I have following code that prints the output on console, But along with follower and friend account name, I need the screenname of the user account. Following code works well for one screen_name provided I need to collect follower and following account for multiple users along with screenname of user. for user in tweepy.Cursor(api.followers, screen_name="Billgates").items(): ..
I have a dataframe: event job_id_num JOB_START 12345 — — JOB_END 12345 RETOOL 99999 JOB_START 12346 — — JOB_END 12346 Between the JOB_START and JOB_END events there can be x number of rows depending on what job steps take place for a given job. The JOB_START and END events are marked with the job_id_number, but ..
I have a Dataframe df as follows: id lob addr a1 001 1234 a1 001 1233 a3 003 1221 a4 009 1234 I want to generate n (let’s take 4) rows per id, with the other columns being null/na/nan values. So, the above table is to be transformed to: id lob addr a1 001 1234 ..
Does Anyone have any ideas of how I can optimise this code? I don’t really know how to condense anything, I’m still very new to coding and this is the first time I’ve really created anything besides "Hello World" XD import pandas as pd import csv #Method that calculates the Current Budget of the contents ..
I am trying to generate a 4-4-5 calendar. I have a dataframe of dates from 29-03-2020 to 2022-04-09 consisting of their equivalent week number. 29-03-2020 is the starting date of the fiscal year. I am trying to generate a column with their respective quarters it would belong to. Here is the final df I am ..
I’m working with a huge dataframe in python and sometimes I need to add an empty row or several rows in a definite position to dataframe. For this question I created a small dataframe df in order to show, what I want to achieve. cars = {‘Brand’: [‘Honda Civic’,’Toyota Corolla’,’Ford Focus’,’Audi A4′], ‘Price’: [22000,25000,27000,35000] } ..
I want to be able to reset my DataFrame to its original state whenever I need to. I tried declaring 2 DataFrame variables (say df_1 and df_2) with the exact same data so I can edit df_1, then reset it to it’s original form (inside df_2) whenever I need to. I tried doing this in ..
I need to create a new column as Billing and Non-Billing based on the Billable column. If the Billable is ‘Yes’ then i should create a new column as Billing and if its ‘No’ then need to create a new column as ‘Non-Billable’ and need to calculate it. Calculation should be in row axis. Calculation ..
Imagine you have the following df: dffinal = {‘BTW’: ‘2.81’, ‘ExclBTW’: ‘31.17’, Totaal: ‘33.98’} BTW ExclBTW Totaal 0 2.81 31.17 33.98 dtype: I want to do a reconciliation check on these numbers, such that Totaal equals ExclBTW + BTW: dffinal[‘VATcheck’] = np.where(dffinal[‘Totaal’].astype(‘float’) == (dffinal[‘ExclBTW’].astype(float).round(2) + dffinal[‘BTW’].astype(float).round(2)), True, False) However, the VATcheck returns False, while 2.81 ..
I have a DataFrame in pandas like this: In[1]: df Out [1]: City Cluster 0 Paris A 1 Paris A 2 Paris B 3 Paris B 4 Paris B 5 Nairobi C 6 Nairobi A 7 Nairobi C 8 Phnom_penh B 9 Phnom_penh B 10 Phnom_penh B I want to count for each cluster/city how ..
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