Data Cleansing an offenen Daten aus Köln

Data about the different quarters of Cologne, Germany

This visualization presents a multi-faceted demographic profile of Cologne’s 86 districts using a CSV from a public gist, with each row representing a district. It shows how metrics like population density, age distribution, and migration background vary across the city through an interactive scatter plot, where each point is a district colored by its borough. Built with D3’s `forceSimulation` for layout and `geoPath`-based Voronoi tessellation for district shapes, the tooltip dumps all raw column values on hover. The data source is the city’s open statistics portal, loaded directly into the page for filtering.

59% match

Playing around with geopy using "Geburtsorte" - Data from Moers - [https://www.offenesdatenportal.de/dataset/geburtsorte-der-moerser-bevoelkerung]

This example visualizes the birthplaces of Moers’ population, mapping place names and counts from the city’s open data portal onto a geographic context. It uses Python with `pandas` to load and clean a CSV file, dropping rows for unknown or unclear entries, and prepares the data for geocoding. The code imports `geopy` to convert the listed birth locations into coordinates, which would then support plotting on a map, though no specific mapping or rendering library is shown in the provided excerpts.

56% match

Cleaning Up

This visualization shows voter turnout percentages across Cologne’s distinct neighborhoods, or *Veedel*, as horizontal bars sorted in descending order. Using D3 v3, it employs an ordinal scale with `rangeRoundBands` for the y-axis and linear scales for the x-axis, rendering SVG rectangles and dual top/bottom axes via `d3.svg.axis`. The data, sourced from Offene Daten Köln’s 2014 local election records, is loaded from a CSV, with hover effects and tooltips revealing exact percentages.

56% match

Created on Cognitive Class Labs

This example demonstrates the process of data wrangling on an automobile dataset using Python. It shows how to import data from a URL into a Pandas DataFrame, assign descriptive column headers, and prepare the data for analysis. The notebook uses Pandas for data manipulation and matplotlib for potential visualization. The dataset is sourced from an IBM-hosted CSV file, and the code handles tasks such as reading the data and setting up the structure for further cleaning and transformation.

DDAVINDER

Created on Cognitive Class Labs

DDAVINDER

Data for Public aid in cologne city quarters

This visualization shows the long-term trend of public aid recipients in Cologne, comparing categories such as total persons in need, single parents, and couples with children from 2005 to 2012. It uses a small-multiple line chart layout, with each facet representing a different household type. The data is read from CSV files and reshaped via JavaScript, then plotted using D3.js’s `d3.line` and `d3.points` APIs. The y-axis scale is shared across facets to enable direct comparison, while the x-axis encodes the year. The source data comes from an open gist.

Gist fa05e9e0684e37e537c08085e5900435

This example shows a data-cleaning pipeline for an Instagram influencer dataset, highlighting the number of unique users and missing values across key columns. The code uses pandas to inspect and transform the data, filling missing industry labels with "General," dropping rows without captions, removing duplicate entries, and converting the post date column to datetime format for time-series indexing. The visualization relies on pandas' built-in data manipulation functions, such as `fillna`, `drop_duplicates`, and `set_index`, rather than a plotting library. The final output would be a cleaned dataframe ready for trend analysis, though no chart is generated in the provided script.

AArizonaTay