cut method and creating bins in Python

plt.style.use("ggplot")
plt.figure(figsize=(12, 8))
plt.bar(df_converted['Year'], df_converted['Dividends'], color = 'b')

# Set x-axis ticks to display every year between 1 and rotate them
plt.xticks(np.arange(df_converted['Year'].min(), df_converted['Year'].max() + 1, 1), rotation=45)

plt.xlabel('Year')
plt.ylabel('Total Dividends')
plt.title('Dividends by Year')

# Show grid and plot
plt.grid(True)
plt.show()

########################################################################################################3
age_bins = [0, 10, 18, 30, 55, 100]
cats = pd.cut(titanic.age, age_bins, right = False)
titanic["age_cat"] = cats
titanic.groupby("age_cat").survived.mean()

group_names = ["child", "teenager", "young_adult", "adult", "elderly"]
titanic["age_cat"] = pd.cut(titanic.age, age_bins, right = False, labels = group_names)

########################################################################################
titanic["fare_cat"] = pd.cut(titanic.fare, 5, precision= 0)
titanic.fare_cat.value_counts()

########################################################################################
titanic["fare_cat"] = pd.qcut(titanic.fare, 5)

fare_labels =["very_cheap", "cheap", "moderate", "exp", "very_exp"]
titanic["fare_cat"] =  pd.qcut(titanic.fare, [0, 0.1, 0.25, 0.5, 0.9, 1], precision = 0, labels = fare_labels)
titanic.fare_cat.value_counts()
titanic.groupby(["age_cat", "fare_cat"]).survived.mean().unstack()