Get particular row as series from pandas dataframe

How do we get a particular filtered row as series?

Example dataframe:

>>> df = pd.DataFrame({'date': [20130101, 20130101, 20130102], 'location': ['a', 'a', 'c']})
>>> df
       date location
0  20130101        a
1  20130101        a
2  20130102        c

I need to select the row where location is c as a series.

I tried:

row = df[df["location"] == "c"].head(1)  # gives a dataframe
row = df.ix[df["location"] == "c"]       # also gives a dataframe with single row

In either cases I can't the row as series.

Use the squeeze function that will remove one dimension from the dataframe:

df[df["location"] == "c"].squeeze()
Out[5]: 
date        20130102
location           c
Name: 2, dtype: object

DataFrame.squeeze method acts the same way of the squeeze argument of the read_csv function when set to True: if the resulting dataframe is a 1-len dataframe, i.e. it has only one dimension (a column or a row), then the object is squeezed down to the smaller dimension object.

In your case, you get a Series object from the DataFrame. The same logic applies if you squeeze a Panel down to a DataFrame.

squeeze is explicit in your code and shows clearly your intent to "cast down" the object in hands because its dimension can be projected to a smaller one.

If the dataframe has more than one column or row, squeeze has no effect.

You can just take first row with integer indexing (iloc() function):

>>> df[df["location"] == "c"].iloc[0]
date        20130102
location           c
Name: 2, dtype: object