Visualizing data patterns often involves re-arrangement and elimination to determine patterns. For example, in a list of data with yearly rainfall amounts, to quickly determine the years with the most rainfall, the data can be sorted according to rainfall in descending order. A filter could be used to limit the amount of data observed, for example, to only show rainfall amounts greater than an inch. A merge can be used to join two datasets together, for example rainfall and temperature data from two different sources. The ability to sort, merge and filter data has always existed using SQL with database data, now it can be done in application memory space using Python.
Merge, Sort and Filter Data in Python Pandas
Taught in English
Instructor: David Dalsveen
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What you'll learn
Use a Filter to remove outliers in the data.
Combine the data using Pandas Merge.
Sort the data by rainfall and temperature.
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Guided Project
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Read the data into a Pandas DataFrame.
Use a Filter to remove outliers in the data.
Combine the data using Pandas Merge.
Sort the data by rainfall and temperature.
Use the Seaborn package to create a regression plot.
Recommended experience
Learners should be familiar with some Python, including variables and input/output, and the basic control constructs.
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