How to convert pandas single column data frame to series or numpy vector [duplicate]

Question

I have the following data frame just single column.

import pandas as pd tdf = pd.DataFrame({'s1' : [0,1,23.4,10,23]})

Currently it has the following shape.

In [54]: tdf.shape Out[54]: (5, 1)

How can I convert it to a Series or a numpy vector so that the shape is simply (5,)

Anand S Kumar · Accepted Answer · 2015-10-28 07:33:24Z

50

You can simply index the series you want. Example -

tdf['s1']

Demo -

In [24]: tdf = pd.DataFrame({'s1' : [0,1,23.4,10,23]}) In [25]: tdf['s1'] Out[25]: 0 0.0 1 1.0 2 23.4 3 10.0 4 23.0 Name: s1, dtype: float64 In [26]: tdf['s1'].shape Out[26]: (5,)

If you want the values in the series as numpy array, you can use .values accessor , Example -

In [27]: tdf['s1'].values Out[27]: array([ 0. , 1. , 23.4, 10. , 23. ])

answered Oct 28, 2015 at 7:33

Anand S Kumar

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4 Comments

Anzel Over a year ago

or perhaps assign the series to a numpy array for further manipulation, ie. np.asarray(tdf['s1'])

Anand S Kumar Over a year ago

Yea, but I think tdf['s1'].values would be cleaner.

Keith Over a year ago

This can have the following error stackoverflow.com/questions/39474056/…

Ben Mares Over a year ago

If you wish to avoid reference to the column name, you can alternatively do tdf.iloc[:,0] to get the leftmost column, tdf.iloc[:,1] for the next one...

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