Live Programming and Computing with Python, R, Sage, Octave, Maxima, Singular, Gap, GP, HTML & Macaulay2

[Eli]

Output HTML figure with Plotly (See more options):

Code: [Select all] [Expand/Collapse]

1. from plotly.offline import plot
2. import plotly.graph_objects as go
3.  
4. trace0 = go.Scatter(
5.     x=[1, 2, 3, 4],
6.     y=[10, 15, 13, 17]
7. )
8. trace1 = go.Scatter(
9.     x=[1, 2, 3, 4],
10.     y=[16, 5, 11, 9]
11. )
12. data = [trace0, trace1]
13.  
14. plot(data)

Code: [Select all] [Expand/Collapse]

1. import plotly.graph_objects as go
2. import numpy as np
3. from plotly.offline import plot
4. np.random.seed(1)
5.  
6. N = 100
7. x = np.random.rand(N)
8. y = np.random.rand(N)
9. colors = np.random.rand(N)
10. sz = np.random.rand(N) * 30
11.  
12. fig = go.Figure()
13. fig.add_trace(go.Scatter(
14.     x=x,
15.     y=y,
16.     mode="markers",
17.     marker=go.scatter.Marker(
18.         size=sz,
19.         color=colors,
20.         opacity=0.6,
21.         colorscale="Viridis"
22.     )
23. ))
24.  
25. plot(fig)

[Joseph Bundala]

Massive development in this forum, congrats guys.

[Eli]

The code below taken from here intends to test xarray, netCDF4, and how to project heat (outgoing long wave radiation) data on a spherical map. Check the netCDF4 datasets from here (details are ignored). The resulting output is an animated sphere.

This code uses cartopy instead of Basemap

In this code, we have imported cartopy.feature and used its COASTLINE and BORDERS attributes to retrieve the coastline and country borders, respectively. We have also made the necessary changes in the get_coastline_traces and get_country_traces functions to use these attributes.

Code: [Select all] [Expand/Collapse]

1. import plotly
2. print(plotly.__version__)
3.  
4. import chart_studio.plotly as py
5. from plotly.graph_objs import *
6. from plotly.offline import plot
7.  
8. import numpy as np          
9. import cartopy.crs as ccrs
10. import cartopy.feature as cfeature
11. import netCDF4 as nc
12. from netCDF4 import *
13. import urllib.request
14.  
15. urllib.request.urlretrieve("https://www.dropbox.com/s/h2tbnnf972i9ai8/compday.KsNOdsJTz1.nc?dl=1", "compday.KsNOdsJTz1.nc")
16.  
17. with nc.Dataset("./compday.KsNOdsJTz1.nc", 'r') as f:
18.     lon = f.variables['lon'][::]
19.     lat = f.variables['lat'][::-1]
20.     olr = f.variables['olr'][0,::-1,:]
21.  
22. tmp_lon = np.array([lon[n]-360 if l>=180 else lon[n]
23.                    for n,l in enumerate(lon)])
24.  
25. i_east, = np.where(tmp_lon>=0)
26. i_west, = np.where(tmp_lon<0)  
27. lon = np.hstack((tmp_lon[i_west], tmp_lon[i_east]))
28.  
29. olr_ground = np.array(olr)
30. olr = np.hstack((olr_ground[:,i_west], olr_ground[:,i_east]))
31.  
32. trace1 = Contour(
33.     z=olr,
34.     x=lon,
35.     y=lat,
36.     colorscale= [[0.0, '#313695'], [0.07692307692307693, '#3a67af'], [0.15384615384615385, '#5994c5'], [0.23076923076923078, '#84bbd8'], [0.3076923076923077, '#afdbea'], [0.38461538461538464, '#d8eff5'], [0.46153846153846156, '#d6ffe1'], [0.5384615384615384, '#fef4ac'], [0.6153846153846154, '#fed987'], [0.6923076923076923, '#fdb264'], [0.7692307692307693, '#f78249'], [0.8461538461538461, '#e75435'], [0.9230769230769231, '#cc2727'], [1.0, '#a50026']],
37.     zauto=False,
38.     zmin=-20,
39.     zmax=20,
40.  
41.     colorbar= {
42.         "borderwidth": 0,
43.         "outlinewidth": 0,
44.         "thickness": 15,
45.         "tickfont": {"size": 14},
46.         "title": "W/m²"
47.     },
48.  
49.     contours= {
50.         "end": 20,
51.         "showlines": False,
52.         "size": 2,
53.         "start": -20
54.     }
55. )    
56.  
57. m = ccrs.PlateCarree()
58.  
59. def make_scatter(x,y):
60.     return Scatter(
61.         x=x,
62.         y=y,
63.         mode='lines',
64.         line=Line(color="black"),
65.         name=' '
66.     )
67.  
68. def polygons_to_traces(poly_paths, N_poly):
69.     data = dict(
70.         x=[],
71.         y=[],
72.         mode='lines',
73.         line=Line(color="black"),
74.         name=' '
75.     )
76.  
77.     for i_poly in range(N_poly):
78.         poly_path = poly_paths[i_poly]
79.  
80.         coords_cc = np.array(
81.             [(vertex[0], vertex[1])
82.              for (vertex, code) in poly_path.iter_segments(simplify=False)]
83.         )
84.  
85.         lon_cc, lat_cc = poly_path.boundary.xy
86.  
87.         data['x'] = data['x'] + lon_cc.tolist() + [np.nan]
88.         data['y'] = data['y'] + lat_cc.tolist() + [np.nan]
89.  
90.     return [data]
91.  
92. def get_coastline_traces():
93.     poly_paths = cfeature.COASTLINE.geometries()
94.     N_poly = 1  # only single coastline path
95.     return polygons_to_traces(poly_paths, N_poly)
96.  
97. def get_country_traces():
98.     poly_paths = cfeature.BORDERS.geometries()
99.     N_poly = len(poly_paths)
100.     return polygons_to_traces(poly_paths, N_poly)
101.  
102. traces_cc = get_coastline_traces() + get_country_traces()
103.  
104. data = Data([trace1] + traces_cc)
105.  
106. title = u"Outgoing Longwave Radiation Anomalies<br>"
107. anno_text = ""
108.  
109. axis_style = dict(
110.     zeroline=False,
111.     showline=False,
112.     showgrid=False,
113.     ticks='',
114.     showticklabels=False,
115. )
116.  
117. layout = Layout(
118.     title=title,
119.     showlegend=False,
120.     hovermode="closest",        
121.     xaxis=XAxis(
122.         axis_style,
123.         range=[lon[0],lon[-1]]  
124.     ),
125.     yaxis=YAxis(
126.         axis_style,
127.     ),
128.     annotations=Annotations([
129.         Annotation(
130.             text=anno_text,
131.             xref='paper',
132.             yref='paper',
133.             x=0,
134.             y=1,
135.             yanchor='bottom',
136.             showarrow=False
137.         )
138.     ]),
139.     autosize=False,
140.     width=1200,
141.     height=800,
142. )
143.  
144. fig = Figure(data=data, layout=layout)
145. plot(fig, filename="NCEP_OLR.html")

This code uses Basemap

Code: [Select all] [Expand/Collapse]

1. import chart_studio.plotly  as py
2. from plotly.offline import plot
3. import numpy as np          
4. from scipy.io import netcdf  
5. from mpl_toolkits.basemap import Basemap
6. import warnings
7.  
8. import netCDF4 as nc
9. from netCDF4 import *
10. import urllib.request
11. #import xarray as xr
12.  
13. urllib.request.urlretrieve("https://www.dropbox.com/s/h2tbnnf972i9ai8/compday.KsNOdsJTz1.nc?dl=1", "compday.KsNOdsJTz1.nc")
14.  
15. #Read data from a netCDF file:
16. with warnings.catch_warnings():
17.     warnings.simplefilter("ignore")
18.     #with xr.open_dataset('./olr-daily_v01r02-preliminary_20200101_20201231.nc') as f:
19.     with nc.Dataset('./compday.KsNOdsJTz1.nc', 'r') as f:
20.         lon = f.variables['lon'][::]       # copy longitude as list
21.         lat = f.variables['lat'][::-1]     # invert the latitude vector -> South to North
22.         olr = f.variables['olr'][0,::-1,:] # olr= outgoing longwave radiation
23.     #f.fp  
24.  
25. # Shift 'lon' from [0,360] to [-180,180]
26. tmp_lon = np.array([lon[n]-360 if l>=180 else lon[n]
27.                    for n,l in enumerate(lon)])  # => [0,180]U[-180,2.5]
28.  
29. i_east, = np.where(tmp_lon>=0)  # indices of east lon
30. i_west, = np.where(tmp_lon<0)   # indices of west lon
31. lon = np.hstack((tmp_lon[i_west], tmp_lon[i_east]))  # stack the 2 halves
32.  
33. # Correspondingly, shift the olr array
34. olr_ground = np.array(olr)
35. olr = np.hstack((olr_ground[:,i_west], olr_ground[:,i_east]))
36.  
37. #The lon and lat are the geographical coordinates on a planar map, and the array olr
38. #represents the scalar field defined on this map.
39. from numpy import pi, sin, cos
40.  
41. def degree2radians(degree):
42.     #convert degrees to radians
43.     return degree*pi/180
44.  
45. def mapping_map_to_sphere(lon, lat, radius=1):
46.     #this function maps the points of coords (lon, lat) to points onto the  sphere of radius radius
47.  
48.     lon=np.array(lon, dtype=np.float64)
49.     lat=np.array(lat, dtype=np.float64)
50.     lon=degree2radians(lon)
51.     lat=degree2radians(lat)
52.     xs=radius*cos(lon)*cos(lat)
53.     ys=radius*sin(lon)*cos(lat)
54.     zs=radius*sin(lat)
55.     return xs, ys, zs
56.  
57. #The next cell functions are modified versions of the functions with the
58. #same name from a notebook by the Plotly user @Dreamshot, https://plot.ly/~Dreamshot/9152.
59. #They extract the lon and lat coordinates of the points on the coastlines and country boundaries:
60.  
61. # Make shortcut to Basemap object,
62. # not specifying projection type for this example
63. m = Basemap()
64.  
65.  
66. # Functions converting coastline/country polygons to lon/lat traces
67. def polygons_to_traces(poly_paths, N_poly):
68.     '''
69.   pos arg 1. (poly_paths): paths to polygons
70.   pos arg 2. (N_poly): number of polygon to convert
71.   '''
72.     # init. plotting list
73.     lons=[]
74.     lats=[]
75.  
76.     for i_poly in range(N_poly):
77.         poly_path = poly_paths[i_poly]
78.  
79.         # get the Basemap coordinates of each segment
80.         coords_cc = np.array(
81.             [(vertex[0],vertex[1])
82.              for (vertex,code) in poly_path.iter_segments(simplify=False)]
83.         )
84.  
85.         # convert coordinates to lon/lat by 'inverting' the Basemap projection
86.         lon_cc, lat_cc = m(coords_cc[:,0],coords_cc[:,1], inverse=True)
87.  
88.  
89.         lats.extend(lat_cc.tolist()+[None])
90.         lons.extend(lon_cc.tolist()+[None])
91.  
92.  
93.     return lons, lats
94.  
95. # Function generating coastline lon/lat
96. def get_coastline_traces():
97.     poly_paths = m.drawcoastlines().get_paths() # coastline polygon paths
98.     N_poly = 91  # use only the 91st biggest coastlines (i.e. no rivers)
99.     cc_lons, cc_lats= polygons_to_traces(poly_paths, N_poly)
100.     return cc_lons, cc_lats
101.  
102. # Function generating country lon/lat
103. def get_country_traces():
104.     poly_paths = m.drawcountries().get_paths() # country polygon paths
105.     N_poly = len(poly_paths)  # use all countries
106.     country_lons, country_lats= polygons_to_traces(poly_paths, N_poly)
107.     return country_lons, country_lats
108.  
109. # Get list of of coastline, country, and state lon/lat
110.  
111. cc_lons, cc_lats=get_coastline_traces()
112. country_lons, country_lats=get_country_traces()
113.  
114. #concatenate the lon/lat for coastlines and country boundaries:
115. lons=cc_lons+[None]+country_lons
116. lats=cc_lats+[None]+country_lats
117.  
118. #Map them onto the sphere:
119.  
120. xs, ys, zs=mapping_map_to_sphere(lons, lats, radius=1.01)# here the radius is slightly greater than 1
121.                                                          #to ensure lines visibility; otherwise (with radius=1)
122.                                                          # some lines are hidden by contours colors
123.  
124. boundaries=dict(type='scatter3d',
125.                x=xs,
126.                y=ys,
127.                z=zs,
128.                mode='lines',
129.                line=dict(color='black', width=1)
130.               )
131.  
132.  
133. #Define a Plotly colorscale:
134.  
135. colorscale=[[0.0, '#313695'],
136.  [0.07692307692307693, '#3a67af'],
137.  [0.15384615384615385, '#5994c5'],
138.  [0.23076923076923078, '#84bbd8'],
139.  [0.3076923076923077, '#afdbea'],
140.  [0.38461538461538464, '#d8eff5'],
141.  [0.46153846153846156, '#d6ffe1'],
142.  [0.5384615384615384, '#fef4ac'],
143.  [0.6153846153846154, '#fed987'],
144.  [0.6923076923076923, '#fdb264'],
145.  [0.7692307692307693, '#f78249'],
146.  [0.8461538461538461, '#e75435'],
147.  [0.9230769230769231, '#cc2727'],
148.  [1.0, '#a50026']]
149.  
150. #In order to plot the scalar field as a heatmap onto the sphere,
151. #we define the sphere as a surface colored according to the olr values.
152. #To ensure color continuity we extend the lon list with [180]
153. #(its last value was lon[-1]=177.5). In this way we can identify lon=-180 with lon=180.
154.  
155. clons=np.array(lon.tolist()+[180], dtype=np.float64)
156. clats=np.array(lat, dtype=np.float64)
157. clons, clats=np.meshgrid(clons, clats)
158.  
159. #Map the meshgrids clons, clats onto the sphere:
160.  
161. XS, YS, ZS=mapping_map_to_sphere(clons, clats)
162.  
163. #The sphere points are colormapped according to the values of the numpy
164. #array, OLR, that extends the array olr, to ensure color continuity:
165.  
166. nrows, ncolumns=clons.shape
167. OLR=np.zeros(clons.shape, dtype=np.float64)
168. OLR[:, :ncolumns-1]=np.copy(np.array(olr,  dtype=np.float64))
169. OLR[:, ncolumns-1]=np.copy(olr[:, 0])
170.  
171. #Define the text to be displayed on hover:
172.  
173. text=[['lon: '+'{:.2f}'.format(clons[i,j])+'<br>lat: '+'{:.2f}'.format(clats[i, j])+
174.         '<br>W: '+'{:.2f}'.format(OLR[i][j]) for j in range(ncolumns)] for i in range(nrows)]
175.  
176. sphere=dict(type='surface',
177.             x=XS,
178.             y=YS,
179.             z=ZS,
180.             colorscale=colorscale,
181.             surfacecolor=OLR,
182.             cmin=-20,
183.             cmax=20,
184.             colorbar=dict(thickness=20, len=0.75, ticklen=4, title= 'W/m²'),
185.             text=text,
186.             hoverinfo='text')
187.  
188. #Set the plot layout:
189.  
190. noaxis=dict(showbackground=False,
191.             showgrid=False,
192.             showline=False,
193.             showticklabels=False,
194.             ticks='',
195.             title='',
196.             zeroline=False)
197.  
198. layout3d=dict(title='Outgoing Longwave Radiation Anomalies<br>Mock Projection',
199.               font=dict(family='Balto', size=14),
200.               width=800,
201.               height=800,
202.               scene=dict(xaxis=noaxis,
203.                          yaxis=noaxis,
204.                          zaxis=noaxis,
205.                          aspectratio=dict(x=1,
206.                                           y=1,
207.                                           z=1),
208.                          camera=dict(eye=dict(x=1.15,
209.                                      y=1.15,
210.                                      z=1.15)
211.                                     )
212.             ),
213.             paper_bgcolor='rgba(235,235,235, 0.9)'  
214.            )
215.  
216. fig=dict(data=[sphere, boundaries], layout=layout3d)
217. #py.sign_in('empet', 'api_key')
218. #py.iplot(fig, filename='radiation-map2sphere')
219. plot(fig, filename='radiation-map2sphere')

Reproduced using the original data:

[Eli]

This code is taken from here. Test Plotly by running it directly from our forum, go here.

Code: [Select all] [Expand/Collapse]

1. # Get this figure: fig = py.get_figure("https://plotly.com/~jackp/8612/")
2. # Get this figure's data: data = py.get_figure("https://plotly.com/~jackp/8612/").get_data()
3. # Add data to this figure: py.plot(Data([Scatter(x=[1, 2], y=[2, 3])]), filename ="d3-globe", fileopt="extend")
4.  
5. # Get figure documentation: https://plotly.com/python/get-requests/
6. # Add data documentation: https://plotly.com/python/file-options/
7.  
8. # If you're using unicode in your file, you may need to specify the encoding.
9. # You can reproduce this figure in Python with the following code!
10.  
11. # Learn about API authentication here: https://plotly.com/python/getting-started
12. # Find your api_key here: https://plotly.com/settings/api
13.  
14. import chart_studio.plotly as py
15. from plotly.graph_objs import *
16. from plotly.offline import plot
17. #py.sign_in('TSSFL', 'VIrC8tjxOn2nwujbiwrk')
18. trace1 = {
19.   "line": {
20.     "color": "rgb(213,62,79)",
21.     "width": 2
22.   },
23.   "mode": "lines",
24.   "type": "scattergeo",
25.   "lat": [28.88868792, 28.68086614, 28.06371535, 27.05598738, 25.68830153, 24.00221423, 22.04895641, 19.8878768, 17.58463872, 15.2092249, 12.83381107, 10.530573, 8.36949339, 6.416235563, 4.730148269, 3.362462415, 2.354734444, 1.737583661, 1.529761881, 1.737583661, 2.354734444, 3.362462415, 4.730148269, 6.416235563, 8.36949339, 10.530573, 12.83381107, 15.2092249, 17.58463872, 19.8878768, 22.04895641, 24.00221423, 25.68830153, 27.05598738, 28.06371535, 28.68086614, 28.88868792, 28.68086614, 28.06371535, 27.05598738, 25.68830153, 24.00221423, 22.04895641, 19.8878768, 17.58463872, 15.2092249, 12.83381107, 10.530573, 8.36949339, 6.416235563, 4.730148269, 3.362462415, 2.354734444, 1.737583661, 1.529761881, 1.737583661, 2.354734444, 3.362462415, 4.730148269, 6.416235563, 8.36949339, 10.530573, 12.83381107, 15.2092249, 17.58463872, 19.8878768, 22.04895641, 24.00221423, 25.68830153, 27.05598738, 28.06371535, 28.68086614, 28.88868792, 28.68086614, 28.06371535, 27.05598738, 25.68830153, 24.00221423, 22.04895641, 19.8878768, 17.58463872, 15.2092249, 12.83381107, 10.530573, 8.36949339, 6.416235563, 4.730148269, 3.362462415, 2.354734444, 1.737583661, 1.529761881, 1.737583661, 2.354734444, 3.362462415, 4.730148269, 6.416235563, 8.36949339, 10.530573, 12.83381107, 15.2092249, 17.58463872, 19.8878768, 22.04895641, 24.00221423, 25.68830153, 27.05598738, 28.06371535, 28.68086614, 28.88868792, 28.68086614, 28.06371535, 27.05598738, 25.68830153, 24.00221423, 22.04895641, 19.8878768, 17.58463872, 15.2092249, 12.83381107, 10.530573, 8.36949339, 6.416235563, 4.730148269, 3.362462415, 2.354734444, 1.737583661, 1.529761881, 1.737583661, 2.354734444, 3.362462415, 4.730148269, 6.416235563, 8.36949339, 10.530573, 12.83381107, 15.2092249, 17.58463872, 19.8878768, 22.04895641, 24.00221423, 25.68830153, 27.05598738, 28.06371535, 28.68086614, 28.88868792],
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167. }
168. trace16 = {
169.   "line": {
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173.   "mode": "lines",
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177. }
178. trace17 = {
179.   "line": {
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182.   },
183.   "mode": "lines",
184.   "type": "scattergeo",
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187. }
188. trace18 = {
189.   "line": {
190.     "color": "rgb(50,136,189)",
191.     "width": 2
192.   },
193.   "mode": "lines",
194.   "type": "scattergeo",
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197. }
198. data = Data([trace1, trace2, trace3, trace4, trace5, trace6, trace7, trace8, trace9, trace10, trace11, trace12, trace13, trace14, trace15, trace16, trace17, trace18])
199. layout = {
200.   "geo": {
201.     "lataxis": {
202.       "showgrid": True,
203.       "gridcolor": "rgb(102, 102, 102)",
204.       "gridwidth": 0.5
205.     },
206.     "lonaxis": {
207.       "showgrid": True,
208.       "gridcolor": "rgb(102, 102, 102)",
209.       "gridwidth": 0.5
210.     },
211.     "showland": True,
212.     "lakecolor": "rgb(0, 255, 255)",
213.     "landcolor": "rgb(230, 145, 56)",
214.     "showlakes": True,
215.     "showocean": True,
216.     "oceancolor": "rgb(0, 255, 255)",
217.     "projection": {
218.       "type": "orthographic",
219.       "rotation": {
220.         "lat": 40,
221.         "lon": -100,
222.         "roll": 0
223.       }
224.     },
225.     "countrywidth": 0.5,
226.     "showcountries": True
227.   },
228.   "title": "Contour lines over globe<br>(Click and drag to rotate)",
229.   "showlegend": False
230. }
231. fig = Figure(data=data, layout=layout)
232. #plot_url = py.plot(fig)
233. plot(fig, filename="contour_lines.html")

Output:

[Eli]

Plotly templating of the Outgoing Longwave Radiation Anomalies map.

This code uses cartopy instead of the code below that uses Basemap:

In this code, we have imported cartopy.feature and used its COASTLINE and BORDERS attributes to retrieve the coastline and country borders, respectively. We have also made the necessary changes in the get_coastline_traces and get_country_traces functions to use these attributes.

Code: [Select all] [Expand/Collapse]

1. import plotly
2. print(plotly.__version__)
3.  
4. import chart_studio.plotly as py
5. from plotly.graph_objs import *
6. from plotly.offline import plot
7.  
8. import numpy as np          
9. import cartopy.crs as ccrs
10. import cartopy.feature as cfeature
11. import netCDF4 as nc
12. from netCDF4 import *
13. import urllib.request
14.  
15. urllib.request.urlretrieve("https://www.dropbox.com/s/h2tbnnf972i9ai8/compday.KsNOdsJTz1.nc?dl=1", "compday.KsNOdsJTz1.nc")
16.  
17. with nc.Dataset("./compday.KsNOdsJTz1.nc", 'r') as f:
18.     lon = f.variables['lon'][::]
19.     lat = f.variables['lat'][::-1]
20.     olr = f.variables['olr'][0,::-1,:]
21.  
22. tmp_lon = np.array([lon[n]-360 if l>=180 else lon[n]
23.                    for n,l in enumerate(lon)])
24.  
25. i_east, = np.where(tmp_lon>=0)
26. i_west, = np.where(tmp_lon<0)  
27. lon = np.hstack((tmp_lon[i_west], tmp_lon[i_east]))
28.  
29. olr_ground = np.array(olr)
30. olr = np.hstack((olr_ground[:,i_west], olr_ground[:,i_east]))
31.  
32. trace1 = Contour(
33.     z=olr,
34.     x=lon,
35.     y=lat,
36.     colorscale= [[0.0, '#313695'], [0.07692307692307693, '#3a67af'], [0.15384615384615385, '#5994c5'], [0.23076923076923078, '#84bbd8'], [0.3076923076923077, '#afdbea'], [0.38461538461538464, '#d8eff5'], [0.46153846153846156, '#d6ffe1'], [0.5384615384615384, '#fef4ac'], [0.6153846153846154, '#fed987'], [0.6923076923076923, '#fdb264'], [0.7692307692307693, '#f78249'], [0.8461538461538461, '#e75435'], [0.9230769230769231, '#cc2727'], [1.0, '#a50026']],
37.     zauto=False,
38.     zmin=-20,
39.     zmax=20,
40.  
41.     colorbar= {
42.         "borderwidth": 0,
43.         "outlinewidth": 0,
44.         "thickness": 15,
45.         "tickfont": {"size": 14},
46.         "title": "W/m²"
47.     },
48.  
49.     contours= {
50.         "end": 20,
51.         "showlines": False,
52.         "size": 2,
53.         "start": -20
54.     }
55. )    
56.  
57. m = ccrs.PlateCarree()
58.  
59. def make_scatter(x,y):
60.     return Scatter(
61.         x=x,
62.         y=y,
63.         mode='lines',
64.         line=Line(color="black"),
65.         name=' '
66.     )
67.  
68. def polygons_to_traces(poly_paths, N_poly):
69.     data = dict(
70.         x=[],
71.         y=[],
72.         mode='lines',
73.         line=Line(color="black"),
74.         name=' '
75.     )
76.  
77.     for i_poly in range(N_poly):
78.         poly_path = poly_paths[i_poly]
79.  
80.         coords_cc = np.array(
81.             [(vertex[0], vertex[1])
82.              for (vertex, code) in poly_path.iter_segments(simplify=False)]
83.         )
84.  
85.         lon_cc, lat_cc = poly_path.boundary.xy
86.  
87.         data['x'] = data['x'] + lon_cc.tolist() + [np.nan]
88.         data['y'] = data['y'] + lat_cc.tolist() + [np.nan]
89.  
90.     return [data]
91.  
92. def get_coastline_traces():
93.     poly_paths = cfeature.COASTLINE.geometries()
94.     N_poly = 1  # only single coastline path
95.     return polygons_to_traces(poly_paths, N_poly)
96.  
97. def get_country_traces():
98.     poly_paths = cfeature.BORDERS.geometries()
99.     N_poly = len(poly_paths)
100.     return polygons_to_traces(poly_paths, N_poly)
101.  
102. traces_cc = get_coastline_traces() + get_country_traces()
103.  
104. data = Data([trace1] + traces_cc)
105.  
106. title = u"Outgoing Longwave Radiation Anomalies<br>"
107. anno_text = ""
108.  
109. axis_style = dict(
110.     zeroline=False,
111.     showline=False,
112.     showgrid=False,
113.     ticks='',
114.     showticklabels=False,
115. )
116.  
117. layout = Layout(
118.     title=title,
119.     showlegend=False,
120.     hovermode="closest",        
121.     xaxis=XAxis(
122.         axis_style,
123.         range=[lon[0],lon[-1]]  
124.     ),
125.     yaxis=YAxis(
126.         axis_style,
127.     ),
128.     annotations=Annotations([
129.         Annotation(
130.             text=anno_text,
131.             xref='paper',
132.             yref='paper',
133.             x=0,
134.             y=1,
135.             yanchor='bottom',
136.             showarrow=False
137.         )
138.     ]),
139.     autosize=False,
140.     width=1200,
141.     height=800,
142. )
143.  
144. fig = Figure(data=data, layout=layout)
145. plot(fig, filename="NCEP_OLR.html")

This code uses Basemap

Code: [Select all] [Expand/Collapse]

1. import plotly
2. print(plotly.__version__)
3.  
4. import chart_studio.plotly as py
5. from plotly.graph_objs import *
6. from plotly.offline import plot
7.  
8. import numpy as np          
9. #from scipy.io import netcdf  
10.  
11. from mpl_toolkits.basemap import Basemap
12. import netCDF4 as nc
13. from netCDF4 import *
14. import urllib.request
15. #import xarray as xr
16.  
17. urllib.request.urlretrieve("https://www.dropbox.com/s/h2tbnnf972i9ai8/compday.KsNOdsJTz1.nc?dl=1", "compday.KsNOdsJTz1.nc")#your file from the NCEP reanalysis plotter
18.  
19. # Retrieve data from NetCDF file
20. with nc.Dataset("./compday.KsNOdsJTz1.nc", 'r') as f:
21.     lon = f.variables['lon'][::]    # copy as list
22.     lat = f.variables['lat'][::-1]  # invert the latitude vector -> South to North
23.     olr = f.variables['olr'][0,::-1,:]  # squeeze out the time dimension,
24.                                         # invert latitude index
25.  
26. # Shift 'lon' from [0,360] to [-180,180], make numpy array
27. tmp_lon = np.array([lon[n]-360 if l>=180 else lon[n]
28.                    for n,l in enumerate(lon)])  # => [0,180]U[-180,2.5]
29.  
30. i_east, = np.where(tmp_lon>=0)  # indices of east lon
31. i_west, = np.where(tmp_lon<0)   # indices of west lon
32. lon = np.hstack((tmp_lon[i_west], tmp_lon[i_east]))  # stack the 2 halves
33.  
34. # Correspondingly, shift the 'precip' array
35. olr_ground = np.array(olr)
36. olr = np.hstack((olr_ground[:,i_west], olr_ground[:,i_east]))
37.  
38. trace1 = Contour(
39.     z=olr,
40.     x=lon,
41.     y=lat,
42.     colorscale= [[0.0, '#313695'], [0.07692307692307693, '#3a67af'], [0.15384615384615385, '#5994c5'], [0.23076923076923078, '#84bbd8'], [0.3076923076923077, '#afdbea'], [0.38461538461538464, '#d8eff5'], [0.46153846153846156, '#d6ffe1'], [0.5384615384615384, '#fef4ac'], [0.6153846153846154, '#fed987'], [0.6923076923076923, '#fdb264'], [0.7692307692307693, '#f78249'], [0.8461538461538461, '#e75435'], [0.9230769230769231, '#cc2727'], [1.0, '#a50026']],
43.     zauto=False,  # custom contour levels
44.     zmin=-20,      # first contour level
45.     zmax=20,        # last contour level  => colorscale is centered about 0
46.  
47.  
48. colorbar= {
49.     "borderwidth": 0,
50.     "outlinewidth": 0,
51.     "thickness": 15,
52.     "tickfont": {"size": 14},
53.     "title": "W/m²"}, #gives your legend some units                                                                    
54.  
55. contours= {
56.     "end": 20,
57.     "showlines": False,
58.     "size": 2, #this is your contour interval
59.     "start": -20}    
60.  
61. )    
62.  
63. # Make shortcut to Basemap object,
64. # not specifying projection type for this example
65. m = Basemap()
66.  
67. # Make trace-generating function (return a Scatter object)
68. def make_scatter(x,y):
69.     return Scatter(
70.         x=x,
71.         y=y,
72.         mode='lines',
73.         line=Line(color="black"),
74.         name=' '  # no name on hover
75.     )
76.  
77. # Functions converting coastline/country polygons to lon/lat traces
78. def polygons_to_traces(poly_paths, N_poly):
79.     '''
80.   pos arg 1. (poly_paths): paths to polygons
81.   pos arg 2. (N_poly): number of polygon to convert
82.   '''
83.     # init. plotting list
84.     data = dict(
85.         x=[],
86.         y=[],
87.         mode='lines',
88.         line=Line(color="black"),
89.         name=' '
90.     )
91.  
92.     for i_poly in range(N_poly):
93.         poly_path = poly_paths[i_poly]
94.  
95.         # get the Basemap coordinates of each segment
96.         coords_cc = np.array(
97.             [(vertex[0],vertex[1])
98.              for (vertex,code) in poly_path.iter_segments(simplify=False)]
99.         )
100.  
101.         # convert coordinates to lon/lat by 'inverting' the Basemap projection
102.         lon_cc, lat_cc = m(coords_cc[:,0],coords_cc[:,1], inverse=True)
103.  
104.  
105.         # add plot.ly plotting options
106.         data['x'] = data['x'] + lon_cc.tolist() + [np.nan]
107.         data['y'] = data['y'] + lat_cc.tolist() + [np.nan]
108.  
109.         # traces.append(make_scatter(lon_cc,lat_cc))
110.  
111.     return [data]
112.  
113. # Function generating coastline lon/lat traces
114. def get_coastline_traces():
115.     poly_paths = m.drawcoastlines().get_paths() # coastline polygon paths
116.     N_poly = 91  # use only the 91st biggest coastlines (i.e. no rivers)
117.     return polygons_to_traces(poly_paths, N_poly)
118.  
119. # Function generating country lon/lat traces
120. def get_country_traces():
121.     poly_paths = m.drawcountries().get_paths() # country polygon paths
122.     N_poly = len(poly_paths)  # use all countries
123.     return polygons_to_traces(poly_paths, N_poly)
124.  
125. # Get list of of coastline, country, and state lon/lat traces
126. traces_cc = get_coastline_traces()+get_country_traces()
127.  
128. data = Data([trace1]+traces_cc)
129.  
130. title = u"Outgoing Longwave Radiation Anomalies<br>"
131. anno_text = ""
132. #anno_text = "Data courtesy of \
133. #<a href='http://www.esrl.noaa.gov/psd/data/composites/day/'>\
134. #NOAA Earth System Research Laboratory</a>"
135.  
136. axis_style = dict(
137.     zeroline=False,
138.     showline=False,
139.     showgrid=False,
140.     ticks='',
141.     showticklabels=False,
142. )
143.  
144. layout = Layout(
145.     title=title,
146.     showlegend=False,
147.     hovermode="closest",        # highlight closest point on hover
148.     xaxis=XAxis(
149.         axis_style,
150.         range=[lon[0],lon[-1]]  # restrict y-axis to range of lon
151.     ),
152.     yaxis=YAxis(
153.         axis_style,
154.     ),
155.     annotations=Annotations([
156.         Annotation(
157.             text=anno_text,
158.             xref='paper',
159.             yref='paper',
160.             x=0,
161.             y=1,
162.             yanchor='bottom',
163.             showarrow=False
164.         )
165.     ]),
166.     autosize=False,
167.     width=1200,
168.     height=800,
169. )
170.  
171. fig = Figure(data=data, layout=layout)
172. #py.iplot(fig, filename="NCEP OLR", width=1000)
173. plot(fig, filename="NCEP_OLR.html")

Reproduced with original data:

[Eli]

An example code that uses SpiceyPy to locate the position of Cassini (the code is from here):

Code: [Select all] [Expand/Collapse]

1. #Below is an example that uses spiceypy to plot the position
2. #of the Cassini spacecraft relative to the barycenter of Saturn.
3. import numpy as np
4. import matplotlib.pyplot as plt
5. from mpl_toolkits.mplot3d import Axes3D
6.  
7. #First import spiceypy and test it out.
8.  
9. import spiceypy as spice
10.  
11. # Print out the toolkit version
12. print(spice.tkvrsn("TOOLKIT"))
13.  
14. #You will need to download the following kernels from the NAIF
15. #servers via the links provided. After the kernels have been
16. #downloaded to a common directory write a metakernel
17. #containing the file names for each downloaded kernel
18.  
19. import urllib.request
20.  
21. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/generic_kernels/lsk/a_old_versions/naif0009.tls", 'naif0009.tls')
22. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/CASSINI/kernels/sclk/cas00084.tsc", 'cas00084.tsc')
23. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/CASSINI/kernels/pck/cpck05Mar2004.tpc", 'cpck05Mar2004.tpc')  
24. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/CASSINI/kernels/fk/release.11/cas_v37.tf", 'cas_v37.tf')  
25. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/CASSINI/kernels/ck/04135_04171pc_psiv2.bc", '04135_04171pc_psiv2.bc')  
26. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/CASSINI/kernels/spk/030201AP_SK_SM546_T45.bsp", '030201AP_SK_SM546_T45.bsp')  
27. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/CASSINI/kernels/ik/release.11/cas_iss_v09.ti", 'cas_iss_v09.ti')    
28. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/CASSINI/kernels/spk/020514_SE_SAT105.bsp", '020514_SE_SAT105.bsp')
29. urllib.request.urlretrieve("https://naif.jpl.nasa.gov/pub/naif/CASSINI/kernels/spk/981005_PLTEPH-DE405S.bsp", '981005_PLTEPH-DE405S.bsp')
30.  
31. spice.furnsh("./naif0009.tls")  
32. #spice.furnsh("./cas00084.tsc")
33. spice.furnsh("./cpck05Mar2004.tpc")
34. spice.furnsh("./cas_v37.tf")
35. spice.furnsh("./04135_04171pc_psiv2.bc")
36. spice.furnsh("./030201AP_SK_SM546_T45.bsp")
37. #spice.furnsh("./cas_iss_v09.ti")
38. #spice.furnsh("./020514_SE_SAT105.bsp")
39. #spice.furnsh("./981005_PLTEPH-DE405S.bsp")
40.  
41. step = 4000
42. # we are going to get positions between these two dates
43. utc = ['Jun 20, 2004', 'Dec 1, 2005']
44.  
45. #Get et values one and two, we could vectorize str2et
46. etOne = spice.str2et(utc[0])
47. etTwo = spice.str2et(utc[1])
48. print("ET One: {}, ET Two: {}".format(etOne, etTwo))
49.  
50. #Get times
51. times = [x*(etTwo-etOne)/step + etOne for x in range(step)]
52.  
53. #Check first few times:
54. print(times[0:3])
55.  
56. #Check the documentation on spkpos before continuing
57. help(spice.spkpos)
58.  
59. #Run spkpos as a vectorized function
60. positions, lightTimes = spice.spkpos('Cassini', times, 'J2000', 'NONE', 'SATURN BARYCENTER')
61.  
62. # Positions is a 3xN vector of XYZ positions
63. print("Positions: ")
64. print(positions[0])
65.  
66. # Light times is a N vector of time
67. print("Light Times: ")
68. print(lightTimes[0])
69.  
70. # Clean up the kernels
71. spice.kclear()
72.  
73. #We will use matplotlib’s 3D plotting to visualize Cassini’s coordinates.
74. #We first convert the positions list to a 2D numpy array for easier indexing in the plot.
75.  
76. positions = positions.T # positions is shaped (4000, 3), let's transpose to (3, 4000) for easier indexing
77. fig = plt.figure(figsize=(9, 9))
78. ax  = fig.add_subplot(111, projection='3d')
79. ax.plot(positions[0], positions[1], positions[2])
80. plt.title('SpiceyPy Cassini Position Example from Jun 20, 2004 to Dec 1, 2005')
81. plt.show()

[Eli]

Folium is a Python library used for visualizing geospatial data. It is easy to use and yet a powerful library. Folium is a Python wrapper for Leaflet. js which is a leading open-source JavaScript library for plotting interactive maps.

Test Folium with the following codes (taken from this Jupyter notebook, see this kaggle post as well):

Code: [Select all] [Expand/Collapse]

1. import numpy as np
2. import folium
3. from folium import plugins
4.  
5. m = folium.Map(location=[35.68159659061569, 139.76451516151428], zoom_start=16)
6.  
7. # Lon, Lat order.
8. lines = [
9.     {
10.         "coordinates": [
11.             [139.76451516151428, 35.68159659061569],
12.             [139.75964426994324, 35.682590062684206],
13.         ],
14.         "dates": ["2017-06-02T00:00:00", "2017-06-02T00:10:00"],
15.         "color": "red",
16.     },
17.     {
18.         "coordinates": [
19.             [139.75964426994324, 35.682590062684206],
20.             [139.7575843334198, 35.679505030038506],
21.         ],
22.         "dates": ["2017-06-02T00:10:00", "2017-06-02T00:20:00"],
23.         "color": "blue",
24.     },
25.     {
26.         "coordinates": [
27.             [139.7575843334198, 35.679505030038506],
28.             [139.76337790489197, 35.678040905014065],
29.         ],
30.         "dates": ["2017-06-02T00:20:00", "2017-06-02T00:30:00"],
31.         "color": "green",
32.         "weight": 15,
33.     },
34.     {
35.         "coordinates": [
36.             [139.76337790489197, 35.678040905014065],
37.             [139.76451516151428, 35.68159659061569],
38.         ],
39.         "dates": ["2017-06-02T00:30:00", "2017-06-02T00:40:00"],
40.         "color": "#FFFFFF",
41.     },
42. ]
43.  
44. features = [
45.     {
46.         "type": "Feature",
47.         "geometry": {
48.             "type": "LineString",
49.             "coordinates": line["coordinates"],
50.         },
51.         "properties": {
52.             "times": line["dates"],
53.             "style": {
54.                 "color": line["color"],
55.                 "weight": line["weight"] if "weight" in line else 5,
56.             },
57.         },
58.     }
59.     for line in lines
60. ]
61.  
62. plugins.TimestampedGeoJson(
63.     {
64.         "type": "FeatureCollection",
65.         "features": features,
66.     },
67.     period="PT1M",
68.     add_last_point=True,
69. ).add_to(m)
70.  
71. display(m)

Code: [Select all] [Expand/Collapse]

1. import folium
2. from folium import plugins
3. import numpy as np
4.  
5. N = 100
6. data = np.array(
7.     [
8.         np.random.uniform(low=35, high=60, size=N),  # Random latitudes in Europe.
9.         np.random.uniform(low=-12, high=30, size=N),  # Random longitudes in Europe.
10.     ]
11. ).T
12. popups = [str(i) for i in range(N)]  # Popups texts are simple numbers.
13.  
14. m = folium.Map([45, 3], zoom_start=4)
15.  
16. plugins.MarkerCluster(data, popups=popups).add_to(m)
17.  
18. display(m)

Code: [Select all] [Expand/Collapse]

1. import folium
2. from folium import plugins
3.  
4. m = folium.Map([41.97, 2.81])
5.  
6. plugins.LocateControl().add_to(m)
7.  
8. # If you want get the user device positon after load the map, set auto_start=True
9. plugins.LocateControl(auto_start=True).add_to(m)
10. display(m)

Code: [Select all] [Expand/Collapse]

1. import folium
2. from folium import plugins
3.  
4. table = """\
5. <table style=\'width:100%\'>
6.  <tr>
7.    <th>Firstname</th>
8.    <th>Lastname</th>
9.    <th>Age</th>
10.  </tr>
11.  <tr>
12.    <td>Jill</td>
13.    <td>Smith</td>
14.    <td>50</td>
15.  </tr>
16.  <tr>
17.    <td>Eve</td>
18.    <td>Jackson</td>
19.    <td>94</td>
20.  </tr>
21. </table>
22. """
23.  
24. points = [
25.     {
26.         "time": "2017-06-02",
27.         "popup": "<h1>address1</h1>",
28.         "coordinates": [-2.548828, 51.467697],
29.     },
30.     {
31.         "time": "2017-07-02",
32.         "popup": "<h2 style='color:blue;'>address2<h2>",
33.         "coordinates": [-0.087891, 51.536086],
34.     },
35.     {
36.         "time": "2017-08-02",
37.         "popup": "<h2 style='color:orange;'>address3<h2>",
38.         "coordinates": [-6.240234, 53.383328],
39.     },
40.     {
41.         "time": "2017-09-02",
42.         "popup": "<h2 style='color:green;'>address4<h2>",
43.         "coordinates": [-1.40625, 60.261617],
44.     },
45.     {"time": "2017-10-02", "popup": table, "coordinates": [-1.516113, 53.800651]},
46. ]
47.  
48. features = [
49.     {
50.         "type": "Feature",
51.         "geometry": {
52.             "type": "Point",
53.             "coordinates": point["coordinates"],
54.         },
55.         "properties": {
56.             "time": point["time"],
57.             "popup": point["popup"],
58.             "id": "house",
59.             "icon": "marker",
60.             "iconstyle": {
61.                 "iconUrl": "https://leafletjs.com/examples/geojson/baseball-marker.png",
62.                 "iconSize": [20, 20],
63.             },
64.         },
65.     }
66.     for point in points
67. ]
68.  
69. features.append(
70.     {
71.         "type": "Feature",
72.         "geometry": {
73.             "type": "LineString",
74.             "coordinates": [
75.                 [-2.548828, 51.467697],
76.                 [-0.087891, 51.536086],
77.                 [-6.240234, 53.383328],
78.                 [-1.40625, 60.261617],
79.                 [-1.516113, 53.800651],
80.             ],
81.         },
82.         "properties": {
83.             "popup": "Current address",
84.             "times": [
85.                 "2017-06-02",
86.                 "2017-07-02",
87.                 "2017-08-02",
88.                 "2017-09-02",
89.                 "2017-10-02",
90.             ],
91.             "icon": "circle",
92.             "iconstyle": {
93.                 "fillColor": "green",
94.                 "fillOpacity": 0.6,
95.                 "stroke": "false",
96.                 "radius": 13,
97.             },
98.             "style": {"weight": 0},
99.             "id": "man",
100.         },
101.     }
102. )
103.  
104. m = folium.Map(
105.     location=[56.096555, -3.64746],
106.     tiles="cartodbpositron",
107.     zoom_start=5,
108. )
109.  
110. plugins.TimestampedGeoJson(
111.     {"type": "FeatureCollection", "features": features},
112.     period="P1M",
113.     add_last_point=True,
114.     auto_play=False,
115.     loop=False,
116.     max_speed=1,
117.     loop_button=True,
118.     date_options="YYYY/MM/DD",
119.     time_slider_drag_update=True,
120.     duration="P2M",
121. ).add_to(m)
122.  
123. display(m)

Code: [Select all] [Expand/Collapse]

1. import folium
2. from folium import plugins
3.  
4. m = folium.Map(location=[0, 0], zoom_start=6)
5.  
6. fg = folium.FeatureGroup(name="groups")
7. m.add_child(fg)
8.  
9. g1 = plugins.FeatureGroupSubGroup(fg, "group1")
10. m.add_child(g1)
11.  
12. g2 = plugins.FeatureGroupSubGroup(fg, "group2")
13. m.add_child(g2)
14.  
15. folium.Marker([-1, -1]).add_to(g1)
16. folium.Marker([1, 1]).add_to(g1)
17.  
18. folium.Marker([-1, 1]).add_to(g2)
19. folium.Marker([1, -1]).add_to(g2)
20.  
21. folium.LayerControl(collapsed=False).add_to(m)
22.  
23. display(m)

[Eli]

openpyxl is a Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files. It was born from lack of existing library to read/write natively from Python the Office Open XML format. Test it with this example (see more here):

Code: [Select all] [Expand/Collapse]

1. from openpyxl import Workbook
2. wb = Workbook()
3.  
4. # grab the active worksheet
5. ws = wb.active
6.  
7. # Data can be assigned directly to cells
8. ws['A1'] = 42
9. ws['F10'] = 99
10.  
11. # Rows can also be appended
12. ws.append([1, 2, 3])
13.  
14. # Python types will automatically be converted
15. import datetime
16. ws['A2'] = datetime.datetime.now()
17.  
18. # Save the file
19. wb.save("Test_sheet.xlsx")
20.  
21. #Read a spreadsheet
22. import openpyxl
23. #from openpyxl import load_workbook
24. workbook = openpyxl.load_workbook("Test_sheet.xlsx") #Or load_workbook(filename="Test_sheet.xlsx")
25. print(workbook.sheetnames)
26. sheet = workbook.active
27. print(sheet)
28. print(sheet.title)
29.  
30. #Retrieve some values
31. print(sheet["A1"])
32.  
33. print(sheet["A1"].value)
34.  
35. print(sheet["F10"].value)

[Eli]

ConfigParser class implements a basic configuration language which provides a structure similar to what’s found in Microsoft Windows INI files. It is very useful for writing Python programs which can be customized by end users easily (See more examples here). Run this code here.

Code: [Select all] [Expand/Collapse]

1. import configparser
2. config = configparser.ConfigParser()
3.  
4. config['DEFAULT'] = {'ServerAliveInterval': '45', 'Compression': 'yes', 'CompressionLevel': '9'}
5. config['bitbucket.org'] = {}
6. config['bitbucket.org']['User'] = 'hg'
7. config['topsecret.server.com'] = {}
8. topsecret = config['topsecret.server.com']
9. topsecret['Port'] = '50022'     # mutates the parser
10. topsecret['ForwardX11'] = 'no'  # same here
11. config['DEFAULT']['ForwardX11'] = 'yes'
12. with open('example.ini', 'w') as configfile:
13.     config.write(configfile)
14.    
15. config.sections()
16.  
17. config.read('example.ini')
18. print(config.sections())
19.  
20. config['bitbucket.org']['User']
21. config['DEFAULT']['Compression']
22.  
23. topsecret = config['topsecret.server.com']
24. print(topsecret)
25. topsecret['ForwardX11']
26.  
27. print(topsecret['Port'])
28.  
29. for key in config['bitbucket.org']:  
30.     print(key)
31.  
32. config['bitbucket.org']['ForwardX11']

[Eli]

Here is a demo code (from here) that illustrates how to automate the boring excel report with OpenPyXL and Python. Run this code here.

Code: [Select all] [Expand/Collapse]

1. #Loading our Libraries
2. import pandas as pd
3.  
4. from openpyxl import load_workbook
5. from openpyxl.styles import Font
6. from openpyxl.chart import BarChart, Reference
7.  
8. #Loading our Data
9. df = pd.read_excel('https://github.com/datagy/pivot_table_pandas/raw/master/sample_pivot.xlsx', parse_dates=['Date'])
10. print(df.head())
11.  
12. #Testing Pivot Tables
13. filtered = df[df['Region'] == 'East']
14. quarterly_sales = pd.pivot_table(filtered, index = filtered['Date'].dt.quarter, columns = 'Type', values = 'Sales', aggfunc='sum')
15.  
16. print("Quarterly Sales Pivot Table:")
17. print(quarterly_sales.head())
18.  
19. #Creating an Excel Workbook
20. file_path = "./sample_pivot.xlsx" #Path to where you want your file saved
21. quarterly_sales.to_excel(file_path, sheet_name = 'Quarterly Sales', startrow=3)
22.  
23. #Loading the Workbook
24. wb = load_workbook(file_path)
25. sheet1 = wb['Quarterly Sales']
26.  
27. # Section 06 - Formatting the First Sheet
28. sheet1['A1'] = 'Quarterly Sales'
29. sheet1['A2'] = 'datagy.io'
30. sheet1['A4'] = 'Quarter'
31.  
32. sheet1['A1'].style = 'Title'
33. sheet1['A2'].style = 'Headline 2'
34.  
35. for i in range(5, 9):
36.     sheet1[f'B{i}'].style='Currency'
37.     sheet1[f'C{i}'].style='Currency'
38.     sheet1[f'D{i}'].style='Currency'
39.  
40. # Section 07 - Adding a Bar Chart
41. bar_chart = BarChart()
42. data = Reference(sheet1, min_col=2, max_col=4, min_row=4, max_row=8)
43. categories = Reference(sheet1, min_col=1, max_col=1, min_row=5, max_row=8)
44. bar_chart.add_data(data, titles_from_data=True)
45. bar_chart.set_categories(categories)
46. sheet1.add_chart(bar_chart, "F4")
47.  
48. bar_chart.title = 'Sales by Type'
49. bar_chart.style = 3
50. wb.save(filename = file_path)

The output is: