Edward Jackson

Edward Jackson

1564565299

10 Python Pandas tricks that make your work more efficient

Some commands you may know already but may not know they can be used this way. Pandas is a widely used Python package for structured data. There’re many nice tutorials of it, but here I’d still like to introduce a few cool tricks the readers may not know before and I believe they’re useful.

1. read_csv

Everyone knows this command. But the data you’re trying to read is large, try adding this argument: nrows = 5 to only read in a tiny portion of the table before actually loading the whole table. Then you could avoid the mistake by choosing wrong delimiter (it may not always be comma separated).

(Or, you can use ‘head’ command in linux to check out the first 5 rows (say) in any text file: head -n 5 data.txt (Thanks Ilya Levinson for pointing out a typo here))

Then, you can extract the column list by using df.columns.tolist() to extract all columns, and then add usecols = [‘c1’, ‘c2’, …] argument to load the columns you need. Also, if you know the data types of a few specific columns, you can add the argument dtype = {‘c1’: str, ‘c2’: int, …} so it would load faster. Another advantage of this argument that if you have a column which contains both strings and numbers, it’s a good practice to declare its type to be string, so you won’t get errors while trying to merge tables using this column as a key.

2. select_dtypes

If data preprocessing has to be done in Python, then this command would save you some time. After reading in a table, the default data types for each column could be bool, int64, float64, object, category, timedelta64, or datetime64. You can first check the distribution by

df.dtypes.value_counts()

to know all possible data types of your dataframe, then do

df.select_dtypes(include=['float64', 'int64'])

to select a sub-dataframe with only numerical features.

3. copy

This is an important command if you haven’t heard of it already. If you do the following commands:

import pandas as pd
df1 = pd.DataFrame({ 'a':[0,0,0], 'b': [1,1,1]})
df2 = df1
df2['a'] = df2['a'] + 1
df1.head()

You’ll find that df1 is changed. This is because df2 = df1 is not making a copy of df1 and assign it to df2, but setting up a pointer pointing to df1. So any changes in df2 would result in changes in df1. To fix this, you can do either

df2 = df1.copy()

or

from copy import deepcopy
df2 = deepcopy(df1)

4. map

This is a cool command to do easy data transformations. You first define a dictionary with ‘keys’ being the old values and ‘values’ being the new values.

level_map = {1: 'high', 2: 'medium', 3: 'low'}
df['c_level'] = df['c'].map(level_map)

Some examples: True, False to 1, 0 (for modeling); defining levels; user defined lexical encodings.

5. apply or not apply?

If we’d like to create a new column with a few other columns as inputs, apply function would be quite useful sometimes.

def rule(x, y):
    if x == 'high' and y > 10:
         return 1
    else:
         return 0

df = pd.DataFrame({ 'c1':[ 'high' ,'high', 'low', 'low'], 'c2': [0, 23, 17, 4]})
df['new'] = df.apply(lambda x: rule(x['c1'], x['c2']), axis =  1)
df.head()

In the codes above, we define a function with two input variables, and use the apply function to apply it to columns ‘c1’ and ‘c2’.

but the problem of ‘apply’ is that it’s sometimes too slow. Say if you’d like to calculate the maximum of two columns ‘c1’ and ‘c2’, of course you can do

df['maximum'] = df.apply(lambda x: max(x['c1'], x['c2']), axis = 1)

but you’ll find it much slower than this command:

df['maximum'] = df[['c1','c2']].max(axis =1)

Takeaway: Don’t use apply if you can get the same work done with other built-in functions (they’re often faster). For example, if you want to round column ‘c’ to integers, do round(df[‘c’], 0) or df[‘c’].round(0) instead of using the apply function: df.apply(lambda x: round(x['c'], 0), axis = 1).

6. value counts

This is a command to check value distributions. For example, if you’d like to check what are the possible values and the frequency for each individual value in column ‘c’ you can do

df['c'].value_counts()

There’re some useful tricks / arguments of it:

A. normalize = True: if you want to check the frequency instead of counts.

B. dropna = False: if you also want to include missing values in the stats.

C. df['c'].value_counts().reset_index(): if you want to convert the stats table into a pandas dataframe and manipulate it

D. df['c'].value_counts().reset_index().sort_values(by='index') : show the stats sorted by distinct values in column ‘c’ instead of counts.

7. number of missing values

When building models, you might want to exclude the row with too many missing values / the rows with all missing values. You can use .isnull() and .sum() to count the number of missing values within the specified columns.

import pandas as pd
import numpy as np

df = pd.DataFrame({ 'id': [1,2,3], 'c1':[0,0,np.nan], 'c2': [np.nan,1,1]})
df = df[['id', 'c1', 'c2']]
df['num_nulls'] = df[['c1', 'c2']].isnull().sum(axis=1)
df.head()

8. select rows with specific IDs

In SQL we can do this using SELECT * FROM … WHERE ID in (‘A001’, ‘C022’, …) to get records with specific IDs. If you want to do the same thing with pandas, you can do

df_filter = df['ID'].isin(['A001','C022',...])
df[df_filter]

9. Percentile groups

You have a numerical column, and would like to classify the values in that column into groups, say top 5% into group 1, 5–20% into group 2, 20%-50% into group 3, bottom 50% into group 4. Of course, you can do it with pandas.cut, but I’d like to provide another option here:

import numpy as np
cut_points = [np.percentile(df['c'], i) for i in [50, 80, 95]]
df['group'] = 1
for i in range(3):
    df['group'] = df['group'] + (df['c'] < cut_points[i])
# or <= cut_points[i]

which is fast to run (no apply function used).

10. to_csv

Again this is a command that everyone would use. I’d like to point out two tricks here. The first one is

print(df[:5].to_csv())

You can use this command to print out the first five rows of what are going to be written into the file exactly.

Another trick is dealing with integers and missing values mixed together. If a column contains both missing values and integers, the data type would still be float instead of int. When you export the table, you can add float_format=‘%.0f’ to round all the floats to integers. Use this trick if you only want integer outputs for all columns — you’ll get rid of all annoying ‘.0’s.

Thanks for reading ❤

If you liked this post, share it with all of your programming buddies!

#python #pandas

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Buddha Community

10 Python Pandas tricks that make your work more efficient

4 Tricks for Making Python Pandas More Efficient

Making the most out of Pandas

Pandas is arguably the most popular data analysis and manipulation library in the data science ecosystem. The user-friendly and intuitive Python syntax is a significant factor in the popularity of Pandas. However, it is not the only reason why Pandas is adapted by a vast majority of data scientists.

Pandas provides numerous functions and methods that expedite the data analysis and manipulation operations. In this article, we will go over 4 tricks for using these functions even more efficiently.

Let’s start with creating a data frame. We will use the Melbourne housing dataset available on Kaggle.

#programming #artificial-intelligence #python #data-science #4 tricks for making python pandas more efficient #tricks for making python pandas

How to work with Pandas in Python

The complete guide to Pandas for beginners

When we talk about data science, we usually refer to the data analysis through summarization, visualizations, sophisticated algorithms that learn patterns in data (machine learning), and other fancy tools. When we discuss the term with software developers, we also hear a lot of Python, the popular programming language.

But why is Python so popular and special in the data science world? There are many reasons, and an important one is the Python ecosystem and libraries that make data science seem natural to Python.

One of these libraries is pandas , which every data science in the world uses, used, or at least heard of (if you are a data scientist who never used pandas, scream in comments).

Pandas is an essential part of the ecosystem that many other data science tools build on top or provide specific functionalities for pandas.

This guide introduces pandas for developers and aims to cover the what, why, and how of pandas’ most commonly used features.

Before we get started, if you want to access the full source code for this project to follow along, you can download the project’s source code from GitHub .

#how to work with pandas in python #python #pandas #work #pandas in python

Paula  Hall

Paula Hall

1623488340

3 Python Pandas Tricks for Efficient Data Analysis

Explained with examples.

Pandas is one of the predominant data analysis tools which is highly appreciated among data scientists. It provides numerous flexible and versatile functions to perform efficient data analysis.

In this article, we will go over 3 pandas tricks that I think will make you a more happy pandas user. It is better to explain these tricks with some examples. Thus, we start by creating a data frame to wok on.

The data frame contains daily sales quantities of 3 different stores. We first create a period of 10 days using the date_range function of pandas.

import numpy as np
import pandas as pd

days = pd.date_range("2020-01-01", periods=10, freq="D")

The days variable will be used as a column. We also need a sales quantity column which can be generated by the randint function of numpy. Then, we create a data frame with 3 columns for each store.

#machine-learning #data-science #python #python pandas tricks #efficient data analysis #python pandas tricks for efficient data analysis

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Jamison  Fisher

Jamison Fisher

1642995900

Pandas Bokeh: Bokeh Plotting Backend for Pandas and GeoPandas

Pandas-Bokeh provides a Bokeh plotting backend for Pandas, GeoPandas and Pyspark DataFrames, similar to the already existing Visualization feature of Pandas. Importing the library adds a complementary plotting method plot_bokeh() on DataFrames and Series.

With Pandas-Bokeh, creating stunning, interactive, HTML-based visualization is as easy as calling:

df.plot_bokeh()

Pandas-Bokeh also provides native support as a Pandas Plotting backend for Pandas >= 0.25. When Pandas-Bokeh is installed, switchting the default Pandas plotting backend to Bokeh can be done via:

pd.set_option('plotting.backend', 'pandas_bokeh')

More details about the new Pandas backend can be found below.

Interactive Documentation

Please visit:

https://patrikhlobil.github.io/Pandas-Bokeh/

for an interactive version of the documentation below, where you can play with the dynamic Bokeh plots.

For more information have a look at the Examples below or at notebooks on the Github Repository of this project.

Startimage

Installation

You can install Pandas-Bokeh from PyPI via pip

pip install pandas-bokeh

or conda:

conda install -c patrikhlobil pandas-bokeh

With the current release 0.5.5, Pandas-Bokeh officially supports Python 3.6 and newer. For more details, see Release Notes.

How To Use

Classical Use

 

The Pandas-Bokeh library should be imported after Pandas, GeoPandas and/or Pyspark. 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 or the Bokeh documentation.

Notebook output (see also bokeh.io.output_notebook)

import pandas as pd
import pandas_bokeh
pandas_bokeh.output_notebook()

File output to "Interactive Plot.html" (see also bokeh.io.output_file)

import pandas as pd
import pandas_bokeh
pandas_bokeh.output_file("Interactive Plot.html")

Pandas-Bokeh as native Pandas plotting backend

For pandas >= 0.25, a plotting backend switch is natively supported. It can be achievied by calling:

import pandas as pd
pd.set_option('plotting.backend', 'pandas_bokeh')

Now, the plotting API is accessible for a Pandas DataFrame via:

df.plot(...)

All additional functionalities of Pandas-Bokeh are then accessible at pd.plotting. So, setting the output to notebook is:

pd.plotting.output_notebook()

or calling the grid layout functionality:

pd.plotting.plot_grid(...)

Note: Backwards compatibility is kept since there will still be the df.plot_bokeh(...) methods for a DataFrame.

Plot types

Supported plottypes are at the moment:

Also, check out the complementary chapter Outputs, Formatting & Layouts about:

Lineplot

Basic Lineplot

This simple lineplot in Pandas-Bokeh 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

Consider the following simple example:

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")       #equivalent to df.plot_bokeh.line()

ApplevsGoogle_1

Note, that similar to the regular pandas.DataFrame.plot method, there are also additional accessors to directly access the different plotting types like:

  • df.plot_bokeh(kind="line", ...)df.plot_bokeh.line(...)
  • df.plot_bokeh(kind="bar", ...)df.plot_bokeh.bar(...)
  • df.plot_bokeh(kind="hist", ...)df.plot_bokeh.hist(...)
  • ...

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.
  • x: Name of the column to use for the horizontal x-axis. If the x parameter is not specified, the index is used for the x-values of the plot. Alternative, also an array of values can be passed that has the same number of elements as the DataFrame.
  • y: Name of column or list of names of columns to use for the vertical y-axis.
  • 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: Can be used to specify multiple colors to plot. Can be either a list of colors or the name of a Bokeh color palette
  • hovertool: If True a Hovertool is active, else if False no Hovertool is drawn.
  • hovertool_string: If specified, this string will be used for the hovertool (@{column} will be replaced by the value of the column for the element the mouse hovers over, see also Bokeh documentation and here)
  • toolbar_location: Specify the position of the toolbar location (None, "above", "below", "left" or "right"). Default: "right"
  • zooming: Enables/Disables zooming. Default: True
  • panning: Enables/Disables panning. Default: True
  • fontsize_label/fontsize_ticks/fontsize_title/fontsize_legend: Set fontsize of labels, ticks, title or legend (int or string of form "15pt")
  • rangetool Enables a range tool scroller. Default False
  • kwargs**: Optional keyword arguments of bokeh.plotting.figure.line

Try them out to get a feeling for the effects. Let us consider now:

df.plot_bokeh.line(
    figsize=(800, 450),
    y="Apple",
    title="Apple vs Google",
    xlabel="Date",
    ylabel="Stock price [$]",
    yticks=[0, 100, 200, 300, 400],
    ylim=(0, 400),
    toolbar_location=None,
    colormap=["red", "blue"],
    hovertool_string=r"""<img
                        src='https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Apple_logo_black.svg/170px-Apple_logo_black.svg.png' 
                        height="42" alt="@imgs" width="42"
                        style="float: left; margin: 0px 15px 15px 0px;"
                        border="2"></img> Apple 
                        
                        <h4> Stock Price: </h4> @{Apple}""",
    panning=False,
    zooming=False)

ApplevsGoogle_2

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

Let us use this information to have another version of the same plot:

df.plot_bokeh.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")

ApplevsGoogle_3

Lineplot with rangetool

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
df = df.cumsum()

df.plot_bokeh(rangetool=True)

rangetool

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 like marker or size:

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.point(
    x="x",
    xticks=range(-3, 4),
    size=5,
    colormap=["#009933", "#ff3399"],
    title="Pointplot (Parabula vs. Cube)",
    marker="x")

Pointplot

Stepplot

With a similar API as the line- & pointplots, one can generate a stepplot. Additional keyword arguments for this plot type are passes to bokeh.plotting.figure.step, e.g. mode (before, after, center), see the following example

import numpy as np

x = np.arange(-3, 3, 1)
y2 = x**2
y3 = x**3
df = pd.DataFrame({"x": x, "Parabula": y2, "Cube": y3})
df.plot_bokeh.step(
    x="x",
    xticks=range(-1, 1),
    colormap=["#009933", "#ff3399"],
    title="Pointplot (Parabula vs. Cube)",
    figsize=(800,300),
    fontsize_title=30,
    fontsize_label=25,
    fontsize_ticks=15,
    fontsize_legend=5,
    )

df.plot_bokeh.step(
    x="x",
    xticks=range(-1, 1),
    colormap=["#009933", "#ff3399"],
    title="Pointplot (Parabula vs. Cube)",
    mode="after",
    figsize=(800,300)
    )

Stepplot

Note that the step-plot API of Bokeh does so far not support a hovertool functionality.

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

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).

In the example below, we use the building grid layout support of Pandas-Bokeh to display both the DataFrame (using a Bokeh DataTable) and the resulting scatterplot:

# Load Iris Dataset:
df = pd.read_csv(
    r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/iris/iris.csv"
)
df = df.sample(frac=1)

# Create Bokeh-Table with DataFrame:
from bokeh.models.widgets import DataTable, TableColumn
from bokeh.models import ColumnDataSource

data_table = DataTable(
    columns=[TableColumn(field=Ci, title=Ci) for Ci in df.columns],
    source=ColumnDataSource(df),
    height=300,
)

# Create Scatterplot:
p_scatter = df.plot_bokeh.scatter(
    x="petal length (cm)",
    y="sepal width (cm)",
    category="species",
    title="Iris DataSet Visualization",
    show_figure=False,
)

# Combine Table and Scatterplot via grid layout:
pandas_bokeh.plot_grid([[data_table, p_scatter]], plot_width=400, plot_height=350)

 

Scatterplot

A possible optional keyword parameters that can be passed to bokeh.plotting.figure.scatter is size. Below, we use the sepal length of the Iris data as reference for the size:

#Change one value to clearly see the effect of the size keyword
df.loc[13, "sepal length (cm)"] = 15

#Make scatterplot:
p_scatter = df.plot_bokeh.scatter(
    x="petal length (cm)",
    y="sepal width (cm)",
    category="species",
    title="Iris DataSet Visualization with Size Keyword",
    size="sepal length (cm)")

Scatterplot2

In this example you can see, that the additional dimension sepal length cannot be used to clearly differentiate between the virginica and versicolor species.

Barplot

The barplot API has no special keyword arguments, but accepts optional kwargs of bokeh.plotting.figure.vbar like alpha. It uses per default the index for the bar categories (however, also columns can be used as x-axis category using the x argument).

data = {
    'fruits':
    ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'],
    '2015': [2, 1, 4, 3, 2, 4],
    '2016': [5, 3, 3, 2, 4, 6],
    '2017': [3, 2, 4, 4, 5, 3]
}
df = pd.DataFrame(data).set_index("fruits")

p_bar = df.plot_bokeh.bar(
    ylabel="Price per Unit [€]", 
    title="Fruit prices per Year", 
    alpha=0.6)

Barplot

Using the stacked keyword argument you also maked stacked barplots:

p_stacked_bar = df.plot_bokeh.bar(
    ylabel="Price per Unit [€]",
    title="Fruit prices per Year",
    stacked=True,
    alpha=0.6)

Barplot2

Also horizontal versions of the above barplot are supported with the keyword kind="barh" or the accessor plot_bokeh.barh. You can still specify a column of the DataFrame as the bar category via the x argument if you do not wish to use the index.

#Reset index, such that "fruits" is now a column of the DataFrame:
df.reset_index(inplace=True)

#Create horizontal bar (via kind keyword):
p_hbar = df.plot_bokeh(
    kind="barh",
    x="fruits",
    xlabel="Price per Unit [€]",
    title="Fruit prices per Year",
    alpha=0.6,
    legend = "bottom_right",
    show_figure=False)

#Create stacked horizontal bar (via barh accessor):
p_stacked_hbar = df.plot_bokeh.barh(
    x="fruits",
    stacked=True,
    xlabel="Price per Unit [€]",
    title="Fruit prices per Year",
    alpha=0.6,
    legend = "bottom_right",
    show_figure=False)

#Plot all barplot examples in a grid:
pandas_bokeh.plot_grid([[p_bar, p_stacked_bar],
                        [p_hbar, p_stacked_hbar]], 
                       plot_width=450)

Barplot3

Histogram

For drawing histograms (kind="hist"), Pandas-Bokeh has a lot of customization features. Optional keyword arguments for histogram plots are:

  • bins: Determines bins to use for the histogram. If bins is an int, it defines the number of equal-width bins in the given range (10, by default). If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. If bins is a string, it defines the method used to calculate the optimal bin width, as defined by histogram_bin_edges.
  • histogram_type: Either "sidebyside", "topontop" or "stacked". Default: "topontop"
  • stacked: Boolean that overrides the histogram_type as "stacked" if given. Default: False
  • kwargs**: Optional keyword arguments of bokeh.plotting.figure.quad

Below examples of the different histogram types:

import numpy as np

df_hist = pd.DataFrame({
    'a': np.random.randn(1000) + 1,
    'b': np.random.randn(1000),
    'c': np.random.randn(1000) - 1
    },
    columns=['a', 'b', 'c'])

#Top-on-Top Histogram (Default):
df_hist.plot_bokeh.hist(
    bins=np.linspace(-5, 5, 41),
    vertical_xlabel=True,
    hovertool=False,
    title="Normal distributions (Top-on-Top)",
    line_color="black")

#Side-by-Side Histogram (multiple bars share bin side-by-side) also accessible via
#kind="hist":
df_hist.plot_bokeh(
    kind="hist",
    bins=np.linspace(-5, 5, 41),
    histogram_type="sidebyside",
    vertical_xlabel=True,
    hovertool=False,
    title="Normal distributions (Side-by-Side)",
    line_color="black")

#Stacked histogram:
df_hist.plot_bokeh.hist(
    bins=np.linspace(-5, 5, 41),
    histogram_type="stacked",
    vertical_xlabel=True,
    hovertool=False,
    title="Normal distributions (Stacked)",
    line_color="black")

Histogram

Further, advanced keyword arguments for histograms are:

  • weights: A column of the DataFrame that is used as weight for the histogramm aggregation (see also numpy.histogram)
  • normed: If True, histogram values are normed to 1 (sum of histogram values=1). It is also possible to pass an integer, e.g. normed=100 would result in a histogram with percentage y-axis (sum of histogram values=100). Default: False
  • cumulative: If True, a cumulative histogram is shown. Default: False
  • show_average: If True, the average of the histogram is also shown. Default: False

Their usage is shown in these examples:

p_hist = df_hist.plot_bokeh.hist(
    y=["a", "b"],
    bins=np.arange(-4, 6.5, 0.5),
    normed=100,
    vertical_xlabel=True,
    ylabel="Share[%]",
    title="Normal distributions (normed)",
    show_average=True,
    xlim=(-4, 6),
    ylim=(0, 30),
    show_figure=False)

p_hist_cum = df_hist.plot_bokeh.hist(
    y=["a", "b"],
    bins=np.arange(-4, 6.5, 0.5),
    normed=100,
    cumulative=True,
    vertical_xlabel=True,
    ylabel="Share[%]",
    title="Normal distributions (normed & cumulative)",
    show_figure=False)

pandas_bokeh.plot_grid([[p_hist, p_hist_cum]], plot_width=450, plot_height=300)

Histogram2

Areaplot

Areaplot (kind="area") can be either drawn on top of each other or stacked. The important parameters are:

stacked: If True, the areaplots are stacked. If False, plots are drawn on top of each other. Default: False

kwargs**: Optional keyword arguments of bokeh.plotting.figure.patch

Let us consider the energy consumption split by source that can be downloaded as DataFrame via:

df_energy = pd.read_csv(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/energy/energy.csv", 
parse_dates=["Year"])
df_energy.head()
YearOilGasCoalNuclear EnergyHydroelectricityOther Renewable
1970-01-012291.5826.71467.317.7265.85.8
1971-01-012427.7884.81459.224.9276.46.3
1972-01-012613.9933.71475.734.1288.96.8
1973-01-012818.1978.01519.645.9292.57.3
1974-01-012777.31001.91520.959.6321.17.7

Creating the Areaplot can be achieved via:

df_energy.plot_bokeh.area(
    x="Year",
    stacked=True,
    legend="top_left",
    colormap=["brown", "orange", "black", "grey", "blue", "green"],
    title="Worldwide energy consumption split by energy source",
    ylabel="Million tonnes oil equivalent",
    ylim=(0, 16000))

areaplot

Note that the energy consumption of fossile energy is still increasing and renewable energy sources are still small in comparison 😢!!! However, when we norm the plot using the normed keyword, there is a clear trend towards renewable energies in the last decade:

df_energy.plot_bokeh.area(
    x="Year",
    stacked=True,
    normed=100,
    legend="bottom_left",
    colormap=["brown", "orange", "black", "grey", "blue", "green"],
    title="Worldwide energy consumption split by energy source",
    ylabel="Million tonnes oil equivalent")

areaplot2

Pieplot

For Pieplots, let us consider a dataset showing the results of all Bundestags elections in Germany since 2002:

df_pie = pd.read_csv(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/Bundestagswahl/Bundestagswahl.csv")
df_pie
Partei20022005200920132017
CDU/CSU38.535.233.841.532.9
SPD38.534.223.025.720.5
FDP7.49.814.64.810.7
Grünen8.68.110.78.48.9
Linke/PDS4.08.711.98.69.2
AfD0.00.00.00.012.6
Sonstige3.04.06.011.05.0

We can create a Pieplot of the last election in 2017 by specifying the "Partei" (german for party) column as the x column and the "2017" column as the y column for values:

df_pie.plot_bokeh.pie(
    x="Partei",
    y="2017",
    colormap=["blue", "red", "yellow", "green", "purple", "orange", "grey"],
    title="Results of German Bundestag Election 2017",
    )

pieplot

When you pass several columns to the y parameter (not providing the y-parameter assumes you plot all columns), multiple nested pieplots will be shown in one plot:

df_pie.plot_bokeh.pie(
    x="Partei",
    colormap=["blue", "red", "yellow", "green", "purple", "orange", "grey"],
    title="Results of German Bundestag Elections [2002-2017]",
    line_color="grey")

pieplot2

Mapplot

The mapplot method of Pandas-Bokeh allows for plotting geographic points stored in a Pandas DataFrame on an interactive map. For more advanced Geoplots for line and polygon shapes have a look at the Geoplots examples for the GeoPandas API of Pandas-Bokeh.

For mapplots, only (latitude, longitude) pairs in geographic projection (WGS84) can be plotted on a map. The basic API has the following 2 base parameters:

  • x: name of the longitude column of the DataFrame
  • y: name of the latitude column of the DataFrame

The other optional keyword arguments are discussed in the section about the GeoPandas API, e.g. category for coloring the points.

Below an example of plotting all cities for more than 1 million inhabitants:

df_mapplot = pd.read_csv(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/populated%20places/populated_places.csv")
df_mapplot.head()
namepop_maxlatitudelongitudesize
Mesa108539433.423915-111.7360841.085394
Sharjah110302725.37138355.4064781.103027
Changwon108149935.219102128.5835621.081499
Sheffield129290053.366677-1.4999971.292900
Abbottabad118364734.14950373.1995011.183647
df_mapplot["size"] = df_mapplot["pop_max"] / 1000000
df_mapplot.plot_bokeh.map(
    x="longitude",
    y="latitude",
    hovertool_string="""<h2> @{name} </h2> 
    
                        <h3> Population: @{pop_max} </h3>""",
    tile_provider="STAMEN_TERRAIN_RETINA",
    size="size", 
    figsize=(900, 600),
    title="World cities with more than 1.000.000 inhabitants")

 

Mapplot

Geoplots

Pandas-Bokeh also allows for interactive plotting of Maps using GeoPandas by providing a geopandas.GeoDataFrame.plot_bokeh() method. It allows to plot the following geodata on a map :

  • Points/MultiPoints
  • Lines/MultiLines
  • Polygons/MultiPolygons

Note: t is not possible to mix up the objects types, i.e. a GeoDataFrame with Points and Lines is for example not allowed.

Les us start with a simple example using the "World Borders Dataset" . Let us first import all neccessary libraries and read the shapefile:

import geopandas as gpd
import pandas as pd
import pandas_bokeh
pandas_bokeh.output_notebook()

#Read in GeoJSON from URL:
df_states = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/states/states.geojson")
df_states.head()
STATE_NAMEREGIONPOPESTIMATE2010POPESTIMATE2011POPESTIMATE2012POPESTIMATE2013POPESTIMATE2014POPESTIMATE2015POPESTIMATE2016POPESTIMATE2017geometry
Hawaii413638171378323139277214080381417710142632014286831427538(POLYGON ((-160.0738033454681 22.0041773479577...
Washington467413866819155689089969634107046931715281872809347405743(POLYGON ((-122.4020153103835 48.2252163723779...
Montana4990507996866100352210119211019931102831710386561050493POLYGON ((-111.4754253002074 44.70216236909688...
Maine113275681327968132810113279751328903132778713302321335907(POLYGON ((-69.77727626137293 44.0741483685119...
North Dakota2674518684830701380722908738658754859755548755393POLYGON ((-98.73043728833767 45.93827137024809...

Plotting the data on a map is as simple as calling:

df_states.plot_bokeh(simplify_shapes=10000)

US_States_1

We also passed the optional parameter simplify_shapes (~meter) to improve plotting performance (for a reference see shapely.object.simplify). The above geolayer thus has an accuracy of about 10km.

Many keyword arguments like xlabel, ylabel, xlim, ylim, title, colormap, hovertool, zooming, panning, ... for costumizing the plot are also available for the geoplotting API and can be uses as in the examples shown above. There are however also many other options especially for plotting geodata:

  • geometry_column: Specify the column that stores the geometry-information (default: "geometry")
  • hovertool_columns: Specify column names, for which values should be shown in hovertool
  • hovertool_string: If specified, this string will be used for the hovertool (@{column} will be replaced by the value of the column for the element the mouse hovers over, see also Bokeh documentation)
  • colormap_uselog: If set True, the colormapper is using a logscale. Default: False
  • colormap_range: Specify the value range of the colormapper via (min, max) tuple
  • tile_provider: Define build-in tile provider for background maps. Possible values: None, 'CARTODBPOSITRON', 'CARTODBPOSITRON_RETINA', 'STAMEN_TERRAIN', 'STAMEN_TERRAIN_RETINA', 'STAMEN_TONER', 'STAMEN_TONER_BACKGROUND', 'STAMEN_TONER_LABELS'. Default: CARTODBPOSITRON_RETINA
  • tile_provider_url: An arbitraty tile_provider_url of the form '/{Z}/{X}/{Y}*.png' can be passed to be used as background map.
  • tile_attribution: String (also HTML accepted) for showing attribution for tile source in the lower right corner
  • tile_alpha: Sets the alpha value of the background tile between [0, 1]. Default: 1

One of the most common usage of map plots are choropleth maps, where the color of a the objects is determined by the property of the object itself. There are 3 ways of drawing choropleth maps using Pandas-Bokeh, which are described below.

Categories

This is the simplest way. Just provide the category keyword for the selection of the property column:

  • category: Specifies the column of the GeoDataFrame that should be used to draw a choropleth map
  • show_colorbar: Whether or not to show a colorbar for categorical plots. Default: True

Let us now draw the regions as a choropleth plot using the category keyword (at the moment, only numerical columns are supported for choropleth plots):

df_states.plot_bokeh(
    figsize=(900, 600),
    simplify_shapes=5000,
    category="REGION",
    show_colorbar=False,
    colormap=["blue", "yellow", "green", "red"],
    hovertool_columns=["STATE_NAME", "REGION"],
    tile_provider="STAMEN_TERRAIN_RETINA")

When hovering over the states, the state-name and the region are shown as specified in the hovertool_columns argument.

US_States_2

Dropdown

By passing a list of column names of the GeoDataFrame as the dropdown keyword argument, a dropdown menu is shown above the map. This dropdown menu can be used to select the choropleth layer by the user. :

df_states["STATE_NAME_SMALL"] = df_states["STATE_NAME"].str.lower()

df_states.plot_bokeh(
    figsize=(900, 600),
    simplify_shapes=5000,
    dropdown=["POPESTIMATE2010", "POPESTIMATE2017"],
    colormap="Viridis",
    hovertool_string="""
                        <img
                        src="https://www.states101.com/img/flags/gif/small/@STATE_NAME_SMALL.gif" 
                        height="42" alt="@imgs" width="42"
                        style="float: left; margin: 0px 15px 15px 0px;"
                        border="2"></img>
                
                        <h2>  @STATE_NAME </h2>
                        <h3> 2010: @POPESTIMATE2010 </h3>
                        <h3> 2017: @POPESTIMATE2017 </h3>""",
    tile_provider_url=r"http://c.tile.stamen.com/watercolor/{Z}/{X}/{Y}.jpg",
    tile_attribution='Map tiles by <a href="http://stamen.com">Stamen Design</a>, under <a href="http://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>. Data by <a href="http://openstreetmap.org">OpenStreetMap</a>, under <a href="http://www.openstreetmap.org/copyright">ODbL</a>.'
    )

US_States_3

Using hovertool_string, one can pass a string that can contain arbitrary HTML elements (including divs, images, ...) that is shown when hovering over the geographies (@{column} will be replaced by the value of the column for the element the mouse hovers over, see also Bokeh documentation).

Here, we also used an OSM tile server with watercolor style via tile_provider_url and added the attribution via tile_attribution.

Sliders

Another option for interactive choropleth maps is the slider implementation of Pandas-Bokeh. The possible keyword arguments are here:

  • slider: By passing a list of column names of the GeoDataFrame, a slider can be used to . This dropdown menu can be used to select the choropleth layer by the user.
  • slider_range: Pass a range (or numpy.arange) of numbers object to relate the sliders values with the slider columns. By passing range(0,10), the slider will have values [0, 1, 2, ..., 9], when passing numpy.arange(3,5,0.5), the slider will have values [3, 3.5, 4, 4.5]. Default: range(0, len(slider))
  • slider_name: Specifies the title of the slider. Default is an empty string.

This can be used to display the change in population relative to the year 2010:

#Calculate change of population relative to 2010:
for i in range(8):
    df_states["Delta_Population_201%d"%i] = ((df_states["POPESTIMATE201%d"%i] / df_states["POPESTIMATE2010"]) -1 ) * 100

#Specify slider columns:
slider_columns = ["Delta_Population_201%d"%i for i in range(8)]

#Specify slider-range (Maps "Delta_Population_2010" -> 2010, 
#                           "Delta_Population_2011" -> 2011, ...):
slider_range = range(2010, 2018)

#Make slider plot:
df_states.plot_bokeh(
    figsize=(900, 600),
    simplify_shapes=5000,
    slider=slider_columns,
    slider_range=slider_range,
    slider_name="Year", 
    colormap="Inferno",
    hovertool_columns=["STATE_NAME"] + slider_columns,
    title="Change of Population [%]")

US_States_4

Plot multiple geolayers

If you wish to display multiple geolayers, you can pass the Bokeh figure of a Pandas-Bokeh plot via the figure keyword to the next plot_bokeh() call:

import geopandas as gpd
import pandas_bokeh
pandas_bokeh.output_notebook()

# Read in GeoJSONs from URL:
df_states = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/states/states.geojson")
df_cities = gpd.read_file(
    r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/populated%20places/ne_10m_populated_places_simple_bigcities.geojson"
)
df_cities["size"] = df_cities.pop_max / 400000

#Plot shapes of US states (pass figure options to this initial plot):
figure = df_states.plot_bokeh(
    figsize=(800, 450),
    simplify_shapes=10000,
    show_figure=False,
    xlim=[-170, -80],
    ylim=[10, 70],
    category="REGION",
    colormap="Dark2",
    legend="States",
    show_colorbar=False,
)

#Plot cities as points on top of the US states layer by passing the figure:
df_cities.plot_bokeh(
    figure=figure,         # <== pass figure here!
    category="pop_max",
    colormap="Viridis",
    colormap_uselog=True,
    size="size",
    hovertool_string="""<h1>@name</h1>
                        <h3>Population: @pop_max </h3>""",
    marker="inverted_triangle",
    legend="Cities",
)

Multiple Geolayers

Point & Line plots:

Below, you can see an example that use Pandas-Bokeh to plot point data on a map. The plot shows all cities with a population larger than 1.000.000. For point plots, you can select the marker as keyword argument (since it is passed to bokeh.plotting.figure.scatter). Here an overview of all available marker types:

gdf = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/populated%20places/ne_10m_populated_places_simple_bigcities.geojson")
gdf["size"] = gdf.pop_max / 400000

gdf.plot_bokeh(
    category="pop_max",
    colormap="Viridis",
    colormap_uselog=True,
    size="size",
    hovertool_string="""<h1>@name</h1>
                        <h3>Population: @pop_max </h3>""",
    xlim=[-15, 35],
    ylim=[30,60],
    marker="inverted_triangle");

Pointmap

In a similar way, also GeoDataFrames with (multi)line shapes can be drawn using Pandas-Bokeh.

Colorbar formatting:

If you want to display the numerical labels on your colorbar with an alternative to the scientific format, you can pass in a one of the bokeh number string formats or an instance of one of the bokeh.models.formatters to the colorbar_tick_format argument in the geoplot

An example of using the string format argument:

df_states = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/states/states.geojson")

df_states["STATE_NAME_SMALL"] = df_states["STATE_NAME"].str.lower()

# pass in a string format to colorbar_tick_format to display the ticks as 10m rather than 1e7
df_states.plot_bokeh(
    figsize=(900, 600),
    category="POPESTIMATE2017",
    simplify_shapes=5000,    
    colormap="Inferno",
    colormap_uselog=True,
    colorbar_tick_format="0.0a")

colorbar_tick_format with string argument

An example of using the bokeh PrintfTickFormatter:

df_states = gpd.read_file(r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/states/states.geojson")

df_states["STATE_NAME_SMALL"] = df_states["STATE_NAME"].str.lower()

for i in range(8):
    df_states["Delta_Population_201%d"%i] = ((df_states["POPESTIMATE201%d"%i] / df_states["POPESTIMATE2010"]) -1 ) * 100

# pass in a PrintfTickFormatter instance colorbar_tick_format to display the ticks with 2 decimal places  
df_states.plot_bokeh(
    figsize=(900, 600),
    category="Delta_Population_2017",
    simplify_shapes=5000,    
    colormap="Inferno",
    colorbar_tick_format=PrintfTickFormatter(format="%4.2f"))

colorbar_tick_format with bokeh.models.formatter_instance

Outputs, Formatting & Layouts

 

Output options

The pandas.DataFrame.plot_bokeh API has the following additional keyword arguments:

  • show_figure: If True, the resulting figure is shown (either in the notebook or exported and shown as HTML file, see Basics. If False, None is returned. Default: True
  • return_html: If True, the method call returns an HTML string that contains all Bokeh CSS&JS resources and the figure embedded in a div. This HTML representation of the plot can be used for embedding the plot in an HTML document. Default: False

If you have a Bokeh figure or layout, you can also use the pandas_bokeh.embedded_html function to generate an embeddable HTML representation of the plot. This can be included into any valid HTML (note that this is not possible directly with the HTML generated by the pandas_bokeh.output_file output option, because it includes an HTML header). Let us consider the following simple example:

#Import Pandas and Pandas-Bokeh (if you do not specify an output option, the standard is
#output_file):
import pandas as pd
import pandas_bokeh

#Create DataFrame to Plot:
import numpy as np
x = np.arange(-10, 10, 0.1)
sin = np.sin(x)
cos = np.cos(x)
tan = np.tan(x)
df = pd.DataFrame({"x": x, "sin(x)": sin, "cos(x)": cos, "tan(x)": tan})

#Make Bokeh plot from DataFrame using Pandas-Bokeh. Do not show the plot, but export
#it to an embeddable HTML string:
html_plot = df.plot_bokeh(
    kind="line",
    x="x",
    y=["sin(x)", "cos(x)", "tan(x)"],
    xticks=range(-20, 20),
    title="Trigonometric functions",
    show_figure=False,
    return_html=True,
    ylim=(-1.5, 1.5))

#Write some HTML and embed the HTML plot below it. For production use, please use
#Templates and the awesome Jinja library.
html = r"""
<script type="text/x-mathjax-config">
  MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}});
</script>
<script type="text/javascript"
  src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>

<h1> Trigonometric functions </h1>

<p> The basic trigonometric functions are:</p>

<p>$ sin(x) $</p>
<p>$ cos(x) $</p>
<p>$ tan(x) = \frac{sin(x)}{cos(x)}$</p>

<p>Below is a plot that shows them</p>

""" + html_plot

#Export the HTML string to an external HTML file and show it:
with open("test.html" , "w") as f:
    f.write(html)
    
import webbrowser
webbrowser.open("test.html")

This code will open up a webbrowser and show the following page. As you can see, the interactive Bokeh plot is embedded nicely into the HTML layout. The return_html option is ideal for the use in a templating engine like Jinja.

Embedded HTML

Auto Scaling Plots

For single plots that have a number of x axis values or for larger monitors, you can auto scale the figure to the width of the entire jupyter cell by setting the sizing_mode parameter.

df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])

df.plot_bokeh(kind="bar", figsize=(500, 200), sizing_mode="scale_width")

Scaled Plot

The figsize parameter can be used to change the height and width as well as act as a scaling multiplier against the axis that is not being scaled.

 

Number formats

To change the formats of numbers in the hovertool, use the number_format keyword argument. For a documentation about the format to pass, have a look at the Bokeh documentation.Let us consider some examples for the number 3.141592653589793:

FormatOutput
03
0.0003.141
0.00 $3.14 $

This number format will be applied to all numeric columns of the hovertool. If you want to make a very custom or complicated hovertool, you should probably use the hovertool_string keyword argument, see e.g. this example. Below, we use the number_format parameter to specify the "Stock Price" format to 2 decimal digits and an additional $ sign.

import numpy as np

#Lineplot:
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",
    title="Apple vs Google",
    xlabel="Date",
    ylabel="Stock price [$]",
    yticks=[0, 100, 200, 300, 400],
    ylim=(0, 400),
    colormap=["red", "blue"],
    number_format="1.00 $")

Number format

Suppress scientific notation for axes

If you want to suppress the scientific notation for axes, you can use the disable_scientific_axes parameter, which accepts one of "x", "y", "xy":

df = pd.DataFrame({"Animal": ["Mouse", "Rabbit", "Dog", "Tiger", "Elefant", "Wale"],
                   "Weight [g]": [19, 3000, 40000, 200000, 6000000, 50000000]})
p_scientific = df.plot_bokeh(x="Animal", y="Weight [g]", show_figure=False)
p_non_scientific = df.plot_bokeh(x="Animal", y="Weight [g]", disable_scientific_axes="y", show_figure=False,)
pandas_bokeh.plot_grid([[p_scientific, p_non_scientific]], plot_width = 450)

Number format

 

Dashboard Layouts

As shown in the Scatterplot Example, combining plots with plots or other HTML elements is straighforward in Pandas-Bokeh due to the layout capabilities of Bokeh. The easiest way to generate a dashboard layout is using the pandas_bokeh.plot_grid method (which is an extension of bokeh.layouts.gridplot):

import pandas as pd
import numpy as np
import pandas_bokeh
pandas_bokeh.output_notebook()

#Barplot:
data = {
    'fruits':
    ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'],
    '2015': [2, 1, 4, 3, 2, 4],
    '2016': [5, 3, 3, 2, 4, 6],
    '2017': [3, 2, 4, 4, 5, 3]
}
df = pd.DataFrame(data).set_index("fruits")
p_bar = df.plot_bokeh(
    kind="bar",
    ylabel="Price per Unit [€]",
    title="Fruit prices per Year",
    show_figure=False)

#Lineplot:
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
p_line = df.plot_bokeh(
    kind="line",
    title="Apple vs Google",
    xlabel="Date",
    ylabel="Stock price [$]",
    yticks=[0, 100, 200, 300, 400],
    ylim=(0, 400),
    colormap=["red", "blue"],
    show_figure=False)

#Scatterplot:
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"])))
p_scatter = df.plot_bokeh(
    kind="scatter",
    x="petal length (cm)",
    y="sepal width (cm)",
    category="species",
    title="Iris DataSet Visualization",
    show_figure=False)

#Histogram:
df_hist = pd.DataFrame({
    'a': np.random.randn(1000) + 1,
    'b': np.random.randn(1000),
    'c': np.random.randn(1000) - 1
},
                       columns=['a', 'b', 'c'])

p_hist = df_hist.plot_bokeh(
    kind="hist",
    bins=np.arange(-6, 6.5, 0.5),
    vertical_xlabel=True,
    normed=100,
    hovertool=False,
    title="Normal distributions",
    show_figure=False)

#Make Dashboard with Grid Layout:
pandas_bokeh.plot_grid([[p_line, p_bar], 
                        [p_scatter, p_hist]], plot_width=450)

Dashboard Layout

Using a combination of row and column elements (see also Bokeh Layouts) allow for a very easy general arrangement of elements. An alternative layout to the one above is:

p_line.plot_width = 900
p_hist.plot_width = 900

layout = pandas_bokeh.column(p_line,
                pandas_bokeh.row(p_scatter, p_bar),
                p_hist)

pandas_bokeh.show(layout)

Alternative Dashboard Layout

Release Notes

Release Notes can be found here.

Contributing to Pandas-Bokeh

If you wish to contribute to the development of Pandas-Bokeh you can follow the instructions on the CONTRIBUTING.md.

Download Details:
Author: PatrikHlobil
Source Code: https://github.com/PatrikHlobil/Pandas-Bokeh
License: MIT License

#pandas  #python #bokeh #Ploty