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port matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

# creating a dictionary
plt.rc('font', size=16) #controls default text size
plt.rc('axes', titlesize=16) #fontsize of the title
plt.rc('axes', labelsize=16) #fontsize of the x and y labels
plt.rc('xtick', labelsize=16) #fontsize of the x tick labels
plt.rc('ytick', labelsize=16) #fontsize of the y tick labels
plt.rc('legend', fontsize=16) #fontsize of the legend

# load dataset - census income
census_income = pd.read_csv(r'../input/income/train.csv')

# define figure
fig, (ax1, ax2) = plt.subplots(2)
fig.set_size_inches(18.5, 10.5)

# plot age histogram
age_count = census_income.groupby(by=["age"])["age"].count(), age_count, color='black')

# binning age
def age_bins(age):
    if age < 29:
        return "1 - young"
    if age < 60 and age >= 29:
        return "2 - middle-aged"
        return "3 - old-aged"

# apply trans. function
census_income["age_bins"] = census_income["age"].apply(age_bins)

# group and count all entries in the same bin
age_bins_df = census_income.groupby(by=["age_bins"])["age_bins"].count(), age_bins_df, color='grey')

Tue Jan 10 2023 07:25:32 GMT+0000 (Coordinated Universal Time)

#python #pandas #binning

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