pandas-bokeh-0.0.2.tar.gz

# Pandas Bokeh **Pandas Bokeh** provides a [Bokeh](https://bokeh.pydata.org/en/latest/) plotting backend for [Pandas](https://pandas.pydata.org/) and [GeoPandas](http://geopandas.org/), similar to the already existing [Visualization](https://pandas.pydata.org/pandas-docs/stable/visualization.html) feature of Pandas. Importing the library adds a complementary plotting method ***plot_bokeh()*** on **DataFrames** and **Series** (and also on **GeoDataFrames**). With **Pandas Bokeh**, creating stunning, interactive, HTML-based visualization is as easy as calling: ```python df.plot_bokeh() ``` For more information have a look at the [Examples](#Examples) below or at notebooks on the [Github Repository](https://github.com/PatrikHlobil/Pandas-Bokeh/tree/master/Documentation) of this project.  <br> ## Installation You can install **Pandas Bokeh** from [PyPI](TODO) via *pip*: pip install pandas-bokeh **Pandas Bokeh** is supported on Python 2.7, as well as Python 3.6 and above. <br> ## How To Use <p id="Basics"> </p> The **Pandas-Bokeh** library should be imported after **Pandas**. After the import, one should define the plotting output, which can be: * **pandas_bokeh.output_notebook()**: Embeds the Plots in the cell outputs of the notebook. Ideal when working in Jupyter Notebooks. * **pandas_bokeh.output_file(filename)**: Exports the plot to the provided filename as an HTML. For more details about the plotting outputs, see the reference [here](#Layouts) or the [Bokeh documentation](https://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html#getting-started). ### Notebook output (see also [bokeh.io.output_notebook](https://bokeh.pydata.org/en/latest/docs/reference/io.html#bokeh.io.output_notebook)) ```python import pandas as pd import pandas_bokeh pandas_bokeh.output_notebook() ``` <p id="output_file"> </p> ### File output to "Interactive Plot.html" (see also [bokeh.io.output_file](https://bokeh.pydata.org/en/latest/docs/reference/io.html#bokeh.io.output_file)) ```python import pandas as pd import pandas_bokeh pandas_bokeh.output_file("Interactive Plot.html") ``` <br> <p id="Examples"></p> ## Lineplot ### Basic Lineplot This simple **lineplot** already contains various interactive elements: * a pannable and zoomable (zoom in plotarea and zoom on axis) plot * by clicking on the legend elements, one can hide and show the individual lines * a Hovertool for the plotted lines **Note**: If the **x** parameter is not specified, the index is used for the x-values of the plot. ```python import numpy as np np.random.seed(42) df = pd.DataFrame({"Google": np.random.randn(1000)+0.2, "Apple": np.random.randn(1000)+0.17}, index=pd.date_range('1/1/2000', periods=1000)) df = df.cumsum() df = df + 50 df.plot_bokeh(kind="line") ```  #### Advanced Lineplot There are various optional parameters to tune the plots, for example: * **kind**: Which kind of plot should be produced. Currently supported are: *"line", "point", "scatter", "bar"* and *"histogram"*. In the near future many more will be implemented as horizontal barplot, boxplots, pie-charts, etc. * **figsize**: Choose width & height of the plot * **title**: Sets title of the plot * **xlim**/**ylim**: Set visibler range of plot for x- and y-axis (also works for *datetime x-axis*) * **xlabel**/**ylabel**: Set x- and y-labels * **logx**/**logy**: Set log-scale on x-/y-axis * **xticks**/**yticks**: Explicitly set the ticks on the axes * **color**: Defines a single color for a plot. * **colormap**: Defines the colors to plot. Can be either a list of colors or the name of a [Bokeh color palette](https://bokeh.pydata.org/en/latest/docs/reference/palettes.html) * **hovertool**: If True a Hovertool is active, else if False no Hovertool is drawn. * **kwargs****: Optional keyword arguments of [bokeh.plotting.figure.line](https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.line) Try them out to get a feeling for the effects. Let us consider now: ```python df.plot_bokeh( kind="line", figsize=(800, 450), title="Apple vs Google", xlabel="Date", ylabel="Stock price [$]", yticks=[0,100,200,300,400], ylim=(0,400), colormap=["red", "blue"]) ```  #### Lineplot with data points For **lineplots**, as for many other plot-kinds, there are some special keyword arguments that only work for this plotting type. For lineplots, these are: * **plot_data_points**: Plot also the data points on the lines * **plot_data_points_size**: Determines the size of the data points * **marker**: Defines the point type *(Default: "circle")*. Possible values are: 'circle', 'square', 'triangle', 'asterisk', 'circle_x', 'square_x', 'inverted_triangle', 'x', 'circle_cross', 'square_cross', 'diamond', 'cross' * **kwargs****: Optional keyword arguments of [bokeh.plotting.figure.line](https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.line) Let us use this information to have another version of the same plot: ```python df.plot_bokeh( kind="line", figsize=(800, 450), title="Apple vs Google", xlabel="Date", ylabel="Stock price [$]", yticks=[0,100,200,300,400], ylim=(100,200), xlim=("2001-01-01","2001-02-01"), colormap=["red", "blue"], plot_data_points=True, plot_data_points_size=10, marker="asterisk", toolbar_location="right" ) ```  <br> ## Pointplot If you just wish to draw the date points for curves, the **pointplot** option is the right choice. It also accepts the **kwargs** of [bokeh.plotting.figure.scatter](https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.scatter) like *marker* or *size*: ```python import numpy as np x = np.arange(-3, 3, 0.1) y2 = x**2 y3 = x**3 df = pd.DataFrame({"x": x, "Parabula": y2, "Cube": y3}) df.plot_bokeh( kind="point", x="x", xticks=range(-3, 4), size=5, colormap=["#009933", "#ff3399"], title="Pointplot (Parabula vs. Cube)", marker="x") ```  <br> ## Scatterplot A basic **scatterplot** can be created using the *kind="scatter"* option. For **scatterplots**, the **x** and **y** parameters have to be specified and the following optional keyword argument is allowed: * **category**: Determines the category column to use for coloring the scatter points * **kwargs****: Optional keyword arguments of [bokeh.plotting.figure.scatter](https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.scatter) Note, that the **pandas.DataFrame.plot_bokeh()** method return per default a Bokeh figure, which can be embedded in Dashboard layouts with other figures and **Bokeh** objects (for more details about (sub)plot layouts and embedding the resulting Bokeh plots as HTML click [here](#Layouts)). In the example below, we use the building *grid layout* support of **Pandas Bokeh** to display both the DataFrame (embedded in a *Div*) and the resulting **scatterplot**: ```python #Load Iris Dataset from Scikit Learn: from sklearn.datasets import load_iris iris = load_iris() df = pd.DataFrame(iris["data"]) df.columns = iris["feature_names"] df["species"] = iris["target"] df["species"] = df["species"].map(dict(zip(range(3), iris["target_names"]))) df = df.sample(frac=1) #Create Div with DataFrame: from bokeh.models import Div div_df = Div(text=df.head(10).to_html(index=False), width=550) #Create Scatte