How do I print a simple Python statement based on Pandas dataframe?

I've made a little function which you can call on whatever df you want.

I find this solution more readable and flexible for further requests.

def equip_avail(df):
  for i in df['Train Number'].unique():
    date_start = df.Date.loc[(df['Train Number']==i)].min()
    date_end = df.Date.loc[(df['Train Number']==i)].max()
    from_start = df.Station1.loc[(df['Train Number']==i)].values[0]
    to_end = df.Station2.loc[(df['Train Number']==i)].values[0]
    print(f'For Train Number {i} {from_start} to {to_end}, we have equipment available from {date_start} to {date_end}.')

Then you call it like this:

equip_avail(df)

Result:

For Train Number 1111 North to Central, we have equipment available from 2022-06-16 to 2022-06-20.
For Train Number 2222 North to South, we have equipment available from 2022-06-01 to 2022-06-08.

You could get the min and max values for each Train's Date with a groupby, dedupe the DataFrame to get the other columns (as they are repeated) and then print the results with some datetime formatting

df.loc[:, 'Date'] = pd.to_datetime(df['Date'])

g = df.groupby(['Train Number']).agg(date_min=pd.NamedAgg(column='Date', aggfunc='min'), date_max=pd.NamedAgg(column='Date', aggfunc='max'))
g = g.join(df_deduped, how='inner')

df_deduped = df.loc[:, 'Train Number':].drop_duplicates().set_index('Train Number')

for index, values in g.reset_index().iterrows():
    print(f'For Train Number {values["Train Number"]}, {values["Station 1"]} to {values["Station 2"]}, we have equipment available from {values["date_min"].strftime("%b %d")} to {values["date_max"].strftime("%b %d")}')

The output is -

For Train Number 1111, North to Central, we have equipment available from Jun 16 to Jun 20
For Train Number 2222, North to South, we have equipment available from Jun 01 to Jun 08

here is one way to do it. Group by Train, station1, station2, taking both min and max of the dates

Finally printing them out from the resulting df from groupby

df2=df.groupby(['TrainNumber', 'Station1', 'Station2'])['Date'].aggregate([min, max]).reset_index()
for idx, row in df2.iterrows():
    print("For Train Number {0} {1} to {2}, we have equipment available on {3} and {4}".format(
          row[0],row[1],row[2],  row[3] , row[4] ))

For Train Number 1111 North to Central, we have equipment available on 2022-06-16 and 2022-06-20
For Train Number 2222 North to South, we have equipment available on 2022-06-01 and 2022-06-08