I have a dataset like this index Date_Time Pass_ID El 0 3/30/23 05:12:36.36 A 1 1 3/30/23 05:12:38.38 A 2 1 3/30/23 05:12:40.40 A 3 1 3/30/23 05:12:42.42 A 4 1 3/30/23 05:12:44.44 A 4 1 3/30/23 12:12:50.50 B 3 1 3/30/23 12:12:52.52 B 4 1 3/30/23 12:12:54.54 B 5 1 3/30/23 12:12:56.56 B 6… Read More Pandas – groupby string field and select by time-of-day range
This is my DataFrame: import pandas as pd df = pd.DataFrame( { ‘a’: [‘x’, ‘x’, ‘x’, ‘x’, ‘x’, ‘y’, ‘y’, ‘y’, ‘y’, ‘y’, ‘y’, ‘y’], ‘b’: [1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2], ‘c’: [9, 8, 11, 13, 14, 3, 104, 106, 11, 100, 70, 7] } ) Expected output:… Read More How can I find the first row that meets conditions of a mask for each group?
I have this Dataframe import pandas as pd import math from pandas import Timestamp Date = [Timestamp(‘2024-03-16 23:59:42’), Timestamp(‘2024-03-16 23:59:42’), Timestamp(‘2024-03-16 23:59:44’), Timestamp(‘2024-03-16 23:59:44’), Timestamp(‘2024-03-16 23:59:44’), Timestamp(‘2024-03-16 23:59:47’), Timestamp(‘2024-03-16 23:59:48’), Timestamp(‘2024-03-16 23:59:48’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16 23:59:49’), Timestamp(‘2024-03-16… Read More Some manipulations with groupby Pandas
This is a follow up to this post This is my DataFrame: df = pd.DataFrame( { ‘a’: [10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 10, 22], ‘b’: [1, 1, 1, -1, -1, -1, -1, 2, 2, 2, 2, -1, -1, -1, -1], ‘c’: [25, 25, 25, 45, 45, 45,… Read More How to select first N number of groups based on values of a column conditionally and groupby two columns?
This is my DataFrame: import pandas as pd df = pd.DataFrame( { ‘a’: [10, 14, 20, 10, 12, 5, 3] } ) And this is the expected output. I want to create four groups: a 0 10 1 14 2 20 a 3 10 4 12 a 5 5 a 6 3 From top to… Read More How can I create groups based on ascending streak of a column?
Say I have the following dataframe covering hundreds of different names: df = pd.DataFrame({"NAME":["A", "A", "A" ,"A", "B", "B", "B", "C", "C", "C", "C", "D", "D",], "CLASS":["LI","BO","EQ","AI", "LI", "EQ", "AI", "LI","BO","EQ","AI", "EQ", "AI", ]}) Using the groupby-function of Pandas, how am I supposed to filter out only those names that contain both EQ AND AI… Read More Filter Groupby if contains specific values
Hi I have a dataframe as below, I need to replace the column value in each group based on the condition using pandas. Please help Input Data: Name Thing type flag Steve Car High NULL Steve Car Low NULL Steve Bike Low NULL Steve Bike Avg X Steve Plane High NULL Steve Plane Low NULL… Read More Replacing column value under a Group in Pandas
I have a table that looks like this: CLI_CD CURA_T1 3 0 3 0 3 0 3 0 3 1 3 1 3 1 3 1 3 1 3 1 3 0 3 0 What I need to do is to add a column named ‘CURA_ALT’ that needs to recognize the first appearence of the… Read More Fill values in each row of pandas dataframe based on condition and groupby
How do you create a dataframe from a csv file and be able to execute the same groupby command that simply works when creating the dataframe by hand? Step-by-step i tried to do: I create a pandas dataframe with multiindexed columns: df = pd.DataFrame({ (‘A’, ‘x’): [1, 2, 3, 4], (‘A’, ‘y’): [10, 15, 20,… Read More groupby on pandas dataframe created with pandas.read_csv
I have a dataframe like this one and would like to make sure to sort it by date and have the same ordering for side for status emtries, I would like to see B entries on top before S for each date. But also keep the dates where there is only one entry. tempDF =… Read More categorical sorting within date group in dataframe