Analyze Arctic Ice Tendencies with Python | by Lee Vaughan | Jun, 2023

The next code was entered into JupyterLab and is described by cell.

Importing Libraries

For this venture, all we’d like are the stalwart libraries of Matplotlib and pandas. You may set up them with conda utilizing:

conda set up matplotlib pandas

and with pip utilizing:

pip set up matplotlib

pip set up pandas

Matplotlib’s mdates module will assist us annotate our plot with the time span throughout which Gore postulated an ice-free Arctic Sea. Listed below are the imports:

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

Loading and Making ready the Knowledge

The next commented code makes use of the pandas library to load the information from the Gist and put together it for plotting. As well as, it generates a yearly shifting common to seize long-term tendencies within the measurements.

# Learn the information:
URL = ''
df = pd.read_csv(URL, skiprows=[1])

# Take away main whitespace from the column names:
df.columns = df.columns.str.strip()

# Drop pointless columns:
df = df.drop(df.columns[[4, 5]], axis=1)

# Group by month-to-month MINIMUM ice extent:
df = df.groupby(['Year', 'Month']).agg({'Extent': ['min']}).reset_index()

# Create a 'date' column from the '12 months' and 'Month' columns:
cols = ['Year', 'Month']
df['date'] = df[cols].apply(lambda x: '-'.be part of(x.values.astype(str)),
df['date'] = pd.to_datetime(df['date'])

# Set the 'date' column because the DataFrame index:
df = df.set_index(df['date'])

# Drop pointless 12 months, month, and date columns:
df = df.drop(df.columns[[0, 1, 3]], axis=1)

# Calculate the yearly shifting common:
df['yearly_ma'] = df.Extent.rolling(12).imply()

# Test the outcomes:

The top of the DataFrame (picture by the creator)

Plotting the Knowledge

The next commented code plots the month-to-month minimal extent knowledge and the yearly shifting common as a line chart. The seven-year interval after Al Gore’s 2009 remarks is highlighted in crimson and labeled, “Gore’s Subsequent 7 Years.”

# Create the plot:
fig, ax = plt.subplots(figsize=(12, 6))
ax.set_title('Arctic Sea Ice Month-to-month MINIMUM Extent', measurement=15)
ax.plot(df['Extent'], lw=2)
ax.plot(df['yearly_ma'], coloration='ok')
ax.set_ylim([0, 20])

# Add a legend:
ax.legend(['Ice Extent (10^6 sq km)', 'Yearly Moving Ave'],
prop={'measurement': 14},

# Add a shaded span for Gore's prediction:
ax.axvspan(*mdates.datestr2num(['2009-12-14', '2016-1-1']),

# Annotate the 7-year span referenced by Gore in 2009:
ax.textual content(0.655, 0.8,
"Gore's Subsequent 7 Years",

# Set the x and y labels:
font1 = {'household': 'arial',
'coloration': 'black',
'measurement': 15}
ax.set_xlabel('12 months', fontdict=font1)
ax.set_ylabel('Arctic Sea Ice Extent (10^6 sq km)',


The ultimate line plot (picture by the creator)

The saw-toothed blue line within the plot tracks the minimal extent of Arctic Sea ice for every month. The height of every oscillation represents the wintertime minimal extent (often highest in March). The trough of every oscillation represents {the summertime} minimal extent (often lowest in September). The black line is the yearly shifting common, which filters out the seasonal “noise” to point out the general pattern in sea ice extent over the 44-year interval.

As a result of we’re utilizing the minimal values recorded every month, moderately than the extra typical month-to-month imply or median values, this plot could not precisely match others that you simply discover on-line.

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