Okay guys, it’s time to get sexy: if you buy a microfibre cloth, you can clean worktops using only water, saving money o

Date 2019-06-30 190.324286 2019-07-31 199.595454 2019-08-31 184.497726 2019-09-30 185.735000 2019-10-31 184.383912 2019-11-30 195.718500 2019-12-31 201.951904 2020-01-31 216.643333 2020-02-29 207.505263 2020-03-31 165.747727 2020-04-30 177.003335 2020-05-31 216.549001 2020-06-30 232.671332 Freq: M, Name: Close, dtype: float64

I know, I know, you can’t imagine using a Mooncup because the concept grosses you out big time. But the thing is, menstrual cups aren’t as weird as the name ‘menstrual cup’ would have you believe. Just like your menstrual cycle is nothing to do with biking. Or men. More importantly, the average woman will spend over £18,000 on period products in their lifetime, so you owe it to your bank balance to at least try out an alternative to (taxed) tampons and sanitary pads, and at only £21.99, a Mooncup is the best alternative. The first time you use one, expect to be confused and grossed out. But persistence pays off — keep practising and you’ll be whipping it out one-handed while scrolling through Insta in no time, smug in the knowledge that you’re not literally flushing money down the toilet every time you’re riding the pricey crimson wave.

There are other countries around the world, who use days first. For example in Americal style June 1st, 2002 is written as ‘6/1/2020’. But in England, South Asian countries like India, Bangladesh, Pakistan, and some other parts of the world write it as ‘1/6/2020’.

It is the analysis of the dataset that has a sequence of time stamps. It has become more and more important with the increasing emphasis on machine learning. So many different types of industries use time-series data now for time series forecasting, seasonality analysis, finding trends, and making important business and research decisions. So it is very important as a data scientist or data analyst to understand the time series data clearly.

Understanding of timezone is important. Probably, you are in one timezone and your client is in another timezone. Pandas have great functionality to deal with different timezones.

The ‘dates’ variable above showing five different formats of date-time settings and all are correct. But we need this specific format to work conveniently. So, convert those dates to the right format.

Bulbs are boring. I’ve spent months weeing in the dark because the bulb has gone in the bathroom and there are so many more interesting things to purchase. And weeing in the dark provides me with a sense of danger. Anyway, one bulb that isn’t boring is the humble CFL — it uses one-third to one-fifth of the electricity of a normal bulb. Which is loads. Drop £6.99 for two on Amazon, swap all the bulbs in your flat for CFLs, and stop crying every time you open your electricity bill.

Bulbs are boring. I’ve spent months weeing in the dark because the bulb has gone in the bathroom and there are so many more interesting things to purchase. And weeing in the dark provides me with a sense of danger. Anyway, one bulb that isn’t boring is the humble CFL — it uses one-third to one-fifth of the electricity of a normal bulb. Which is loads. Drop £6.99 for two on Amazon, swap all the bulbs in your flat for CFLs, and stop crying every time you open your electricity bill.

This is a raw dataset. Doesn’t look bad! Well organized. But we need to change the format of the ‘Date’ column as we discussed earlier. We will make it to the DatetimeIndex format and put it as the index column.

Because we do not always need all the data in a huge dataset. For example, we may need only the data from June 2019. If your date format is in DatetimeIndex, it is very easy:

We have two types of DateTime data. Naive DateTime which has no idea about timezone and time zone aware DateTime that knows the time zone. The data we have is naive DateTime. So, we need to use tz_localize to convert this DateTime.

Find the 1 week total in percentage. for that, we have to shift by 5 days. Right? And then take the difference from today and 5 days early data. Multiply by 100 and divide by the original today data. Let’s see it to understand it better.

Open 1.887500e 02 High 1.920000e 02 Low 1.887500e 02 Close 1.911400e 02 Adj Close 1.911400e 02 Volume 2.275120e 07 Name: 2019-06-21 00:00:00, dtype: float64

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ways feel safe for a woman to laugh or smile, but when she does, it’s certain men will turn to look at her. She’s showing her emotions, and men want to be there for it, especially if they get to be the one to make her laugh or smile.

The way it sounds, the shift function shifts the data to the specified number of times. FB dataset we are using starts on June 20th, 2019. Now, if we shift our data by 1, June 20th, 2019 data will move to June 21st, 2019, June 21st, 2019 data will shift to June 22nd, 2019, and so on.