Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
dataframe.groupby() function is used to split the data into groups based on some criteria. pandas objects can be split on any of their axes. The abstract definition of grouping is to provide a mapping of labels to group names.
Syntax: DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)
by : mapping, function, str, or iterable
axis : int, default 0
level : If the axis is a MultiIndex (hierarchical), group by a particular level or levels
as_index : For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output
sort : 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.
group_keys : When calling apply, add group keys to index to identify pieces
squeeze : Reduce the dimensionality of the return type if possible, otherwise return a consistent type
Returns : GroupBy object
For link to CSV file Used in Code, click here
Example #1: Use
groupby() function to group the data based on the “Team”.
Now apply the
Let’s print the value contained any one of group. For that use the name of the team. We use the function
get_group() to find the entries contained in any of the groups.
Example #2: Use
groupby() function to form groups based on more than one category (i.e. Use more than one column to perform the splitting).
groupby() is a very powerful function with a lot of variations. It makes the task of splitting the dataframe over some criteria really easy and efficient.
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