Data Visualisation

1. Plot a simple bar graph with two arrays.

plt.bar([0,1,2,3],[12,23,21,8])
plt.bar([0,1,2],[34,46,23]) #Production in 2018plt.bar([0,1,2],[23,45,43]) #Production in 2019
pos_x=np.array([0,1,2])plt.bar(pos_x,[34,46,23]) #Production in 2018plt.bar(pos_x+0.25,[23,45,43]) #Production in 2019
pos_x=np.array([0,1,2])plt.bar(pos_x,[34,46,23], width=0.5) #Production in 2018plt.bar(pos_x+0.5,[23,45,43], width=0.5) #Production in 2019
pos_x=np.array([0,1,2])*2plt.bar(pos_x,[34,46,23], width=0.5) #Production in 2018plt.bar(pos_x+0.5,[23,45,43], width=0.5) #Production in 2019

2. Give Labels

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

3. Put labels on x-axis

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

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

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

5. Change the color of bars

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

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Shatakshi Singh

Shatakshi Singh

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