I have a dataframe,
df,
Name Stage Description
0 sri 1 sri is one of the good singer in this two
1 nan 2 thanks for reading
2 ram 1 ram is two of the good cricket player
3 ganesh 1 one driver
4 nan 2 good buddies
tried df["Stage"]=pd.to_numeric(df["Stage"],downcast="float")
but still the values are same
You can use df.Stage.astype(float)
:
In [6]: df.Stage.astype(float)
Out[6]:
0 1.0
1 2.0
2 1.0
3 1.0
4 2.0
Name: Stage, dtype: float64
In [7]: df.Stage.astype(float)
Using pd.to_numeric
is better as it handles the conversion to a float type that takes less memory.
Example
In [23]: df.Stage
Out[23]:
0 1
1 2
2 1
3 1
4 2
Name: Stage, dtype: int64
In [24]: import sys
In [25]: sys.getsizeof(df.Stage)
Out[25]: 272
In [26]: sys.getsizeof(df.Stage.astype(float))
Out[26]: 272
In [27]: sys.getsizeof(pd.to_numeric(df.Stage, downcast='float'))
Out[27]: 252
In case there are bad data in df.Stage, coerce the value to NaN
pd.to_numeric(df.Stage, errors='coerce', downcast='float')