Pretty seaborn bar chart, differentiate data by color

pretty_seaborn_bar_chart.py

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.style as style

style.use('ggplot')

f, ax1 = plt.subplots(1, 1, figsize=(20, 7), sharex=True)
df_mth_plot = pd.DataFrame(list(monthly_growth_dict.items()), columns=['Market', 'Average Monthly Growth Rate %'])
df_mth_plot = df_mth_plot.sort_values('Average Monthly Growth Rate %', ascending=False)
ax1.set_title("B787 Average Monthly Growth Rates")
clrs = ['royalblue' if x > 0 else 'salmon' for x in df_mth_plot['Average Monthly Growth Rate %']]
barplt_mth = sns.barplot(x=df_mth_plot['Market'], y=df_mth_plot['Average Monthly Growth Rate %'], ax=ax1, palette=clrs) #color='royalblue')
for item in barplt_mth.get_xticklabels():
    item.set_rotation(90)

f, ax1 = plt.subplots(1, 1, figsize=(20, 7), sharex=True)
df_mth_plot = pd.DataFrame(list(yearly_growth_dict.items()), columns=['Market', 'Average Yearly Growth Rate %'])
df_mth_plot = df_mth_plot.sort_values('Average Yearly Growth Rate %', ascending=False)
ax1.set_title("B787 Average Yearly Growth Rates")
clrs = ['royalblue' if x > 0 else 'salmon' for x in df_mth_plot['Average Yearly Growth Rate %']]
barplt_yr = sns.barplot(x=df_mth_plot['Market'], y=df_mth_plot['Average Yearly Growth Rate %'], ax=ax1, palette=clrs)
for item in barplt_yr.get_xticklabels():
    item.set_rotation(90)