Following a previous question I’m trying to analyze the number of occurrences of price areas, i.e., I’m trying to count the number of hours that different price areas converge. This is my data: df1: Time DK1 DK2 … SE3 SE4 FI 0 2017-01-01T00:00:00+01:00 20.96 20.96 … 24.03 24.03 24.03 1 2017-01-01T01:00:00+01:00 20.90 20.90 … 24.03 ..
Category : dataframe
I want to get the data from some columns and create new rows with it. In my case, the same product was can be sold many times, and each time it was sold again, my df created a new column, I’d like to take the data from those columns and put them in rows, keeping ..
I have this dataframe: Name Phone / Mail Max 0176348334 Celine [email protected] … … How do I edit all the cells containin "@"? So the result should be like this: Name Phone / Mail Max 0176348334 Celine Please fill in Phone Number … … Thank you! Source: Python..
I have one dataframe that include transactions. I want to group data. buyDate category subcategory product actual_price sell_price 1/1/2021 Cloth women shirt style A 5 4 1/1/2021 Cloth men skirt style A 7 6.5 1/1/2021 Accessories ear sky wing 2 1 2/1/2021 Automotive wheel small 21 18 2/1/2021 Automotive wheel big 34 30 1/14/2021 Accessories ..
I have dataframe in below format import pandas as pd df=pd.DataFrame({‘From’: [‘A’,’B’,’C’,’A’,’C’,’D’,’E’], ‘To’:[‘B’,’C’,’A’,’C’,’D’,’E’,’C’], ‘User’:[1,1,1,1,1,1,1]}) I want to extract all possible journeys for a user. Expected output: [A],[B],[C],[D],[E],[A,B],[B,C],[C,A],[A,C],[C,D],[D,E],[E,C],[A,B,C],[B,C,A]………… I tried heuristic model, i get the graph, but i am not able to convert into tabular data. How can i achieve this using python operations or by ..
I have two dataframes that have the following structure. Dataframe A: id date price 1 2021-09-01 null 1 2021-09-02 null 2 2021-09-01 null 2 2021-09-02 null 3 2021-09-01 null 3 2021-09-02 null Dataframe B: id price 1 100 2 200 3 300 I need to set the price in dataframe A, for each id, to ..
I have a Dask DataFrame with a date-time column and other numeric columns. The successive entries in the DataFrame rows differ by a fixed time interval t mins. I want to aggregate the data hourly so that the average of the rows for the other columns data is computed for every hour. How can this ..
I would like to add multiple data frames together, with differing columns, but if there are similar columns/cells, then the values will be added together. Here is an example: Input: df1 Region Code (Index) Number of police stations Amount of crime Average Age B 2 2 35 A 1 5 45 C 3 5 56 ..
I have a database where I query for this kind of result with panda read_sql (millions results by query), Id is linked to an other table . ID Date Value 369 2021-06-15 13:06:54 0.33 370 2021-06-15 13:06:54 0.02 377 2021-06-15 13:06:54 0.30 378 2021-06-15 13:06:54 0.36 390 2021-06-15 13:06:54 535.27 391 2021-06-15 13:06:54 35.55 264 ..
I have created the data frame "kpssfirmdate" . Then I used sql procedure to deal with two data frames including "kpssfirmdate", but I get an error that kpssfirmdate does not exist. I searched and found some solutions which seem complicated to me. Idk how to handle with that. Any suggestions? How can I connect my ..
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