In this article, let’s see how we can count distinct in pandas aggregation. So to count the distinct in pandas aggregation we are going to use groupby() and agg() method.
- groupby(): This method is used to split the data into groups based on some criteria. Pandas objects can be split on any of their axes. We can create a grouping of categories and apply a function to the categories. The abstract definition of grouping is to provide a mapping of labels to group names
- agg(): This method is used to pass a function or list of functions to be applied on a series or even each element of series separately. In the case of a list of functions, multiple results are returned by agg() method.
Below are some examples which depict how to count distinct in Pandas aggregation:
Example 1:
Python
# import moduleimport pandas as pdimport numpy as np# create Data framedf = pd.DataFrame({'Video_Upload_Date': ['2020-01-17', '2020-01-17', '2020-01-19', '2020-01-19', '2020-01-19'], 'Viewer_Id': ['031', '031', '032', '032', '032'], 'Watch_Time': [34, 43, 43, 41, 40]})# print original Dataframeprint(df)# let's Count distinct in Pandas aggregationdf = df.groupby("Video_Upload_Date").agg( {"Watch_Time": np.sum, "Viewer_Id": pd.Series.nunique})# print final outputprint(df) |
Output:
Example 2:
Python
# import moduleimport pandas as pdimport numpy as np# create Data framedf = pd.DataFrame({'Order Date': ['2021-02-22', '2021-02-22', '2021-02-22', '2021-02-24', '2021-02-24'], 'Product Id': ['021', '021', '022', '022', '022'], 'Order Quantity': [23, 22, 22, 45, 10]})# print original Dataframeprint(df)# let's Count distinct in Pandas aggregationdf = df.groupby("Order Date").agg({"Order Quantity": np.sum, "Product Id": pd.Series.nunique})# print final outputprint(df) |
Output:
