Data Wrangling with Python 🐍

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InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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  • TechNuggets

  • Pandas can import data from a wide variety of data sources. Usually it uses the function read_x where "x" is csv, excel, json, ..., etc. In this tutorial, we will use a csv (comma separated values) file representing a transactions (purchases) log in a grocery store. Each row represents one item in a single transaction. One transaction can have multiple items. Sometimes, manual entries can happen 😬. You can find the sample dataset and all the code in this tutorial in this Github repo. The goal of this tutorial is to wrangle this file to find out what is the total revenue per item category and the top 3 purchased items.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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