I have a time-series pivot table with struct timestamp column including start and end of time frame of records as follow: Since later I will use timestamps as the index for time-series analysis, I need to convert it into timestamps with just end/start. I have tried to find the solution using regex maybe unsuccessfully based on this post as follows:
Tag: pandas
Python – Write a row into an array into a text file
I have to work on a flat file (size > 500 Mo) and I need to create to split file on one criterion. My original file as this structure (simplified): JournalCode|JournalLib|EcritureNum|EcritureDate|CompteNum| I need to create to file depending on the first digit from ‘CompteNum’. I have started my code as well It seems ok, my concern is to create my
How to concatenate the column by column name in pandas?
Is there any efficient way to concatenate Pandas column name, and don’t use loop. My current method is very slow. input : Output : Answer You could rework your dictionary to form groups and use groupby+agg(list): output:
Python Pandas Column Formation
Is there an easy way to reformat the columns from to Thanks Answer To reorder columns dynamically based on value, here is a way to do it: This returns a dataframe with columns (axis=1) ordered based on sorted value of first row. Here is a full example using your data sample:
Operation between 2 arrays for many rows based on date
I have a dataset df_1 that looks like this: date stock A stock B stock C stock D 2020-11-01 4 8 14 30 2020-11-10 0.4 0.6 0.8 0.2 2020-11-30 6 10 20 35 2020-12-01 6 10 20 35 2020-11-31 8 12 25 0.1 And a second dataset, df_2: date output1 output2 11/2020 stock A,stock B stock C, stock D 12/2020
How to reverse a pandas series
I have a pandas series that must be flipped upside-down before I concatenate it to the main DataFrame. I can easily flip it with myseries = myseries.iloc[::-1] But when I attach it to the main DataFrame, it attaches the default series and not the flipped version. Why doesn’t the flipped series stay in place? EDIT: So my guess is that
Can we use iterables in pandas groupby agg function?
I have a pandas groupby function. I have another input in the form of dict which has {column:aggfunc} structure as shown below: I want to use this dict to apply aggregate function as follows: Is there some way I can achieve this using the input dict d (may be by using dict comprehensions)? Answer If dictionary contains columns name and
Python Dataframe – only keep oldest records from each month
I have a Pandas Dataframe with a date column. I want to only have the oldest records for each month and remove any records that came before. There will be duplicates and I want to keep them. I also need a new column with only the month and year. Input Provider date Apple 01/01/2022 Apple 05/01/2022 Apple 20/01/2022 Apple 20/01/2022
Dropping duplicate rows ignoring case (lowercase or Uppercase)
I have a data frame with one column (col). I’m trying to remove duplicate records regardless of lowercase or Uppercase, for example output: Expected Output: How can this Dropping be done regardless of case-insensitively? Answer You could use: output:
Efficient chaining of boolean indexers in pandas DataFrames
I am trying to very efficiently chain a variable amount of boolean pandas Series, to be used as a filter on a DataFrame through boolean indexing. Normally when dealing with multiple boolean conditions, one chains them like this but this becomes a problem with a variable amount of conditions. I have tried out some possible solutions, but I am convinced