I am trying to plot this time series in a chart, but the canvas is empty.
As you can see in the image above, my time series is quite simple. I want to plot DATE in x-axis and PAYEMS in the y-axis.
At first, I was getting an error because my dates were strings, so I converted it in cell 11.
You do not want to use a tsplot to plot a time series. The name is a bit confusing, but as the documentation puts it, tsplot is "intended to be used with data where observations are nested within sampling units that were measured at multiple timepoints". As a rule of thumb: If you understand this sentence, you will know when to use it, if you don't understand this sentence, don't use it. Apart, tsplot will even be removed or significantly altered in the future, so its use is deprecated.
But that doesn't matter, because you can directly use pandas to plot the time series.
df.plot(x="Date", y="Payems")
Related
I am trying to plot the availability of my network per hour. So,I have a massive dataframe containing multiple variables including the availability and hour. I can clearly visualise everything I want on my plot I want to plot when I do the following:
mond_data= mond_data.groupby('Hour')['Availability'].mean()
The only problem is, if I bracket the whole code and plot it (I mean this (the code above).plot); I do not get any value on my x-axis that says 'Hour'.How can plot this showing the values of my x-axis (Hour). I should have 24 values as the code above bring an aaverage for the whole day for midnight to 11pm.
Here is how I solved it.
plt.plot(mon_data.index,mond_data.groupby('Hour')['Availability'].mean())
for some reason python was not plotting the index, only if called. I have not tested many cases. So additional explanation to this problem is still welcome.
I am trying to decompose a Time Series, however my data does not have Dates, it is composed of entries taken at regular (and unknown) time intervals.
This solution is great and exactly what I want, however it assumed that my series has a datetime index, which it does not.
I can estimate the frequency parameter in this specific case, however this will need to be automated for different data, and as such I can not use the freq parameter of the seasonal_decompose function (unless there is some way to automatically calculate this) to make do for the fact that my series lacks a datetime index.
I have managed to estimate season lenght by utilizing the seasonal python package.
Using fit_seasons function and then seeing the lenght of the returned seasons.
I have a relatively large data set of accidents, which contains a column called 'Time'. Each row has a 'time'. I would like to plot a histogram showing the frequency distribution of time periods. These are datetime objects.
On x-axis I would have time-periods, or starting of time periods. And on y-axis the number of rows/datapoints that fall in those time periods. Don't think of this as a bi-variate data, with time serving as index. Think of just one series - Time. I only need frequency distribution. All the questions and answers relate to some data in context of time-series. But, data is really not relevant here.
This worked. It was pretty straightforward.
df['Time'].hist(bins=24)
I'm currently pumping out some histograms with matplotlib. The issue is that because of one or two outliers my whole graph is incredibly small and almost impossible to read due to having two separate histograms being plotted. The solution I am having problems with is dropping the outliers at around a 99/99.5 percentile. I have tried using:
plt.xlim([np.percentile(df,0), np.percentile(df,99.5)])
plt.xlim([df.min(),np.percentile(df,99.5)])
Seems like it should be a simple fix, but I'm missing some key information to make it happen. Any input would be much appreciated, thanks in advance.
To restrict focus to just the middle 99% of the values, you could do something like this:
trimmed_data = df[(df.Column > df.Column.quantile(0.005)) & (df.Column < df.Column.quantile(0.995))]
Then you could do your histogram on trimmed_data. Exactly how to exclude outliers is more of a stats question than a Python question, but basically the idea I was suggesting in a comment is to clean up the data set using whatever methods you can defend, and then do everything (plots, stats, etc.) on only the cleaned dataset, rather than trying to tweak each individual plot to make it look right while still having the outlier data in there.
Note from maintainers: this question is about the obsolete bokeh.charts API removed several years ago. For an example of timeseries charts in modern Bokeh, see here:
https://docs.bokeh.org/en/latest/docs/gallery/range_tool.html
I'm trying to create a timeseries graph with bokeh. This is my first time using bokeh, and my first time dealing with pandas as well. Our customers receive reviews on their products. I'm trying to create a graph which shows how their average review rating has changed over time.
Our database contains the dates of each review. We also have the average review value for that date. I need to plot a line with the x axis being dates and the y axis being the review value range (1 through 10).
When I accepted this project I thought it would be easy. How wrong I was. I found a timeseries example that looks good. Unfortunately, the example completely glosses over what is the most difficult part about creating a solution. Specifically, it does not show how to create an appropriate data structure from your source data. The example is retrieving pre-built datastructures from the yahoo api. I've tried examining these structures, but they don't exactly look straightforward to me.
I found a page explaining pandas structs. It is a little difficult for me to understand. Particularly confusing to me is how to represent points in the graph without necessarily labeling those points. For example the y axis should display whole numbers, but data points need not intersect with the whole number value. The page I found is linked below:
http://pandas.pydata.org/pandas-docs/stable/dsintro.html
Does anyone know of a working example for the timeseries chart type which exemplifies how to build the necessary data structure?
UPDATE:
Thanks to the answer below I toyed around with just passing lists into lines. It didn't occur to me that I could do this, but it works very well. For example:
date = [1/11/2011, 1/12/2011. 1/13/2011, 4/5/2014]
rating = [4, 4, 5, 2]
line(
date, # x coordinates
rating, # y coordinates
color='#A6CEE3', # set a color for the line
x_axis_type = "datetime", # NOTE: only needed on first
tools="pan,wheel_zoom,box_zoom,reset,previewsave" # NOTE: only needed on first
)
You don't have to use Pandas, you simply need to supply a sequence of x-values and a sequence of y-values. These can be plain Python lists of numbers, or NumPy arrays, or Pandas Series. Here is another time series example that uses just NumPy arrays:
http://docs.bokeh.org/en/latest/docs/gallery/color_scatter.html
EDIT: link updated