Untitled

# coding: utf-8

# # 1. Import pandas and Numpy

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import pandas as pd
import numpy as np


# # 2. Read Csv from file

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ecom=pd.read_csv('Ecommerce Purchases.csv')


# # Display csv

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ecom


# # How many rows and columns are there

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ecom.info()


# # 3. Check the head of the dataframe

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ecom.head()


# # Check the tail of the dataframne

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ecom.tail()


# # What is the average purchase price

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ecom['Purchase Price'].mean()


# # Highest purchase price

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ecom['Purchase Price'].max()


# # Lowest purchase price

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ecom['Purchase Price'].min()


# # How many people have english as their language of choice on the website

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ecom[ecom['Language']=='en'].count()


# # How many people have job title of lawyer

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ecom[ecom['Job']=='Lawyer'].info()


# # How many people made the purchase during the AM and how many people made the purchase during PM?

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ecom['AM or PM'].value_counts()


# # What are the five most common job title

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ecom['Job'].value_counts().head(5)


# # Someone made a purchase that came from Lot: "90 WT", What was the purchase price for this transaction

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ecom[ecom['Lot']=='90 WT']['Purchase Price']


# # What is the Email of the person With the following Credit card number

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ecom[ecom['Credit Card']==4926535242672853]['Email']


# # How many people have American Express as their Credit Card Provider and made a purchase above $95

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ecom[(ecom['CC Provider']=='American Express') & (ecom['Purchase Price']>95)].count()


# # How many people have a credit card that expires in year 2025

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sum(ecom['CC Exp Date'].apply(lambda x: x[3:]) =='25')