How to get rid of grid lines when plotting with Seaborn + Pandas with secondary_y?

When plotting with Pandas using secondary_y, grid lines are enabled by default. You can remove them by setting grid=False in the plot method.

Creating a DataFrame with Sample Data

First, let's create a DataFrame with two columns of data ?

import pandas as pd
import matplotlib.pyplot as plt

# Set figure parameters
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True

# Create sample data
data = pd.DataFrame({
    "column1": [4, 6, 7, 1, 8], 
    "column2": [1, 5, 7, 8, 1]
})

print(data)

   column1  column2
0        4        1
1        6        5
2        7        7
3        1        8
4        8        1

Plotting with Secondary Y-axis and No Grid

To remove grid lines when using secondary_y, set the grid parameter to False ?

import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True

# Create sample data
data = pd.DataFrame({
    "column1": [4, 6, 7, 1, 8], 
    "column2": [1, 5, 7, 8, 1]
})

# Plot with secondary y-axis and no grid
ax = data.plot(secondary_y=["column2"], grid=False)
plt.title("Plot without Grid Lines")
plt.show()

Alternative: Removing Grid After Plotting

You can also remove grid lines from both axes after creating the plot ?

import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True

# Create sample data
data = pd.DataFrame({
    "column1": [4, 6, 7, 1, 8], 
    "column2": [1, 5, 7, 8, 1]
})

# Plot with secondary y-axis
ax = data.plot(secondary_y=["column2"])

# Remove grid from both primary and secondary axes
ax.grid(False)
ax.right_ax.grid(False)

plt.title("Grid Removed After Plotting")
plt.show()

Key Parameters

Conclusion

Use grid=False in the plot method to remove grid lines when plotting with secondary y-axis. For more control, access both axes individually using ax.grid(False) and ax.right_ax.grid(False).