# Quantile Quantile plots - GeeksforGeeks (2024)

- AI ML DS
- Data Science
- Data Analysis
- Data Visualization
- Machine Learning
- Deep Learning
- NLP
- Computer Vision
- Artificial Intelligence
- AI ML DS Interview Series
- AI ML DS Projects series
- Data Engineering
- Web Scrapping

Open In App

Last Updated : 11 Feb, 2024

Comments

Improve

The quantile-quantile( q-q plot) plot is a graphical method for determining if a dataset follows a certain probability distribution or whether two samples of data came from the same population or not. Q-Q plots are particularly useful for assessing whether a dataset is normally distributed or if it follows some other known distribution. They are commonly used in statistics, data analysis, and quality control to check assumptions and identify departures from expected distributions.

### Quantiles And Percentiles

Quantiles are points in a dataset that divide the data into intervals containing equal probabilities or proportions of the total distribution. They are often used to describe the spread or distribution of a dataset. The most common quantiles are:

- Median
: The median is the middle value of a dataset when it is ordered from smallest to largest. It divides the dataset into two equal halves.**(50th percentile)** - Quartiles
: Quartiles divide the dataset into four equal parts. The first quartile (Q1) is the value below which 25% of the data falls, the second quartile (Q2) is the median, and the third quartile (Q3) is the value below which 75% of the data falls.**(25th, 50th, and 75th percentiles)** - Percentiles: Percentiles are similar to quartiles but divide the dataset into 100 equal parts. For example, the 90th percentile is the value below which 90% of the data falls.

**Note:**

- A q-q plot is a plot of the quantiles of the first data set against the quantiles of the second data set.
- For reference purposes, a 45% line is also plotted;
if the samples are from the same population then the points are along this line.**For**

Normal Distribution:

The normal distribution (aka Gaussian distribution Bell curve) is a continuous probability distribution representing distribution obtained from the randomly generated real values.

.

Normal Distribution with Area Under CUrve

### How to Draw Q-Q plot?

To draw a Quantile-Quantile (Q-Q) plot, you can follow these steps:

: Gather the dataset for which you want to create the Q-Q plot. Ensure that the data are numerical and represent a random sample from the population of interest.**Collect the Data**: Arrange the data in either ascending or descending order. This step is essential for computing quantiles accurately.**Sort the Data**: Determine the theoretical distribution against which you want to compare your dataset. Common choices include the normal distribution, exponential distribution, or any other distribution that fits your data well.**Choose a Theoretical Distribution**: Compute the quantiles for the chosen theoretical distribution. For example, if you’re comparing against a normal distribution, you would use the inverse cumulative distribution function (CDF) of the normal distribution to find the expected quantiles.**Calculate Theoretical Quantiles**:**Plotting**- Plot the sorted dataset values on the x-axis.
- Plot the corresponding theoretical quantiles on the y-axis.
- Each data point (x, y) represents a pair of observed and expected values.
- Connect the data points to visually inspect the relationship between the dataset and the theoretical distribution.

Interpretation of Q-Q plot

- If the points on the plot fall approximately along a straight line, it suggests that your dataset follows the assumed distribution.
- Deviations from the straight line indicate departures from the assumed distribution, requiring further investigation.

### Exploring Distribution Similarity with Q-Q Plots

Exploring distribution similarity using Q-Q plots is a fundamental task in statistics. Comparing two datasets to determine if they originate from the same distribution is vital for various analytical purposes. When the assumption of a common distribution holds, merging datasets can improve parameter estimation accuracy, such as for location and scale. Q-Q plots, short for quantile-quantile plots, offer a visual method for assessing distribution similarity. In these plots, quantiles from one dataset are plotted against quantiles from another. If the points closely align along a diagonal line, it suggests similarity between the distributions. Deviations from this diagonal line indicate differences in distribution characteristics.

While tests like the chi-square and Kolmogorov-Smirnov tests can evaluate overall distribution differences, Q-Q plots provide a nuanced perspective by directly comparing quantiles. This enables analysts to discern specific differences, such as shifts in location or changes in scale, which may not be evident from formal statistical tests alone.

### Python Implementation Of Q-Q Plot

## Python3

`import`

`numpy as np`

`import`

`matplotlib.pyplot as plt`

`import`

`scipy.stats as stats`

`# Generate example data`

`np.random.seed(`

`0`

`)`

`data `

`=`

`np.random.normal(loc`

`=`

`0`

`, scale`

`=`

`1`

`, size`

`=`

`1000`

`)`

`# Create Q-Q plot`

`stats.probplot(data, dist`

`=`

`"norm"`

`, plot`

`=`

`plt)`

`plt.title(`

`'Normal Q-Q plot'`

`)`

`plt.xlabel(`

`'Theoretical quantiles'`

`)`

`plt.ylabel(`

`'Ordered Values'`

`)`

`plt.grid(`

`True`

`)`

`plt.show()`

**Output:**

Q-Q plot

Here, as the data points approximately follow a straight line in the Q-Q plot, it suggests that the dataset is consistent with the assumed theoretical distribution, which in this case we assumed to be the normal distribution.

### Advantages of Q-Q plot

: Q-Q plots can compare datasets of different sizes without**Flexible Comparison****requiring equal sample sizes.**: They are dimensionless, making them suitable for comparing datasets with**Dimensionless Analysis****different units or scales.**: Provides a clear visual representation of data distribution compared to a theoretical distribution.**Visual Interpretation**: Easily detects departures from assumed distributions, aiding in identifying data discrepancies.**Sensitive to Deviations**: Helps in assessing distributional assumptions, identifying outliers, and understanding data patterns.**Diagnostic Tool**

### Applications Of Quantile-Quantile Plot

The Quantile-Quantile plot is used for the following purpose:

: Q-Q plots are frequently used to visually inspect whether a dataset follows a specific probability distribution, such as the normal distribution. By comparing the quantiles of the observed data to the quantiles of the assumed distribution, deviations from the assumed distribution can be detected. This is crucial in many statistical analyses, where the validity of distributional assumptions impacts the accuracy of statistical inferences.**Assessing Distributional Assumptions**: Outliers are data points that deviate significantly from the rest of the dataset. Q-Q plots can help identify outliers by revealing data points that fall far from the expected pattern of the distribution. Outliers may appear as points that deviate from the expected straight line in the plot.**Detecting Outliers**: Q-Q plots can be used to compare two datasets to see if they come from the same distribution. This is achieved by plotting the quantiles of one dataset against the quantiles of another dataset. If the points fall approximately along a straight line, it suggests that the two datasets are drawn from the same distribution.**Comparing Distributions**: Q-Q plots are particularly useful for assessing the normality of a dataset. If the data points in the plot closely follow a straight line, it indicates that the dataset is approximately normally distributed. Deviations from the line suggest departures from normality, which may require further investigation or non-parametric statistical techniques.**Assessing Normality**: In fields like econometrics and machine learning, Q-Q plots are used to validate predictive models. By comparing the quantiles of observed responses with the quantiles predicted by a model, one can assess how well the model fits the data. Deviations from the expected pattern may indicate areas where the model needs improvement.**Model Validation**: Q-Q plots are employed in quality control processes to monitor the distribution of measured or observed values over time or across different batches. Departures from expected patterns in the plot may signal changes in the underlying processes, prompting further investigation.**Quality Control**

### Types of Q-Q plots

There are several types of Q-Q plots commonly used in statistics and data analysis, each suited to different scenarios or purposes:

: A symmetric distribution where the Q-Q plot would show points approximately along a diagonal line if the data adheres to a normal distribution.**Normal Distribution**: A distribution where the Q-Q plot would display a pattern where the observed quantiles deviate from the straight line towards the upper end, indicating a longer tail on the right side.**Right-skewed Distribution**: A distribution where the Q-Q plot would exhibit a pattern where the observed quantiles deviate from the straight line towards the lower end, indicating a longer tail on the left side.**Left-skewed Distribution**: A distribution where the Q-Q plot would show observed quantiles clustered more tightly around the diagonal line compared to the theoretical quantiles, suggesting lower variance.**Under-dispersed Distribution**: A distribution where the Q-Q plot would display observed quantiles more spread out or deviating from the diagonal line, indicating higher variance or dispersion compared to the theoretical distribution.**Over-dispersed Distribution**

## Python3

`import`

`numpy as np`

`import`

`matplotlib.pyplot as plt`

`import`

`scipy.stats as stats`

`# Generate a random sample from a normal distribution`

`normal_data `

`=`

`np.random.normal(loc`

`=`

`0`

`, scale`

`=`

`1`

`, size`

`=`

`1000`

`)`

`# Generate a random sample from a right-skewed distribution (exponential distribution)`

`right_skewed_data `

`=`

`np.random.exponential(scale`

`=`

`1`

`, size`

`=`

`1000`

`)`

`# Generate a random sample from a left-skewed distribution (negative exponential distribution)`

`left_skewed_data `

`=`

`-`

`np.random.exponential(scale`

`=`

`1`

`, size`

`=`

`1000`

`)`

`# Generate a random sample from an under-dispersed distribution (truncated normal distribution)`

`under_dispersed_data `

`=`

`np.random.normal(loc`

`=`

`0`

`, scale`

`=`

`0.5`

`, size`

`=`

`1000`

`)`

`under_dispersed_data `

`=`

`under_dispersed_data[(under_dispersed_data > `

`-`

`1`

`) & (under_dispersed_data < `

`1`

`)] `

`# Truncate`

`# Generate a random sample from an over-dispersed distribution (mixture of normals)`

`over_dispersed_data `

`=`

`np.concatenate((np.random.normal(loc`

`=`

`-`

`2`

`, scale`

`=`

`1`

`, size`

`=`

`500`

`),`

`np.random.normal(loc`

`=`

`2`

`, scale`

`=`

`1`

`, size`

`=`

`500`

`)))`

`# Create Q-Q plots`

`plt.figure(figsize`

`=`

`(`

`15`

`, `

`10`

`))`

`plt.subplot(`

`2`

`, `

`3`

`, `

`1`

`)`

`stats.probplot(normal_data, dist`

`=`

`"norm"`

`, plot`

`=`

`plt)`

`plt.title(`

`'Q-Q Plot - Normal Distribution'`

`)`

`plt.subplot(`

`2`

`, `

`3`

`, `

`2`

`)`

`stats.probplot(right_skewed_data, dist`

`=`

`"expon"`

`, plot`

`=`

`plt)`

`plt.title(`

`'Q-Q Plot - Right-skewed Distribution'`

`)`

`plt.subplot(`

`2`

`, `

`3`

`, `

`3`

`)`

`stats.probplot(left_skewed_data, dist`

`=`

`"expon"`

`, plot`

`=`

`plt)`

`plt.title(`

`'Q-Q Plot - Left-skewed Distribution'`

`)`

`plt.subplot(`

`2`

`, `

`3`

`, `

`4`

`)`

`stats.probplot(under_dispersed_data, dist`

`=`

`"norm"`

`, plot`

`=`

`plt)`

`plt.title(`

`'Q-Q Plot - Under-dispersed Distribution'`

`)`

`plt.subplot(`

`2`

`, `

`3`

`, `

`5`

`)`

`stats.probplot(over_dispersed_data, dist`

`=`

`"norm"`

`, plot`

`=`

`plt)`

`plt.title(`

`'Q-Q Plot - Over-dispersed Distribution'`

`)`

`plt.tight_layout()`

`plt.show()`

**Output:**

Q-Q plot for different distributions

Previous Article

Box Plot

Next Article

### Please __Login__ to comment...

### Similar Reads

Article Tags :

- AI-ML-DS
- Machine Learning
- Data Visualization
- ML-EDA

Practice Tags :

- Machine Learning

Trending in News

- California Lawmakers Pass Bill to Limit AI Replicas
- Best 10 IPTV Service Providers in Germany
- Python 3.13 Releases | Enhanced REPL for Developers
- IPTV Anbieter in Deutschland - Top IPTV Anbieter Abonnements
- Content Improvement League 2024: From Good To A Great Article

We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy

## FAQs

### What is a normal quantile-quantile plot? ›

A QQ plot is a scatterplot created by plotting two sets of quantiles against one another. If both sets of quantiles came from the same distribution, we should see the points forming a line that's roughly straight. Here's an example of a normal QQ plot **when both sets of quantiles truly come from normal distributions**.

**How is quantile-quantile plot different from quantile plot? ›**

**Quantile plots directly display the quantiles of a set of values**. The sample quantiles are plotted against the fraction of the sample they correspond to. There is no built-in quantile plot in R, but it is relatively simple to produce one. Quantile-quantile plots allow us to compare the quantiles of two sets of numbers.

**How to plot qq in Python? ›**

**Plotting:**

- Plot the sorted dataset values on the x-axis.
- Plot the corresponding theoretical quantiles on the y-axis.
- Each data point (x, y) represents a pair of observed and expected values.
- Connect the data points to visually inspect the relationship between the dataset and the theoretical distribution.

**What is the Q-Q plot program? ›**

Quantile-quantile (QQ) plots are **an exploratory tool used to assess the similarity between the distribution of one numeric variable and a normal distribution, or between the distributions of two numeric variables**.

**What is a good quantile score? ›**

For example, a student's Quantile measure should be at **1350Q** by high school graduation to handle the math needed in college and most careers. A student Quantile measure helps you to know: Which skills and concepts students are ready to learn.

**What is the 95% quantile of standard normal? ›**

So the 95th percentile is 1.645. In other words, there is a 95% probability that a standard normal will be **less than 1.645**. Eg: z-scores on an IQ test have a standard normal distribution.

**What is the primary purpose of a QQ plot quantile-quantile plot? ›**

The quantile-quantile (q-q) plot is a graphical technique for **determining if two data sets come from populations with a common distribution**. A q-q plot is a plot of the quantiles of the first data set against the quantiles of the second data set.

**What do QQ plots tell us? ›**

A Q–Q plot is used to **compare the shapes of distributions, providing a graphical view of how properties such as location, scale, and skewness are similar or different in the two distributions**. Q–Q plots can be used to compare collections of data, or theoretical distributions.

**Does a Q-Q plot show linearity? ›**

**Q-Q plots are graphical tools that help you assess the validity of some assumptions in regression models, such as normality, linearity, and homoscedasticity**.

**How to generate a normal quantile plot? ›**

**Here are steps for creating a normal quantile plot in Excel:**

- Place or load your data values into the first column. ...
- Label the second column as Rank. ...
- Label the third column as Rank Proportion. ...
- Label the fourth column as Rank-based z-scores. ...
- Copy the first column to the fifth column. ...
- Select the fourth and fifth column.

### Can Q-Q plot be used for categorical data? ›

The The Q-Q plot helps determine whether the distribution of a numeric variable is normally distributed. plot_qq_numeric() shows Q-Q plots of several numeric variables on one screen. **This function can also display a Q-Q plot for each level of a specific categorical variable**.

**What are the disadvantages of Q-Q plot? ›**

The drawback is that for a two-sample Q-Q plot, **quantifying the results becomes more complicated than a simple linear correlation**, especially for a Q-Q plot comparing two data sets rather than a single data set to a known distribution function.

**What is the difference between a Q-Q plot and a box plot? ›**

**The whiskers in the boxplot show only the extent of the tails for most of the data** (with outside values denoted separately); more detailed information about the shape of the tails, such as skewness and “weight” relative to a standard reference distribution, is much better displayed via quantile–quantile (q-q) plots.

**What is the Z score of the Q-Q plot? ›**

When the option Q-Q plot is selected, the horizontal axis shows the z-scores of the observed values, **z=(x−mean)/SD**. A straight reference line represents the Normal distribution. If the sample data are near a Normal distribution, the data points will be near this straight line.

**What is a normal QQ plot in R? ›**

The QQ plot shows the data on the vertical axis ranked in order from smallest to largest (“sample quantiles” in the figure below). On the horizontal axis, **it shows the expected value of an individual with the same quantile if the distribution were normal** (“theoretical quantiles” in the same figure).

**What is standard normal quantile? ›**

Quantiles of the normal distribution

Some quantiles of the standard normal distribution (i.e., **the normal distribution having zero mean and unit variance**) are often used as critical values in hypothesis testing.

**What is the 5 quantile of the normal distribution? ›**

When we look into PDF, the 5th quantile is **the point that cuts off an area of 5% in the lower tail of the distribution**: The lower 5% quantile for normal distribution N(0,1). Image by the author. The area under PDF on the left from the red line is exactly 5% of the total area under the curve.

## References

- https://whitlockschluter3e.zoology.ubc.ca/Tutorials%20using%20R/R_tutorial_Normal_and_sample_means.html
- https://www.geeksforgeeks.org/quantile-quantile-plots/
- https://www.itl.nist.gov/div898/handbook/eda/section3/qqplot.htm
- https://pro.arcgis.com/en/pro-app/latest/help/analysis/geoprocessing/charts/qq-plot.htm
- https://pages.stern.nyu.edu/~churvich/MBA/Handouts/8-Normal.pdf
- https://www.stat.auckland.ac.nz/~ihaka/787/lectures-quantiles.pdf
- http://facweb.cs.depaul.edu/cmiller/it223/normQuant.html
- https://www.quantiles.com/educators/understanding-quantile-measures/what-is-a-quantile-measure/
- http://library.virginia.edu/data/articles/understanding-q-q-plots
- https://www.medcalc.org/manual/normal-plot.php
- https://digitalcommons.usu.edu/context/mp_facpub/article/1046/viewcontent/Andersen_2018_AJP_QQ_PrePrint.pdf
- https://www.tandfonline.com/doi/full/10.1080/10618600.2021.1938586
- https://www.library.virginia.edu/data/articles/understanding-q-q-plots
- https://towardsdatascience.com/quantiles-key-to-probability-distributions-ce1786d479a9
- https://www.statlect.com/fundamentals-of-probability/quantile
- https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot
- https://builtin.com/data-science/q-q-plot
- https://www.linkedin.com/advice/0/how-can-you-interpret-q-q-plots-regression-models-skills-statistics
- https://choonghyunryu.github.io/dlookr/reference/plot_qq_numeric.data.frame.html

- The influence of turbulence on a columnar vortex
- Aozora Kirai No Usotsuki Semiko Season 1 Episode 14
- When to Eat, Sleep & Exercise by the CHINESE CLOCK –
- I Tried Doe Magnetic Lashes—And I’ll Never Touch Traditional Falsies Again
- Why Is The Wolf Among Us 2 So Good
- Given: Uragawa no Sonzai
- Download You're My Sky Movie
- Rooney Mara Haircut 2017
- How Many Episodes Of Yankee-Kun To Megane-Chan Will There Be
- Motorhome & Leisure Gas - Refillable gas bottles, cylinders - Safefill bottles & adapters
- Espn Speed World On Nintendo Switch
- Deutsches Haus Staffel 1 Episodenguide: Alle Folgen im Überblick!
- Sakura No Mori Dreamers Discord Servers
- Utsu No Miko 2 King
- Bugsnax: A Faith-Filled Adventure In A Colorful World
- High Score Specials Viewing Order
- Download & Play Ludo SuperStar on PC & Mac (Emulator)
- Приложение N 1. Порядок проведения обязательных предварительных и периодических медицинских осмотров работников, предусмотренных частью четвертой статьи 213 Трудового кодекса Российской Федерации. Приказ Министерства здравоохранения РФ от 28.01.2021 N 29н
- From Stephen King to Tom Cruise: Florida celebs brace for Hurricane Milton
- Body Glitter and a Mall Safety Kit
- ATEEZ turns 6: Mapping the octet's inspirational journey from humble start to becoming 1st K-pop boy group to perform at Coachella
- The Sound Of Magic 4K Release
- Naïm Qassem élu nouveau chef du Hezbollah
- The Quickest, Easiest Way to Get More Voluminous Lashes
- 12 Effective Home Remedies For Hand, Foot, And Mouth Disease
- Freddie Freeman’s historic Home Run was eerily identical to ’88 Dodgers World Series heroics - Pubity
- Lee Yoo Ri July 2024
- No+baby+blisters+scam | New & Used Personal Care Items for Sale | NL Classifieds
- 10-Gatsu No Hanabi Ep 7 Sub
- Stickers for Sale | TeePublic
- These Ultra-Gentle Moisturizers for Sensitive Skin Instantly Soothe
- Hbo Max Hannah Barefoot
- How Long Is 粉色樱与大眠王 (2021) Film
- Dream Tower Season 2 Ep 14
- Sorcery Update Date
- What Can You Do with Hair Clips? 10 Surprising Uses You Never Knew! - HairSpeaks
- Morning Headache as an Obstructive Sleep Apnea-Related Symptom among Sleep Clinic Patients—A Cross-Section Analysis
- Is G.i. Joe Extreme On Netflix
- Colectomy (Colon Resection Surgery): Definition & Procedure
- Polis Evo (2015) Streaming English
- The Clean Knife Elements
- Kabuki-Bu!: Oogiri "Chiyocolate-Gassen" Mini Figure
- Makeup Brush Holder | Office of Advanced Manufacturing
- Have Diabetes? Take That Toenail Fungus Seriously - DSM
- Marc Anthony Old Pictures
- What is Ion Implantation? - An Overview
- Who has Kristin Kreuk dated? Boyfriends List, Dating History
- Exploding Wall Blog
- Mushishi Special: Hihamukage Doblaje
- Scarlet: Boku Ga Kimi Ni Dekiru Koto Anime Season 4
- Dance With Devils Lock Screen
- Kindaichi Shounen no Jikenbo: File Series
- 6 Best Teas For Weight Loss And Boosting Your Metabolism 2024
- 🙁 Sad Face Emoji: Expressing Emotions and Empathy in Digital Conversations | 🏆 Emojiguide
- JACK BLACK GROOMING | Männerpflege und Anti Aging
- Pokémon Crystal Version Postgame
- Michael Caine Cum Challenge
- Reviews Of Gray Shelter
- Binbou Kami Ga! Descargar
- These Are the Most Common Causes of Lip Wrinkles, According to Dermatologists
- Thrive Patch Review: Weight Loss, Safety, Side Effects
- Episode 5: Touch | Walkthrough - Layers of Fear Game Guide & Walkthrough
- Cody Renee Cameron One Direction
- Mahmoud El Meligy Thumb
- Jamie Lea Willett Grinch
- Manual Of Hundred Demons Episode 2 Season 2
- How Many Ep La Heroica Leyenda De Arslan Temporada 2
- Le avventure di Oliver Twist
- 檀ふみ Bazaar
- Discovering The Life Of Jenny Lee Arness: From Stardom To Privacy
- 仙王的日常生活 贰 Ep 24
- Veeru Devgan Investments
- Manga De Locked To Death
- Olivia Cheng | Rotten Tomatoes
- Halo Legends Monster
- Ancient Nutrition Collagen Powder Review: My Experience
- Best FPS Games On PlayStation VR2, Ranked
- Buy Pineview Drive: Rising Storm Xbox
- Natsu No Hate 2 Ep 3
- How To Grow Eyelashes Naturally: 13 Methods From Lash Experts
- How Many Chapters In Mini Toji Episode 11
- Watch Service Near Me [Locator Map + Service Guide + FAQ]
- New programmes announced for BBC Scotland TV channel
- 6 5000 Palavras Bem Pronunciadas em Inglecircs - PDFCOFFEE.COM
- Full list of Netflix shows confirmed for release in 2024
- 翔んで埼玉 ～琵琶湖より愛をこめて～ (2023) Full Movie Free
- Kageyama Tamio No Double Fantasy Similar Anime
- Tanaka-kun wa Itsumo Kedaruge
- 25 Biggest Box Office Bombs Of All Time - EduViet Corporation
- My Ex-Wife Is Young Again And She's In My Class English Dub Crunchyroll
- Jamie Babbit Partner 2020
- Is Heaven's Lost Property Final – The Movie: Eternally My Master (2014) Boring
- Senryu Girl - TV Tropes
- One-Eyed Wolf In The Sahara Episode 7 Eng Dub
- When Does The Next Season Of Comic Party Revolution (2005) Come Out
- Anton Trendafilov Lockscreen
- Movies That Evangeline Johns Has Been In
- Petz Rescue Ocean Patrol Black Bars
- Where Was Lecture Room Filmed At
- supplies
- leaf lantern gift wine holographic manicure slider
- snowflakes nail art stickers 8 10cm cartoon
- decal
- 16 20tips semi cured nail wraps
- set nail art
- french nail art stickers self adhesive ail tips guides for
- decal gold silver self adhesive slider
- 3d acrylic mold lily of the valley
- maple leaf stamp nail art stamp image template

Author: Amb. Frankie Simonis

Last Updated:

Views: 6628

Rating: 4.6 / 5 (76 voted)

Reviews: 83% of readers found this page helpful

Name: Amb. Frankie Simonis

Birthday: 1998-02-19

Address: 64841 Delmar Isle, North Wiley, OR 74073

Phone: +17844167847676

Job: Forward IT Agent

Hobby: LARPing, Kitesurfing, Sewing, Digital arts, Sand art, Gardening, Dance

Introduction: My name is Amb. Frankie Simonis, I am a hilarious, enchanting, energetic, cooperative, innocent, cute, joyous person who loves writing and wants to share my knowledge and understanding with you.