Wells Fargo Zelle limit $500- Steps to increase it?

How to Increase Zelle Transfer Limit?

 

The amount of money you can transfer through Zelle is governed by your bank. As with any other payment service, there is a maximum amount you can transfer through the service each day. The transfer limit is set by your financial institution. In the United States, the limit is set by your bank. However, you can use Zelle to send and receive money from any of your banks. In fact, if you want to transfer money to someone who doesn't have a bank account, you must first transfer $500 via the Zelle platform.

 

The Zelle transfer limit depends on the bank you are using. Some banks have a daily or weekly limit, while others have a daily or monthly restriction. In any case, you can send and receive money from anyone with a U.S. phone number or email address. Other banks have a weekly or monthly transfer limit of up to $20,000, and you cannot exceed the limit in any one day. In any case, you can always increase the Zelle transfer limit if you meet the requirements.

 

Although the maximum amount you can send and receive through Zelle is $10,000 per month, you should not worry about the limits. You can send as much money as you need, provided that you hold the same bank account for a certain period of time. If the Zelle transfer limit is not enough, you can still make multiple transfers. The limits are based on your bank's rules and regulations. If you go over the maximum limit, it will expire after a few days.

 

What is my Zelle transfer limit?

 

The maximum Zelle transfer limit for Chase accounts is $2,500 per day and $40,000 per month. Keep in mind that the limits can change without notice. You can send up to three payments per day through Zelle, but the number of transactions you can make a day depends on your bank. The limit for a Citibank checking account varies by type of account and deposit history. So, the maximum amount you can transfer through Zelle is different from your bank.

 

The Zelle transfer limit is set by your bank and the company that offers the service. This limit will depend on your bank's agreement with Zelle. For instance, the maximum amount you can transfer through the Zelle service will be determined by your financial institution. The maximum amount is dependent on your financial institution. If you've reached your daily limit, you're not allowed to make further transfers. You can increase the Zelle limit as needed, but you should not transfer money in excess of this amount.

 

The maximum amount you can send with Zelle is $5,000 per day, with a $10,000 limit per month. This is a considerable amount, but it's still much higher than many other services. The average amount of money you can send through Zelle is $500 per day, but you can send up to $40,000 a month. If you're sending large amounts of money, you can choose to transfer up to two million dollars per day.

 

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Matplotlib Cheat Sheet: Plotting in Python

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. 

For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.

However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.   

DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python. 

You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots. 

Check out the infographic by clicking on the button below:

With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.

What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.

Matplotlib 

Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

Prepare the Data 

1D Data 

>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)

2D Data or Images 

>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

Create Plot

>>> import matplotlib.pyplot as plt

Figure 

>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))

Axes 

>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) #row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)

Save Plot 

>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png',  transparent=True) #Save transparent figures

Show Plot

>>> plt.show()

Plotting Routines 

1D Data 

>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 0

2D Data 

>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
      cmap= 'gist_earth', 
      interpolation= 'nearest',
      vmin=-2,
      vmax=2)
>>> axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2[2].contourf(data1) #Plot filled contours
>>> axes2[2]= ax.clabel(CS) #Label a contour plot

Vector Fields 

>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows

Data Distributions 

>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z)  #Make a violin plot

Plot Anatomy & Workflow 

Plot Anatomy 

 y-axis      

                           x-axis 

Workflow 

The basic steps to creating plots with matplotlib are:

1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot

>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4]  #Step 1
>>> y = [10,20,25,30] 
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
>>> ax.scatter([2,4,6],
          [5,15,25],
          color= 'darkgreen',
          marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> plt.show() #Step 6

Close and Clear 

>>> plt.cla()  #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window

Plotting Customize Plot 

Colors, Color Bars & Color Maps 

>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
            cmap= 'seismic' )

Markers 

>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")

Linestyles 

>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid') 
>>> plt.plot(x,y,ls= '--') 
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' ) 
>>> plt.setp(lines,color= 'r',linewidth=4.0)

Text & Annotations 

>>> ax.text(1,
           -2.1, 
           'Example Graph', 
            style= 'italic' )
>>> ax.annotate("Sine", 
xy=(8, 0),
xycoords= 'data', 
xytext=(10.5, 0),
textcoords= 'data', 
arrowprops=dict(arrowstyle= "->", 
connectionstyle="arc3"),)

Mathtext 

>>> plt.title(r '$sigma_i=15$', fontsize=20)

Limits, Legends and Layouts 

Limits & Autoscaling 

>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal')  #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axis

Legends 

>>> ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
            ylabel= 'Y-Axis', 
            xlabel= 'X-Axis')
>>> ax.legend(loc= 'best')  #No overlapping plot elements

Ticks 

>>> ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
             ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
             direction= 'inout', 
              length=10)

Subplot Spacing 

>>> fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
             hspace=0.3,
             left=0.125,
             right=0.9,
             top=0.9,
             bottom=0.1)
>>> fig.tight_layout() #Fit subplot(s) in to the figure area

Axis Spines 

>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#matplotlib #cheatsheet #python

Python Data Visualization: Bokeh Cheat Sheet

A handy cheat sheet for interactive plotting and statistical charts with Bokeh.

Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn in the fact that it is an interactive visualization library that is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. 

Bokeh is also known for enabling high-performance visual presentation of large data sets in modern web browsers. 

For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily; But there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let's not forget that the wide variety of visualization customization options makes this Python library an indispensable tool for your data science toolbox.

Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started.

In short, you'll see that this cheat sheet not only presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts. 

In no time, this Bokeh cheat sheet will make you familiar with how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. And the creation of basic statistical charts will hold no secrets for you any longer. 

Boost your Python data visualizations now with the help of Bokeh! :)

---

Plotting With Bokeh

The Python interactive visualization library Bokeh enables high-performance visual presentation of large datasets in modern web browsers.

Bokeh's mid-level general-purpose bokeh. plotting interface is centered around two main components: data and glyphs.

The basic steps to creating plots with the bokeh. plotting interface are:

1. Prepare some data (Python lists, NumPy arrays, Pandas DataFrames and other sequences of values)
2. Create a new plot
3. Add renderers for your data, with visual customizations
4. Specify where to generate the output
5. Show or save the results

>>> from bokeh.plotting import figure
>>> from bokeh.io import output_file, show
>>> x = [1, 2, 3, 4, 5] #Step 1
>>> y = [6, 7, 2, 4, 5]
>>> p = figure(title="simple line example", #Step 2
x_axis_label='x',
y_axis_label='y')
>>> p.line(x, y, legend="Temp.", line_width=2) #Step 3
>>> output_file("lines.html") #Step 4
>>> show(p) #Step 5

1. Data 

Under the hood, your data is converted to Column Data Sources. You can also do this manually:

>>> import numpy as np
>>> import pandas as pd
>>> df = pd.OataFrame(np.array([[33.9,4,65, 'US'], [32.4, 4, 66, 'Asia'], [21.4, 4, 109, 'Europe']]),
                     columns= ['mpg', 'cyl',   'hp',   'origin'],
                      index=['Toyota', 'Fiat', 'Volvo'])


>>> from bokeh.models import ColumnOataSource
>>> cds_df = ColumnOataSource(df)

2. Plotting 

>>> from bokeh.plotting import figure
>>>p1= figure(plot_width=300, tools='pan,box_zoom')
>>> p2 = figure(plot_width=300, plot_height=300,
x_range=(0, 8), y_range=(0, 8))
>>> p3 = figure()

3. Renderers & Visual Customizations 

Glyphs 

Scatter Markers 

>>> p1.circle(np.array([1,2,3]), np.array([3,2,1]), fill_color='white')
>>> p2.square(np.array([1.5,3.5,5.5]), [1,4,3],
color='blue', size=1)

Line Glyphs 

>>> pl.line([1,2,3,4], [3,4,5,6], line_width=2)
>>> p2.multi_line(pd.DataFrame([[1,2,3],[5,6,7]]),
pd.DataFrame([[3,4,5],[3,2,1]]),
color="blue")

Customized Glyphs

Selection and Non-Selection Glyphs 

>>> p = figure(tools='box_select')
>>> p. circle ('mpg', 'cyl', source=cds_df,
selection_color='red',
nonselection_alpha=0.1)

Hover Glyphs

>>> from bokeh.models import HoverTool
>>>hover= HoverTool(tooltips=None, mode='vline')
>>> p3.add_tools(hover)

Color Mapping 

>>> from bokeh.models import CategoricalColorMapper
>>> color_mapper = CategoricalColorMapper(
             factors= ['US', 'Asia', 'Europe'],
             palette= ['blue', 'red', 'green'])
>>>  p3. circle ('mpg', 'cyl', source=cds_df,
            color=dict(field='origin',
                 transform=color_mapper), legend='Origin')

4. Output & Export 

Notebook

>>> from bokeh.io import output_notebook, show
>>> output_notebook()

HTML 

Standalone HTML 

>>> from bokeh.embed import file_html
>>> from bokeh.resources import CON
>>> html = file_html(p, CON, "my_plot")

>>> from  bokeh.io  import  output_file,  show
>>> output_file('my_bar_chart.html',  mode='cdn')

Components

>>> from bokeh.embed import components
>>> script, div= components(p)

PNG

>>> from bokeh.io import export_png
>>> export_png(p, filename="plot.png")

SVG 

>>> from bokeh.io import export_svgs
>>> p. output_backend = "svg"
>>> export_svgs(p,filename="plot.svg")

Legend Location 

Inside Plot Area 

>>> p.legend.location = 'bottom left'

Outside Plot Area 

>>> from bokeh.models import Legend
>>> r1 = p2.asterisk(np.array([1,2,3]), np.array([3,2,1])
>>> r2 = p2.line([1,2,3,4], [3,4,5,6])
>>> legend = Legend(items=[("One" ,[p1, r1]),("Two",[r2])], location=(0, -30))
>>> p.add_layout(legend, 'right')

Legend Background & Border 

>>> p.legend. border_line_color = "navy"
>>> p.legend.background_fill_color = "white"

Legend Orientation 

>>> p.legend.orientation = "horizontal"
>>> p.legend.orientation = "vertical"

Rows & Columns Layout

Rows

>>> from bokeh.layouts import row
>>>layout= row(p1,p2,p3)

Columns

>>> from bokeh.layouts import columns
>>>layout= column(p1,p2,p3)

Nesting Rows & Columns 

>>>layout= row(column(p1,p2), p3)

Grid Layout 

>>> from bokeh.layouts import gridplot
>>> rowl = [p1,p2]
>>> row2 = [p3]
>>> layout = gridplot([[p1, p2],[p3]])

Tabbed Layout 

>>> from bokeh.models.widgets import Panel, Tabs
>>> tab1 = Panel(child=p1, title="tab1")
>>> tab2 = Panel(child=p2, title="tab2")
>>> layout = Tabs(tabs=[tab1, tab2])

Linked Plots

Linked Axes 

Linked Axes
>>> p2.x_range = p1.x_range
>>> p2.y_range = p1.y_range

Linked Brushing 

>>> p4 = figure(plot_width = 100, tools='box_select,lasso_select')
>>> p4.circle('mpg', 'cyl' , source=cds_df)
>>> p5 = figure(plot_width = 200, tools='box_select,lasso_select')
>>> p5.circle('mpg', 'hp', source=cds df)
>>>layout= row(p4,p5)

5. Show or Save Your Plots  

>>> show(p1)
>>> show(layout)
>>> save(p1)

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#python #datavisualization #bokeh #cheatsheet

Wells Fargo Zelle limit $500- Steps to increase it?

How to Increase Zelle Transfer Limit?

 

The amount of money you can transfer through Zelle is governed by your bank. As with any other payment service, there is a maximum amount you can transfer through the service each day. The transfer limit is set by your financial institution. In the United States, the limit is set by your bank. However, you can use Zelle to send and receive money from any of your banks. In fact, if you want to transfer money to someone who doesn't have a bank account, you must first transfer $500 via the Zelle platform.

 

The Zelle transfer limit depends on the bank you are using. Some banks have a daily or weekly limit, while others have a daily or monthly restriction. In any case, you can send and receive money from anyone with a U.S. phone number or email address. Other banks have a weekly or monthly transfer limit of up to $20,000, and you cannot exceed the limit in any one day. In any case, you can always increase the Zelle transfer limit if you meet the requirements.

 

Although the maximum amount you can send and receive through Zelle is $10,000 per month, you should not worry about the limits. You can send as much money as you need, provided that you hold the same bank account for a certain period of time. If the Zelle transfer limit is not enough, you can still make multiple transfers. The limits are based on your bank's rules and regulations. If you go over the maximum limit, it will expire after a few days.

 

What is my Zelle transfer limit?

 

The maximum Zelle transfer limit for Chase accounts is $2,500 per day and $40,000 per month. Keep in mind that the limits can change without notice. You can send up to three payments per day through Zelle, but the number of transactions you can make a day depends on your bank. The limit for a Citibank checking account varies by type of account and deposit history. So, the maximum amount you can transfer through Zelle is different from your bank.

 

The Zelle transfer limit is set by your bank and the company that offers the service. This limit will depend on your bank's agreement with Zelle. For instance, the maximum amount you can transfer through the Zelle service will be determined by your financial institution. The maximum amount is dependent on your financial institution. If you've reached your daily limit, you're not allowed to make further transfers. You can increase the Zelle limit as needed, but you should not transfer money in excess of this amount.

 

The maximum amount you can send with Zelle is $5,000 per day, with a $10,000 limit per month. This is a considerable amount, but it's still much higher than many other services. The average amount of money you can send through Zelle is $500 per day, but you can send up to $40,000 a month. If you're sending large amounts of money, you can choose to transfer up to two million dollars per day.

 

Mastering Tableau Step by Step | Simpliv

Description
Tableau is a widely used data analytics tool. It is the most powerful, secure, end to end platform for your data. Designed for the individual but scaled for the enterprise. Tableau is the only data intelligence platform that turns your data into insights that drive action. Learn data visualization in an easy step by step manner that even a non-analyst can understand.

In this course, you will learn what you need to know to analyze and display data using Tableau Desktop - and make better, more data-driven decisions for your company.

Basic knowledge
Basic knowledge of Excel expected
What will you learn
Difference between Tableau and Excel
Data types in Tableau
Live v/s Extract Data
View Data
Measure Names and Values
Joining Tables
Splitting Columns
Introduction to Maps
Defining Groups
Defining other Groups
Editing Groups
Creating Dashboards
Editing Dashboards
Creating a new storyline

#Tableau Step by Step #Mastering Tableau Step by Step #dataandanalytics #Tableau

Django Chartit: A Django App to Plot Charts and Pivot Charts Directly

Django Chartit is a Django app that can be used to easily create charts from the data in your database. The charts are rendered using Highcharts and jQuery JavaScript libraries. Data in your database can be plotted as simple line charts, column charts, area charts, scatter plots, and many more chart types. Data can also be plotted as Pivot Charts where the data is grouped and/or pivoted by specific column(s).

Changelog

Features

• Plot charts from models.
• Plot data from multiple models on the same axis on a chart.
• Plot pivot charts from models. Data can be pivoted by across multiple columns.
• Legend pivot charts by multiple columns.
• Combine data from multiple models to plot on same pivot charts.
• Plot a pareto chart, paretoed by a specific column.
• Plot only a top few items per category in a pivot chart.
• Python 3 compatibility
• Django 1.8 and 1.9 compatibility
• Documentation to ReadTheDocs
• Automated testing via Travis CI
• Test coverage tracking via Coveralls

Installation

You can install Django-Chartit from PyPI. Just do

$ pip install django_chartit

Then, add chartit to INSTALLED_APPS in "settings.py".

You also need supporting JavaScript libraries. See the Required JavaScript Libraries section for more details.

How to Use

Plotting a chart or pivot chart on a webpage involves the following steps.

1. Create a DataPool or PivotDataPool object that specifies what data you need to retrieve and from where.
2. Create a Chart or PivotChart object to plot the data in the DataPool or PivotDataPool respectively.
3. Return the Chart/PivotChart object from a django view function to the django template.
4. Use the load_charts template tag to load the charts to HTML tags with specific ids.

It is easier to explain the steps above with examples. So read on.

How to Create Charts

Here is a short example of how to create a line chart. Let's say we have a simple model with 3 fields - one for month and two for temperatures of Boston and Houston.

class MonthlyWeatherByCity(models.Model):
    month = models.IntegerField()
    boston_temp = models.DecimalField(max_digits=5, decimal_places=1)
    houston_temp = models.DecimalField(max_digits=5, decimal_places=1)

And let's say we want to create a simple line chart of month on the x-axis and the temperatures of the two cities on the y-axis.

from chartit import DataPool, Chart

def weather_chart_view(request):
    #Step 1: Create a DataPool with the data we want to retrieve.
    weatherdata = \
        DataPool(
           series=
            [{'options': {
               'source': MonthlyWeatherByCity.objects.all()},
              'terms': [
                'month',
                'houston_temp',
                'boston_temp']}
             ])

    #Step 2: Create the Chart object
    cht = Chart(
            datasource = weatherdata,
            series_options =
              [{'options':{
                  'type': 'line',
                  'stacking': False},
                'terms':{
                  'month': [
                    'boston_temp',
                    'houston_temp']
                  }}],
            chart_options =
              {'title': {
                   'text': 'Weather Data of Boston and Houston'},
               'xAxis': {
                    'title': {
                       'text': 'Month number'}}})

    #Step 3: Send the chart object to the template.
    return render_to_response({'weatherchart': cht})

And you can use the load_charts filter in the django template to render the chart.

<head>
    <!-- code to include the highcharts and jQuery libraries goes here -->
    <!-- load_charts filter takes a comma-separated list of id's where -->
    <!-- the charts need to be rendered to                             -->
    {% load chartit %}
    {{ weatherchart|load_charts:"container" }}
</head>
<body>
    <div id='container'> Chart will be rendered here </div>
</body>

How to Create Pivot Charts

Here is an example of how to create a pivot chart. Let's say we have the following model.

class DailyWeather(models.Model):
    month = models.IntegerField()
    day = models.IntegerField()
    temperature = models.DecimalField(max_digits=5, decimal_places=1)
    rainfall = models.DecimalField(max_digits=5, decimal_places=1)
    city = models.CharField(max_length=50)
    state = models.CharField(max_length=2)

We want to plot a pivot chart of month (along the x-axis) versus the average rainfall (along the y-axis) of the top 3 cities with highest average rainfall in each month.

from django.db.models import Avg
from chartit import PivotDataPool, PivotChart

def rainfall_pivot_chart_view(request):
    # Step 1: Create a PivotDataPool with the data we want to retrieve.
    rainpivotdata = PivotDataPool(
        series=[{
            'options': {
                'source': DailyWeather.objects.all(),
                'categories': ['month'],
                'legend_by': 'city',
                'top_n_per_cat': 3,
            },
            'terms': {
                'avg_rain': Avg('rainfall'),
            }
        }]
    )

    # Step 2: Create the PivotChart object
    rainpivcht = PivotChart(
        datasource=rainpivotdata,
        series_options=[{
            'options': {
                'type': 'column',
                'stacking': True
            },
            'terms': ['avg_rain']
        }],
        chart_options={
            'title': {
                'text': 'Rain by Month in top 3 cities'
            },
            'xAxis': {
                'title': {
                    'text': 'Month'
                }
            }
        }
    )

    # Step 3: Send the PivotChart object to the template.
    return render_to_response({'rainpivchart': rainpivcht})

And you can use the load_charts filter in the django template to render the chart.

<head>
    <!-- code to include the highcharts and jQuery libraries goes here -->
    <!-- load_charts filter takes a comma-separated list of id's where -->
    <!-- the charts need to be rendered to                             -->
    {% load chartit %}
    {{ rainpivchart|load_charts:"container" }}
</head>
<body>
    <div id='container'> Chart will be rendered here </div>
</body>

Rendering multiple charts

It is possible to render multiple charts in the same template. The first argument to load_charts is the Chart object or a list of Chart objects, and the second is a comma separated list of HTML IDs where the charts will be rendered.

When calling Django's render you have to pass all you charts as a list:

return render(request, 'index.html',
             {
                'chart_list' : [chart_1, chart_2],
             }
        )

Then in your template you have to use the proper syntax:

<head>
    {% load chartit %}
    {{ chart_list|load_charts:"chart_1,chart_2" }}
</head>
<body>
    <div id="chart_1">First chart will be rendered here</div>
    <div id="chart_2">Second chart will be rendered here</div>
</body>

Demo

The above examples are just a brief taste of what you can do with Django-Chartit. For more examples and to look at the charts in actions, check out the demoproject/ directory. To execute the demo run the commands

cd demoproject/
PYTHONPATH=../ python ./manage.py migrate
PYTHONPATH=../ python ./manage.py runserver

Documentation

Full documentation is available here .

Required JavaScript Libraries

The following JavaScript Libraries are required for using Django-Chartit.

• jQuery - versions 1.6.4 and 1.7 are known to work well with django-chartit.
• Highcharts - versions 2.1.7 and 2.2.0 are known to work well with django-chartit.

Note

While Django-Chartit itself is licensed under the BSD license, Highcharts is licensed under the Highcharts license and jQuery is licensed under both MIT License and GNU General Public License (GPL) Version 2. It is your own responsibility to abide by respective licenses when downloading and using the supporting JavaScript libraries.

0.1 (November 5, 2011)

• Initial release of django-chartit

0.2.0 as django-chartit2 (January 20, 2016):

• Fixed issue that could prevent installation via PyPI

0.2.2 as django-chartit2 (January 28, 2016)

• Fixed another issue that prevented installation via PyPI

0.2.3 (July 30, 2016)

• New to_json() method for charts. Useful for creating Highcharts in AJAX
• Merged with django-chartit2 fork by Grant McConnaughey which adds Python 3 and latest Django 1.8.x and 1.9.x support
• Allow dictionary fields in conjunction with lambda fields. Closes #26
• Documentation improvements
• Lots of code cleanups and style improvements

0.2.4 (August 2, 2016)

• Fix for get_all_field_names() and get_field_by_name() removal in Django 1.10. Fixes #39
• Updated for django.db.sql.query.Query.aggregates removal

0.2.5 (August 3, 2016)

• Workaround Python 3 vs. Python 2 list sort issue which breaks charts with multiple data sources displayed on the same axis!
• Make demoproject/ compatible with Django 1.10

0.2.6 (August 16, 2016)

• Merge chartit_tests/ with demoproject/
• Load test DB with real data to use during testing
• Add more tests
• Update the path to demoproject.settings when building docs. Fixes a problem which caused some API docs to be empty
• Fix ValueError: not enough values to unpack (expected 2, got 0) with PivotChart when the QuerySet returns empty data
• Dropped requirement on simplejson
• Properly handle unicode data in Pivot charts. Fixes #5
• Demo project updated with Chart and PivotChart examples of rendering DateField values on the X axis
• Allow charting of extra() or annotate() fields. Fixes #8 and #12
• Refactor RecursiveDefaultDict to allow chart objects to be serialized to/from cache. Fixes #10
• Add information about supported 3rd party JavaScript versions. Fixes #14

0.2.7 (September 14, 2016)

• Don't use super(self.__class__) b/c that breaks chart class inheritance. Fixes #41

0.2.8 (December 4, 2016)

• PivotChart and PivotDataPool will be deprecated soon. Both are marked with deprecation warnings. There is a lot of duplication and special handling between those classes and the Chart and DataPool classes which make it harder to expand the feature set for django-chartit. The next release will focus on consolidating all the functionality into Chart and DataPool so that users will still be able to draw pivot charts. You will have to construct your pivot charts manually though!
• DataPool terms now supports model properties. Fixes #35. Model properties are not supported for PivotDataPool! WARNING: when using model properties chartit can't make use of ``QuerySet.values()`` internally. This means results will not be groupped by the values of the fields you supplied. This may lead to unexpected query results/charts!
• DataPool now supports RawQuerySet as data source. Fixes #44. RawQuerySet is not supported for PivotDataPool! WARNING: when using ``RawQuerySet`` don't use double underscores in field names because these are interpreted internally by chartit and will cause exceptions. For example don't do this ``SELECT AVG(rating) as rating__avg`` instead write it as ``SELECT AVG(rating) as rating_avg``!
• README now tells how to execute demoproject/

0.2.9 (January 17, 2017)

• Enable pylint during testing but don't block Travis-CI on failures. Closes #42.
• Handle unicode data in pie and scatter plot charts under Python 2.7. PR#47.

master

• Update demo with an example of how to pass legendIndex as an option to a data serie. Closes #48.
• Update demo with an example of how to change the label of any term instead of using the default one. Closes #46.

Download Details:
Author: chartit
Source Code: https://github.com/chartit/django-chartit
License: View license

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