📊 Day 64 of My Data Analytics Journey!

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— Learning Matplotlib (All Charts Explained!)

Today, I worked with Matplotlib, the most fundamental Python library for data visualization.
It allows you to turn raw data into meaningful graphs that help in understanding patterns, trends, and insights.

What I Learned — Matplotlib Chart Types


Below are the essential chart types every Data Analyst should know:

Line Chart


Used to show trends over time.
Use case: Monthly sales trend.


import matplotlib.pyplot as plt

x = [1,2,3,4,5]
y = [10, 20, 15, 25, 30]

plt.plot(x, y)
plt.title("Line Chart")
plt.xlabel("Months")
plt.ylabel("Sales")
plt.show()



Bar Chart


Used to compare categories.


categories = ["A", "B", "C"]
values = [20, 35, 30]

plt.bar(categories, values)
plt.title("Bar Chart")
plt.show()



Histogram


Shows the distribution of data.


data = [22,25,29,21,28,30,27,26,32,24]

plt.hist(data, bins=5)
plt.title("Histogram")
plt.show()



Scatter Plot


Used to find the relationship between two variables.


x = [1,2,3,4,5]
y = [5,20,15,25,30]

plt.scatter(x, y)
plt.title("Scatter Plot")
plt.show()



Pie Chart


Used to show parts of a whole.


sizes = [30,25,20,25]
labels = ["A","B","C","D"]

plt.pie(sizes, labels=labels, autopct="%1.1f%%")
plt.title("Pie Chart")
plt.show()



Box Plot


Helps identify data spread and outliers.


data = [22,25,29,21,28,30,27,26,32,24]

plt.boxplot(data)
plt.title("Box Plot")
plt.show()



Area Chart


Shows cumulative progress.


x = [1,2,3,4,5]
y = [3,8,2,10,5]

plt.fill_between(x, y)
plt.title("Area Chart")
plt.show()



Heatmap (Using seaborn for better visuals)


Helps show patterns between variables.


import seaborn as sns
import numpy as np

data = np.random.rand(5,5)
sns.heatmap(data, annot=True)
plt.title("Heatmap")
plt.show()



Additional Things I Practiced

• Customized chart colors and styles
• Adjusted figure sizes
• Added labels, legends, and titles
• Created multiple plots using subplot()
• Saved charts as images

These visualizations help communicate insights clearly — a must-have skill for Data Analysts.

Consistency is my strength. Day 64 completed.


GitHub Link:

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Источник:

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