Convert pandas data frame to series

It's not smart enough to realize it's still a "vector" in math terms.

Say rather that it's smart enough to recognize a difference in dimensionality. :-)

I think the simplest thing you can do is select that row positionally using iloc, which gives you a Series with the columns as the new index and the values as the values:

>>> df = pd.DataFrame([list(range(5))], columns=["a{}".format(i) for i in range(5)])>>> df   a0  a1  a2  a3  a40   0   1   2   3   4>>> df.iloc[0]a0    0a1    1a2    2a3    3a4    4Name: 0, dtype: int64>>> type(_)<class 'pandas.core.series.Series'>

You can transpose the single-row dataframe (which still results in a dataframe) and then squeeze the results into a series (the inverse of to_frame).

df = pd.DataFrame([list(range(5))], columns=["a{}".format(i) for i in range(5)])>>> df.squeeze(axis=0)a0    0a1    1a2    2a3    3a4    4Name: 0, dtype: int64

Note: To accommodate the point raised by @IanS (even though it is not in the OP's question), test for the dataframe's size. I am assuming that df is a dataframe, but the edge cases are an empty dataframe, a dataframe of shape (1, 1), and a dataframe with more than one row in which case the use should implement their desired functionality.

if df.empty:    # Empty dataframe, so convert to empty Series.    result = pd.Series()elif df.shape == (1, 1)    # DataFrame with one value, so convert to series with appropriate index.    result = pd.Series(df.iat[0, 0], index=df.columns)elif len(df) == 1:    # Convert to series per OP's question.    result = df.T.squeeze()else:    # Dataframe with multiple rows.  Implement desired behavior.    pass

This can also be simplified along the lines of the answer provided by @themachinist.

if len(df) > 1:    # Dataframe with multiple rows.  Implement desired behavior.    passelse:    result = pd.Series() if df.empty else df.iloc[0, :]

You can retrieve the series through slicing your dataframe using one of these two methods:

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iloc.htmlhttp://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.loc.html

import pandas as pdimport numpy as npdf = pd.DataFrame(data=np.random.randn(1,8))series1=df.iloc[0,:]type(series1)pandas.core.series.Series