Negative energy acts like a vacuum. It sucks you in. Quitting your addiction to negative energy is like quitting cigaret

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_df

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.

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.

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?

http://live-stream.munich.es/rqh/videos-pontevedra-v-cadiz-v-es-es-1hld-17.php

http://live07.colomboserboli.com/tie/videos-SD-Amorebieta-Sporting-Gijon-v-en-gb-1euh-19.php

http://streaming7.actiup.com/nez/video-Atromitos-Athens-PAOK-v-gr-gr-1bow-5.php

http://live-stream.munich.es/rqh/Video-Ashdod-SC-Hapoel-Tel-Aviv-v-en-gb-1ply30122020-.php

https://assifonte.org/media/hvc/v-ideos-SD-Amorebieta-Sporting-Gijon-v-en-gb-1dut-.php

http://live-stream.munich.es/rqh/videos-Ashdod-SC-Hapoel-Tel-Aviv-v-en-gb-1vsp30122020-17.php

https://assifonte.org/media/hvc/video-SD-Amorebieta-Sporting-Gijon-v-en-gb-1xbs-1.php

http://streaming7.actiup.com/nez/Video-Atromitos-Athens-PAOK-v-gr-gr-1mcv-27.php

http://streaming7.actiup.com/nez/Video-El-Entag-El-Harby-Smouha-v-en-gb-1cbq-.php

http://live-stream.munich.es/rqh/videos-Ashdod-SC-Hapoel-Tel-Aviv-v-en-gb-1ecv30122020-11.php

http://streaming7.actiup.com/nez/Video-El-Entag-El-Harby-Smouha-v-en-gb-1vlg30122020-13.php

http://go.negronicocktailbar.com/jze/video-amorebieta-v-sporting-gijon-v-es-es-1sir-1.php

https://assifonte.org/media/hvc/v-ideos-SD-Amorebieta-Sporting-Gijon-v-en-gb-1oyb-3.php

http://news7.totssants.com/qds/v-ideos-Atromitos-Athens-PAOK-v-gr-gr-1dnf-4.php

http://live07.colomboserboli.com/tie/video-amorebieta-v-sporting-gijon-v-es-es-1egy-6.php

http://live-stream.munich.es/rqh/Video-SD-Amorebieta-Sporting-Gijon-v-en-gb-1hoz-.php

http://streaming7.actiup.com/nez/v-ideos-El-Entag-El-Harby-Smouha-v-en-gb-1lcr-19.php

http://live-stream.munich.es/rqh/Video-SD-Amorebieta-Sporting-Gijon-v-en-gb-1aqi30122020-17.php

http://go.negronicocktailbar.com/jze/videos-amorebieta-v-sporting-gijon-v-es-es-1mwy-1.php

http://news7.totssants.com/qds/video-Atromitos-Athens-PAOK-v-gr-gr-1vrx-1.php

Billy Mitchell demonstrated in 1921 that battleships were too vulnerable to air attack, forward-thinking military leaders have known for years that the battlefield utility of the tank was drawing to an end. Of course there are deniers who, in the words of Mark Twain, claim that “the report of the death of the tank has been greatly exaggerated,” but the evidence says otherwise. To the drone operator, a tank is a big, slow-moving target easily seen in infrared, and throughout all military history, whoever holds the high ground has a big advantage.

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.

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.

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.

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.

When you think of yourself as an ant in a massive open world, you take yourself less seriously. Your opinion doesn’t matter nearly as much as you think. Your sweater with a cappuccino stain on it will probably go unnoticed.

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.

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.

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.

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.

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.