Many times, when working with data in Python, you may come across a situation where you need to combine or merge two dataframes based on a common column. One common use case is when you want to group and merge values from one dataframe to another. In this article, we will explore how to accomplish this using the groupby and merge functions in pandas.
Before we dive into the details, let's first understand what a dataframe is. A dataframe is a two-dimensional labeled data structure in pandas, similar to a table in a relational database. It consists of rows and columns, where each column can have a different data type.
Now, let's say we have two dataframes - df1 and df2. df1 contains information about employees, such as their names, departments, and salaries. On the other hand, df2 contains information about departments, such as their names and budgets. We want to merge the department budgets from df2 into df1 based on the department names.
To accomplish this, we can use the groupby function in pandas. The groupby function allows us to group the data based on a specific column or columns. In our case, we want to group the data in df2 by department names.
grouped_df2 = df2.groupby('Department Name')
After grouping the data, we can perform various operations on the grouped data, such as aggregations, transformations, or filtering. In our case, we want to merge the department budgets from df2 into df1. To achieve this, we can use the merge function in pandas.
merged_df = df1.merge(grouped_df2['Budget'].sum(), left_on='Department', right_index=True)
In the above code, we are merging df1 with the sum of department budgets from grouped_df2 based on the department names. We specify the left dataframe (df1) using the left_on parameter and the right dataframe (grouped_df2['Budget'].sum()) using the right_index parameter.
After merging the data, merged_df will contain the combined information from both dataframes. It will have all the columns from df1 as well as the department budgets from df2. The merged data will be based on the department names.
Here is the complete code to groupby merge values from one dataframe to another:
import pandas as pd
# Create df1
df1 = pd.DataFrame({
'Name': ['John', 'Jane', 'Mike', 'Emily'],
'Department': ['Sales', 'HR', 'Finance', 'Sales'],
'Salary': [5000, 6000, 5500, 4500]
})
# Create df2
df2 = pd.DataFrame({
'Department Name': ['Sales', 'HR', 'Finance'],
'Budget': [100000, 50000, 75000]
})
# Group df2 by department names
grouped_df2 = df2.groupby('Department Name')
# Merge department budgets from df2 into df1
merged_df = df1.merge(grouped_df2['Budget'].sum(), left_on='Department', right_index=True)
By running the above code, you will get the desired merged dataframe merged_df with the department budgets included.
In conclusion, merging values from one dataframe to another based on a common column can be easily achieved using the groupby and merge functions in pandas. The groupby function allows us to group the data based on a specific column, and the merge function allows us to combine the data from different dataframes based on common columns. By understanding and applying these functions, you can efficiently work with and manipulate data in Python.
References
| Number | Source |
|---|---|
| 1 | pandas.DataFrame.groupby() |
| 2 | pandas.DataFrame.merge() |