def get_dataframe_and_axes():
''' arbitrary data for now '''
from bokeh.sampledata.unemployment1948 import data
data['Year'] = data['Year'].astype(str)
data = data.set_index('Year')
data.drop('Annual', axis=1, inplace=True)
data.columns.name = 'Month'
years = list(data.index)
months = list(data.columns)
# reshape to 1D array or rates with a month and year for each row.
df = pd.DataFrame(data.stack(), columns=['rate']).reset_index()
return df, years, months
from math import pi
from bokeh.models import HoverTool
from bokeh.plotting import ColumnDataSource, figure, show, output_file
from bokeh.sampledata.unemployment1948 import data
data['Year'] = [str(x) for x in data['Year']]
years = list(data['Year'])
months = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
data = data.set_index('Year')
# this is the colormap from the original NYTimes plot
colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce",
"#ddb7b1", "#cc7878", "#933b41", "#550b1d"]
# Set up the data for plotting. We will need to have values for every
# pair of year/month names. Map the rate to a color.
month = []
year = []
color = []
rate = []
for y in years:
for m in months:
month.append(m)
year.append(y)
monthly_rate = data[m][y]
rate.append(monthly_rate)
color.append(colors[min(int(monthly_rate)-2, 8)])
from math import pi
import pandas as pd
from bokeh.io import show
from bokeh.models import LinearColorMapper, BasicTicker, PrintfTickFormatter, ColorBar
from bokeh.plotting import figure
from bokeh.sampledata.unemployment1948 import data
data['Year'] = data['Year'].astype(str)
data = data.set_index('Year')
data.drop('Annual', axis=1, inplace=True)
data.columns.name = 'Month'
years = list(data.index)
months = list(data.columns)
# reshape to 1D array or rates with a month and year for each row.
df = pd.DataFrame(data.stack(), columns=['rate']).reset_index()
# this is the colormap from the original NYTimes plot
colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"]
mapper = LinearColorMapper(palette=colors, low=df.rate.min(), high=df.rate.max())
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
p = figure(title="US Unemployment ({0} - {1})".format(years[0], years[-1]),
x_range=years, y_range=list(reversed(months)),
x_axis_location="above", plot_width=900, plot_height=400,
tools=TOOLS, toolbar_location='below',
tooltips=[('date', '@Month @Year'), ('rate', '@rate%')])
from math import pi
from bokeh.models import HoverTool
from bokeh.plotting import ColumnDataSource, figure, show, output_file
from bokeh.sampledata.unemployment1948 import data
data["Year"] = [str(x) for x in data["Year"]]
years = list(data["Year"])
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
data = data.set_index("Year")
# this is the colormap from the original NYTimes plot
colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"]
# Set up the data for plotting. We will need to have values for every
# pair of year/month names. Map the rate to a color.
month = []
year = []
color = []
rate = []
for y in years:
for m in months:
month.append(m)
year.append(y)
monthly_rate = data[m][y]
rate.append(monthly_rate)
color.append(colors[min(int(monthly_rate) - 2, 8)])
source = ColumnDataSource(data=dict(month=month, year=year, color=color, rate=rate))
import cudf
import pandas as pd
from bokeh.sampledata.unemployment1948 import data
data["Year"] = data["Year"].astype(str)
data = data.set_index("Year")
data.drop("Annual", axis=1, inplace=True)
data.columns.name = "Month"
years = list(data.index)
months = list(data.columns)
# reshape to 1D array or rates with a month and year for each row.
df = pd.DataFrame(data.stack(), columns=["rate"]).reset_index()
df["Month"] = pd.to_datetime(df.Month, format="%b").dt.month
df["Year"] = df["Year"].astype("float64")
df["Month"] = df["Month"].astype("float64")
df = cudf.DataFrame.from_pandas(df)