I am having a table which contains over 600000 records and a column named implementer_userid, value in which may get repeated for more than one record. Now i want to store how many times a particular distinct value is occuring in that column. COUNTIF(Excel), GroupBy(sql) and similar functions wont work as i dont want a count of a specific value and instead replace all distinct values with their frequencies. Help me by doing so in any one of the three frameworks: Excel, Pandas(Python) & SQL.
If I understand your problem correctly, you can just construct a frequency table using value_counts() function, and then go through your column, replacing keys (row values) with the respective frequencies, as retrieved from the dictionary you've constructed earlier. For example:
frequencies = your_pandas_dataframe['Your column'].value_counts()
your_pandas_dataframe['Result column'] = your_pandas_dataframe['Your column'].apply(lambda x: frequencies[x])
If you don't want this extra column, you can probably do something like this instead:
# ...
your_pandas_dataframe['Your column'] = your_pandas_dataframe['Your column'].apply(lambda x: frequencies[x])
Does this answer your question?