#!/usr/bin/env python
# coding: utf-8

# - Import Package 
# We import the pandas package with the alias pd
# - Import Data
# We import the Boston Dataset from 
# - Select Data by Row number
# - Select Data by Column Name
# - Select Data by Row and Column
# - Select Data by Condition

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import pandas as pd


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Boston =pd.read_csv("http://vincentarelbundock.github.io/Rdatasets/csv/MASS/Boston.csv")


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Boston.head(5) #Gives first few rows. In R we typically use head(object) while in Python we would use object.head()


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Boston=Boston.drop('Unnamed: 0', 1) #Dropping a particular variable


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Boston.dtypes


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Boston.info()# Gives information of object. In R this is given by str


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type(Boston) #Gives type of object. In R this is given by class


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Boston.shape #Gives rows and columns. In R this is given by dim


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dir(Boston) #What are the various commands we can run on Boston


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Boston[0:3] #Gives rows from first row to third row. In python index starts from 0 while in R it starts from 1.


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Boston[-5:504] #Gives rows from 506(total rows)-5 to the row number specified. 
#Note index is one less than row number unlike R


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Boston[['medv','rm','chas']].head() #Note the double square brackets [[]]


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Boston.ix[3:10,['medv','rm','chas','nox']] 
#Note the use of command ix as well as putting row numbers and column names seperately


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Boston['medv']>49


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Boston[Boston['medv']>49]


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Boston[Boston['medv']>49 ][Boston['chas']==0]


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Boston.query('medv >49 and rm >8')


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Boston.query('medv >49 or rm <4')


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