+

Plotly maps¶

+

with Plotly's Python API library and Basemap¶

+

This notebook comes in response to this Rhett Allain tweet.

+

+
Although Plotly does not feature built-in maps functionality (yet), this notebook demonstrates how to plotly-fy maps generated by Basemap.
+

+

First, check the version which version of the Python API library installed on your machine:

+

import plotly
+plotly.__version__
+

+'1.2.6'
+

+

Next, if you have a plotly account as well as a credentials file set up on your machine, singing in to Plotly's servers is done automatically while importing plotly.plotly.

+

import plotly.plotly as py
+

+

Import the plotly graph objects (in particular Contour) to help build our figure:

+

from plotly.graph_objs import *
+

+

Data with this notebook will be taken from a NetCDF file, so import netcdf class from the scipy.io module, along with numpy:

+

import numpy as np           
+from scipy.io import netcdf  
+

+

Finally, import the Matplotlib Basemap Toolkit, its installation instructions can found here.

+

from mpl_toolkits.basemap import Basemap
+

+

1. Get the data!¶

+

The data is taken from NOAA Earth System Research Laboratory.

+

Unfortunately, this website does not allow to code your output demand and/or use wget to download the data.

+

That said, the data used for this notebook can be downloaded in a only a few clicks:

+

Select Air Temperature in Varaibles
Select Surface in Analysis level?
Select Jul | 1 and Jul | 31
Enter 2014 in the Enter Year of last day of range field
Select Anomaly in Plot type?
Select All in Region of globe
Click on Create Plot

+

Then on the following page, click on Get a copy of the netcdf data file used for the plot to download the NetCDF on your machine.

+

Note that the data represents the average daily surface air temperature anomaly (in deg. C) for July 2014 with respect to 1981-2010 climatology.

+

Now, import the NetCDF file into this IPython session. The following was inspired by this earthpy blog post.

+

# Path the downloaded NetCDF file (different for each download)
+f_path = '/home/etienne/Downloads/compday.Bo3cypJYyE.nc'
+
+# Retrieve data from NetCDF file
+with netcdf.netcdf_file(f_path, 'r') as f:
+    lon = f.variables['lon'][::]    # copy as list
+    lat = f.variables['lat'][::-1]  # invert the latitude vector -> South to North
+    air = f.variables['air'][0,::-1,:]  # squeeze out the time dimension, 
+                                        # invert latitude index
+

+

The values lon start a 0 degrees and increase eastward to 360 degrees. So, the air array is centered about the Pacific Ocean. For a better-looking plot, shift the data so that it is centered about the 0 meridian:

+

# Shift 'lon' from [0,360] to [-180,180], make numpy array
+tmp_lon = np.array([lon[n]-360 if l>=180 else lon[n] 
+                   for n,l in enumerate(lon)])  # => [0,180]U[-180,2.5]
+
+i_east, = np.where(tmp_lon>=0)  # indices of east lon
+i_west, = np.where(tmp_lon<0)   # indices of west lon
+lon = np.hstack((tmp_lon[i_west], tmp_lon[i_east]))  # stack the 2 halves
+
+# Correspondingly, shift the 'air' array
+tmp_air = np.array(air)
+air = np.hstack((tmp_air[:,i_west], tmp_air[:,i_east]))
+

+

2. Make Contour graph object¶

+

Very simply,

+

trace1 = Contour(
+    z=air,
+    x=lon,
+    y=lat,
+    colorscale="RdBu",
+    zauto=False,  # custom contour levels
+    zmin=-5,      # first contour level
+    zmax=5        # last contour level  => colorscale is centered about 0
+)
+

+

3. Get the coastlines and country boundaries with Basemap¶

+

The Basemap module includes data for drawing coastlines and country boundaries onto world maps. Adding coastlines and/or country boundaries on a matplotlib figure is done with the .drawcoaslines() or .drawcountries() Basemap methods.

+

Next, we will retrieve the Basemap plotting data (or polygons) and convert them to longitude/latitude arrays (inspired by this stackoverflow post) and then package them into Plotly Scatter graph objects .

+

In other words, the goal is to plot each continuous coastline and country boundary lines as 1 Plolty scatter line trace.

+

# Make shortcut to Basemap object, 
+# not specifying projection type for this example
+m = Basemap()     
+

# Make trace-generating function (return a Scatter object)
+def make_scatter(x,y):
+    return Scatter(
+        x=x,
+        y=y,
+        mode='lines',
+        line=Line(color="black"),
+        name=' '  # no name on hover
+    )
+
+# Functions converting coastline/country polygons to lon/lat traces
+def polygons_to_traces(poly_paths, N_poly):
+    ''' 
+    pos arg 1. (poly_paths): paths to polygons
+    pos arg 2. (N_poly): number of polygon to convert
+    '''
+    traces = []  # init. plotting list 
+
+    for i_poly in range(N_poly):
+        poly_path = poly_paths[i_poly]
+        
+        # get the Basemap coordinates of each segment
+        coords_cc = np.array(
+            [(vertex[0],vertex[1]) 
+             for (vertex,code) in poly_path.iter_segments(simplify=False)]
+        )
+        
+        # convert coordinates to lon/lat by 'inverting' the Basemap projection
+        lon_cc, lat_cc = m(coords_cc[:,0],coords_cc[:,1], inverse=True)
+        
+        # add plot.ly plotting options
+        traces.append(make_scatter(lon_cc,lat_cc))
+     
+    return traces
+
+# Function generating coastline lon/lat traces
+def get_coastline_traces():
+    poly_paths = m.drawcoastlines().get_paths() # coastline polygon paths
+    N_poly = 91  # use only the 91st biggest coastlines (i.e. no rivers)
+    return polygons_to_traces(poly_paths, N_poly)
+
+# Function generating country lon/lat traces
+def get_country_traces():
+    poly_paths = m.drawcountries().get_paths() # country polygon paths
+    N_poly = len(poly_paths)  # use all countries
+    return polygons_to_traces(poly_paths, N_poly)
+

+

Then,

+

# Get list of of coastline and country lon/lat traces
+traces_cc = get_coastline_traces()+get_country_traces()
+

+

4. Make a figue object and plot!¶

+

Package the Contour trace with the coastline and country traces. Note that the Contour trace must be placed before the coastline and country traces in order to make all traces visible.

+

data = Data([trace1]+traces_cc)
+

+

Layout options are set in a Layout object:

+

title = u"Average daily surface air temperature anomalies [\u2103]<br> \
+in July 2014 with respect to 1981-2010 climatology"
+
+anno_text = "Data courtesy of \
+<a href='http://www.esrl.noaa.gov/psd/data/composites/day/'>\
+NOAA Earth System Research Laboratory</a>"
+
+axis_style = dict(
+    zeroline=False,
+    showline=False,
+    showgrid=False,
+    ticks='',
+    showticklabels=False,
+)
+
+layout = Layout(
+    title=title,
+    showlegend=False,
+    hovermode="closest",        # highlight closest point on hover
+    xaxis=XAxis(
+        axis_style,
+        range=[lon[0],lon[-1]]  # restrict y-axis to range of lon
+    ),
+    yaxis=YAxis(
+        axis_style,
+    ),
+    annotations=Annotations([
+        Annotation(
+            text=anno_text,
+            xref='paper',
+            yref='paper',
+            x=0,
+            y=1,
+            yanchor='bottom',
+            showarrow=False
+        )
+    ]),
+    autosize=False,
+    width=1000,
+    height=500,
+)
+

+

Package data and layout in a Figure object and send it to plotly:

+

fig = Figure(data=data, layout=layout)
+
+py.iplot(fig, filename="maps", width=1000)
+

+

See this graph in full screen here.

+

To learn more about Plotly's Python API¶

+

Refer to

+

our online documentation page or
our User Guide.

+

+ +

Got Questions or Feedback?

+ +

About Plotly

+

email: feedback@plot.ly
tweet: +@plotlygraphs

+

Notebook styling ideas

+ +

Big thanks to

+

Cam Davidson-Pilon
Lorena A. Barba

+

from IPython.display import display, HTML
+import urllib2
+url = 'https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css'
+display(HTML(urllib2.urlopen(url).read()))
+

+

Online Dashboards with Excel, Python, & Plotly¶

Building Interactive Graphs at the Push of an (Excel) Button Using Plot.ly and xlwings¶

This notebook is a primer on building interacitve web-based visualizations straight from an excel workbook with

+

pandas: A library with easy-to-use data structures and data analysis tools. Also, interfaces to out-of-memory databases like SQLite.
IPython notebook: An interface for writing and sharing python code, text, and plots.
xlwings: A python library with tools to connect pandas to data stored in excel workbooks.
Plotly: A platform for publishing beautiful, interactive graphs from Python to the web.

+

In Short... How you can go from this:¶

+

from IPython.display import Image
+Image(filename='assets/prices.png', width = 700)
+

+

To this:¶

+

import plotly.tools as tls
+tls.embed('https://plot.ly/~otto.stegmaier/609/previous-min-and-max-prices/')
+

Image(filename='assets/logo.png')
+

+

Why we are working on this:¶

At Liftopia we are working on bringing dynamic pricing into the ski industry. We help consumers ski more by offering tickets for purchase in advance at lower prices in exchange for their commitment. We help resorts control their pricing, drive more predictable revenue and grow their businesses.

+

Since one of our core business channels is pricing and selling lift tickets for our resort partners, our analytics team needs to be able to communicate our pricing plans to our resort partners in a simple, but effective manner. The ski areas we work with often offer tickets on 120 days of the year at upwards of 10 different price points on each day of the season. If you do the math - that can mean trying to communicate 1,200 different prices for one product. Some resorts offer over 10 different products. Now we are at 12,000 data points. Want to see the junior and child ticket pricing too? Now that's 36,000 data points.

+

In an effort to communicate our pricing plans more effecitvely - we decided to use Plot.ly to help us build web based interactive visualizations we can share with our partners.

+

To do this we connected one of our pricing tools to a python script that interacts with Plotly's API.This notebook walks through a simplified version of that process. Note - the data used in this example is intended to show how we use Plotly from Excel - if you want to talk to us about our beliefs abour pricing - get in touch! (ostegmaier@liftopia.com)

+

Covered in this notebook¶

Connect to an excel workbook with XLWings
Clean and prepare your data with pandas
Plot with Plotly
Building the VBA connection to your python code
Sharing and Collaborating with Plotly

+ +

+

+ +

+

Step 1 Connect to your data in Excel using xlwings¶

To show how we use plotly with XLWings and Excel - we put together some simulated data in an excel workbook. For more on XLWings Check out their documentation or this great tutorial

+ +

from IPython.display import IFrame
+#A few imports we will need later
+from xlwings import Workbook, Sheet, Range, Chart
+import pandas as pd
+import numpy as np
+import plotly.plotly as py
+import plotly.tools as tlsM
+from IPython.display import HTML
+from plotly.graph_objs import *
+

#workbook connection - When connecting to a file from a VBA macro you use Workbook.call() instead of Workbook(<filepath>)
+wb = Workbook('C:\Users\Otto S.OttoS-PC\Desktop\Plotly_Post\Example Workbook.xlsm')
+

+

+Using Excel as a Plotly Dashboard...

+

Ok - so maybe its not so high speed - but its a good fit for our users! Plotly has a ton of great GUI tools to edit the graphs once they're made, but we needed a way to make it easy on our users to get the graphs out of excel and into Plotly so they can edit the graphs there. So we built a "Dashboard" with some controls:

+ +

Image(filename="assets/dashboard.png", width="700")
+

+

+Now we can use some of these user-input values to control what elements get plotted

+ +

#Now we can use some of these controls to customize the 
+folder_name = Range('Dashboard','B2').value
+graph_title = Range('Dashboard','B3').value
+

+

Step 2 Clean and prepare your data for plotting using Pandas¶

To show how we use plotly with XLWings and Excel - we put together some simulated data in an excel workbook. For more on XLWings Check out their documentation or this great tutorial

+ +

#short function to create a new dataframe using xlwings
+def new_df(shtnm, startcell = 'A1'):
+    data = Range(shtnm, startcell).table.value
+    temp_df = pd.DataFrame(data[1:], columns = data[0])
+    return(temp_df)
+
+###Make some dataframes from the workbook sheets
+#Core Product
+shtnm1 = Range('Dashboard','B6').value
+df = new_df(shtnm1)
+

+

+Based on user input from the "dashboard" sheet in excel - we can choose to create a new dataframe for the 2nd product

+ +

Image(filename="assets/toggle.png", width="600")
+

#2nd Product
+product_2 = False
+if Range('Dashboard','C7').value == "Yes":
+    shtnm2 = Range('Dashboard','B7').value
+    df2 = new_df(shtnm2)
+    product_2 = True
+

#3rd Product
+product_3 = False
+if Range('Dashboard','C8').value == "Yes":
+    shtnm3 = Range('Dashboard','B8').value
+    df3 = new_df(shtnm3)
+    product_3 = True       
+

+

Its easier to work with the column headers once they're cleaned up, so let's clean them up a bit

+ +

#Clean up the charaters in the columns 
+names2 = []
+def clean_names(column_list):
+    #Short function to make our column headers easier to reference later.
+    names2=[]
+    for name in column_list:
+        name = name.replace(" ","").lower()
+        names2.append(name)
+    return names2
+

df.columns = clean_names(df.columns.values)
+if product_2 == True:
+    df2.columns = clean_names(df2.columns.values)
+if product_3 == True:
+    df3.columns = clean_names(df3.columns.values)  
+

+

We found it useful to be using a common index across the products - at least for our purpose, so we reset the index on the date column and convert the rest of the data to float

+ +

df= df.set_index('date').tz_localize('MST').astype(float)
+if product_2 == True:
+    df2= df2.set_index('date').tz_localize('MST').astype(float)
+if product_3 == True:
+    df3= df3.set_index('date').tz_localize('MST').astype(float)
+

+

Step 3 Plot your data with plotly.¶

+

#set a few global variables so we can use them throughout the plots
+X = df.index
+
+try:  
+    ymin = min(df['minpriceoffered'].min(),df2['minpriceoffered'].min(),df3['minpriceoffered'].min()) - 10
+    ymax = max(df['walkupprice'].max(),df2['walkupprice'].max(),df3['walkupprice'].max()) + 10
+    
+except:
+    #If that doesn't work, just go edit it on Plotly's web based plot editor. 
+    ymin = df['minpriceoffered'].min() - 10
+    ymax = df['walkupprice'].max() + 10
+

+

For our particular use case - we were rebuilding traces of similar type, so we wrote a short function to simplify this step

+ +

#function to create a "trace" (line) for each item we want to plot
+def new_trace(price_column, color, name, x=X, fill = 'none', qty_column = []):
+    trace = Scatter(
+    x=X,
+    y=price_column,  
+    fill=fill,
+    mode='lines',
+    name=name,
+    text=['Quantity: {}'.format(q) for q in qty_column],
+    line=Line(
+        color=color,
+        width=2,
+        dash='solid',
+        opacity=1,),
+    xaxis='x1',
+    yaxis='y1')
+    return trace
+
+#Set up the 3 core traces
+trace1 = new_trace(df['walkupprice'], '#FF9966','Core Product Walkup Price') 
+trace2 = new_trace(df['maxpriceoffered'], '#5EA5D1',shtnm1 + 'Highest Price Offered', qty_column=df['unitsmax']) 
+trace3 = new_trace(df['minpriceoffered'], '#5EA5D1',shtnm1+' Starting Price',  qty_column= df['unitsmin'], fill='tonexty') 
+trace_list = [trace1, trace2, trace3]
+

#add additional traces if toggled on by user
+if product_2 == True:   #Using the input from the Dashboard Sheet in Excel
+    trace4 = new_trace(df2['minpriceoffered'], '##66ff66',shtnm2+' Lowest Price Offered')
+    trace_list.append(trace4)  
+
+if product_3 == True:   #Using the input from the Dashboard Sheet in Excel
+    trace5 = new_trace(df3['minpriceoffered'], '#e6e600',shtnm3+' Lowest Price Offered') 
+    trace_list.append(trace5) 
+    
+

+

Lastly we set some general Layout controls. If needed, these could be added as user controls pretty easily in the Excel dashboard - or you could just edit the graph from Plotly's GUI.

+ +

y_axis = YAxis(
+        title='Price',
+        titlefont=Font(
+            size=11.0,
+            color='#262626'
+        ),
+        range=[ymin, ymax],
+        domain=[0.0, 1.0],
+        type='linear',
+        showgrid=True,
+        zeroline=False,
+        showline=True,
+        nticks=7,
+        ticks='inside',
+        tickfont=Font(
+            size=10.0
+        ),
+        mirror='ticks',
+        anchor='x1',
+        side='left'
+    )
+

x_axis = XAxis(
+        title='Trip Date',
+        titlefont=Font(
+            size=11.0,
+            color='#262626'
+        ),
+        range=[X.min(),X.max()],
+        domain=[0.0, 1.0],
+        type='date',
+        showgrid=True,
+        zeroline=False,
+        showline=True,
+        nticks=8,
+        ticks='inside',
+        tickfont=Font(
+            size=10.0
+        ),
+        mirror='ticks',
+        anchor='y1',
+        side='bottom'
+    )
+

layout = Layout(
+    title=graph_title,  #Using the input from the Dashboard Sheet in Excel
+    titlefont=Font(
+        size=12.0,
+        color='#262626'
+    ),
+    showlegend=True,
+    hovermode='compare',
+    xaxis1= x_axis,
+    yaxis1= y_axis
+)
+

#Short function for pushing private graphs to plotly
+def private_plot(*args, **kwargs):
+    kwargs['auto_open'] = False     #Controls whether a new tab is opened in your browser with the new plot
+    url = py.plot(*args, **kwargs)
+    return (url)
+

+

Now We are ready to plot!

+ +

fig = Figure(data=trace_list, layout=layout)
+url = private_plot(fig,  filename='%s/%s' %(folder_name, graph_title), world_readable=True)
+tls.embed(url)
+

+

Step 4 - Running your python code directly from Excel¶

+ +

+

Save the python script to file. Make sure to use Workbook.caller() rather than the file path. This allows XLWings to access the current notebook that the user has open. +

+ +

Image(filename= 'assets/workbookcaller.png', width="500")
+

+

Build a Macro in python that references the script you've written: +

+ +

Image(filename= "assets/macro.png", width="700")
+

+

Assign the macro to a button of your choosing +

+ +

Image(filename= "assets/assignmacro.png", width="500")
+

+

Make any final edits using Plotly's web based editor +

+ +

Image(filename= "assets/plotlyeditor.png", width="800")
+

+

+ +

+

One of the main reasons we wanted to use Plotly was the ability to share these interacitve visualizations via a URL. This makes it easy for our account managers to communicate the pricing plans with a simple email containging a link to the plot.

+

Plotly has built out some great functionality that makes sharing and collaborating really easy. When we have multiple analysts on a pricing build we can all work on a plot and once its done, its easy to share a private link with our partners.

+ +

Image(filename='assets/sharing.png')
+

+

	state	murder	Forcible_rate	Robbery	aggravated_assult	burglary	larceny_theft	motor_vehicle_theft	population
0	Alabama	8.2	34.3	141.4	247.8	953.8	2650.0	288.3	4627851
1	Alaska	4.8	81.1	80.9	465.1	622.5	2599.1	391.0	686293

	country	year	pop	continent	lifeExp	gdpPercap
20	Albania	1992	3326498	Europe	71.581	2497.437901
21	Albania	1997	3428038	Europe	72.950	3193.054604
22	Albania	2002	3508512	Europe	75.651	4604.211737
23	Albania	2007	3600523	Europe	76.423	5937.029526
24	Algeria	1952	9279525	Africa	43.077	2449.008185
25	Algeria	1957	10270856	Africa	45.685	3013.976023
26	Algeria	1962	11000948	Africa	48.303	2550.816880
27	Algeria	1967	12760499	Africa	51.407	3246.991771
28	Algeria	1972	14760787	Africa	54.518	4182.663766
29	Algeria	1977	17152804	Africa	58.014	4910.416756

	A	B	C
count	100.000000	100.000000	100.000000
mean	0.466735	0.497376	0.506264
std	0.272216	0.286994	0.266733
min	0.007997	0.007294	0.000326
25%	0.234741	0.243540	0.296997
50%	0.462034	0.521211	0.511778
75%	0.690845	0.711072	0.730888
max	0.986756	0.993439	0.979052

	A	B	C
count	100.000000	100.000000	100.000000
mean	0.545290	0.529573	0.483429
std	0.286990	0.299712	0.271988
min	0.011408	0.001798	0.001315
25%	0.311730	0.245510	0.268717
50%	0.561711	0.558073	0.488095
75%	0.796309	0.803161	0.716313
max	0.989962	0.988582	0.982066

	uniprot_id	Astrocyte__cerebellum_donor1CNhs1132111500119F6	Astrocyte__cerebral_cortex_donor1CNhs1086411235116D2	breast_adult_donor1CNhs1179210080102A8	cerebellum_adult_donor10252CNhs1232310166103B4	cerebellum_newborn_donor10223CNhs1407510357105E6	...	thalamus__adult_donor10196CNhs1379410168103B6	thalamus_adult_donor10252CNhs1231410154103A1	thalamus_adult_donor10258_tech_rep1CNhs1422310370105G1	thalamus_adult_donor10258_tech_rep2CNhs1455110370105G1	throat_fetal_donor1CNhs1177010061101H7	thyroid_fetal_donor1CNhs1176910060101H6	tongue_epidermis_fungiform_papillae_donor1CNhs1346010288104F9	umbilical_cord_fetal_donor1CNhs1176510057101H3	uterus_fetal_donor1CNhs1176310055101H1
0	NA	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0
1	uniprot:Q96JB6	0.12	11.45	2.17	0.22	1.03	...	5.8	0.31	5.65	2.99	1.19	1.01	2.58	7.04	4.48
2	NA	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0
3	NA	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0
4	NA	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0

Plotly graphs with LaTeX

IPython supports lovely Latex typesetting through MathJax:

And Plotly has jumped on the boat! Plotly graphs now process Latex markup. Below are some examples.

Questions? Issues? jack [at] plot [dot] ly

To get hooked up with a free Plotly account, go here

Example 1: Taylor Series for Sine

Example 2: Colors, Size, and Opacity

Plotly maps¶

with Plotly's Python API library and Basemap¶

1. Get the data!¶

2. Make Contour graph object¶

3. Get the coastlines and country boundaries with Basemap¶

4. Make a figue object and plot!¶

To learn more about Plotly's Python API¶

Got Questions or Feedback?

Notebook styling ideas

Got Questions or Feedback?

Notebook styling ideas

Online Dashboards with Excel, Python, & Plotly¶

Building Interactive Graphs at the Push of an (Excel) Button Using Plot.ly and xlwings¶

In Short... How you can go from this:¶

To this:¶

Why we are working on this:¶

Covered in this notebook¶

Step 1 Connect to your data in Excel using xlwings¶

Step 2 Clean and prepare your data for plotting using Pandas¶

Step 3 Plot your data with plotly.¶

Step 4 - Running your python code directly from Excel¶

	mag	magtype	type	time_plotly	place	depth
3749	7.3	mww	earthquake	2014-10-14 03:51:35	67km WSW of Jiquilillo, Nicaragua	40.00
1686	7.1	mww	earthquake	2014-10-09 02:14:32	Southern East Pacific Rise	15.50
4602	7.1	mwc	earthquake	2014-11-01 18:57:22	141km NE of Ndoi Island, Fiji	434.41
2855	6.6	mww	earthquake	2014-10-09 02:32:05	Southern East Pacific Rise	10.00
7186	6.3	mwp	earthquake	2014-10-11 02:35:46	154km ENE of Hachinohe, Japan	13.48

	Chile	Hungary	Uruguay
x	[1960, 1961, 1962, 1963, 1964, 1965, 1966, 196...	[1960, 1961, 1962, 1963, 1964, 1965, 1966, 196...	[1960, 1961, 1962, 1963, 1964, 1965, 1966, 196...
y	[None, None, 600, 640, 660, 650, 740, 760, 770...	[None, None, None, None, None, None, None, Non...	[None, None, 580, 610, 660, 680, 720, 640, 610...

	sepal_len	sepal_wid	petal_len	petal_wid	class
145	6.7	3.0	5.2	2.3	Iris-virginica
146	6.3	2.5	5.0	1.9	Iris-virginica
147	6.5	3.0	5.2	2.0	Iris-virginica
148	6.2	3.4	5.4	2.3	Iris-virginica
149	5.9	3.0	5.1	1.8	Iris-virginica

	Amount	CloseDate	ExpectedRevenue	Id	Name	Owner	Probability	StageName
0	15000	2015-06-03	9000	0061a000002vYrwAAE	Grand Hotels Kitchen Generator	Bill C	60	Id. Decision Makers
1	90000	2015-05-03	81000	0061a000002vYsIAAU	Grand Hotels SLA	Chuck Brockerson	90	Negotiation/Review
2	80000	2015-05-22	60000	0061a000002vYs3AAE	Express Logistics Portable Truck Generators	Bill C	75	Proposal/Price Quote
3	22000	2015-05-07	11000	0061a000002vYruAAE	Express Logistics Standby Generator	Chuck Brockerson	50	Value Proposition
4	100000	2015-06-17	90000	0061a000002vYsCAAU	University of AZ Installations	Bill C	90	Negotiation/Review

	Unique Key	Created Date	Closed Date	Agency	Agency Name	Complaint Type	Descriptor	Location Type	Incident Zip	Incident Address	...	Bridge Highway Name	Bridge Highway Direction	Road Ramp	Bridge Highway Segment	Garage Lot Name	Ferry Direction	Ferry Terminal Name	Latitude	Longitude	Location
0	29300358	11/16/2014 11:46:00 PM	11/16/2014 11:46:00 PM	DSNY	BCC - Queens East	Derelict Vehicles	14 Derelict Vehicles	Street	11432	80-25 PARSONS BOULEVARD	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.719411	-73.808882	(40.719410639341916, -73.80888158860446)
1	29299837	11/16/2014 02:24:35 AM	11/16/2014 02:24:35 AM	DOB	Department of Buildings	Building/Use	Illegal Conversion Of Residential Building/Space	NaN	10465	938 HUNTINGTON AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.827862	-73.830641	(40.827862046105416, -73.83064067165407)

	index	CreatedDate	ClosedDate	Agency	ComplaintType	Descriptor	City
0	1	2014-11-16 23:46:00.000000	2014-11-16 23:46:00.000000	DSNY	Derelict Vehicles	14 Derelict Vehicles	Jamaica
1	2	2014-11-16 02:24:35.000000	2014-11-16 02:24:35.000000	DOB	Building/Use	Illegal Conversion Of Residential Building/Space	BRONX
2	3	2014-11-16 02:17:12.000000	2014-11-16 02:50:48.000000	NYPD	Illegal Parking	Blocked Sidewalk	BROOKLYN

	City	num_complaints
0	BROOKLYN	2441941
1	NEW YORK	1544421
2	BRONX	1470746
3	None	654158
4	STATEN ISLAND	408095
5	JAMAICA	141940
6	FLUSHING	112519
7	ASTORIA	86051
8	RIDGEWOOD	63400
9	Jamaica	54876

	CreatedDate	hour	Complaints per Hour
0	2003-02-26 00:47:27.000000	00	3178595
1	2003-02-26 01:36:31.000000	01	71993
2	2003-03-04 02:00:46.000000	02	56362
3	2003-02-25 03:07:01.000000	03	33396
4	2003-03-04 04:32:11.000000	04	30434

	CreatedDate	hour	Complaints per Hour
0	2004-08-19 00:54:43.000000	00	41373
1	2008-08-29 01:07:39.000000	01	34588

	CreatedDate	interval
0	2014-11-16 23:46:00.000000	2014-11-16 23:45:00
1	2014-11-16 02:24:35.000000	2014-11-16 02:15:00
2	2014-11-16 02:17:12.000000	2014-11-16 02:15:00
3	2014-11-16 02:15:13.000000	2014-11-16 02:15:00
4	2014-11-16 02:14:01.000000	2014-11-16 02:00:00

	uniprot_id	Astrocyte__cerebellum_donor1CNhs1132111500119F6	Astrocyte__cerebral_cortex_donor1CNhs1086411235116D2	brain_adult_donor1CNhs1179610084102B3	brain_adult_pool1CNhs1061710012101C3	brain_fetal_pool1CNhs1179710085102B4	breast_adult_donor1CNhs1179210080102A8	cerebellum__adult_donor10196CNhs1379910173103C2	cerebellum_adult_donor10252CNhs1232310166103B4	cerebellum_newborn_donor10223CNhs1407510357105E6	...	thalamus__adult_donor10196CNhs1379410168103B6	thalamus_adult_donor10252CNhs1231410154103A1	thalamus_adult_donor10258_tech_rep1CNhs1422310370105G1	thalamus_adult_donor10258_tech_rep2CNhs1455110370105G1	thalamus_newborn_donor10223CNhs1408410366105F6	throat_fetal_donor1CNhs1177010061101H7	thyroid_fetal_donor1CNhs1176910060101H6	tongue_epidermis_fungiform_papillae_donor1CNhs1346010288104F9	umbilical_cord_fetal_donor1CNhs1176510057101H3	uterus_fetal_donor1CNhs1176310055101H1
1	uniprot:Q96JB6	0.12	11.45	0	0	0	2.17	0	0.22	1.03	...	5.8	0.31	5.65	2.99	0	1.19	1.01	2.58	7.04	4.48
6	uniprot:Q8N2H3	7.51	6.3	3.88	3.71	2	5.07	1.53	1.99	6.72	...	4.15	7.34	4.23	3.84	4.28	15.24	11.92	7.74	7.04	15.87
7	uniprot:Q8N2H3	3.69	3.43	1.94	0.65	0	0	1.53	0.55	1.55	...	0.83	2.69	2.82	1.92	0	3.33	3.03	2.58	2.35	5.29
14	uniprot:Q92902,uniprot:Q658M9,uniprot:Q8WXE5	35.58	20.61	30.05	21.71	19.96	31.9	13.73	20.79	21.72	...	15.75	26.89	24	26.24	12.83	19.76	22.03	12.9	32.85	17.09
23	uniprot:Q8WWQ2	0	0	0	1.02	0	0	0	2.99	0	...	5.8	8.38	4.23	8.96	0.71	0.71	0	0	7.04	5.7

	Agency	num_complaints
0	3-1-1	22029
1	ACS	2
2	AJC	2
3	ART	3
4	CAU	7

	interval	Complaints / interval
0	2003-02-24 09:15:00	1
1	2003-02-24 09:30:00	2
2	2003-02-24 09:45:00	2
3	2003-02-24 10:00:00	2
4	2003-02-24 10:15:00	1

	interval	Complaints / interval
495	2003-03-13 07:30:00	2
496	2003-03-13 08:45:00	1
497	2003-03-13 09:00:00	1
498	2003-03-13 09:15:00	1
499	2003-03-13 09:30:00	2

Plotly graphs with LaTeX

IPython supports lovely Latex typesetting through MathJax:

And Plotly has jumped on the boat! Plotly graphs now process Latex markup. Below are some examples.Questions? Issues? jack [at] plot [dot] ly

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Example 1: Taylor Series for Sine

Example 2: Colors, Size, and Opacity

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2. Make Contour graph object¶

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4. Make a figue object and plot!¶

To learn more about Plotly's Python API¶

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Notebook styling ideas

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Notebook styling ideas

Online Dashboards with Excel, Python, & Plotly¶

Building Interactive Graphs at the Push of an (Excel) Button Using Plot.ly and xlwings¶

In Short... How you can go from this:¶

To this:¶

Why we are working on this:¶

Covered in this notebook¶

Step 1 Connect to your data in Excel using xlwings¶

Step 2 Clean and prepare your data for plotting using Pandas¶

Step 3 Plot your data with plotly.¶

Step 4 - Running your python code directly from Excel¶

Step 5 - Sharing your work and Collaborating with Others¶

And Plotly has jumped on the boat! Plotly graphs now process Latex markup. Below are some examples.

Questions? Issues? jack [at] plot [dot] ly