Plotting in Python

Nextjournal’s default Python environment comes with the matplotlib and plotly packages pre-installed. It also includes numpy and pandas for numeric data analysis. Simply import them to make them available.

The following examples show how to plot temperature predication data in various scenarios for the Chicago area based on a subset of the OpenNEX DCP30 dataset.

OpenNEX-chicago-climate.csv

Inspecting Data Frames

Inspecting their contents often comes handy when working with data frames. Simply returning a data frame will produce a table viewer that makes it easy to browse the data frame’s contents:

import pandas as pd
data = pd.read_csv(OpenNEX-chicago-climate.csv
)
data.head(30)

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Python

Matplotlib

Check out matplotlib’s documentation for more examples.

2.6s

Python

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt

# Read in the CSV
data = pd.read_csv(OpenNEX-chicago-climate.csv
)

# Specify categorical data
for col in ['Model', 'Scenario', 'Variable']:
    data[col] = data[col].astype('category')

# Coax date strings to beginning of year dates
data['Year'] = data['Date'] \
	.astype('datetime64') \
	.map(lambda d: "%d-01-01" % d.year) \
	.astype('datetime64')

# Convert temperatures from Kelvin to Celsius
data['Temperature'] = data['Value'] - 273.15

# Get max temperature and group by year and scenario
max_per_year = data.groupby(['Year', 'Scenario']).max().loc[:,['Temperature']]
groups = max_per_year.reset_index().set_index('Year').groupby('Scenario')

# Plot all groups and set up label for legend to use
fig, ax = plt.subplots()
for key, group in groups:
	ax.plot(group.index, group['Temperature'], label=key)

# Add legend to plot
plt.legend()

# Set axis labels and title
model = data.loc[1, 'Model']
ax.set(
  title="Maximum mean temperature for warmest month using model %s" % model,
  xlabel="Year",
  ylabel="Temperature [Celsius]"
)

# Returning the figure tells Nextjournal to display it
fig

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Python

Plotly

Check out plotly’s documentation for more examples.

2.7s

Python

import pandas as pd
import plotly.plotly as py
import plotly.graph_objs as go

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt

# Read in the CSV
data = pd.read_csv(OpenNEX-chicago-climate.csv
)

# Specify categorical data
for col in ['Model', 'Scenario', 'Variable']:
    data[col] = data[col].astype('category')

# Coax date strings to beginning of year dates
data['Year'] = data['Date'] \
	.astype('datetime64') \
	.map(lambda d: "%d-01-01" % d.year) \
	.astype('datetime64')

# Convert temperatures from Kelvin to Celsius
data['Temperature'] = data['Value'] - 273.15

# Get max temperature and group by year and scenario
max_per_year = data.groupby(['Year', 'Scenario']).max().loc[:,['Temperature']]
groups = max_per_year.reset_index().set_index('Year').groupby('Scenario')

# Plot all groups and set up 'name' for legend to use
plot_data = [{
  'x': group.index,
  'y': group['Temperature'],
  'name': key
} for key, group in groups]

# Set axis labels and title
model = data.loc[1, 'Model']
layout = {
  'title': 'Maximum mean temperature for warmest month using model %s' % model,
  'xaxis': {'title': 'Year'},
  'yaxis': {'title': 'Temperature [Celsius]'}
}

# Returning the figure tells Nextjournal to display it
go.Figure(data=plot_data, layout=layout)

2.7s

Python

Vega (Altair)

Any Python cell in Nextjournal, that is returning a valid Vega grammar or a file with an .vl.json extension will display a Vega plot. A more convenient way to do this in Python is to install and work with the altair package.

Check out Altair’s documentation for more examples or learn more about Vega here.

pip install altair

3.3s

Bash in Python

10.1s

Python

import pandas as pd
import altair as alt

# Read in the CSV
data = pd.read_csv(OpenNEX-chicago-climate.csv
)

# Specify categorical data
for col in ['Model', 'Scenario', 'Variable']:
    data[col] = data[col].astype('category')

# Coax date strings to beginning of year dates
data['Year'] = data['Date'] \
	.astype('datetime64') \
	.map(lambda d: "%d-01-01" % d.year) \
	.astype('datetime64')

# Convert temperatures from Kelvin to Celsius
data['Temperature'] = data['Value'] - 273.15

# Plot maximum temperature by year
model = data.loc[1, 'Model']
title = 'Maximum mean temperature for warmest month using model %s' % model
alt.Chart(data, title=title).mark_line().encode(
  x='Year:T',
  y=alt.Y('max_temp:Q',
       title='Temperature [Celsius]',
       scale=alt.Scale(
         domain=(25, 40),
         clamp=True
       )
  ),
  color='Scenario'
).transform_aggregate(
	max_temp='max(Temperature)',
  groupby=['Year', 'Scenario']
)

10.1s

Python