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Pandas Need to Convert 0.0 Float to Empty String Without Messing Up Other Numbers in Pandas

I am pulling data from an API and doing calculations and transformations using Pandas.

These calculations create floats that I have to the second decimal place. 0.03, 0.04, 0.0 etc.

I then store this data into BigQuery and visualize in Google Data Studio(GDS). The problem is data studio cannot handle division with 0 numbers. So I need to remove the 0.0 from my series. The way I would think to do it is convert to a string, remove 0.0 and make it empty something like .str.replace('0.0', '')

But when I remove 0.0 I also remove the lead for all of the other decimals. When I do .round(decimals=3) to the series the 0.0 just stays as 0.0 so I cannot remove 0.00.

Any thoughts on how I can take that series of numbers and convert any 0.0's to empty?

Example

#In my dataframe I have something like Quantities
df = pd.DataFrame('Store': ['X', 'Y', 'Z', 'A', 'B'],
'Quantities': ['0.0', '0.034', '0.402', '1.0', '0.0'])

#I want to remove 0.0 and make it blank, but keep 0.034 etc
df = pd.DataFrame('Store': ['X', 'Y', 'Z'],
'Quantities': ['', '0.034', '0.402', '1.0', ''])


df.loc[df.calc.eq(0)] = # do something

df.mask(df.calc.eq(0)).dropna()

df.mask(df.eq('0.0')).fillna('')

1 Answer
1

Use mask:

mask

df = pd.DataFrame('Store': ['X', 'Y', 'Z', 'A', 'B'],
'Quantities': ['0.0', '0.034', '0.402', '1.0', '0.0'])

df['Quantities'] = df['Quantities'].mask(df['Quantities']=='0.0', '')
print(df)


Output:

Store Quantities
0 X
1 Y 0.034
2 Z 0.402
3 A 1.0
4 B


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