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

Now that you know the answer, or rather, an answer, what will you do with it? What is your answer? Did you even understand the question? Was this worth your time? Is anything?

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.

Staying at home has given me a lot of time to think about my personal design goals. One thing that became clear to me over this time was that I didn’t want to be stagnant. I want to be someone who never stops learning and exploring topics that I’m passionate or curious about.

Are you the smartest man? Did you know the question? Do you know what it means it not knowing the question? Does any of this make sense to you? Who are you? Why are you here? Does life have a meaning? Does anything matter? What is reality? What is the nature of reality? What is existence? Is it 42?

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 want to share my design process; what I’ve learned along the way, and how I might apply these skills to make them relevant — hopefully I can inspire others in some capacity.

I studied the rationale behind every step, memorizing the result of each tool or technique used. To create my own future projects, I would most likely need to refer back to these concepts.

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was the importance of a goal that stretches you beyond your comfort zone. If I had undertaken a one month running challenge, I would not have pushed myself as much as I did. Because of the goal I set, I tracked Day 1 to Day 70 and pushed myself along the way.

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.

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

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.

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:

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.

With every new skill, you learn to walk before you can run (generally). To start, many Blender artists recommended the popular donut tutorial from Andrew Price, the Blender Guru. So, for the first couple days, I learned how to make a donut.

This tutorial helped establish a solid foundation of techniques and core concepts. Mainly; creating and applying materials; an introduction to sculpting; applying the subsurface division modifier (makes things smooth).