Count distinct in Pandas aggregation
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 add() 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:
# 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:
Last Updated on October 24, 2021 by admin