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convert UNIX timestamp to US/Central using tz_convert

I'm trying to convert my UNIX timestamp to the US/Central timezone timestamp, but i keep getting the UTC output. I don't know what i'm doing wrong in the code.
import ccxt
import pandas as pd
from dateutil import tz
binance = ccxt.binance({
'enableRateLimit': True,
'apiKey': 'xxxxxxxxxxxxxxxxxxx',
'secret': 'xxxxxxxxxxxxx'
})
symbol = 'ETHUSDT'
timeframe = '15m'
limit = 500
bars = binance.fetch_ohlcv (symbol, timeframe = timeframe, limit = limit)
df = pd.DataFrame(bars, columns = ['timestamp','open','high','low', 'close', 'volume'])
df['timestamp'] = pd.to_datetime(df['timestamp'], unit = 'ms').dt.tz_localize(tz='US/Central')
df['timestamp'] = pd.to_datetime(df['timestamp'], unit = 'ms').dt.tz_convert(tz='US/Central')
print(df)
timestamp open high low close volume
0 2022-11-21 12:15:00-06:00 1120.63 1122.74 1118.26 1119.31 3278.5060
1 2022-11-21 12:30:00-06:00 1119.30 1127.48 1115.10 1125.31 11065.4442
2 2022-11-21 12:45:00-06:00 1125.32 1128.36 1123.92 1127.30 5447.6054
3 2022-11-21 13:00:00-06:00 1127.30 1136.75 1125.67 1133.81 15977.1500
4 2022-11-21 13:15:00-06:00 1133.82 1146.99 1132.77 1139.39 21009.7356
.. ... ... ... ... ... ...
495 2022-11-26 16:00:00-06:00 1210.90 1212.87 1208.77 1212.54 3822.1327
496 2022-11-26 16:15:00-06:00 1212.55 1213.92 1212.09 1213.63 2414.2695
497 2022-11-26 16:30:00-06:00 1213.62 1213.63 1211.05 1212.89 2461.4644
498 2022-11-26 16:45:00-06:00 1212.89 1212.94 1209.00 1209.76 2544.8965
499 2022-11-26 17:00:00-06:00 1209.75 1210.00 1207.74 1209.77 1638.1446

I think you want.
df["timestamp"] = (
pd.to_datetime(df["timestamp"], unit="ms")
.dt.tz_localize("UTC")
.dt.tz_convert("US/Central")
.dt.tz_localize(None)
)

Concatenating data frames pandas

I would like to historical close prices from the yfinance module and create a data frame with a column with these closing prices for each of the tickers stored in the Holdings list. I can do everything except creating that data frame at the end. Can someone please help?:
Holdings = ['RL', 'AMC', 'BYND', 'BRK-B',
'BBY', 'AYX', 'AAPL', 'KO',
'FB', 'RACE', 'INTC', 'PFE',
'CRM', 'WFC', 'JPM', 'GOOG']
Hist_Holdings = []
for symbol in Holdings:
Ticker = yf.Ticker(symbol)
Hist = Ticker.history(period = "6mo", interval = "1d")
Hist = Hist['Close']
Hist.columns = [symbol]
Hist_Holdings.append(Hist)

The desired data frame format is not known, but the following code will concatenate the stocks you want to get with spaces. It is fast and returns the data in a data frame format. The code below specifies only the closing price.
import yfinance as yf
import datetime
now_ = datetime.datetime.today()
start = datetime.datetime(now_.year, now_.month - 6, now_.day + 1)
end = datetime.datetime(now_.year, now_.month, now_.day - 1)
Holdings = 'RL AMC BYND BRK-B BBY AYX AAPL KO FB RACE INTC PFE CRM WFC JPM GOOG'
data = yf.download(Holdings, start=start, end=end)['Close']
AAPL AMC AYX BBY BRK-B BYND CRM FB GOOG INTC JPM KO PFE RACE RL WFC
Date
2020-06-12 84.699997 5.89 141.130005 77.760002 181.210007 144.740005 175.110001 228.580002 1413.180054 59.330002 99.870003 45.599998 32.020874 167.889999 74.769997 27.969999
2020-06-15 85.747498 5.80 142.940002 80.010002 181.550003 154.000000 178.610001 232.500000 1419.849976 60.099998 101.250000 46.299999 31.650854 169.699997 73.739998 28.209999
2020-06-16 88.019997 5.56 145.690002 83.470001 182.300003 151.940002 180.479996 235.649994 1442.719971 60.400002 102.059998 46.770000 31.688805 169.690002 76.419998 28.520000
2020-06-17 87.897499 5.42 150.990005 83.239998 180.860001 156.339996 181.399994 235.529999 1451.119995 60.490002 99.480003 46.580002 31.840607 169.809998 74.480003 27.450001
2020-06-18 87.932503 5.63 160.779999 82.300003 180.729996 158.199997 187.660004 235.940002 1435.959961 60.080002 98.940002 46.990002 31.537003 168.580002 73.940002 27.549999

A list of ticker to get setor and name

import pandas as pd
import datetime as dt
from pandas_datareader import data as web
import yfinance as yf
yf.pdr_override()
filename=r'C:\Users\User\Desktop\from_python\data_from_python.xlsx'
yeah = pd.read_excel(filename, sheet_name='entry')
stock = []
stock = list(yeah['name'])
stock = [ s.replace('\xa0', '') for s in stock if not pd.isna(s) ]
adj_close=pd.DataFrame([])
high_price=pd.DataFrame([])
low_price=pd.DataFrame([])
volume=pd.DataFrame([])
print(stock)
['^GSPC', 'NQ=F', 'AAU', 'ALB', 'AOS', 'APPS', 'AQB', 'ASPN', 'ATHM', 'AZRE', 'BCYC', 'BGNE', 'CAT', 'CC', 'CLAR', 'CLCT', 'CMBM', 'CMT', 'CRDF', 'CYD', 'DE', 'DKNG', 'EARN', 'EMN', 'FBIO', 'FBRX', 'FCX', 'FLXS', 'FMC', 'FMCI', 'GME', 'GRVY', 'HAIN', 'HBM', 'HIBB', 'IEX', 'IOR', 'KFS', 'MAXR', 'MPX', 'MRTX', 'NSTG', 'NVCR', 'NVO', 'OESX', 'PENN', 'PLL', 'PRTK', 'RDY', 'REGI', 'REKR', 'SBE', 'SQM', 'TCON', 'TCS', 'TGB', 'TPTX', 'TRIL', 'UEC', 'VCEL', 'VOXX', 'WIT', 'WKHS', 'XNCR']
for symbol in stock:
adj_close[symbol] = web.get_data_yahoo([symbol],start,end)['Adj Close']
I have a list of tickers, I have got the adj close price, how can get these tickers NAME and SECTORS?
for single ticker I found in web, it can be done like as below
sbux = yf.Ticker("SBUX")
tlry = yf.Ticker("TLRY")
print(sbux.info['sector'])
print(tlry.info['sector'])
How can I make it as a dataframe that I can put the data into excel as I am doing for adj price.
Thanks a lot!

You can try this answer using a package called yahooquery. Disclaimer: I am the author of the package.
from yahooquery import Ticker
import pandas as pd
symbols = ['^GSPC', 'NQ=F', 'AAU', 'ALB', 'AOS', 'APPS', 'AQB', 'ASPN', 'ATHM', 'AZRE', 'BCYC', 'BGNE', 'CAT', 'CC', 'CLAR', 'CLCT', 'CMBM', 'CMT', 'CRDF', 'CYD', 'DE', 'DKNG', 'EARN', 'EMN', 'FBIO', 'FBRX', 'FCX', 'FLXS', 'FMC', 'FMCI', 'GME', 'GRVY', 'HAIN', 'HBM', 'HIBB', 'IEX', 'IOR', 'KFS', 'MAXR', 'MPX', 'MRTX', 'NSTG', 'NVCR', 'NVO', 'OESX', 'PENN', 'PLL', 'PRTK', 'RDY', 'REGI', 'REKR', 'SBE', 'SQM', 'TCON', 'TCS', 'TGB', 'TPTX', 'TRIL', 'UEC', 'VCEL', 'VOXX', 'WIT', 'WKHS', 'XNCR']
# Create Ticker instance, passing symbols as first argument
# Optional asynchronous argument allows for asynchronous requests
tickers = Ticker(symbols, asynchronous=True)
data = tickers.get_modules("summaryProfile quoteType")
df = pd.DataFrame.from_dict(data).T
# flatten dicts within each column, creating new dataframes
dataframes = [pd.json_normalize([x for x in df[module] if isinstance(x, dict)]) for module in ['summaryProfile', 'quoteType']]
# concat dataframes from previous step
df = pd.concat(dataframes, axis=1)
# View columns
df.columns
Index(['address1', 'address2', 'city', 'state', 'zip', 'country', 'phone',
'fax', 'website', 'industry', 'sector', 'longBusinessSummary',
'fullTimeEmployees', 'companyOfficers', 'maxAge', 'exchange',
'quoteType', 'symbol', 'underlyingSymbol', 'shortName', 'longName',
'firstTradeDateEpochUtc', 'timeZoneFullName', 'timeZoneShortName',
'uuid', 'messageBoardId', 'gmtOffSetMilliseconds', 'maxAge'],
dtype='object')
# Data you're looking for
df[['symbol', 'shortName', 'sector']].head(10)
symbol shortName sector
0 NQZ20.CME Nasdaq 100 Dec 20 NaN
1 ALB Albemarle Corporation Basic Materials
2 AOS A.O. Smith Corporation Industrials
3 ASPN Aspen Aerogels, Inc. Industrials
4 AAU Almaden Minerals, Ltd. Basic Materials
5 ^GSPC S&P 500 NaN
6 ATHM Autohome Inc. Communication Services
7 AQB AquaBounty Technologies, Inc. Consumer Defensive
8 APPS Digital Turbine, Inc. Technology
9 BCYC Bicycle Therapeutics plc Healthcare

It processes stocks and sectors at the same time. However, some stocks do not have a sector, so an error countermeasure is added.
Since the issue column name consists of sector and issue name, we change it to a hierarchical column and update the retrieved data frame. Finally, I save it in CSV format to import it into Excel. I've only tried some of the stocks due to the large number of stocks, so there may be some issues.
import datetime
import pandas as pd
import yfinance as yf
import pandas_datareader.data as web
yf.pdr_override()
start = "2018-01-01"
end = "2019-01-01"
# symbol = ['^GSPC', 'NQ=F', 'AAU', 'ALB', 'AOS', 'APPS', 'AQB', 'ASPN', 'ATHM', 'AZRE', 'BCYC', 'BGNE', 'CAT',
#'CC', 'CLAR', 'CLCT', 'CMBM', 'CMT', 'CRDF', 'CYD', 'DE', 'DKNG', 'EARN', 'EMN', 'FBIO', 'FBRX', 'FCX', 'FLXS',
#'FMC', 'FMCI', 'GME', 'GRVY', 'HAIN', 'HBM', 'HIBB', 'IEX', 'IOR', 'KFS', 'MAXR', 'MPX', 'MRTX', 'NSTG', 'NVCR',
#'NVO', 'OESX', 'PENN', 'PLL', 'PRTK', 'RDY', 'REGI', 'REKR', 'SBE', 'SQM', 'TCON', 'TCS', 'TGB', 'TPTX', 'TRIL',
#'UEC', 'VCEL', 'VOXX', 'WIT', 'WKHS', 'XNCR']
stock = ['^GSPC', 'NQ=F', 'AAU', 'ALB', 'AOS', 'APPS']
adj_close = pd.DataFrame([])
for symbol in stock:
try:
sector = yf.Ticker(symbol).info['sector']
name = yf.Ticker(symbol).info['shortName']
except:
sector = 'None'
name = 'None'
adj_close[sector, symbol] = web.get_data_yahoo(symbol, start=start, end=end)['Adj Close']
idx = pd.MultiIndex.from_tuples(adj_close.columns)
adj_close.columns = idx
adj_close.head()
None Basic Materials Industrials Technology
^GSPC_None NQ=F_None AAU_None ALB_Albemarle Corporation AOS_A.O. Smith Corporation APPS_Digital Turbine, Inc.
2018-01-02 2695.810059 6514.75 1.03 125.321663 58.657742 1.79
2018-01-03 2713.060059 6584.50 1.00 125.569397 59.010468 1.87
2018-01-04 2723.989990 6603.50 0.98 124.073502 59.286930 1.86
2018-01-05 2743.149902 6667.75 1.00 125.502716 60.049587 1.96
2018-01-08 2747.709961 6688.00 0.95 130.962250 60.335583 1.96
# for excel
adj_close.to_csv('stock.csv', sep=',')

pandas_datareader.DataReader returns data for just one date

On using the pandas_datareader library to access the S&P 500 historical data, I manage to get the date of JUST the current date and not until the start date as mentioned below.
import pandas as pd
import pandas_datareader as web
import datetime
def test_run():
end_date = datetime.datetime.today()
start_date = datetime.date(end_date.year,2,28)
#DataReader method name is case sensitive
df=web.DataReader('^SPX','yahoo',start=start_date,end=end_date)
path_out = 'Data/'
df.to_csv(path_out+'SPY.csv')
if __name__ == "__main__":
test_run()
Date,High,Low,Open,Close,Volume,Adj Close
2020-03-13,2711.330078125,2492.3701171875,2569.989990234375,2711.02001953125,708668739,2711.02001953125
The output is in a csv file as:
enter image description here
I cant seem to figure out where the error lies.

The symbol/ticker is '^GSPC', not '^SPX'.
import pandas as pd
import datetime
import pandas_datareader.data as web
start=datetime.datetime(2019,3,13)
end=datetime.datetime(2020,3,13)
df=web.DataReader('^GSPC','yahoo',start,end)
print(df)
Result:
High Low ... Volume Adj Close
Date ...
2019-03-13 2821.239990 2799.780029 ... 3766150000 2810.919922
2019-03-14 2815.000000 2803.459961 ... 3469730000 2808.479980
2019-03-15 2830.729980 2810.790039 ... 5962730000 2822.479980
2019-03-18 2835.409912 2821.989990 ... 3552190000 2832.939941
2019-03-19 2852.419922 2823.270020 ... 3620220000 2832.570068
... ... ... ... ...
2020-03-09 2863.889893 2734.429932 ... 8423050000 2746.560059
2020-03-10 2882.590088 2734.000000 ... 7635960000 2882.229980
2020-03-11 2825.600098 2707.219971 ... 7374110000 2741.379883
2020-03-12 2660.949951 2478.860107 ... 8829380000 2480.639893
2020-03-13 2711.330078 2492.370117 ... 8258670000 2711.020020

Try to include a column based on input and file name in Pandas Dataframe in Python

I have a several csv files which have the following structure:
Erster Hoch Tief Schlusskurs Stuecke Volumen
Datum
14.02.2017 151.55 152.35 151.05 152.25 110.043 16.687.376
13.02.2017 149.85 152.20 149.25 151.25 415.76 62.835.200
10.02.2017 149.00 150.05 148.65 149.40 473.664 70.746.088
09.02.2017 144.75 148.45 144.35 148.00 642.175 94.348.392
Erster Hoch Tief Schlusskurs Stuecke Volumen
Datum
14.02.2017 111.454 111.776 111.454 111.776 44 4.918
13.02.2017 110.570 110.989 110.570 110.989 122 13.535
10.02.2017 109.796 110.705 109.796 110.705 0 0
09.02.2017 107.993 108.750 107.993 108.750 496 53.933
all are different based on the naming of the file name:
wkn_A1EWWW_historic.csv
wkn_A0YAQA_historic.csv
I want to have the following Output:
Date wkn Open High low Close pieced Volume
14.02.2017 A1EWWW 151.55 152.35 151.05 152.25 110.043 16.687.376
13.02.2017 A1EWWW 149.85 152.20 149.25 151.25 415.76 62.835.200
10.02.2017 A1EWWW 149.00 150.05 148.65 149.40 473.664 70.746.088
09.02.2017 A1EWWW 144.75 148.45 144.35 148.00 642.175 94.348.392
Date wkn Open High low Close pieced Volume
14.02.2017 A0YAQA 111.454 111.776 111.454 111.776 44 4.918
13.02.2017 A0YAQA 110.570 110.989 110.570 110.989 122 13.535
10.02.2017 A0YAQA 109.796 110.705 109.796 110.705 0 0
09.02.2017 A0YAQA 107.993 108.750 107.993 108.750 496 53.933
The code looks like the following:
import pandas as pd
wkn_list_dummy = {'A0YAQA','A1EWWW'}
for w_list in wkn_list_dummy:
url = 'C:/wkn_'+str(w_list)+'_historic.csv'
df = pd.read_csv(url, encoding='cp1252', sep=';', decimal=',', index_col=0)
print(df)
I tried using melt but it was not working.

You can add column by just assigning a value to it:
df['new_column'] = 'string'
All together:
import pandas as pd
wkn_list_dummy = {'A0YAQA','A1EWWW'}
final_df = pd.DataFrame()
for w_list in wkn_list_dummy:
url = 'C:/wkn_'+str(w_list)+'_historic.csv'
df = pd.read_csv(url, encoding='cp1252', sep=';', decimal=',', index_col=0)
df['wkn'] = w_list
final_df = final_df.append(df)
final_df.reset_index(inplace=True)
print(final_df)