Suppose I have a dataframe, df1, that has zeros and nans:
dates = pd.date_range('20170101',periods=20)
df1 = pd.DataFrame(np.random.randint(10,size=(20,3)),index=dates,columns=['foo','bar','see'])
df1.iloc[3:12,0] = np.nan
df1.iloc[6:17,1] = 0
What's the succinct way to forward fill both zeors and nans? I tried the below:
df1 = (df1.fillna(method='ffill', inplace=True)).replace(to_replace=0, method='ffill')
AttributeError: 'NoneType' object has no attribute 'replace'
Let's use replace to replace zeros with nan then ffill:
df1.replace(0, np.nan).ffill()
Output:
foo bar see
2017-01-01 2.0 1.0 4
2017-01-02 2.0 2.0 6
2017-01-03 2.0 8.0 3
2017-01-04 2.0 6.0 1
2017-01-05 2.0 8.0 4
2017-01-06 2.0 9.0 6
2017-01-07 2.0 9.0 8
2017-01-08 2.0 9.0 5
2017-01-09 2.0 9.0 8
2017-01-10 2.0 9.0 7
2017-01-11 2.0 9.0 3
2017-01-12 2.0 9.0 6
2017-01-13 5.0 9.0 4
2017-01-14 6.0 9.0 9
2017-01-15 7.0 9.0 4
2017-01-16 6.0 9.0 2
2017-01-17 2.0 9.0 5
2017-01-18 3.0 1.0 1
2017-01-19 3.0 8.0 1
2017-01-20 2.0 5.0 7