I'm trying to copy data over from one Panadas DataFrame into another and I'm getting some strange results. For example if I have:
[In]:
A = {'Types':['Falcon', 'Eagle', 'sparrow'],
'Speed':[100, 75, 50]}
df_A = pd.DataFrame(A)
df_B = pd.DataFrame()
df_B['Type'] = df_A['Types']
df_B['tags'] = ['FLCN', 'EGLE', 'SPRW']
df_B['ID'] = [543.76, 534.32, 645.25]
df_A['Tags'] = df_B['tags']
df_A['ID'] = df_B['ID']
df_A
What I'm expecting to get is:
[Out]:
Types Speed Tags ID
0 Falcon 100 FLCN 543.76
1 Eagle 75 EGLE 534.32
2 sparrow 50 SPRW 645.25
But what I'm getting instead is:
[Out]:
Types Speed Tags ID
0 Falcon 100 FLCN NaN
1 Eagle 75 EGLE NaN
2 sparrow 50 SPRW NaN
I've tried doing this in a Jupyter Notebook to trouble shoot and received a TypeError: "'Method' object is not subscriptable". Here is an example of the type error that I received:
ex. 2:
[In]:
df_A['ID'] = df_B['ID']
[Out]:
TypeError: 'method' object is not subscriptable
Once I decided to write a question I wrote the code for these examples in Jupyter and got the expected results without any issue, so I'm stumped.
Edit to Add: I've tried using the following as a work around:
[In]:
df_A['Tags'] = df_B['tags']
df_A = pd.concat(df_A, df_B['ID'], axis = 1)
but I'm still getting funky results. With this sample code I end up with:
[Out]:
Types Speed Tags ID
0 Falcon 100 FLCN 543.76
1 Eagle 75 EGLE 534.32
2 sparrow 50 SPRW 645.25
but when I use my larger data set, the results look like this:
[Out]:
Types Speed Tags ID
0 NaN NaN NaN 543.76
1 NaN NaN NaN 534.32
2 NaN NaN NaN 645.25
3 Falcon 100 FLCN NaN
4 Eagle 75 EGLE NaN
5 sparrow 50 SPRW NaN
despite using 'axis=1' as a parameter in pd.concat.
At each step, with the provided code, I get no NaN neither.
import pandas as pd
def print_df_A_df_B(stage, df_A, df_B):
print(stage, "assignment","\ndf_A\n",df_A,"\ndf_B\n",df_B,"\n")
pass
A = {'Types':['Falcon', 'Eagle', 'sparrow'],
'Speed':[100, 75, 50]}
df_A = pd.DataFrame(A)
df_B = pd.DataFrame()
df_B['Type'] = df_A['Types']
print_df_A_df_B("Type", df_A, df_B)
df_B['tags'] = ['FLCN', 'EGLE', 'SPRW']
df_B['ID'] = [543.76, 534.32, 645.25]
df_A['Tags'] = df_B['tags']
print_df_A_df_B("Tags", df_A, df_B)
df_A['ID'] = df_B['ID']
print_df_A_df_B("ID", df_A, df_B)
Out
Type assignment
df_A
Types Speed
0 Falcon 100
1 Eagle 75
2 sparrow 50
df_B
Type
0 Falcon
1 Eagle
2 sparrow
Tags assignment
df_A
Types Speed Tags
0 Falcon 100 FLCN
1 Eagle 75 EGLE
2 sparrow 50 SPRW
df_B
Type tags ID
0 Falcon FLCN 543.76
1 Eagle EGLE 534.32
2 sparrow SPRW 645.25
ID assignment
df_A
Types Speed Tags ID
0 Falcon 100 FLCN 543.76
1 Eagle 75 EGLE 534.32
2 sparrow 50 SPRW 645.25
df_B
Type tags ID
0 Falcon FLCN 543.76
1 Eagle EGLE 534.32
2 sparrow SPRW 645.25
Perhaps try in your full code: print(type(df_A['ID']), type(df_B['ID'])) to check if both are pandas.core.series.Series.
For your reported workaround perhaps add ignore_index=True as pd.concat([df_A, df_B['tags']], axis=1, ignore_index=True) and fix the column names thereafter.