If pass dictionary to Series from keys is created index by default:

country_capital_dict = {'Germany':'Berlin', 'US':'Washington',
                         'Italy':'Rome', 'France':'Paris',
                         'Russia':'Moscow','Spain':'Madrid',
                         'Austria':'Vienna','Greece':'Athens'}
country_capital_series = pd.Series(country_capital_dict)
print(country_capital_series)
Germany        Berlin
US         Washington
Italy            Rome
France          Paris
Russia         Moscow
Spain          Madrid
Austria        Vienna
Greece         Athens
dtype: object

If need change index you can assign it:

country_capital_series.index = ['a','b','c','d','e','f','g','h']

print(country_capital_series)
a        Berlin
b    Washington
c          Rome
d         Paris
e        Moscow
f        Madrid
g        Vienna
h        Athens
dtype: object

Or pass only values of dictionary to Series:

country_capital_series = pd.Series(country_capital_dict.values(), 
                                   index = ['a','b','c','d','e','f','g','h'])
print(country_capital_series)
a        Berlin
b    Washington
c          Rome
d         Paris
e        Moscow
f        Madrid
g        Vienna
h        Athens
dtype: object

Reason why get all missing values is mismatch between index from list and index from keys of dictionary - because different pandas try change original index by new from list and dont know new values, so assigned all NaNs:

country_capital_series = pd.Series(country_capital_dict, 
                                   index = ['a','b','c','d','e','f','g','h'])
print(country_capital_series)
a    NaN
b    NaN
c    NaN
d    NaN
e    NaN
f    NaN
g    NaN
h    NaN
dtype: object

If only some values matching are assigned NaNs only for not match values:

country_capital_series = pd.Series(country_capital_dict, 
                                   index = ['a','Germany','c','d','e','Austria','g','h'])
print(country_capital_series)
a             NaN
Germany    Berlin
c             NaN
d             NaN
e             NaN
Austria    Vienna
g             NaN
h             NaN
dtype: object

Thank you, it worked.

As well I created a data frame and changed the index following your suggestion. And it was also OK. cars_details = {'Honda':['Civic','Accord','Acura','Pilot','Odyssey','Fit','Ringline', 'Civic Hatchback','HR-V'] , 'Year':['2005','2012','2018','2020','2013','2019','2020','2019', '2014'] , 'Mileage':[98600,55000,16000,126000,150000,17000,500,10000,110000] , 'Price': [6500,12300,15500,18000,77000,16600,34000,19000,12000] , 'Category': ['sedan','sedan','sedan','suv','minivan','hatchback','truck', 'hatchback','suv']

             }

df_cars_details = pd.DataFrame(cars_details)

index from 0 to 9 is

df_cars_details.index = ['1','2','3','4','5','6','7','8','9'] print('df_cars_details with new indexes') print(df_cars_details)

the index from 1 to 9

I don't have any specific goal to change the index, just put my hands on Pandas to practice. But I appreciate your advice. It is useful.