I have two Pandas dataframes; let’s call them df_a and df_b.

I have two DataFrames that contain some log data, lets call them CorrectData and WrongData

I have a multi-index pandas dataframe, and I need assign the value to a slice of the dataframe (based on one index), with a calculation on another slice of the same dataframe (based on the same index).
I have tried to assign the values using loc, but the entire slice ends up NaN.
I have written this simple example code to make clear the problem I’m having.

This question already has answers here: Pandas DataFrame: replace all values in a column, based on condition (10 answers) Closed last month. I have a pandas dataframe (let’s call it df), and one of the columns of this df (let’s say column 10) specifies if a sensor has acquired data (1 it did, 0 it […]

I have 2 dfs, the first df has transactions that each have a category id, the categories are multi layered and the number of layers varies. The 2. df has the categories. for each category it has the category id and the parent id. I would like to prepare df 2 in such a way that i can merge it with df 1 and then have all the layers of the categories in df1.

If I have a data frame df, which has five columns: ‘A’, ‘B’, ‘C’, ‘D’, and ‘E’, which contains python strings. Currently, ‘B’, ‘C’, ‘D’, and ‘E’ has unbalanced unique values (i.e., some unique values have more rows than the others). How can I sample df so that column ‘B’, ‘C’, ‘D’, and ‘E’ have balanced number of unique values (i.e., each unique value in a specific column has the same number of rows)? I want to sample with replacement so that the resulting data frame has the same length as the original data frame, though some rows may be duplicated and some may be omitted. Thanks!

I want to divide all values from particular columns in the dataframe rpk by the same value from the dataframe scaling_factor, according to sample_name. I know how to do it for a particular value (e.g. for the column ‘P1-6’ in rpk all the values should be divided by 2 – according to value factor for ‘P1-6’ in scaling_factor dataframe) but how to do it for all samples?.

I have two lists of people in two separate systems. One is our data warehouse, and the other is an autodialer. We recently had an issue where some of the data got duplicated in the dialer. We are trying to identify the problem records and I’m trying to create two lists, one that is a priority list with only active people, and the other is a complete list with everybody. I have two DataFrames that I merged into a single DataFrame and then substituted a blank string for the NaN’s so it’s more readable for the end users. However, now I’m trying to remove the blank records for the “priority” list (the ID gets blanked out as part of the process when the person is made inactive). However when I try it’s not working. Here is what I’ve got:

I am trying to count the number of separate events in a Dataframe (not number of occurences)

How can I select rows from a DataFrame based on values in some column in Pandas?