Using Groupby in Pandas


  1. upon doing a groupby, we either get a SeriesGroupBy object, or a DataFrameGroupBy object.
    • “This grouped variable is now a GroupBy object. It has not actually computed anything yet except for some intermediate data about the group key df[‘key1’]. The idea is that this object has all of the information needed to then apply some operation to each of the groups.” – Python for Data Analysis
  2. using aggregate functions on the grouped object.
    • some common aggregations are provided by default as instance methods on the GroupBy object
      • .sum()
      • .mean()
      • .size()
        • size has a slightly different output than others
        • there are some examples which show using count().  but i had trouble using count()
    • applying multiple functions / applying different functions of different columns
      • look up section in reference [1]
  3. column selection in group by.
    • In [37]: grouped = df.groupby(['A'])
      In [38]: grouped_C = grouped['C']
      In [39]: grouped_D = grouped['D']

      This is mainly syntactic sugar for the alternative and much more verbose:

      In [40]: df['C'].groupby(df['A'])
      Out[40]: <pandas.core.groupby.SeriesGroupBy object at 0x129fce310>
  4.  as_index=False
    • do note that using as_index=False still returns a groupby object
  5. reset_index
    • there are some oddities when using groupby (reference [3]). In those cases, using reset_index will be useful
  6. using unstack()
    • the typical use of unstack is to remove the effects of hierarchical indexing
    • see reference [2] for a nice example
  7. iterate operations over groups

    # Group the dataframe by regiment, and for each regiment,
    for name, group in df.groupby(‘regiment’):
    # print the name of the regiment
    # print the data of that regiment

  8. applying multiple functions at once
    • look up section in reference [1] around applying multiple functions






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