Pyspark Fill Na With Mean

Since the intercept is the expected mean value when X=0, it is the mean value only for the reference group (when all other X=0). If the data has missing values, they will become NaNs in the resulting Numpy arrays. Since domain understanding is an important aspect when deciding how to encode various categorical values - this. Introduction Before we get started, we shall take a quick look at the […]. Introduction. This is used by vformat() to break the string into either literal text, or replacement fields. fill which could be another option. This article provides a comprehensive introduction to Apache Spark, its benefits, APIs, RDDs, Dataframes & solves a machine learning problem. For more information, see the discussions in the Apache Spark Git pull requests 24359 and 24375. Or PySpark, as the Olgivy inspired geniuses at Apache marketing call it. Anita Graser. Pandas drop function allows you to drop/remove one or more columns from a dataframe. 1581147779916. In this post I perform equivalent operations on a small dataset using RDDs, Dataframes in Pyspark & SparkR and HiveQL. Machine Learning Case Study With Pyspark 0. class pyspark. 下面介绍几种常用Spark应用程序提交方式. 6 was the ability to pivot data, creating pivot tables, with a DataFrame (with Scala, Java, or Python). first_name last_name age sex preTestScore postTestScore; 0: Fill in missing in preTestScore with the mean value of preTestScore. sub are the same. notnull()] 4. Content Data Loading and Parsing Data Manipulation Feature Engineering Apply Spark ml/mllib models 1. HiveContext Main entry point for accessing data stored in Apache Hive. In practice, larger num ber of. /inputs/dist. In the first part of this series, we looked at advances in leveraging the power of relational databases "at scale" using Apache Spark SQL and DataFrames. Finally, you may want to keep only the rows with at least 4 non-na values: dataframe. 2222222 When you import dataset from other statistical applications the missing values might be coded with a number, for example 99. Also, it has been ranked as the seventh most popular programming language in the world amongst software developers. Or, you want to calculate the average temperature on a particular day over a 10-year time span. Data can have missing values for a number of reasons such as observations that were not recorded and data corruption. I'm not talking about Scala yet, or Java, those are whole other language. 4 Release 2. Welcome to the Forum Archive! Years of conversation fill a ton of digital pages, and we've kept all of it accessible to browse or copy over. In any real world data science situation with Python, you'll be about 10 minutes in when you'll need to merge or join Pandas Dataframes together to form your analysis dataset. Let's run this. Customers downgrade or discontinue service for various reasons, and the service provider often cannot know when or why customers leave until they leave!. Most people likely have experience with pivot tables in Excel. spark 交互式执行环境. 3, “Date and Time Literals”. table are amazing packages that make data manipulation in R fun. Draw small vertical lines to show each observation in a distribution. 注意,最后一个name为空。 需要解决这么几个问题: 将name字段进行空值填充;; 添加一列并赋值为‘abc’ 2. Data Flow Diagram. seed(1) or set. Ireland QGIS User Group Blog. A2A: I would use the replace() method: [code]>>> import pandas as pd >>> import numpy as np >>> df = pd. 4, you can finally port pretty much any relevant piece of Pandas' DataFrame computation to Apache Spark parallel computation framework using Spark SQL's DataFrame. For example, using a simple example DataFrame: df = pandas. DataType or a datatype string or a list of column names, default is None. Or PySpark, as the Olgivy inspired geniuses at Apache marketing call it. Neverth ele ss, the response rate was hig h when compa red with the numbe r of. This lesson describes how to construct a confidence interval around the slope of a regression line. GISpark为数据工程专家、大数据科学家、地理与空间研究的学者而设计,将理论、方法、模型. Related Posts: Pandas : Find duplicate rows in a Dataframe based on all or selected columns using DataFrame. An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1. There are often problems with inconsistent setups of ROOT and python, but also PyYAML, so adding a conda channel that makes use of the already existing ROOT one might and fixing the PyYAML version to something we know to work could make things bulletproof. Join Coursera for free and transform your career with degrees, certificates, Specializations, & MOOCs in data science, computer science, business, and dozens of other topics. If you are installing or upgrading to CDS 2. txt' as: 1 1 2. After running this on my example data, I get a mean conversion rate (across all tests) of about 3% and a variance of 0. Let's run this. empty(df),times=100) Unit: microseconds expr min lq mean median uq max neval is. In first and second pages, user has to fill the information and click on Next button. Although, doesn’t mean you should stop keeping up like others are using AI. If x is grouped, this is the number (or fraction) of rows per group. By comparable we mean that a. Visualizing the distribution of a dataset¶ When dealing with a set of data, often the first thing you’ll want to do is get a sense for how the variables are distributed. As in some of my earlier posts, I have used the tendulkar. n: number of rows to return for top_n(), fraction of rows to return for top_frac(). frame with another ( not 0) value. project-management Jobs in Bangalore , Karnataka on WisdomJobs. It is also important to keep in mind that a bar plot shows only the mean (or other estimator) value, but in many cases it may be more informative to show the distribution of values at each level of the categorical variables. Categories. how much the individual data points are spread out from the mean. Often you may want to filter a Pandas dataframe such that you would like to keep the rows if values of certain column is NOT NA/NAN. 2222222 When you import dataset from other statistical applications the missing values might be coded with a number, for example 99. Simply calculate the mean of the observed values for that variable for all individuals who are non-missing. Pandas is a foundational library for analytics, data processing, and data science. This post is in answer to his question. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Short Description: A quick tutorial on how to query cryptocurrency transactions with Spark Streaming with Python. Also, it has been ranked as the seventh most popular programming language in the world amongst software developers. This lesson of the Python Tutorial for Data Analysis covers grouping data with pandas. Join Coursera for free and transform your career with degrees, certificates, Specializations, & MOOCs in data science, computer science, business, and dozens of other topics. Handling Missing values of column in pandas python can be handled either by dropping the missing values or replacing the missing values with mean, median. I am playing with several functions, and some use set. This feature is not available right now. na Finite list of text values For the most part, there is no need to worry about determining if you should try to explicitly force the pandas type to a corresponding to NumPy type. What can this tool do? Choose from the following 5 JSON conversions offered by this tool: CSV to JSON - array of JSON structures matching your CSV plus JSONLines (MongoDB) mode. club | minimal plan 1$. RandomForestClassifier. But we will not prefer this way for large dataset, as this will return TRUE/FALSE matrix for each data point, instead we would interested to know the counts or a simple check if dataset is holding NULL or not. The Data Set. 3 Building the Prediction Model. I want to summarize my best practice so that others will take less detour. If enough records are missing entries, any analysis you perform will be skewed and the results of […]. Window functions allow users of Spark SQL to calculate results such as the rank of a given row or a moving average over a range of input rows. Join Coursera for free and transform your career with degrees, certificates, Specializations, & MOOCs in data science, computer science, business, and dozens of other topics. Pandas DataFrame provides a fillna() function that can fill missing data items with any string or number. iloc, which require you to specify a location to update with some value. $ pyspark K-means on Spark. apply to send a column of every row to a function. Forward-fill missing data in Spark Posted on Fri 22 September 2017 • 4 min read Since I've started using Apache Spark, one of the frequent annoyances I've come up against is having an idea that would be very easy to implement in Pandas, but turns out to require a really verbose workaround in Spark. Because Toronto’s crime data was simply not available. It is a GNU project which is similar to the S language and environment. Big Data-1: Move into the big league:Graduate from Python to Pyspark 2. MLlib includes three major parts: Transformer, Estimator and Pipeline. Rules for Multi value compression 1. The replicate T-SQL function in Microsoft’s SQL Server makes it simple to add leading zeros to a number value. But what I don't get is what do the values themselves mean. Big Data-2: Move into the big league:Graduate from R to SparkR. Apache Spark's meteoric rise has been incredible. heatmap (flights, annot = True, fmt = "d"). import com. SparkSession (sparkContext, jsparkSession=None) [source] ¶. Although DataFrames are meant to be populated by reading already organized data from external files, many times you will need to somehow manage and modify already existing columns (and rows) in a DF. ” This means if you click on the link and purchase the item, I will receive an affiliate commission. By following steps we mean all the operations that you may want to perform on the tree: code validation, interpretation, compilation, etc. Parameters axis {0 or 'index', 1 or 'columns'}, default 0. DataFrame Spark SQL和DataFrames重要的类有: pyspark. Given either regular expression or a vector of character positions, separate() turns a single character column into multiple columns. One of the most common and simplest strategies to handle imbalanced data is to undersample the majority class. Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. age favorite_color name test_one test_two; 0: 20: blue: Willard Morris: 88: 78: 1: 19: blue: Al Jennings: 92: 100: 2: 22: yellow: Omar Mullins: 95: 90: 3: 21: green. The following list includes issues fixed in CDS 2. It has the advantage of keeping the same mean and the same sample size, but many, many disadvantages. I am playing with several functions, and some use set. Let’s discuss how to add new columns to existing DataFrame in Pandas. 4, use Release 2 or higher. Parameters value scalar, dict, Series, or DataFrame. The Blues Brothers started in 1978 as a living salute to the blues, soul and R&B music of the 1950s and 1960s that was not in vogue by the late 1970s. FirstName, C. So it appears the values in the list are used to fill the 'holes' in order, if the list has the same length as number of holes. what I really mean is this: “I think that with higher education, people would. For the PHP coders out there, using the == syntax is the same as == in Python, where using the is keyword is equivalent to the === syntax. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions. An SQL join clause - corresponding to a join operation in relational algebra - combines columns from one or more tables in a relational database. Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. The aim of this blog is to explain how to use SparkML to build a Classification model. class pyspark. Provided by Data Interview Questions, a mailing list for coding and data interview problems. imputeDF = imputeDF. DataType or a datatype string or a list of column names, default is None. Parameters axis {0 or 'index', 1 or 'columns'}, default 0. Value to use to fill holes (e. Forward-fill missing data in Spark Posted on Fri 22 September 2017 • 4 min read Since I've started using Apache Spark, one of the frequent annoyances I've come up against is having an idea that would be very easy to implement in Pandas, but turns out to require a really verbose workaround in Spark. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. How this formula works. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of the cluster. 이번엔 무슨 글을 작성할 것인가? 사실 저는 글또 1~3기를 하며 한번도 예치금을 잃어본 적이 없어서(Pass도 한번도 쓰지 않은 것 같네요) 예치금 없이 Pass 꼭 해야지!. We called the "agg" function on the df_ordered dataset that we have created in the previous tutorial. GISpark为数据工程专家、大数据科学家、地理与空间研究的学者而设计,将理论、方法、模型. This lesson describes how to construct a confidence interval around the slope of a regression line. We can see that the mean value for air_temp 9am matches the mean value computed in the summary statistics of the data frame where the missing values were removed. Latest data-entry Jobs* Free data-entry Alerts Wisdomjobs. Often you may want to filter a Pandas dataframe such that you would like to keep the rows if values of certain column is NOT NA/NAN. This article is showing a geometric and intuitive explanation of the covariance matrix and the way it describes the shape of a data set. But what I don't get is what do the values themselves mean. ipynb import pandas as pd Use. 第14章TensorFlowOnSpark详解. Chris Albon. fill(0) 등으로 0을 넣거나 하는데, 여기서 알아둘 것이 있다. frame with "000/000" how do I achieve. Viewed 41k times 17. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. The other day I encountered a SAS Knowledge Base article that shows how to count the number of missing and nonmissing values for each variable in a data set. Before you start. txt' as: 1 1 2. # filter out rows ina. 0104166666666667, which is the decimal value that represents 15 minutes, and rounds using that value. How to replace all values in a data. How to calculate the mean of ratings of each user? HTTP-Authentication: how to quickly test client software through BASIC and DIGEST? chrome. Dismiss Join GitHub today. As you can see, in the training data, we know the values of predictors and we also know the corresponding answer: we have the ground truth. ) is an element. Since the intercept is the expected mean value when X=0, it is the mean value only for the reference group (when all other X=0). types import DoubleType # user defined function def complexFun(x): return results. how much the individual data points are spread out from the mean. The learning curve is not easy my pretties, but luckily for you, I’ve managed to sort out some of the basic ecosystem and how it all operates. apply to send a column of every row to a function. txt) or view presentation slides online. com Twitter : @bigdataconf 3. 0008 (which translates to a standard deviation of about 3%). When you need to analyze really big data , the use of Pandas, sometime, cannot fit the problems. # TODO: Replace with appropriate code # You are welcome to structure your solution in a different way, so long as # you ensure the variables used in the next Test section are defined # DataFrame containing all accesses that did not return a code 200 from pyspark. pdf), Text File (. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. DataFrame provides a member function drop() i. r m x p toggle line displays. imputeDF = imputeDF. If you are installing or upgrading to CDS 2. One of the common tasks of dealing with missing data is to filter out the part with missing values in a few ways. Iteration is a general term for taking each item of something, one after another. In this section, we will discuss some general considerations for missing data, discuss how Pandas chooses to represent it, and demonstrate some built-in Pandas tools for handling missing data in Python. 下面介绍几种常用Spark应用程序提交方式. Published on Mar 5, 2019. there not-null varchar2(20) (txn_id) column contains numeric values. data - airquality data[4:10,3] - rep(NA,7) data[1:5,4] - NA As far as categorical variables are concerned, replacing categorical variables is usually not advisable. Version 2 May 2015 - [Draft – Mark Graph – mark dot the dot graph at gmail dot com – @Mark_Graph on twitter] 3 Working with Columns A DataFrame column is a pandas Series object. fill(meanValue, [x]). using the toarray() method of the class) first before applying the method. pyplot as plt import numpy as np import pandas as pd from metpy. The data type string format equals to pyspark. Merging and joining dataframes is a core process that any aspiring data analyst will need to master. With this option, the result will broadcast correctly against the original array a. Regression Slope: Confidence Interval. disk) to avoid being constrained by memory size. Much better! So once you have your list nicely formatted, you may perform some additional actions, such as appending values to the list. Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Recently I finished up Python Graph series by using Matplotlib to represent data in different types of charts. Not a member of Pastebin yet? Sign Up, it unlocks many cool features!. In lesson 01, we read a CSV into a python Pandas DataFrame. Apache Spark is a popular open-source distributed querying and processing engine. Using the following code, my intention is to check whether the variable 'char' belongs to either small_case OR camel_case OR space. Values of the DataFrame are replaced with other values dynamically. 4, use Release 2 or higher. This step is a bit more involved than the last, as it requires the data within the PySpark RDD to be formatted in a specific way. Regex substitution is performed under the hood with re. line, each data point is represented as a vertex (which location is given by the x and y columns) of a polyline mark in 2D space. I plan to have two main topics, one that focusses on players at specific positions, and another focussing on team dynamics and patterns. convert to logical, integer, numeric, complex or (depending on as. This was not the case in older versions of pandas, but users were generally discarding the NA group anyway (and supporting it was an implementation headache). The first part of the book has lots of trees and covers the storage side of databases both persistent and in-memory with examples from actual databases but without going too deep in specific database implementation details. Pandas is one of those packages and makes importing and analyzing data much easier. While the earlier post discussed parallel constructs in Python and Pyspark, this post elaborates similar and key constructs in R and SparkR. One of the many new features added in Spark 1. How to interpret OOB and confusion matrix for random forest? Ask Question Asked 7 years, 7 months ago. In the following article, I’m going to show you how and when to use mode imputation. print(x, meanValue) Now let's update our new DataFrame, replacing the missing values with the mean value. def crosstab (self, col1, col2): """ Computes a pair-wise frequency table of the given columns. What can this tool do? Choose from the following 5 JSON conversions offered by this tool: CSV to JSON - array of JSON structures matching your CSV plus JSONLines (MongoDB) mode. I am playing with several functions, and some use set. Depending on the scenario, you may use either of the 4 methods described in order to replace NaN values with zeros in pandas DataFrame. MySQL recognizes DATE, DATETIME, and TIMESTAMP values in several formats, described in Section 9. You can achieve the same results by using either lambada, or just sticking with pandas. fill(meanValue, [x]). My previous post ‘Outlier removal in R using IQR rule’ has been one of the most visited posts on here. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. pkgutil: Utilities for the import system. In this article, we will show you how to add a column to a data frame. 明明学过那么多专业知识却不知怎么应用在工作中,明明知道这样做可以解决问题却无可奈何。 你不仅仅需要学习专业数学模型,更需要学习怎么应用数学的方法。. In this post we'll walk through a number of different data cleaning tasks using Python's Pandas library. The SQL FULL JOIN combines the results of both left and right outer joins. k-Means is not actually a *clustering* algorithm; it is a *partitioning* algorithm. DataFrame([1, '', ''], ['a', 'b', 'c']) >>> df 0 a 1 b c. Python Cheat Sheets - Free download as PDF File (. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. Joining data Description Function #Data joinleft. Pandas Filter Filtering rows of a DataFrame is an almost mandatory task for Data Analysis with Python. Missing data in pandas dataframes. In this post I perform equivalent operations on a small dataset using RDDs, Dataframes in Pyspark & SparkR and HiveQL. While it is exceedingly useful, I frequently find myself struggling to remember how to use the syntax to format the output for my needs. pyspark 에서는 python이 형 명시가 없는 것을 고려해 string을 넣으면 문자열 빈값에 넣어지고, int를 넣으면 숫자 빈값에 넣어준다. using the toarray() method of the class) first before applying the method. Aanconda对接FusionInsight HD¶ 适用场景¶. How to use Stop If True when conditional formatting in Excel? In Microsoft Excel, you can apply multiple conditional formatting rules together. Mode Function in python pandas is used to calculate the mode or most repeated value of a given set of numbers. date to see whether it is advisable to set these other options. Operations in PySpark DataFrame are lazy in nature but, in case of pandas we get the result as soon as we apply any operation. Example, in a book I am working through- they use set. spark-shell --master < master-url > # scala pyspark --master < master-url > # python. A complementary Domino project is available. I know I can use pyspark. I have a pandas dataframe and there are few values that is shown as NaN. Recently I finished up Python Graph series by using Matplotlib to represent data in different types of charts. How to interpret OOB and confusion matrix for random forest? Ask Question Asked 7 years, 7 months ago. I would appreciate any help to apply the transparent background colours below to divide into two parts the plot area based on x-values as illustrated in the plot below (vertical division). Iteration is a general term for taking each item of something, one after another. mean() function return the mean of the values for the requested axis. I am using HDP 2. This time we’ll be using Pandas and…. Missing values can be imputed with a provided constant value, or using the statistics (mean, median or most frequent) of each column in which the missing values are located. Pandas is one of those packages, and makes importing and analyzing data much easier. First of all, create a DataFrame object of students records i. table is, in some cases, faster (see benchmark here) and it […]. Latest project-management Jobs in Bangalore* Free Jobs Alerts ** Wisdomjobs. But looking for a. Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated. Here is my code:. Notice that if a previous value is not available during a forward fill, the NA value remains. Most people likely have experience with pivot tables in Excel. Or PySpark, as the Olgivy inspired geniuses at Apache marketing call it. We are going to use the machine learning module of Spark called MLlib designed to invoke machine learning algorithms on numerical data sets represented in RDD. data - airquality data[4:10,3] - rep(NA,7) data[1:5,4] - NA As far as categorical variables are concerned, replacing categorical variables is usually not advisable. How this formula works. If enough records are missing entries, any analysis you perform will be skewed and the results of […]. Let's discuss how to add new columns to existing DataFrame in Pandas. Note that you can perform any operation inside the pipeline. Need your help, 1) Before enabling SSL, Hive with Kerberos was working from local hiveserver. The learning curve is not easy my pretties, but luckily for you, I've managed to sort out some of the basic ecosystem and how it all operates. For instance, with the current dataset, you need to standardize the continuous variables and convert the categorical data. One of the most important concerns for companies with subscription-based business models is customer churn. Conversely, a value of 100 will mean that all data will need to be present for the average to be calculated, else it is recorded as NA. To do so, click the Spell check button. and na¨ıve Bayes classifiers. I decided for this competition to continue my learning process of spark environment and invest time in understanding how to do recommendation using Apache Spark. Can plot many sets of data together. So we use na. Introduction. Pyspark count null. I see most people referring to the Null (None in Python) value as being “Nothing. Say for example that X1 is a continuous variable centered at its mean. You can also create new variables. 5 (Spark2x) 产品介绍¶. It provides flexibility and extensibility of MapReduce but at significantly higher speeds. MROUND rounds to nearest values based on a supplied multiple. In this example, each cell ('Mock', 'Dataset', 'Python', 'Pandas', etc. It creates a set that can be saved as a table or used as it is. there not-null varchar2(20) (txn_id) column contains numeric values. 1 (one) first highlighted chunk. R is a language and environment for statistical computing and graphics. If you do want to apply a NumPy function to these matrices, first check if SciPy has its own implementation for the given sparse matrix class, or convert the sparse matrix to a NumPy array (e. Alexandre Neto's blog. A scatterplot where one variable is categorical. Therefore, applymap() will apply a function to each of. We will now do a simple tutorial based on a real-world dataset to look at how to use Spark SQL. we are going to use a real world dataset from Home Credit Default Risk competition on kaggle. We will be using preprocessing method from scikitlearn package. SparkSession (sparkContext, jsparkSession=None) [source] ¶. Forgot your password? Pyspark count null. 1), using Titanic dataset, which can be found here (train. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: floating point, integer, boolean, and general object. Learning Objectives. The Microsoft Excel MATCH function searches for a value in an array and returns the relative position of that item. txt and sends it to the standard output stream. You may have observations at the wrong frequency. What can this tool do? Choose from the following 5 JSON conversions offered by this tool: CSV to JSON - array of JSON structures matching your CSV plus JSONLines (MongoDB) mode. Spark is a general distributed in-memory computing framework developed at AmpLab, UCB. Bonus Tip - Fill blank cells with "0" (zero) Most of the we want to fill blank cells with zeroes as opposed to the situation Asmat mentioned. In order to let R know that is a missing value you need to recode it. This lesson describes how to construct a confidence interval around the slope of a regression line. The pandas package provides various methods for combining DataFrames including merge and concat. Not until the summer of this year, Toronto police launch a public safety data portal to increase transparency between the public and officers. class pyspark. 0 (zero) top of page. The entry point to programming Spark with the Dataset and DataFrame API. replace (self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad') [source] ¶ Replace values given in to_replace with value. And what is your solution to empty df? Thanks!. Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated. I guess it is the best time, since you can deal with millions of data points with relatively limited computing power, and without having to know every single bit of computer science. Recently Kaggle master Kazanova along with some of his friends released a "How to win a data science competition" Coursera course. Tengo un Spark Dataframe con algunos valores faltantes. Can have MVC on as many columns as desired, no such thing as too many columns. Indexing, Slicing and Subsetting DataFrames in Python. Before going through this article, I highly recommend reading A Complete Tutorial on Time Series Modeling in R and taking the free Time Series Forecasting course. In this post I am giving a brief intro of Exploratory data analysis(EDA) in Python. It has the advantage of keeping the same mean and the same sample size, but many, many disadvantages. In this way, you can enjoy your favorite ST Patrick’s Day Parade 2020 Live Stream Online. This post is in answer to his question. My goal is to improve PySpark user experience and allow for a smoother transition from Pandas to Spark DataFrames, making it easier to perform exploratory data analysis and visualize the data. dropna (self, axis=0, how='any', thresh=None, subset=None, inplace=False) [source] ¶ Remove missing values. Since the intercept is the expected mean value when X=0, it is the mean value only for the reference group (when all other X=0). How to replace all values in a data. Appreciate any guidance you might have.