Python训练营-Day8

字典的简单介绍

dict={'name': 'Ming', 'age':21 ,'city':'shenzhen'}
dict
dict['name']

标签编码

import pandas as pd
df= pd.read_csv('data.csv')
df.head()
df["Home Ownership"].value_counts()
mapping = {
    'Rent': 0,
    'Own Home': 1,
    'Have Mortgage': 2,
    'HOME Mortgage': 3
    }
df['Home Ownership'].head()
data["Home Ownership"] = data["Home Ownership"].map(mapping)
data["Home Ownership"].head()
df['Term'].value_counts()
mapping = {
    'Short Term': 0,
    'Long Term': 1
    }
df['Term']= df['Term'].map(mapping)
df['Term'].head()
df=pd.read_csv('data.csv')
mapping = {
    'Term':{
        'Short Term': 0,
        'Long Term': 1
    },
    'Home Ownership':{
        'Rent': 0,
        'Own Home': 1,
        'Have Mortgage': 2,
        'Home Mortgage': 3
    }
}
mapping['Term']
df['Home Ownership'] = df['Home Ownership'].map(mapping['Home Ownership'])
df['Term'] = df['Term'].map(mapping['Term'])
df.head()
def manual_normalize(df):
    min_val = df.min()
    max_val = df.max()
    normalized_df = (df - min_val) / (max_val - min_val)
    return normalized_df
df['Annual Income']= manual_normalize(df['Annual Income'])
df['Annual Income'].head()
from sklearn.preprocessing import StandardScaler, MinMaxScaler
df= pd.read_csv('data.csv')
min_max_scaler = MinMaxScaler()
df['Annual Income'] = min_max_scaler.fit_transform(df[['Annual Income']])
df['Annual Income'].head()
df = pd.read_csv('data.csv')
scaler = StandardScaler()
df['Annual Income'] = scaler.fit_transform(df[['Annual Income']])
df['Annual Income'].head()