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 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 module
import pandas as pd
import numpy as np

# create Data frame
df = 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 Dataframe
print(df)

# let's Count distinct in Pandas aggregation
df = df.groupby("Video_Upload_Date").agg(
    {"Watch_Time": np.sum, "Viewer_Id": pd.Series.nunique})

# print final output
print(df)

Output:

Example 2:

Python

# import module
import pandas as pd
import numpy as np

# create Data frame
df = 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 Dataframe
print(df)

# let's Count distinct in Pandas aggregation
df = df.groupby("Order Date").agg({"Order Quantity": np.sum,
                                   "Product Id": pd.Series.nunique})

# print final output
print(df)

Output: