In this short article, you will find pandas groupby() examples with source code. Pandas groupby is used for grouping the data according to the categories and applying a function to the categories. It also helps to aggregate data efficiently. Pandas 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.
All the source code of pandas groupby() examples are taken from Kaggle.
What is pandas groupby()?
Pandas groupby() is a powerful feature of pandas that helps to split the dataframe into groups. For example, let us say, we want to analyze how many people are affected by Covid-19 every day around the world on average. Will this approach be effective for all countries? The answer is no. There might be countries that are very less effected by Covid-19. In such cases, it is always better to use pandas groupby() method to divide the population into groups and then analyze data for each group.
GroupBy allows us to group our data based on different features and get a more accurate idea about your data. It is a one-stop shop for deriving deep insights from your data.
Parameters of Pandas groupby()
Pandas groupby() method takes various kinds of parameters and most of the values are default. The pandas groupby() takes the following parameter values.
DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=_NoDefault.no_default, squeeze=_NoDefault.no_default, observed=False, dropna=True)
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.
Pandas groupby() examples with source code
Now let us jump into the pandas groupby() with source code. We will gather different examples from Kaggle and show them here for you. All the credits for coding go to the owners.
Example 1: Pandas groupby() on dataframe to create subgroups
The first example of pandas groupby() is simple. The first part is importing the dataset and then counting the total values.
In the next step, the apply() method has been used to apply the different methods to the dataframe. You can use your own function and apply() it to the data frame. Then the data has been divided into different groups and subgroups as well.
As shown above, there are two groups. The first group is based on the country and the second group is based on the provinces.
Example 2: Pandas groupby() to find statistics of each group
Another important feature of groups in pandas is that we can apply different methods to them and get inside. For example, the following example shows how we can get the min, max and average values of each group we created.
In the above, country is the name of the column on which we want to make groups. You can replace it with your own column name.
Example 3: Pandas groupby() multi indexing
We can also use pandas groupby() for multi indexing as well as shown below:
The country and province are the names of the columns. Replace them with your own column names.
Example 4: Pandas groupby() on single column
Here is another example of pandas groupby() used to group the data based on one column and then will apply to mean() method.
As you can see, groupby() method was applied to group the data based on the parking_lot column.
Example 5: Pandas groupby() titanic example
Now, we will look at the example to apply the pandas group() to the titanic dataset.
The groupby() will now split the data based on the Class column and count each total value in each of the groups.
As you can see, the data has been grouped into different classes and we get the total count of each class.
Example 6: Many columns grouped
The next example will explain how we can make groups based on multiple columns and find the mean in one line of code.
Example 7: pandas groupby()
This time the code will help not only group the data based on multiple columns but will also give information about multiple columns based on the groups.
As you can see, the above example shows how we can group data based on multiple columns and then find inside more than one column based on the groups.
Example 8: Evaluate map score without groupby() in python
Here is another full example of groupby() in pandas
Example 9:
The next example is about creating your own dataframe and then using the pandas groupby() method.
The next step is to use pandas group method to create groups.
Summary
You can group DataFrame rows into a list by using pandas. DataFrame. groupby() function on the column of interest, select the column you want as a list from the group and then use Series. apply(list) to get the list for every group. In this short article, we went through various examples from Kaggle which shows how we can use the groupby method in different scenarios.
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