# Data Visualization
course: Academy — 65-GENAI-for-Engineers
module: Academy/65-GENAI-for-Engineers
type: notebook
source_url: https://personal-learn.armco.dev/files/Academy/65-GENAI-for-Engineers/folders/Live_Intervention_Visualisation_and_Scikit_Folder/Data_Visualization.ipynb
---
[cell 1 code]
import pandas as pd
data = {
"Category" : ["Electronics", "Clothing", "Groceries", "Clothing","Electronics","Groceries"],
"Sales": [200,250,150,100,220,130],
"Date":pd.to_datetime(["2025-01-01","2025-01-02","2025-01-03","2025-01-04","2025-01-05","2025-01-06"])
}
df = pd.DataFrame(data)
print(df)
[cell 3 code]
#matplotlib and seaborn
# Bar Chart
import matplotlib.pyplot as plt
import seaborn as sns
#matplot
bars = plt.bar(df["Category"],df["Sales"],color="green")
for bar in bars:
yval = bar.get_height()
plt.text(bar.get_x()+bar.get_width()/3,yval,str(yval),ha="center",va="top")
plt.xlabel("Category")
plt.ylabel("Sales")
plt.title("Sales by Category")
plt.show()
[cell 4 code]
plt.figure(figsize=(3,4))
sns.barplot(x="Category",y="Sales",data=df, palette=["red","blue","Green"])
plt.show()
[cell 5 code]
plt.plot(df["Category"],df["Sales"],marker="o",linestyle="-",color="blue",linewidth = 2)
for x,y in zip(df["Date"],df["Sales"]):
plt.text(x,y,str(y),ha="center",va='bottom')
plt.xlabel("Date")
plt.ylabel("Sales")
plt.title("Sales Over Time")
plt.show()
[cell 6 code]
sns.lineplot(x="Date",y="Sales",data=df,marker="o")
for x,y in zip(df["Date"],df["Sales"]):
plt.text(x,y,str(y),ha="center",va='bottom')
plt.show()
[cell 7 code]
data = {"Date":["2025-01-01","2025-01-02","2025-01-03","2025-01-04"],
"Sales":[100,180,200,250],
#"Category":["Electronics","Clothing","Electronics","Clothing"],
"Electronics":[12,13,14,15],
"Clothing":[34,78,90,100],
"Toys":[67,89,45,56]}
df = pd.DataFrame(data)
print(df)
[cell 8 code]
plt.plot(df["Sales"],df["Electronics"],label="Electronics")
plt.plot(df["Sales"],df["Clothing"],label="Clothing")
plt.plot(df["Sales"],df["Toys"],label="Toys")
plt.legend()
plt.show()
[cell 9 code]
sns.lineplot(x="Date",y="Sales", hue="Category",data=df,marker="o")
plt.plot(df["Date"],df["Electronics"],label="Electronics")
plt.show()