Learn Power Query with Microsoft Power BI

1. Introduction: Hi there, welcome to this course on Power Query with Microsoft Power BI, minor associate an amine instructor for this class. In this class you will learning various types of functions and options in Power Query use for performing various kinds of operations on a dataset or a spreadsheet. You will be learning how to use various Power Query functions that you can use to transform your dataset into the format that you require. For religious reasons, for data analytics, for data visualization is storytelling, business intelligence, and much more. You would be able to clean your data or data cleaning. You would be able to alter your dataset. You can create new columns and rows based on a certain logic provided by you with a matter of just a few clicks, without any requirement for writing a single piece of code. However, you can write a code as well. Here in this class you will learn in various Power Query functions in Power BI, such as row deletion, column is played replacing values, column Merge, adding suffix and prefix, converting to uppercase or lowercase. Adding transformation texts. Extract function, creating conditional or index column, date and time function sorting and much more. So if you're curious to learn these Power Query skills in Microsoft Power BI, it started learning right now. See you in the class. 2. Row deletion and Column SPLIT: Hi there, Welcome to this lesson. We are going to learn about Power Query in Microsoft Power BI. So let's start with this. So here, uh, we just need to get our data. So we'll click on Get Data and locate a particular data source. So here we are using Excel file. You can use CASB database, anything like that, any data source. And once you import it, just select the sheet that you want to import. You could import multiple worksheet, but for the meantime, just take one sheet for example. I don't need to load this. If you have to perform Power Query obstetricians, click on the transform option so that you can perform ETL extract transform load operations. So the Query Editor is a different window on itself. It is different from the Power BI dashboard. So you can switch between these two very easily. Okay, So this is the editor that allows you to edit everything. So you've got multiple columns and multiple rows. And here, just let start with a road deletions. So here we got first row with null values. It could be possible because we have an Excel file and just go to Home and select, Remove top rows, and just provide number of rows that you want to delete. So if you provide what, it will remove the first row from the top. If you provide 35, 10, it will remove 10 rows from the top in the same order. So in the similar way, you can also delete rows from bottom rows or from the end. And you can perform the same operations. So this is the simplest and basic operations that you can perform. Power Query will allow powerful editing options, both in Excel as well as Power BI. So if you're comfortable with MS. Excel, you can continue these operations near as well. If you are comfortable with Power BI, you could continue here. It provides a wide range of options, wide range of features that you can perform on your data set. So here we have deleted. Top row is our bottom rows. In the similar way, you can also perform various operations like column is played, replace column values and much more. So on the right-hand side you've got various applied E-step. So it will show you the history of steps that you perform. If you want to remove it, you can just click on the cross icon and it will remove step you perform. So here, if we remove the last step, it would be canceled out. So it perform action just like you undo anything. Okay, So you can do this. So as we undo, we can just perform the same wish to have one's more time. Just removed the rows. So if you by mistake perform certain operations that you don't want to reflect, you can just revert back to the previous state. So this is how your dashboard looks like. On the top side, you've got multiple tabs. The basic is Home tab by default, you can go to transform and add columns and various options as well. It got advanced functionalities. And you can generally perform any kind of power query operation with the help of few clicks. So it makes it very easily and intuitive for anyone is. Now just go to Split Column. You can find a split column here, just next to remove rows. And here just click on by delimiter. So if you want to split a single column into two or three different columns, you can do so. Say here it could be multiple delimiters based on what the columns are displayed. So here we got the column and we are, we got something like if you can provide a character, you can provide a space, tab, comma, anything. You can just do that thing. But here we are going to split with a delimiter hi event or dash. And we will split at each occurrence of the delimiter. So here we've got single occurrence of a delimiter and we can just remove it. So it has extracted some values from one column and cleared it to different columns. This is called a split operation. Similar kind of operations can be performed on databases. Using SQL queries. You can perform such operations using Python on big data. And here we are performing the operation on a dataset that is in Excel or CSV using Power Query. So if you want to revert back to the previous state, you can easily revert back by going to the applied steps and just click on the cross icon, it will just nullify. So previously we got two columns flip, and when we revert back three steps, we had and we got the same original column. You can explain why once more, or you can perform any number of operations multiple times. Say here. Say maybe if you delimited with a different character, it'll be better to revert back to the original state and perform. So once you are done with all the edges, you have to warm clothes and apply. So it will just save your performance and the operations that you perform in the Power Query. And it will apply in the dashboard. So here, after it has been applied, you can find these columns in the feels category. The main power, we add edge four and you can create an enum kind of chart, visualization chart for dashboard creation or report generation. So here are just create a simple table using two columns, the product ID and product ID number. We have split these two columns and we are using these two columns to show previously they were in a single column. And using certain operations, we have performed row deletion as well as columns. So just try to practice these things in Power Query and Power BI or axial. Keep learning and keep pulling ahead. 3. Replace Col Values: Hi, welcome back, print here. In this lesson you're going to learn about replacing various column values i in Power BI using Power Query. So let's start with this. Currently we are in the Power BI dashboard. And here we have to first get our data source for which we have to click on Get Data and we will fetch the data we are using Excel file. So just look into the file that you want to import and hit. Okay? So the select the table that you want to edit and just go to transform data. Otherwise you can click to load if you don't want to perform query editing. Okay? So here, what is the purpose of replacing various columns? Say if you have a spreadsheet that has a 50 thousand records and each record consists of a lot of information. So if you want to have to edit some of these data, say 1000 records, it would be very time taking approach to one way one performed these things. Little better to optimize this thing using Power Query. So it will automate everything based on certain logic. So power Query allows you to perform logical operations. So for that, we will go to the country column. And here we got two countries, United States of America and Canada. So we have to just right-click here and we will replace some of these values. Okay? So just right-click here, you will get multiple option to select Replace Values. And in the Replace values, you can replace anything with a different thing. So here we have United States of America and let us replace it with USE. So United States of America will be a big entity. And if you have a lot of columns, it will consume a lot of data on your memory. So if you want to optimize this thing, you can just replace it with USC and just place it here. So value in texts will be united States of America and replace with will be USE. So hit. Okay, and we can find here it has been replaced with USA very easily. All the data, relevant information, relevant values in our spreadsheet has been replaced pretty easily with a matter of a few clicks. So this is the power of Power Query. So if we want to perform this operation multiple times, you can do this. So here we got USE and we can replace it with United States. So earlier we have United States of America. Just omit the last word. And here we have just the United States. So USA will be a lot of short-form on here. We can easily do this thing. You can replace just anything. You can replace blue with red, red with blue. Anything, any number, any character, anything. Say if you have some values like 20 million. So if you want to replace million with multiple zeros, you can do it. Okay? So you can perform a similar kind of operations. So just try to imagine this thing. You will learn more about what you can do by practicing it. So just take a dataset, open the Power Query, Query Editor, and just start performing certain operations. You already know how to delete some rows from top or bottom. You also know how to perform column is split, splitting one column into multiple columns or joining them together. You also know how to replace various values in the column. And sustaining. Suggest. Give it a try, keep practicing, keep learning, and keep moving ahead. 4. Column MERGE: Hi, welcome back. In this lesson, you are going to learn about column merge in Power BI using Power Query. So let's start with this. So we have this dataset, we have earlier imported it, and now we are using the query editor to perform various operations. So here we are just going to merge two different columns together. Or earlier we have is split to one column into two different columns. And we want to merge them together. So just hold the Control key on your keyboard and just select the columns that you want too much. So here we want to merge these two columns, product ID and correct name go to transform. And here in the transform tab, you have to go to Merge Columns option. So just go to on which column options here you will get two options. One is separation. You can select separator as colon, comma equal is pace or any other character. You can use it as a separator if you want to combine two columns together. So these values will be indifferent. You can also provide name for a new column and just hit Okay once you're done. So here we've got a new column with the product name. And we've got different values combined together with a space, and we can use it for various purpose. So if you've got multiple columns, say multiple values, psi house, door, door number, Lynn number, area number, and these things. And if you want to combine them together into the address field, you can perform it. So there could be various scenarios where you have to perform certain operations on your data sheet. You want to split. You can combine multiple columns together. You can also replace the values. Say here, we can perform the replace operation by replacing colon with a space comma or any other delimiter. So if you want to split this column, the functionality will be seen. Just click the split option, and you've got various options you can split based on a delimiter. Okay? So we placed a delimiter when we are merging two columns together, she colon. And if you want to split them, again, we can use this same delimiter for separating. Don't forget to add delimiter, at least add some space, okay? There could be situations where you want to just and combine those two values together without any space. In that case, you can also delimit after a certain parameter. Little bit tedious. You may want to combine firstName and lastName as the name of a person. You can combine using the column merge operations. So it could be widely used in a lot of scenarios. You may have a lot of datasets. So just try to practice this column merge operation and Power BI. Keep learning, keep moving. You're learning more in the coming lessons. 5. Adding SUFFIX and PREFIX: Hi, welcome back, friend. Here in this lesson you're going to learn about adding suffix and prefix in Power BI using Power Query. So let's start with this. We have opened a query editor and we have this dataset we are, we are working upon. So in a particular column, if you want to add something before the existing data, you can put a prefix. Or if you want to add after that data, you can add suffix. So it is easy to add some things. So just first select the column. Here, I'm selecting total price. So price is numerical and just go to Transform tab and go to Format option. And in the format option you will find multiple options where you need to go to add prefix or suffix option. So first we will add a prefix. So we just move to add prefix option, just click it once and then you can add anything. So just like, right, You are €4. Or you can also write symbols like US dollar symbol, Indian rupees symbol, Japanese, yen, a different thing. So it will add a currency value to this dataset, okay? Later on you can also split it into multiple columns as well. If it is a quack. So if you require any suffix, like Doctor, mr, anything before a person's name, you can also use this functionality to add prefix. The same way, say select anything else like profit and put dollar sign before that. So just provide this dollar sign into the value option and hit Okay, it will add this as a prefix to your values. This will be useful in a lot of scenarios. Say if you want to add some visualization, you want to show some KPIs. It will show in dollars. But if you have to perform some mathematical operations like some count, countable, we will perform because it will count the number of rows. But if you try to perform some of values, these signs will confuse the calculator. Okay? So if you want to perform some mathematical operations, just don't add any character value into a numerical value. Are you, even if you want to add, you can create a separate column as well. It depends on you, okay? So here it is. We can similarly add prefix, sorry, suffix. So the process will be same. Just go to Format tab and you can put suffix. We can write Euros. You can write anything else. In the same way we can perform other operations like we can convert the text to lowercase, uppercase capitalize letters performed cream of lean operations and much more. So just try to create this thing. Move to a dataset, go to various values, try to add some suffix or prefix in your columns and you will be able to modify this thing. So one by one, you are learning various functionalities in the query editor where you can perform various operations. It may look very easy to do things in earlier level. But if you know a lot of these things, it will be very handy. You will have to work upon. Some serious projects are big projects, okay, so this will be solved. Sometimes we will only start with a simple, basic problems. So Query Editor has a great potential to solve a lot of problem. Try to create a listing on your own. Keep learning and keep moving ahead. 6. UPPERCASE and Lowercase: Hi, we'll come back print here in this lesson, you are going to learn about converting some column data into uppercase or lowercase characters. So let's start with this. So we have this dataset and we are, we have opened the Query Editor and just select a particular column. C is states. It will have a state's information name of the state. And just move to Format option in transform and just hit capitalized. So it will convert everything into capital letters or uppercase. Okay? So here we got the name office States. And right now choose a different column C, product category. It or you can say moist, it has a different information. Here is a combination of the small or capital letter. When we click lowercase, everything will be converted to lowercase. So the things that are already in lowercase there will be intact. They will be lowercase. And things that are in uppercase will be converted to lowercase. Digits or numbers or special characters are untouched. In the same way when we convert anything to uppercase, lowercase things will convert it to uppercase digits. Special characters will be intact. Okay, So it depends, say here we got united states that all it's rooted in a combination of lowercase and uppercase. And we can convert it to either of one. So there are various options that we get into the format option in transform tab, and we can make use of these operations. So it will be useful if you want some data to be in a particular formatting options. It will not change the values here, but it will show you how they are appearing. Say if you have something, if you want to highlight something, you want to convert it to aggravations or short forms. You can convert it to uppercase if you want to convert it to lowercase, like email address. So for example, people generally type e-mail IDs in a combination of capital or small. You want to convert it to a small case. It will be useful. Try to create this thing on your own, keep learning and keep growing. 7. Add and Transform Column Extract function: Hi, welcome back friend here in this lesson you're going to learn about extract function in Power BI using Power Query. Here, you will be going to learn about transforming a particular column. Or you can also create a different column using the Extract functionality. So let's start with transform column. So just go to the transform tab next to the Home tab. And here we got a simple spreadsheet. We're going to select a column that is indeed column. And here we can extract some information. As you can see here, the date is in format of date, month, and year, separated by delimiter. And here we can find the abstract function to extract some of the components of distinct, say, just select, Extract first character. So you will be able to extract some characters. So just select two or writing to, you will be able to extract first two characters. So these are the dates. So we have successfully created an extract function for the next weekend, extract month or year if you want, say if you select, Extract last characters and provide for, it will extract the year on a particular column. And if you want to select a month, it is in-between these two delimiters. You can do like selecting a range. Just go to extract and select a range option. Within the range option, you have to provide the starting index and the number of characters. So we starting index would be three, because our third character is a delimiter. After that, it will start. And then we can find a number of characters as two. So it will extract the month data. In some countries, month is written before date, like us. So you can just perform your operations accordingly. Okay, So now let's move to add tab. Here you can add a column. You got the same extract functionality. What it will differ with transform in the sense that when you apply any functionality like extract functionality using transform, it will modify the existing column. But if you don't want to modify existing column, you can go to Add Column, perform the same steps as mentioned. So it will create a separate column. So here we have created a column for date, you or month. You can create a column for year. So just getting to extract option in the Add Column tab. And here just go to extract functionality. And here you can extract the year providing with the last date. Just select a date column. The column that you want to perform, the last character provided for in the count value and hit. Okay. So it will extract the ear. Just rename this column to make it here. And here we have extracted month and year information from the Date column. So we have created two different columns. You can also calculate H. You based on the difference of two columns. You can perform the time difference and it will be useful. So you can also extract some of the information from these things. So you can extract last character, first character, a character in-between a text. Say for example, if we have an e-mail ID, you can extract the domain name like alpha, beta, gamma at dairy something.com. So you can extract or something.com. So it will be a domain, or you can identify the person's name using a delimiter. So it will be easy for you if you've got some pattern, okay, if there is no, Not any pattern, you have to use a different function based on a delimiter. You can extract some information. So just try to perform this ad and transform column option and we extract functionality in Power BI or keep learning and keep going. 8. Extract text based on delimiter: Hi, welcome back friend. Here in this lesson you're going to learn about extracting text based on a delimiter. So here we got our dataset. We've got a column called date, and we can extract some information based on delimiter. So here we got data information. Just move to the Add Column tab and just select extract. And here you got three different options. You can either extract picked before, delimiter, after, or in-between. So if you want to extract some time before, just provide a delimiter. Here we have our delimiter that is hyphen or dash. It is between the month and date and year. So here we've got two hyphens and we can extract something before or after it out. So if you want to extract date or year, this way, you can easily lead to you can also go to the advanced option for extracting texts after a delimiter. And you got two options. You can scan from the n or is time. So if you scan from the start, it will count with the first character and move to the last. One is the delimiter is found and it will perform operations just after the delimited. You can also specify the number of delimiters to escape. Say if you're delimiter occur multiple times, you can escape a few of them. You can provide how many delimiters you want to escape. Say if you want to extract ear from the starting of the deque, we have to escape when delimiter between date and month. We will count the second eliminated. In the same way. If you want to extract information between two delimiters, we have to define the starting delimiter. So here we've got hyphens on the two sides. So we'll be providing the hyphen. And if you want to use the advanced options, like you scanning from the end or beginning, or the number of delimiters you want to skip. You can provide that in also. So it will be helpful if you want to extract minute sticking out from the time stamp. Or if you want to extract be the month or year. For me. You can also extract some information in different way, like fax number and PIN code addresses multiple things. It will be used for various purpose. You can also use comma has any special character in the delimiter. Also any numbers or characters. Like if you have a pan number, 10 number, tax number or anything like that, it can be characterized using alphanumeric values as well. So just try to perform this text extraction using delimiter in Power BI, keep learning, keep moving ahead. 9. Adding Conditional and Index column: Hi, welcome back friend. Here in this lesson you're going to learn about adding index and conditional columns in Power BI. So let us first get our dataset. Here is our dataset in the Power Query Editor. And let's start creating an index column. For that, you just need to move to Add Column tab just next to transform tab. And here you can just go to create an index column. So what is an index column in this column? Just generate the numbers based on the starting or the end range. Okay, so here we got 20 rows. So it will generate a column ranging a value from one to two. You can select a range from 0 to start or when to start. Or you can create a custom range. So here we, when we created a, a general range starting from one to 20 here we got 20 rows. So this, it created one to 20 values. If you want to create a custom index column, you can provide a starting value, starting index or an increment operator. Say if you start with 101 and you can have an increment of ten, it will generate these numbers based on the gap. Okay? So there could be different scenarios where this kind of index column would be useful. Not just a basic count, but you can create a custom range, okay? So another thing is creating a conditional column. So you can find this option just next to the index column and Conditional column. We generate values. It will be numeric or alphabetic based on another column information. Okay? So if you're familiar with programming languages, all the SQL, you might already know the if and else clause. Otherwise, even if you're not familiar with these terms, it will do very easy. Just take it as a simple English language in tech, you have to first provide the name of a new column. You have to define which column you have to take. So just read it. If product and the name of the column equals to milk. So if you find the milk keyword in the product column, it will generate ES has an output. It will be stored. So here we create a new column which consists of yes and no. Yes will be provided only for the information we find milk as a taste. You can also would add some conditional like if you find a number or anything else. So just do it once more time. So let us create the expense product, expensive product information. And let take a profit here. Okay? So based on the profit value, we can identify whether the product was expensive or a mid-range or a cheap product. So we can use the same thing. You have to first provide the name of the column here, just take price and then choose the operator. Here we take equals. You can also take is greater than. If it is greater than, you can provide a number that is a value, say 45. If the price is greater than 45, it will output as expensive. It will create this output, and this output will be stored in the new column. And you can also define the else clause. You can also choose a different column value, okay? You can take any value from the column as well. But here, just write something that we want to write. So just provide a value. And you can also add some more class, like if you want to perform this kind of conditions, condition checking one more time, you can add an if else clause. It will follow the same conditions as this. Okay? So here, just add this, add a new clause here, and take the price as the same column for checking. This time, just make the operator as less than or equals to. So we will check less than or equals to 45. It will generate a mid-range. Okay? So earlier it was expensive and now it has merged range. You can also specify one more condition or you can just leave it for the moment. You can define multiple if-else clauses here to check various conditions. Okay, this is a simple if else later, it will generate a later if her family or with their programming language. This is one of the easiest thing that you can do without writing a single quote. If you understand the logic, just try to understand this logic, how it works, how it created. It is very powerful. So you can generate a some operations without reading a piece of code. So you can perform various operations here. You can identify whether your product is expensive or mutterings. Just based on the price value. You define a condition and it generated a value. Based on this data, you can create amazing charts with the Power BI as well. For data visualization. Otherwise, for now, just focus on creating new columns on different conditional columns. The condition columns is very powerful to say. I'll just do it once more time. Here we got, we take the date column. Okay? So if date, yeah, just take the operator value is like is after or before. So you can take a particular date. It will check row-wise. So in each row it will generate a different output, okay? So it will check whether a date is before or after. Let us take before providing any data, say one July 2021, and generate the output as this quarter. Okay? So if a date falls after one July 2021, it will generate this quarter. Otherwise, if it is before one July, it will make it as earlier times. Again. You can define any of the conditions. It can be used for different things. Say if you have the sales data, if you have anything informative, you can define whether it is, it falls within this decade, a particular timeframe. So your timeframe need not to just start with first January for a year. You're going to start anywhere, say, for financial calculation. They start with first off April and May 31st, March. You can define this kind of danger as well. This year are sometimes so financial year may differ. So just try to create this conditional columns and various index columns in Power BI. Try to create this thing on Iran. Keep learning, keep moving ahead. 10. Date functions in Power Query: Hi, welcome back friend here in this lesson we're going to learn about date function in Power BI using Power Query. So let's start with this. We've got a dataset and let's choose the date column. And we can slide it any, we're just move it towards the last side. And because we are going to create multiple columns, we want a date column to be visible. Just move to add columns. Move to the functional teeth on here, you've got multiple functions for the date. You can generate, find the year. Based on the D. You can find month, various parameters. Month of the year, number of months. You can find a quarter of the year. Say it would be it would be four quarters. First-quarter is again current or fourth quarter, you can generate you. So here we have generated a quarter of the year. So if you require that information, you can easily extract. You don't need to perform any calculation afterwards when you're creating visualizations. You're going to also find more information like week of the year. It could create which week of the year, if that is okay, the week number. So in a year we got 52 weeks. So we can find the weeks of the year. You can find the date of the year. Okay. So there are 365 or 366 days based on the leap or normal year. You can find which number of days. It is rarely used, but it could be used in various functionalities. Say you can find a 50% of the year is passed or 40 percent of the year has passed. You can generate such calculations based on this information. You can also find day of the week. You can find age. If you've got a range of the year, say, if you've got a bolt here or a date year, or the current year, you can find the age of a person. So in that case, we've got two columns for data elements. Currently we are using one column, so we cannot calculate it. It will give some obvious values we don't need. So just to stick two different functionalities that we can perform. You can find the other one. You can find weekdays. We can any information. So did functional allow you to generate multiple data based on the date element in Power BI. If you've got time element, you can also generate various time element based on a seconds, minutes, hours. You can calculate a very rich functionalities. It makes calculation very easily and it would be helpful in visualization phase. So just try to create various columns using the date functionalities in Power Query. Keep learning hanky, moving ahead. 11. Date and Time function age and more: Hi, welcome back brand here in this lesson you're going to learn about date and time function in Power BI. So let's start with this. So you have to open the query editor and here we have the sample dataset. And here we got a column that consists of both information, date as well as column. So it will be a little bit trickier if you want to separate date and time into two different columns from this combined dataset. So how you can do this? Just go to Add column option. And here you got on the right-hand side, date and column function sterile, barely 30 functions here. So just go to say time and just select time only. So it will extract time information and store it into a new column. In the same way you can extract date only using date only function. So running these two functions, we can extract the row and column individually. Moreover, you can also extract various other information. You can calculate age of a person. So if you just assume this is our date of birth of a person, and you, when you click on date, age, it will calculate a duration in number of days. So just go to date and just convert it into total years based on the duration. And it will convert into the duration. See, here we've got this data on. When we go to the Duration option just next to time, we can find various options and just select our total years. It will convert this from a number of days to the ER. So currently everything is between decimal place because the data is very recent. So if you imagine if you have a date of birth, you can calculate the age or the time gap. For sake. You can also convert this into total number of seconds that can be used for grading a timestamp or various other reasons. You can convert it into different functions as well. Moreover, you can also perform various operations such as division and multiplication. So if you want to divide it within any given number, say if you want to divide it by 365 number of days in a year, we generally divide by the number of days in a year. But if you have a leap year, you can customize this light 366 days and so on. It can be used with various purposes, a number of months in a different way if you want to use it. So just make use of date and time functions to calculate H, extract date and time and various other functionalities. Keep learning and keep moving ahead. We're learning more in the coming lessons. 12. Time function: Hi, welcome back friend here in this session you're going to learn about various time function in Power BI. So let's start with this. So here we got some dataset open in the Query Editor. And not just move to add column and move to time. Here you've got various options. So first, convert it into our format. Just go to time hour. And now you can extract our formula time permission. So time half, hour, minutes and ticking information. If you want to extract just only our minute or second, you can extract them separately into a different column. You can also find it starting off an hour, say if it is 1030, it will extract 10 fold a starting up the hour. If you want to select end of the hour, it will round off to 11. Okay? And the same way, you can also calculate seconds as well as minutes from the data, the time data. You could also perform various other operations. Say, if you want to subtract two different time and find a difference between time, how time has been patched. Say if you wanted to, if you have an employee data where you got to the chicken information and a checkout login information and a lot about information when your customer or your employee login into the system and logged out. So you can find a working hour or rocking duration. So you can easily create distinct, say, here. We just duplicate the same data. Just for demonstration. If you have these two different data, just imagine. First one is logging data and the second one is love our data. You could just simply find a difference between just go to time and subtract. So it will subtract and find a difference between if it is a negative just sound of it, it will be there. So you can actually find a working difference. Here. We duplicated the same column. That's why we find a d 0 value. If you have a different value, it will show you the difference between those two values. Say a person login the system at nine AM and went out at 05:00 PM. So it will show you five plus three. That equals to eight hours. So it working hours as area. You can calculate various things using a time method, keep learning and keep growing. 13. query editor options: Hey, we'll come back print here in this lesson, you are going to learn about various Query Editor options in Power BI. So let's start with this. So once you open a Power BI app, you have editorial content and just applied, saved and applied it into the Power Query Editor. Now Power Query Editor is gone. So how you can reopen the power query editor? It is simple. Just select any of the field. Either in the Data Visualization mode or data editing mode. You have to just select a particular field. It is visible on the right-hand side. Just select the sheet, right-click on it, and just choose the option for edit query. It will open the dataset or the spreadsheet in the Query Editor. So here you can continue with performing various Query Editor functionalities. Next is how you can enable or disable the formula bar. So Formula bar is used by some of these professionals. If you require it, you can enable it or turn it off. Because mostly we require a matter of few clicks. So generally we don't require the formula, but if you require it, you can enable it. So next, you've got multiple options in the View tab. You've got the simple column quantity option. So it will show you the quality of the column, not quantity. Quality, waste on three parameters, valid error and empty information. So if you have null values, it will show you in the empty. Or if there is some error in the formatting, say in the date column you've got say, time value and a particular cell, it will show you error. If it is valid, 100 percent valid, you can go with this. Okay? So if the quality of a column is not good, you can just easily and quickly check it. In the same way. You can also check column distribution and column profile. It will show you the value distribution in their particular age. It will just summarize everything. You don't need to create a final visual addition to get the insights. If you have the raw data, you can quickly gathered this information and decide your next steps. Okay? So if you have the median falling over a particular range, you can remove the outliers and complex things and make, you can make critical decisions before starting anything else. So don't worry, it will be very simple to you. Most other people don't make use of these things. Until you have a very complex set of data and you need to simplify things before. You can also go to Advanced Editor. It will show you the Advanced Editor window where you can just type the query if you want to write, okay? In case you want to write all you can check. These are generally auto-generated based on the actions, all the clicks you perform in the Query Editor. So don't worry about distinct. It will be generally correct. Even if you want to edit, you can edit it in various ways. So these are the basic query editor profiles that you can make changes in the settings on. Just make use of some of them if you require, I'd rather just use a query return functions to make your data in the format that you want. So just try to use these things. Keep learning and keep moving ahead. 14. Sorting: Hi, welcome back friend. Here in this lesson, you're going to learn sorting your data based on ascending or descending order. So let's start with this. So we have our dataset and we can sort generally any column in Power BI. So if your column is alphabetic, just move to Home, go to sort. And you can sort from a to Z or Z to a. So if you sort from alphabetic order, starting with a, you have to just click on this option, the a2. It is routing or otherwise, if you want to sort in reverse order starting with z, you can also select this thing. The same option is used for numerical values, sorting numerical values of all kinds. Be it simple numbers, decimal numbers, whole number and ink. You can sort the same thing. It will work as an increasing or decreasing order. A to Z will act unlike an increasing order. Or Z to a will work as a decreasing order. Okay? So just select a particular column if you want to sort, you can sort date starting with first January. Or if you have multiple years, it will start with the year. It will give first preference to year, second refresh to month, and current reference to a particular day in the month. So if you can sort data in this format, you can also sort time. Emp am format is also deer. Or you can start with an hourly basis. It depends on what amount of thing you thought you got to use, just simply these two options. It will do the work. So you either have increasing order or decreasing order. On some fetus. It won't work if it is a special character, say, but especially characters also have a precedence order. But generally, you can change it. Our customize it as per your requirement. So here just select a time stamp based on date and time. So when we have a combination of data in time, it will arrange with data because data is given a higher priority over time. So this is how you can sort these things. Just use these two options. So if you want your data to look arranged without going into each and every column or row, you can just simply make use of these two options. It will not just sort particular column. It will say sort entire spreadsheet, okay? But it will be sorting based on a particular column. So if you sort a particular data based on the product in the alphabetic order. So everything would be arranged in the alphabetic order. Say bread will appear before then milk, okay, because B appears before m and everything else would be sorted in this way. So try to create an O sorting function in Power BI. Keep learning and keep moving. 15. bar chart: Hi friends, welcome to this lesson where you are going to learn about are creating a bar chart in Power BI. So let's start with this. We're going to just create a simple bar chart. And for that, we need a dataset. So just go to Get Data and choose a format that you have here I'm using an Excel file. So importing this Excel file for grocery price dataset, just select this table and hit the load option. If you want to load. Otherwise you can go to Transform for changing the values of the columns and so on. You're learning this thing in the later exercises. Here. We got this dataset and here we've got three different columns. And we can go to the visualizations to create a chart. So when the first row, we got all the tag is tagged, clustered bar charts and column charts. So just select anyone here. I'm just using a stacked bar chart and just move the values to different values, say date to access, more product column to legion, and then price two values. So here we've got three data points. So this just create a simple bar chart here we've got four different bars. And we can change it to represent based on a different timeframe. Say on an annual basis, we got one chart, on a quarterly basis, we've got two yards. And for the monthly basis, if you want to represent, you can get testing. And there are four different bars and different colors. And the light blue, the dark blue, orange, and the violet, each representing a different category of values are the regions. You can go to the Format tab to change a few values. Say if you want to change the value of lesion, you can increase the size of Legion simply to make them visible to your audience. So here we've got four different prereqs, red, grapes, milk, and orange juice. So we got the price data of these for food prereqs distributed over a month. And we can see the price change of each, correct? It is simple bar chart. So we can simply note here the price of the milk has been increasing over the past few months. And the price of oranges have gradually drop. So the oranges are cheaper in the later months and a milk is more expensive. So we can decide more things. Here. We can change the title as well. Just write the price variation over time. You can edit your title, you can change the values. You've got multiple formatting options. Just go through them. If you want to change the color of individual bars and you can change, say, if you want to change the color of bread to lighter tone of blue or see any different color, yellow, green, purple, anything like that. You can change the color of gray, anything, and you get a C gene. So color of pink and change the color of orange is still yellow. You can change anything. Just make oranges and the orange color. And change the color of the graves to a different color. You can change the value of individual colors. You can change the size for the text. You can see the data colors. You can change different formatting options. You've got, you've got x axis, y axis, with the different charts, you've got different kinds of formatting options. Say a pie chart may have a different formatting options, then a bar chart and so on. You are a variety of charts. And bar chart would be the first thing that you should write to query is simply represent four different categories and a price change or the time frame. So bar chart is very useful. So this is how you can create a simple bar chart. If we have our data in different sources, say web data you can use. You're learning this thing in the coming lessons. You will also learn about importing other datasets. You can use a database, you can use the CSV files, you can use a library data from the web that I mentioned. You will be learning to create different charts, pie charts, tables, treemaps, funnel chart, a scatter plot, and other chunks. So this is how you create a simple bar chart. Just try to create a finger on Iran. Keep learning and keep moving. 16. Line chart: Hi, welcome back friend. Here in this lesson you're going to learn about creating a line chart in Power BI. So let's start with this. Let us first create a line chart using visualization tab. And just use the columns on the sheets. Here I'm just dropping date column to the axis, product to legions, and price to value. So price are the measures that are in numerical values. You can find a sigma sign between that column and you can drop it into the value stamp. And here we got this simple line chart. We can go to the Format tab and go to some other options like data labels. And we can change the size of this line. You can change the size of the text. We can perform multiple edits. Like we can go to the shapes and just change it from the solid line, two dashed line, or a dotted line. So if you want a dashed line, you can use it to change the shape of your line. Or you can use the dotted line to change the value of your dotted line. Otherwise, just keep it solid if you want a straight line without any spaces or dots, okay? So sometimes you want a projection, you may have a different columns and you want a different kind of projection. You want a dotted line as well as a solid line. You can use it. You can change the stroke width. You can either increase it to have a solid stroke, you can decrease it. You can't change other values. You can make it tip. So when there is a change of value, your line chart will form as I step. Otherwise it will be constant parallel to x-axis. But line charts are generally find slopes. So if you want to have is sloped line chart, you can set a default and turned I step off of. Otherwise, if you want to show in a step manner, it will be a new kind of visualization for some people who just don't want a simple line chart, they want to add some misstep. Maybe you may want to add a bar chart or some more insights. You can just use a strip chart. You can change the title, you can change or other texts, the colors, the legions, and anything is just say, increase the size of the title. Because by default it is quite small. You can make it large. Edit the title, you can write your own text. You can just do any other areas. So this is how you can create a line chart. I just go to the Format tab for multiple options. Say, here we've got a stepped option and different shapes that we can identify. This option was not available in the bar chart or a pie chart because bar chart cannot have a stepped bar, okay? Each individual bar is a different thing. And a step functionality is only available in the line charts. So in the format tab of different charts, you will have different options. Say, here it is. You have to practice and by creating multiple charts or different kind on just refer to different formatting options. You will learn how to format each individual charts. And this is how you can read a chart. When you just hover around to the chart with the tool tip, if you want to add a tooltip, we can also drop something to retool trip. Otherwise, it will just generate a default values. On. Here you see this thing. And this is how you create a simple line chart. For some data. You don't want to represent it with a bar charts. You can represent it with a line chart or a different chart. So here we have just taken the same dataset, created one bar chart, hand-drawn lines. And now you can decide which one to use for a visualization and assumption Aereo's botch, I do regret here sometimes lunch I do better. You can use a combination of more charts or create your own custom charts in Power BI. Just keep learning and keep moving ahead. 17. Donut Ring chart: Hey, welcome back friend here in this lesson you're going to learn about creating a doughnut chart or a ring chart in Power BI. So let's start with this. So we have just created a pie chart in the previous lesson. And here, in order to create a new chart, you can either create a new worksheet or you can just create multiple charts on the same worksheet. So here I'm just reading the string shot over the top of this pie chart next to it. Just reposition it. And once you are done, you can just draw up the values into legions and values. The alphabetic values of the categorical data can go to the region. It'll be categorized, or the numerical value of the measures can go into the values. So I'm using order quantity in values and put him into the Legion. And you can just go to the Format tab after you have provided values. So here just formatted little bit. We can change the size of the text. We can just go to the Data Labels and go to Type and choose this to represent with a category and percentage form. We don't want the amount to display in this ring chart. You can either turn the allegiant on and off. Here, you can use all kind of categorical data. But to make these two chart synchronized together, the pie and the ring, we just want to have these two information, name of the category and person teach as these two charts or related, because on the pie chart, we have use the product category. And here in the ring chart we are using subcategory. So food is a category that can have a different subcategory, fruits, vegetables, milk, and other things. And then similarly, we can have categorical and some categorical segregation. So these two charts are related. Now sometimes if you want to represent our story are some things, some data. And we may require one or more charges together to create a dashboard. So in order to show the established order, order quantity by category, we have the pie chart. And if you want to deep dive into what particular kind of food or the subcategory of food high the maximum order quantity. You can refer to the ring chart. Otherwise, in order to have an abstract view, you can have this pie chart or with a detailed view, you can have the ring chart. So this is how we can go with this. In the pie chart, we have put the data inside the label values. And now we in the ring chart, we put them outside. You can change the colors, all the data values, and align it with a pie chart. With a pie chart we had a food. So food category in the yellow, orange color zone. And in the subcategory, you can have a similar tone. In order to make these two charts look similar or identical. Otherwise, you can use a different color combination because it will have fewer entities. While the subcategories may have multiple entities or the larger number of entities. So the colors should be distinguishable. So in the previous lessons, we line chart, bar chart, or the simple bot pie chart. You learned how to create these individual charts. And here, you're not just learning how to create a ring chart, you are learning how to combine it to present a story, make it aligned. So in this way, we can have two different charts to have a specific detail about the information. You can apply more features that are called with Power BI, suggest, drill down. You can add filters later on, you're learning everything. In this course on heritage. It is simple. Just give it a try. Take a dataset and start creating different charts. Try to link them. Here I've just taught you how to create two different chart, the pie chart and the ring chart. In a similar way, you can also represent the subcategories with a bar chart. Or you can also show with a line chart. But I believe if you have fewer NTDs, it is better to use line charts. Otherwise, if you've got a lot of data, a lot of subcategories here. Say if you have 20 subcategories, understanding 20 lines on the body on the line chart would be difficult. So it is better to use a ring chart. When if you have a bar chart, it would be difficult to how are different allegiant. Allegiant, but you still can represent, you can use a different kind of chart. You can turned a dark mode on and off. You can use a different mode if you want to convey. Just create a combination of different charts. So this is how you create a ring chart and combine it with a dashboard. Listing on Arrow. Keep learning and keep practicing. 18. Treemap- Inter Related charts in a dashboard: Hello, welcome back friend. Here in this lesson you're going to learn about putting a treemap chart in Microsoft Power BI. So let's start with this. Earlier, we have created a pie chart and a ring chart, four categories and subcategories. And this time we're just going to procreate a TreeMap Jack. So treemap chart as simple chart, which consists of rectangular. These are represented with rectangles. And the area for a particular category will show you the weightage. So just realign here. I'm just putting country column into the group and order quantity into the values. So here we got two countries for our dataset, the United States and Canada. So these are represented. So as we can see here, simply that maximum orders were placed from international exchange and from, from Canada. We have somewhat less number of orders. But if you want to realign these P3 charts together or create a relationship between the three charts together. No need to worry. These are automatically created. If you create a single worksheet and create multiple charts using the similar dataset. It will be interlinked. So if you click on a particular category, everything will be reflected. So first of all, just go to the Format tab here. You can do a few formatting. You can increase the size as we have done with other charged. Now, you may find some other formatting options. Here. I'll just change the display unit, perform thousands to auto or anything. If you want to change the display unit of numbers, you can change. You can change the color. You can change the label, our data labels, you can turn the legend on and off. So let's change the color. Here. United States is represented with blue color amp current rate. So just place them around. You can always readjust the charts as you like. So you can just make this ring chart little bit smaller and just increase the size of our treemap chart. So it aligns perfectly with here we got three different charts. So when we click on a particular option in any of the chart, say in a Treemap, you click United States option, all other charts, pie chart and the chart will be updated. So once we click United States, as you can see, other charts or shrink, this is a powerful animation in Power BI. So we can find in the United States, maximum orders workplace for the food category, less proportion for medicines, mode provision for cosmetics, 100 percent of the cosmetics order to replace from registers. So we can see that no cosmetic order was placed from Canada. And when you click our Canada, you can find Kennedy's tend to order more medicines then food. So this is how you can write insects. So once you click on a particular category, you can either just select food and find which country have the maximum proportion of food technology. Say just click on any of the category or subcategory. You can click anywhere on the chart to exit or say just selected Canada. Here we find that Kennedy US order less fruit as compared to dairy predict and vegetables. And a rentier states, maximum number of food, fruits were ordered from us. So in a similar way, this is a dummy data. This is not a real data, so don't worry, this is not a difference between an attitude States and Canada. We are taking a subset of few pupils. And this is a dummy data for practice. Okay? So just select a particular category here we select, say, fruits. So you can see United States have maximum order for fruits when compared to Canada. When you select medicines, Kennedy's tend to buy more medicines datasets. This is how you drive the business insights. So you have multiple dataset. You just don't need to read the tables and go through everything. You can create multiple charts in an attractive manner. So this is how it looks. Very attractive. Power BI makes it feel very attractive, okay, when you present this thing in confidence or the third teams are collaborators. They will feel amazed with this visual. So just use these charts and create amazing visuals. Just create a TreeMap. Interact with other charts, such as PI earring, line charts. So keep learning and keep moving ahead. 19. Table and Matrix: Hi, we'll come back print here in this lesson you're going to learn about creating tables and matrices in Power BI. So let's start with. The tables are generally represent data in two-dimensional values. And you can only add column in two tables of power BI. So as you can see here, you've got only one option values. You can add any column to the values. We eat, a measure or dimension, alphabetic or numerical value. You can use it. So here, just let me drop producted agree here. You can add more columns into this table anytime we want. So we got formatting options. We can change the size and appearance. You can change their style to contract alternative rows or have anything else. Later on we can just add more columns such as order, quantity and profit. And after we have created this table, you can also create mattress. Both are different. Sometimes you can use either of them or you can combine both of them. But let me just tell you a difference between table and matches. Table is simple 2D representation of data where you can only add column in Power BI. And mattresses are similar to pivot table in Excel. If you have used Microsoft Excel previously, you may have used pivot table. And in the pivot table, we got the two-dimensions, but we can add rows, columns as well as values. And it can be used for drill down. And you can apply drill-down functionality very easily into matrices. Okay? Here you get the option to add rows, columns, and values. And we can classify. So in the table we got only values and no matches. We got row, column and values. And it helps you access deeper levels of hierarchy for our dataset visually. And you can examine something in further details. And a drill-down allows you to do that. So this is a simple table. Now, let us just create mattress. We can change the size and just keep it on the left-hand side. For reference. Here we got predicating the order, quantity and profit. We have turned title bar off. So simply we can see these useful information next and just click on creating a matrix. It is next to that table. And you can see here, we've got three options for rows, columns, and values. Let us draw a few columns from the table or dataset, sorry. We have the states and just put it into the rows, particular degree into the column, and values into profit. One thing. You can note here that you may put multiple measure or dimension into either of these things. So as in tables, so we have added three columns into the values. You can add multiple values in rows, multiple values in columns, as well as multiple values. And here it is. And we had just added profit two values. And you can see you've got one column for profit. Later on, we can add some more information like total price. You can find it a few of the cell, our empty. So we don't have any corresponding data for that particular cell. Maybe that is the case. But you're going to add more columns share with you. So just try to add total price into the values. And here you can see the total price as well as profit is added to each column. So here we've got column and you've got SAP column. The matrix is that everyone's computation. Sometimes you have multidimensional data. You can use mattress, could represent. Just as a mathematical matrix, you get damage and four dimension or a higher dimension computation. You can represent using mattress where something table, simple table cannot fit a lot of data. Just go to the Formatting tab to format a mattress as you call my other chart, you can have minimal styling contrast. Alternative routes is parsing or flashing rows. Let us choose a flashing the rows. Now it looks visually good. So here on the left-hand side we've got a small table, and on the right-hand side we've got a detail matches. Sometimes there could be information that cannot be simply represented with a PI or the bar chart or any other chart. You may require matters. Is it still it can allow you to drill down or drivel up and add predictors and advanced functionalities, you can add a slicer Center thing is that we will be learning in the coming lessons. So here you learned how to create a table and a matches in Power BI. This can allow you to represent data. Try to create your own mattress, multi-dimensional mattress, or a simple two-dimensional table. Try to add different values, measures or dimensions into multiple columns, rows and values. You can use a dataset and just try to create this thing. Keep learning and keep moving ahead. 20. Drill Down Table and Matrix: In the previous lesson, we have just created a table and a matrix. And now in this lesson you're going to learn about implementing this drill-down functionality on table add metrics. Let's talk. So what is drill-down? Simply drill-down allows you to access deeper level of hierarchies of a dataset visually. So when we click on a particular row or column, so your technology in table, it will update the mattress only further technology in the similar way, if V-Q, say California from the mattress, it will only highlight the relevant information in table. So if you want to dive deeper into a particular category or entity, a particular thing, and get a relevant information for that thing. We can find out. Just keep it simple. See the product category, order, quantity, or profit details are shown in the table. And by default it shows the entire state or from the United States and Canada, all the profit combined together in the food category, cosmetics am and he sings technology. But if we select a particular state, it will only update information for the artistic. Same way. If we choose a particular category, it will update only particular category information in the mattress. Here let us create one more chart. This time, a column, a pie chart, and just put a country into the Legion and order quantity into the values. So here we find a simple pie chart and we can also format it further, but let it be. We are focused on drilling down. So here we've got three charts. These are connected to each other with each other because we have the similar dataset. And when we click say select Canada, it will show you the information only for the Canada. Both tables and matrices are updated and will show you only the information for the Canada. So this is a drill-down technique based on multiple charts, safe use electric food. It will only show the food data for US and Canada. So in the pie chart, you find relevant information highlighted on the right-hand side in the mattress, you find that particular information is violated. So if you want to focus on target, on the particular category, say geographical location, or a particular category, product category. And you can just focus on that product line. Okay? So you may update with a really went information. Say if you are making a presentation with your peers, colleagues, you have a data, sales data for different states. And so somebody popped up and said, let me focus just on California into the technology sector. So you can select just hold Control click, Control key on the keyboard and the single click. Or you can select multiple columns or categories with the mouse and control. So this way, you can focus on a combination of things, okay? It will show you the relevant information. It makes life very easy and drill down is really useful. Rather than creating different tables are different charts for a particular solution. It may take time, but here you drill down in real time without requiring to create separate charts. And you can just focus on things. Dark matter, try to create listing on Iran. Keep learning and keep moving ahead. 21. Slicer filter: Hi, welcome back friend here in this lesson you're going to learn about getting a slicer and Power BI. So what is a slicer? Is? Slicer is a kind of filter that provides you multiple options in form of check boxes where you can select a particular option. And that will be used as a filter criteria to modify your visualization. Here we have the simple visualization of this table. And here we can create a slicer on. We can drop any field here. So just drop the country into the field or you can add States. So when we have the country, we got checkbox with country name. And when we expand it, we also get dish taste name. So these two columns are related to when we select a particular checkbox, say a particular state in Canada, like Ontario, Alberta, it will show you information regarding to data states Only. You can also select multiple columns, are multiple checkboxes by holding the Control key on the keyboard and just single click on a particular column. You can also select States from other options, like in the US, you can select New York, new macrocycle, a dao, Illinois, different states. You can use this kind of a slicer to create a filter to show you information relevant to that particular state. Say currently you have the sales data for all over the country, in the US and Canada. But when you want a specific data for a particular state, you can create a slicer rather than creating multiple charts. It is sometimes useful to create a slicer. So it is a basic kind of filter. You can create a slicer 4D time. You're learning that in next lesson. But you can also create a slicer 40 variety of thing. Say, if you have, we're just kind of categories and subcategories. It can be used to dive into deeper. Say, if you have a category like technology, food, different kidding reason the continent, you can just divide. So there's no limit to the imagination and you can use a lot of things. You can also create various filters. Advanced filters were to eat useful. I slicer would work in a lot of scenario. You can format these slicers just like you format the general charts and visualizations and just go to the Formula tab and you will get multiple options. You can play with. That. This is how you create a simple slicer. You can also select multiple fields, multiple columns. You can also create multiple slicers on the same chart. So whenever you create a slicer with the same dataset, which is used to create a visualization in a Power BI dashboard. Is slicer get automatically linked to your chart. So you can use a slicer with multiple chars or a combination of charts as well. We tried to create your own is slicer and used it with various charts and Power BI, Dylan, keep learning and keep moving ahead. 22. Date Slicer: Hi, welcome back friend. Here in this lesson you are going to learn about creating a date slicer in Power BI. In the previous lesson, you learned to create a basic is slicer. And here, just moving forward at any slicer, you can use this as lysate as a filter. So let us first create a slicer. Just go to the visualization and in the lower row, you've got this option. Just click it. So here are we dropping order date into the field. So when we drop order date, it will fetch year, month, end date data, and it will show you I slicer is leader by default. So here you've got two points, the starting date and end date. So you can select a range of dates by just moving two points of the slicer. So if you want to select, say, one week, 10 days, any amount of D is starting from say, 25th March or 11 December, ending date. You can select, or you can just click on a particular date to select a custom date. Okay, so you've got two options. You can slice. You can a slide. You can just go to a particular date, year, and month, just like you do in any web page. Let us very cool thing you can just create. This date is slicer and just keep it in the sight of a visualization. And when you're showing your presentation, you can use the basic slicer as a flicker, four categories, and date or time, a slicer to select a time frame. So let us first also create one more data slicer. You can create a 3D Slicer in two ways. Just go to this option and you can select the hierarchy. So it will show you date in terms of checkboxes arranged in a hierarchy or categories to add sub category format. So you can select them just like a basic is slicer. So you've got years, quarters. When you expand a quarter, you will get month. You can expand a month to get a particular date, and so on. So you can use a combination of its leader, the date hierarchy, and basic slicer to just navigate to your visualization. People generally do drill down. In charts. You can use drill down. But when you require a filter, particularly I slicer, you can just create these things. Though these are very empowering thing. It may look like they are really simple. But once you combine them with a charge that you create all the charts, TreeMap by bar, line, any map chart, or a combination of custom charts. It will simply take your visualization to the next level with very easy, say if you have visualization for sales data across a region and your client ask your friend us simply. Now, I want to get the sales data of Texas from July 2019 to February 2021. You can just use these to slicers and navigate through the exit data a person is asking for. Alternatively, when we use a database query language, SQL can be used for performing such kind of query. But here we have the power to show it visually in real time without writing any script or query. So slicers are very powerful filters that can be used with your charts for real-time query handling. So great to create their own slicers and integrate with your charts to create an amazing story. So stay motivated. Tried to create these kind of filters is licensed and charts Power BI, keep learning and keep moving ahead. 23. Simple Map and modes: Hi, welcome back, friend here in this S and you're going to learn about simple map and different modes format in Power BI. So let's start with this. Here, we will create a simplistic map. So you've gotten to visualization tab, just hit the globe icon. And this very tiny you are three different kinds of map in Power BI will first begin with a simple map and just click this globe icon. And you've got various options. Just draw one way, one column or field into the different categories. Just drop country. Or you can also use a state or any other geographical information into the location. Then drop is tape into legion. Here we are using estates. Then we are dropping it to the region. You can also drop multiple categories into legion core location as well. Then we will draw order quantity into the size. And we can also place some other informations into different things. So here it is. So we've got a simple map for United States and Canada. And you can see it's small and big bubbles emerging on different states. Okay? So these are the different states and let us just define. So these are the, these bubbles represent where we got some information or data about order, quantity, yours, profit or something is there in a different state, say, and you can increase the size. You can just go to the bubble and increase the size so the impression or the bubble will be in that way. So if you want to average looking bubble, you can increase it. Or if you want small, you can also change the color of your bubbles. Okay? So the size it based on order quantity. So the maximum number of orders placed from a different exchange, it will be represented. So here we can see in North Carolina, We got large number of orders, okay? Not Carolina. Ontario in Canada regard larger quantity. And we've got a smaller quantity in states like Nevada, Wisconsin, we got less order. So the size of bubble matters. If you change the order quantity with profit, sales data, it will reflect with that thing. Okay? So here we are considering order quantity and everything is colored in blue, in the same color. You could add a different color and you can add something to the tooltip, say profit into tooltip. If you drop, you can change the color mode, map, tile from ADL to black to dark mode to light gray squiggle under root. By default it was set to the road type. You can churn, turn it to dark mode, grayscale, a real geographical map. If you want to show a forest, mountains, oceans, seas, lakes, all the geographical things, physical map. You can use this aerial map. If you want to add roads and the landmarks, you can use a road, you can use the gray-scale. If you want to have this night vision kind of look, then you can use separate ArcMap. So these are the different map is ties that you can use. So Power BI not just provide you a simple kind of map, you can use a complex variety of map. You can also use ArcGIS or other informations and what we have to import your data in that format. So for the regular format, you have this map. So for using map, your Internet connectivity should be turned on. Because if you're viewing this thing offline, it has to load a map from online sources. Okay? So just make your Power BI, connected to the internet, okay? So this is how you create a simple map in Power BI. Just take any dataset that has some geographical information. You don't need latitude and longitude. You just need simple name of his tes, countries, territories, cities, and it will be reflected to the map. So just try to create a map chart using different modes. Keep learning and keep moving ahead. 24. View Data and Export in CSV : Hey, we'll come back print here in this lesson, you are going to learn about exporting a subset of chart data into csv file. So let's start with this. So we have created multiple visualization charts. Here we have the pie chart or ring chart or treemap chart. And we can export a subset of data, say, if we select a particular option from either of the chart and we can extract relevant information. Say if we want to know data only for the UN Security states or simply Canada, or a simple pedigree, we have to just select the chart, select a particular section, and right-click on show data point as a table. So it will generate a table folder data points. Here we have selected a fruit category. So it just created a data for fruits. In the same way, we can select any other thing, say technology. We'll show you the data for the sheet, just for the technology category. Here we'll leave all other categories. Ignore all of the categories, and we'll show you this information. So in case if you need any particular information, you have a large dataset and you want to filter it. On this point. You can do this thing and you can also export it into the CSV. Just go to the more options on the top right corner and hit Export Data. Just provide a name for your CSV file, comma-separated value. Or you can also save it into the spreadsheet or any other format of your choice. By default, people generally use CSV file because it gets easily connected. Or we tell web data. Database is our cloud data and other visualization tools. So here we have. So this is how you can just view a particular segment of data. You can have the charge. You can select a particular segment of the chart. You can extract the data and export it and save it. For further analytics. It is vital because, say if you're working in a team and you have created a visualization and your friends or collaborators quanta particular chunk say they want only the technology data. All we want. You have a team for different divisions, a US West, US East, US Nautilus out. And you want to provide them with the data relevant to the domains. They are, specializations there areas. So you can just extract that chunk of data very easily in Power BI, create a spreadsheet and share with them so they can perform folder more analytics that are relevant to the case of our Any other reasons. So this is how you can extract data and export NC as we keep learning and keep moving it.