astype_vs._to_numeric []

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==== astype vs. to_numeric ====
Example 1:
Say we are trying to convert a column of strings to floats
<code>
In [1]:
import pandas as pd
df = pd.DataFrame({'foo': ['1', '1.52', '', '1.6']})
df

Out[1]:
    foo
0     1
1  1.52
2
3   1.6
</code>
astype('float') gives a ValueError.
<code>
In [2]:
df['foo'].astype('float')
...
ValueError: could not convert string to float: ''
</code>
But to_numeric() with errors='coerce' works.
<code>
In [3]:
pd.to_numeric(df['foo'], errors='coerce')
Out[3]:
0    1.00
1    1.52
2     NaN
3    1.60
Name: foo, dtype: float64
</code>

tags | astype float but set empty string to NaN

Ref:
  * https://stackoverflow.com/questions/35465741/pandas-convert-column-with-empty-strings-to-float
  * https://pandas.pydata.org/docs/reference/api/pandas.to_numeric.html

==== code snippets ====
Strip '%' character at the end and convert it to a number. Empty strings will be converted to NaNs.
<code>
df["pctchange"] = (
        df["pctchange"].str.rstrip("%").apply(pd.to_numeric, errors="coerce")
    )
</code>

Ref: http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html