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:
# 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:
# 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:
