[Pandas-dev] Drop all columns which are not normal numeric nor text values

[Tom Augspurger]

> On Nov 16, 2020, at 10:51 AM, Shaozhong SHI <shishaozhong at gmail.com> wrote:
> 
> After applying myDF = json_normalize(result)
> 
> We get 
> brandId	brandName	careHome	constituency	currentRatings.overall.keyQuestionRatings	currentRatings.overall.rating	currentRatings.overall.reportDate	currentRatings.overall.reportLinkId	currentRatings.reportDate	dormancy	...	providerId	region	registrationDate	registrationStatus	regulatedActivities	relationships	reports	specialisms	type	uprn
> 0	BD510	BRAND MACC Care	Y	Birmingham, Northfield	[{u'reportDate': u'2020-10-01', u'rating': u'R...	Requires improvement	2020-10-01	1157c975-c2f1-423e-a2b4-66901779e014	2020-10-01	N	...	1-101641521	West Midlands	2013-12-16	Registered	[{u'code': u'RA2', u'name': u'Accommodation fo...	[]	[{u'reportDate': u'2020-10-01', u'linkId': u'1...	[{u'name': u'Caring for adults over 65 yrs'}, ...	Social Care Org	100070537642
> 
> 
> 
> 
> 
> 
> How to replace or drop all columns which are neither numeric nor text values?
> 
> What is the fastest way?
> 
> Regards,
> 
> David

Hi David,

This mailing list is for pandas development. We recommend stack overflow for usage questions.

Tom


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