I want to plot 5 different data frames in 1 plot. Containing the same measurement but done at different times. The plot should share the x-Axis for all measurement.
First thing i did was to calculate the time between the measurement points. It differs between 5-10 ms but sometimes also big gaps of 200 ms.
Then i calculated the running sum over this column. Then i set this column as the index (dtype "timedelta64[ns]")
Now i want to plot those 5 times.series in one plot which share the x-Axis (as time in ms)
But i don´t now how because they have almost no common index together. The plot should have one common x-Axis from 0-3 seconds containing the 5 measurements.
Thank you!
2 Example DataFrames:
example for measuremt01
example for measuremt02
Related
I am constructing a pdf matrix, for an data which looks like:
Date
Reference
Secondary
10.01.2023
2
4
11.01.2023
5
6
12.01.2023
5
3
I formed a matrix between Reference and Secondary using pd.crosstab and normalizing it column wise and later plotted it using seaborn.heatmap. It looks something like this:
Please ignore the lower panel. The green tabs are column normalised pdf matrix for the Reference and the Secondary from the above table. X-axis is the Secondary and y-axis is the Reference. My problem is matrix is not populated for higher bins. For example in the figure you see in the x-axis, the bin 17 is missing. It simply means that Secondary has no values on overlapping days with the Reference. However, I want to populate this bin (bin 17) by doing a Monte Carlo simulation and getting a distribution like other bins.
Is there any easy way to do this?
Originally I had a dataframe containing power consumption of some devices like this:
and I wanted to plot power consumption vs time for different devices, one plot per one of 6 possible dates. After grouping by date I got plots like this one (for each group = date):
Then I tried to create similar plot, but switch date and device roles so that it is grouped by device and colored by date. In order to do it I prepared this dataframe:
It is similar to the previous one, but has many NaN values due to differing measurement times. I thought it won't be a problem, but then after grouping by device, subplots look like this one (ex is just a name of sub-dataframe extracted from loop going through groups = devices):
This is the ex dataframe (mean lag between observations is around 20 seconds)
Question: What should I do to make plot grouped by device look like ones grouped by date? (I'd like to use ex dataframe but handle NaNs somehow.)
I found solution in answer to similar question: ex.interpolate(method='linear').plot(). This line will fill gaps between data points via interpolation between plotting. This is the result:
Another thing that can help is adding .plot(marker='o', ms = 3) which won't fill gaps between points, but at least will make points visible (previously some points, mainly the peaks in energy consumption were too small in scale of whole plot). This is the result:
Across a list of dataframes (dflist), each showing some sensor readings in a 24 hour window, I am setting the y axis limits for these readings in matplotlib.
axes[3].set_ylim(dflist[day]['AS_%s_WE_%d(mv)' %(gas,sensor)].min(),dflist[day]['AS_%s_WE_%d(mv)' %(gas,sensor)].max())
So for each df in my list, a graph is produced. Unfortunately the first 10 minutes of readings throws of the scale dramatically, and I can't interpret the readings.
Now, for each df, instead of setting the minimum sensor reading as the ymin, could I tell the df to ignore the first 10 minutes (which is the first 10 readings, as I have 1 minute a reading) and take the min in the rest of the data?
You can use a boolean mask in pandas that filters out undesired values.
You didn't provide the structure of your dataframe, so I'm just writing something that gives you the right idea:
dflist[day[day['minute'] > 10]]['AS_%s_WE_%d(mv)' %(gas,sensor)].min()
Essentially you are indexing each row of day with a boolean value that is mapped to the dataframe using a conditional expression.
I have a data series of about 50,000 points. Say it's roughly 60Hz, but not perfectly regular, for about 15 minutes. I would like to do a visualization like it this writeup.
So I want to divide up the data into slices and then perform a histogram on each slice, then visualize the histogram using some colormap. So if I wanted 10 second bins I would need to compute about 90 different histograms. If it was 1 second bins it would be 900 histograms. Is there a way to do this efficiently? I was thinking just numpy.where and numpy.histogram for each slice but I'm wondering if that will be slow. Is there a better way?
I'm using Core Plot to draw graphs in my app.
I just encountered a problem:
I have dates on the X-Axis and I use a custom labeling policy.
If I only have a few records everything works fine
If I have many records all the labels are near and not useful :-(
So the question is: How can I decide which values display and which not to always have 10 labels, separated one from the other.
Divide the number of points by the number of labels you want and round up. For example, if you have 25 data points and want roughly 10 labels, label every third data point. You'll end up with 9 evenly spaced labels.