Check for sequence in column of Pandas DataFrame

Question

My DataFrame looks like this:

    Category       Date
81    Monate 2020-01-01
88    Monate 2020-01-02
58    Monate 2020-01-03
3     Monate 2020-01-04
23    Monate 2020-01-05
..       ...        ...
134   Wochen 2020-05-24
145     Tage 2020-05-25
147     Tage 2020-05-26
146     Tage 2020-05-27
148     Tage 2020-05-28

It is ordered by Date . I need to run a check if on each row Monate follows Monate, Wochen follows Wochen and so on. It is allowed that Wochen follows Monate and Tage follows Wochen. I hope it is clear that I mean. Something looks this should cause an error, since the sequence is invalid.

    Category       Date
81    Monate 2020-01-01
88    Monate 2020-01-02
58    Tage   2020-01-03
3     Monate 2020-01-04
23    Monate 2020-01-05
..       ...        ...
134   Wochen 2020-05-24
145     Tage 2020-05-25
147     Tage 2020-05-26
146   Wochen 2020-05-27
148     Tage 2020-05-28

I could try to write a pretty complicated and probably slow iteration over each row.

for row in result_df.iterrows():
    do xyz

Is there a better and quicker way to check for an ongoing sequence in a Series or a maybe in a list, dictionary etc.?

Answer 1

I believe you can create a numeric dictionary stating the order and replace the values of the Category column and check if series.diff is never negative with series.all :

def check(dataframe):
    d = {'Monate':1,'Wochen':2,'Tage':3}
    return dataframe['Category'].replace(d).diff().fillna(0).ge(0).all()

Test Runs:

print(df,'\n\n',f"Valid? : {check(df)}",'\n\n',df1,'\n\n',f"Valid? : {check(df1)}")

 Category        Date
81    Monate  2020-01-01
88    Monate  2020-01-02
58    Monate  2020-01-03
3     Monate  2020-01-04
23    Monate  2020-01-05
134   Wochen  2020-05-24
145     Tage  2020-05-25
147     Tage  2020-05-26
146     Tage  2020-05-27
148     Tage  2020-05-28 

 Valid? : True 

     Category        Date
81    Monate  2020-01-01
88    Monate  2020-01-02
58      Tage  2020-01-03
3     Monate  2020-01-04
23    Monate  2020-01-05
134   Wochen  2020-05-24
145     Tage  2020-05-25
147     Tage  2020-05-26
146   Wochen  2020-05-27
148     Tage  2020-05-28 

 Valid? : False