This python3 script uses OGR (you can run it from OSGeo4W Shell), to count all features of shapefiles in the current directory, sum the total, and give an average

100%

This python3 script uses OGR (you can run it from OSGeo4W Shell), to count all features of shapefiles in the current directory, sum the total, and give an average

✓ Published0🌍 Public

Last edited Jan 14, 2021

Created on Jan 14, 2021

This script calculates aggregate statistics for shapefiles in the current directory, reporting the total feature count and the average per file. It walks the directory tree using Python’s `os.walk`, opens each `.shp` file with OGR’s `ogr.Open`, and retrieves feature counts via `layer.GetFeatureCount()`. Results are accumulated in a list, then summed and divided by the number of files to produce the final average. The output prints each shapefile’s name and count, followed by the sum and average, using only the `os` and `osgeo.ogr` libraries.

AI-generated description

Similar vizzes

Loading thumbnail…

Get shp count from directory

This example scans a user-supplied directory for ESRI Shapefiles and reports the number of features in each one. It walks through subdirectories recursively using Python’s `os.walk`, opens each `.shp` file with the OGR driver from the `osgeo` library, and prints the feature count via `layer.GetFeatureCount()`. The script takes the directory path as a command-line argument and outputs the file path along with its feature total, demonstrating a simple batch inspection workflow for geospatial data.

TThomasG77

70% match

Loading thumbnail…

Gist 745c7c03b5bf6b6f59d9

This example splits a single ESRI Shapefile into multiple new Shapefiles, one for each unique value found in a specified attribute field. It demonstrates how to use the OGR library from GDAL to read the original dataset, extract all distinct attribute values, and then create filtered copies. The process first enumerates unique values, then iterates through them to create new Shapefile layers, applies an SQL attribute filter to exclude features not matching the current value, and deletes the remaining unwanted features.

RRCura

52% match

Loading thumbnail…

Simple shell script to merge shapefiles from INE

This example shows a shell script that downloads, reprojects, and merges Mexican electoral geography shapefiles from the INE’s distritación 2017 federal collection into a single output file. It processes layers such as `SECCION`, `ENTIDAD`, and `DISTRITO` for all 32 states, using `ogr2ogr` from GDAL to reproject each source into EPSG:4326 and append the features. The script handles a structural anomaly for file number 06 by adjusting the source path, and it relies on `/vsizip//vsicurl/` for remote zip access. The merged result is saved as a new shapefile named with the layer and `-wgs84` suffix.

GGeoWill

51% match

Loading thumbnail…

Gist 6857a0243ad24b95924f

This example uses pandas and Matplotlib within an IPython notebook to aggregate and compare FreeSurfer brain segmentation statistics across multiple CSV files. It processes volume, area, thickness, and aseg measurements, grouping them by subject ID and averaging values to produce normalized and unnormalized output tables. The code merges separate CSV files, collapses left/right hemisphere labels into combined metrics, and then saves the resulting aggregated data for each measurement type.

Aakeshavan

50% match

Loading thumbnail…

Gist 1353003

This example demonstrates a spatial SQL query that computes raster statistics for a custom polygon region. It uses PostGIS functions to sum pixel values, count pixels, and calculate the average value across a 10x10 grid of sub-pixels within each raster tile. The query filters raster tiles intersecting the target polygon and then intersects translated grid cells against the same polygon. The code relies on `ST_Value`, `ST_Intersects`, `ST_Translate`, `ST_MakeEnvelope`, and `generate_series` to perform the analysis, extracting data from the `carbon_query_test_s` table.

Aandrewxhill

50% match

Loading thumbnail…

Gist 788c07fefb1d6fb9dfe6d546010188e1

The visualization displays a pie chart of file type counts across a directory tree, highlighting dominant extensions while omitting labels and percentages for slices under 5%. It also computes and prints statistics for GLTF generators, JSON format types, and directory extension combinations. The chart is rendered with Matplotlib’s `plt.pie`, using `autopct_func` to conditionally show percentages and larger font sizes for readability. Data is collected by scanning subdirectories with `os.walk` and JSON parsing via `json.load`.

Aandybak

50% match

Loading thumbnail…

subdirectory files

This example demonstrates how to recursively traverse a directory structure using Python’s `os.walk` API to collect all file paths into a set, then prints the count and the set of relative paths. The visualization shows the logical hierarchy of files and folders, with the code relying on `os.path.relpath` to generate clean, relative file locations from the root directory. The rendering approach is text-based output rather than a graphical chart, emphasizing the data collection and enumeration process. The script’s behavior is limited to listing files, with no sorting, filtering, or graphical mapping applied.

CCameron-Grams

49% match

Loading thumbnail…

Gist 243f658d1001b2e31c18d383a6757c77

This example demonstrates how GDAL’s `ogrmerge.py` script combines multiple country shapefiles into a single merged dataset, adding a custom field that records each source layer’s identity. The shell script downloads Germany and France boundaries from the geo-countries GitHub repository via `ogr2ogr`, then merges them into `merged.shp`. The `-src_layer_field_name` and `-src_layer_field_content` options populate the custom column with template keywords like `{DS_NAME}` and `{AUTO_NAME}`, which `ogrinfo` later displays. It highlights how GDAL’s vector merge tools can trace data provenance during batch processing.

TThomasG77

49% match