Get value of item when it first and last appears

I have a dataframe like

Item	Date	Count
Item_1	01/06/2023	25
Item_1	02/06/2023	14
Item_1	03/06/2023	5
Item_1	04/06/2023	7
Item_2	03/06/2023	41
Item_2	04/06/2023	49
Item_2	05/06/2023	2
Item_2	06/06/2023	38

and would like to aggregate to a new df with a line per item to show

1 - when it first appeared
2 - the value of "Count" when it first appeared 
3 - when it last appeared 
4 - the value of "Count" when it last appeared 
5 - the minimum value of "Count" per item
6 - the maximum value of "Count" per item

So it should look like

Item	Earliest_date	Count_at_earliest_date	Latest_date	Count_at_latest_date	Min_of_count	Max_of_count
Item_1	01/06/2023	25	04/06/2023	25	5	25
Item_2	03/06/2023	41	06/06/2023	41	2	49

I can get 1, 3, 5, 6 with

df2 = df.groupby('Item').agg(earliest_date =('Date', 'min'), latest_date =('Date', 'max'), min_count = ('Count', 'min'), max_count = ('Count', 'max'))
df2.reset_index(inplace=True)

But I can't figure how to get 2 and 4.

Please help!

Let's first create a df:
import pandas as pd

# Let's first create a df
data = {
    'Item': ['Item_1', 'Item_1', 'Item_1', 'Item_1', 'Item_2', 'Item_2', 'Item_2', 'Item_2'],
    'Date': ['01/06/2023', '02/06/2023', '03/06/2023', '04/06/2023', '03/06/2023', '04/06/2023', '05/06/2023', '06/06/2023'],
    'Count': [25, 14, 5, 7, 41, 49, 2, 38]
}

df = pd.DataFrame(data)

Then applying the main logic

# change date format first
df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%Y')

# sort based on Datew
df = df.sort_values('Date')

# group based on Items
df_first = df.groupby('Item').first().reset_index()
df_last = df.groupby('Item').last().reset_index()

# Rename columns
df_first = df_first.rename(columns={'Date': 'Earliest_date', 'Count': 'Count_at_earliest_date'})
df_last = df_last.rename(columns={'Date': 'Latest_date', 'Count': 'Count_at_latest_date'})

# get min and mac
df_min_max = df.groupby('Item').agg(Min_of_count=('Count', 'min'), Max_of_count=('Count', 'max')).reset_index()

# and merge them at last
df_result = pd.merge(df_first, df_last, on='Item')
df_result = pd.merge(df_result, df_min_max, on='Item')

df_result

Sort your dataframe by dates first and use first and last to get -2- and -4-:

df2 = (df.sort_values('Date').groupby('Item', as_index=False)
         .agg(earliest_date =('Date', 'min'),
              count_at_earliest_date=('Count', 'first'),
              latest_date =('Date', 'max'),
              count_at_latest_date=('Count', 'last'),
              min_count = ('Count', 'min'),
              max_count = ('Count', 'max')))

Output:

>>> df2
     Item earliest_date  count_at_earliest_date latest_date  count_at_latest_date  min_count  max_count
0  Item_1    01/06/2023                      25  04/06/2023                     7          5         25
1  Item_2    03/06/2023                      41  06/06/2023                    38          2         49