I working with a forex dataset, trying to fill in my dataframe with open, high, low, close updated every tick. Here is my code: So as you can see, with for loop I’m getting groups. Now I want to fill the following columns in my dataframe: idx be my df[‘candle_number’] df[‘1h_open’] must be equal to the very first df.bid in
Tag: dataframe
Remove default formatting in header when converting pandas DataFrame to excel sheet
This is something that has been answered and re-answered time and time again because the answer keeps changing with updates to pandas. I tried some of the solutions I found here and elsewhere online and none of them have worked for me on the current version of pandas. Does anyone know the current, March 2019, pandas 0.24.2, fix for removing
Rename dataframe columns with a mapper function that takes parameters
How can I pass along parameters to a mapper function in pandas.DataFrame.rename? The example below uses a mapper function test. I want to change it’s behavior based on an additional parameter that I pass along. In this example, the mapper function appends an “A” to each column name. I want the mapper not always to append an “A” but a
Python DataFrame: How to connect different columns with the same name and merge them into one column
Problem I have a df that has many columns with the same column name. I wish to use the same column name as a key to do like UNION in SQL. Example see example data: df: df.T: I need to combine the two y columns since I want to calculate how many times the words in y leads to the
pandas df – sort on index but exclude first column from sort
I want to sort this df on rows (‘bad job’) but I want to exclude the first column from the sort so it remains where it is: expected output: I don’t know to edit my code below to exclude the 1st column from the sort: Answer Use argsort with add 1 for possible add first value 0 by reindex for
Check if all values in dataframe column are the same
I want to do a quick and easy check if all column values for counts are the same in a dataframe: In: Out: I want just a simple condition that if all counts = same value then print(‘True’). Is there a fast way to do this? Answer An efficient way to do this is by comparing the first value with
difference between “&” and “and” in pandas
I have some code that runs on a cron (via kubernetes) for several months now. Yesterday, part of my code didn’t work that normally does: This statement, all of a sudden, wasnt ‘True’ (both df_temp and df_temp4 have data in them: however, this worked: Was there some sort of code push that would cause this change? Since I’ve run this
GroupBy columns on column header prefix
I have a dataframe with column names that start with a set list of prefixes. I want to get the sum of the values in the dataframe grouped by columns that start with the same prefix. The only way I could figure out how to do it was to loop through the prefix list, get the columns from the dataframe
Search for a value anywhere in a pandas DataFrame
This seems like a simple question, but I couldn’t find it asked before (this and this are close but the answers aren’t great). The question is: if I want to search for a value somewhere in my df (I don’t know which column it’s in) and return all rows with a match. What’s the most Pandaic way to do it?
Using result_type with pandas apply function
I want to use apply on a pandas.DataFrame that I created, and return for each row a list of values, where each value is a column in itself. I wrote the following code: When I add result_type=’expand’ in order to change the returned array into separate columns I get the following error: However if I drop the result_type field it