Suppose I have a dataframe as follows
earningspersharebasic earningsPerShareBasic 2019 -0.19 NaN 2018 NaN 4.00 2017 NaN 0.21 2016 0.01 NaN
how can I merge the two columns into one using pandas? The desired output is
output
earningspersharebasic 2019 -0.19 2018 4.00 2017 0.21 2016 0.01
Thank you!
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Answer
Use Series.fillna
with DataFrame.pop
for replace missing values to another column with drop second column:
df['earningspersharebasic'] = df['earningspersharebasic'].fillna(df.pop('earningsPerShareBasic')) print (df) earningspersharebasic 2019 -0.19 2018 4.00 2017 0.21 2016 0.01
Or you can back filling missing values with select first column by DataFrame.iloc
with [[0]]
for one column DataFrame from first column:
df = df.bfill(axis=1).iloc[:, [0]]