Matplotlib Mini Project (EDA charts pack)

Goal

Create a reusable set of plots you can apply to any dataset:

Histogram (distribution)
Bar chart (category counts)
Line plot (trend)
Scatter plot (relationship)

Sample dataset

Sample data

import pandas as pd
 
# Small example you can replace with any CSV
df = pd.DataFrame({
    "date": pd.date_range("2025-01-01", periods=10, freq="D"),
    "orders": [120, 140, 130, 160, 155, 170, 180, 175, 190, 200],
    "city": ["Pune", "Delhi", "Pune", "Delhi", "Pune", "Mumbai", "Pune", "Delhi", "Mumbai", "Pune"],
    "amount": [100, 140, 110, 160, 130, 90, 180, 150, 120, 200],
})

Sample data

import pandas as pd
 
# Small example you can replace with any CSV
df = pd.DataFrame({
    "date": pd.date_range("2025-01-01", periods=10, freq="D"),
    "orders": [120, 140, 130, 160, 155, 170, 180, 175, 190, 200],
    "city": ["Pune", "Delhi", "Pune", "Delhi", "Pune", "Mumbai", "Pune", "Delhi", "Mumbai", "Pune"],
    "amount": [100, 140, 110, 160, 130, 90, 180, 150, 120, 200],
})

1) Histogram

Histogram

import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
plt.hist(df["amount"], bins=8, edgecolor="black")
plt.title("Amount distribution")
plt.xlabel("Amount")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

Histogram

import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
plt.hist(df["amount"], bins=8, edgecolor="black")
plt.title("Amount distribution")
plt.xlabel("Amount")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

2) Bar chart (counts)

Bar counts

import matplotlib.pyplot as plt
 
counts = df["city"].value_counts()
 
plt.figure(figsize=(7, 4))
plt.bar(counts.index, counts.values)
plt.title("Orders count by city")
plt.xlabel("City")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

Bar counts

import matplotlib.pyplot as plt
 
counts = df["city"].value_counts()
 
plt.figure(figsize=(7, 4))
plt.bar(counts.index, counts.values)
plt.title("Orders count by city")
plt.xlabel("City")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

3) Line plot (trend)

Trend

import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
plt.plot(df["date"], df["orders"], marker="o")
plt.title("Orders trend")
plt.xlabel("Date")
plt.ylabel("Orders")
plt.xticks(rotation=20)
plt.tight_layout()
plt.show()

Trend

import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
plt.plot(df["date"], df["orders"], marker="o")
plt.title("Orders trend")
plt.xlabel("Date")
plt.ylabel("Orders")
plt.xticks(rotation=20)
plt.tight_layout()
plt.show()

4) Scatter plot

Scatter

import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
plt.scatter(df["orders"], df["amount"], alpha=0.7)
plt.title("Orders vs amount")
plt.xlabel("Orders")
plt.ylabel("Amount")
plt.tight_layout()
plt.show()

Scatter

import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
plt.scatter(df["orders"], df["amount"], alpha=0.7)
plt.title("Orders vs amount")
plt.xlabel("Orders")
plt.ylabel("Amount")
plt.tight_layout()
plt.show()

Deliverable

Save some plots with plt.savefig(...)plt.savefig(...) and reuse this structure for your datasets.

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