I have the above data for 1 month and I want to create a new column delta_rank_7 which tells me the change in rank in last 7 days for each id (NaNs for 2021-06-01 to 2021-06-07) I can do something like mentioned here Calculating difference between two rows in Python / Pandas but I have multiple entries for each date
Tag: calculated-columns
Calculated boolean column showing if column is newest
I have a table with a structure that more or less (I’ve simplified it for the question) looks like the following: id (P.K.) creation_ts some_field 1 2021-08-19 foo 2 2021-08-18 foo 3 2021-08-17 foo 4 NULL bar 5 2021-01-01 bar 6 2021-01-02 bar And I’m trying build a query to show a calculated column that per each row with the
How can I show only some columns using Python Pandas?
I have tried the following code and it works however it shows excess columns that I don’t require. This is the output showing the extra columns: I have tried to add the following code in after sorting the vacancy values however it gives me an error: Answer City1 and City2 are in index since you applied a groupby on it.
Pandas: calculate first purchase amount
I need to calculate the first purchase amount for every client. This is my code: ticket.groupby([‘user_reference_id’,’total_amount’]).reference_date.min().reset_index()“ And i have this result: user_reference_id total_amount reference_date* enter image description here I need it grouped by user_reference_id with the minimum reference_date (first date when a customer made the purchase) and corresponding total_amount. In this case i need the next output: reference_date 2019-06-14, user_reference_id
Pandas – iloc – comparing value to the cell below
For the following table: Using Pandas – I would like achieve the desired_output column, that is TRUE when the value below the current cell i different – otherwise FALSE. I have tried the following code – but error occurs. Answer