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I'm writing an article on use of Pandas for data analysis in museum collections data, and have been making great use of yours and MoMa's datasets for this, for which many thanks.
One of the things I wanted to use Pandas for was find errata in the data, comparing common fields between the datasets. After a bit of data mangling, the following differences in the artist gender were flagged up.
Gender incorrect (according to MoMa)
Artist
Tate Gender
MoMa Gender
Misch Kohn
Female
Male
Gender unknown (but known by MoMa)
Artist
Tate Gender
MoMa Gender
Shusaku Arakawa
NaN
Male
Juan Downey
NaN
Male
Jack Goldstein
NaN
Male
Lawrence Abu Hamdan
NaN
Male
Vladimir Kozlinskii
NaN
Male
Vladimir Mayakovsky
NaN
Male
Melik Ohanian
NaN
Male
R. H. Quaytman
NaN
Female
Monika Sosnowska
NaN
Female
Likewise have differences for artists birth & death dates, will lodge these as separate issues if of use.
The text was updated successfully, but these errors were encountered:
I'm writing an article on use of Pandas for data analysis in museum collections data, and have been making great use of yours and MoMa's datasets for this, for which many thanks.
One of the things I wanted to use Pandas for was find errata in the data, comparing common fields between the datasets. After a bit of data mangling, the following differences in the artist gender were flagged up.
Gender incorrect (according to MoMa)
Gender unknown (but known by MoMa)
Likewise have differences for artists birth & death dates, will lodge these as separate issues if of use.
The text was updated successfully, but these errors were encountered: