I have a dataframe structured like this I have data for all days and months from 2018 to 2021, with around 50k observations How can I aggregate all the data for the same month and perform a Train-Test splitting for each month? I.e. for all the data of the months of January, February, March and so on. Answer try this:
Tag: dataframe
Plotting top 10 Values in Big Data
I need help plotting some categorical and numerical Values in python. the code is given below: However, the data size is so huge (Big data) that I’m not even able to make meaningful plotting in python. Basically, I just want to take the top 5 or top 10 values in python and make a plot of that as given below:-
Get the sum of each column, with recursive values in each cell
Given a parameter p, be any float or integer. For example, let p=4 time 1 2 3 4 5 Numbers a1 a1*(0.5)^(1/p)^(2-1) a1*(0.5)^(1/p)^(2-1) a1*(0.5)^(1/p)^(3-1) a1*(0.5)^(1/p)^(4-1) Numbers nan a2 a2*(0.5)^(1/p)^(3-2) a2*(0.5)^(1/p)^(4-2) a2*(0.5)^(1/p)^(5-2) Numbers nan nan a3 a3*(0.5)^(1/p)^(4-3) a3*(0.5)^(1/p)^(5-3) Numbers nan nan nan a4 a4*(0.5)^(1/p)^(5-4) Number nan nan nan nan a5 Final Results a1 sum of column 2 sum of column 3
pandas sort alphabetically for every row based on column content
I have a dataframe that looks like this: Col1 Col2 Bonnie Anna Connor Ethan Sophia Daniel And I want to sort its content alphabetically so that the final result is: Col1 Col2 Anna Bonnie Connor Ethan Daniel Sophia I want each pair to be ordered alphabetically. As they are in different columns, I don’t know how to sort them directly
How to get a value in a column as an index
I assign the eligible index value to A column and then df.ffill() Now I want to use the value of A column as an index and assign the obtained value to the expcted column I try df[‘expected’]=df[‘price’][df[‘A’]] but it doesn’t work. input expected result table Answer Try this:
How to reset the incrementing values when assigning values to groups in a pandas dataframe?
I have a pandas dataframe which looks like this after the following code: For clarity, row_l0 relates to Category, row_l1 relates to Process and row_l2 to Parent. The row_l0 is correct, but I can’t seem to be able to reset the count/grouping for the subsequent groups (row_l1 and row_l2) when I get to category B (and beyond). E.g. at index
Separate columns of a DataFrame by days of the week
Let it be the following Python Panda Dataframe (the original could include dates for several months): Hours 2022-06-06 2022-06-07 2022-06-08 2022-06-09 2022-06-10 2022-06-11 2022-06-12 2022-06-13 2022-06-14 2022-06-15 2022-06-16 2022-06-17 2022-06-18 2022-06-19 00:00 3 0 0 3 23 43 1 2 3 3 7 3 1 0 05:00 5 4 0 3 32 31 3 9 3 3 5 3 0
python jupyter Same condition tested in an if statement behave differently
Have a Jupyter Lab notebook which at a certain point compares two dataframes. df_lastweek is an extraction of only last week’s data while the df_lastmonth is the extraction of the last 30 days. The two dataframes are different the latter having more rows than the former. The following if comparing the two different dataframes does not trigger: while the next
How add value in second row into first row?
I would like to add a new columns from a values of ‘Pr’ in second rows for each value same id and date. Input a: ID Date order Date restock Pr Infos 778005 2022-04-07 11:34:46.0 NaN 87.0;113001.0;00 a 778005 2022-04-07 11:34:46.0 NaN 87.0;113159.0;FC at 7001 2021-12-10 13:50:46.0 2021-12-13 00:00:00.0 87.0;271007.0;BV b 7001 2021-12-10 13:50:46.0 2021-12-13 00:00:00.0 87.0;286005.0;BV bt 778005 2022-05-24
How do I append a repeating list to a dataframe?
I have a list sub = [“A”,”B”,”C”,”D”,”E”,”F”] and a dataframe of the following format: I need to write a code for my dataframe to finally look like the following format: Answer You can create a cycle using itertools.cycle, and cut it to the appropriate length using itertools.islice. So, in your case, you can just cut it to the length of