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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:
 Introduction
Databaker is an Open Source Python library for converting semi-structured spreadsheets into computer-friendly datatables.  The resulting data can be stored into Pandas data tables or the ONS-specific WDA format.
The system is embedded into the interactive programming environment called Jupyter for fast prototyping and development, and depends for its spreadsheet processing on messytables and xypath.
Install it with the command
Step1:  Conversion segments
Databaker gives you tools to help you write the code to navigate around the spreadsheet and select the cells and their correspondences.  
When you are done your code will look like the following.  
You can click on the OBS (observation) cells to see how they connect to the headings.
Step2:  Output in pandas
Pandas data tables provides an enormous scope for further processing and cleaning of the data.  
To make full use of its power you should become familiar with its Time series functionality, which will allows you to plot, resample and align multple data sources at once.
Step3:  Output in WDA Observation File
The WDA system in the ONS has been the primary use for this library.  If you need output into WDA the result would look like the following
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Python Code:
from databaker.framework import *
tab = loadxlstabs("example1.xls", "beatles", verbose=False)[0]
savepreviewhtml(tab, verbose=False)
Explanation: Introduction
Databaker is an Open Source Python library for converting semi-structured spreadsheets into computer-friendly datatables.  The resulting data can be stored into Pandas data tables or the ONS-specific WDA format.
The system is embedded into the interactive programming environment called Jupyter for fast prototyping and development, and depends for its spreadsheet processing on messytables and xypath.
Install it with the command:
pip3 install databaker
Your main interaction with databaker is through the Jupyter notebook interface.  There are many tutorials to show you how to master this system elsewhere on-line.  
Once you've have a working program to converts a particular spreadsheet style into the output which you want, there are ways to rerun the notebook on other spreadsheets externally or from the command line.  
Example
Although Databaker can handle spreadsheets of any size, here is a tiny example from the tutorials to illustrate what it does.
End of explanation
r1 = tab.excel_ref('B3').expand(RIGHT)
r2 = tab.excel_ref('A3').fill(DOWN)
dimensions = [ 
    HDim(tab.excel_ref('B1'), TIME, CLOSEST, ABOVE), 
    HDim(r1, "Vehicles", DIRECTLY, ABOVE), 
    HDim(r2, "Name", DIRECTLY, LEFT), 
    HDimConst("Category", "Beatles")
]
observations = tab.excel_ref('B4').expand(DOWN).expand(RIGHT).is_not_blank().is_not_whitespace()
c1 = ConversionSegment(observations, dimensions)
savepreviewhtml(c1)
Explanation: Conversion segments
Databaker gives you tools to help you write the code to navigate around the spreadsheet and select the cells and their correspondences.  
When you are done your code will look like the following.  
You can click on the OBS (observation) cells to see how they connect to the headings.
End of explanation
c1.topandas()
Explanation: Output in pandas
Pandas data tables provides an enormous scope for further processing and cleaning of the data.  
To make full use of its power you should become familiar with its Time series functionality, which will allows you to plot, resample and align multple data sources at once.
End of explanation
print(writetechnicalCSV(None, c1))
Explanation: Output in WDA Observation File
The WDA system in the ONS has been the primary use for this library.  If you need output into WDA the result would look like the following:
End of explanation
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