I have a following problem. My data has this structure:
import pandas as pd import numpy as np input = { "Name": ["Tom", "Tom", "nick", "krish", "krish", "jack", "krish"], "Age": [20, 20, 21, 19, 19, 18, 19], "Time": [ "2021-09-23 00:01:00", "2021-09-24 00:02:00", "2021-09-23 00:01:00", "2021-09-23 00:01:00", "2021-09-23 00:10:00", "2021-09-23 00:01:00", "2021-09-25 00:03:00", ], "Value": [1, 5, 1, 1, 17, 2, 8], } df_input = pd.DataFrame(input)
I would like to calculate difference in minutes based on:
Name
- and
Value
starts with 1 and ends with 9 or 17.
Desired output is:
output = { "Name": ["Tom", "Tom", "nick", "krish", "krish", "jack", "krish"], "Age": [20, 20, 21, 19, 19, 18, 19], "Time": [ "2021-09-23 00:01:00", "2021-09-24 00:02:00", "2021-09-23 00:01:00", "2021-09-23 00:01:00", "2021-09-23 00:10:00", "2021-09-23 00:01:00", "2021-09-25 00:03:00", ], "Value": [1, 5, 1, 1, 17, 2, 8], "Diff_hours": [np.NaN, np.NaN, np.NaN, # becuase no 9 or 17 at the end in Value 9, # because 2021-09-23 00:01:00 minus 2021-09-23 00:10:00 9, np.NaN, # because neither 1 at beginning and 9 or 17 at the end in Value 9 ], } df_output = pd.DataFrame(output)
I found this, but it did not help me: Time difference in day based on specific condition in pandas
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Answer
Solution I come with, but there might be better one:
help = df_input[["Name", "Time", "Value"]] help = help[(help["Value"] == 1 ) | (help["Value"] == 9 ) | (help["Value"] == 17 ) ] help["Time"] = pd.to_datetime(help["Time"]) help['diff'] = help.sort_values(['Name','Time']).groupby('Name')['Time'].diff() help['diff'] = help['diff'].fillna(pd.Timedelta(seconds=0)) help['diff'] = help['diff'].dt.total_seconds().div(60).astype(int) help = help[help["diff"] != 0][["Name", "diff"]] df_output = df_input.merge( help, how="left", on="Name" ) print(df_output)