10 Geoprocessing Applications of GeoPandas Library

hi guys welcome to another tutorial in this tutorial i'm going to take you through 10 most useful geoprocessing applications of jio pandas library now before we get started i would like to give you a bit of a context about the data that we are going to use so that you'll be familiar with the different geoprocessing operations that we'll be doing in the next few minutes so i'm using spider as my python ide and since we are using the jio pandas library also you have to make sure that you have already installed the jio pandas library before you proceed with this tutorial now over here in my file explorer you can see that i have some s3 shape files over here you can see that i have a shape file called river and another shape file called study area 1 and study area 2 and over here i also have a shape file which is actually of the different states of the us so to get started i'm going to create a new python script in my working fold which happens to be this and i'm going to right click over here and create a new module and i'm going to name this as jio pandas applications dot dui all right so the first thing that i'm going to do is i'm going to import the jio pandas library and i would like to first visualize these different shape files so that we can get an idea of what sort of geospatial data we are working with so first i would like to maybe import this study area one shape file so to import the shape files i can very easily use jio pandas library i'm going to name this one as let's say study area 1 sa1 in shortened form and i'm going to say gpd dot read file and since i have kept my python script file inside the same folder i'm just going to specify the name of the shapefile over here but in case if you happen to keep your data in separate place you have to actually provide the full path so that's something to keep in mind but in this case i'm going to simply copy this name just like this and i'm going to say studyarea1.shp and i would like to do a quick plot in order to visualize this now to run this you can either click on this triangle button over here or you can simply hit f5 and when you do that you can see on the right side in your python console you'll be able to see how the study area one looks and similarly if i were to import the study area 2 as you can see over here i have another shape file called study area 2 so i can simply copy this and paste it but just change the name of the variable as well as the name of the shape file over here so if i run this command now you can see that this is study area 1 and this is how my study area 2 looks and we also have one shape file called river over here so let's go ahead and import that one as well i'm going to name this one as river equals gpd dot read file and that's going to be reword.shp and i'm going to plot that as well it's going to be river.plot and over here you can see actually it's just a line it's just a polyline so this is how that river looks so just keep in mind that this actu this is actually how my different study areas look but now i'm thinking that it would be convenient actually if we can visualize these different information just in one figure so that we know especially where each of these different item would be at so i'm going to do that using matplotlib objects so i'm going to go ahead and import mat plot lib dot pi plot as plt and then i'm going to actually get rid of this plotting commands over here i'm just going to import the three shape files just like this so that study area 1 study area 2 and river and the shapefile of river will get imported and by the way guys if you wanted to follow along with me you can always download this set of data from the link given in the description so don't forget to check that out as well and now what i'm going to do is i'm going to say figure access and that's going to be equal to plt dot subplots and now i'm going to plot this study area 1 with its axis to be the same as this axis and i would like to specify the color to be let's say blue and i will say the edge color is black so that it will create a black color outline across the perimeter of the study area one and i'm going to similarly say i would like to plot the study area too as well but in the same axis as you can see over here but the study area too i'm going to let's say say red and the edge color i'm going to return it as as black and finally i'm going to plot the river as well in the same axis and and i'm basically going to get rid of all of this so that the default colors will be applied for that as well this one should be changed to edge color and now i'm going to run this one [Music] and over here you can see that we have the three different shape files actually plotted just in one single figure but now you can see there is an issue we cannot really see the extent the full extent of this study area one which is represented in blue color because that layer is actually behind this study area two layer so what i'm going to do is i'm i'm actually going to get rid of the fill color of the study area too so here instead of specifying red i'm going to specify the fill color to be none and then i can simply run this one and now you can see that there are actually some common areas for study area 1 and two over here which happens to be in this middle region and over here you can see that the river is actually a line which starts from somewhere over here and ends at this particular point all right guys so i took a few minutes to actually explore the data so that everything that i'm going to be discussing with you guys is going to be crystal clear to you especially when you have a good context about what sort of data or what sort of geospatial data that we are working with all right so without further ado let's get started with the tutorial 10 extremely useful geospatial applications of jio pandas python library all right so the first application of jio pandas that we're going to talk about today is its ability to perform polygon intersections to demonstrate this to you guys i'm going to use this study area 1 and study area 2. as you can recall this blue color area correspond to study area 1 and and you can see the extent of the study area 2 through this black color outline so the area which corresponds to the intersection of these two will be this mid region as you can see over here so how can we perform that geoprocessing operation using geopandas i'm going to create a new variable called intersection and that's going to be equal to gpd dot overlay and we provide the two layers which we would like to intersect and that's going to be study area one which is this which we read in through this line and next i provide the second layer which is going to be study area 2 and then we specify how we would like to do this overlay operation and that's going to be intersection and after that if i say i want to plot this intersection layer and after that i can run and from here you can see that we get this part which actually corresponds to the intersection of these two layers which happens to be study area one and study area two all right let's move on to number two and number two is union of polygons so i'm going to change this one to be union of polygons and that's quite easy as well i'll change the name of the variable to be union and that's going to be equal to gpd.overlay and the rest is going to be the same because we are going to get the union of the two layers which is study area one and study area two and just over here we're going to change the method from being intersection to union over here and after that i'm going to plot [Music] this union right over here and after that if i go ahead and run and now over here you can see the union of the two layers now if you would like to see how that geopandas geodata frame looks in terms of its attributes table you can actually check that out from here yeah this is the union and you can see over here that it's made out of three individual polygons because as you can see from the original figure when you take the union you'll be getting one single jio pandas geodata frame which consists of three different parts the part which corresponds to only the study area too and the part which corresponds to only study area 1 as well as the part which corresponds to both study area 1 and 2 together that's why you see three different attributes over here in this union jio pandas geodata frame and if you would like to save any of these files as as an ester shape file back into one of your folders you can simply do that as well without any issue all right that's how we do the union and now we will go to the third item and that's how to obtain the symmetrical differences of polygons using geopandas so here i'm going to specify that this is going to be symmetric difference symmetric difference of polygons and the corresponding or the resulting geopandas geodata frame i'm going to name that as sd and sd is going to be equal to gpd dot overlay and the two layers that we are going to use for this are going to be the same and just over here we're going to change symmetric difference after that i'm going to plot this sd jio pandas jio data frame simply by running this script and over here you can see the part which corresponds to the symmetric difference so what happens in the symmetric difference is that if you come over here you can see that this middle part is actually the part which corresponds to the intersection and the way to interpret the symmetric difference let's say in terms of these two polygons or these two study areas now if you look at this original figure of the two study areas the way to interpret this symmetric differences simply by considering the union of the two polygons and if you take out the part which corresponds to the intersection then what you're left with is basically the symmetric difference of the two polygons as you can see from here and if you would like to take a look at the the attributes table you can see that it's made out of only two parts this upper part which comes from the study area one and this lower part which comes from the study area too all right next we'll move on to see how we can take the difference of polygons using geopandas so in that case i'm going to get rid of this symmetric part and over here i will just retain this difference part and i'm going to name this one as difference and let's look at the plot of difference and see how it looks if i run the script now now you can see that when i consider the difference again this is the original figure which corresponds to both study area one and two and this is the figure which corresponds to the difference of the two polygons now you can see that the way the difference has been calculated is simply by subtracting study area 2 from study area 1. that means when you consider this blue color area just the part which is covered by the study area 2 has been subtracted from that and what you are left with is basically the difference now what if you wanted to get the difference on the other way means you want to minus out the part of the study area one from the study area too well all you have to do is to switch these two up instead of instead of keeping study area one on the left side what i'm going to do is i'm going to keep study area 2 on the left side i'm and i'm going to minus out the part which corresponds to study area 1 from the study area 2 just like this so i flip these two and if i run the command now you will see that the difference that i get is basically this white color region of the study area too yeah it's just as simple as that all right now we'll move on to the fifth geo processing operation and that's how to use the dissolve geoprocessing operation using geopandas so i'm going to commend that one out all right so what does it mean by dissolving a polygon to explain this i'm going to first create the union again gpd dot study area 1 study area 2 and how by union and after that i can plot this byte taking union dot plot and if i run this you can see that now we get the union of the study area 1 and study area 2 but as you can see over here this union is actually made out of three individual polygons but let's say if you wanted to merge these three polygons so that you so that instead of having three different attributes you you will just have one single attribute which happens to be just a complete polygon like that that's actually one of the examples of dissolving polygons now in order to do that in the attributes table you need to have a column which contains a common value for each of these three polygons as you can see over here so what i'm going to do is i'm going to open up the attributes table over here for a second and if you wanted to and if you want to dissolve something as i mentioned to you guys i need to add some sort of a common column so that i can perform the dissolve operation based on that based on the value of that particular column but as you can see over here i do not really have a column which contains a common value so what i'm going to do is i'm going to go ahead and create a column called let's say common call something like common column actually the name doesn't really matter it just needs to be a random column and you just have to make sure that that random column has the same value so either you can just pass to pass a string over here doesn't really matter what you enter or you can just specify a number so that that particular value will get added into all of the different attributes of that particular jio pandas geodata frame so let's go ahead and execute this command and after that if i check the union the attributes table of the of the union geopandas geodata frame now you can see that each of these three polygons have received a value of one which is actually a common value to all these three attributes and now you we can utilize this common value in order to perform our dissolve operation so what i'm going to do is i'm actually going to copy this one because i would like to execute it right over here as well and after that i will say that dissolved study area is equal to union dot and we are going to dissolve this by the common column which happens to have the name common column as well and as i told you guys again we don't really need to have this same name over here it can be just any name which contains a common value for the different attributes which you would like to merge together and i can run this well and after that i can simply plot [Music] dissolved study area dot plot and now you can see it's just a continuous polygon which happens to be an aggregation of all these three different polygons together because we did take the union and we actually dissolved it based on a common column and now even if you open up the attributes table which corresponds to this dissolved study area you can see that now it contains just one attribute and that happens to be this this entire area which which gets covered by study area 1 and study area 2 just like this all right so i hope you guys got the idea of using dissolve geoprocessing operation using geopandas and now we'll move on to the next geoprocessing operation and that's how to create a buffer all right so to demonstrate this to you guys what i'm going to do is i'm going to actually use this line or this river shape file or this river geopandas geode frame that we have over here and i'm going to create a buffer around this line for a certain distance i'm not sure maybe about 500 meters of a buffer so that it creates a polygon around that specific line demarcating that buffer which extends 500 meters further away from the light for the entire stretch of the line so now we'll see how we can do that and before i proceed with that i might need to actually check a few things for example first i would like to see what the coordinate reference system of this river shape file is so what i'm going to do is i'm going to say river and if i say crs over here i can get to know the fact that over here my coordinate reference system is epsg 4326 which corresponds to the geographic coordinate reference system and since i'm going to deal with meters when i'm specifying the buffer zone i would actually like to change the this coordinate reference system from being a geographic coordinate reference system to a projected coordinate reference system and this area is actually corresponding to one part in malaysia so what i'm going to do is i'm going to use one of the projected coordinate reference systems used in malaysia by making use of its epsg code and i'm going to first transform the coordinate reference system from this wgs1984 geographic coordinate reference system into a projected coordinate reference system so the first thing that i'm going to do is reprojecting the river geopandas geodata frame into a projected coordinate reference system so you can simply do that by specifying river dot 2 crs and over here i'm going to specify the eps g code and the eps record of the corresponding coordinate reference system for this part of malaysia is 24 547 but in your case if you are actually doing this for a different part in the world then i think you might be able to very easily find what the corresponding eps g code of that particular projected coordinate reference system and from there you can actually first do this conversion and i'm going to make this one equal to a new file called river river projected first run let's run this one and see how it looks here after i run this one i can first plot the previous river geodata frame and you can see the xy coordinates are actually in decimal degrees and now if i go ahead and plot the river projected geodata frame now you can see that the xy coordinates have been changed from this particular from these values to be in meters as you can see over here well you will be able to see that from this attributes table as well under this geometry column you can see that these items in this line string are basically in decimal degrees but in the river projected you can see that these items are now in in utm coordinates which happens to be in meters and the other thing that you need to keep in mind when doing the buffer operation is that you can do the buffer you can perform the buffer operations only on jio series now the difference between the jio series and the geo data frame is basically that if you take this entire thing it's actually called a jio data frame a geopandas geodata frame but if you just consider this geometry column without everything else then actually it happens to be a jio series for example you can check the type of river projected and you can see over here it's joe pandas jio data frame but if you take only the geometry column of river projected from here you can see its jio series so we are going to perform this buffer operation on this jio series so i'm going to simply copy this one and paste it over here and i'm going to name this one as buffer let's say 500 meters that's going to be equal to river projected and its geometry column dot buffer and we can specify the distance [Music] to be 500 meters and after that i'm going to ask the program to plot [Music] well first let's run this one and see what happens here you can see that the command ran without any issue so now i'm going to again plot the river projected first and after that i'm going to plot this buffer 500 meters just so that we can see the difference and over here you can see that now the reverse stretch has been actually expanded using this given buffer of 500 meters well if you would like to enlarge this maybe we can increase the figure size to be about 10 or maybe 7 by 7 and now you can see that a bit more clearly where this is the buffers on which corresponds to that particular line that we had before with a buffer distance of 500 meters and you can do this for polygons and even points depending on your specific case but the method that you have to follow will basically be the same as you can see over here so now we'll move on to the next geo-pricing operation and that's actually how to obtain the centroid of certain polygons all right so i'm going to get rid of this one and and similar to the buffer operation we can obtain the centroid but we have to make sure that we are executing this on a jio series instead of a jio pandas geodata frame so to demonstrate this to you guys let's see that maybe i think i might go with the union again because the once we once i get the union i i will actually get three different attributes which means three different pieces of polygons as you can see and after that i will try to see if i can get the centroid of each of those polygons in the form of a jio series again so first i'm going to create the union similar to what we did before going to overlay gpd1 overlays a1 and s82 and how we are going to do that is by taking the union and i will plot to make sure we get what we want and as you can see over here it's made out of three different polygons maybe when i plot this i can specify the edge color to be something like black so that you guys will be able to see the boundary of each of the polygons each of the polygon attributes as you can see over here now if i were to get the centroid of each of these three different polygon attributes i can get that simply by creating a variable i'm going to create a variable called centroid and that's going to be equal to union but again we are going to consider the geometry column of this union jio pandas geodata frame so that this particular thing that you see over here will be a jio series and from here i'm going to create the centroid yeah after you run the command [Music] we can actually plot this centroid over here and see how it looks yeah as you can see over here the centroid of course happens to be just a point so this is how the three points actually appear so this first point that you see over here should be the centroid of this particular polygon and the second point that you see over here should be the centroid of this second polygon attribute over here and this third point is actually the centroid of this third polygon attribute that you can see over here i think it would be quite clear if i were to plot these two again in the same figure so let's go ahead and maybe quickly maybe do a quick visualization i'm going to create [Music] so since i'm going to plot two different things i'm actually going to create something called the figure one and axis one and over here the two things that i'm going to plot are the union and the centroid and the centroid will be actually on top of the union and of course i need to change the axis to be axis one over here in this case and for the centroid i'm going to change the color to be black and there will be no edge color over here [Music] here we'll see how this appears here now you can see that how this is how the centroid actually appears for each of these polygons so the centroid of this upper part of the study area is this this intersected part is this dot and this part of the study area too is basically this point that you see over here i guess that's clear for you guys all right let's move on to the next geoprocessing operation using jio pandas and we are going to discuss how to convert a given set of xy coordinates into point geometries using joe pandas so i think i won't be needing any of these items anymore so i'm just going to go ahead and take them out like this and the list of points that i'm going to use for this task is actually coming from this csv file that you see over here which i downloaded from kaggle.com which lists out different airports in the united states we have something like a code over here and we have the name of the airport the city the state and the countries usa we have the latitude and the longitude information and i'm going to use geopandas to actually convert this information into a set of point geometry so that we can visually see where exactly each of these airports are located at all right so just keep in mind that this particular csv file is located inside of our working folder so i'm also going to use pandas to first read in that csv file so i'm going to first go ahead and import pandas as pd and if i open up the file explorer over here you can see that [Music] i have that particular csv file over here which is this us underscore airports so i'm going to create a variable called airpods data and that's going to be equal to pd dot read csv now we are using the pandas library and again since this file is located inside my working folder in which i have saved my python script i do not really have to worry about specifying this full path i can simply say us airpods.csp and after that i can run the command and now if i check airports data dot head maybe we'll call for the first 10 items just to see whether the data got imported properly or not and now you can see that the data is actually already here and we get to see the different columns of this airpods data pandas data frame simply by saying columns and you can see the different columns over here as well and now what i'm going to do is i'm going to make use of this latitude and longitude information to create the special geometry column which is an essential item which we need to have when we create a geopandas geodata frame so i'm first going to create the corresponding geometry column and to create this geometry column i'm going to import one more shape one more library called shapely well what i'm going to import is i'm going to import from shapely dot geometry i'm going to import point so that i can use that in here this is going to be point x y for x y in zip and we are going to use the same data that we imported over here but not really the entire pandas data frame we are going to use the longitude [Music] by selecting that column and in addition to that we're going to again use the latitude information i'm just going to copy and paste it from the these column headings as you can see over here and then let's run this one and see here now if you would like to check the type of this geometric you can see that it's actually a list and you can even have a look at the items as well as you can see over here and now what we're going to do is we're going to actually embed this in a jio pandas joe data frame so the way to create a new jio pandas geodata frame is similarly by signing the variable name first i'm going to name this jio pandas geodata from as airports underscore us and that's going to be gpd dot jio data frame and i'm going to use the same pandas data frame that we used over here and we need to specify the geometric column and i'm going to assign the geometry column to be this list which we created over here and finally i need to assign the crs and the crs is going to be let's say if i wanted to use the crs from one of these existing shape files that you can see over here i have one shapefile of the different states of the us so what if i use the same crs by first importing this shape file over here and then we can check the crs of this shapefile and we can directly assign that crs to be the crs of this new jio new jio pandas geodata frame which we are going to create right over here so for that over here let's go ahead and first [Music] import stats s3 [Music] yeah i'm going to create a variable called u.s states and that's going to be equal to gpd dot read file and that's simply going to be this usstates.shp yeah if you happen to run this one in your console first so that you can do a quick check and if you happen to and if you happen to check the crs dot crs you can see the crs is actually a psg 4326 so what i'm going to do is i'm going to assign the same crs of this particular shape file or this particular geopandas geodata frame in this particular in this case simply by saying that take the crs of this u.s states geopandas geodata frame yeah that's an easy way to avoid typing out this right here so that it will directly extract that crs from this existing uh usted's geodata frame here after that i can run this one and as you can see it took about two seconds to run that command and now if i wanted to plot this airport us dot plot you can see that the point you can see now the point geometries get plotted just like this now you can always go ahead and inspect the attributes table we can go to these airports us and over here now you can see that all of that information have been imported into this attributes table along with this special geometry column which actually makes it possible for us to plot this on a map just like this and of course if you would like to reduce the marker size you can do that as well but as you can see over here now we've successfully managed to import a set of xy coordinate into python and we managed to create point geometries using jio pandas by making use of those light long information all right let's move on to the next geoprocessing task that we are going to talk about today and that's how to perform attribute joins using geopandas alright so to demonstrate to you guys how to perform an attributes join using jio pandas i'm going to use two files first i'm going to use this airport's us jopanna's geodata frame which we just created so if i wanted to have a look at the attributes table you can see that the items that we have inside over here we have this iata code we have the airport name the city the state code the country the latitude and the longitude i have another csv file which contains the state code and the actual name of the state so what i'm going to do is i'm going to use geopandas to actually merge these two layers in a way that i would be able to transfer this state name correctly as a new column into this job and as jio data frame based on these two columns which actually consist of the state the state code now now in order to do that you have to make sure that you actually have the same heading for the column by which you're going to perform this attributes join so what i'm going to do is i'm going to first import this state names and code csv file and after that i will go ahead and change this name of this column from being state to state code so that we have the heading of the two columns to be exactly the same all right so first i'm going to import the csv file and to do that we can simply use the pandas library i'm going to create this variable called state names codes that's going to be equal to pd dot read csv and that csv file is actually located right inside here this file so i'm going to copy the file name [Music] and paste it over here [Music] and after that i'm going to run the script and now if i check the first 10 items you can see that the items actually got import you can see that the csv file got imported without any issue and the next thing that i'm going to do is i'm going to take this airpods us and i'm going to change the column name from being the state to the state code so this is going to be renaming the column heading and [Music] that's going to be airport us dot rename columns the original name is state and i'm going to rename it with this name right over here and i'm going to say in place equals true just to make this a permanent change and now we can simply run this and we can see this airport's us dot columns and now you can see that the state has been changed to state code you can see that from here as well and now you can see that this what was initially state has now been changed to state underscore code all right now i think we can go ahead and perform the attributes join and we can do that attributes join simply on this airport's us geopanda zero data frame and that's going to be equal to again airports us i'm going to put this on the left side of this merge operation and on the right side i'm going to specify from which pandas data frame i'm going to extract the corresponding information and i'm going to perform this merge operation on [Music] the specific column name which happens to be state code [Music] and after that we can hit run and now if i open up this airport's us jio pandas geodata frame you will be able to see that we managed to extract this particular column from that pandas data frame so that you can see what is the corresponding what is the full name of the state which corresponds to this state code that you can see over here so in this case this nm corresponds to new mexico tx is texas similarly you can see that wa is washington nd is not decoder just like that you can see that the attributes join happened perfectly with no issues at all all right so now let's move on to the final geoprocessing operation that we are going to discuss today in this tutorial and that's how to perform spatial joins using geopandas and first of all i'm going to get rid of all of these which i did to demonstrate to you guys the attributes join operations and for this i'm going to be using this usdates.shp shapefile so i would like to actually plot this right over here as well and if you see visually this is how that shape file looks and we can have a look at the attributes the individual attributes as well so if i check the attributes maybe the first 15 attributes and over here you can see in terms of the attributes we just have two columns over here we have the name of the state and we just have this special geometry column right for this i'm also going to do a bit of cleaning up of this airport's us geopandas geodata frame over here now you can see that we have actually quite a number of columns over here but let's say in case if we happen to have only just only the airport name and this geometry column so i'm going to extract just those two columns i'm going to take only this airport and this geometry column [Music] and we can go ahead and run this [Music] and now if i check this airport's us you see that we only have this airport column and this geometry column and just because i retained this geometry column it actually didn't lose its geometrical properties now even if i simply say airports us dot plot you will see that we are actually still able we are still able to plot these two together and just for the purposes of visualizing let's go ahead and visualize these two together as well so i'm going to create the figure and the axis and that's going to be plt dot [Music] 8x8 and now i'm going to plot first the base layer which is going to be this u.s states dot plot and over here i'm going to specify the axis to be equal to the axis and color will be blue and the edge color will be black and similarly the us the airports us i'm going to put them in the same axis and now we'll see how this looks we managed to plot these two together but of course we need to make some changes but i think i can reduce the marker size to be about let's say two and the color to be let's say green yeah even though it's not so clear the points the individual points because we have actually quite a number of points you can see that the two layers have been plotted on top of each other you can see that the airport's us layer has been now plotted on top of this u.s states layer so what i'm going to do in order to demonstrate this special joints is basically based on the special location of each of this airport i'm going to extract the corresponding state so in this case we are not going to do any attributes join we are going to extract the information from this u.s state's base layer in which we have the name of the state and we are going to plug them in to this airport's us layer so that we can see as a new column in which state each of these airports are situated at now for your specific purpose it could be a different case but of course you can apply the the same concept in order to perform a spatial join and special joins are completely dependent on the special location of each of the different attributes so now let's go ahead and perform that special join so i'm going to use the same layer airpods us and i'm going to make it equal to geopandas spatial join s join and the things that i'm going to join are these airports us layer with this u.s states layer and over here we have to specify two arguments the two arguments are how and this op argument so what basically this how refers to is that the way you need to specify your indexes now let's say if you specify the how argument to be left then what happens is that it'll use the index from the first or or the left geodata frame that you have provided over here in this case that's going to be this airpods us and it will retain only the geometric column of that particular layer but in case if you happen to provide the argument to be right in that case it's actually going to be this particular layer and it'll be using the index from that layer and it'll retain the geometric column of that layer but in case if you use the argument to be inner in that case it will actually use the intersection of the index value from both geodata frames and it will retain only the geometry column of the left layer that you have specified over here so for this case i'm just going to go with inner and over here we also have to specify this op argument let's say if i specify the op argument to be intersect in that case the attributes will be joined if the boundary and the interior of the object intersect in any way with the boundary and the interior of the other object so for this i'm also going to go with intersect let's see how how it works out so now i think we can just go ahead and run this command yes as you can see over here the code ran without any issue so we'll open up this airport's us jio pandas geodata frame and now you can see that it extracted the corresponding state name from this base layer and plug that and plug that in over here so that we can see where each of these in which state each of this airport is actually located at and this happened completely based on the special location of each of the attributes that's the most that's the most interesting part alright guys that brings us to the end of this tutorial we discussed quite a lot of things pertaining to the applications of jio pandas library using python and if you do have any questions regarding this tutorial don't forget to comment them down below and if you did enjoy the tutorial don't forget to show your support by hitting that like button and also if you would like to see interesting tutorials like this in the weeks to come you can consider subscribing to this channel as well thanks a lot for watching guys i'll see you in the next one

Channel: GeoDelta Labs

Views: 9,245

Keywords: applications of geopandas, introduction to python geopandas, introduction to geopandas, basics of geopandas, remote sensing and gis, Intersection, Union, Symmetric Difference Difference, Dissolve, Buffer, Centroid, XY Coordinates to Point Geometries, Attribute Joins, Spatial Joins, geopandas, how to create a buffer, gis programming

Id: HtYxzt55-1w

Length: 51min 32sec (3092 seconds)

Published: Mon Nov 02 2020