Beta
Introduction to Data Visualization with Matplotlib
Run the hidden code cell below to import the data used in this course.
# Importing the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Importing the course datasets
climate_change = pd.read_csv('datasets/climate_change.csv', parse_dates=["date"], index_col="date")
medals = pd.read_csv('datasets/medals_by_country_2016.csv', index_col=0)
summer_2016 = pd.read_csv('datasets/summer2016.csv')
austin_weather = pd.read_csv("datasets/austin_weather.csv", index_col="DATE")
weather = pd.read_csv("datasets/seattle_weather.csv", index_col="DATE")
# Some pre-processing on the weather datasets, including adding a month column
seattle_weather = weather[weather["STATION"] == "USW00094290"]
month = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
seattle_weather["MONTH"] = month
austin_weather["MONTH"] = month
Take Notes
Add notes about the concepts you've learned and code cells with code you want to keep.
Cuando esta la fecha en el index, se puede subsetear un df solamente usando df[desde este index : hasta este index]
# Create variable seventies with data from "1970-01-01" to "1979-12-31"
seventies = climate_change["1970-01-01":"1979-12-31"]
Explore Datasets
Use the DataFrames imported in the first cell to explore the data and practice your skills!
- Using
austin_weather
andseattle_weather
, create a Figure with an array of two Axes objects that share a y-axis range (MONTHS
in this case). Plot Seattle's and Austin'sMLY-TAVG-NORMAL
(for average temperature) in the top Axes and plot theirMLY-PRCP-NORMAL
(for average precipitation) in the bottom axes. The cities should have different colors and the line style should be different between precipitation and temperature. Make sure to label your viz! - Using
climate_change
, create a twin Axes object with the shared x-axis as time. There should be two lines of different colors not sharing a y-axis:co2
andrelative_temp
. Only include dates from the 2000s and annotate the first date at whichco2
exceeded 400. - Create a scatter plot from
medals
comparing the number of Gold medals vs the number of Silver medals with each point labeled with the country name. - Explore if the distribution of
Age
varies in different sports by creating histograms fromsummer_2016
. - Try out the different Matplotlib styles available and save your visualizations as a PNG file.