Pandas Cannot Create a DataFrame from a Numpy Array Of Timestamps

Question

I have a numpy array of Pandas Timestamps:

array([[Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T'),
        Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T'),
        Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T')],
       [Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T'),
        Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T'),
        Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T')],
       [Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T'),
        Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T'),
        Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T')]], dtype=object)

I cannot create a DataFrame from this array, as attempting to do so throws the following error:

AssertionError: Number of Block dimensions (1) must equal number of axes (2)

You can see that the array is clearly 2 dimensional, which i verified by using ndim.

Why can't I create a DataFrame?

jezrael · Accepted Answer · 2016-05-25 19:04:13Z

1

I think you can use list comprehension:

import pandas as pd
import numpy as np

a =np.array([[pd.Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T'),
        pd.Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T'),
        pd.Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T')],
       [pd.Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T'),
        pd.Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T'),
        pd.Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T')],
       [pd.Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T'),
        pd.Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T'),
        pd.Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T')]], dtype=object)

df = pd.DataFrame([x for x in a], columns=['a','b','c'])
print (df)
                          a                         b  \
0 2016-05-02 15:50:00+00:00 2016-05-02 15:50:00+00:00   
1 2016-05-02 17:10:00+00:00 2016-05-02 17:10:00+00:00   
2 2016-05-02 20:25:00+00:00 2016-05-02 20:25:00+00:00   

                          c  
0 2016-05-02 15:50:00+00:00  
1 2016-05-02 17:10:00+00:00  
2 2016-05-02 20:25:00+00:00

Another solution is DataFrame.from_records:

print (pd.DataFrame.from_records(a, columns=['a','b','c']))
                          a                         b  \
0 2016-05-02 15:50:00+00:00 2016-05-02 15:50:00+00:00   
1 2016-05-02 17:10:00+00:00 2016-05-02 17:10:00+00:00   
2 2016-05-02 20:25:00+00:00 2016-05-02 20:25:00+00:00   

                          c  
0 2016-05-02 15:50:00+00:00  
1 2016-05-02 17:10:00+00:00  
2 2016-05-02 20:25:00+00:00

See alternate constructors of df.

edited May 25, 2016 at 19:04

answered May 25, 2016 at 18:55

jezrael

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1 Comment

James Kelleher Over a year ago

This definitely answers my question, so thank you. I was asking because the original problem I had was that I was attempting to transpose a DataFrame of Timestamps. Even when constructing a DataFrame with from_records, transposing throws the same AssertionError as before. For now, I am transposing the numpy array before I construct the DataFrame.

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