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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:  CSV
CSV Input
Step2:  CSV Output
Step3:  Excel
Pandas can read and write excel files, keep in mind, this only imports data. Not formulas or images, having images or macros may cause this read_excel method to crash. 
Excel Input
Step4:  Excel Output
Step5:  HTML
You may need to install htmllib5,lxml, and BeautifulSoup4. In your terminal/command prompt run
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Python Code:
import numpy as np
import pandas as pd
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
 Typically we will just be either reading csv files directly or using pandas-datareader or quandl. Consider this lecture just a quick overview of what is possible with pandas (we won't be working with SQL or excel files in this course) 
Data Input and Output
This notebook is the reference code for getting input and output, pandas can read a variety of file types using its pd.read_ methods. Let's take a look at the most common data types:
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df = pd.read_csv('example')
df
Explanation: CSV
CSV Input
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df.to_csv('example',
          index = False)
Explanation: CSV Output
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pd.read_excel('Excel_Sample.xlsx',
              sheetname = 'Sheet1')
Explanation: Excel
Pandas can read and write excel files, keep in mind, this only imports data. Not formulas or images, having images or macros may cause this read_excel method to crash. 
Excel Input
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df.to_excel('Excel_Sample.xlsx',
            sheet_name = 'Sheet1')
Explanation: Excel Output
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df = pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html')
df[0]
Explanation: HTML
You may need to install htmllib5,lxml, and BeautifulSoup4. In your terminal/command prompt run:
conda install lxml
conda install html5lib
conda install BeautifulSoup4
Then restart Jupyter Notebook.
(or use pip install if you aren't using the Anaconda Distribution)
Pandas can read table tabs off of html. For example:
HTML Input
Pandas read_html function will read tables off of a webpage and return a list of DataFrame objects:
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