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Pandas read csv only returns the first column when column names are duplicate

I have OHLC data in a .csv file with the stock name is repeated in the header rows, like this:
M6A=F, M6A=F,M6A=F, M6A=F, M6A=F
Open, High, Low, Close, Volume
I am using pandas read_csv to get it, and parse all (and only) the 'M6A=F' columns to FastAPI. So far nothing I do will get all the columns. I either get the first column if I filter with "usecols=" or the last column if I filter with "names=".
I don't want to load the entire .csv file then dump unwanted data due to speed of use, so need to filter before extracting the data.
Here is my code example:
symbol = ['M6A=F']
df = pd.read_csv('myOHCLVdata.csv', skipinitialspace=True, usecols=lambda x: x in symbol)
def parse_csv(df):
res = df.to_json(orient="records")
parsed = json.loads(res)
return parsed
#app.get("/test")
def historic():
return parse_csv(df)
What I have done so far:
I checked the documentation for pandas.read_csv and it says "names=" will not allow duplicates.
I use lambdas in the above code to prevent the symbol hanging FastAPI if it does not match a column.
My understanding from other stackoverflow questions on this is that mangle_dupe_cols=True should be incrementing the duplicates with M6A=F.1, M6A=F.2, M6A=F.3 etc... when pandas reads it into a dataframe, but that isnt happening and I tried setting it to false, but it says it is not implemented yet.
And answers like I found in this stackoverflow solution dont seem to tally with what is happening in my code, since I am only getting the first column returned, or the last column with the others over-written. (I included FastAPI code here as it might be related to the issue or a workaround).

Replacing dot with comma from a dataframe using Python

I have a dataframe for example df :
I'm trying to replace the dot with a comma to be able to do calculations in excel.
I used :
df = df.stack().str.replace('.', ',').unstack()
or
df = df.apply(lambda x: x.str.replace('.', ','))
Results :
Nothing changes but I receive his warning at the end of an execution without errors :
FutureWarning: The default value of regex will change from True to
False in a future version. In addition, single character regular
expressions willnot be treated as literal strings when regex=True.
View of what I have :
Expected Results :
Updated Question for more information thanks to #Pythonista anonymous:
print(df.dtypes)
returns :
Date object
Open object
High object
Low object
Close object
Adj Close object
Volume object
dtype: object
I'm extracting data with the to_excel method:
df.to_excel()
I'm not exporting the dataframe in a .csv file but an .xlsx file

Where does the dataframe come from - how was it generated? Was it imported from a CSV file?
Your code works if you apply it to columns which are strings, as long as you remember to do
df = df.apply() and not just df.apply() , e.g.:
import pandas as pd
df = pd.DataFrame()
df['a'] =['some . text', 'some . other . text']
df = df.apply(lambda x: x.str.replace('.', ','))
print(df)
However, you are trying to do this with numbers, not strings.
To be precise, the other question is: what are the dtypes of your dataframe?
If you type
df.dtypes
what's the output?
I presume your columns are numeric and not strings, right? After all, if they are numbers they should be stored as such in your dataframe.
The next question: how are you exporting this table to Excel?
If you are saving a csv file, pandas' to_csv() method has a decimal argument which lets you specify what should be the separator for the decimals (tyipically, dot in the English-speaking world and comma in many countries in continental Europe). Look up the syntax.
If you are using the to_excel() method, it shouldn't matter because Excel should treat it internally as a number, and how it displays it (whether with a dot or comma for decimal separator) will typically depend on the options set in your computer.
Please clarify how you are exporting the data and what happens when you open it in Excel: does Excel treat it as a string? Or as a number, but you would like to see a different separator for the decimals?
Also look here for how to change decimal separators in Excel: https://www.officetooltips.com/excel_2016/tips/change_the_decimal_point_to_a_comma_or_vice_versa.html
UPDATE
OP, you have still not explained where the dataframe comes from. Do you import it from an external source? Do you create it/ calculate it yourself?
The fact that the columns are objects makes me think they are either stored as strings, or maybe some rows are numeric and some are not.
What happens if you try to convert a column to float?
df['Open'] = df['Open'].astype('float64')
If the entire column should be numeric but it's not, then start by cleansing your data.
Second question: what happens when you use Excel to open the file you have just created? Excel displays a comma, but what character Excel sues to separate decimals depends on the Windows/Mac/Excel settings, not on how pandas created the file. Have you tried the link I gave above, can you change how Excel displays decimals? Also, does Excel treat those numbers as numbers or as strings?

Using python pandas how can we select very specific rows and associated column

I am still learning python, kindly excuse if the question looks trivial to some.
I have a csv file with following format and I want to extract a small segment of it and write to another csv file:
So, this is what I want to do:
Just extract the entries under actor_list2 and the corresponding id column and write it to a csv file in following format.
Since the format is not a regular column headers followed by some values, I am not sure how to select starting point based on a cell value in a particular column.e.g. even if we consider actor_list2, then it may have any number of entries under that. Please help me understand if it can be done using pandas dataframe processing capability.
Update: The reason why I would like to automate it is because there can be thousands of such files and it would be impractical to manually get that info to create the final csv file which will essentially have a row for each file.

As Nour-Allah has pointed out the formatting here is not very regular to say the least. The best you can do if that is the case that your data comes out like this every time is to skip some rows of the file:
import pandas as pd
df = pd.read_csv('blabla.csv', skiprows=list(range(17)), nrows=8)
df_res = df.loc[:, ['actor_list2', 'ID']]
This should get you the result but given how erratic formatting is, this is no way to automate. What if next time there's another actor? Or one fewer? Even Nour-Allah's solution would not help there.
Honestly, you should just get better data.

As the CSV file you have is not regular, so a lot of empty position, that contains 'nan' objects. Meanwhile, the columns will be indexed.
I will use pandas to read
import pandas as pd
df = pd.read_csv("not_regular_format.csv", header=None)
Then, initialize and empty dictionary to store the results in, and use it to build an output DataFram, which finally send its content to a CSV file
target={}
Now you need to find actor_list2 in the second columns which is the column with the index 0, and if it exists, start store the names and scores from in the next rows and columns 1 and 2 in the dictionary target
rows_index = df[df[1] == 'actor_list2'].index
if len(rows_index) > 0:
i = rows_index[0]
while True:
i += 1
name = df.iloc[i, 1]
score = df.iloc[i, 2]
if pd.isna(name): # the names sequence is finished and 'nan' object exists.
break
target[name] = [score]
and finally, construct DataFrame and write the new output.csv file
df_output=pd.DataFrame(target)
df_output.to_csv('output.csv')
Now, you can go anywhere with the given example above.
Good Luck

Reading Date times from Excel to Python using Pandas

I'm trying to read from an Excel file that gets converted to python and then gets split into numbers (Integers and floats) and everything else. There are numerous columns of different types.
I currently bring in the data with
pd.read_excel
and then split the data up with
DataFrame.select_dtypes("number")
When users upload a time (so 12:30:00) they expect for it to be recognized as a time. However python (currently) treats it as dtype object.
If I specify the column with parse_dates then it works, however since I don't know what the data is in advance I ideally want this to be done automatically. I`ve tried setting parse_dates = True however it doesn't seem to make a difference.
I'm not sure if there is a way to recognize the datatime after the file is uploaded. Again however I would want this to be done without having to specify the column (so anything that can be converted is)
Many Thanks

If your data contains only one column with dtype object (I assume it is a string) you can do the following:
1) filter the column with dtype object
import pandas as pd
datatime_col = df.select_dtypes(object)
2) convert it to seconds
datetime_col_in_seconds = pd.to_timedelta(datatime_col.loc[0]).dt.total_seconds()
Then you can re-append the converted column to your original data and/or do whatever processing you want.
Eventually, you can convert it back to datetime.
datetime_col = pd.to_datetime(datetime_col_in_seconds, unit='s')
if you have more than one column with dtype object you might have to do some more pre-processing but I guess this is a good way to start tackling your particular case.

This does what I need
for column_name in df.columns:
try:
df.loc[:, column_name] = pd.to_timedelta(df.loc[:, column_name].astype(str))
except ValueError:
pass
This tries to convert every column into a timedelta format. If it isn't capable of transforming it, it returns a value error and moves onto the next column.
After being run any columns that could be recognized as a timedelta format are transformed.

How do I prevent a value from converting to a date or executing as division?

I have a column in a dataframe that has values in the format XX/XX (Ex: 05/23, 4/22, etc.) When I convert it to a csv, it converts to a date. How do I prevent this from happening?
I tried putting an equals sign in front but then it executes like division (Ex: =4/20 comes out to 0.5).
df['unique_id'] = '=' + df['unique_id']
I want the output to be in the original format XX/XX (Ex: 5/23 stays 5/23 in the csv file in Excel).

Check the datatypes of your dataframe with df.dtypes. I assume your column is interpreted as date. Then you can do df[col] = df[col].astype(np_type_you_want)
If that doenst bring the wished result, check why the column is interpreted as date when creating the df. Solution depends on where you get the data from.

The issue is not an issue with python or pandas. The issue is that excel thinks its clever and assumes it knows your data type. you were close with trying to put an = before your data but your data needs to be wrapped in qoutes and prefixed with an =. I cant claim to have come up with this answer myself. I obtained it from this answer
The following code will allow you to write a CSV file that will then open in excel without any formating trying to convert to date or executing division. However it shoudl be noted that this is only really a strategy if you will only be opening the CSV in excel. as you are wrapping formating info around your data which will then be stripped out by excel. If you are using this csv in any other software you might need to rethink about it.
import pandas as pd
import csv
data = {'key1': [r'4/5']}
df = pd.DataFrame.from_dict(data)
df['key1'] = '="' + df['key1'] + '"'
print(df)
print(df.dtypes)
with open(r'C:\Users\cd00119621\myfile.csv', 'w') as output:
df.to_csv(output)
RAW OUTPUT in file
,key1
0,"=""4/5"""
EXCEL OUTPUT