Typeerror: Cannot Concatenate Object of Type '<Class 'Str'>'; Only Series and Dataframe Objs Are Valid

TypeError: cannot concatenate object of type '<class 'str'>'; only Series and DataFrame objs are valid

You can use itertools.product to build a set of all of the pairs, filter to remove when the source and destination are the same location, and then construct a new data frame.

import pandas as pd
from itertools import product

df = pd.DataFrame({'sources': [
    "CAULAINCOURT",
    "MARCHE DE L'EUROPE",
    "AU MAIRE"
]})

df_pairs = pd.DataFrame(
    filter(lambda x: x[0]!=x[1], product(df.sources, df.sources)), 
    columns=['source', 'dest']
)

df_pairs
# returns:
               source                dest
0        CAULAINCOURT  MARCHE DE L'EUROPE
1        CAULAINCOURT            AU MAIRE
2  MARCHE DE L'EUROPE        CAULAINCOURT
3  MARCHE DE L'EUROPE            AU MAIRE
4            AU MAIRE        CAULAINCOURT
5            AU MAIRE  MARCHE DE L'EUROPE

TypeError: cannot concatenate object of type '<class 'str'>'; only Series and DataFrame objs are valid I got this error

Your code can be updated in the following way:

import pandas as pd

# set data
data = {"colummen1": ["Kenbele1", "Kenbele1", "Kenbele1", "Kenbele1", "Kenbele2", "Kenbele2", "Kenbele2", "Kenbele2"], 
        "colummun2": ["Commutity1", "Commutity2", "Commutity3", "Commutity4", "Commutity1", "Commutity2", "Commutity3", "Commutity4"]}

# create dataframe
df = pd.DataFrame(data)

dfs = df.groupby('colummen1')

dfs_updated = []

for _, df in dfs:
    df['id'] = df.groupby(['colummen1','colummun2']).ngroup()+1
    dfs_updated.append(df)
    
df_new = pd.concat(dfs_updated)

df_new

Returns

TypeError: cannot concatenate object of type '<class 'list'>'; only Series and DataFrame objs are valid

s1 is already a list. Doing what you did called pd.concat with a list of a list with DataFrames, which pandas doesn't allow. You should do it like this instead:

s2=pd.concat(s1)

TypeError: cannot concatenate object of type '<class 'yfinance.ticker.Options'>'; only Series and DataFrame objs are valid

Sequential appends to a DataFrame are extremely costly as it requires a new DataFrame to be build every iteration. For this reason, they are generally avoided. Since option_chain returns an iterable, instead of appending to the list we should extend the list. Then perform a single concat at the end.

import pandas as pd
import yfinance as yf

tickers = ['AAPL', 'AA', 'AAL']

opts_list = []

for symbol in tickers:
    try:
        ticker = yf.Ticker(symbol)
        opt = ticker.option_chain('2021-07-30')
        opts_list.extend(opt)
    except ValueError:
        continue

new_df = pd.concat(opts_list)

new_df:

         contractSymbol       lastTradeDate  ...  contractSize  currency
0   AAPL210730C00065000 2021-07-28 19:37:45  ...       REGULAR       USD
1   AAPL210730C00070000 2021-07-22 18:17:27  ...       REGULAR       USD
2   AAPL210730C00075000 2021-07-28 17:19:38  ...       REGULAR       USD
3   AAPL210730C00080000 2021-07-22 14:59:05  ...       REGULAR       USD
4   AAPL210730C00085000 2021-07-27 16:09:57  ...       REGULAR       USD
..                  ...                 ...  ...           ...       ...
28   AAL210730P00029000 2021-07-26 13:31:18  ...       REGULAR       USD
29   AAL210730P00029500 2021-07-26 13:32:22  ...       REGULAR       USD
30   AAL210730P00030000 2021-07-22 16:52:08  ...       REGULAR       USD
31   AAL210730P00031000 2021-07-22 15:53:55  ...       REGULAR       USD
32   AAL210730P00032000 2021-07-26 13:30:11  ...       REGULAR       USD

[253 rows x 14 columns]

Python Loop[ Today Stock Data

I think you need:

L = ['AAPL', 'MSFT']

increased_symbol = []

for stock in L:
    daily_data = pd.DataFrame()
    daily_data = daily_data.ta.ticker(stock, start="2020-01-01", end="2021-09-30")
    daily_data.ta.stochrsi(append=True)
    daily_data = daily_data.iloc[-1:]
    #append dataframes to list
    increased_symbol.append(daily_data)

#concat list of DataFrames
df = pd.concat(increased_symbol)
print(df)
                  Open        High         Low       Close    Volume  \
Date                                                                   
2021-09-29  142.470001  144.449997  142.029999  142.830002  74602000   
2021-09-29  285.100006  286.769989  283.010010  284.000000  26353700   

            Dividends  Stock Splits  STOCHRSIk_14_14_3_3  STOCHRSId_14_14_3_3  
Date                                                                           
2021-09-29        0.0           0.0            18.203772            22.490302  
2021-09-29        0.0           0.0             0.839166            13.396303  

If need also ticker in column:

L = ['AAPL', 'MSFT']

increased_symbol = []

for stock in L:
    daily_data = pd.DataFrame()
    daily_data = daily_data.ta.ticker(stock, start="2020-01-01", end="2021-09-30")
    daily_data.ta.stochrsi(append=True)
    daily_data = daily_data.iloc[-1:]
    increased_symbol.append(daily_data.assign(ticker=stock))


df = pd.concat(increased_symbol)
print(df)

                  Open        High         Low       Close    Volume  \
Date                                                                   
2021-09-29  142.470001  144.449997  142.029999  142.830002  74602000   
2021-09-29  285.100006  286.769989  283.010010  284.000000  26353700   

            Dividends  Stock Splits  STOCHRSIk_14_14_3_3  STOCHRSId_14_14_3_3  \
Date                                                                            
2021-09-29        0.0           0.0            18.203772            22.490302   
2021-09-29        0.0           0.0             0.839166            13.396303   

           ticker  
Date               
2021-09-29   AAPL  
2021-09-29   MSFT  

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