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Make Better Bar Charts in Python using Pandas Plot

14 Comments / blog, Data Visualisation, Pandas, python, Tutorials, Uncategorized /

Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data are composed. The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.

In this tutorial, we’ll examine every aspect of creating bar charts with the Pandas library in Python.

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Batch CSV Geocoding in Python with Google Maps API

74 Comments / blog, data science, Pandas, python, Tutorials, Uncategorized /

Geocode your addresses for free with Python and Google For a recent project, I ported the “batch geocoding in R” script over to Python. The script allows geocoding of large numbers of string addresses to latitude and longitude values using the Google Maps Geocoding API. The Google Geocoding API is one of the most accurate geocoding

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Cyclist in the rain. Blog about python scraping data from wunderground rainfall data.

Analysis of Weather data using Pandas, Python, and Seaborn

47 Comments / blog, data science, Data Visualisation, python, Uncategorized /

The most recent post on this site was an analysis of how often people cycling to work actually get rained on in different cities around the world. You can check it out here. The analysis was completed using data from the Wunderground weather website, Python, specifically the Pandas and Seaborn libraries. In this post, I will

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AWS Elastic Beanstalk – Logging to Logentries & InsightOps from Python

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[Short version] The S3 ingestion script for Amazon applications provided by Logentries will not work for the gzip compressed log files produced by the Elastic Beanstalk log rotation system. A slightly edited script will work instead and can be found on Github here.[/Short Version] Logentries is a brilliant startup originating here in Dublin for collecting and

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Use Pandas Groupby to Group and Summarise DataFrames

139 Comments / blog, data science, python, Uncategorized /

Aggregation and data grouping of Dataframes is accomplished in Python Pandas using “groupby()” and “agg()” functions. In this post, we’ll look at every aspect of grouping by single or multiple columns, applying aggregation functions such as max, min, count, and naming the resulting Dataframes and Pandas Series.

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