python - pandas: combine two columns in a DataFrame

Question

I have a pandas DataFrame that has multiple columns in it:

Index: 239897 entries, 2012-05-11 15:20:00 to 2012-06-02 23:44:51
Data columns:
foo                   11516  non-null values
bar                   228381  non-null values
Time_UTC              239897  non-null values
dtstamp               239897  non-null values
dtypes: float64(4), object(1)

where foo and bar are columns which contain the same data yet are named differently. Is there are a way to move the rows which make up foo into bar, ideally whilst maintaining the name of bar?

In the end the DataFrame should appear as:

Index: 239897 entries, 2012-05-11 15:20:00 to 2012-06-02 23:44:51
Data columns:
bar                   239897  non-null values
Time_UTC              239897  non-null values
dtstamp               239897  non-null values
dtypes: float64(4), object(1)

That is the NaN values that made up bar were replaced by the values from foo.

Answer 1

you can use directly fillna and assigning the result to the column 'bar'

df['bar'].fillna(df['foo'], inplace=True)
del df['foo']

general example:

import pandas as pd
#creating the table with two missing values
df1 = pd.DataFrame({'a':[1,2],'b':[3,4]}, index = [1,2])
df2 = pd.DataFrame({'b':[5,6]}, index = [3,4])
dftot = pd.concat((df1, df2))
print dftot
#creating the dataframe to fill the missing values
filldf = pd.DataFrame({'a':[7,7,7,7]})

#filling 
print dftot.fillna(filldf)