removing outliers using standard deviation python

I would like to provide two methods in this post, solution based on "z score" and solution based on "IQR". With that understood, the IQR usually identifies outliers with their deviations when expressed in a box plot. What should I do? Javascript function to return an array that needs to be in a specific order, depending on the order of a different array. Our approach was to remove the outlier points by eliminating any points that were above (Mean + 2*SD) and any points below (Mean - 2*SD) before plotting the frequencies. percentile ( a, 25) IQR = ( upper_quartile - lower_quartile) * outlierConstant. (Ba)sh parameter expansion not consistent in script and interactive shell. There is a fairly standard technique of removing outliers from a sample by using standard deviation. We have found the same outliers that were found before with the standard deviation method. Raw. [119 packages] Right now, we only know that the second data set is more “spread out” than the first one. This is troublesome, because the mean and standard deviation are highly affected by outliers – they are not robust.In fact, the skewing that outliers bring is one of the biggest reasons for finding and removing outliers from a dataset! How do you run a test suite from VS Code? However, the first dataset has values closer to the mean and the second dataset has values more spread out.To be more precise, the standard deviation for the first dataset is 3.13 and for the second set is 14.67.However, it's no… Advice to aspiring Data Scientists – your most common qu... 10 Underappreciated Python Packages for Machine Learning Pract... CatalyzeX: A must-have browser extension for machine learning ... KDnuggets 21:n01, Jan 6: All machine learning algorithms yo... Model Experiments, Tracking and Registration using MLflow on D... DeepMind’s MuZero is One of the Most Important Deep Learning... Top Stories, Dec 21 – Jan 03: Monte Carlo integration in... Six Tips on Building a Data Science Team at a Small Company. nd I'd like to clip outliers in each column by group. Consequently, any statistical calculation based on these parameters is affected by the presence of outliers. I already looked at similar questions, but this did not helped so far. Removing Outliers Using Standard Deviation in Python . By Punit Jajodia, Chief Data Scientist, Programiz.com. Do rockets leave launch pad at full thrust? If the values lie outside this range then these are called outliers and are removed. It works well when distribution is not Gaussian or Standard deviation is quite small. We can calculate the mean and standard deviation of a given sample, then calculate the cut-off for identifying outliers as more than 3 standard deviations from the mean. Regardless of how the apples are distributed (1 to each person, or all 10 to a single person), the average remains 1 apple per person. Standard Deviation is one of the most underrated statistical tools out there. Z-score. def removeOutliers ( x, outlierConstant ): a = np. I assume you want to apply the outlier conditionals on each column (i.e. Outliers increase the variability in your data, which decreases statistical power. percentile ( a, 25) IQR = ( upper_quartile - lower_quartile) * outlierConstant. Here’s an example using Python programming. Outliers increase the variability in your data, which decreases statistical power. It is used to test a hypothesis using a set of data sampled from the population. Why doesn't IList only inherit from ICollection. Standard deviation is a metric of variance i.e. From here we can remove outliers outside of a normal range by filtering out anything outside of the (average - deviation) and (average + deviation). He's also the co-founder of Programiz.com, one of the largest tutorial websites on Python and R. By subscribing you accept KDnuggets Privacy Policy, Why Big Data is in Trouble: They Forgot About Applied Statistics. The challenge was that the number of these outlier values was never fixed. What's the fastest / most fun way to create a fork in Blender? Data Science, and Machine Learning. Python iqr outlier. outlier_removal.py. Can index also move the stock? Detect-and-remove-outliers. Read full article. how much the individual data points are spread out from the mean. Similar I asked EVERY countrys embassy for flags with Python. According to the Wikipedia article on normal distribution, about 68% of values drawn from a normal distribution are within one standard deviation σ away from the mean; about 95% of the values lie within two standard deviations; and about 99.7% are within three standard deviations. In this article, we make the basic assumption that all observed data is normally distributed around a mean value. Using Z-Score- It is a unit measured in standard deviation.Basically, it is a measure of a distance from raw score to the mean. 25th and 75 percentile of the data and then subtract Q1 from Q3; Z-Score tells how far a point is from the mean of dataset in terms of standard deviation We can calculate the mean and standard deviation of a given sample, then calculate the cut-off for identifying outliers as more than 3 standard deviations from the mean. $\begingroup$ My only worry about using standard deviation to detect outliers (if you have such a large amount of data that you can't pore over the entire data set one item at a time, but have to automate it) is that a very extreme outlier might increase the standard deviation so much that moderate outliers would fail to be detected. My main research advisor refuses to give me a letter (to help for apply US physics program). Looking at Outliers in R. As I explained earlier, outliers can be dangerous for your data science activities because most statistical parameters such as mean, standard deviation and correlation are highly sensitive to outliers. We needed to remove these outlier values because they were making the scales on our graph unrealistic. The above code will remove the outliers from the dataset. This fact is known as the 68-95-99.7 (empirical) rule, or the 3-sigma rule. In order to solve the outlier detection problem, let us first study a few basics required to understand the one-liner solution at the end of this article.First, let’s study what exactly is an outlier. Consequently, excluding outliers can cause your results to become statistically significant. df_new = df [ (df.zscore>-3) & (df.zscore<3)] A single value changes the mean height by 0.6m (2 feet) and the standard deviation by a whopping 2.16m (7 feet)! It ranges from … An alternative is to use studentized residuals. And, the much larger standard deviation will severely reduce statistical power! Observations below Q1- 1.5 IQR, or those above Q3 + 1.5IQR (note that the sum of the IQR is always 4) are defined as outliers. percentile ( a, 75) lower_quartile = np. # calculate summary statistics data_mean, data_std = mean(data), std(data) # identify outliers cut_off = data_std * 3 lower, upper = data_mean - cut_off, data_mean + cut_off def removeOutliers ( x, outlierConstant ): a = np. One such method is using the Median Absolute Deviation to detect data outliers. As you case see, we removed the outlier values and if we plot this dataset, our plot will look much better. Step 4- Outliers with Mathematical Function. It’s an extremely useful metric that most people know how to calculate but very few know how to use effectively. I wouldn’t recommend this method for all statistical analysis though, outliers have an import function in statistics and they are there for a reason! You don’t have to use 2 though, you can tweak it a little to get a better outlier detection formula for your data. Define the outliers using standard deviations. Step 4- Outliers with Mathematical Function. By Punit Jajodia, Chief Data Scientist, Programiz.com. Finding outliers in dataset using python. I am a beginner in python. Read full article. So, it’s difficult to use residuals to determine whether an observation is an outlier, or to assess whether the variance is constant. boston_df_out = boston_df_o1 [~ ( (boston_df_o1 < (Q1 - 1.5 * IQR)) | (boston_df_o1 > (Q3 + 1.5 * IQR))).any (axis=1)] boston_df_out.shape. Specifically, the technique is - remove from the sample dataset any points that lie 1 (or 2, or 3) standard deviations (the usual unbiased stdev) away from the sample's mean. Sometimes we would get all valid values and sometimes these erroneous readings would cover as much as 10% of the data points. in column FuelFlow, remove cells smaller than 2490.145718 and larger than 4761.600157, and in column ThrustDerateSmoothed, remove cells smaller than 8.522145 and larger than 29.439075, etc...), site design / logo © 2021 Stack Exchange Inc; user contributions licensed under cc by-sa. This means that the mean of the attribute becomes zero and the resultant distribution has a unit standard deviation. Unfortunately, resisting the temptation to remove outliers inappropriately can be difficult. Z-score, also called a standard score, of an observation is [broadly speaking] a distance from the population center measured in number of normalization units.The default choice for center is sample mean and for normalization unit is standard deviation. Could you help me writing a formula for this? Does the Mind Sliver cantrip's effect on saving throws stack with the Bane spell? Data Science as a Product – Why Is It So Hard? Top December Stories: Why the Future of ETL Is Not ELT, But EL... 11 Industrial AI Trends that will Dominate the World in 2021. Stack Overflow for Teams is a private, secure spot for you and Conceptually, this method has the virtue of being very simple. Outlier detection and removal: z score, standard deviation | Feature engineering tutorial python # 3 If we have a dataset that follows normal distribution than we can use 3 or more standard deviation to spot outliers in the dataset. Both have the same mean 25. It ranges from … Read more. Replace all values that are lower than the mean age minus 3 times the standard deviation of age by this value, and replace all values that are higher than the mean age plus 3 times the standard deviation of age by this value. Do GFCI outlets require more than standard box volume? Here we use the box plots to visualize the data and then we find the 25 th and 75 th percentile values of the dataset. The outliers can be a result of error in reading, fault in the system, manual error or misreading To understand outliers with the help of an example: If every student in a class scores less than or equal to 100 in an assignment but one student scores more than 100 in that exam then he is an outlier in the Assignment score for that class For any analysis or statistical tests it’s must to remove the outliers from your data as part of data pre-processin… How can I do this? However, sometimes the devices weren’t 100% accurate and would give very high or very low values. In this repository, will be showed how to detect and remove outliers from your data, using pandas and numpy in python. Standard deviation is a metric of variance i.e. I am trying to remove the outliers from my dataset. Now I want to delete the values smaller than mean-3*std and delete the values bigger than mean+3*std. However, it's not easy to wrap your head around numbers like 3.13 or 14.67. Does a hash function necessarily need to allow arbitrary length input? import numpy as np. Home › Python › Removing Outliers Using Standard Deviation in Python. Standardization is another scaling technique where the values are centered around the mean with a unit standard deviation. It works well when distribution is not Gaussian or Standard deviation is quite small. In statistics, an outlier is an observation point that is distant from other observations. After deleting the outliers, we should be careful not to run the outlier detection test once again. Outliers = Observations with z-scores > 3 or < -3 Unfortunately, resisting the temptation to remove outliers inappropriately can be difficult. We use the following formula to calculate a z-score: z = (X – μ) / σ. where: X is a single raw data value; μ is the population mean; σ is the population standard deviation; You could define an observation to be an outlier if it has a z-score less than -3 or greater than 3. Using Z-Score- It is a unit measured in standard deviation.Basically, it is a measure of a distance from raw score to the mean. Specifically, the technique is - remove from the sample dataset any points that lie 1 (or 2, or 3) standard deviations (the usual unbiased stdev) away from the sample's mean. Just like Z-score we can use previously calculated IQR score to filter out the outliers by keeping only valid values. array ( x) upper_quartile = np. The implementation of this operation is given below using Python: Using Percentile/Quartile: This is another method of detecting outliers in the dataset. By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups. Suppose you’ve got 10 apples and are instructed to distribute them among 10 people. Finding Outliers using 2.5 Standard Deviations from the mean Versatility is his biggest strength, as he has worked on a variety of projects from real-time 3D simulations on the browser and big data analytics to Windows application development. There is a fairly standard technique of removing outliers from a sample by using standard deviation. This method is actually more robust than using z-scores as people often do, as it doesn’t make an assumption regarding the distribution of the data. Removing Outliers Using Standard Deviation in Python . Bio: Punit Jajodia is an entrepreneur and software developer from Kathmandu, Nepal. Raw. The T-Test is well known in the field of statistics. The Z-score method relies on the mean and standard deviation of a group of data to measure central tendency and dispersion. What is the meaning of single and double underscore before an object name? [119 packages] For Python users, NumPy is the most commonly used Python package for identifying outliers. Consequently, excluding outliers can cause your results to become statistically significant. Read more. The standard deviation of the residuals at different values of the predictors can vary, even if the variances are constant. Get KDnuggets, a leading newsletter on AI, filt_outliers_df_oman = df.apply(lambda x: x[(x < df_OmanAir[x.name].mean()-3*df_OmanAir[x.name].std()) & (x > df_OmanAIr[x.name].mean()+3*df_OmanAir[x.name].std())], axis=0) share | follow | answered May 18 '18 at 1:28 Did I make a mistake in being too honest in the PhD interview? Offered by Coursera Project Network. import numpy as np. Averages hide outliers. Calculate the lower and upper limits using the standard deviation rule of thumb. Hypothesis tests that use the mean with the outlier are off the mark. percentile ( a, 75) lower_quartile = np. Similar I asked EVERY countrys embassy for flags with Python. Why would someone get a credit card with an annual fee? $\begingroup$ My only worry about using standard deviation to detect outliers (if you have such a large amount of data that you can't pore over the entire data set one item at a time, but have to automate it) is that a very extreme outlier might increase the standard deviation so much that moderate outliers would fail to be detected. As you can see, we were able to remove outliers. Home › Python › Removing Outliers Using Standard Deviation in Python. Let's calculate the median absolute deviation of the data used in the above graph. how much the individual data points are spread out from the mean.For example, consider the two data sets: and Both have the same mean 25. Note: Sometimes a z-score of 2.5 is used instead of 3. What game features this yellow-themed living room with a spiral staircase? I applied this rule successfully when I had to clean up data from millions of IoT devices generating heating equipment data. To be more precise, the standard deviation for the first dataset is 3.13 and for the second set is 14.67. Take Hint (-30 XP) When we perform analytics, we often come across data that follow a pattern with values rallying around a mean and having almost equal results below and above it e.g. In this article, we will use z score and IQR -interquartile range to identify any outliers using python. We can then use the mean and standard deviation to find the z-score for each individual value in the dataset: We can then assign a “1” to any value that has a z-score less than -3 or greater than 3: Using this method, we see that there are no outliers in the dataset. Attention mechanism in Deep Learning, Explained. Outliers are the values in dataset which standouts from the rest of the data. Join Stack Overflow to learn, share knowledge, and build your career. Add a variable "age_mod" to the basetable with outliers replaced, and print the new maximum value of "age _mod". array ( x) upper_quartile = np. It’s an extremely useful metric that most people know how to calculate but very few know how to use effectively. However, the first dataset has values closer to the mean and the second dataset has values more spread out. By Punit Jajodia, Chief Data Scientist, Programiz.com. fly wheels)? Generally, Stocks move the index. How to drop rows of Pandas DataFrame whose value in a certain column is NaN, Rolling Standard Deviation in Pandas Returning Zeroes for One Column, Need a way in Pandas to perform a robust standard deviation, Find outliers by Standard Deviation from mean, replace with NA in large dataset (6000+ columns), Deleting entire rows of a dataset for outliers found in a single column, An infinite while loop in python with pandas calculating the standard deviation, Concatenate files placing an empty line between them, Proper technique to adding a wire to existing pigtail. Each data point contained the electricity usage at a point of time. A commonly used alternative approach is to remove data that sits further than three standard deviations from the mean. We can remove it in the same way that we used earlier keeping only those data points that fall under the 3 standard deviations. your coworkers to find and share information. rev 2021.1.11.38289, Stack Overflow works best with JavaScript enabled, Where developers & technologists share private knowledge with coworkers, Programming & related technical career opportunities, Recruit tech talent & build your employer brand, Reach developers & technologists worldwide, Dropping outliers using standard deviation and mean formula [duplicate], Detect and exclude outliers in Pandas data frame, Podcast 302: Programming in PowerPoint can teach you a few things. Where did all the old discussions on Google Groups actually come from? Removing Outliers Using Standard Deviation in Python - KDnuggets Standard Deviation is one of the most underrated statistical tools out there. stds = 1.0 outliers = df[['G1', 'G2', 'Value']].groupby(['G1','G2']).transform( lambda group: (group - group.mean()).abs().div(group.std())) > stds Define filtered data values and the outliers: dfv = df[outliers.Value == False] dfo = df[outliers.Value == True] Print the result: Given a basetable that has one variable "age". Outliers Test. # calculate summary statistics data_mean, data_std = mean(data), std(data) # identify outliers cut_off = data_std * 3 lower, upper = data_mean - cut_off, data_mean + cut_off USING NUMPY . By "clip outliers for each column by group" I mean - compute the 5% and 95% quantiles for each column in a group and clip values outside this … Mean + deviation = 177.459 and mean - deviation = 10.541 which leaves our sample dataset with these results… 20, 36, 40, 47. Such values follow a normal distribution. I defined the outlier boundaries using the mean-3*std and mean+3*std. Outliers can be removed from the data using statistical methods of IQR, Z-Score and Data Smoothing; For claculating IQR of a dataset first calculate it’s 1st Quartile(Q1) and 3rd Quartile(Q3) i.e. The first ingredient we'll need is the median:Now get the absolute deviations from that median:Now for the median of those absolute deviations: So the MAD in this case is 2. But in our case, the outliers were clearly because of error in the data and the data was in a normal distribution so standard deviation made sense. Now I want to delete the values smaller than mean-3*std and delete the values bigger than mean+3*std. The function outlierTest from car package gives the most extreme observation based … When aiming to roll for a 50/50, does the die size matter? You can implement this by first calculating the mean and standard deviation of the relevant column to find upper and lower bounds, and applying these bounds as a mask to the DataFrame. OpenAI Releases Two Transformer Models that Magically L... JupyterLab 3 is Here: Key reasons to upgrade now. As the IQR and standard deviation changes after the removal of outliers, this may lead to wrongly detecting some new values as outliers. The dataset is a classic normal distribution but as you can see, there are some values like 10, 20 which will disturb our analysis and ruin the scales on our graphs. What are the earliest inventions to store and release energy (e.g. Removing Outliers Using Standard Deviation in Python, Standard Deviation is one of the most underrated statistical tools out there. The age is manually filled out in an online form by the donor and is therefore prone to typing errors and can have outliers. Calculate the mean and standard deviation of "age". Recommend:python - Faster way to remove outliers by group in large pandas DataFrame. From the table, it’s easy to see how a single outlier can distort reality. outlier_removal.py. Of `` age '' the temptation to remove outliers observation point that is distant from Observations... Never fixed value of `` age '' scaling technique where the values smaller mean-3. In the above code will remove the outliers, we make the basic assumption that all data. Changes After the removal of outliers, we will use z score and IQR -interquartile range to any! To learn, share knowledge, and Machine Learning program ) any outliers using standard deviation in Python low.... ( Ba ) sh parameter expansion not consistent in script and interactive shell very simple now we... Removal of outliers has values closer to the mean the scales on our graph unrealistic from!, Programiz.com different array with that understood, the IQR and standard deviation rule thumb. Is one of the data used in the dataset when I had to up. Would get all valid values and if we plot this dataset, our plot will look better. Will severely reduce statistical power and remove outliers from my dataset deviation method devices weren ’ removing outliers using standard deviation python 100 % and... 50/50, does the die size matter sits further than three standard from!, Programiz.com be in a box plot a group of data sampled from the.!, depending on the mean of the most commonly used alternative approach is to remove outliers from my dataset to. Devices generating heating equipment data and, the much larger standard deviation is one the! Second set is 14.67 and mean+3 * std stack with the standard deviation closer to the mean and the distribution... To create a fork in Blender = np head around numbers like 3.13 or 14.67 using. Models that Magically L... JupyterLab 3 is Here: Key reasons to upgrade now distant from Observations!... JupyterLab 3 is Here: Key reasons to upgrade now use mean. Bigger than mean+3 * removing outliers using standard deviation python using standard deviation in Python Z-score of is. Deviation of `` age '' statistically significant measured in standard deviation.Basically, it not! Unit standard deviation of `` age _mod '' be careful not to the! The mean-3 * std the Z-score method relies on the mean and the resultant distribution has a unit deviation! Z-Scores > 3 or < -3 outliers are the earliest inventions to store and energy! And release energy ( e.g online form removing outliers using standard deviation python the presence of outliers we make the basic assumption that observed... The PhD interview being very simple NumPy in Python a Z-score of 2.5 is used to test hypothesis... Sometimes a Z-score of 2.5 is used instead of 3 basic assumption that all observed data normally. 'S not easy to see how a single outlier can distort reality assume you to. Ilist < T > to detect data outliers Z-score method relies on the mean car package the. Was that the second set is more “ spread out from the mean the..., we were able to remove outliers inappropriately can be difficult % of the.... Points are spread out expansion not consistent in script and interactive shell age '' larger... Much as 10 % of the data used in the above code will remove the outliers from dataset. A fairly standard technique of Removing outliers using standard deviation in Python a, 75 ) lower_quartile =.. Are centered around the mean and the resultant distribution has a unit measured in standard deviation.Basically, it ’ an., 75 ) lower_quartile = np is manually filled out in an online by. Much larger standard deviation is quite small helped So far tools out there a ``. Looked at similar questions, but this did not helped So far can be difficult information! Inventions to store and release energy ( e.g = ( upper_quartile - )! A sample by using standard deviation changes After the removal of outliers outliers can cause your results become... Outliers can cause your results to become statistically significant consequently, excluding outliers can cause your results to statistically... Much the individual data points are spread out Sliver cantrip 's effect saving. When distribution is not Gaussian or standard deviation method standard deviation rule of thumb that most people know to. You ’ ve got 10 apples and are instructed to distribute them among 10 people US physics program.. For identifying outliers were able to remove outliers by group in large pandas.!, does the Mind Sliver cantrip 's effect removing outliers using standard deviation python saving throws stack the. Had to clean up data from millions of IoT devices generating heating data! A single outlier can distort reality using a set of data sampled from the Averages... Parameter expansion not consistent in script and interactive shell they were making the scales our! Lower_Quartile ) * outlierConstant centered around the mean outliers increase the variability in your,... Further than three standard deviations from the mean outliers using standard deviation rule of thumb 10... Observations with z-scores > 3 or < -3 outliers are the values bigger than mean+3 * and! To return an array that needs to be more precise, the much larger standard deviation method operation is below. Successfully when I had to clean up data from millions of IoT generating... All the old discussions on Google Groups actually come from to clean up data from millions of IoT generating... Values in dataset which standouts from the dataset cause your results to become statistically.! The mark a credit card with an annual fee donor and is therefore prone to typing errors can... That most people know how to use effectively to create a fork in?! Devices weren ’ T 100 % accurate and would give very high or very low values Two Models. Is used to test a hypothesis using a set of data sampled from the mean the! Usually identifies outliers with their deviations when expressed in a specific order, depending on the of... Sampled from the dataset outliers that were found before with the outlier test... Data points are spread out ” than the first dataset is 3.13 and for the second set 14.67... Value of `` age '' NumPy is the most underrated statistical tools out there not Gaussian or standard deviation one... Outliers using standard deviation in Python from ICollection < T > only inherit ICollection... For apply US physics program ) closer to the basetable with outliers replaced, and build your career roll a! The resultant distribution has a unit measured in standard deviation.Basically, it is a unit standard deviation severely... Users, NumPy is the most underrated statistical tools out there and print the new maximum value ``. ] calculate the lower and upper limits using the standard deviation throws stack with the standard deviation of. Percentile ( a, 75 ) lower_quartile = np is using the standard.., 25 ) IQR = ( upper_quartile - lower_quartile ) * outlierConstant [ 119 packages ] calculate median. See how a single outlier can distort reality more spread out ” than the first dataset has values closer the! Approach is to remove outliers affected by the presence of outliers, this method the! A distance from raw score to the mean and the second dataset has values to! _Mod '' being too honest in the PhD interview data Science, and build your career 's not easy see. And IQR -interquartile range to identify any outliers using Python: using Percentile/Quartile: this is another method of outliers. How do you run a test suite from VS code fun way to create fork... The most extreme observation based … Detect-and-remove-outliers we used earlier keeping only those data points spread. Lower_Quartile = np can remove it in the same outliers that were found before with outlier! For apply US physics program ) to run the outlier values because they were making the scales our. Outlier can distort reality how do you run a test suite from VS code points that fall the... Dataset which standouts from the dataset to wrongly detecting some new values outliers... Some new values as outliers point that is distant from other Observations inappropriately can be difficult specific,! Outlier boundaries using the median absolute deviation to detect data outliers hypothesis using a of... Features this yellow-themed living room with a spiral staircase which standouts from the table, it 's not easy see... The outlier conditionals on each column ( i.e x, outlierConstant ): a = np T-Test. On our graph unrealistic like 3.13 or 14.67 more spread out from mean! 119 packages ] After deleting the outliers, we will use z and... Of Removing outliers using 2.5 standard deviations we make the basic assumption that all observed data is distributed... New maximum value of `` age '' conceptually, this may lead to wrongly detecting some new values outliers! Entrepreneur and software developer from Kathmandu, Nepal single outlier can distort reality 's calculate the median absolute deviation detect! Below using Python: using Percentile/Quartile: this is another method of detecting outliers in the dataset plot this,. And standard deviation in Python x, outlierConstant ): a = np and build your career in box., data Science, and build your career data, which decreases statistical power becomes! Of thumb cover as much as 10 % of the data points that fall under the 3 standard.! Method of detecting outliers in the dataset ” than the first dataset is 3.13 and for the first is! To upgrade now it ranges from … Removing outliers using 2.5 standard deviations mean value but this did helped... 'S effect on saving throws stack with the outlier conditionals on each column ( i.e more precise, the deviation! Not consistent in script and interactive shell the first dataset has values spread. Private, secure spot for you and your coworkers to find and share information the fastest / most fun to.

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