What is GroupBy Function and Examples

In this blog, we would discuss What is GroupBy function and Examples. Python is an excellent language for performing data analysis, primarily due to the robust ecosystem of python programs that are focused on handling data. One of the packages that make importing and analyzing data considerably simpler is Pandas.

 

 

The data is grouped using the Pandas groupby function, which applies a function to the categories. Effective data aggregation is aided by it as well. The data is divided into groups according to specific criteria using the dataframe.groupby() method of Pandas. Objects made of pandas can be divided along any axis. To offer a mapping of labels to group names is the abstract concept of grouping.

 

 

 

What is GroupBy Function

Splitting the object, applying a function, and merging the results are all components of a groupby action. Using this, it is possible to organize enormous data sets and perform computations on them.

 

 

Parameters

by: label, list of labels, mapping, or function

 

 

Used to decide which groups the groupby should include. Each value of the object’s index calls by if it is a function. When a series or dict is supplied, the groups will be chosen based on the values in the series or dict.

 

 

Whenever a list or ndarray with a length equal to the chosen axis is passed, the values are used directly to create the groups. When using the self-grouping columns, a label or list of labels may be given.

 

Axis: 0 or “index,” 1 or “columns,” with 0 as the default

 

 

Divide into (0) rows or (0) columns (1).

 

 

level: int, level name, or series of similar levels; default None

Group data based on a specific level or levels if the axis is a MultiIndex

 

 

as_index: bool, default True

 

 

Return an object with group labels as the index for aggregated output. applicable only to DataFrame input. Effectively, “SQL-style” grouped output is as index=False.

 

 

sort: bool, initial value: True

 

 

group keys in order. By turning this off, your performance will improve. Note that the sequence of observations within each group is unaffected by this. The order of the rows within each group is preserved by groupby.

 

 

group keys: Boolean, defaults to True
Add group keys to the index when calling apply to identify the parts.

 

 

bool: squeeze, defaults to False
If at all possible, reduce the return type’s dimensionality; if not, return a consistent type.

 

 

Returns: DataFrameGroupBy

provides a groupby object with information about the groups as a response.

 

 

 

Examples of GroupBy Function

 

import pandas as pd
df = pd.DataFrame({'name': ['a', 'a',
                              'b', 'c','b','c'],
                   'Marks': [89., 100., 95., 60.,80.,76
                   ]})
df

df.groupby(['name']).mean()
df.groupby(['name']).median()

 

 

 

 

Using the level option, we can group by multiple levels of a hierarchical index.

 

arrays = [['a','a','b','b'],
          ['c','d','c','d']]
index = pd.MultiIndex.from_arrays(arrays, names=('Name', 'Marks))
df = pd.DataFrame({'Marks': [89., 100., 95., 60.]},
                  index=index)
df

 

 

 

df.groupby(level=0).mean()

 

 

 

df.groupby(level="Type").mean()

 

 

 

 

Also, read – Implementation of SVM in Python