Simplify Your Data Manipulation with Pandas Groupby

We need an efficient and time-saving tool for finding insights from large and complex datasets.

As we know without a reliable data manipulation tool, analyzing large datasets can be a daunting task, leading to inaccurate or incomplete results.

Manually handling missing and complex data can be time-consuming and error-prone, resulting in incomplete or incorrect analyses.

“I choose a lazy person to do a hard job. Because a lazy person will find an easy way to do it.”

Bill Gates

It's time to find a convenient tool to do a complex job.


Pandas groupby is a powerful data manipulation tool in Python that allows grouping data based on specified criteria.

Imagine you have a large bag of marbles with different colors, sizes, and patterns.

You want to group them by color, so you start picking out all the blue marbles and putting them in one pile, all the red marbles in another pile, and so on.

This is similar to what Pandas groupby does with your data. It helps you group your data based on certain criteria, such as color in our example, so that you can easily analyze and compare different groups of data.

Just like how it's easier to compare and count the blue marbles when they're all in one pile, it's easier to analyze and draw insights from your data when it's grouped together with Pandas groupby.

Some useful and handy groupby methods

grouped = df.groupby('year')
  • .groups: Grouped objects can be used to extract groups in dictionary format


    Output: {2020: [0, 6, 17, 19, 20, 22], 2021: [1, 2, 3, 4], we now have year (unique_key) mapped to row_ids.

  • .ngroups: Find out how many groups were formed.

  • .size(): How many rows does each group have? Let's say if you group by city, this method gives you a count of how many records contain city names.

  • .count(): Gives the count of values in every column in a group, doesn't count NaN values.

  • .first() and .last(): is used to get the first and last record from each group.

  • .nth(row_number) : .nth doesn’t change anything and gives the result as per order even if the first record of the group has a null value in it. Also, it has extra capabilities. It has some special powers .nth([0,3]) can be used to select the 1st and 4th row.

Groupby with multiple criteria

Grouping data by multiple criteria simultaneously

  • Analyzing data across multiple criteria can be complex and difficult to manage.

There are multiple ways to perform group by and aggregation, instead of exploring different syntaxes, we will go ahead with syntax that caters to wide use cases.

# Groupby multiple columns & multiple aggregations
result = df.groupby('column').aggregate({'column1':'count','column2':['min','max']})

.aggregate(): takes a dictionary, where the

  • key has to be column names.

  • values have to be aggregation operations like count, min, max,avg. you can either specify a single operation as str or multiple operations in list

The output of the groupby is of pandas.core.groupby.generic.DataFrameGroupBy type and every group is a tuple of 2 element

  • tuple[0]: the value of column which represent a group

  • tuple[1]: data frame that contains all the data.

Collect all the values of a group for a column in a list

groups.aggregate({"column_name":lambda x: list(x)})

groupby can also be performed on multiple columns at once

  • create a list of columns list_of_column = ['column1', 'column2']

  • pass the list fo groupby method df.groupby(list_of_columns)



  1. Enables grouping of large datasets based on desired criteria for easier analysis

  2. Allows for efficient aggregation of data using various functions, such as sum, mean, min, max, etc.

  3. Provides flexibility in grouping data by multiple criteria simultaneously

  4. Enables easy handling of missing data by excluding or filling values in the grouped data

  5. Enables merging of multiple data frames based on grouped criteria


  1. Groupby operations may be slower on larger datasets due to the computational overhead of grouping and aggregating data.

  2. Can sometimes lead to complex, nested code that may be difficult to understand and maintain.

  3. Can be resource-intensive, requiring significant amounts of memory to perform operations on large datasets.

  4. May require advanced knowledge of Python and Pandas to fully utilize all of the available functions.

What should be the next action plan:

  1. Start using Pandas groupby in your data analysis workflows today for more efficient and insightful data manipulation.

  2. Learn advanced techniques for Pandas groupby to unlock its full potential in your data analysis tasks.

  3. Join online communities and forums to collaborate with other data analysts and learn from their experiences with Pandas groupby.

  4. Experiment with different grouping and aggregation functions to explore new insights in your data.

  5. Consider using other Python libraries, such as NumPy and Matplotlib, in conjunction with Pandas groupby for even more powerful data analysis capabilities.