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In programming, even small tips or tools can make a big difference.
For example, a shortcut key or a helpful package might simplify a lot of work and double your efficiency.
Here I’ll share a few small tricks I often use.
pandas_profiling to Inspect DataFramesUnderstanding your data is essential before doing any analysis.
Although df.describe() and df.info() provide basic summaries, they’re limited with large or complex datasets.
The pandas_profiling library offers detailed profiling through profile_report().
pip install pandas-profiling
# or
conda install -c anaconda pandas-profiling
It’s very easy to use:
import pandas as pd
import pandas_profiling
df = pd.read_csv("train.csv")
df.profile_report()
You can also export the report to HTML:
html = df.profile_report(title='Titanic Profiling Report')
html.to_file(outputfile="titanic_Profiling_Report.html")
cufflinksPandas has built-in plotting via .plot(), but it’s not interactive.
If you want interactivity, try the cufflinks package.
在编程的世界里，经常一些小小的建议或者帮助就可以起到很大的作用。 比如说有个快捷键或者一个包简单应用就可以起到简化大量工作，达到事半功倍的效果，这里我就介绍几种我经常用到的小技巧。
在分析数据前，了解我们的数据是数据分析里面很重要的一个环节，虽然在pandas里面有一些像df.describe，df.info()函数虽然起到了一定的查看数据的功能。但是对于较大的数据和比较复杂的功能他们就不再提供了，而pandas_profiling里面的profile_report函数却可以得到比较详细的数据情况。