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Replacing NULL value with Zero in geodatabase table using Python parser of ArcGIS field calculator?


I am having some difficulties to change "NULL" values to zero in geodatabase. I have tried the following python code in ArcGIS 10.1 ("Field Calculator") to change it but did not work.

Pre-Logic Script Code:

def RemoveNULL(x): if x is None: return"elif x ==": return '0' else: return x

Calculation field:

RemoveNULL(str( !DEAD_VOL_PER_HA_SPP1_125!))

Error message:

ERROR: 999999: Error executing function The value type is incompatible with the field type. [dead_pine] Failed to execute (calculate field)

I can see from your field name!DEAD_VOL_PER_HA_SPP1_125!that the field you are calculating is likely a float or integer type field. Assuming this is true, you cannot write a space or"into a number-based field (e.g.return"). Valid values for number-based fields includeNoneor a numeric value.

You will need to either create a new text field to store the special character (i.e. a space) or change thereturnoutput to an integer or float.

The following works for both text and numeric fields (Python Parser):

def replaceNull(x): if x is None: return 0 else: return x

replaceNull(!YourFieldName!)

Create a new field and put field type is text after that you should use field calculator the value by simply select all "nulls" and change it in to "0".


How To: Replace the text in multiple fields in an attribute table using the Field Calculator tool

An attribute table is a database that contains non-spatial information about a set of geographic features in a geographic information system (GIS). The information is arranged so that each row represents a feature and each column represents one feature attribute. In ArcGIS, rows are known as records and columns are fields. Each field stores a specific type of data, such as a number, date, or piece of text.

The instructions provided describe how to use the Replace function in the Field Calculator tool to replace the text in multiple fields within an attribute table without having to edit every record individually.


Name/Alias

The feature class name is a unique handle that identifies the feature class. The most popular way to name a feature class is with mixed case or using an underscore, such as MajorRoads or Major_Roads.

When you create a feature class, you should give it a name that indicates what data the feature class stores. Feature class names must be unique in a database or geodatabase—you can't have more than one feature class with the same name. Having two feature classes with the same name in the same geodatabase, even if included in different feature datasets, is not allowed.

The name you indicate when you create the feature class in ArcGIS for Desktop , however, is not the name of the feature class as it appears in the database or geodatabase. The database or geodatabase appends the name of the schema in which the feature class is stored. In all databases but Oracle, the name of the database is also appended to the name. This is referred to as the fully qualified feature class name. For example, if user Werther creates a feature class called alpacas in the spdata database, the fully qualified name of the feature class is

Therefore, it is possible for other users to create feature classes called alpacas because the feature classes they create will have their user names appended to the feature class names. For example, if user Gretchen created her own alpacas feature class, the fully qualified name would be

However, it is not recommended that you reuse feature class names even if they are stored in different schemas or databases. In this example, if both feature classes contained information about alpacas, there would be no reason to have two separate feature classes. If the data was distinctly different between the two feature classes, the feature class names should reflect that.

In Informix, even if you are storing the feature classes in separate schemas, they cannot have the same name.

  • Names must begin with a letter, not a number or special character such as an asterisk (*) or percent sign (%).
  • Names should not contain spaces. If you have a two-part name for your table or feature class, connect the words with an underscore (_), for example, garbage_routes.
  • Names should not contain reserved words, such as select or add . Consult your DBMS documentation for additional reserved words.
  • The length of feature class and table names depends on the underlying database. The maximum name length for file geodatabase feature classes is 160 characters. Be sure to consult your DBMS documentation for maximum name lengths.

Table or feature class names with the following prefixes are not supported:

Renaming fields

You can rename fields in a table or feature class from the Fields tab of the Properties dialog box. Fields in a geodatabase from the ArcGIS 10 release and later support renaming, and fields in database tables can be renamed.

To rename a field, right-click the feature class or table in the Catalog tree and click Properties . Click the Fields tab to see a list of fields in that table or feature class. Click on the text of the field you want to rename and type a new name. Click OK to apply your changes and close the Properties dialog box.

Restrictions for field names are similar to those for table and feature class names.

  • Names must begin with a letter, not a number or special character such as an asterisk (*) or percent sign (%).
  • Names should not contain spaces.

If you have a two-part field name, connect the words with an underscore (_), for example, customer_address.

Consult your DBMS documentation for additional reserved words.

The following fields cannot be renamed:

  • ObjectID and GlobalID fields
  • Any Shape-related field Shape, shape length, shape area
  • The enabled, ancillary role or network weight fields of a network feature class
  • Representation fields
  • Fields in a feature class participating in a Network Dataset, Terrain, or Cadastral Fabric
  • Fields used for Editor Tracking
  • Relationship Class Primary Key and Foreign Key fields
  • The Subtype field
  • Raster fields

Aliases

When you create a table or feature class in a geodatabase, you can assign an alias to it. An alias is an alternate name. If you assign an alias to a table or feature class, that is the name users see when they add it to ArcMap. Users can still look up the name of the table or feature class by going to the Source tab of the Layer Properties dialog box.


Arcmap Field Calculator Python Parser If Statement

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Форматы данных ГИС. Оформление и качество

In this course, the second in the Geographic Information Systems (GIS) Specialization, you will go in-depth with common data types (such as raster and vector data), structures, quality and storage during four week-long modules: Week 1: Learn about data models and formats, including a full understanding of vector data and raster concepts. You will also learn about the implications of a data’s scale and how to load layers from web services. Week 2: Create a vector data model by using vector attribute tables, writing query strings, defining queries, and adding and calculating fields. You'll also learn how to create new data through the process of digitizing and you'll use the built-in Editor tools in ArcGIS. Week 3: Learn about common data storage mechanisms within GIS, including geodatabases and shapefiles. Learn how to choose between them for your projects and how to optimize them for speed and size. You'll also work with rasters for the first time, using digital elevation models and creating slope and distance analysis products. Week 4: Explore datasets and assess them for quality and uncertainty. You will also learn how to bring your maps and data to the Internet and create web maps quickly with ArcGIS Online. Take GIS Data Formats, Design and Quality as a standalone course or as part of the Geographic Information Systems (GIS) Specialization. You should have equivalent experience to completing the first course in this specialization, Fundamentals of GIS, before taking this course. By completing the second class in the Specialization you will gain the skills needed to succeed in the full program.

Получаемые навыки

Spatial Analysis, Analytics, Workflow, Data Management

Рецензии

Nick, you and your team did good job on making the course enjoyable, only problem I faced was having trouble downloading the tutorial 2 assignment data. Still it was a very good experience.

Good course, well structured to deliver the invaluable skills, ranging from data management to final output after processing. Good exposure to the toolbox, expecting more in the next course.

This module is all about working with vector data. We'll review geoprocessing and introduce the intersect tool. This module also covers writing query strings to subset data, adding and calculating fields, configuring selections, editing and creating feature classes, and everything you need to know about digitizing data.

Преподаватели

Nick Santos

Текст видео

[MUSIC] Hello everyone, and welcome back. In this lesson, we're going to continue the series that we have on workflows, where we bring together components of GIS into more realistic ways that you would actually work with your data. In this lecture on workflows we're going to cover data preparation. And how we're going to do that is we're going to take a look at the final assignment from the first class in this specialization on the voting data for one of the propositions in California's 2012 election. And we're going to go through the data clean up I did to provide the data to you for that assignment. As nice as it would have been if the data just worked for this assignment right out the website I got it from. It didn't and I needed to do some preparation to make it clear to you what you needed to do for the assignment. Even as you advance in your use of GIS you'll still find plenty of situations where you need to beside data or. Work with the attributes a bit to get it into the format that you need to do your analysis. It's not just as nice as plugging it all into a geoprocessing tool and saying, go. Because sometimes your data types don't match, or sometimes one tool needs a certain bit of information. And you need to use the field calculator to generate that information based upon another tool. So in this lesson, I'm going to show you the data preparation I did in order to prep the data for you for that previous assignment so that you understand more realistically how this happens when you do GIS analysis. For those of you who didn't do that assignment or who are into this class without having done the previous class what we are looking at here is a layer of California voting precincts. And if we zoom in a bit we will see them more where the urban area have much smaller precincts. Precincts being where people go to vote. An attribute table for this. We see all of the data for all of the propositions here. Along with a number of other fields. So, we see the number of voters for all these propositions. Some of these values have nulls. If I go to select by attributes to find some, and make sure statewide ballot datas. Chosen and I find the proposition 37 field that we were using, and I'll say, is null, because the equal sign doesn't work with null, it's an, is, statement in this case. So I'll do, apply, and I'll see that some of these are nulls. And if we reduce it, we'll see that they are nulls almost across the board here. So we just don't have data for those pre-syncs. And this creates problems when we go to the spatial join step, to try to aggregate the data to the counties level. And in fact it even creates symbology problems. So if we go to properties and we switch to quantities for symbology, and find the proposition 37 field now. It says maximum sample sized reached, that's one additional issue, but if I click apply. Those no values are just holes here, so which make sense we don't know the value so we can't symbolize it. But at the same time it shows us that we have some data problems. So let's run the spacial join that we did for the previous class. And the target features are the counties. And the joint features are the statewide ballot data. And I'll leave that just fine there. And down below here, we'll leave it joined to one to one. But we're going to aggregate this attribute remember. So merge rule, we're going to take the sum. And if I click okay and run it. We get our result back as counties, but if I go to the attributes table and go to the prop 37 field, we still. So, now this is surprising too actually. When I run to this originally either something I did or the fact that I was running the previous version of arc GIS meant that, when I had polygons that were noes being aggregated in special joint, I got noes back in this step right here. In this case Arc GIS handled them correctly, and there is not anything that I need to do. So, working with data is messy and something that even after many, many years, you can still be getting the hang of. Because it behaves differently in different situations. Now, if we pretend that we did have trouble and these were coming back as null. I'll still show you how to clean out this data a little bit, so that you can symbolize it a little more evenly. And that you can use it in spatial join if we were to assume the problem that I was talking about before. So, if we go to statewide ballot data and open the actuary table, and we click on this field, we're going to use the field calculator to clean up the data. Now, remember when we learned about data types how I said that nil is not equal to zero. Null and zero are not the same thing. Well, they aren't but sometimes you do want to treat them that way, right? Because. If I need to sum things up for all intents and purposes, null is equivalent to 0. There's no information there. And in a sum, whether it's null or 0 doesn't make any difference. So we can replace null with 0 in the event that we are summing our data up right now. Even though, in reality, null means we don't actually know the value there. So, this is one of those instances where I'm going to use that code block that I mentioned before. And I'm going to write a function, I'm going to call it fill_nulls, and it's going to take an original value. And I'm going to put a colon at the end because that's what you do. So def means defining a function. I give it a name and in parentheses I can put the parameters it can take, the inputs it can take. And since Python I need to Indent to say what's inside the function. And what I'm going to be parsing in as the original value is the value of the prop 37 yes votes. So prop 37 yes. So i'm going to put in fill. Nulls. Prop 37, yes. So what's going to happen is prop 37 yes is going to be equal to the result of the function I'm writing when I pass in the current value of prop 37 yes. And what we're going to do is say if original value which is, since this is a parameter, this becomes original value when it answers the function. So if original value, which is prop 37s's current value, and I don't actually need the parentheses here. If original value is none, and none is the way you write null in Python. So then call on it again In depth further. I'm going to return, I'm going to say return means exit the function and give back the value. Return 0 and then enter again and then else basically saying, otherwise if that condition is not made. If original value is something other than none. Return, get back the original value. So basically what we're saying is pass the value of the record through this function. And what the function does is if the original value is a null value, it replaces it with zero, returns a zero to be passed back to the prop 37 yes field. If it's something other than a null value, it just returns the value that we passed in. So it doesn't modify it. So I can now click okay to run this [SOUND]. It's calculating the records. We can see that these records didn't change, but that our null value was replaced with a zero. This is a really handy way that we can use the field calculator to modify data in place. Preserving certain parts but using a conditional to match parts that we. We need to clean or modify. Now I did a number of other things to clean up the data before I gave it to you. So I removed fields that weren't important to you. So that you don't have to sort through them all. So I could have right click and done delete field on all of this. And then it gives me a warning here and then I could yes and it deletes the field. Or it fails to delete the selected field largely because this geo-database is stored in a synchronized folder, and the synchronization software Google Drive just interfered. One tip, in general, you don't want to run your data out of synchronized folders. It creates headaches. Headaches like this where the data was just corrupted because I tried the. Delete a field in place while Google Drive was trying to synchronize the data. So, this is just another type of problem that you can run into working with your data. So I could go back into Google Drive and restore a previous version of the data, but I have another copy of it. And since we're just doing a demonstration here, I'll work with that. Okay, so I've restored my data from backup and if we go to the properties in catalog, I can go to the fields pane and continue prepping the data. And if I scroll down to all these fields in here, I can see that a lot of these fields are not named very clearly. Somebody who didn't go find this data and understand exactly what we're working with, might not know what it is. And, so if I, go to field name, one thing I did, was I renamed these fields. And if I click Apply, it saves the field name. Now, I had some of the same problems renaming the fields previously, and actually corrupted my data a few times when I was actually prepping it. So I made, numerous backups of the data as I was working with it in the event that I messed up my data. But now the field is renamed. And if I brought it back into ArcMap, I would see that new name on it. I also selected many of these fields, and I deleted them. And so I can click on them quickly here. And then when I'm done, click on delete them. So I hit the delete key, after I have the rows selected, and then if I click apply. It deletes the fields and updates the table for me, and now if I scroll back down, those fields are gone. So those are a handful of the things I did. A number of other things I did. So I had to go out and retrieve this data from the Internet. I retrieved it as a shapefile, and I wanted you guys working with geodatabase. Feature classes because they're nicer to work with, they're smaller, and more modern, and I imported it into a geo database. I did the processing we just talked about in terms of checking for null values and renaming fields, and deleting the fields that you didn't need that would just confused the situation. And then I was ready to run it through the analysis workflow. This all takes much longer than what we just showed in this demo, but what I want you to see is that this stuff doesn't come in perfectly, and it's okay when errors happen. It's okay when you've accidentally corrupted your geo-database, as long as you have a backup. And it's okay that You have to try multiple things sometimes, that's just the process that you'll often find yourself going through. There'll be days where you do everything right on the first try, and there will be days that you mess up in the middle of a demo, like what I have right here. So, I hope that helps understand some of the cleaning that happens on data for your assignments. And some of the cleaning that you might need to do, when you're working with your own data. See you next time.


Much Ado About Nothing

See my nullness summary in the NumPy Repository​ - from there:

(There are other examples dealing with geometry)

Python doesn't have a data type called NULL/Null/null. The Python Built-in Types documentation states for the null object, "There is exactly one null object, named None (a built-in name)." If you are used to working with NULL in databases and SQL be careful, None in Python doesn't behave exactly the same as NULL even though None is referred to as "the null object" in some documentation.

In Python there are several falsy values, i.e., values that are not false but evaluate to false in a Boolean context. Some common examples are None (the NoneType), "" (empty string), [] empty list, () (empty tuple), and 0 (the number zero). When using the Python parser with the Field Calculator or cursors with ArcPy, NULLs need to be converted, and Esri has chosen to convert them to None , which is understandable given the built-in data types available with Python.

Although Python Conditional Expressions have their limits, they can be used in this and similar cases to avoid using a code block:

I have been working the last year converting to Python from VBA. I still have issues with Python's handling of NULLS! From my perspective results are almost always inconsistent so my level of trust using Python to handle null is very low. I almost always use vbscript isnull() function to check.

I learned something new thanks to Dan, I did not realize that nulls are treated as "false". Coming from a VB background nulls are neither True or False. this concept may help me with my own inconsistencies with Pythons and Nulls.

Another thing to check or to make sure that your tests includes Blanks,embedded tabs or other control character (see a lot of this when cut and pasting from excel). these are not nulls although they act and appear to be nulls. These include all characters with ASCII values between 0 and 31.

I made some tests using Python's of treating Null values as false . works great if the data is clean. however on my "dirty tests" (real life cut/paste from external programs) I would still have to strip all embedded control characters before the Python script would work whereas the vbscript had no issues.

This exercise has shown me that I have been lazy relying on VB to test for nulls. my data is not as clean as I thought it was.

In ArcGIS, NULL in a data table is converted to None in Python. The equivalent of IsNull in VBA is is None in Python.

In pure GIS tables I do not doubt your correctness about ESRI Nulls equating to None. However, in My real world, as a state authority, we are beggars of data and in many cases have little or no say in the formatting or the QA process the data we receive undergoes.

Using my data, None is useless for testing for nullness ( hit or miss). The best pythonic approach I have used with my data is using the old fashion trim and concatenation approach to strip out all the Ascii codes 31 or less and final test for a single space or as many here indicated, assign a comparison number or string to represent nullness for the down and dirty. My clean approach, when I have time is to validate and correct the data before it gets into GIS (Then None and Dan's boolean approach works 100%).

I still state that Python has issues with Nulls. It quacks like a duck, looks like a duck and feels like a duck -- But it is not a duck!

Here's my solution: often for this problem you want to put something in there to indicate a null value was there in Y.

CopyNonNull(!Y!) # Null Y values are assigned None (Null, sort of. )

CopyNonNull(!Y!, -9999) # Null Y values are assigned -9999

In pure GIS tables I do not doubt your correctness about ESRI Nulls equating to None. However, in My real world, as a state authority, we are beggars of data and in many cases have little or no say in the formatting or the QA process the data we receive undergoes.

Using my data, None is useless for testing for nullness ( hit or miss). The best pythonic approach I have used with my data is using the old fashion trim and concatenation approach to strip out all the Ascii codes 31 or less and final test for a single space or as many here indicated, assign a comparison number or string to represent nullness for the down and dirty. My clean approach, when I have time is to validate and correct the data before it gets into GIS (Then None and Dan's boolean approach works 100%).

I still state that Python has issues with Nulls. It quacks like a duck, looks like a duck and feels like a duck -- But it is not a duck!

Ted. still not ready to move on to 'not a number ' until you can accept nulls

I guess I see it different, i.e., it is a data issue and not a Python issue.

I have worked for government in a variety of positions ranging from planning to information management and now information technology. I have been on both the producing and consuming side of data, including data problems. I agree that messy data doesn't fit cleaning into code, regardless of the language.

Regardless of the language (Python, SQL , etc. ), most of what you describe is neither Null or None. An empty string might be falsy, but it certainly isn't Null. The same holds true for strings with ASCII codes 31 or less. I am interested in how you would handle these types of situations in other programming languages because I don't really see how this is specifically a Python issue, regardless of whether one wants to argue None is Null or not Null exactly.

Good to know about np.NaNs for that. I have been running into issues with that at work recently. NP methods for scrubbing NaNs seem best, but I have found masked arrays to be annoying in practice. Pandas seems to handle it a little better on the surface.

I am a LRS shop and NaN will be an issue when Python incorporates dynamic seg and measures. nice that I have that to look forward to.

Thankfully I wrote all my LRS code in VB.Net/ESRI SDK which will be around for awhile. I have read a few interesting papers about the possible redesign of VB, on which will incorporate and built around the HTML5 engine. If this comes to be, it will be a game changer and breath new life into vb script again!

I completely agree with Joshua that only real Null values should be found by Python, since only real Null values will be found by SQL if I write an expression that uses IS NULL for any of the databases I use. Whitespace and control characters are not Null and will not be selected using IS NULL with standard implementations of SQL. Python follows the SQL standard. Access queries might return results that are more similar to the VB implementation, but Access is in no way a reliable reference for established database "standards" recognized by professionals.

There is only one object in python that is None, it is

There are, however many null objects, in the mathematical sense (ie lists, sets, dictionaries, collections any container)

Then there are objects that are not, but their are not equal in their none-ness (ie np.NaN)

Their truth equality in testing is another issue. the idea that None of this matters is False and this is True

And I again, will leave geometry out of this. of which I have posted several times before

Only thing I might add is this leverages Falsy behavior in python. 2.3.1 Truth Value Testing

Something to keep in mind is that 0 can also be Falsy along with a few other values (empty strings etc).
Using

is what I see over and over again.

Yes confusing isn't it. you have to know what an object or what it belongs to. a common mistake in numpy

because you can't use zero. and the list goes on

There are issues with nulls in all languages even my beloved vb and also with both the Oracle and MS SQL server -- so Python following the SQL standard is not 100% true either because each deals with nullness differently. So it is not really any specific language problem. My issue with Python's and Null is mostly personal. Python reminds me of the Perl scripting language (Which I really hated). I spend more time with python trying to figure out why an apparent record that both MSSQL ,ACCESS and EXCEL says its null but fails when testing in Python. In the end I use VB or other language scripts for null testing because I find the results both consistent and repeatable across time. VB, MS access, MS SQL treat Nulls slightly differently but in those cases I found I could handle the differences with little to no effort. VB distinguishes between space and nothing. Whereas MS Access does not except when you use the built in function as well as MS SQL. Oracle has more of a java/javascript approach to Null. But Python is very indirect and obscure when dealing with null within the language context (Probably makes Python much more flexible in the long run and can easily test all variants of nullness). I am probably not yet knowledgeable in Python to adequately determine which of the many variants I should test for.

I think the problem is with the definition of what we consider is NULL. In my definition an empty string is not null, however nothing and null are same. Some other engineers I know disagree with my definition stating that nothing is also not null . to them NULL is the absence of Nothing (that I cannot put my head around). Other folks will say that the < 31 ASCII Codes are nothing because they are artifacts from buffered cut and paste operation.

My ideal language tells me if something is null adhering to my expectation of nullness consistently. Python does not do this yet for me. My custom Python isNull function is constantly growing/being modified . trying to capture and test for my definition of a null value.

Did a little isNull test using different Languages and DB's for a test record from sqlserver that had a test field containing an ascii <NUL> and <TAB> char

Language Test Boolean result
VB6IsNull()False
VB.NETIsNull()False (but: True if DB Context isNull -- go figure)
C#== nullFalse (but: True if DB Context isNull -- go figure)
PythonIs NoneFalse
MS AccessIS NULLTrue
SQL ServerIS NULLTrue
OracleIS NULLFalse

I fear your ideal language is one you will likely have to create yourself. Everyone is entitled to have his/her own views of the world, but not all views are commonly held. In this case your idea of null-ness is quite uncommon, which is fine in and of itself, but you should expect to run into plenty of frustrations with most (if not all) computer languages and even possibly some areas of mathematics.

Before getting back to Null, I think saying that ASCII codes < 31 are "nothing because they are artifacts from buffered cut and paste operation" doesn't give enough credit to those characters. Sure, control characters aren't printable/viewable, but I can't imagine trying to work with computers without control characters. Typical business users that only enter text into e-mail, word processing, and other business productivity applications might not understand control characters, or even know that such things exist, but it doesn't mean they are nothing.

Regarding nulls, an ASCII null character ( NUL or ) is null within the domain or context of the ASCII character set, but it is still a character. Type systems can define their own nullable type, but the ASCII null character is not a universal null object/type. It is possible there is a programming language that has adopted the ASCII null character as its own null representation, but that isn't the case for any of the languages that I interact with.

Regarding SQL Server and MS Access, I get the opposite results you do, i.e., neither returns a field with only an ASCII null when the WHERE condition includes IS NULL. Looking at a SQL Server example:

I do get the same results running the isnull script in SQL server as you, however when I import a record from excel with embedded codes, the isnull function returned true which differs from you singular controlled value list.

In any event, I whole-heartedly agree with your statements, the other definitions I stated were not necessarily my own but view points of folks that supply me with data. I do not infer that the ASCII null is a universal standard of nullness, but gave it as one example of where the simple is none python test fails (for me. ). I believe my issue is not one of testing for null, but in data standards in which I am struggling to employ in-house as well as data received from the outside (lots of resistance in my case). I won't even go into uni-text,ut8 and binary strings/null issues.


It's the index column, pass pd.to_csv(. index=False) to not write out an unnamed index column in the first place, see the to_csv() docs.

You could also optionally tell read_csv that the first column is the index column by passing index_col=0 :

This is usually caused by your CSV having been saved along with an (unnamed) index ( RangeIndex ).

(The fix would actually need to be done when saving the DataFrame, but this isn't always an option.)

Workaround: read_csv with index_col=[0] argument

IMO, the simplest solution would be to read the unnamed column as the index. Specify an index_col=[0] argument to pd.read_csv , this reads in the first column as the index. (Note the square brackets).

Note
You could have avoided this in the first place by using index=False if the output CSV was created in pandas, if your DataFrame does not have an index to begin with:

But as mentioned above, this isn't always an option.


Building label expressions

You can use label expressions to adjust the formatting of your labels. In addition to inserting characters and scripting functions, you can use ArcGIS formatting tags in label expressions. These are special characters for changing the appearance of all or part of your labels. For example, you might use the bold formatting tag to make the first line bold in a stacked, multiline label.

A label expression is limited to a single line of code unless you check the Advanced box on the Label Expression dialog box. Checking the Advanced box allows you to enter a function containing programming logic and spanning multiple lines of code.

Field values are automatically cast to text strings. Therefore, if you wish to use a numeric value in an arithmetic operation, or when making a comparison, you will need to cast it back to a numeric data type. The examples below add two integer fields:

Python
VBScript
JScript

  1. Click the Label Manager button on the Labeling toolbar.
  2. Click a label class in the Label Classes list.
  3. Click the Expression button.
  4. Choose a language on the Parser menu.
  5. Type a Python, VBScript, or JScript expression. You can also create an expression by double-clicking the field to add it to the expression or by selecting the field and clicking the Append button to append the field to the end of the expression separated by a space.

Fields are enclosed in square brackets [ ] irrespective of the data type of the layer's data source.

Optionally, enter ArcGIS text formatting tags in the Expression box to apply formatting to a portion of your label text.

If your expression will span multiple lines of code, check the Advanced check box and enter your label expression.


Introduction to ArcGIS Insights

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    • ArcGIS Insights (Esri Product Page)
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                  • Configure the portal to support Insights

                  1 minute videos on Insights capabilities) Insights Capabilities by Industry (Short


                  An SQL NULL value reserves one or two bytes in the record directory. Besides that, an SQL NULL value reserves zero bytes in the data part of the record if stored in a variable length column. In a fixed-length column, it reserves the fixed length of the column in the data part of the record. Reserving the fixed space for NULL values enables an update of the column from NULL to a non-NULL value to be done in place without causing fragmentation of the index page.

                  The variable-length part of the record header contains a bit vector for indicating NULL columns. If the number of columns in the index that can be NULL is N, the bit vector occupies CEILING(N/8) bytes. (For example, if there are anywhere from 9 to 15 columns that can be NULL, the bit vector uses two bytes.) Columns that are NULL do not occupy space other than the bit in this vector. The variable-length part of the header also contains the lengths of variable-length columns. Each length takes one or two bytes, depending on the maximum length of the column. If all columns in the index are NOT NULL and have a fixed length, the record header has no variable-length part.

                  Based on these bulletpoints, here is what a NULL value takes up for a column's storage

                  • variable length: a NULL value takes up no storage in the row itself
                  • fixed length: Takes up the reserved space

                  Now, you must decide between use CHAR and VARCHAR because of what the first point brought out

                  Reserving the fixed space for NULL values enables an update of the column from NULL to a non-NULL value to be done in place without causing fragmentation of the index page

                  This will prevent introducing any fragmentation of a row going down the road once non-NULL data is stored. This is something I have discussed before with regards to MyISAM : See my old post What is the performance impact of using CHAR vs VARCHAR on a fixed-size field?.


                  Watch the video: Filling Attribute Table Automatically with Simple Steps in ArcMap Using Field Calculator (September 2021).