I have dataframes named a, b, c (and more), and I'd like to concat them with a datafram named d.
I'm doing as below:
a= pd.concat([a, d], axis = 0)
b= pd.concat([b, d], axis = 0)
c= pd.concat([c, d], axis = 0)
...
As I have more tham 3 dataframe a,b,c, I'm looking for some ways to do it faster, like in a loop for eg. I'm using code as below but it doesn't work:
for x in [a,b,c]:
global d
x = pd.concat([x, d], axis = 0)
import pandas as pd
a = pd.DataFrame({'task':['one'],'label':['tag_01'] })
b = pd.DataFrame({'task':['one'],'label':['tag_01'] })
c = pd.DataFrame({'task':['one'],'label':['tag_01'] })
d = pd.DataFrame({'task':['two'],'label':['tag_02'] })
A common solution to iterate over dataframes while using their name is to use another list with dataframes names, then enumerate and get value of name by position:
# create a dict containing dfs
names = ['a','b','c']
dict_dfs = ({names[i]: df for i, df in enumerate([a, b, c])})
Now we can use globals() to generate desidered output.
The globals() method returns the dictionary of the current global symbol table. A symbol table is a data structure maintained by a compiler.
for i in dict_dfs:
globals()[str(i)] = pd.concat([dict_dfs[i], d], axis = 0)
Results:
a
Out[1]:
task label
0 one tag_01
0 two tag_02
b
Out[2]:
task label
0 one tag_01
0 two tag_02
c
Out[3]:
task label
0 one tag_01
0 two tag_02
