AttributeError: 'Series' object has no attribute 'to_numeric'

Question

I'm trying to sort dataframe by values. got an AttributeError: 'Series' object has no attribute 'to_numeric'. version '0.20.3', so to numeric should work, but not. Please help.

import pandas as pd
    tables = pd.read_html("https://www.sec.gov/Archives/edgar/data/949012/000156761919015285/xslForm13F_X01/form13fInfoTable.xml")
    len(tables)
    ren=tables[3]
    ren.drop(ren.index[[0,1,2]], inplace=True)
    ren.to_numeric(ren[3], errors='coerce')
    #ren[3].convert_objects(convert_numeric=True)
    ren.sort_values(by=[3],ascending=False)

Solution

import pandas as pd
tables = pd.read_html("https://www.sec.gov/Archives/edgar/data/949012/000156761919015285/xslForm13F_X01/form13fInfoTable.xml")
len(tables)
ren=tables[3]
ren.drop(ren.index[[0,1,2]], inplace=True)
ren[3] = pd.to_numeric(ren[3], errors='coerce')
ren.sort_values([3],ascending=False, inplace=True)
ren


        0               1   2              3    ...
101 JPMorgan          COM   46625h100   48532   ...
44  Cisco             COM   17275r102   47376   ...
204 Waste Management  COM   94106L109   41558   ...
117 Microsoft         COM   594918104   37492   ...   
99  Johnson & Johnson COM   478160104   31491   ...

Comments

[Derek Eden]

pandas.pydata.org/pandas-docs/stable/reference/api/… its a pd function not a series method

[vb_rises]

pd.to_numeric(ren[3], errors='coerce')

[Rajith Thennakoon]

convert your series to dataframe ren.to_frame() and apply to_numeric method

[vb_rises]

@IgBell change to the line from ren.to_numeric(ren[3], errors='coerce') to pd.to_numeric(ren[3], errors='coerce').

[IgBell]

@RajithThennakoon got another error instead AttributeError: 'DataFrame' object has no attribute 'to_frame'

[IgBell]

@vb_rises got another error instead AttributeError: 'DataFrame' object has no attribute 'sort_value'

[IgBell]

@DerekEden so, how to fix it?

[IgBell]

Appreciate it!!! Thanks!!! Everything works now. I stumble on that for already 2 days. The best answer obviously!