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pd.concat not stacking columns with same names

I'm in awe that this isn't working because I've done this a hundred times. I want to stack two dataframes vertically and pandas is creating duplicate columns and refusing to put the data in the right columns.
df1 looks like this:
df2 looks like this:
then I run this:
frames = [df1,df2]
final = pd.concat(frames, ignore_index = True, axis = 0)
final
and get 6 columns instead of 3 like this:
I have no idea why two dataframes with identical column names and data types would not simply stack on top of each other. Any help appreciated.
Thanks.
update:
Big Thanks to #Naveed there was trailing whitespace in one of the dataframe's columns.
Here is the final fix:
df2.columns = [x.strip() for x in df2.columns]
frames = [df1,df2]
final = pd.concat(frames,ignore_index = True, axis = 0)
final

Try
check the column names, there might be white spaces that results in mis-alignment of column after the concat.
display(df1.columns, df2.columns)
# make axis=0 and remove ignore_index
final = pd.concat(frames, axis = 0)
final

Concatenate two pandas dataframe and follow a sequence of uid

I have a pandas dataframe with the following data: (in csv)
#list1
poke_id,symbol
0,BTC
1,ETB
2,USDC
#list2
5,SOL
6,XRP
I am able to concatenate them into one dataframe using the following code:
df = pd.concat([df1, df2], ignore_index = True)
df = df.reset_index(drop = True)
df['poke_id'] = df.index
df = df[['poke_id','symbol']]
which gives me the output: (in csv)
poke_id,symbol
0,BTC
1,ETB
2,USDC
3,SOL
4,XRP
Is there any other way to do the same. I think calling the whole data frame of ~4000 entries just to add ~100 more will be a little pointless and cumbersome. How can I make it in such a way that it picks list 1 (or dataframe 1) and picks the highest poke_id; and just does i + 1 to the later entries in list 2.

Your solution is good, is possible simplify:
df = pd.concat([df1, df2], ignore_index = True).rename_axis('poke_id').reset_index()

use indexes to get what data you want from the dataframe, although this is not effective if you want large amounts of data from the dataframe, this method allows you to take specific amounts of data from the dataframe

Pandas "A value is trying to be set on a copy of a slice from a DataFrame"

Having a bit of trouble understanding the documentation
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
dfbreed['x'] = dfbreed.apply(testbreed, axis=1)
C:/Users/erasmuss/PycharmProjects/Sarah/farmdata.py:38: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
Code is basically to re-arrange and clean some data to make analysis easier.
Code in given row-by per each animal, but has repetitions, blanks, and some other sparse values
Idea is to basically stack rows into columns and grab the useful data (Weight by date and final BCS) per animal
Initial DF
few snippets of the dataframe
Output Format
Output DF/csv
import pandas as pd
import numpy as np
#Function for cleaning up multiple entries of breeds
def testbreed(x):
if x.first_valid_index() is None:
return None
else:
return x[x.first_valid_index()]
#Read Data
df1 = pd.read_csv("farmdata.csv")
#Drop empty rows
df1.dropna(how='all', axis=1, inplace=True)
#Copy to extract Weights in DF2
df2 = df1.copy()
df2 = df2.drop(['BCS', 'Breed','Age'], axis=1)
#Pivot for ID names in DF1
df1 = df1.pivot(index='ID', columns='Date', values=['Breed','Weight', 'BCS'])
#Pivot for weights in DF2
df2 = df2.pivot(index='ID', columns='Date', values = 'Weight')
#Split out Breeds and BCS into individual dataframes w/Duplicate/missing data for each ID
df3 = df1.copy()
dfbreed = df3[['Breed']]
dfBCS = df3[['BCS']]
#Drop empty BCS columns
df1.dropna(how='all', axis=1, inplace=True)
#Shorten Breed and BCS to single Column by grabbing first value that is real. see function above
dfbreed['x'] = dfbreed.apply(testbreed, axis=1)
dfBCS['x'] = dfBCS.apply(testbreed, axis=1)
#Populate BCS and Breed into new DF
df5= pd.DataFrame(data=None)
df5['Breed'] = dfbreed['x']
df5['BCS'] = dfBCS['x']
#Join Weights
df5 = df5.join(df2)
#Write output
df5.to_csv(r'.\out1.csv')
I want to take the BCS and Breed dataframes which are multi-indexed on the column by Breed or BCS and then by date to take the first non-NaN value in the rows of dates and set that into a column named breed.
I had a lot of trouble getting the columns to pick the first unique values in-situ on the DF
I found a work-around with a 2015 answer:
2015 Answer
which defined the function at the top.
reading through the setting a value on the copy-of a slice makes sense intuitively,
but I can't seem to think of a way to make it work as a direct-replacement or index-based.
Should I be looping through?
Trying from The second answer here
I get
dfbreed.loc[:,'Breed'] = dfbreed['Breed'].apply(testbreed, axis=1)
dfBCS.loc[:, 'BCS'] = dfBCS.apply['BCS'](testbreed, axis=1)
which returns
ValueError: Must have equal len keys and value when setting with an iterable
I'm thinking this has something to do with the multi-index
keys come up as:
MultiIndex([('Breed', '1/28/2021'),
('Breed', '2/12/2021'),
('Breed', '2/4/2021'),
('Breed', '3/18/2021'),
('Breed', '7/30/2021')],
names=[None, 'Date'])
MultiIndex([('BCS', '1/28/2021'),
('BCS', '2/12/2021'),
('BCS', '2/4/2021'),
('BCS', '3/18/2021'),
('BCS', '7/30/2021')],
names=[None, 'Date'])
Sorry for the long question(s?)
Can anyone help me out?
Thanks.

You created dfbreed as:
dfbreed = df3[['Breed']]
So it is a view of the original DataFrame (limited to just this one column).
Remember that a view has not any own data buffer, it is only a tool to "view"
a fragment of the original DataFrame, with read only access.
When you attempt to perform dfbreed['x'] = dfbreed.apply(...), you
actually attempt to violate the read-only access mode.
To avoid this error, create dfbreed as an "independent" DataFrame:
dfbreed = df3[['Breed']].copy()
Now dfbreed has its own data buffer and you are free to change the data.

Concatenate two dataframes with different row indices

I want to concatenate two data frames of the same length, by adding a column to the first one (df).
But because certain df rows are being filtered, it seems the index isn't matching.
import pandas as pd
pd.read_csv(io.StringIO(uploaded['customer.csv'].decode('utf-8')), sep=";")
df["Margin"] = df["Sales"]-df["Cost"]
df = df.loc[df["Margin"]>-100000]
df = df.loc[df["Sales"]> 1000]
df.reindex()
df
This returns:
So this operation:
customerCluster = pd.concat([df, clusters], axis = 1, ignore_index= True)
print(customerCluster)
Is returning:
So, I've tried reindex and the argument ignore_index = True as you can see in above code snippet.

Thanks for all the answers. If anyone encounters the same problem, the solution I found was this:
customerID = df["CustomerID"]
customerID = customerID.reset_index(drop=True)
df = df.reset_index(drop=True)
So, basically, the indexes of both data frames are now matching, thus:
customerCluster = pd.concat((customerID, clusters), axis = 1)
This will concatenate correctly the two data frames.

pandas appending df1 to df2 get 0s/NaNs in result

I have 2 dataframes. df1 comprises a Series of values.
df1 = pd.DataFrame({'winnings': cumsums_winnings_s, 'returns':cumsums_returns_s, 'spent': cumsums_spent_s, 'runs': cumsums_runs_s, 'wins': cumsums_wins_s, 'expected': cumsums_expected_s}, columns=["winnings", "returns", "runs", "wins", "expected"])
df2 runs each row through a function which takes 3 columns and produces a result for each row - specialSauce
df2= pd.DataFrame(list(map(lambda w,r,e: doStuff(w,r,e), df1['wins'], df1['runs'], df1['expected'])), columns=["specialSauce"])
print(df2.append(df1))
produces all the df1 columns but NaN for the df1 (and vice versa if df1/df2 switched in append)
So the problem I has is how to append these 2 dataframes correctly.

As I understand things, your issue seems to be related to the fact that you get NaN's in the result DataFrame.
The reason for this is that you are trying to .append() one dataframe to the other while they don't have the same columns.
df2 has one extra column, the one created with apply() and doStuff, while df1 does not have that column. When trying to append one pd.DataFrame to the other the result will have all columns both pd.DataFrame objects. Naturally, you will have some NaN's for ['specialSauce'] since this column does not exist in df1.
This would be the same if you were to use pd.concat(), both methods do the same thing in this case. The one thing that you could do to bring the result closer to your desired result is use the ignore_index flag like this:
>> df2.append(df1, ignore_index=True)
This would at least give you a 'fresh' index for the result pd.DataFrame.
EDIT
If what you're looking for is to "append" the result of doStuff to the end of your existing df, in the form of a new column (['specialSauce']), then what you'll have to do is use pd.concat() like this:
>> pd.concat([df1, df2], axis=1)
This will return the result pd.DataFrame as you want it.
If you had a pd.Series to add to the columns of df1 then you'd need to add it like this:
>> df1['specialSauce'] = <'specialSauce values'>
I hope that helps, if not please rephrase the description of what you're after.

Ok, there are a couple of things going on here. You've left code out and I had to fill in the gaps. For example you did not define doStuff, so I had to.
doStuff = lambda w, r, e: w + r + e
With that defined, your code does not run. I had to guess what you were trying to do. I'm guessing that you want to have an additional column called 'specialSauce' adjacent to your other columns.
So, this is how I set it up and solved the problem.
Setup and Solution
import pandas as pd
import numpy as np
np.random.seed(314)
df = pd.DataFrame(np.random.randn(100, 6),
columns=["winnings", "returns",
"spent", "runs",
"wins", "expected"]).cumsum()
doStuff = lambda w, r, e: w + r + e
df['specialSauce'] = df[['wins', 'runs', 'expected']].apply(lambda x: doStuff(*x), axis=1)
print df.head()
winnings returns spent runs wins expected specialSauce
0 0.166085 0.781964 0.852285 -0.707071 -0.931657 0.886661 -0.752067
1 -0.055704 1.163688 0.079710 0.155916 -1.212917 -0.045265 -1.102266
2 -0.554241 1.928014 0.271214 -0.462848 0.452802 1.692924 1.682878
3 0.627985 3.047389 -1.594841 -1.099262 -0.308115 4.356977 2.949601
4 0.796156 3.228755 -0.273482 -0.661442 -0.111355 2.827409 2.054611
Also
You tried to use pd.DataFrame.append(). Per the linked documentation, it attaches the DataFrame specified as the argument to the end of the DataFrame that is being appended to. You would have wanted to use pd.DataFrame.concat().