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Each company wishes to remain present in the industry, and for that purpose, it indulges in user profile creation sites list to become popular in the online field. To achieve this, however, you need robust and productive strategies to maintain your existing customers’ loyalty. These plans should also attract new customers at precisely the same time.
Only the latest consulting firms are effective enough to observe the suitable approaches that can help you transform your business into your favourite brand. At Vowels Advertising in Dubai, Abu Dhabi, we offer various courses and suggestions to help your business and building to be effective by becoming a brand consultant. Branding advisers build new communication methods and a new identity. These advisers offer the brand of goods as required. They research your brand, which is made up of its identity and values. Brand Advisors have fantastic ideas and approaches to branding your products.
The most significant advantage of hiring a new consulting firm is that it has years of experience combined with the latest construction approaches. Since then, such companies are used to market trends and are continually being improved; they can quickly assess the competition and track competitor companies. In this way, they can analyze the manufacturer’s promotional requirements and evaluate the market to target a new product more effectively.
None of the other popular brands you see in the market today have started on a large scale right from the start. They achieved their present position thanks to ongoing trials and planned procedures. The whole long-term strategy was to introduce a new identity, place it precisely in the current market, and strengthen it by copying it to acceptable masses. Planning these elements and then implementing them appropriately is not a simple job. That is why companies from all over the world employ new consulting companies to implement the project.
If best, a new consulting firm can be tricky as there are an endless number of firms offering such services? The key to finding the perfect supplier for you is to analyze your condition first. As soon as you write down all the things, you can compare providers on the Internet and look for any gaps that may be useful to you. Also, start looking for reviews and connect with all previous customers to find out how satisfied they were with all the services.
By opting for Enterprises, they are responsible for creating the perfect brand identity for virtually any business, placing or repositioning it according to the market in which it operates, designing campaigns that advertise the business professionally and economically, and conducting thorough target market, competition, and market research. Thanks to all these activities, companies help to establish a small business and increase its visibility on the market. In the case of your small business, you can undoubtedly see it grow and reach a wider audience. You can find suitable new employees for a consulting company.
So this is how you can hire professional brand consulters to promote your brand amongst the customers effectively.
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We have penned down this article to cover virtually everything there is to know about employer branding, such as its benefits, how to measure it, how can you improve it, and some coverage on top global companies using market-leading employer branding strategies.
To know more about this refer-
https://blog.digitalogy.co/employer-branding-strategy/
#best employer branding #best employer branding strategy for 2021 #employer branding #, employer branding 2021 #employer branding and recruitment #employer branding best practices
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Making a proper IT strategy is the key to launch the business in the present times. The best thing about this is to formulate strategies and derive a solution for the complexities of IT infrastructure. That’s how it works! Now companies look proactive to prepare anything in advance to tackle any unprecedented situation. Though 2020 would be called an exception!
Now through this piece, we’ll look at some top IT consulting firms that’ll help entrepreneurs by a considerable margin.
Read Full Blog- https://www.valuecoders.com/blog/top-and-best-companies/top-10-it-consulting-companies-to-seek-for-business-transformation/
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Today, IT experts are a priority for enterprises. The IT consulting market is worth $476 billion in 2021. With operational segments divided into niches, companies are trusting IT consultants to provide functional advice on how to solve specific IT problems. They have become an inevitable part of the enterprise that wants experts or an outside perspective on the existing product or problem.
Specialized knowledge is a key component that IT consulting firms bring to the table. Whether it’s related to optimizing a process or simply testing the software you have – IT consulting companies ensure you have the right resources to tackle the problem. They develop a critical understanding of the problem and provide advice based on years of experience.
Apart from that, IT consulting companies are highly useful for redefining your existing IT strategy. A software development company can help you build a product or solution – an IT consulting company provides a blueprint for whether and how the product will work. If there are any issues, the IT consultants will create a new map to resolve the problem so that the technical functions operate smoothly.
This article will focus on why you need to be hiring an IT consulting company. We will highlight 7 significant reasons to hire a professional IT consultant for your tech department. Whether you hire an in-house consultant or outsource the task, the professional will deliver better outcomes by identifying what your IT team was missing until now.
The most important reason to hire IT consultants is to solve the technology challenges that companies face. For example, in today’s world, digital transformation implementation is a big problem. IT consultants help enterprises by creating a roadmap and overlooking the successful implementation of technologies like AI, ML, and RPA.
The best IT consulting companies in the USA bring years of experience in solving challenges. Even for recent technologies, consultants have developed expertise through their knowledge and skills.
Here are 7 reasons to hire professional IT consultants for your business –
IT consulting is not an option anymore – it has become a necessity. Companies need specialized knowledge and technical help that only experienced consultants can provide. IT consulting companies in India and the US are solely focused on serving niches related to the digital transformation of enterprises.
BoTree Technologies is a leading IT consulting company that provides experts in multiple domains, technologies, and industries to solve your IT problems.
Contact us today for a FREE CONSULTATION.
Source: https://www.botreetechnologies.com/blog/7-reasons-to-hire-a-professional-it-consulting-company/
#it consultants #it consulting company #it consulting firms #it consulting services
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IT consulting services have become a prominent part of the business culture across the world. A consultant is synonymous with niche expertise, outsourced in the hour of need for perspective. This has allowed companies to compartmentalize their core business activities by saving time and improving business sustainability. The technology transformation wave growing over the recent years has made IT consulting services a business necessity. Technology has transformed the way businesses now operate.
With the versatile and ever-changing trends in IT, maintaining a full-time, in-house IT department happens to involve great costs. An IT solutions consulting services company provides efficient solutions with low costs and higher Return On Investment (ROI). Therefore, many small or medium scale businesses take up IT consulting services for small business companies for their IT-based work. The world may see a big shift within the consulting industry in the next 5 years, taking into account its massive contribution to the global economy.
There are plenty of categories of IT consulting services examples ranging from helpdesk services to data backups. No matter small or large, businesses are always on the lookout for finding smarter technology solutions to incorporate. Both IT products and services help companies to grow their ventures in existing and newer domains.
Some of the most popular types of IT consulting services are:
Enterprise IT consulting services include Enterprise Resource Planning (ERP), a business process management software that allows an organization to use integrated applications. It helps businesses automate the various functions- technology, services, and human resources, thus reducing the cost of manpower.
Cloud Services lead the IT consulting services list due to its increasing demand across all businesses. They are not only budget-friendly but also ensure high performance and efficiency. With the help of cloud services, businesses have access to everything they need on the go without having the burdens of physical data storage. This is one of the best IT consulting services for small businesses which run on lower operational costs. They also provide better security and control strategies to the companies.
All offices have IT support equipment such as printers, servers, scanners, etc. These often happen to be the road bump in day-to-day operations, thereby responsible for the low productivity of office spaces. With the Help Desk IT services helping with installation, set-up, maintenance, and repair of the office equipment.
Data is the heart of any business. It is one of the most valuable resources and forms the foundation for decision making, strategy planning, and business growth plans. The data is shared among company stakeholders, and hence, network security and cyber data protection are of utmost importance.
The IT consulting services list is not limited to the ones mentioned above. It includes a range of other IT consulting services examples like data storage and management, data backup services, social media consulting, repair services, web designing services, to name the popular few.
According to the U.S. Small Business Administration, IT consulting services in the USA have proven to scale up small businesses without creating additional overheads. IT solutions consulting services have a huge financial and logistical impact on businesses, especially small businesses. An outsourced IT consulting services company reaps many benefits such as:
In comparison to using an in-house IT department, an outsourced IT consulting team reduces costs on the employee training, taxes, employee benefits, and overheads incurred. A substantial amount of money is also saved on lost downtime, prevented by quick detection and problem-solving. According to a Forrester Research study by BCM Software, “the routine IT problems across all employees cost Fortune 100 companies more than $100 billion dollars annually.”
On the other hand, using an IT consultancy service comes with the option of pay-py-project or pay-by-hour. Such payment flexibility also benefits small businesses to control and optimize their costs.
The decision to hire an IT consultancy is promising because of its company’s collective knowledge and domain expertise. IT consultancies employ experienced IT experts who hold expertise in various IT fields across industries.
This attribute of cross-functional and industry-wide knowledge provided by IT consultancies ensures top-notch technology development to companies. They ensure that their clients have the most effective and latest technology as per market trends. As long as businesses choose the right IT consulting services, they are always one step ahead of their competitors.
Technology helps in improving business productivity by allowing communication, collaborations, and knowledge sharing. When the best technology practices are properly planned and implemented, true productivity is achieved by companies. This improves the quality of work for its employees, and in turn, makes them more productive.
On the parallel road, to improve customer relationships, companies turn up to IT consultancies to effectively manage big data and reach their customers. Turning to big data is a daunting and time-intensive task which the service providers do very efficiently. Businesses use this service to come up with effective marketing campaigns and better audience targeting methods. This, in turn, leads to a substantial boost in company revenues.
In the growing era of technology, even technology breaches are growing at a rapid speed. Cyber attacks and security threats are happening across all business sizes, industries, and countries. By outsourcing IT consulting services that hold expertise in internet security, companies can rely on them and better focus on the core business activities. IT consulting companies also provide best-in-class training to their employees for the technology they are working on and the prospective threats to it.
#it consulting services #it consulting #it consultancy #it consulting companies #it consulting
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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.
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.
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.
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:
For more details about the plotting outputs, see the reference here or the Bokeh documentation.
import pandas as pd
import pandas_bokeh
pandas_bokeh.output_notebook()
import pandas as pd
import pandas_bokeh
pandas_bokeh.output_file("Interactive Plot.html")
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.
Supported plottypes are at the moment:
Also, check out the complementary chapter Outputs, Formatting & Layouts about:
This simple lineplot in Pandas-Bokeh already contains various interactive elements:
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()
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(...)
There are various optional parameters to tune the plots, for example:
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)
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:
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")
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)
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")
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)
)
Note that the step-plot API of Bokeh does so far not support a hovertool functionality.
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)
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)")
In this example you can see, that the additional dimension sepal length cannot be used to clearly differentiate between the virginica and versicolor species.
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)
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)
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)
For drawing histograms (kind="hist"), Pandas-Bokeh has a lot of customization features. Optional keyword arguments for histogram plots are:
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")
Further, advanced keyword arguments for histograms are:
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)
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()
Year | Oil | Gas | Coal | Nuclear Energy | Hydroelectricity | Other Renewable |
---|---|---|---|---|---|---|
1970-01-01 | 2291.5 | 826.7 | 1467.3 | 17.7 | 265.8 | 5.8 |
1971-01-01 | 2427.7 | 884.8 | 1459.2 | 24.9 | 276.4 | 6.3 |
1972-01-01 | 2613.9 | 933.7 | 1475.7 | 34.1 | 288.9 | 6.8 |
1973-01-01 | 2818.1 | 978.0 | 1519.6 | 45.9 | 292.5 | 7.3 |
1974-01-01 | 2777.3 | 1001.9 | 1520.9 | 59.6 | 321.1 | 7.7 |
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))
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")
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
Partei | 2002 | 2005 | 2009 | 2013 | 2017 |
---|---|---|---|---|---|
CDU/CSU | 38.5 | 35.2 | 33.8 | 41.5 | 32.9 |
SPD | 38.5 | 34.2 | 23.0 | 25.7 | 20.5 |
FDP | 7.4 | 9.8 | 14.6 | 4.8 | 10.7 |
Grünen | 8.6 | 8.1 | 10.7 | 8.4 | 8.9 |
Linke/PDS | 4.0 | 8.7 | 11.9 | 8.6 | 9.2 |
AfD | 0.0 | 0.0 | 0.0 | 0.0 | 12.6 |
Sonstige | 3.0 | 4.0 | 6.0 | 11.0 | 5.0 |
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",
)
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")
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:
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()
name | pop_max | latitude | longitude | size |
---|---|---|---|---|
Mesa | 1085394 | 33.423915 | -111.736084 | 1.085394 |
Sharjah | 1103027 | 25.371383 | 55.406478 | 1.103027 |
Changwon | 1081499 | 35.219102 | 128.583562 | 1.081499 |
Sheffield | 1292900 | 53.366677 | -1.499997 | 1.292900 |
Abbottabad | 1183647 | 34.149503 | 73.199501 | 1.183647 |
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")
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 :
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_NAME | REGION | POPESTIMATE2010 | POPESTIMATE2011 | POPESTIMATE2012 | POPESTIMATE2013 | POPESTIMATE2014 | POPESTIMATE2015 | POPESTIMATE2016 | POPESTIMATE2017 | geometry |
---|---|---|---|---|---|---|---|---|---|---|
Hawaii | 4 | 1363817 | 1378323 | 1392772 | 1408038 | 1417710 | 1426320 | 1428683 | 1427538 | (POLYGON ((-160.0738033454681 22.0041773479577... |
Washington | 4 | 6741386 | 6819155 | 6890899 | 6963410 | 7046931 | 7152818 | 7280934 | 7405743 | (POLYGON ((-122.4020153103835 48.2252163723779... |
Montana | 4 | 990507 | 996866 | 1003522 | 1011921 | 1019931 | 1028317 | 1038656 | 1050493 | POLYGON ((-111.4754253002074 44.70216236909688... |
Maine | 1 | 1327568 | 1327968 | 1328101 | 1327975 | 1328903 | 1327787 | 1330232 | 1335907 | (POLYGON ((-69.77727626137293 44.0741483685119... |
North Dakota | 2 | 674518 | 684830 | 701380 | 722908 | 738658 | 754859 | 755548 | 755393 | POLYGON ((-98.73043728833767 45.93827137024809... |
Plotting the data on a map is as simple as calling:
df_states.plot_bokeh(simplify_shapes=10000)
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:
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.
This is the simplest way. Just provide the category keyword for the selection of the property column:
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.
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>.'
)
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.
Another option for interactive choropleth maps is the slider implementation of Pandas-Bokeh. The possible keyword arguments are here:
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 [%]")
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",
)
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");
In a similar way, also GeoDataFrames with (multi)line shapes can be drawn using Pandas-Bokeh.
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")
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"))
The pandas.DataFrame.plot_bokeh API has the following additional keyword arguments:
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.
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")
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.
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:
Format | Output |
---|---|
0 | 3 |
0.000 | 3.141 |
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 $")
If you want to suppress the scientific notation for axes, you can use the disable_scientific_axes parameter, which accepts one of "x", "y", "xy":
df = pd.DataFrame({"Animal": ["Mouse", "Rabbit", "Dog", "Tiger", "Elefant", "Wale"],
"Weight [g]": [19, 3000, 40000, 200000, 6000000, 50000000]})
p_scientific = df.plot_bokeh(x="Animal", y="Weight [g]", show_figure=False)
p_non_scientific = df.plot_bokeh(x="Animal", y="Weight [g]", disable_scientific_axes="y", show_figure=False,)
pandas_bokeh.plot_grid([[p_scientific, p_non_scientific]], plot_width = 450)
As shown in the Scatterplot Example, combining plots with plots or other HTML elements is straighforward in Pandas-Bokeh due to the layout capabilities of Bokeh. The easiest way to generate a dashboard layout is using the pandas_bokeh.plot_grid method (which is an extension of bokeh.layouts.gridplot):
import pandas as pd
import numpy as np
import pandas_bokeh
pandas_bokeh.output_notebook()
#Barplot:
data = {
'fruits':
['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'],
'2015': [2, 1, 4, 3, 2, 4],
'2016': [5, 3, 3, 2, 4, 6],
'2017': [3, 2, 4, 4, 5, 3]
}
df = pd.DataFrame(data).set_index("fruits")
p_bar = df.plot_bokeh(
kind="bar",
ylabel="Price per Unit [€]",
title="Fruit prices per Year",
show_figure=False)
#Lineplot:
np.random.seed(42)
df = pd.DataFrame({
"Google": np.random.randn(1000) + 0.2,
"Apple": np.random.randn(1000) + 0.17
},
index=pd.date_range('1/1/2000', periods=1000))
df = df.cumsum()
df = df + 50
p_line = df.plot_bokeh(
kind="line",
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
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)
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)
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