Counting the number of occurrences per year in a groupby

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I have a pandas dataframe which looks like:

df = pd.DataFrame(data={'id':[1234, 1234, 1234, 1234, 1234], 'year':['2017', '2017', '2018', '2018', '2018'], 'count_to_today':[1, 2, 3, 3, 4]})
df
     id  year  count_to_today
0  1234  2017               1
1  1234  2017               2
2  1234  2018               3
3  1234  2018               3
4  1234  2018               4

And I need to count how many times count_to_today happens in each year per id. i.e. I have a running count since the beginning of time, and I want to count the number of times it increments per year.

           count_in_year
id   year               
1234 2017              2
     2018              2

I'm a bit confused about how to do this. I know I need to groupby id and year but I can't figure out how to get .count() or .value_counts() to give me the counts per year.

You can use diff and groupby:

df.count_to_today.diff().ne(0).groupby([df.id, df.year]).sum()

id    year
1234  2017    2.0
      2018    2.0
Name: count_to_today, dtype: float64

(df.count_to_today.diff()
   .ne(0)
   .groupby([df.id, df.year])
   .sum()
   .astype(int)
   .reset_index())

     id  year  count_to_today
0  1234  2017               2
1  1234  2018               2

How to count grouped occurrences?, You can use diff and groupby : df.count_to_today.diff().ne(0).groupby([df.id, df. year]).sum() id year 1234 2017 2.0 2018 2.0 Name: count_to_today, dtype: float64� count() is similar but calls group_by() before and ungroup() after. If the data is already grouped, count() adds an additional group that is removed afterwards. add_tally() adds a column n to a table based on the number of items within each existing group, while add_count() is a shortcut that does the grouping as

If you want to count ID per Year try using -

df[['ID','Year']].groupby('Year').count()

or-

df[['ID','Year']].groupby('Year').agg('count')

Change variables as you want to get your result.

How to Use Pandas GroupBy, Counts and Value Counts, You can do that by >>> import pandas as pd >>> m = pd.DataFrame({'gender': [1, 2, 2, 1, 1, 2, 1], 'rating': [3, 4, 2, 1, 3, 1, 5]}) >� For each group, it includes an index to the rows in the original DataFrame that belong to each group. The input to groupby is quite flexible. You can choose to group by multiple columns. For example, if we had a year column available, we could group by both stock symbol and year to perform year-over-year analysis on our stock data.

Use this structure:

df[['ID','Year']].groupby('Year').count()

and

df[['ID','Year']].groupby('Year').agg('count')

I hope this will work fine.Try this

pandas.core.groupby.GroupBy.count — pandas 1.1.0 documentation, Compute count of group, excluding missing values. Returns. Series or DataFrame. Count of values within each group. See also. Series� Actually, we can also create a PivotTable to count the number of occurrences per year, month, quarter, hours, seconds, etc. Please do as follows: 1. Select the table you will work with, and click Insert > PivotTable. See screenshot: 2.

How can I count the number of rows per group in Pandas?, To do this you can use the groupby method and pass in a list of columns to group by and then you can use the aggregate method to aggregate� SQL COUNT ( ) group by and order by in descending . To get data of 'working_area' and number of agents for this 'working_area' from the 'agents' table with the following conditions - 1. ' working_area' should come uniquely, 2. counting for each group should come in descending order, the following SQL statement can be used :

Summarize Your SQL Results with the GROUP BY clause, The SQL GROUP BY Clause is used to output a row across specified column values. COUNT(expression), Count occurrences of non-null values returned by the Kris Wenzel has been working with databases over the past 28 years as a� If you have Kutools for Excel installed, you can apply itsSelect Specific Cellsutility to count the number of occurrences by specified date, year, or date range in Excel easily. Kutools for Excel- Includes more than 300 handy tools for Excel. Full feature free trial 30 days, no credit card required!

Getting frequency counts of a columns in Pandas DataFrame , After grouping a DataFrame object on one column, we can apply count() method on the resulting groupby object to get a DataFrame object� I have data arranged like this and I'm trying to count the number of non-zero occurrences for each ID value. I'm trying to do this without using a PROC step and to only use a DATA step. Please forgive me if this question has been solved elsewhere - I couldn't find this in the community. ID A B. 1000 0 1

Comments
  • Guys, "I want to count the number of times it increments per year."
  • This does not address the part of the question that asks "I want to count the number of times it increments per year."...