Data Visualisation

Part-3

Bar Graphs are commonly used to represent data. Lets see how to plot attractive and insightful bar graphs using matplotlib.

1. Plot a simple bar graph with two arrays.

plt.bar([0,1,2,3],[12,23,21,8])

You can see the bars are centered around 0,1,2 and 3 x-coordinate.

Now let's see how can we plot graphs to compare data of two years. Suppose you are given production of grain in 2018 and 2019 for 3 months.

plt.bar([0,1,2],[34,46,23]) #Production in 2018
plt.bar([0,1,2],[23,45,43]) #Production in 2019

OOPS! Data of 2018 and 2019 overlapped. This happened because both are centered around 0,1, and 2.

So we will change the position on the x-axis for one of them. Let’s use and array pos_x=np.array([0,1,2]) to define position on x-axis for one of them. And for other we will add 0.5.

pos_x=np.array([0,1,2])
plt.bar(pos_x,[34,46,23]) #Production in 2018
plt.bar(pos_x+0.25,[23,45,43]) #Production in 2019

It still overlaps. Now we can either reduce the thickness or increase the gap in pos_x values. Let’s try to reduce the width.

pos_x=np.array([0,1,2])
plt.bar(pos_x,[34,46,23], width=0.5) #Production in 2018
plt.bar(pos_x+0.5,[23,45,43], width=0.5) #Production in 2019

Great, No Overlapping now! Still, it would look good if there is some space in between data of different months.

pos_x=np.array([0,1,2])*2
plt.bar(pos_x,[34,46,23], width=0.5) #Production in 2018
plt.bar(pos_x+0.5,[23,45,43], width=0.5) #Production in 2019

Looks good now.

2. Give Labels

We will now give labels to each color and will plot a legend.

pos_x=np.array([0,1,2])*2
plt.bar(pos_x,[34,46,23], width=0.5, label=”2018") #Production in 2018
plt.bar(pos_x+0.5,[23,45,43], width=0.5, label=”2019") #Production in 2019
plt.legend()

3. Put labels on x-axis

To make it easy to interpret we will give the names of months for which production is compared. We do it using the tick_labels parameter.

pos_x=np.array([0,1,2])*2
plt.bar(pos_x,[34,46,23], width=0.5, label=”2018", tick_label=[“Jan”, “Feb”, “Mar”]) #Production in 2018
plt.bar(pos_x+0.5,[23,45,43], width=0.5, label=”2019") #Production in 2019
plt.legend()

4. Give a title to the plot and both the axes

pos_x=np.array([0,1,2])*2
plt.bar(pos_x,[34,46,23], width=0.5, label=”2018", tick_label=[“Jan”, “Feb”, “Mar”]) #Production in 2018
plt.bar(pos_x+0.5,[23,45,43], width=0.5, label=”2019") #Production in 2019
plt.title(“Wheat Production”)
plt.xlabel(“Months”)
plt.ylabel(“Production in Kgs”)
plt.legend()

Now let's try to change the theme to make it look more attractive.

Above your existing code just write this one line.

plt.style.use(“dark_background”)

5. Change the color of bars

plt.style.use(“dark_background”)
pos_x=np.array([0,1,2])*2
plt.bar(pos_x,[34,46,23], width=0.5, label=”2018", tick_label=[“Jan”, “Feb”, “Mar”], color=”blue”) #Production in 2018
plt.bar(pos_x+0.5,[23,45,43], width=0.5, label=”2019", color=”orange”) #Production in 2019
plt.title(“Wheat Production”)
plt.xlabel(“Months”)
plt.ylabel(“Production in Kgs”)
plt.legend()