Hydrofunctions provides several ways to plot your data in a Jupyter notebook. Many of these methods use the graphing methods built-in to the Pandas dataframe. Some of these methods are specific to Hydrofunctions. All of these techniques use matplotlib under the hood to create the charts, so Hydrofunctions includes it during installation.

First Step: Automatic Display of Charts in Jupyter

The first step for creating a graph is to import Hydrofunctions, and then provide Jupyter with a directive to automatically display all charts from matplotlib:

>>> import hydrofunctions as hf
>>> `%matplotlib inline`

Second Step: Preparing our data for plotting

The next step is to request some data from the NWIS for us to plot:

>>> request = hf.NWIS('01585200', 'dv', period='P999D').get_data()

Next, we create a dataframe called ‘data’ from our request:

>> data = request.hf()

The rest of the examples will assume that we have a dataframe called data.

Accessing plotting functions through Hydrofunctions

Hydrofunctions includes a Flow Duration Chart, which you access as a function:

>>> hf.flow_duration(data)

Options include the ability to change the following:

  • xscale: default is ‘logit’; may also be ‘linear’
  • yscale: default is ‘log’; may also be ‘linear’
  • ylabel: default is ‘Discharge’; may be any string.
  • symbol: default is ‘.’ for small points. May also be:
    • pixel point: ‘,’
    • up triangle: ‘^’
    • circle: ‘o’
    • plus: ‘+’

Accessing plotting functions through the dataframe

The Pandas dataframe comes with several different graphing methods associated with the dataframe. To access these methods, use dot notation.

Plotting a hydrograph:

>>> data.plot()

Plotting a Histogram:

>>> data.hist()
>>> data.plot.hist()

Box Plot:


Kernel Density Plot:

>>> data.plot.kde()