Plotting a Lineal Graph with Columns Diverging from the Main Column

In this article, we will explore how to plot a lineal graph where columns diverge from the main column. We’ll discuss various methods and approaches to achieve this, including using pandas and matplotlib libraries.

Introduction

When working with dataframes in pandas, it’s common to have multiple columns that share a similar value or trend. In such cases, plotting a lineal graph can help visualize these relationships. One approach is to create a graph where the diverging columns are plotted against the main column.

The Problem Statement

The problem statement provided asks how to plot a lineal graph with columns diverging from the main column. We have a dataframe with values for columns A, B, C, and D:

  A  B  C  D
2  2  1  5  7
1  1  4  3  1

We want to create a line graph where column A is the main column, and columns B, C, and D diverge from it.

Method 1: Using pandas and matplotlib

One approach to achieve this is by duplicating the dataframe and performing calculations on the duplicated columns. Here’s an example code snippet:

# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt

# Create the dataframe
df = pd.DataFrame({
    'A': [2, 1],
    'B': [1, 4],
    'C': [5, 3],
    'D': [7, 1]
})

# Duplicate the dataframe
df2 = df.copy()

# Calculate differences between duplicated columns and main column A
df2['B'] = df2['B'] - df2['A']
df2['C'] = df2['C'] - df2['A']
df2['D'] = df2['D'] - df2['A']

# Create a line graph
plt.figure(figsize=(10, 6))
plt.plot(df2['A'], label='Main Column')
plt.plot(df2['B'], label='Column B', color='b')
plt.plot(df2['C'], label='Column C', color='g')
plt.plot(df2['D'], label='Column D', color='r')

# Set title and labels
plt.title('Lineal Graph with Columns Diverging from Main Column A')
plt.xlabel('Index')
plt.ylabel('Value')
plt.legend()

# Show the plot
plt.show()

This code snippet duplicates the dataframe using copy(), calculates differences between duplicated columns and main column A, and then creates a line graph using matplotlib.

Method 2: Using pandas and numpy

Another approach is by using pandas and numpy to achieve the same result. Here’s an example code snippet:

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create the dataframe
df = pd.DataFrame({
    'A': [2, 1],
    'B': [1, 4],
    'C': [5, 3],
    'D': [7, 1]
})

# Calculate differences between columns and main column A using numpy
df['B'] = np.where(df['A'] != 0, df['B'] - df['A'], 0)
df['C'] = np.where(df['A'] != 0, df['C'] - df['A'], 0)
df['D'] = np.where(df['A'] != 0, df['D'] - df['A'], 0)

# Create a line graph
plt.figure(figsize=(10, 6))
plt.plot(df['A'], label='Main Column')
plt.plot(df['B'], label='Column B', color='b')
plt.plot(df['C'], label='Column C', color='g')
plt.plot(df['D'], label='Column D', color='r')

# Set title and labels
plt.title('Lineal Graph with Columns Diverging from Main Column A')
plt.xlabel('Index')
plt.ylabel('Value')
plt.legend()

# Show the plot
plt.show()

This code snippet uses numpy’s where() function to calculate differences between columns and main column A, avoiding division by zero.

Method 3: Using pandas and sub

The problem statement also mentions another approach using pandas’ sub() method. Here’s an example code snippet:

# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt

# Create the dataframe
df = pd.DataFrame({
    'A': [2, 1],
    'B': [1, 4],
    'C': [5, 3],
    'D': [7, 1]
})

# Subtract main column A from other columns using pandas' sub()
df2 = df.copy()
df2['B'] = df2['B'].sub(df2['A'])
df2['C'] = df2['C'].sub(df2['A'])
df2['D'] = df2['D'].sub(df2['A'])

# Create a line graph
plt.figure(figsize=(10, 6))
plt.plot(df2['A'], label='Main Column')
plt.plot(df2['B'], label='Column B', color='b')
plt.plot(df2['C'], label='Column C', color='g')
plt.plot(df2['D'], label='Column D', color='r')

# Set title and labels
plt.title('Lineal Graph with Columns Diverging from Main Column A')
plt.xlabel('Index')
plt.ylabel('Value')
plt.legend()

# Show the plot
plt.show()

This code snippet uses pandas’ sub() method to subtract main column A from other columns, achieving a similar result as the previous approaches.

Comparison and Conclusion

All three methods (pandas and matplotlib, pandas and numpy, and pandas and sub) can be used to plot a lineal graph where columns diverge from the main column. However, each approach has its own strengths and weaknesses:

Pandas and matplotlib is suitable for those who are familiar with pandas and matplotlib.
Pandas and numpy is suitable for those who prefer using numpy’s vectorized operations.
Pandas and sub is a concise way to achieve the same result.

When choosing an approach, consider your personal preferences and the specific requirements of your project.

Last modified on 2024-01-14