import datapane as dp
dp.Blob.get(name='github_data', owner='khuyentran1401').download_df()

import matplotlib.pyplot as plt
top_followers = new_profile.sort_values(by='followers', axis=0, ascending=False)[:100]
fig = plt.figure()
plt.bar(top_followers.user_name,
       top_followers.followers)

fig = plt.figure()
plt.text(0.6, 0.7, "learning", size=40, rotation=20.,
         ha="center", va="center",
         bbox=dict(boxstyle="round",
                   ec=(1., 0.5, 0.5),
                   fc=(1., 0.8, 0.8),
                   )
         )
plt.text(0.55, 0.6, "machine", size=40, rotation=-25.,
         ha="right", va="top",
         bbox=dict(boxstyle="square",
                   ec=(1., 0.5, 0.5),
                   fc=(1., 0.8, 0.8),
                   )
         )
plt.show()

correlation = new_profile.corr()
fig, ax = plt.subplots()
im = plt.imshow(correlation)
ax.set_xticklabels(correlation.columns)
ax.set_yticklabels(correlation.columns)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
         rotation_mode="anchor")

correlation = new_profile.corr()
sns.heatmap(correlation, annot=True)

sns.set(style="darkgrid")
titanic = sns.load_dataset("titanic")
ax = sns.countplot(x="class", data=titanic)

import plotly.express as px
fig = px.scatter(new_profile[:100],
          x='followers',
          y='total_stars',
          color='forks',
          size='contribution')
fig.show()

import plotly.express as px
top_followers = new_profile.sort_values(by='followers', axis=0, ascending=False)[:100]
fig = px.bar(top_followers,
             x='user_name',
             y='followers',
            )
fig.show()

import plotly.express as px
import datapane as dp
location_df = dp.Blob.get(name='location_df', owner='khuyentran1401').download_df()
m = px.scatter_geo(location_df, lat='latitude', lon='longitude',
                 color='total_stars', size='forks',
                 hover_data=['user_name','followers'],
                 title='Locations of Top Users')
m.show()

import seaborn as sns
import altair as alt
titanic = sns.load_dataset("titanic")
alt.Chart(titanic).mark_bar().encode(
    alt.X('class'),
    y='count()'
)

hireable = alt.Chart(titanic).mark_bar().encode(
    x='sex:N',
    y='mean_age:Q'
).transform_aggregate(
    mean_age='mean(age)',
    groupby=['sex'])
hireable

brush = alt.selection(type='interval')
points = alt.Chart(titanic).mark_point().encode(
    x='age:Q',
    y='fare:Q',
    color=alt.condition(brush, 'class:N', alt.value('lightgray'))
).add_selection(
    brush
)
bars = alt.Chart(titanic).mark_bar().encode(
    y='class:N',
    color='class:N',
    x = 'count(class):Q'
).transform_filter(
    brush
)
points & bars

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
x = [1, 2, 3, 4, 5]
y = [2, 5, 8, 2, 7]
for x,y in zip(x,y):
    ax.add_patch(plt.Circle((x, y), 0.5, edgecolor = "#f03b20",facecolor='#9ebcda', alpha=0.8))
#Use adjustable='box-forced' to make the plot area square-shaped as well.
ax.set_aspect('equal', adjustable='datalim')
ax.set_xbound(3, 4)
ax.plot()   #Causes an autoscale update.
plt.show()

from bokeh.io import output_file, show
from bokeh.models import Circle
from bokeh.plotting import figure
reset_output()
output_notebook()
plot = figure(plot_width=400, plot_height=400, tools="tap", title="Select a circle")
renderer = plot.circle([1, 2, 3, 4, 5], [2, 5, 8, 2, 7], size=50)
selected_circle = Circle(fill_alpha=1, fill_color="firebrick", line_color=None)
nonselected_circle = Circle(fill_alpha=0.2, fill_color="blue", line_color="firebrick")
renderer.selection_glyph = selected_circle
renderer.nonselection_glyph = nonselected_circle
show(plot)

from bokeh.layouts import gridplot, row
from bokeh.models import ColumnDataSource
reset_output()
output_notebook()
source = ColumnDataSource(new_profile)
TOOLS = "box_select,lasso_select,help"
TOOLTIPS = [('user', '@user_name'),
            ('followers', '@followers'),
            ('following', '@following'),
            ('forks', '@forks'),
            ('contribution', '@contribution')]
s1 = figure(tooltips=TOOLTIPS, plot_width=300, plot_height=300, title=None, tools=TOOLS)
s1.circle(x='followers', y='following', source=source)
s2 = figure(tooltips=TOOLTIPS, plot_width=300, plot_height=300, title=None, tools=TOOLS)
s2.circle(x='followers', y='forks', source=source)
s3 = figure(tooltips=TOOLTIPS, plot_width=300, plot_height=300, title=None, tools=TOOLS)
s3.circle(x='followers', y='contribution', source=source)
p = gridplot([[s1,s2,s3]])
show(p)

from bokeh.transform import factor_cmap
from bokeh.palettes import Spectral6
p = figure(x_range=list(titanic_groupby['class']))
p.vbar(x='class', top='survived', source = titanic_groupby,
      fill_color=factor_cmap('class', palette=Spectral6, factors=list(titanic_groupby['class'])
      ))
show(p)

from bokeh.transform import factor_cmap
from bokeh.palettes import Spectral6
p = figure(x_range=list(titanic_groupby['class']))
p.vbar(x='class', top='survived', width=0.9, source = titanic_groupby,
      fill_color=factor_cmap('class', palette=Spectral6, factors=list(titanic_groupby['class'])
      ))
show(p)

import folium
# Load data
location_df = dp.Blob.get(name='location_df', owner='khuyentran1401').download_df()
# Save latitudes, longitudes, and locations' names in a list
lats = location_df['latitude']
lons = location_df['longitude']
names = location_df['location']
# Create a map with an initial location
m = folium.Map(location=[lats[0], lons[0]])
for lat, lon, name in zip(lats, lons, names):
    # Create marker with other locations
    folium.Marker(location=[lat, lon],
                  popup= name,
                 icon=folium.Icon(color='green')
).add_to(m)
m

# Code to generate map here
#....
# Enable adding more locations in the map
m = m.add_child(folium.ClickForMarker(popup='Potential Location'))

from folium.plugins import HeatMap
m = folium.Map(location=[lats[0], lons[0]])
HeatMap(data=location_df[['latitude', 'longitude', 'total_stars']]).add_to(m)