pandas efficient dataframe set row

Question

First I have the following empty DataFrame preallocated:

df=DataFrame(columns=range(10000),index=range(1000))

Then I want to update the d

Answer 1

df=DataFrame(columns=range(10),index=range(10))
a = np.array( [9,9,9,9,9,9,9,9,9,9] )

Update row:

df.loc[2] = a

Using Jeff's idea...

df2 = DataFrame(data=np.random.randn(10,10), index=arange(10))
df2.head().T

I have written up a notebook answering the question: https://www.wakari.io/sharing/bundle/hrojas/pandas%20efficient%20dataframe%20set%20row

Answer 2

Here's 3 methods, only 100 columns, 1000 rows

In [5]: row = np.random.randn(100)

Row wise assignment

In [6]: def method1():
   ...:     df = DataFrame(columns=range(100),index=range(1000))
   ...:     for i in xrange(len(df)):
   ...:         df.iloc[i] = row
   ...:     return df
   ...: 

Build up the arrays in a list, create the frame all at once

In [9]: def method2():
   ...:     return DataFrame([ row for i in range(1000) ])
   ...: 

Columnwise assignment (with transposes at both ends)

In [13]: def method3():
   ....:     df = DataFrame(columns=range(100),index=range(1000)).T
   ....:     for i in xrange(1000):
   ....:         df[i] = row
   ....:     return df.T
   ....: 

These all have the same output frame

In [22]: (method2() == method1()).all().all()
Out[22]: True

In [23]: (method2() == method3()).all().all()
Out[23]: True


In [8]: %timeit method1()
1 loops, best of 3: 1.76 s per loop

In [10]: %timeit method2()
1000 loops, best of 3: 7.79 ms per loop

In [14]: %timeit method3()
1 loops, best of 3: 1.33 s per loop

It is CLEAR that building up a list, THEN creating the frame all at once is orders of magnitude faster than doing any form of assignment. Assignment involves copying. Building up all at once only copies once.