Python matplotlib

import pandas as pd

data = pd.read_csv(
OpenNEX-chicago-climate.csv)

data.head(100)

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(numeric_only=True) \

  .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(figsize=(8,6),dpi=100)

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]"

)

plt.show()