�� : ��

infpandas

�� bank-full2 �� . �� , � �� .

Dataset bank-full2 �� :

Age - �� 

Job - �� 

Marital - �� 

Education - �� 

Default - �� (no - ��, unknown - ��)

Housing - �� ? (yes - ��, no - ��, unknown - ��)

Loan - �� ?

Contact - �� 

Month - �� 

Duration - �� 

� �� Pandas �� read �� bank-addition-full.cvs
�� , �� , � �� .

�� , �� :

1) �� 100 ��

2) �� 100 �� 200

3) �� 200 �� 400

4) �� 400 � ��

�� .

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('bank-full2.csv',encoding='iso8859-1', sep=';', dayfirst=True )
df.columns = map(str.lower, df.columns)
housing_no_percentage = df[df['housing'] == 'no'].groupby('education').size() / df.groupby('education').size() * 100

housing_no_percentage.plot(kind='bar', color='skyblue', figsize=(10, 6))
plt.title('������� ����� ��� ��������� ������� �� ���������� �����������')
plt.xlabel('������� �����������')
plt.ylabel('������� ����� ��� ��������� �������')
plt.xticks(rotation=45)
plt.grid(axis='y')
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('bank-full2.csv',encoding='iso8859-1', sep=';', dayfirst=True )
df.columns = map(str.lower, df.columns)
 
df['client_duration'] = pd.cut(df['duration'], bins=[0, 100, 200, 400, float('inf')], labels=['<100', '100-200', '200-400', '400+'])
duration_counts = df['client_duration'].value_counts()

duration_counts.plot(kind='pie', autopct='%1.1f%%', colors=['lightgreen', 'lightblue', 'lightcoral', 'lightskyblue'], figsize=(8, 8))
plt.title('���������� �������� ����� �� ����������������� ����������')
plt.ylabel('')
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('bank-full2.csv',encoding='iso8859-1', sep=';', dayfirst=True )
df.columns = map(str.lower, df.columns)

df['age'].plot(kind='hist', bins=20, color='salmon', figsize=(10, 6))
plt.title('������������� �������� �������� �����')
plt.xlabel('�������')
plt.ylabel('�������')
plt.grid(axis='y')
plt.show()

��
bank-full2.csv

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