Is there a way to append dataframes together more memory-wised efficient?
for example, i want to concat/append two dataframes into one and only use the final dataframe later on:

`
import numpy as np
import pandas as pd
from pandas.util import testing as tm
from multiprocessing.pool import ThreadPool

num_rows = 100000

def make_df(num_rows=10000):

```
df = pd.DataFrame(np.random.rand(num_rows, 5), columns=list('abcde'))
df['foo'] = 'foo'
df['bar'] = 'bar'
df['baz'] = 'baz'
df['date'] = pd.date_range('20000101 09:00:00',
                           periods=num_rows,
                           freq='s')
df['int'] = np.arange(num_rows, dtype='int64')
return df
```

df1 = make_df(num_rows=num_rows)
df2 = make_df(num_rows=num_rows)
df = pd.concat([df1,df2])
`

during the last statement execution, the memory usage got doubled due to the copy assignment.

is it possible that we have some method like df1.append_but_modify_lvalue(df2,ignore_index=True) 
, thus we don't need to copy assign return value
