Why does it say that my date is Timestamp when it's not

Question

The following code takes a dict of dates and values and convert it to df, i have the following dict (shortened):

{'2022-09-18 06:00:00': 5.4,
 '2022-09-18 07:00:00': 6.0,
 '2022-09-18 08:00:00': 6.5,
 '2022-09-18 14:00:00': 7.9,
 '2022-09-18 15:00:00': 7.8,
 '2022-09-18 16:00:00': 7.6,
 '2022-09-18 17:00:00': 6.8,
 '2022-09-18 18:00:00': 6.4,
 '2022-09-18 19:00:00': 5.7,
 '2022-09-18 20:00:00': 4.8,
 '2022-09-18 21:00:00': 5.4,
 '2022-09-18 22:00:00': 4.7,
 '2022-09-18 23:00:00': 4.3,
 '2022-09-19 04:00:00': 3.5,
 '2022-09-19 05:00:00': 2.8,
 '2022-09-19 06:00:00': 3.8}

And the following cod:

def dict_to_df(date_dict):
    df = pd.DataFrame(list(date_value.items()), columns=['DateTime', 'Value'])
    df['DateTime'] = pd.to_datetime(df.DateTime, format='%Y-%m-%d %H:%M:%S')
    df['Date'] = df['DateTime'].dt.strftime('%d/%m/%Y')
    df['Time'] = df['DateTime'].dt.strftime('%H:%M')
    df['period'] =  pd.cut(pd.to_datetime(df.DateTime).dt.hour,
            bins=[0, 5, 17, 23],
            labels=['night', 'morning', 'night'],
            include_lowest=True, ordered= False)
    df['DatePeriod']=df['Date'].astype(str) + '-' + df['period'].astype(str)
    result_df = (df.groupby('DatePeriod')
            .agg({"Value": list, ("DateTime"): "first"})
            .set_index("DateTime")["Value"]
            .to_dict())
    return result_df

But when i try to convert my dict i get

{Timestamp('2022-09-18 06:00:00'): [5.4, 6.0, 6.5, 6.7, 6.9, 7.9, 8.5, 7.5, 7.9, 7.8, 6.8],
 Timestamp('2022-09-18 18:00:00'): [6.4, 5.7, 4.8, 5.4, 4.7, 4.3],
 Timestamp('2022-09-19 06:00:00'): [3.8],
 Timestamp('2022-09-19 00:00:00'): [4.1, 4.4, 4.3, 3.8, 3.5, 2.8]}

I want the dates to be the same, It's really confusing that it's saying that the dates are in Timestamp? What's the problem here? Thanks i advance!

Answer 1

Date is still the same, its an instance of Timestamp class from Pandas. There's no any problem at all. But you can convert the timestamp value to string if you want datetimes to be represented as string instead of Timestamp object:

(df.assign(sdatetime=df['DateTime'].astype(str)).groupby('DatePeriod')
 .agg({"Value": list, ("sdatetime"): "first"})
 .set_index("sdatetime")["Value"]
 .to_dict())

{'2022-09-18 06:00:00': [5.4, 6.0, 6.5, 7.9, 7.8, 7.6, 6.8],
 '2022-09-18 18:00:00': [6.4, 5.7, 4.8, 5.4, 4.7, 4.3], 
 '2022-09-19 06:00:00': [3.8], 
 '2022-09-19 04:00:00': [3.5, 2.8]
}