Yoga can increase strength and flexibility—to a point, experts said. Credit: MoMo Productions / Getty Images
Yoga is an effective way to build strength and improve flexibility.
However, it’s unlikely to significantly increase muscle mass or address more complex mobility limitations.
To reach your full potential, experts recommend combining yoga with strength training.
If you stick with yoga long enough, there’s a good chance your downward dogs, cobras, and other poses will start to feel easier. While that ease can signal gains in strength and flexibility, is yoga alone really enough to help you reach your potential in these areas? We tapped two fitness experts to weigh in.
What Yoga Can (and Can’t) Do for Strength
Yoga can certainly help you feel stronger—and relatively quickly, said Sasha Hanway, CPT, RYT, a certified personal trainer and yoga coach. If you practice regularly, you’ll likely feel more stable overall—especially in your core—within just a few weeks.
Noticeable increases in strength and muscle mass, however, can take a bit longer—about six to 12 weeks, Hanway noted. A 2015 study supports this idea, finding that people who participated in a 12-week yoga program showed improvements in curl-up and push-up tests.
But in general, explained Desi Bartlett, CPT, E-RYT, creator of the Bodymind Workout, there’s a limit to how much yoga can boost muscle power. That’s because yoga is designed to primarily enhance stability and muscular endurance, not to build significant muscle mass. For that, you’d need to incorporate strength training, which involves something called progressive external load—the process of increasing weight, reps, or intensity over time.
“Because the practice of yoga generally does not involve progressive external load, there is a natural ceiling,” Bartlett said. “If building significant muscular strength and/or muscle mass is the goal, adding resistance training is key.”
What Yoga Can (and Can't) Do For Flexibility
Yoga is highly effective at improving flexibility, mobility, and overall range of motion, Bartlett said. “It combines many postures that involve stretching with breathwork, which helps the muscles relax and lengthen more effectively over time,” she explained.
Many people notice reduced stiffness and improved mobility within one to two weeks of consistent practice, Bartlett added. More noticeable improvements in flexibility typically occur within four to six weeks. A small 2014 study found that low to moderately active middle-aged women who did as little as one 90-minute yoga session per week improved flexibility in their hamstrings and lower back after six weeks.
Keep in mind that in these early stages, your muscles aren’t physically lengthening, Hanway said. Rather, “your brain is essentially deciding how much range of motion it feels safe allowing you to access,” she explained. “As you practice yoga, you’re repeatedly exposing your body to end ranges of motion, and over time your nervous system becomes more tolerant of those positions.”
That said, yoga alone may not fully address deeper mobility issues, especially if joint limitations, past injuries, or significant muscle imbalances are involved, Bartlett said. “In those cases, more targeted mobility work or therapeutic intervention may be needed.”
How To Craft an Exercise Routine for Strength and Flexibility
To reap the strength and flexibility benefits of yoga, consistency is key—but Bartlett said you don’t need intense, hour-long sessions for yoga to be effective.
What matters more, she added, is the quality of your practice. To that end, she recommended moving with intention rather than rushing through poses, incorporating longer holds that last about 20 to 60 seconds, and choosing styles like vinyasa or power yoga that blend strength and mobility.
But it's important to remember that yoga alone isn’t enough to meet all fitness needs, Bartlett stressed. “I always encourage people to incorporate some form of external resistance in addition to yoga,” she said. “Yoga helps with mobility, stability, and mind-body awareness, while resistance training helps build muscle, bone density, and overall strength. Together, they create a more balanced and resilient body.”
To start resistance training, Bartlett recommended incorporating weightlifting, resistance bands, or progressive bodyweight exercises into your routine. Strategies for building muscle over time include increasing weight, reducing rest time, or adding reps. Aim for two to four days per week, Hanway suggested.
To enhance flexibility, yoga can offer a strong foundation, but pairing it with targeted mobility movements can help address specific limitations, especially in tight or restricted areas, Bartlett said.
“Movements like split squats, Romanian deadlifts, and lunges build strength in lengthened positions, which directly improves mobility,” Hanway said. “That combination— strength plus mobility—is what creates a body that not only moves well, but is strong, resilient, and built to last.”
Power BI is referred to as a Software as a service (SaaS) platform, a Business Intelligence tool that helps analyze organizational data and also helps in creating real-time and interactive dashboards. The Power BI business intelligence tool can be installed on any desktop, mobile or can be used online as well. It also helps in collaborating with every peer in the organization. Power BI is a data visualization tool that helps in developing dashboards and BI reports with business intelligence capabilities. Apart from data visualization, it also allows to perform data exploration, helps in establishing reliable and secure connections to the cloud data sources.
Power BI is used by many organizations because of its amazing features like visualization creation. Some of the visualization formats are column charts, area plots, bar charts, line plots, scatter plots, pie charts, treemaps, scatter plots, etc. It also includes a navigation pane that helps in navigating to the dashboards and reports, associated applications, recent work, etc. The Power BI tool also includes inbuilt functions called DAX functions that can be used for data analysis. These are predefined functions that are available in the library. In Power BI, the data can be imported either from a single or multiple data sources. Power BI is also capable of providing extensive support to both structured and unstructured data. It also includes pre-built templates for dashboard creation. You can also create customized dashboards.
What is Python?
Python is referred to as a high-level, general-purpose, interpreted programming language that includes a set of pre-built functions and libraries which helps in performing complex operations and calculations. It is easy to learn and is used in most fields like data analytics, artificial intelligence, machine learning, etc.
Python includes two libraries called Matplotlib and Pandas. Matplotlib library consists of the predefined functions that help in plotting the data visualizations while the pandas library also includes the predefined functions that help in working with the data available.
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Prerequisites for Power BI Python Integration:
Below is the list of the prerequisites required for Power BI Python Integration to take place.
Python runtime installation: The python runtime installation includes the installation of the execution run time through which the Python script operations can be performed.
Libraries installation: It is essential that some of the important libraries have to be installed which increases the robustness of Power BI. Some of the important libraries are Seaborn and Pandas.
Installation of Visual Studio Code: The installation of Visual Studio code is optional. You can also install any other code editor for writing your python scripts. You can also make use of the Power BI script editor to write the scripts.
Power BI settings update: The final step is to update the settings so that you can work efficiently with Python in Power BI. Through this, you can perform the scripting in Power BI. All you need to do is open up the Power BI desktop, click on the file option. Then click on options followed by settings, then navigate to the opportunities which open a new dialogue box in Power BI. Then you can click on the Python scripting, select the path from the directories and IDE of Python. Once done, you will need to click on the OK button.
Understanding the need for Python Integration in Power BI:
By now, you all might have got an idea of what Power BI and Python is for. Yes, Python is referred to as the powerful tool that helps in creating visualizations while Power BI is for creating well-versed dashboards. These dashboards include all the information which helps in providing a complete view of the organization growth, metrics, KPIs, etc. Hence, if Python and Power BI are integrated, it will be a plus for us to utilize the capabilities that Power BI and Python hold.
Apart from the above, Python and Power BI integration includes several other benefits listed below.
The users are allowed to run the python scripts directly within the Power BI
Through the integration, libraries like Matplotlib and Seaborn can be used in Power BI for data visualization.
Python has become a leading technology and all the machine learning frameworks and data science libraries are written in Python. Through the integration, it allows to create of the data analysis scripts using Power BI
To perform precise calculations and clear the complexities, some of the enriched libraries like NumPy and Pandas can be used.
In order to solve complex problems in an organization, it requires data and analytics. It also requires future predictions which gives us future insights and helps us clear the bypass of the issues. Through the business intelligence tools, all these predictions and data visualizations are represented in different forms for a better understanding and analysis. With the amazing capabilities that Power BI and Python hold, organizations are being successful with their integration attaining benefits.
Any idea on how the Power BI Python Integration is performed? Well, I will help with the step by step process to perform the Power BI Python integration.
Setup the Integrated Environment:
The primary step is to set up an integrated environment ready for the integration process to begin. You will need to have a distribution of Python readily installed in your machine/desktop. I preferred Anaconda for coding related tasks and the base distribution of Python. Sometimes, trying to integrate Anaconda with Power BI is a difficult task.
After the installation process is completed, you will need to install four python packages, each one has its own significance. They are :
Pandas – for data analysis and data manipulation
Matplotlib and seaborn – for plotting purposes
NumPy – for performing scientific calculations
The pip command in the command line tool is used for installing these packages into the machine.
pip install pandas
pip install matplotlib
pip install NumPy
pip install seaborn
Once the above packages are installed, the python scripting needs to be enabled in Power BI. If you want to check, you can open the Power BI and detect whether the python distribution is automatically detected or not. You will need to go to the files, followed by options and settings, then click on options. You will be able to view the home directory for Python under python scripting that is installed in the machine.
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Data Importing using the Python script:
It is time for you to check whether Python is working within Power BI by running a sample test. The primary step to perform this function is to import a small dataset using the Python script in Power BI.
To perform this, you will need to navigate to the Home ribbon, then move to the GetData option and click on the other option. Through this, you will be able to import the data from a different set of sources available. Some of the sources are Spark, Hadoop distributed file system, (HDFS), Web, etc. In the below image, I am going to import the Churn Prediction system that is available in my system.
Once you click on the connect option, you will see a section that allows you to write the Python script.
You will need to click on the OK button which will further ask you to select the churn data. Once done, you will need to click on the Load option. You can also perform a check whether the data has been loaded or not through the data view option. With this, you are now ready to make use of the power query in order to perform the data transformations.
Using Power Query To Transform the data:
All the individuals who have learned Python would know that data transformation is no longer a single task-based activity.
By using the Power Query editor, the user can shape and transform the data as required with just a single click. The Power BI is also capable of keeping the record of all the transformations and operations that take place or happen during the process of transformation. We will now show you how to use the Power query to understand and know the data transformation capabilities.
Once the data is loaded in the Power BI, you will need to click on the transform data option which is available under the Home tab in order to open the query editor.
Once the query editor is opened up, you will see multiple options to perform the operations like clean, reshape and transform the data.
We will now convert the customer_nw_category variable into a text field because these fields represent the worth category. Also, you need to know that it would not be a continuous variable.
To perform the same, you will need to select the column -> Go to the Data Type, and change the data type to a text format. All the steps will be recorded by the Power query under the applied steps section. You can also rename these steps for a better recall or reference. I will rename this step as nw_cat Text. The next step is to transform the churn column into a logical variable. True here represents for churned (1), whereas False here represents not churned (2). The step can be renamed as Churn True/False.
Once the above operation is performed, you will need to click on the close and apply option which will be available on the top left corner so that all the transformations made will be applied.
Using Python’s Statistical within Power BI:
Power BI is considered a library of visualization. Correlation matrix heatmap is an integral component in the data analysis reports.
We will guide you on how to create a correlation matrix heatmap by making use of the Python correlation function. The created heatmap will be available in the reports section in Power BI.
You will need to navigate to the Report section that is available in the Power BI. Then click on the Py symbol which denotes Python visual which is available under the visualizations section. On the left side, you will see an empty Py along with a Python script editor popping up at the bottom of the screen. By this, you might have understood that Power BI is providing an option to create the visualizations with the scripts.
All the value fields will be empty firstly. For correlation heatmap illustration, all the continuous variables will be brought into the value fields, like the age current, previous month balance, monthly balance items, etc. This is considered one of the most essential steps during the integration process. If you forget to perform this step, the Power BI will not be able to recognize the variables.
As the variables are moved into the values fields, the python script will be automatically populated with the below codes.
Let us also write the code for correlation heatmap creation in Python by using the seaborn package.
# import the charting libraries matplotlib and seaborn
import matplotlib.pyplot as plt
import seaborn as sns
# create the correlation matrix on the dataset
corr = dataset.corr()
# create a heatmap of the correlation matrix
sns.heatmap(corr, cmap="YlGnBu")
# show plot
plt.show()
You can now use the run script button and run the script, which will produce a correlation matrix heatmap.
Generating analytical reports:
After the heatmap is generated, we can analyze the heatmap and will come to a conclusion.
With the above heatmap, below are the set of conclusions made:
There is no correlation with the other variables for the number of dependents and age.
There is a moderate correlation observed for the average monthly balance in the time span of the last two quarters.
There is a high correlation between the average monthly balance with the previous month balance and the current month balance in the last quarter. It is possible to generate a heatmap for the customers who have churned and you can also compare with the customers that do not have. You can apply the filter of churn = False or True so that the heatmap can be observed. Through the analysis, it helps in deriving useful insights from data analysis to the prediction of the behavior.
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Conclusion:
Through this article, you have got a clear idea of the process of integration of Python with Power BI. I hope the above information helps you. To get a clear understanding and in-depth knowledge on the subject, you can get trained and certified in Power BI through the Power BI training. The integrated environment will definitely help the organizations to handle and play with the data as and when needed. It focuses on enhancing the power and capitalizing on the benefits that are available in both tools.
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