How to plot data on primary and inverted secondary axis using python
In this video a detailed description of how we can plot a bar plot of data in primary and secondary axis using python.
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##code
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
df_rainfall = pd.read_excel('path to rainfall data')
df_slope = pd.read_excel('path to slope data')
Assuming both DataFrames have the same length
If not, you may need to interpolate or align them accordingly
df_combined = pd.concat([df_rainfall['cumulative distance'], df_rainfall['Rainfall'], df_slope['slope']], axis=1)
Get the number of data points
num_data_points = len(df_combined)
Plotting
fig, ax1 = plt.subplots(figsize=(12, 7))
# Plotting bar plot with index vs rainfall on primary y-axis
ax1.bar(np.arange(num_data_points), df_combined['Rainfall'].values, color='tab:blue', label='Rainfall')
ax1.set_xlabel('Number of points', fontsize=16) # Change x-axis label
ax1.set_ylabel('Rainfall (mm)', color='tab:blue', fontsize=16)
ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
ax1.set_ylim(0, 180)
Adjust tick sizes of x-axis
ax1.tick_params(axis='x', labelsize=14)
ax1.grid(True)
Create a secondary y-axis for slope
ax2 = ax1.twinx()
# Plotting bar plot with index vs slope on secondary y-axis
ax2.bar(np.arange(num_data_points), df_combined['slope'].values, color='tab:orange', label='Slope')
ax2.set_ylabel('Slope (m/m)',color='tab:orange', fontsize=16)
ax2.tick_params(axis='y', labelcolor='tab:orange', labelsize=14)
Set the limits of the secondary y-axis
ax2.set_ylim(0, 0.2) # Adjust the limits as needed
# Reverse the secondary y-axis
ax2.set_ylim(ax2.get_ylim()[::-1])
plt.title('Rainfall vs Slope ', fontsize=20)
plt.savefig('path to save figure')
plt.show()
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