I'm trying to join two dataframes. 'df' is my initial dataframe containing all the header information I require. 'row' is my first row of data that I want to append to 'df'.
df =
FName E1 E2 E3 E4 E5 E6
0 Nan 2 2 2 2 2 2
1 Nan 1 1 1 1 1 1
2 Nan 3 4 5 6 7 8
3 Nan 4 5 6 7 8 10
4 Nan 1002003004 1002004005 1002005006 1002006007 1002007008 1002008010
row =
0 1 2 3 4 5 6
0 501#_ZMB_2019-04-03_070528_reciprocals 30.0193 30.0193 30.0193 34.8858 34.8858 34.8858
I'm trying to create this:
FName E1 E2 E3 E4 E5 E6
0 Nan 2 2 2 2 2 2
1 Nan 1 1 1 1 1 1
2 Nan 3 4 5 6 7 8
3 Nan 4 5 6 7 8 10
4 Nan 1002003004 1002004005 1002005006 1002006007 1002007008 1002008010
5 501#_ZMB_2019-04-03_070528_reciprocals 30.0193 30.0193 30.0193 34.8858 34.8858 34.8858
I have tried the following:
df = df.append(row, ignore_index=True)
and
df = pd.concat([df, row], ignore_index=True)
Both of these result in the loss of all the data in the first df, which should contain all the header information.
0 1 2 3 4 5 6
0 Nan Nan Nan Nan Nan Nan Nan
1 Nan Nan Nan Nan Nan Nan Nan
2 Nan Nan Nan Nan Nan Nan Nan
3 Nan Nan Nan Nan Nan Nan Nan
4 Nan Nan Nan Nan Nan Nan Nan
5 501#_ZMB_2019-04-03_070528_reciprocals 30.0193 30.0193 30.0193 34.8858 34.8858 34.8858
I've also tried
df = pd.concat([df.reset_index(drop=True, inplace=True), row.reset_index(drop=True, inplace=True)])
Which produced the following Traceback
Traceback (most recent call last):
File "<ipython-input-146-3c1ecbd1987c>", line 1, in <module>
df = pd.concat([df.reset_index(drop=True, inplace=True), row.reset_index(drop=True, inplace=True)])
File "C:\Users\russells\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\reshape\concat.py", line 228, in concat
copy=copy, sort=sort)
File "C:\Users\russells\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\reshape\concat.py", line 280, in __init__
raise ValueError('All objects passed were None')
ValueError: All objects passed were None
Does anyone know what I'm doing wrong?
When you concatenate extra rows, pandas aligns the columns, which currently do not overlap. rename will get the job done:
pd.concat([df, row.rename(columns=dict(zip(row.columns, df.columns)))],
ignore_index=True)
FName E1 E2 E3 E4 E5 E6
0 Nan 2 2 2 2 2 2
1 Nan 1 1 1 1 1 1
2 Nan 3 4 5 6 7 8
3 Nan 4 5 6 7 8 10
4 Nan 1002003004 1002004005 1002005006 1002006007 1002007008 1002008010
5 501#_ZMB_2019-04-03_070528_reciprocals 30.0193 30.0193 30.0193 34.8858 34.8858 34.8858
Or if you just need to assign one row at the end and you have a RangeIndex on df:
df.loc[df.shape[0], :] = row.to_numpy()