I have a dataframe called orbital returns which I pulled from a csv:
orbitalreturns = pd.DataFrame.from_csv('Orbital returns.csv',index_col=0,header=0)
2014-02-28 NaN
2014-03-31 1.17%
2014-04-30 1.01%
2014-05-31 2.77%
2014-06-30 2.41%
2014-07-31 -5.44%
I simply want to plot it but get:
TypeError: Empty 'DataFrame': no numeric data to plot
I have tried:
orbitalreturns['OrbitalReturns'].strip('%')
but get:
AttributeError: 'Series' object has no attribute 'strip'
To work with strings you need to use .str method as described here: https://pandas.pydata.org/pandas-docs/stable/text.html#indexing-with-str
This code should work (errors will result in NaN-values - thanks for comment):
orbitalreturns['OrbitalReturns'] = pd.to_numeric(orbitalreturns['OrbitalReturns'].str.strip('%'),errors='coerce')
When printing:
orbitalreturns["OrbitalReturns"]
You get (which looks perfectly fine):
0 1.17
1 1.01
2 2.77
3 2.41
4 -5.44
Name: OrbitalReturns, dtype: float64
Inspect the values in each serie below :
orbitalreturns['OrbitalReturns'].values
# array([nan, '1.17%', '1.01%', '2.77%', '2.41%', '-5.44%'], dtype=object)
orbitalreturns['OrbitalReturns'].str.strip("%").values
# array([nan, '1.17', '1.01', '2.77', '2.41', '-5.44'], dtype=object)
pd.to_numeric(orbitalreturns['OrbitalReturns'].str.strip("%")).values
# array([ nan, 1.17, 1.01, 2.77, 2.41, -5.44])
Remove the % sign and convert to a floating point number:
orbitalreturns['OrbitalReturns'] = orbitalreturns['OrbitalReturns']\
.str.strip('%').astype(float)
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