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I have time series data about water consumption by day and hour in all of the apartments in a specific residential building located in Moscow, Russia. It covers the time period from 1st of January 2006 to 10th of February 2006. The example output of pandas DataFrame for hot water consumption (in liters):

                 Time  1   2  3  4  5    6  7  ...   77  78  79  80  81  82  83  84
0     01.01.2006 0:00  0   0  0  0  0    0  0  ...    0   0   0   0   0   0  40   0
1     01.01.2006 1:00  0   0  0  0  0    0  0  ...    0   0   0   0  40  10   0   0
2     01.01.2006 2:00  0  30  0  0  0    0  0  ...    0  20   0   0   0   0   0   0
3     01.01.2006 3:00  0   0  0  0  0    0  0  ...    0   0   0   0   0   0   0   0
4     01.01.2006 4:00  0   0  0  0  0   10  0  ...    0   0   0   0   0   0   0   0
..                ... ..  .. .. .. ..  ... ..  ...  ...  ..  ..  ..  ..  ..  ..  ..
979  10.02.2006 19:00  0  20  0  0  0   20  0  ...  120   0   0   0   0  10   0   0
980  10.02.2006 20:00  0  10  0  0  0   20  0  ...    0   0   0  20   0  10  30   0
981  10.02.2006 21:00  0  70  0  0  0  110  0  ...   10  20  10   0  10  80  10   0
982  10.02.2006 22:00  0   0  0  0  0   10  0  ...    0  50   0  30   0  40   0   0
983  10.02.2006 23:00  0   0  0  0  0   10  0  ...   10   0   0   0   0  10  40   0

Every column except the first (Time) marks the apartment number. The values in the Time column have a period of one hour. At the intersection of rows and columns there is the amount of water distributed to the specific apartment in the last hour. For example, a cell with timestamp 01.01.2006 2:00 in column 2 indicates that at 1st of January 2006 apartment 2 has consumed 30 liters of hot water between 1 pm and 2 pm.

Original dataset also has a count of registered residents in each apartment, which is not included in the example above. The cold water consumption dataframe looks the same.

In the process of developing the Python application I got to the point where I need to analyze more data.

I am looking for a dataset that covers at least a whole year (to analyze the seasonality) and includes more than a single building (to assign some of the results to buildings with certain characteristics).

So, the needs are as follows:

  • Daily or hourly data, the shorter the period, the better
  • Two buildings or more, preferably with the basic characteristics about each building (e.g. the number of registered residents in each apartment, level of quality, location) or with some info through which I can get these characteristics (e.g. address)
  • It has to be Russian, but any other country will be helpful too in some way

The type of water (cold or hot) doesn't really matter.

I am open to the data with totally not the same structure as in the example above, because the requirements are already strong enough.

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