Pandas Dataframe.groupby() method is used to split the data into groups based on some criteria. The abstract definition of grouping is to provide a mapping of labels to the group name.
To concatenate string from several rows using Dataframe.groupby(), perform the following steps:
- Group the data using Dataframe.groupby() method whose attributes you need to concatenate.
- Concatenate the string by using the join function and transform the value of that column using lambda statement.
We will use the CSV file having 2 columns, the content of the file is shown in the below image:
Example 1: We will concatenate the data in the branch column having the same name.
Python3
# import pandas libraryimport pandas as pd # read csv filedf = pd.read_csv("Book2.csv") # concatenate the stringdf['branch'] = df.groupby(['Name'])['branch'].transform(lambda x : ' '.join(x)) # drop duplicate datadf = df.drop_duplicates() # show the dataframeprint(df) |
Output:
Example 2: We can perform Pandas groupby on multiple columns as well.
We will use the CSV file having 3 columns, the content of the file is shown in the below image:
Apply groupby on Name and year column
Python3
# import pandas libraryimport pandas as pd # read a csv filedf = pd.read_csv("Book1.csv") # concatenate the stringdf['branch'] = df.groupby(['Name', 'year'])['branch'].transform( lambda x: ' '.join(x)) # drop duplicate datadf = df.drop_duplicates() # show the dataframedf |
Output:
