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  1. Polar plots and shaded errors in matplotlib

    Polar graphs can be a good way to represent cyclical data, such as traffic by day of week. In this post I take stack overflow data and plot the usage of tags throughout the week. The method also adds errors to the matplotlib polar plot as a shaded region to help understand the variability in the data. The data is available here ...read more

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  2. Pandas date parsing performance

    Dates and times provide an unlimited source of hassles for anyone working with them. In this post I'll discuss a potential performance pitfall I encountered parsing dates in pandas. Conclusion: Create DatetimeIndices by parsing data with to_datetime(my_dates, format='my_format').

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  3. Analyzing 10 years of digital photography with python and pandas

    I recently switched DSLR camera systems from Canon to Nikon for reasons of marital harmony. That meant choosing which Nikon lenses would replace the four Canon lenses I owned. To make an optimal decision I needed to know my historical usage, so I wrote some python to analyze image metadata from 10 years of digital photography.

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  4. Saving time and space by working with gzip and bzip2 compressed files in python

    File compression tools like gzip and bzip2 can compress text files into a fraction of their size, often to as little as 20% of the original. Data files often come compressed to save storage space and network bandwidth. A typical workflow is to uncompress the file before analysis, but it can be more convenient to leave the file in its compressed form, especially if the uncompressed file would take up a significant amount of space. In this post I'll show how to work directly with compressed files in python.

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  5. Cleaning, reshaping, and plotting BART time series data with pandas

    Introduction

    I recently starting collecting data from the BART API, specifically estimated time to departure for trains at the two stations I use most frequently. In this notebook I'll show how I parsed the data from a csv file, reshaped it to fit the questions at hand, and made a few plots. Download notebook ...read more

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  6. Installing python for data science

    Installing all the python libraries required for data science can be a challenge, especially on windows machine. Unfortunately the same thing that makes the libraries fast also makes them difficult to distribute to different system types. Luckily there are a few free options for getting up and running painlessly. I ...

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