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data columns not returned as numeric #104
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Yeah.... Iirc this was because of a change in pandas. They removed Should be a very simple change! |
Why do neither of these functions ( @ljwolf, it's looking to me like Also, @dfolch, how do you get colored syntax highlighting in your markdown? Is it because you copied from a notebook? |
Is there ever a case where you wouldn't want data columns to be of integer type? Of course, you never want the geography columns to be of a numeric type. |
Yes, fips codes for geographic identifiers ought to be kept as strings |
@ljwolf, how does one get the In
|
Could move |
@dfolch I'm pretty sure this takes care of the import issues. Should all of the functions that are now in In |
Is it the case that you want all or no data columns to be converted to integers or do you want to convert all of the ones that can be converted? |
I made If |
@ljwolf I'm sorry, I didn't see your comment until after I made the PR. I will work on this. What about the rest of the functions in |
some data columns should be float, not int -- most anything that has 'median', 'average' or 'rate' in the table name. I've used This gives some sense of the range of valid |
I've also since realized that the real problem is with the Census API, which returns numbers as quoted strings. JSON numbers shouldn't be quoted. See (and upvote) uscensusbureau/api#5 |
@JoeGermuska Would you still recommend using the Here's a quick solution doing just that (staged inside df = {some recently pulled data inside class ApiConnection}
type_dict = {
k: eval(self.variables.predicateType.loc[k.upper()])
for k in df.columns
}
df = df.astype(type_dict, errors='ignore') Note: This would also require some data cleansing of the
df.predicateType = df.predicateType.replace(['string', np.nan], 'str') |
Previous versions of cenpy retuned data columns as numeric values. Running that older code today returns objects. This is a feature request to go back to the previous approach.
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