This example uses historical data since 1880 on average global surface temperature changes from NASA's GISS Surface Temperature Analysis (GISTEMP) (original file) That site has lots of other data sets in csv format, too.
Click the "play" icon or press shift+enter to execute each cell.
In [1]:
# import the software packages needed
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
inline_rc = dict(mpl.rcParams)
In [2]:
# Combined land and ocean temperature averages (LOTI: Land Ocean Temperature Index)
data1 = pd.read_csv('http://github.com/adamlamee/CODINGinK12-data/raw/master/LOTI.csv', header=1).replace(to_replace="***", value=np.NaN)
data_LOTI = data1.apply(lambda x: pd.to_numeric(x, errors='ignore'))
# Only land temperature averages
data2 = pd.read_csv('http://github.com/adamlamee/CODINGinK12-data/raw/master/LAND.csv', header=1).replace(to_replace="***", value=np.NaN)
data_LAND = data2.apply(lambda x: pd.to_numeric(x, errors='ignore'))
We can view the first few rows of the file we just imported.
In [3]:
# The .head(n) command displays the first n rows of the file.
data_LAND.head(5)
Out[3]:
In [4]:
x1 = data_LOTI.Year
y1 = data_LOTI.JanDec
# plt.plot() makes a line graph, by default
fig = plt.figure(figsize=(10, 5))
plt.plot(x1, y1)
plt.title('Average land an docean temperature readings')
plt.xlabel('Year')
plt.ylabel('Percent temp change')
Out[4]:
In [5]:
x2 = data_LAND.Year
y2 = data_LAND.JanDec
# plt.plot() makes a line graph, by default
fig = plt.figure(figsize=(10, 5))
plt.plot(x2, y2)
plt.title('Land temperature readings')
plt.xlabel('Year')
plt.ylabel('Percent temp change')
Out[5]:
In [6]:
# Wow, this needs a title and axis labels!
fig = plt.figure(figsize=(10, 5))
plt.plot(x1, y1, label="Land and Ocean")
plt.plot(x2, y2, label="Land only")
plt.legend()
plt.show()
If you like Randall Monroe's webcomic XKCD as much as I do, you can make your plots look like his hand-drawn ones. Thanks to Jake VanderPlas for sorting that out.
In [7]:
plt.xkcd()
fig = plt.figure(figsize=(10, 5))
plt.plot(x1, y1)
Out[7]:
In [8]:
# to make normal plots again
mpl.rcParams.update(inline_rc)
Try editing some code and re-running the cell to see the effects. For more information on formatting the markdown text in a cell like this one, go to Help > Markdown > Basic Writing and Formatting Text.
To save your work: go to File > Save and Checkpoint. That only saves your edits as long as you're working inthis notebook. All is lost after 10 minutes of inactivity.
To really save your work: go to File > Download as > iPython notebook (or save as pdf if you just want to show someone a snapshot of what your code and output look like.
In [ ]: