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?

I have an excel-sheet which has a lot of unneccessary information. Therefore I would like to filter out only those rows which contain specific values from a dataframe. However, the dataframe has no column names and I will need to filter the rows with the certain values from multiple dataframes with possibly different amounts of columns.
Therefore I like to filter the rows out based on their values but without knowing the column name.

The accent of this question is on the words names and in place, because I want to drop columns having equal names (no matter the values) but I don’t want to drop columns having equal values but different names, and I want do it efficiently.

I have a pretty simple pandas DataFrame and I want to select the portion of the DataFrame that has data within a column that contains within it another string

Closed 2 days ago.

Example data:

I’m having trouble modifying a new column.

I want to get the information per Name.