#Run this cell first before moving on

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

plt$.$style.use$("$ggplot$")$

$\%$matplotlib inline

#Use the variables$/$urls to load the data

# url for covid cases:

covid$\_$cases $="$https:$//$github$.$com$/$CSSEGISandData$/$COVID$-19/$raw$/$master$/$csse$\_$covid$\_19\_$data$/$csse$\_$covid$\_19\_$time$\_$series$/$time$\_$series$\_$covid$19\_$confirmed$\_$US$.$csv$"$

# url for covid deaths:

covid$\_$deaths $="$https:$//$github$.$com$/$CSSEGISandData$/$COVID$-19/$raw$/$master$/$csse$\_$covid$\_19\_$data$/$csse$\_$covid$\_19\_$time$\_$series$/$time$\_$series$\_$covid$19\_$deaths$\_$US$.$csv$"$

# It is okay to have a horizontal scroll bar for this cell.

df$\_$cases $=$ pd$.$read$\_$csv$($covid$\_$cases$)$

df$\_$deaths $=$ pd$.$read$\_$csv$(---)$ # include covid$\_$deaths variable in place of $---$

#Run the cell, review the output, and move to the next cell

df$\_$cases

#Run the cell, review the output, and move to the next cell

df$\_$deaths

$"""$Problem $0"""$

# Run the cell, notice the additional data column in df$\_$deaths which will be remove in problem $1$b$.$

print$("$Cases DataFrame:", list$($df$\_$cases.columns$[0$:$12]))$

print$("")$ # Do not modify $-$ this displays blank line for space between the Cases and Deaths output

print$("$Deaths DataFrame:", list$($df$\_$deaths.columns$[0$:$13]))$

$"""$Problem $1$a$"""$

#Remove the following columns for df$\_$cases using the drop method:

# "UID", "iso$2",$ "iso$3",$ "code$3",$ "FIPS", "Country$\_$Region",

# "Lat", "Long$\_"$

# Use the inplace$=$True.

df$\_$cases.drop$($columns$=[---])$ #remove $---$ and enter the columns names in the list, each as a string.

df$\_$cases.head$()$

$"""$Problem $1$b$"""$

#Remove the same columns for df$\_$deaths but also include the extra data column in df$\_$deaths:

df$\_$deaths.drop$(---)$ #remove $---$ and enter code

df$\_$deaths.tail$(3)$

$"""$Problem $2"""$

#Rename the following columns for both dataframes using the rename method:

# Admin$2$ to county, Province$\_$State to state, Combined$\_$key to county$\_$state

print$("$Before rename:", list$($df$\_$cases.columns$))$ # displays data column names for df$\_$cases

df$\_$cases.rename$($columns$=\{---\},$ inplace$=---)$

print$("$After rename:", list$($df$\_$cases.columns$))$

print$("")$

print$("$Before rename:", list$(---.$columns$))$ # displays data column names for df$\_$deaths

df$\_$deaths.rename$(---,---)$

print$($list$($df$\_$deaths.$---))$ # displays data column names for df$\_$deaths

$"""$Problem $3"""$

#Melt$/$reshape df$\_$cases. Columns to assign to

# the id$\_$vars parameter are: county, state, and county$\_$state.

# Make sure that var$\_$name$=$"dates" and value$\_$name$=$"cases"

#Complete the melt function code below.

df$\_$cases$\_$melted $=$ pd$.$melt$(---,$ id$\_$vars$=[---],$ var$\_$name$=---,$ value$\_$name$=---)$

df$\_$cases$\_$melted.tail$(3)$

$"""$Problem $4"""$

#Melt$/$reshape df$\_$deaths. Columns to assign to

# the id$\_$vars parameter are: county, state, and county$\_$state.

#Make sure that var$\_$name$=$"dates" and value$\_$name$=$"deaths"

#Complete the melt function code below.

df$\_$deaths$\_$melted $=$ pd$.$melt$(---)$

df$\_$deaths$\_$melted.tail$(3)$

$"""$Problem $5"""$

#Merge the melted dataframes for cases and deaths.

#Complete the merged function code below.

df$\_$merged $=$ pd$.$merge$(---)$

df$\_$merged.tail$()$

$"""$Problem $6"""$

#Using df$\_$merged, change data type for dates to a datetime object.

df$\_$merged.dates $=$ pd$.$to$\_$datetime$(---)$

df$\_$merged.dtypes

$"""$Problem $7"""$

#Calculate the number of days into the outbreak for each record.

#The name of the new column will be us$\_$outbreak

df$\_$merged$["$us$\_$outbreak"$]=---$

df$\_$merged$[["$dates$","$us$\_$outbreak"$]].$iloc$[-1]$ # this code outputs the number of days since the outbreak $($Day $0)$

#Run cell, review the dataframe output which include the new column $($us$\_$outbreak$)$

df$\_$merged

$"""$

After completing problem $7,$ run this cell and review the output.

Understand what the as$\_$index parameter does.

$"""$

df $=$ df$\_$merged.groupby$(["$us$\_$outbreak", "date"$],$ as$\_$index$=$False$)["$cases$"].$sum$()$

df$.$tail$()$

$"""$Problem $8"""$

#Using df from above, create a derived data column using the diff method on cases.

#The name of the new column is: new$\_$cases

df$["$new$\_$cases"$]=---$

df$.$tail$()$

$"""$ Problem $9"""$

#Use plt$.$bar$()$ function to create a bar plot,

# have the dates on the x$-$axis and new$\_$cases on the y$-$axis.

#Provide x & y axis labels and an appropriate chart title that

# explains visual.

#Rotate the x$-$axis ticks to $45$ using the xticks function.

# Below are the functions used $-$ complete the code:

plt$.$figure$(---)$ # include figsize keyword argument

plt$.$bar$(---)$

plt$.$xticks$(---)$ # include rotation keyword argument

plt$.$xlabel$(---)$

plt$.$ylabel$(---)$

plt$.$title$(---)$

plt$.$tight$\_$layout$()$

plt$.$show$()$

$"""$ Problem $10"""$

# Provide the code that shows what the average number of

# new$\_$cases that were reported in $2021$

Please help me solve the problems above please. I need to fill in the $---.$ Please use Jupyter notebook to solve these problems please