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| 1 | +原文:[Handy Python Libraries for Formatting and Cleaning Data](https://blog.modeanalytics.com/python-data-cleaning-libraries/) |
11 | 2 |
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34 | | - |
35 | | -[Mode Blog](https://blog.modeanalytics.com/) |
36 | | - |
37 | | -# [Handy Python Libraries for Formatting and Cleaning |
38 | | -Data](https://blog.modeanalytics.com/python-data-cleaning-libraries/) |
39 | | - |
40 | | -August 23, 2016 | [Melissa Bierly](http://www.twitter.com/melissa_bierly) -- |
41 | | -Content Marketing at Mode |
| 3 | +--- |
42 | 4 |
|
43 | 5 | The real world is messy, and so too is its data. So messy, that a [recent |
44 | 6 | survey](http://visit.crowdflower.com/data-science-report.html) reported data |
@@ -179,73 +141,3 @@ Here are a couple of our favorite reads on munging/wrangling/cleansing data. |
179 | 141 | * [Cohort Analysis That Helps You Look Ahead](https://blog.modeanalytics.com/cohort-analysis-helps-look-ahead/?utm_medium=recommended&utm_source=blog&utm_content=data_cleaning) |
180 | 142 | * [10 Useful Python Data Visualization Libraries for Any Discipline](https://blog.modeanalytics.com/python-data-visualization-libraries/?utm_medium=recommended&utm_source=blog&utm_content=data_cleaning) |
181 | 143 | * [Thinking in SQL vs Thinking in Python](https://blog.modeanalytics.com/learning-python-sql/?utm_medium=recommended&utm_source=blog&utm_content=data_cleaning) |
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