$64$

The standard deviations of df$2$ are LOAN $0\mathrm{.}269531$

MORTDUE $0\mathrm{.}247183$

VALUE $0\mathrm{.}187587$

YOJ $0\mathrm{.}237945$

CLAGE $0\mathrm{.}226345$

CLNO $0\mathrm{.}188681$

DEBTINC $0\mathrm{.}222946$

dtype: float$64$

import pandas as pd

from sklearn.preprocessing import StandardScaler, Normalizer

# Read data

hmeq $=$ pd$.$read$\_$csv$(\text{'}$hmeq$\_$small.csv$\text{'})$

# Fill NaNs

hmeq $=$ hmeq.fillna$($hmeq$.$mean$())$

# Standardize

from sklearn.preprocessing import StandardScaler

standardized $=$ StandardScaler$().$fit$\_$transform$($hmeq$)$

df$1=$ pd$.$DataFrame$($standardized$,$ columns$=$hmeq.columns$)$

# Normalize

from sklearn.preprocessing import Normalizer

normalized $=$ Normalizer$().$fit$\_$transform$($hmeq$)$

df$2=$ pd$.$DataFrame$($normalized$,$ columns$=$hmeq.columns$)$

# Print statistics in correct order

print$($df$1.$mean$())$

print$($df$1.$std$())$

print$($df$2.$mean$())$

print$($df$2.$std$())$