Fill empty Dataframe with filled Dataframe and different column names

Question

Columns: [TERMID, NAME, TYP, NAMECHANGE, ALIASES, SUCHREGELN, ZUW, SEOTEXT1, SEOTEXT2, SEOKommentar, DBIKommentar]

This is my empty dataframe.

      one  two  three  four
0  1.0  4.0    2.4   6.4
1  2.0  3.0    4.4   4.1
2  3.0  2.0    7.0   1.0
3  4.0  1.0    9.0   5.0

I need to fill these values into my empty dataframe.

So lets say'TERMID' takes the value from 'one', 'TYP' the value of 'two', 'ZUW' the value from 'three' and last but not least 'SEOKommentar' takes the value from 'four'

The empty dataframe needs to get filled row by row, and the ones which are not filled should say NaN.

How can I do this in an accurate way?

Answer 1

IIUC, you can rename the second dataframe and then reindex the columns to the original empty dataframe columns:

Creating the empty data frame:

s = 'TERMID,NAME,TYP,NAMECHANGE,ALIASES,SUCHREGELN,ZUW,SEOTEXT1,SEOTEXT2,SEOKommentar,DBIKommentar'
df = pd.DataFrame(columns=s.split(','))

---

Empty DataFrame
Columns: [TERMID, NAME, TYP, NAMECHANGE, ALIASES, SUCHREGELN, ZUW, SEOTEXT1, SEOTEXT2, SEOKommentar, DBIKommentar]
Index: []

---

Solution (df1 is the second dataframe in your example):

d = {'one': 'TERMID', 'two': 'TYP', 'three': 'ZUW', 'four': 'SEOKommentar'}
df = df1.rename(columns=d).reindex(columns=df.columns)

---

   TERMID  NAME  TYP  NAMECHANGE  ALIASES  SUCHREGELN  ZUW  SEOTEXT1  \
0     1.0   NaN  4.0         NaN      NaN         NaN  2.4       NaN   
1     2.0   NaN  3.0         NaN      NaN         NaN  4.4       NaN   
2     3.0   NaN  2.0         NaN      NaN         NaN  7.0       NaN   
3     4.0   NaN  1.0         NaN      NaN         NaN  9.0       NaN   

   SEOTEXT2  SEOKommentar  DBIKommentar  
0       NaN           6.4           NaN  
1       NaN           4.1           NaN  
2       NaN           1.0           NaN  
3       NaN           5.0           NaN