problems with reading csv file.

[MassiJames]

i have watched a tutorial about a neural network which i would like to train with this csv file. however, i have the problem that i always get this error when running it:

Traceback (most recent call last):
File "c:\xxxx\xxxx\xxxx\xxxx\xxxx.py", line 7, in <module>
data.index = pd.to_datetime(data.Time, format='%Y.%m.%d %H:%M:%S')
File "C:\xxxx\xxxx\xxxx\xxxx\xxxx\xxxx\xxxx\xxxx\xxxx\generic.py", line 5989, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'Time'

the csv file is build like this:
<DATE> <TIME> <BID> <ASK> <LAST> <VOLUME> <FLAGS>
2023.06.01 01:00:07.592 1962.69 1963.08 6
2023.06.01 01:00:07.831 1962.60 1963.05 6
etc.

and my code looks like this:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime

data = pd.read_csv("XAUUSD2020_2023.csv")
data.index = pd.to_datetime(data.Time, format='%Y.%m.%d %H:%M:%S') 
data1hrAsk = data.Ask.resample('60min').ohlc()
data1hrAsk = data1hrAsk.dropna()
train_data = data1hrAsk.close.to_list()
ret = data1hrAsk.close.pct_change()

--rest of the code--

im pretty new to coding in python i really tried everything i could. please help me.:(

[deanhystad]

Exactly what the message says,

Error:
AttributeError: 'DataFrame' object has no attribute 'Time'

You do that here:

data.index = pd.to_datetime(data.Time, format='%Y.%m.%d %H:%M:%S') 

Did you mean this:

data.index = pd.to_datetime(data["Time"], format='%Y.%m.%d %H:%M:%S') 

You can also do the conversion when reading the CSV:

data = pd.read_csv(
    "XAUUSD2020_2023.csv",
    parse_dates=["Time"],
    date_format='%Y.%m.%d %H:%M:%S')

[Pedroski55]

Isn't it likely that you might get a non-unique index with just TIME, especially if you have a lot of data?

Add DATE and TIME as index maybe? (I don't know why you had <DATE> etc. Is that important?)

df.index = pd.to_datetime(df['DATE'] + ' '  + df['TIME'], format='%Y.%m.%d %H:%M:%S.%f')

Then you could forget the columns DATE and TIME, the index is carried over

df2 = df[['BID','ASK','LAST','VOLUME','FLAGS']]

df2

Output:
                             BID      ASK  LAST  VOLUME FLAGS
2023-06-01 01:00:07.592  1962.69  1963.08     6     100     G
2023-06-02 13:01:07.891  1962.60  1963.05     6     200     B

[snippsat]

the csv file is build like this:

You should post raw sample of the .csv,so don't have to guess.
So something like this to get a working DataFrame,adjust as needed to your .csv.
stock_data.csv:

Output:
2023.06.01 01:00:07.592 1962.69 1963.08 6	
2023.06.01 01:00:07.831 1962.60 1963.05 6	
2023.06.01 01:00:08.123 1962.65 1963.10 6	
2023.06.01 01:00:08.456 1962.68 1963.12 6

import pandas as pd

df = pd.read_csv("stock_data.csv", sep=' ', names=['Date', 'Time', 'Bid', 'Ask', 'Last'])
df['DateTime'] = pd.to_datetime(df['Date'] + ' ' + df['Time'])
df.set_index('DateTime', inplace=True)
df = df.drop(columns=['Date', 'Time'])
df = df.reset_index()

Test,look at DataFrame and check that types are ok.

>>> df
                 DateTime      Bid      Ask  Last
0 2023-06-01 01:00:07.592  1962.69  1963.08     6
1 2023-06-01 01:00:07.831  1962.60  1963.05     6
2 2023-06-01 01:00:08.123  1962.65  1963.10     6
3 2023-06-01 01:00:08.456  1962.68  1963.12     6

>>> df.dtypes
DateTime    datetime64[ns]
Bid                float64
Ask                float64
Last                 int64
dtype: object