Showing posts with label matplotlib. Show all posts
Creating Histograms using matplotlib of Python [Hands-on]
Histogram is the best way to display frequency of a data and here we are to create one. So far we've dealt with text files and now it's time to show some progress and work with some real-world data hence this time, it's going to be a csv (comma-separated value) file from openflights.org.
Unlike text files, to process csv files, we need to import a package called csv . Also going forward in the program we need to calculate geo distance which is quite different from our normal distance calculation as the former deals with longitudes and latitudes so we've to download the python program geo_distance and import the function geo_distance into our program.
Let's dive deeper into the code. As you see below, we are working with two different input dataset 1. airports.dat to get airport details and 2. routes.dat to get route details. And now we've to calculate geo_distance from both those data and record it in a list distance[]
Now our data is ready and it's time for some storytelling. Let's create a histogram with hist().
Once you execute the code, a beautiful bluish histogram appears. Here it is:
Download the source code here!
Unlike text files, to process csv files, we need to import a package called csv . Also going forward in the program we need to calculate geo distance which is quite different from our normal distance calculation as the former deals with longitudes and latitudes so we've to download the python program geo_distance and import the function geo_distance into our program.
import matplotlib.pyplot as plt import csv import geo_distance #for calculating dist b/w lats. and longs.
Let's dive deeper into the code. As you see below, we are working with two different input dataset 1. airports.dat to get airport details and 2. routes.dat to get route details. And now we've to calculate geo_distance from both those data and record it in a list distance[]
d = open("airports.dat.txt") latitudes = {} longitudes = {} distances = [] for row in csv.reader(d): airport_id = row[0] latitudes[airport_id] = float(row[6]) longitudes[airport_id] = float(row[7]) f = open("routes.dat") for row in csv.reader(f): source_airport = row[3] dest_airport = row[5] if source_airport in latitudes and dest_airport in latitudes: source_lat = latitudes[source_airport] source_long = longitudes[source_airport] dest_lat = latitudes[dest_airport] dest_long = longitudes[dest_airport] distances.append(geo_distance.distance(source_lat,source_long,dest_lat,dest_long))
Now our data is ready and it's time for some storytelling. Let's create a histogram with hist().
plt.hist(distances, 100, facecolor='b') plt.xlabel("Distance (km)") plt.ylabel("Number of flights")
Creating Charts using matplotlib in Python [Hands-on]
Data Storytelling is a very important branch of Data Science. Your world may not be as fond of numbers as you are hence it's very important to show them your results in the language that they understand. Hence for any language to be a member of the data science world, it's not only their data processing capabilities should be great but also the data visualizations should be exceptional and hence Python with packages like matplotlib is capable of competing in the world of R.
So let's try to represent the data of our previous post in terms of graphs/charts.
Problem:
Draw a bar graph with a dictionary counts that we built in our previous blogpost.
Takeaways:
Approach:
As we do for every new package, the first job is to import matplotlib package.
Now let's draw a bar graph with the values (vote) of the dictionary counts
Our graph is ready now but it's kind of naked (without labels ;) ) but let's show it!
But a graph with no labels would make no sense to anyone hence it's our duty to make sure that the graph's x-axis and y-axis are labelled correctly. Let's add them too!
And here's how the bar graph looks: beautiful isn't?
Download the source code here.
So let's try to represent the data of our previous post in terms of graphs/charts.
Problem:
Draw a bar graph with a dictionary counts that we built in our previous blogpost.
Takeaways:
- Basics of matplotlib
Approach:
As we do for every new package, the first job is to import matplotlib package.
import matplotlib.pyplot as plt
Now let's draw a bar graph with the values (vote) of the dictionary counts
plt.bar(range(len(counts)), counts.values(), align='center')
plt.show()
plt.ylabel(s = "Votes") plt.xticks(range(len(counts)), counts.keys(),rotation=90)
And here's how the bar graph looks: beautiful isn't?
