How to extract elements from a filename and move them to different columns?

I have a filenames which I converted into a list. The list has the following elements: My goal is to extract elements from this list and fill out a dataframe, which should look like this: LINK TO THE GOOGLE SHEETS CONTAINING THE IMAGE ABOVE: https://docs.google.com/spreadsheets/d/1kuX3M4RFCNWtNoE7Hm1ejxWMwF-Cs4p8SsjA3JzdidA/edit?usp=sharing WHAT I’VE DONE SO FAR is the following code: But, this one does not leave empty spaces thus not doing what I needed. Thank you very much in advanced. Answer As per number of the comments. It’s a pain because the tokens in the filename are not fully fixed format. Quite a lot of conditional

How to use pandas to create a column that stores count of first occurrences on a group-by?

Q1. Given data frame 1, I am trying to get group-by unique new occurrences & another column that gives me existing ID count per month Expected output for unique newly added group-by ID values & for existing sum of ID values Note: Mar-2020 ID_Count is ZERO because ID 1, 2, and 3 were present in previous months. Note: Existing count is 0 for Jan-2020 because there were zero IDs before Jan. The existing count for Feb-2020 is 1 because before Feb there was only 1. Mar-2020 has 3 existing counts as it adds Jan + Feb and so on Answer

Groupby names replace values with there max value in all columns pandas

I have this DataFrame which looks like this I want this replaced all values with the maximum value. we choose the maximum value from both val1 and val2 if i do this i will get the maximum from only val1 Answer Try using pd.wide_to_long to melt that dataframe into a long form, then use groupby with transform to find the max value. Map that max value to ‘name’ and reshape back to four column (wide) dataframe: Output:

Count Number of Rows within Time Interval in Pandas Dataframe

Say we have this data: I want to count, for each year, how many rows (“index”) fall within each year, but excluding the Y0. So say we start at the first available year, 1990: How many rows do we count? 0. 1991: Three (row 1, 2, 3) 1992: Four (row 1, 2, 3, 4) … 2009: Four (row 1, 2, 3, 4) So I want to end up with a dataframe that says: My attempt: But the result doesn’t look right. Appreciate any help. Answer you could do:

GroupBy Column1, then get all elements with the first/last element on Column2 (Python)

I want to group by user_id, then get the first element of survey_id, and get all elements related to this selection In the same way I want to group by user_id, then get the last element of survey_id, and get all elements related to this selection Is there a quick groupby command to get this? I can do this by merging dataframes but I think there is some better way to do this in less command lines. Thank you in advance Answer Solution with no merging: result: result: Idea is to calculate min / max of survey_id per user_id and

Pandas: groupby().apply() custom function when groups variables aren’t the same length?

I have a large dataset of over 2M rows with the following structure: If I wanted to calculate the net debt for each person at each month I would do this: However the result is full of NA values, which I believe is a result of the dataframe not having the same amount of cash and debt variables for each person and month. Is there a way for me to avoid this and simply get the net debt for each month/person when possible and an NA for when it’s not? Also, I’m kind of new to python and as I

filter for rows with n largest values for each group

Context I want, for each team, the rows of the data frame that contains the top three scoring players. In my head, it is a combination of Dataframe.nlargest() and Dataframe.groupby() but I don’t think this is supported. My ideal solution is: performed directly on df without having to create other dataframes legible, and relatively performant (real df shape is 7M rows and 5 col) Input Desired Output Answer You can use df.groupby.rank method:

How to change index and transposing in pandas

I’m new in pandas and trying to do some converting on the dateframe but I reach closed path. my data-frame is: I need this dataframe to be like the following: as it shown I take the entity_name column as index without duplicates and the columns names from request_status column and the value from dcount so please any one can help me to do that ? many thanks Answer you can use pivot_table:

Panda is printing true and false values

I have written some code to extract data in pandas, however i am getting true and false values and not the ouput extract data using groupby pandas Input file Output file should look like Output file looks like Goes on like this up to last line of data in input file Answer import pandas as pd df = pd.read_csv(“All.csv”,encoding=”ISO-8859-1″) CLO=df.groupby(“CLO”) AE=(CLO.get_group(“xxxx”)) AE.to_csv(“AE1.csv”,index=False)