# Importing required modules import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.da

Dudes pay attention to how you handle negative things. Are you resilient? Do you lash out when you’re upset? Do you pay attention to and respond to red flags, both in others and in yourself? Do what you need to do to take care of yourself, and men will be attracted to that.,Time resampling is a way to aggregate data with respect to a defined time period. We have the stock price data for each day, but this doesn’t make much sense if we want to see the trend for a financial institution. What is useful is the aggregated information for every month or every quarter. This helps the management to get an overview instantly and then make decisions based on this overview.,So there is a dip in stock prices around the last week of October and the first week of November. One could investigate it further by finding out if there was some special event on that day.,Do you see what happened in the resulting table? The ‘High’ and ‘Low’ data is ‘20–06–19’ is the difference in ‘High’ and ‘Low’ data of 21–06–19 and 20–06–19. And it is set in 21–06–19. That’s why it’s null in 20–06–19. Because there no data before that to subtract.,If you add a day or two it will add a day or two. But the date I put here is February 28th. That is different, right? In leap years we have 29 days in February and the other years we have 28 days in February.,Python’s necessary objects for working with dates and times reside in the built-in datetime module. In pandas, a single point in time is represented as a Timestamp And we can use datetime() function to create Timestamps from strings in a wide variety of date/time formats.,It appears that the Date column is being treated as a string rather than as dates. Let’s make things right. For this, we shall use the pandas’ to_datetime feature, which converts the arguments to dates. Lastly, we want to make sure that the Date column is the index column.,Instead of working with the entire data, it is prudent to slice the time series data to highlight the portion of the data we are interested in. Since the volume-weighted average price (VWAP) is a trading benchmark, we shall limit our analysis to only that column.,Rolling function aggregates data for a specified number of DateTime. Here I will take the mean of every three days. I will explain some more after working on this example:,In our data, there is a trend observable. After January 2020 the values start dropping and the curve is steep. In time series analysis we sometimes work for finding the trend. But sometimes we need to remove the trends from the data. Especially when we need to use the time series data for machine learning or forecasting.,If there is any trend in the data, it is not good for modeling, forecasting, or observing seasonality. To improve model performance, or to observe any seasonality or any noise in the data, differencing is a common practice. It takes the difference in data for a specified number of days. Here is an example:,What exactly happened here? I passed 3 as an argument in the rolling function and the aggregate function is mean. So, it is taking a mean of 20th, 21st, and 24th June ‘High’ data and putting on 24th. Doing the same for 21st, 24th, and 25th data and putting on 25th and so on. Lots of time we use the weekly average or 3-day average results to make decisions.,This process of differencing is supposed to remove the trend. By any chance it does not, try with a 3 day differencing or 7 days differencing. This is how to take a 3 day differencing:,It appears that Maruti had a more or less steady increase in its stock price from 2004 to the mid-2018 window. There seems to be some drop in 2019, though. Let’s further analyze the data for the year 2018.,Weekday has an effect on those data, right? On Wednesday ‘High’, ‘Low’ and ‘Volume’ everything is higher. On Monday it’s the opposite. Boxplots give a lot of information in one bundle. If you need a refresher on how to extract all the data from boxplots, here is a detailed article: