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:
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:
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 #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:
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 #5:
You can do this using numpy too.
df['bar'] = np.where(pd.isnull(df['bar']),df['foo'],df['bar'])
