0

I'm trying to rotate the x-axis labels by 90 degrees, which typically works with the last line of the category_amts() function below. However, because this is a dual-axis visual, the approach is not working.

How do you rotate axis labels on a dual-axis chart like this?

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

df = pd.DataFrame({'place': ['restaurant', 'gas station', 'movie theater', 'grocery store'],
                   'amount': [50, 65, 32, 70]})
df = df.sort_values('amount', ascending = False)
df['cumpercentage'] = df['amount'].cumsum() / df['amount'].sum() 
x_pos = np.arange(len(df.index))                  

def category_amts():
    plt.rcParams['figure.figsize'] = (18,8)
    plt.rcParams["font.size"] = 12

    fig, ax = plt.subplots()
    ax.bar(x_pos, df['amount'], color = 'C0')
    ax2 = ax.twinx()
    ax2.plot(x_pos, df['cumpercentage'], color = 'C3', marker = 'D', ms = 7)
    ax.tick_params(axis = 'y', colors = 'C0')
    ax2.tick_params(axis = 'y', colors = 'C3')
    ax.xaxis.label.set_color('black')
    ax2.xaxis.label.set_color('black')
    ax.grid(False)
    ax2.grid(False)
    plt.title('Transactions by Merchant Category')
    ax.set_xlabel('Merchant Category')
    ax.set_ylabel('Transaction Count')
    ax2.set_ylabel('Cummulative % of Transaction Amounts', rotation = 270, labelpad = 15)
    plt.xticks(x_pos, df['place'], rotation = 90)

category_amts()
Trenton McKinney
  • 56,955
  • 33
  • 144
  • 158
bshelt141
  • 1,183
  • 15
  • 31
  • 1
    See [this question](https://stackoverflow.com/questions/11264521/date-ticks-and-rotation-in-matplotlib) perhaps - one solution is to move the `plt.xticks` call to before you actually plot. – BigBen Jan 27 '20 at 20:52

1 Answers1

1

per @BigBen comment, I needed to move the where I was calling plt.xticks. See reproducible solutions below:

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

df = pd.DataFrame({'place': ['restaurant', 'gas station', 'movie theater', 'grocery store'],
                   'amount': [50, 65, 32, 70]})
df = df.sort_values('amount', ascending = False)
df['cumpercentage'] = df['amount'].cumsum() / df['amount'].sum() 
x_pos = np.arange(len(df.index))                  

def category_amts():
    plt.rcParams['figure.figsize'] = (18,8)
    plt.rcParams["font.size"] = 12

    fig, ax = plt.subplots()
    plt.xticks(x_pos, df['place'], rotation=90)
    ax.bar(x_pos, df['amount'], color = 'C0')
    ax2 = ax.twinx()
    ax2.plot(x_pos, df['cumpercentage'], color = 'C3', marker = 'D', ms = 7)
    ax.tick_params(axis = 'y', colors = 'C0')
    ax2.tick_params(axis = 'y', colors = 'C3')
    ax.xaxis.label.set_color('black')
    ax2.xaxis.label.set_color('black')
    ax.grid(False)
    ax2.grid(False)
    plt.title('Transactions by Merchant Category')
    ax.set_xlabel('Merchant Category')
    ax.set_ylabel('Transaction Count')
    ax2.set_ylabel('Cummulative % of Transaction Amounts', rotation = 270, labelpad = 15)

category_amts()
bshelt141
  • 1,183
  • 15
  • 31