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           


        

Answer 1

Try this:

pandas.concat([df['foo'].dropna(), df['bar'].dropna()]).reindex_like(df)

If you want that data to become the new column bar, just assign the result to df['bar'].

Answer 2

Another option, use the .apply() method on the frame. You can do reassign a column with deference to existing data...

import pandas as pd
import numpy as np

# get your data into a dataframe

# replace content in "bar" with "foo" if "bar" is null
df["bar"] = df.apply(lambda row: row["foo"] if row["bar"] == np.NaN else row["bar"], axis=1) 

# note: change 'np.NaN' with null values you have like an empty string

Answer 3

More modern pandas versions (since at least 0.12) have the combine_first() and update() methods for DataFrame and Series objects. For example if your DataFrame were called df, you would do:

df.bar.combine_first(df.foo)

which would only alter Nan values of the bar column to match the foo column, and would do so inplace. To overwrite non-Nan values in bar with those in foo, you would use the update() method.

Answer 4

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)

Answer 5

You can do this using numpy too.

df['bar'] = np.where(pd.isnull(df['bar']),df['foo'],df['bar'])