Bar Charts and Heatmaps¶

Now that you can create your own line charts, it's time to learn about more chart types!

By the way, if this is your first experience with writing code in Python, you should be very proud of all that you have accomplished so far, because it's never easy to learn a completely new skill! If you stick with the course, you'll notice that everything will only get easier (while the charts you'll build will get more impressive!), since the code is pretty similar for all of the charts. Like any skill, coding becomes natural over time, and with repetition.

In this tutorial, you'll learn about bar charts and heatmaps.

Set up the notebook¶

As always, we begin by setting up the coding environment. (This code is hidden, but you can un-hide it by clicking on the "Code" button immediately below this text, on the right.)

In [1]:

import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")

import pandas as pd pd.plotting.register_matplotlib_converters() import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns print("Setup Complete")

Setup Complete

Select a dataset¶

In this tutorial, we'll work with a dataset from the US Department of Transportation that tracks flight delays.

Opening this CSV file in Excel shows a row for each month (where 1 = January, 2 = February, etc) and a column for each airline code.

Each entry shows the average arrival delay (in minutes) for a different airline and month (all in year 2015). Negative entries denote flights that (on average) tended to arrive early. For instance, the average American Airlines flight (airline code: AA) in January arrived roughly 7 minutes late, and the average Alaska Airlines flight (airline code: AS) in April arrived roughly 3 minutes early.

Load the data¶

As before, we load the dataset using the pd.read_csv command.

In [2]:

# Path of the file to read
flight_filepath = "../input/flight_delays.csv"

# Read the file into a variable flight_data
flight_data = pd.read_csv(flight_filepath, index_col="Month")

# Path of the file to read flight_filepath = "../input/flight_delays.csv" # Read the file into a variable flight_data flight_data = pd.read_csv(flight_filepath, index_col="Month")

You may notice that the code is slightly shorter than what we used in the previous tutorial. In this case, since the row labels (from the 'Month' column) don't correspond to dates, we don't add parse_dates=True in the parentheses. But, we keep the first two pieces of text as before, to provide both:

• the filepath for the dataset (in this case, flight_filepath), and
• the name of the column that will be used to index the rows (in this case, index_col="Month").

Examine the data¶

Since the dataset is small, we can easily print all of its contents. This is done by writing a single line of code with just the name of the dataset.

In [3]:

# Print the data
flight_data

# Print the data flight_data

Out[3]:

	AA	AS	B6	DL	EV	F9	HA	MQ	NK	OO	UA	US	VX	WN
Month
1	6.955843	-0.320888	7.347281	-2.043847	8.537497	18.357238	3.512640	18.164974	11.398054	10.889894	6.352729	3.107457	1.420702	3.389466
2	7.530204	-0.782923	18.657673	5.614745	10.417236	27.424179	6.029967	21.301627	16.474466	9.588895	7.260662	7.114455	7.784410	3.501363
3	6.693587	-0.544731	10.741317	2.077965	6.730101	20.074855	3.468383	11.018418	10.039118	3.181693	4.892212	3.330787	5.348207	3.263341
4	4.931778	-3.009003	2.780105	0.083343	4.821253	12.640440	0.011022	5.131228	8.766224	3.223796	4.376092	2.660290	0.995507	2.996399
5	5.173878	-1.716398	-0.709019	0.149333	7.724290	13.007554	0.826426	5.466790	22.397347	4.141162	6.827695	0.681605	7.102021	5.680777
6	8.191017	-0.220621	5.047155	4.419594	13.952793	19.712951	0.882786	9.639323	35.561501	8.338477	16.932663	5.766296	5.779415	10.743462
7	3.870440	0.377408	5.841454	1.204862	6.926421	14.464543	2.001586	3.980289	14.352382	6.790333	10.262551	NaN	7.135773	10.504942
8	3.193907	2.503899	9.280950	0.653114	5.154422	9.175737	7.448029	1.896565	20.519018	5.606689	5.014041	NaN	5.106221	5.532108
9	-1.432732	-1.813800	3.539154	-3.703377	0.851062	0.978460	3.696915	-2.167268	8.000101	1.530896	-1.794265	NaN	0.070998	-1.336260
10	-0.580930	-2.993617	3.676787	-5.011516	2.303760	0.082127	0.467074	-3.735054	6.810736	1.750897	-2.456542	NaN	2.254278	-0.688851
11	0.772630	-1.916516	1.418299	-3.175414	4.415930	11.164527	-2.719894	0.220061	7.543881	4.925548	0.281064	NaN	0.116370	0.995684
12	4.149684	-1.846681	13.839290	2.504595	6.685176	9.346221	-1.706475	0.662486	12.733123	10.947612	7.012079	NaN	13.498720	6.720893

Bar chart¶

Say we'd like to create a bar chart showing the average arrival delay for Spirit Airlines (airline code: NK) flights, by month.

In [4]:

# Set the width and height of the figure
plt.figure(figsize=(10,6))

# Add title
plt.title("Average Arrival Delay for Spirit Airlines Flights, by Month")

# Bar chart showing average arrival delay for Spirit Airlines flights by month
sns.barplot(x=flight_data.index, y=flight_data['NK'])

# Add label for vertical axis
plt.ylabel("Arrival delay (in minutes)")

# Set the width and height of the figure plt.figure(figsize=(10,6)) # Add title plt.title("Average Arrival Delay for Spirit Airlines Flights, by Month") # Bar chart showing average arrival delay for Spirit Airlines flights by month sns.barplot(x=flight_data.index, y=flight_data['NK']) # Add label for vertical axis plt.ylabel("Arrival delay (in minutes)")

Out[4]:

Text(0, 0.5, 'Arrival delay (in minutes)')

The commands for customizing the text (title and vertical axis label) and size of the figure are familiar from the previous tutorial. The code that creates the bar chart is new:

# Bar chart showing average arrival delay for Spirit Airlines flights by month
sns.barplot(x=flight_data.index, y=flight_data['NK'])

It has three main components:

• sns.barplot - This tells the notebook that we want to create a bar chart.
  • Remember that sns refers to the seaborn package, and all of the commands that you use to create charts in this course will start with this prefix.
• x=flight_data.index - This determines what to use on the horizontal axis. In this case, we have selected the column that indexes the rows (in this case, the column containing the months).
• y=flight_data['NK'] - This sets the column in the data that will be used to determine the height of each bar. In this case, we select the 'NK' column.

Important Note: You must select the indexing column with flight_data.index, and it is not possible to use flight_data['Month'] (which will return an error). This is because when we loaded the dataset, the "Month" column was used to index the rows. We always have to use this special notation to select the indexing column.

Heatmap¶

We have one more plot type to learn about: heatmaps!

In the code cell below, we create a heatmap to quickly visualize patterns in flight_data. Each cell is color-coded according to its corresponding value.

In [5]:

# Set the width and height of the figure
plt.figure(figsize=(14,7))

# Add title
plt.title("Average Arrival Delay for Each Airline, by Month")

# Heatmap showing average arrival delay for each airline by month
sns.heatmap(data=flight_data, annot=True)

# Add label for horizontal axis
plt.xlabel("Airline")

# Set the width and height of the figure plt.figure(figsize=(14,7)) # Add title plt.title("Average Arrival Delay for Each Airline, by Month") # Heatmap showing average arrival delay for each airline by month sns.heatmap(data=flight_data, annot=True) # Add label for horizontal axis plt.xlabel("Airline")

Out[5]:

Text(0.5, 42.0, 'Airline')

The relevant code to create the heatmap is as follows:

# Heatmap showing average arrival delay for each airline by month
sns.heatmap(data=flight_data, annot=True)

This code has three main components:

• sns.heatmap - This tells the notebook that we want to create a heatmap.
• data=flight_data - This tells the notebook to use all of the entries in flight_data to create the heatmap.
• annot=True - This ensures that the values for each cell appear on the chart. (Leaving this out removes the numbers from each of the cells!)

What patterns can you detect in the table? For instance, if you look closely, the months toward the end of the year (especially months 9-11) appear relatively dark for all airlines. This suggests that airlines are better (on average) at keeping schedule during these months!

What's next?¶

Create your own visualizations with a coding exercise!

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