lets say I have a brand of list: and I want this list to repeteadly fill a new column called category as much as my rows have. I want the first row have ‘a’, the second row have ‘b’, and the third row have ‘ab’, and the cycle repeats until the last rows like the example below: What I have
Tag: pandas
what would be the most efficient way to do this in pandas
I’m trying to figure out the most efficient way to join two dataframes such as below. I’ve tried pd.merge and maybe using the rank function but cannot seem to figure a way. Thanks in advance df1 What I’m trying to achieve is this df2 Answer You might want to use groupby with unstack as advised in this answer:
Remove rows in pandas dataframe if any of specific columns contains a specific value
I have the following df: Data Frame I have not been able to figure out how to delete a row if any of the columns containing the word “test” is less than 95. For example, I would have to delete the entire index row 1 because the column “heat.test” is 80 (the same for rows 0 and 3). In other
Change structure of dictionary in Python Pandas
Is there a way of changing structure of nested dictionary? I have a column in dataframe with many rows of dictionaries, which looks like that: Is there a way of modifying structure, so that it will looks like without changing actual values? Answer You should read about the function apply() in pandas. You build a function that essentially does your
How to organise multiple stock data in pandas dataframe for plotting
I have over a hundred stocks (actually crypto but that does not matter) I wish to plot, all on the same line plot. I end up with a dataframe that looks like this: I don’t know how to make a line plot from this dataframe, I don’t even know if it is possible. Is there a way? Or is there
I want to select data from different df, how can I speed it up?
I want to take the last data before the specified time from different time intervals df, my code is as follows: On my computer, the running time of get_result_df() is 204ms, how can I speed up the running speed of get_result_df()? I optimized it, and the running time was reduced to 53ms. Is there any room for improvement? Answers to
Pandas – partition a dataframe into two groups with an approximate mean value
I want to split all rows into two groups that have similar means. I have a dataframe of about 50 rows but this could go into several thousands with a column of interest called ‘value’. So far I tried using cumulative sum for which total column was created then I essentially made the split based on where the mid-point of
How to add randomly elements to a column of dataframe (Equally distributed to groups)
Suppose I have the following dataframe: I want to groupby the dataset based on “Type” and then add a new column named as “Sampled” and randomly add yes/no to each row, the yes/no should be distributed equally. The expected dataframe can be: Answer You can use numpy.random.choice: output: equal probability per group: For each group, get an arbitrary column (here
New rows based on a string – Pandas. python
I have this pandas df I need to be able to break down the ‘cast” field in such a way that it is in several rows Example: I understand that I should do it with pandas, but it is very complicated, can you help me? Answer You can use spit and explode:
Iterating through a column and mapping values
Here is what I am trying to do. I want to substitute the values of this data frame. For example. Bernard to be substituted as 1, and then Drake as 2 and so on and so forth. How to iterate through the column to write a function that can do the following. Answer The function already exists – pd.factorize. It