I explained some important Pandas function in the article above that will be used in this

8 min readDec 6, 2020

A Complete Guide to Hypothesis Testing for Data Scientists Using Python
Explained Clearly with Sample Research Questions, Solution Steps, and Complete Codestowardsdatascience.com

Another way of transformation. It keeps adding the cumulative. For example, if you add an expanding function to the ‘High’ column first element remains the same. The second element becomes cumulative of the first and second element, the third element becomes cumulative of the first, second, and third element, and so on. You can use aggregate functions like mean, median, standard deviation, etc. on it too.

While I was dating, I thought he loved me. I checked my blooming relationship with the things I had learned in my college relationship courses to ensure we were on the right track. After we were married, things started coming up that made me wonder if he did love me.
Differencing takes the difference in values of a specified distance. By default, it’s one. If you specify 2 like “df.High.diff(2)’, it will take the difference of first and third element of ‘High’ column, second and fourth element, and so on.
Here I added expanding mean and standard deviation. Look at the daily data and the mean. At the end of 2017, daily data shows a huge spike. But it doesn’t show a spike in the average. Probably if you take the 2017 data only, the expanding average will look different. Please feel free to try it.

In the code above, .div() helps to fill up the missing data. Actually, div() means division. df. div(6) will divide each element in df by 6. But here I used ‘df.Close.shift()’. So, Each element of df will be divided by each element of ‘df.Close.shift()’. We do this to avoid the null values that are created by the ‘shift()’ operation.

In that way, it will provide you with the changing mean, median, sum, or standard deviation with time. Isn’t it really useful for financial data or business sales or profit data?

Remember that first line plot of ‘Volume’ data above. As we discussed before, it was too busy. It can be fixed by resampling. Instead of plotting daily data, plotting monthly average will fix this issue to a large extent. I will use the df_month dataset I prepared already for the bar plot and box plots above for this.

One way to find seasonality is by using a set of boxplots. Here I am going to make boxplots for each month. I will use ‘Open’, ‘Close’, ‘High’ and ‘Low’ data to make this plot.

import calendar
all_month_year_df = pd.pivot_table(df, values=”Open”,
index=[“month”],
columns=[“year”],
fill_value=0,
margins=True)
named_index = [[calendar.month_abbr[i] if isinstance(i, int) else i for i in list(all_month_year_df.index)]] # name months
all_month_year_df = all_month_year_df.set_index(named_index)
all_month_year_dfCommitted love relationships are tricky. They include two distinct individuals with unique upbringing, thoughts, expectations, baggage, hurts, dreams, desires, and more. How do you mesh the two? How can you come together and live a long life together? Is it even possible?In the ‘Volume’ data we are working on right now, we can observe some big spikes here and there. These types of spikes are not helpful for data analysis or for modeling. normally to smooth out the spikes, resampling to a lower frequency and rolling is very helpful.

Rolling is another very helpful way of smoothing out the curve. It takes the average of a specified amount of data. If I want a 7-day rolling, it gives us the 7-d average data.

Multiclass Classification Algorithm from Scratch with a Project in Python: Step by Step Guide
This article explains two methods: The gradient descent method and the optimization function methodtowardsdatascience.com

This is our plot of ‘Volume’ data that looks pretty busy with some big spikes. It will be a good idea to plot all the other columns as well in a plot to examine the curves of all of them at the same time.

My husband’s actions and words didn’t seem to fit what I expected I would have in a loving relationship. I worked and worked on myself. I worked hard to change and make sure my expectations were not unreasonable. I adjusted my communication and started implementing specific strategies that I learned at marriage retreats and from counselors.
If you could run all the code above, Congratulation! You learned enough today to make a great level of time series of data visualization. As I mentioned in the beginning, there are a lot of cool visualization techniques available. I will write more in the future.
So, this article will only deal with the resampling of lower frequencies. Though resampling of higher frequency is also necessary especially for modeling purposes. Not so much in data analysis purpose.

I will use the monthly data that was calculated in the beginning. This time I chose bar plots. It shows the percent change clearly. There is a percent change function available to get the percent_change data.

The line plot I used above is great for showing seasonality. Resampling for months or weeks and making bar plots is another very simple and widely used method of finding seasonality. Here I am making a bar plot of month data for 2016 and 2017. For the index, I will use [2016:]. Because our dataset contains data until 2017. So, 2016 to end should bring 2016 and 2017.

I explained some important Pandas function in the article above that will be used in this

Written by Nguyen Phon