Groupby in Python Pandas

by

In this python tutorial, we will learn everything about Groupby in Python Pandas.

  • Introduction to Groupby in Python Pandas
  • Groupby in Python Pandas
  • Groupby Pandas Example
  • Groupby Pandas Count
  • Groupby Pandas Multiple Columns
  • Groupby Pandas Aggregate
  • Groupby Pandas Without Aggregation
  • Groupby Pandas Sum
  • Groupby Pandas Two Columns
  • Groupby Pandas Sort
  • Groupby Pandas Apply
  • Groupby Pandas agg
  • Groupby Pandas Mean
  • Python Iterate Groupby Pandas

If you are new to Python Pandas, check out Pandas in Python.

We will be using the a food dataset that has been downloaded from this URL.

Groupby Pandas in Python Introduction

  • A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.
  • Let’s say if you want to know the average salary of developers in all the countries. In that case, groupby can be used to display an average of salary country-wise.
  • Groupby in Python Pandas is similar to Group by in SQL.
Groupby in Python Pandas
Groupby in Python Pandas

Syntax:

dataframe.groupby(
              by=None, 
              axis=0, 
              level=None, 
              as_index=True, 
              sort=True, 
              group_keys=True, 
              squeeze=<object object>, 
              observed=False, 
              dropna=True    
)

Parameters:

by– mapping, function, label, or list of labels
– Used to determine the groups for the groupby.
– If by is a function, it’s called on each value of the object’s
index.
– If by is dict or Series, the Series or dict VALUES will be used to determine the groups.
– by default by=None
axis– {0 or ‘index’, 1 or ‘columns’}, default 0
– Split along rows (0) or columns (1).
– eg: axis=0 or axis=1
level– int, level name, or sequence of such, default None.
– If the axis is a MultiIndex (hierarchical), group by a particular level or levels.
as_index– bool, default True
– For aggregated output, return an object with group labels as the index.
– Only relevant for DataFrame input.
– as_index=False is effectively “SQL-style” grouped output.
sort– bool, default True
– Sort group keys.
– Get better performance by turning this off. Note this does not influence the order of observations within each group.
– Groupby preserves the order of rows within each group.
– eg: sort=False
group_keys– bool, default True
– When calling apply, add group keys to the index to identify pieces.
– eg: group_keys = False
squeeze– bool, default False
– Reduce the dimensionality of the return type if possible, otherwise return a consistent type.
– eg: squeeze=True
observed– bool, default False
– This only applies if any of the groupers are Categoricals.
– If True: only show observed values for categorical groupers.
– If False: show all values for categorical groupers.
– eg: observed=True
dropna– bool, default True
– If True, and if group keys contain NA values, NA values together with row/column will be dropped.
– If False, NA values will also be treated as the key in groups
– eg: dropna=True

You may also like Python Pandas CSV Tutorial.

Groupby Pandas DataFrame

  • In this section, we will learn to create and implement Python pandas groupby on DataFrame.
  • A groupby operation involves some combination of splitting the object, applying a function, and combining the results.
  • This can be used to group large amounts of data and compute operations on these groups.

Syntax:

Here is the syntax of implementing groupby in Pandas on dataframe in Python. The parameters are explained in the introduction section of this blog.

DataFrame.groupby(
    by=None, 
    axis=0, 
    level=None, 
    as_index=True, 
    sort=True, 
    group_keys=True, 
    squeeze=<object object>, 
    observed=False, 
    dropna=True
)

Implementation on jupyter notebook:

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Groupby Pandas Example

This is a basic example of Python pandas groupby to demonstrate how it works.

Groupby Pandas Count

Count function in groupby Pandas compute count of group and it excluded missing values.

Syntax:

GroupBy.count()

Groupby Pandas Multiple Columns

In this section, we will learn how to groupby multiple columns in Python Pandas. To do so we need to pass the column names in a list format.

Check out Crosstab in Python Pandas.

Groupby Pandas Aggregate

Aggregate is a function applied on the group in Python groupby Pandas.

Groupby Pandas Without Aggregation

In this section, we will learn how to apply a function without using aggregation in groupby pandas in Python.

Groupby Pandas Sum

Let us see how Groupby Pandas Sum works? It compute sum of grouped values.

Syntax:

GroupBy.sum(
    numeric_only=True, 
    min_count=0
)
numeric_only
bool, default True
Include only float, int, boolean columns.
If None, will attempt to use everything, then use only numeric data.
min_countint, default 0
The required number of valid values to perform the operation.
If fewer than min_count non-NA values are present the result will be NA.

Implementing on jupyter notebook

Groupby Pandas Two Columns

In this section we will learn of to groupby two columns in Python pandas.

Groupby Pandas Sort

Let us see how to do Groupby Pandas Sort in Python.

  • Sort refers to arranging the groups either in ascending or descending order.
  • sorting needs boolean parameter
  • sort=False, this means data is unsorted or unorganised
  • sorted=True, this means data is sorted or organised

Groupby Pandas Apply

In Python Groupby Pandas, the Apply function is used to implement a function on the group. It is used when we don’t want to use aggregation in a program. It takes the function name as a parameter.

Groupby Pandas agg

Let us see how the Groupby Pandas agg works in Python? agg is the shorthand of aggregation and its purpose is to implement a function on the group.

Groupby Pandas Mean

  • In this section, we will learn to find the mean of groupby pandas in Python. The mean is the average or the most common value in a collection of numbers.
  • mean = sum of the terms / total number of terms
  • Groupby mean compute mean of groups, excluding missing values.
  • mean can only be processed on numeric or boolean values. Numeric values can be integer or float.

Syntax:

GroupBy.mean(numeric_only=True)

Parameter:

numeric_only– bool, default True
– Include only float, int, boolean columns.
– If None, will attempt to use everything, then use only numeric data.
– if numeric_only=True, then it will work

Implementation on Jupyter notebook

Python Iterate Groupby Pandas

In this section, we will learn how to iterate over each grouped items in Python pandas groupby.

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In this tutorial, we have learned about groupby in Python pandas also we have covered these topics.

  • Groupby Pandas Introduction
  • Groupby Pandas DataFrame
  • Groupby Pandas Example
  • Groupby Pandas Count
  • Groupby Pandas Multiple Columns
  • Groupby Pandas Aggregate
  • Groupby Pandas Without Aggregation
  • Groupby Pandas Sum
  • Groupby Pandas Two Columns
  • Groupby Pandas Sort
  • Groupby Pandas Apply
  • Groupby Pandas agg
  • Groupby Pandas Mean
  • Python Iterate Groupby Pandas