Data Wrangling

Data Wrangling: Group By, Join, Combine, Pivot,
Melt & Reshape.

Data Wrangling:

The process of cleaning the data enough to input the analytical algorithm is called Data Wrangling. It is also called as Data Munging.  

1. Melt:  Reshaping a date from wide to long in pandas python can be done with melt().

• Step 1: Create a DataFrame: Below data shown is in wide.
  

• Step 2: Use melt() as shown below. This data will be in long. 
• id_vars=['countires']: ids which need to be left unaltered i.e countries in this case.
• var_name='metrics': column names changed to metrics.
• value_name='values': changed to values.
  
  

2. Pivot: Riverse of melt() i.e. from long to wide.

• Step 1: Create a DataFrame: Below data shown is in long.
  

• Step 2: Use pivot() as shown below. This data will be in wide. 
• index='countires': column used as an index.

3. Group By: Similar to group by in SQL.

• Step 1: Create a DataFrame: 
  

• Step 2: Use pivot() as shown below.

  first(): prints first enteries in all the groups formed.

  
  

  
  

  
  
  

• Step 3: mean()  can be used to find the mean of the column/row.
  

Also,

• Step 4: unstack()  can also be used to find the mean after group by.
  
  
  
  Click this for more on Group BY Example