%matplotlib inline
import numpy as np 
import matplotlib.pyplot as plt 
from math import *
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler

G = 6.674 *10**(-11)
M_e = 5.97*10**(24)
R_e = 6.37*10**6

def escape_velocity():
    print (sqrt(2*G*M_e/R_e))

escape_velocity()

import requests #library used to download web pages.
#specify the url
URL = "https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html"

# The GET method indicates that you’re trying to get or retrieve data from a specified resource. 
# Connect to the website using the variable 'page'
# To make a GET request, invoke requests.get().
page = requests.get(URL)

# A Response is a powerful object for inspecting the results of the request.
type(page)

# verify successful connection to website

# To know about the all codes 
# https://www.restapitutorial.com/httpstatuscodes.html
  
#  a 200 OK status means that your request was successful,and the server responded with the data you were requesting,
# whereas a 404 NOT FOUND status means that the resource you were looking for was not found.     
page.status_code

#save string format of website HTML into a variable
HTMLstr = page.text
print(HTMLstr[:1000])

#import the Beautiful soup functions to parse the data returned from the website

# Beautiful Soup is a library that makes it easy to scrape information from web pages. It sits atop an HTML
# or XML parser, providing Pythonic idioms for iterating, searching, and modifying the parse tree.
from bs4 import BeautifulSoup

# parse the html using beautiful soup and store in variable `soup`
# First argument: It is the raw HTML content.
# Second Argument:  Specifying the HTML parser we want to use.
soup = BeautifulSoup(HTMLstr, "html.parser")

print (soup.prettify())

# soup.<tag>: Return content between opening and closing tag including tag.
soup.title

#shows the first <a> tag on the page
soup.a

soup.find_all("a")

#show hyperlink reference for all <a> tags
all_links=soup.find_all("a")

#The following selects the first 20 rows
# If you want to see all, remove [0:20]
for link in all_links[0:20]:
    print (link.get("href"))

all_tables=soup.find_all('table')
all_tables

planet_table=soup.find('table')
planet_table

all_tables=soup.find_all('table')
planet_table=soup.find('table')

#set empty lists to hold data of each column
A=[]#Physical Quantities
B=[]#Mercury
C=[]#Venus
D=[]#Earth
E=[]#Moon
F=[]#Mars
G=[]#Jupiter
H=[]#Saturn
I=[]#Uranus
J=[]#Neptune
K=[]#Pluto

#find all <tr> tags in the table and go through each one (row)
# tr table row tag
for row in planet_table.findAll("tr"):
    body=row.findAll('td') #To store second column data
    #get all the <td> tags for each <tr> tag
    cells = row.findAll('td')
    
    #if there are 11 <td> tags, 11 cells in a row
    if len(cells)==11: 
        
        A.append(cells[0].find(text=True)) #gets info in first column and adds it to list A
        B.append(cells[1].find(text=True)) # gets info of Mercury column and adds it to list B
        C.append(cells[2].find(text=True)) # gets info of Venus column and add it to list C
        D.append(cells[3].find(text=True)) # gets info of Earth and adds it to list D
        E.append(cells[4].find(text=True)) # gets info of Moon column and adds it to list E
        F.append(cells[5].find(text=True)) # gets info of Mars column and adds it to list F
        G.append(cells[6].find(text=True)) # gets info of Jupiter column tand adds it to list G
        H.append(cells[7].find(text=True)) # gets info of Saturn column tand adds to list H
        I.append(cells[8].find(text=True)) # gets info of Uranus column and adds it to list I
        J.append(cells[9].find(text=True)) # gets info of Neptune column and adds it to list J
        K.append(cells[10].find(text=True)) # gets info of NePluto column and adds to list K

#verify data in list A
A

#import pandas to convert list to data frame
import pandas as pd

df=pd.DataFrame(A, columns=['Physical_Measurement']) #turn list A into dataframe first

#add other lists as new columns in my new dataframe
df['Mercury'] = B
df['Venus'] = C
df['Earth'] = D
df['Moon'] = E
df['Mars'] = F
df['Jupiter'] = G
df['Saturn'] = H
df['Uranus'] = I
df['Neptune'] = J
df['Pluto'] = K

df=df.fillna(0)
df=df.replace(to_replace = 'Unknown*', value =0) 
#Planetary Fact Sheet - Ratio to Earth Values

#show first 5 rows of created dataframe
df

df=df.drop(df.index[0])
df=df.drop(df.index[-1])
df

df.dtypes

#getting rid of *
df= df.applymap(lambda x: x.strip('*') if isinstance(x, str) else x)
df
#display the table if everything is OK

df = df.replace(to_replace=['No', 'Yes','Unknown'], value=[0, 1, 2])
df

#change index
df = df.set_index('Physical_Measurement')
df

df.dtypes

df = df.apply(pd.to_numeric, errors='coerce')
df.dtypes

df

#Transpose index and columns.
#Reflect the DataFrame over its main diagonal by 
#writing rows as columns and vice-versa. 
#The property T is an accessor to the method transpose().
dfT = df.T

#check if columns are changed
dfT.columns

#Extract relevant columns 
dfT = dfT[['Mass', 'Diameter', 'Density', 'Gravity', 'Escape Velocity',
       'Rotation Period', 'Length of Day', 'Distance from Sun', 'Perihelion',
       'Aphelion', 'Orbital Period', 'Orbital Velocity']]
dfT

ax=dfT['Mass'].plot(kind='bar',x=dfT.index, figsize=(10,7),color='lightgreen')
ax.set_xlabel("Planets")
ax.set_ylabel("Mass");

ax=dfT['Mass'].plot(kind='bar',x=dfT.index, figsize=(10,7),color='lightgreen')
ax.set_xlabel("Planets")
#ax1.set_xlim([begin, end])
ax.set_ylim([0, 2])
ax.set_ylabel("Mass");

ax=dfT['Escape Velocity'].plot(kind='bar',x=dfT.index, figsize=(10,7),color='g')
ax.set_xlabel("Planets")
ax.set_ylabel("Escape Velocity");

ax=dfT['Length of Day'].plot(kind='bar',x=dfT.index, figsize=(10,7),color='m')
ax.set_xlabel("Planets")
ax.set_ylabel("Length of Day");

ax=dfT['Gravity'].plot(kind='bar',x=dfT.index, figsize=(10,7),color='r')
ax.set_xlabel("Planets")
ax.set_ylabel("Gravity");

ax=dfT['Density'].plot(kind='bar',x=dfT.index, figsize=(10,7),color='darkblue')
ax.set_xlabel("Planets")
ax.set_ylabel("Density");

ax=dfT['Distance from Sun'].plot(kind='bar',x=dfT.index, figsize=(10,7),color='b')
ax.set_xlabel("Planets")
ax.set_ylabel("Distance from Sun ");

dfT1 = pd.DataFrame(dfT, columns=['Gravity', 'Density', 'Escape Velocity'])
colors=['red','blue','lightgreen']
ax=dfT1.plot.bar( figsize=(12,8), color=colors)
ax.set_xlabel("Planets")
ax.set_ylabel("Gravity, Density OR Escape Velocity");

dfT2 = pd.DataFrame(dfT, columns=['Distance from Sun', 'Length of Day'])
ax=dfT2.plot.bar(figsize=(10,7))
ax.set_xlabel("Planets")
ax.set_ylabel("Distance from Sun OR Length of Day");

dfT3 = pd.DataFrame(dfT, columns=['Gravity', 'Density', 'Escape Velocity','Distance from Sun', 'Length of Day','Mass'])
ax=dfT3.plot.bar(stacked=True, figsize=(10,7),colormap='hsv',title='Defferent Physical Measurements of each Planet')
ax.set_xlabel("Planets")
ax.set_ylabel("Physical Parameters");

scaler = MinMaxScaler()
dfT4 = pd.DataFrame(scaler.fit_transform(dfT3), columns=dfT3.columns)

ax=dfT4.plot.bar(stacked=True, figsize=(10,7),colormap='rainbow',title='Defferent Physical Measurements of each Planet')
ax.set_xlabel("Planets")
ax.set_ylabel("Physical Parameters");