Joining Data from Different Sources Using Python

Understanding the Importance of Data Joining

Data joining refers to the process of combining two or more data sets into a single set. Different sources can be used to provide a complete picture of the phenomenon under study. Without data joining, analysts might make inaccurate conclusions or miss important knowledge on various factors influencing a scenario. As such, the technique is critical in generating meaningful insights in circumstances where data from different sources must be pooled, analyzed to inform decisions aligned with organizational goals.

Data joining allows us to combine and generate new data from various sources such as an organization’s in-house data, external datasets, or public open data. The process involves identifying matching or interrelated data fields and concatenating the data into one feature set. The aim is to create a single dataset that provides a complete view of the object or scenario under analysis. In Python, two data sets can be merged into a single dataset with ease using a method called ‘merge,’ which we will discuss later. This article will explore how to join data from different sources in Python.

The Merge Function in Python

One approach to joining datasets using Python is through the merge function in Pandas. This function combines two data frames based on a shared key. The shared element (key) serves as a reference point to identify data that will match between the two datasets.

To merge datasets in Python, we need to import the Pandas module, open two datasets, identify the columns that we would like to join, and then merge the datasets. We use the merge function, and we pass the datasets that we would like to join into this function as arguments. We also identify the shared key that we would like to join the two datasets on.

An important aspect of merging data in Pandas is specifying the type of merge. The merge function provides an option to specify the type of join (Inner, left, right, and outer) depending on the analysis’ objective. For instance, the inner join will merge data with common keys only. Left merge will include all rows from the left table and the matching rows from the right table. Right merge will include all rows from the right table and the matching rows from the left table. Finally, the outer merge will include all rows from both tables, matching the common key.

Joining Data from Different Databases

Python allows us to join data from different databases. For instance, we can join data from MySQL, SQL Server, PostgresSQL, and SQLite, among other databases. One way to join data when using different databases is by importing the required libraries and using the connection settings for each database to create a connection. Once connected, we execute SQL scripts to extract the data we want, join them using the Panda’s merge function, and then store the new output in a new table or a separate database.

Another way to join datasets from different databases is through odo, which is a multipurpose data migration tool that extracts data from various sources and moves it to a destination object. This ETL facilitates a seamless data migration process between any data source or destination. Odo is advantageous, especially when dealing with big data and complex data types like multi-dimensional arrays.

Merging Data from APIs

Data can also be obtained from APIs through HTTP requests. Once the data is obtained, it can be pre-processed, data wrangled, and then used to join datasets. Python has several libraries for retrieving data from APIs, such as requests, httplib2, and urllib, among others. To obtain data from APIs, we merely pass the URL as an argument to our preferred library’s function and then parse the data into the desired format, such as JSON or XML.

Once we have multiple datasets from different APIs, we can use the merge function in Pandas or the combine function to join the datasets. In both cases, we must ensure that we identify the shared key to merge the datasets correctly. For instance, if we collected Twitter data relating to a particular topic and Facebook data, we could join them on the shared key related to the subject matter, such as the hashtag or a keyword. If you wish to learn more about the topic, Click for additional information about this subject, to enhance your study. Find valuable information and new viewpoints!


Data joining is critical in ensuring that different data sources are combined into a single format for analysis. With Python, we can conveniently join datasets from various sources, including databases and APIs. The Pandas merge function offers different types of merges to tailor our objectives. Meanwhile, Python libraries such as odo simplify data migration and the ETL process. Data joining efficiently streamlines an organization’s effort required to make significant decisions promptly.

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