Python Pandas - Plot multiple data columns in a DataFrame?

To plot multiple columns from a DataFrame, we use the plot() method with specific column selection. This is useful for comparing different data series visually using various chart types like bar graphs, line plots, and scatter plots.

Import Required Libraries

First, import pandas and matplotlib for data manipulation and plotting ?

import pandas as pd
import matplotlib.pyplot as plt

Creating Sample Data

Let's create a DataFrame with cricket team rankings data ?

import pandas as pd
import matplotlib.pyplot as plt

# Sample cricket team data
data = [["Australia", 2500, 85], 
        ["Bangladesh", 1000, 70], 
        ["England", 2000, 80], 
        ["India", 3000, 90], 
        ["Sri Lanka", 1500, 75]]

# Create DataFrame
df = pd.DataFrame(data, columns=["Team", "Rank_Points", "Win_Percentage"])
print(df)

        Team  Rank_Points  Win_Percentage
0  Australia         2500              85
1 Bangladesh         1000              70
2    England         2000              80
3      India         3000              90
4  Sri Lanka         1500              75

Plotting Multiple Columns as Bar Chart

Use the plot() method with kind="bar" to create a bar graph ?

import pandas as pd
import matplotlib.pyplot as plt

data = [["Australia", 2500, 85], 
        ["Bangladesh", 1000, 70], 
        ["England", 2000, 80], 
        ["India", 3000, 90], 
        ["Sri Lanka", 1500, 75]]

df = pd.DataFrame(data, columns=["Team", "Rank_Points", "Win_Percentage"])

# Plot multiple columns
df.plot(x="Team", y=["Rank_Points", "Win_Percentage"], kind="bar", figsize=(10, 6))
plt.title("Team Rankings: Points vs Win Percentage")
plt.ylabel("Values")
plt.show()

Alternative Plotting Methods

Line Plot

For trend analysis, use a line plot ?

import pandas as pd
import matplotlib.pyplot as plt

# Time series data
dates = pd.date_range('2023-01-01', periods=5, freq='M')
sales_data = pd.DataFrame({
    'Month': dates,
    'Product_A': [100, 120, 140, 110, 160],
    'Product_B': [80, 95, 105, 120, 135]
})

sales_data.plot(x='Month', y=['Product_A', 'Product_B'], kind='line', marker='o')
plt.title("Monthly Sales Comparison")
plt.show()

Scatter Plot

To show correlation between two variables ?

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Height': [160, 165, 170, 175, 180],
    'Weight': [55, 60, 65, 70, 75],
    'Age': [20, 25, 30, 35, 40]
})

# Scatter plot with color coding
plt.scatter(df['Height'], df['Weight'], c=df['Age'], cmap='viridis')
plt.xlabel('Height (cm)')
plt.ylabel('Weight (kg)')
plt.colorbar(label='Age')
plt.title('Height vs Weight (Color = Age)')
plt.show()

Plot Customization Options

Conclusion

Use DataFrame.plot() with column selection to visualize multiple data series. Choose appropriate chart types like bar graphs for comparisons or line plots for trends based on your data analysis needs.