Research shows that women who strength train see benefits in bone health, longevity, and more. Credit: EyeEm Mobile GmbH / Getty Images
Women who strength train tend to live longer, thanks to reduced risk of death from all causes, including fatal cardiovascular events.
Strength training also staves off age-related muscle and bone density loss, and boosts confidence.
If you’re just starting strength training, aim to do two full-body workouts twice per week.
Strength training is growing increasingly popular among women, which is a good thing for their health and quality of life. In fact, maintaining a regular strength training routine as part of an overall fitness plan offers many science-backed benefits for women. Here are six that will inspire you to hit the weight room.
1. Greater Longevity
Research shows that women who do resistance training tend to live longer than those who don’t.
A 2024 study followed more than 400,000 adults in the United States over a span of 20 years. Data showed that women who strength trained two or three times each week had a 26% reduced risk of death as compared to women who didn't strength train. (Interestingly, men who strength trained enjoyed the same benefit, but saw just an 11% lower mortality risk on average.)
2. Improved Heart Health
Strength training isn't just good for your muscles—it's good for your heart, too.
That same large 2024 study, which showed a longevity boost for women strength trainers, also revealed that this group is less likely to experience fatal cardiovascular events. In fact, a few weekly sessions reduced their risk by 30%.
Other research found that strength training can improve blood pressure readings in men and women with hypertension (high blood pressure) after just a few months. However, the benefits were greater for men.
While people over 65 are most susceptible to sarcopenia, you can begin to lose muscle mass as early as 35 years old. That means it’s never too early for women to establish a strength-training routine.
For women who have experienced sarcopenia, strength training may also help them regain strength and physical capabilities. A recent review and meta-analysis found that older women with sarcopenia who did resistance training improved grip strength, knee extension strength, and other fitness metrics.
4. Stronger Bones
Osteoporosis, a condition that weakens bones and makes them more susceptible to fractures, is more common as people age, especially for women. Of the 10 million Americans with osteoporosis, 8 million are women.
In addition to not smoking, limiting alcohol consumption, and eating a well-balanced diet that contains adequate calcium and vitamin D, older women can improve their bone strength and reduce the risk of osteoporosis with weight training.
A 2025 research review found that postmenopausal women who did strength training saw significantly improved bone density, a measure of bone strength.
Even for women who are still decades away from menopause and osteoporosis-related concerns, strength training could potentially reduce their risk of injury. One study found that female college athletes who established a strength-training routine in high school or junior high were less likely to deal with stress fractures.
5. More Functional Strength
Functional strength training refers to movements that are meant to build strength and balance for activities that people do every day.
Even if you're not necessarily training with functional strength in mind, exercise makes numerous everyday movements easier. For example, bending over to lift a heavy box off the floor is a deadlift, and placing an item on the top shelf of a cabinet is an overhead press.
For both men and women, weight training can create the functional strength necessary for accomplishing daily tasks, living independently, and avoiding falls as they age.
6. Greater Confidence
If you feel invincible after a set of heavy deadlifts, you’re not alone. A study on the relationship between physical activity and women’s sense of self-efficacy found that resistance training was the top-ranked activity for inspiring feelings of empowerment. It also made women feel more accomplished than other forms of exercise.
This could be due to seeing the steady progress that comes with regular weight lifting—either increases in the size or definition of muscles, or the amount of weight lifted. Also, by becoming stronger, women may feel more confident and better equipped to handle physical obstacles, whether it's moving furniture, carrying luggage, or shoveling a walkway.
All of this can improve a woman's physical, mental, and social health.
Tips for Establishing a Strength Training Routine
If you’re new to strength training or trying to work out more effectively, consider the following tips:
Aim for twice a week: According to the Centers for Disease Control and Prevention (CDC), adults should engage in muscle-strengthening activities at least twice a week to keep themselves healthy.
Save time with full-body workouts and compound movements: Rather than having a designated “leg day” or focusing on just one area of the body with each session, save time with full-body workouts that activate more than one muscle group at a time. For example, a dumbbell squat-to-overhead press strengthens the legs, glutes, core, shoulders, and arms in a single exercise.
Start with bodyweight exercises: Before you add dumbbells, kettlebells, or other external resistance, practice strength-training moves with just your body weight. This allows your body to adapt to a new activity and master proper form.
Prioritize nutrition: Muscle growth and recovery require adequate nutrients, especially protein. Your exact protein needs will depend on individual health and activity factors, but current federal guidelines recommend that adults consume 1.2-1.6 grams of protein per kilogram of body weight every day.
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.
Become a Power BI Certified professional by learning this HKR Power BI Training !
Power BI Training
Master Your Craft
Lifetime LMS & Faculty Access
24/7 online expert support
Real-world & Project Based Learning
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.
Acquire Vagrant certification by enrolling in the HKR Vagrant Training program in Hyderabad!
Subscribe to our YouTube channel to get new updates..!
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.
Power BI Training
Weekday / Weekend Batches
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.
To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
Functional
Always active
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
Preferences
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistics
The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
Marketing
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
:max_bytes(150000):strip_icc():format(jpeg)/Health-GettyImages-2164410940-2ddb109ede6c4215add6b02344db07aa.jpg "A woman lifting two dumbbells in a gym setting.")
