Different plotting using pandas and matplotlib

Pandas and Matplotlib are powerful Python libraries for data analysis and visualization. Pandas excels at data manipulation while Matplotlib provides comprehensive plotting capabilities. Together, they offer various plot types to visualize different aspects of your data.

Line Plot

Line plots are ideal for visualizing data trends over time or continuous variables. The plot() function creates connected line segments between data points ?

Syntax

import matplotlib.pyplot as plt
plt.plot(x, y)
plt.show()

Example

import matplotlib.pyplot as plt
import pandas as pd

# Create sample data
data = {"year": [1999, 2000, 2002, 2020, 2023], "sales": [34, 20, 19, 4, 25]} 
df = pd.DataFrame(data)
print("Data:")
print(df)

# Create line plot
plt.figure(figsize=(8, 5))
plt.plot(df["year"], df["sales"], marker='o')
plt.title("Sales Over Years")
plt.xlabel("Year")
plt.ylabel("Sales")
plt.grid(True)
plt.show()

Data:
   year  sales
0  1999     34
1  2000     20
2  2002     19
3  2020      4
4  2023     25

Scatter Plot

Scatter plots display relationships between two numerical variables, with each point representing an observation ?

Example

import matplotlib.pyplot as plt
import pandas as pd

# Create sample data
data = {"height": [150, 160, 170, 180, 190], "weight": [50, 60, 70, 80, 90]} 
df = pd.DataFrame(data)
print("Data:")
print(df)

# Create scatter plot
plt.figure(figsize=(8, 5))
plt.scatter(df["height"], df["weight"], alpha=0.7, s=100)
plt.title("Height vs Weight")
plt.xlabel("Height (cm)")
plt.ylabel("Weight (kg)")
plt.grid(True, alpha=0.3)
plt.show()

Data:
   height  weight
0     150      50
1     160      60
2     170      70
3     180      80
4     190      90

Histogram

Histograms show the frequency distribution of a single numerical variable by dividing data into bins ?

Example

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Create sample data
np.random.seed(42)
scores = np.random.normal(75, 15, 100)  # Normal distribution
df = pd.DataFrame({"test_scores": scores})
print("Sample of data:")
print(df.head())

# Create histogram
plt.figure(figsize=(8, 5))
plt.hist(df["test_scores"], bins=15, alpha=0.7, color='skyblue', edgecolor='black')
plt.title("Distribution of Test Scores")
plt.xlabel("Test Scores")
plt.ylabel("Frequency")
plt.grid(True, alpha=0.3)
plt.show()

Sample of data:
   test_scores
0    82.434452
1    75.235406
2    64.135950
3    78.140515
4    82.130391

Bar Chart

Bar charts compare different categories using rectangular bars with heights proportional to the values ?

Example

import matplotlib.pyplot as plt
import pandas as pd

# Create sample data
data = {"product": ["A", "B", "C", "D", "E"], "revenue": [25000, 35000, 20000, 45000, 30000]} 
df = pd.DataFrame(data)
print("Data:")
print(df)

# Create bar chart
plt.figure(figsize=(8, 5))
plt.bar(df["product"], df["revenue"], color=['red', 'green', 'blue', 'orange', 'purple'])
plt.title("Revenue by Product")
plt.xlabel("Product")
plt.ylabel("Revenue ($)")
plt.grid(True, alpha=0.3)
plt.show()

Data:
  product  revenue
0       A    25000
1       B    35000
2       C    20000
3       D    45000
4       E    30000

Comparison of Plot Types

Conclusion

Choose line plots for time series data, scatter plots for correlations, histograms for distributions, and bar charts for categorical comparisons. Pandas and Matplotlib together provide a complete toolkit for data visualization in Python.