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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 <a href='http
Step1:  Check the head of the DataFrame.
Step2:  How many rows and columns are there?
Step3:  What is the average Purchase Price?
Step4:  What were the highest and lowest purchase prices?
Step5:  How many people have English 'en' as their Language of choice on the website?
Step6:  How many people have the job title of "Lawyer" ?
Step7:  How many people made the purchase during the AM and how many people made the purchase during PM ? 
(Hint
Step8:  What are the 5 most common Job Titles?
Step9:  Someone made a purchase that came from Lot
Step10:  What is the email of the person with the following Credit Card Number
Step11:  How many people have American Express as their Credit Card Provider and made a purchase above $95 ?
Step12:  Hard
Step13:  Hard
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Python Code:
import pandas as pd
ecom = pd.read_csv('Ecommerce Purchases')
Explanation: <a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>
Ecommerce Purchases Exercise
In this Exercise you will be given some Fake Data about some purchases done through Amazon! Just go ahead and follow the directions and try your best to answer the questions and complete the tasks. Feel free to reference the solutions. Most of the tasks can be solved in different ways. For the most part, the questions get progressively harder.
Please excuse anything that doesn't make "Real-World" sense in the dataframe, all the data is fake and made-up.
Also note that all of these questions can be answered with one line of code.
 Import pandas and read in the Ecommerce Purchases csv file and set it to a DataFrame called ecom.
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ecom.head()
Explanation: Check the head of the DataFrame.
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ecom.info()
Explanation: How many rows and columns are there?
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ecom['Purchase Price'].mean()
Explanation: What is the average Purchase Price?
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ecom['Purchase Price'].max()
ecom['Purchase Price'].min()
Explanation: What were the highest and lowest purchase prices?
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ecom[ecom['Language'] == 'en'].count()
Explanation: How many people have English 'en' as their Language of choice on the website?
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ecom[ecom['Job'] =='Lawyer'].info()
Explanation: How many people have the job title of "Lawyer" ?
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ecom['AM or PM'].value_counts()
Explanation: How many people made the purchase during the AM and how many people made the purchase during PM ? 
(Hint: Check out value_counts() )
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ecom['Job'].value_counts().head(5)
Explanation: What are the 5 most common Job Titles?
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ecom[ecom['Lot']=='90 WT']['Purchase Price']
Explanation: Someone made a purchase that came from Lot: "90 WT" , what was the Purchase Price for this transaction?
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ecom[ecom['Credit Card']==4926535242672853]['Email']
Explanation: What is the email of the person with the following Credit Card Number: 4926535242672853
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ecom[(ecom['Purchase Price']>95) & (ecom['CC Provider']=='American Express')].count()
Explanation: How many people have American Express as their Credit Card Provider and made a purchase above $95 ?
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def cc_split(card_year):
    splited = card_year.split('/')
    if(splited[1] == '25'):
        return True
    else:
        return False
sum(ecom['CC Exp Date'].apply(cc_split))
Explanation: Hard: How many people have a credit card that expires in 2025?
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def email_split(email):
    email_arr = email.split('@')
    return email_arr[1]
ecom['Email'].apply(email_split).value_counts().head(5)
Explanation: Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...)
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