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.

Contents

- Groupby Pandas in Python 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

## 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.

**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 is a function, it’s called on each value of the object’s`by` index. – If is dict or Series, the Series or dict VALUES will be used to determine the groups. – by default |

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. – 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:**

You may also like Missing Data in Pandas in Python.

## 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_count | int, 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**.

You may like the following Python tutorials:

- How to create a variable in python
- Python Hello World Program
- Python download and Installation steps
- Remove Unicode characters in python
- Comment lines in Python

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

Entrepreneur, Founder, Author, Blogger, Trainer, and more. Check out my profile.