{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Geopandas\n", "\n", "Geopandas (http://geopandas.org/) makes it possible to work with geospatial data in Python in a relatively easy way. Geopandas combines the capabilities of the data analysis library [pandas](https://pandas.pydata.org/pandas-docs/stable/) with other packages like [shapely](https://shapely.readthedocs.io/en/stable/manual.html) and [fiona](https://fiona.readthedocs.io/en/latest/manual.html) for managing spatial data. \n", "\n", "The main data structures in geopandas; `GeoSeries` and `GeoDataFrame` extend the capabilities of `Series` and `DataFrames` from pandas. This means that we can apply our pandas skills also with geopandas data structures. If you need to refresh your memory about pandas, check out week 5 and 6 lesson materials from the [Geo-Python website](geo-python.github.io). \n", "\n", "The main difference between geodataframes and pandas dataframes is that [a geodataframe should contain one column for geometries](http://geopandas.org/data_structures.html#geodataframe) (by default, the name of this column is `'geometry'`). The geometry column is a [geoseries](http://geopandas.org/data_structures.html#geoseries) which contains the geometries (points, lines, polygons, multipolygons etc.) for each row of data. \n", "\n", "![geodataframe.png](img/geodataframe.png)\n", "\n", "As we learned in the Geo-Python course, it is conventional to import pandas as `pd`. Similarly,we will import geopandas as `gpd`:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this lesson, we will cover basics steps needed for interacting with spatial data in Python using geopandas:\n", "\n", "- Managing filepaths\n", "- Reading a shapefile \n", "- Geometry calculations \n", "- Writing a shapefile\n", "- Grouping and splitting spatial data into multiple layers\n", "\n", "Before diving deeper into geopandas functionalities, let's first acquire some input data to work with. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Input data: Finnish topographic database \n", "\n", "- [Topographic Database from the National Land Survey of Finland (NLS)](https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/topographic-database). \n", "- The data set is licensed under the NLS' [open data licence](https://www.maanmittauslaitos.fi/en/opendata-licence-cc40) (CC BY 4.0).\n", "- Structure of the data is described in a separate Excel file ([download link](http://www.maanmittauslaitos.fi/sites/maanmittauslaitos.fi/files/attachments/2018/10/maastotietokanta_kohdemalli_eng.xlsx)).\n", "- Further information about file naming at [fairdata.fi](https://etsin.fairdata.fi/dataset/5023ecc7-914a-4494-9e32-d0a39d3b56ae).\n", "- We acquired the data via the [CSC open data portal](https://avaa.tdata.fi/web/paituli/latauspalvelu):\n", "\n", "![Paituli data download](img/Paituli_maastotietokanta_download.png)\n", "\n", "\n", "In this lesson, we will focus on **terrain objects** (Feature group: \"Terrain/1\") downloaded as Shapefiles. According to the [naming convention of the Topographic Database](https://etsin.fairdata.fi/dataset/5023ecc7-914a-4494-9e32-d0a39d3b56ae) for Shapefiles, all files that start with a letter `m` and end with `p` contain the objects we are interested in (Terrain/1 polygons). Furthermore, the Terrain/1 feature group contains several feature classes. **Our final task in this lesson is to save all these feature classes into separate files**.\n", "\n", "*Terrain/1 features in the Topographic Database:*\n", "\n", "| feature class | Name of feature | Feature group |\n", "|----------------|------------------------------------------------------------|---------------|\n", "| 32421 | Motor traffic area | Terrain/1 |\n", "| 32200 | Cemetery | Terrain/1 |\n", "| 34300 | Sand | Terrain/1 |\n", "| 34100 | Rock - area | Terrain/1 |\n", "| 34700 | Rocky area | Terrain/1 |\n", "| 32500 | Quarry | Terrain/1 |\n", "| 32112 | Mineral resources extraction area, fine-grained material | Terrain/1 |\n", "| 32111 | Mineral resources extraction area, coarse-grained material | Terrain/1 |\n", "| 32611 | Field | Terrain/1 |\n", "| 32612 | Garden | Terrain/1 |\n", "| 32800 | Meadow | Terrain/1 |\n", "| 32900 | Park | Terrain/1 |\n", "| 35300 | Paludified land | Terrain/1 |\n", "| 35412 | Bog, easy to traverse forested | Terrain/1 |\n", "| 35411 | Open bog, easy to traverse treeless | Terrain/1 |\n", "| 35421 | Open fen, difficult to traverse treeless | Terrain/1 |\n", "| 33000 | Earth fill | Terrain/1 |\n", "| 33100 | Sports and recreation area | Terrain/1 |\n", "| 36200 | Lake water | Terrain/1 |\n", "| 36313 | Watercourse area | Terrain/1 |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading data\n", "\n", "On Binder and CSC Notebook environment, you can use `wget` program to download the data from the command line. Let's download the data ([download link](https://github.com/AutoGIS/data/raw/master/L2_data.zip)) into the same folder with the lesson 2 notebooks (`.../notebooks/L2`):\n", "\n", "- Open up a new terminal window\n", "- Navigate to the correct folder in the terminal:\n", "\n", "```\n", "# Navigate to lesson 2 notebooks directory:\n", "$ cd /home/jovyan/work/autogis/notebooks/notebooks/L2\n", " \n", "```\n", "- use `wget` utility to dowload the data from the dowload link:\n", " \n", "```\n", "$ wget https://github.com/AutoGIS/data/raw/master/L2_data.zip\n", " \n", "```\n", "
\n", "\n", "**Copy-paste**\n", " \n", "You can copy/paste things to JupyterLab Terminal by pressing `SHIFT` + `RIGHT-CLICK` on your mouse and choosing `Paste`.\n", "\n", "
\n", "\n", "Once you have downloaded the `L2_data.zip` file into your home directory, you can unzip the file using `unzip` command in the Terminal (or e.g. 7zip on Windows if working with own computer). Run the following commands in the `.../notebooks/L2` -directory:\n", "\n", "``` \n", "$ unzip L2_data.zip\n", "$ ls L2_data\n", "\n", "```\n", "You can also check the contents of the downloaded and unzipped file in the file browser window. \n", "\n", "The L2_data folder contains several subfolders. After unzipping the downloaded file, you can find the data for this tutorial under: `L2_data/NLS/2018/L4/L41/L4132R.shp`. Notice that Shapefile -fileformat contains many separate files such as `.dbf` that contains the attribute information, and `.prj` -file that contains information about coordinate reference system." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Managing filepaths\n", "\n", "Built-in module `os` provides many useful functions for interacting with the operating system. One of the most useful submodules in the os package is the [os.path-module](https://docs.python.org/2/library/os.path.html) for manipulating file paths. This week, we have data in different sub-folders and we can practice how to use `os` path tools when defining filepaths.\n", "\n", "- Let's import `os` and see how we can construct a filepath by joining a folder path and file name:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L2_data/NLS/2018/L4/L41/L4132R.shp/m_L4132R_p.shp\n" ] } ], "source": [ "import os\n", "\n", "# Define path to folder\n", "input_folder = r\"L2_data/NLS/2018/L4/L41/L4132R.shp\"\n", "\n", "# Join folder path and filename \n", "fp = os.path.join(input_folder, \"m_L4132R_p.shp\")\n", "\n", "# Print out the full file path\n", "print(fp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading a Shapefile\n", "\n", "Typically reading the data into Python is the first step of the analysis pipeline. There are various different GIS data formats available. [Shapefile](https://en.wikipedia.org/wiki/Shapefile), [GeoJSON](https://en.wikipedia.org/wiki/GeoJSON), [KML](https://en.wikipedia.org/wiki/Keyhole_Markup_Language), and [GPKG](https://en.wikipedia.org/wiki/GeoPackage) are one of the most common vector data formats currently in use. [Geopandas](http://geopandas.org/io.html) is capable of reading data from all of these formats (plus many more). \n", "\n", "In geopandas, we use a generic function [.from_file()](http://geopandas.org/reference.html#geopandas.GeoDataFrame.to_file) for reading in different data formats. In the bacground, Geopandas uses [fiona.open()](https://fiona.readthedocs.io/en/latest/fiona.html#fiona.open) when reading in data.\n", "\n", "- When reading in a Shapefile, we only need to pass the filepath when reading data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd\n", "\n", "# Read file using gpd.read_file()\n", "data = gpd.read_file(fp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's see check the data type:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "geopandas.geodataframe.GeoDataFrame" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see that our `data` -variable is a `GeoDataFrame`. GeoDataFrame extends the functionalities of\n", "`pandas.DataFrame` in a way that it is possible to handle spatial data using similar approaches and datastructures as in pandas (hence the name geopandas). \n", "\n", "- Let's take a look at our data and print the first rows using the `head()` -function:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TEKSTI RYHMA LUOKKA TASTAR KORTAR KORARV KULKUTAPA KOHDEOSO \\\n", "0 None 64 32421 5000 0 0.0 0 1812247077 \n", "1 None 64 32421 5000 0 0.0 0 1718796908 \n", "2 None 64 32421 20000 0 0.0 0 411167695 \n", "3 None 64 32421 20000 0 0.0 0 411173768 \n", "4 None 64 32421 20000 0 0.0 0 411173698 \n", "\n", " AINLAHDE SYNTYHETKI ... KARTOGLK ALUEJAKOON VERSUH SUUNTA SIIRT_DX \\\n", "0 1 20180125 ... 0 0 0 0 0 \n", "1 1 20180120 ... 0 0 0 0 0 \n", "2 1 20180120 ... 0 0 0 0 0 \n", "3 1 20180120 ... 0 0 0 0 0 \n", "4 1 20180120 ... 0 0 0 0 0 \n", "\n", " SIIRT_DY KORKEUS ATTR2 ATTR3 \\\n", "0 0 0.0 0 0 \n", "1 0 0.0 0 0 \n", "2 0 0.0 0 0 \n", "3 0 0.0 0 0 \n", "4 0 0.0 0 0 \n", "\n", " geometry \n", "0 POLYGON ((379394.248 6689991.936, 379389.790 6... \n", "1 POLYGON ((378980.811 6689359.377, 378983.401 6... \n", "2 POLYGON ((378804.766 6689256.471, 378817.107 6... \n", "3 POLYGON ((379229.695 6685025.111, 379233.366 6... \n", "4 POLYGON ((379825.199 6685096.247, 379829.651 6... \n", "\n", "[5 rows x 21 columns]\n" ] } ], "source": [ "print(data.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Check all column names:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['TEKSTI', 'RYHMA', 'LUOKKA', 'TASTAR', 'KORTAR', 'KORARV', 'KULKUTAPA',\n", " 'KOHDEOSO', 'AINLAHDE', 'SYNTYHETKI', 'KUOLHETKI', 'KARTOGLK',\n", " 'ALUEJAKOON', 'VERSUH', 'SUUNTA', 'SIIRT_DX', 'SIIRT_DY', 'KORKEUS',\n", " 'ATTR2', 'ATTR3', 'geometry'],\n", " dtype='object')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As most of you probably notice, all the column names are in Finnish... \n", "- Let's select only the useful columns and rename them into English:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data = data[['RYHMA', 'LUOKKA', 'geometry']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Define new column names in a dictionary:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "colnames = {'RYHMA':'GROUP', 'LUOKKA':'CLASS'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- rename the columns:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "data.rename(columns=colnames, inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['GROUP', 'CLASS', 'geometry'], dtype='object')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**Task**\n", " \n", "Figure out the following information from our input data based on your pandas-skills from the Geo-Python course:\n", " \n", "- Number of rows?\n", "- Number of classes?\n", "- Number of groups?\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solutions:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of rows 4311\n", "Number of classes 20\n", "Number of groups 1\n" ] } ], "source": [ "print(\"Number of rows\", len(data['CLASS']))\n", "print(\"Number of classes\", data['CLASS'].nunique())\n", "print(\"Number of groups\", data['GROUP'].nunique())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is always a good idea to explore your data also on a map. Creating a simple map from a `GeoDataFrame` is really easy: you can use ``.plot()`` -function from geopandas that **creates a map based on the geometries of the data**. Geopandas actually uses matplotlib for plotting which we introduced in [Lesson 7 of the Geo-Python course](https://geo-python.github.io/site/notebooks/L7/matplotlib.html).\n", "\n", "- Let's try it out, and plot our GeoDataFrame:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "data.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voilá! As we can see, it is really easy to produce a map out of your Shapefile with geopandas. Geopandas automatically positions your map in a way that it covers the whole extent of your data.\n", "\n", "*If you are living in the Helsinki region, you might recognize the shapes plotted on the map!*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Geometries in Geopandas\n", "\n", "Geopandas takes advantage of Shapely's geometric objects. Geometries are stored in a column called *geometry* that is a default column name for\n", "storing geometric information in geopandas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's print the first 5 rows of the column 'geometry':" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 POLYGON ((379394.248 6689991.936, 379389.790 6...\n", "1 POLYGON ((378980.811 6689359.377, 378983.401 6...\n", "2 POLYGON ((378804.766 6689256.471, 378817.107 6...\n", "3 POLYGON ((379229.695 6685025.111, 379233.366 6...\n", "4 POLYGON ((379825.199 6685096.247, 379829.651 6...\n", "Name: geometry, dtype: geometry\n" ] } ], "source": [ "# It is possible to get a specific column by specifying the column name within square brackets []\n", "print(data['geometry'].head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see the `geometry` column contains familiar looking values, namely Shapely `Polygon` -objects. Since the spatial data is stored as Shapely objects, **it is possible to use all of the functionalities of the Shapely module** when dealing with geometries in geopandas.\n", "\n", "Let's have a closer look at the polygons and try to apply some of the [Shapely methods we learned last week](https://automating-gis-processes.github.io/site/notebooks/L1/geometric-objects.html#Polygon). \n", "\n", "- Let's start by checking the area of the first polygon in the data:\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Polygon: POLYGON ((379394.248 6689991.936, 379389.79 6690000, 379408.646 6690000, 379394.248 6689991.936))\n", "Area: 76.0 square meters\n" ] } ], "source": [ "print(\"Polygon:\", data.at[0, \"geometry\"])\n", "print(\"Area:\", round(data.at[0, \"geometry\"].area,0), \"square meters\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Let's do the same for the first five rows in the data; \n", "\n", "- Iterate over the GeoDataFrame rows using the `iterrows()` -function that we learned [during the Lesson 6 of the Geo-Python course](https://geo-python.github.io/site/notebooks/L6/pandas/advanced-data-processing-with-pandas.html#Iterating-rows-and-using-self-made-functions-in-Pandas).\n", "- For each row, print the area of the polygon (here, we'll limit the for-loop to a selection of the first five rows):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Polygon area at index 0 is: 76.03 m^2\n", "Polygon area at index 1 is: 2652.05 m^2\n", "Polygon area at index 2 is: 3185.65 m^2\n", "Polygon area at index 3 is: 13075.17 m^2\n", "Polygon area at index 4 is: 3980.68 m^2\n" ] } ], "source": [ "# Iterate over rows and print the area of a Polygon\n", "for index, row in data[0:5].iterrows():\n", " \n", " # Get the area from the shapely-object stored in the geometry-column\n", " poly_area = row['geometry'].area\n", " \n", " # Print info\n", " print(\"Polygon area at index {index} is: {area:.2f} m^2\".format(index=index, area=poly_area))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see from here, all the functionalities of **pandas**, such as the `iterrows()` function, are directly available in Geopandas without the need to call pandas separately because Geopandas is an **extension** for pandas. \n", "\n", "In practice, it is not necessary to use the iterrows()-approach to calculate the area for all features. Geodataframes and geoseries have an attribute `area` which we can use for accessing the area for each feature at once: " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 76.027392\n", "1 2652.054186\n", "2 3185.649995\n", "3 13075.165279\n", "4 3980.682621\n", "dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.area.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's next create a new column into our GeoDataFrame where we calculate and store the areas of individual polygons:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Create a new column called 'area' and assign the area of the Polygons into it\n", "data['area'] = data.area" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Check the output:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 76.027392\n", "1 2652.054186\n", "2 3185.649995\n", "3 13075.165279\n", "4 3980.682621\n", "Name: area, dtype: float64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['area'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These values correspond to the ones we saw in previous step when iterating rows.\n", "\n", "- Let's check what is the `min`, `max` and `mean` of those areas using familiar functions from our previous Pandas lessions.\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Maximum area\n", "max_area = data['area'].max()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Minimum area\n", "min_area = data['area'].min()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Mean area\n", "mean_area = data['area'].mean()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max area: 4084558.0 square meters\n", "Min area: 1.0 square meters\n", "Mean area: 11522.0 square meters\n" ] } ], "source": [ "print(\"Max area: {maximum} square meters\".format(maximum=round(max_area, 0)))\n", "print(\"Min area: {minimum} square meters\".format(minimum=round(min_area, 0)))\n", "print(\"Mean area: {mean} square meters\".format(mean=round(mean_area, 0)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing a shapefile\n", "\n", "It is possible to export GeoDataFrames into various data formats using [gpd.to_file()](http://geopandas.org/io.html#writing-spatial-data). Here, we will first learn how to export a subset of the data into a Shapefile.\n", "\n", "- Let's first select one class (class number `36200`, \"Lake water\") from the data as a new GeoDataFrame:\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Select a class\n", "selection = data.loc[data[\"CLASS\"]==36200]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Check the selection:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "selection.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- write this layer into a new Shapefile using the `gpd.to_file()` -function:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Create a output path for the data\n", "output_folder = r\"L2_data/\"\n", "output_fp = os.path.join(output_folder, \"Class_36200.shp\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Write those rows into a new file (the default output file format is Shapefile)\n", "selection.to_file(output_fp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "**Task**\n", " \n", "Read the output Shapefile in a new geodataframe, and check that the data looks ok.\n", "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grouping the Geodataframe\n", "\n", "One really useful function that can be used in Pandas/Geopandas is [.groupby()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) which groups data based on values on selected column(s). We saw and used this function already in [Lesson 6 of the Geo-Python course](https://geo-python.github.io/2018/notebooks/L6/pandas/advanced-data-processing-with-pandas.html#Aggregating-data-in-Pandas-by-grouping). \n", "\n", "Next we will automate the file export task; we will group the data based on column `CLASS` and export a shapefile for each class.\n", "\n", "Let's continue with the same input file we already read previously into the variable `data`. We also selected and renamed a subset of the columns.\n", "\n", "- Check the first rows of the data:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " GROUP CLASS geometry \\\n", "0 64 32421 POLYGON ((379394.248 6689991.936, 379389.790 6... \n", "1 64 32421 POLYGON ((378980.811 6689359.377, 378983.401 6... \n", "2 64 32421 POLYGON ((378804.766 6689256.471, 378817.107 6... \n", "3 64 32421 POLYGON ((379229.695 6685025.111, 379233.366 6... \n", "4 64 32421 POLYGON ((379825.199 6685096.247, 379829.651 6... \n", "\n", " area \n", "0 76.027392 \n", "1 2652.054186 \n", "2 3185.649995 \n", "3 13075.165279 \n", "4 3980.682621 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `CLASS` column in the data contains information about different land use types. With `.unique()` -function we can quickly see all different values in that column:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[32421 32200 34300 34100 34700 32500 32112 32111 32611 32612 32800 32900\n", " 35300 35412 35411 35421 33000 33100 36200 36313]\n" ] } ], "source": [ "# Print all unique values in the column\n", "print(data['CLASS'].unique())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Now we can use that information to group our data and save all land use types into different layers:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Group the data by class\n", "grouped = data.groupby('CLASS')\n", "\n", "# Let's see what we have\n", "grouped" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, `groupby` -function gives us an object called `DataFrameGroupBy` which is similar to list of keys and values (in a dictionary) that we can iterate over. For more information about grouped objects, see [Lesson 6 of the Geo-Python course](https://geo-python.github.io/2018/notebooks/L6/pandas/advanced-data-processing-with-pandas.html#Aggregating-data-in-Pandas-by-grouping).\n", "\n", "- Check out all group keys:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys([32111, 32112, 32200, 32421, 32500, 32611, 32612, 32800, 32900, 33000, 33100, 34100, 34300, 34700, 35300, 35411, 35412, 35421, 36200, 36313])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped.groups.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The group keys are unique values from the column by which we grouped the dataframe.\n", "\n", "- Check how many rows of data each group has:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Terrain class: 32111\n", "Number of rows: 1 \n", "\n", "Terrain class: 32112\n", "Number of rows: 1 \n", "\n", "Terrain class: 32200\n", "Number of rows: 2 \n", "\n", "Terrain class: 32421\n", "Number of rows: 110 \n", "\n", "Terrain class: 32500\n", "Number of rows: 2 \n", "\n", "Terrain class: 32611\n", "Number of rows: 257 \n", "\n", "Terrain class: 32612\n", "Number of rows: 11 \n", "\n", "Terrain class: 32800\n", "Number of rows: 80 \n", "\n", "Terrain class: 32900\n", "Number of rows: 28 \n", "\n", "Terrain class: 33000\n", "Number of rows: 5 \n", "\n", "Terrain class: 33100\n", "Number of rows: 118 \n", "\n", "Terrain class: 34100\n", "Number of rows: 3005 \n", "\n", "Terrain class: 34300\n", "Number of rows: 1 \n", "\n", "Terrain class: 34700\n", "Number of rows: 3 \n", "\n", "Terrain class: 35300\n", "Number of rows: 134 \n", "\n", "Terrain class: 35411\n", "Number of rows: 35 \n", "\n", "Terrain class: 35412\n", "Number of rows: 449 \n", "\n", "Terrain class: 35421\n", "Number of rows: 5 \n", "\n", "Terrain class: 36200\n", "Number of rows: 56 \n", "\n", "Terrain class: 36313\n", "Number of rows: 8 \n", "\n" ] } ], "source": [ "# Iterate over the group object\n", "for key, group in grouped:\n", "\n", " # Let's check how many rows each group has:\n", " print('Terrain class:', key)\n", " print('Number of rows:', len(group), \"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are, for example, 56 lake polygons in the input data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also check how the _last_ group looks like (we have the variables in memory from the last iteration of the for-loop):" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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43076436313POLYGON ((368411.063 6679328.990, 368411.424 6...376.503380
\n", "
" ], "text/plain": [ " GROUP CLASS geometry \\\n", "4303 64 36313 POLYGON ((377127.305 6688073.257, 377116.045 6... \n", "4304 64 36313 POLYGON ((371141.897 6677999.999, 371139.757 6... \n", "4305 64 36313 POLYGON ((371498.720 6680399.799, 371497.585 6... \n", "4306 64 36313 POLYGON ((375668.607 6682942.062, 375671.489 6... \n", "4307 64 36313 POLYGON ((368411.063 6679328.990, 368411.424 6... \n", "\n", " area \n", "4303 9619.307973 \n", "4304 25266.167705 \n", "4305 364.087680 \n", "4306 2651.800270 \n", "4307 376.503380 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "group.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the index numbers refer to the row numbers in the original data -GeoDataFrame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check also the data type of the group:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "geopandas.geodataframe.GeoDataFrame" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(group)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, each set of data are now grouped into separate GeoDataFrames, and we can save them into separate files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saving multiple output files\n", "\n", "Let's **export each class into a separate Shapefile**. While doing this, we also want to **create unique filenames for each class**.\n", "\n", "When looping over the grouped object, information about the class is stored in the variable `key`, and we can use this information for creating new variable names inside the for-loop. For example, we want to name the shapefile containing lake polygons as \"terrain_36200.shp\".\n", "\n", "\n", "
\n", "\n", "**String formatting**\n", " \n", "There are different approaches for formatting strings in Python. Here are a couple of different ways for putting together file-path names using two variables:\n", "\n", "```\n", "basename = \"terrain\"\n", "key = 36200\n", "\n", "# OPTION 1. Concatenating using the `+` operator:\n", "out_fp = basename + \"_\" + str(key) + \".shp\"\n", "\n", "# OPTION 2. Positional formatting using `%` operator\n", "out_fp = \"%s_%s.shp\" %(basename, key)\n", " \n", "# OPTION 3. Positional formatting using `.format()`\n", "out_fp = \"{}_{}.shp\".format(basename, key)\n", "```\n", " \n", "Read more from here: https://pyformat.info/\n", "
\n", "\n", "\n", "Let's now export terrain classes into separate Shapefiles.\n", "\n", "- First, create a new folder for the outputs:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# Determine output directory\n", "output_folder = r\"L2_data/\"\n", "\n", "# Create a new folder called 'Results' \n", "result_folder = os.path.join(output_folder, 'Results')\n", "\n", "# Check if the folder exists already\n", "if not os.path.exists(result_folder):\n", " # If it does not exist, create one\n", " os.makedirs(result_folder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, you can go to the file browser and check that the new folder was created successfully.\n", "\n", "- Iterate over groups, create a file name, and save group to file:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving file terrain_32111.shp\n", "Saving file terrain_32112.shp\n", "Saving file terrain_32200.shp\n", "Saving file terrain_32421.shp\n", "Saving file terrain_32500.shp\n", "Saving file terrain_32611.shp\n", "Saving file terrain_32612.shp\n", "Saving file terrain_32800.shp\n", "Saving file terrain_32900.shp\n", "Saving file terrain_33000.shp\n", "Saving file terrain_33100.shp\n", "Saving file terrain_34100.shp\n", "Saving file terrain_34300.shp\n", "Saving file terrain_34700.shp\n", "Saving file terrain_35300.shp\n", "Saving file terrain_35411.shp\n", "Saving file terrain_35412.shp\n", "Saving file terrain_35421.shp\n", "Saving file terrain_36200.shp\n", "Saving file terrain_36313.shp\n" ] } ], "source": [ "# Iterate over the groups\n", "for key, group in grouped:\n", " # Format the filename \n", " output_name = \"terrain_%s.shp\" % str(key)\n", "\n", " # Print information about the process\n", " print(\"Saving file\", os.path.basename(output_name))\n", "\n", " # Create an output path\n", " outpath = os.path.join(result_folder, output_name)\n", "\n", " # Export the data\n", " group.to_file(outpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Excellent! Now we have saved those individual classes into separate Shapefiles and named the file according to the class name. These kind of grouping operations can be really handy when dealing with layers of spatial data. Doing similar process manually would be really laborious and error-prone." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extra: save data to csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also extract basic statistics from our geodataframe, and save this information as a text file. \n", "\n", "Let's summarize the total area of each group:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "area_info = grouped.area.sum().round()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CLASS\n", "32111 1834.0\n", "32112 2148.0\n", "32200 105737.0\n", "32421 702073.0\n", "32500 109747.0\n", "32611 13135597.0\n", "32612 107343.0\n", "32800 1465278.0\n", "32900 617209.0\n", "33000 659465.0\n", "33100 3777595.0\n", "34100 12381611.0\n", "34300 1627.0\n", "34700 2786.0\n", "35300 1382940.0\n", "35411 411198.0\n", "35412 4710133.0\n", "35421 67864.0\n", "36200 9986966.0\n", "36313 43459.0\n", "Name: area, dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "area_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- save area info to csv using pandas:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# Create an output path\n", "area_info.to_csv(\"terrain_class_areas.csv\", header=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "In this tutorial we introduced the first steps of using geopandas. More specifically you should know how to:\n", "\n", "1. Read data from Shapefile using geopandas\n", "\n", "2. Access geometry information in a geodataframe\n", "\n", "4. Write GeoDataFrame data from Shapefile using geopandas\n", "\n", "5. Automate a task to save specific rows from data into Shapefile based on specific key using `groupby()` -function\n", "\n", "6. Extra: saving attribute information to a csv file.\n", "\n", "\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 4 }