[人工智能]机器学习算法进阶day01

import numpy as np
import pandas as pd
from fuzzywuzzy import fuzz
from fuzzywuzzy import process  #模糊匹配就是要导入这两个东西 暂时不细究


def enum_row(row):
    print(row['state'])


def find_state_code(row):
    if row['state'] != 0:
        print(process.extractOne(row['state'], states, score_cutoff=80))


def capital(str):
    return str.capitalize()  #capitalize将字符串的第一个字母变成大写，其余字母变为小写


def correct_state(row):
    if row['state'] != 0:
        state = process.extractOne(row['state'], states, score_cutoff=80)  #score_cutof分数阈值
        # state是一个这种形式的东西('NORTH CAROLINA', 100)
        if state:
            state_name = state[0]

            print('state:',state_name,state_name.split(' '))
            return ' '.join(map(capital, state_name.split(' ')))
            #map中是将后面state_name.split(' ')的数据放到capital中，然后通过join用‘ ’将数据连接，所以输出的东西都有空格
    return row['state']


def fill_state_code(row):
    if row['state'] != 0:
        state = process.extractOne(row['state'], states, score_cutoff=80)
        #state是一个这种形式的东西('NORTH CAROLINA', 100)
        if state:
            state_name = state[0]
            return state_to_code[state_name]
    return ''


if __name__ == "__main__":
    pd.set_option('display.width', 200)
    # pd.set_option('display.max_columns',None)
    data = pd.read_excel('sales.xlsx', sheet_name='sheet1', header=0)
    #第一个是文件名，sheet_name='sheet是表头名，这里就是主表的意思'，header为确定某一行为列名一般都取第0行
    print('data.head() = \n', data.head())
    print('data.tail() = \n', data.tail())
    print('data.dtypes = \n', data.dtypes)
    print('data.columns = \n', data.columns)
    for c in data.columns:
        print(c)
    data['total'] = data['Jan'] + data['Feb'] + data['Mar']
    print(data.head())
    print(data['Jan'].sum())
    print(data['Jan'].min())
    print(data['Jan'].max())
    print(data['Jan'].mean())

    print('=============')
    # 添加一行
    s1 = data[['Jan', 'Feb', 'Mar', 'total']].sum()
    print(s1)
    s2 = pd.DataFrame(data=s1)
    print(s2)
    print(s2.T)
    print(s2.T.reindex(columns=data.columns))  #reindex是修改索引，重新定义列索引



    # 即：
    s = pd.DataFrame(data=data[['Jan', 'Feb', 'Mar', 'total']].sum()).T
    s = s.reindex(columns=data.columns, fill_value=0)
    print(s)
    data = data.append(s, ignore_index=True)   #把s加到data中  ignore_index=True不要使用索引标签
    data = data.rename(index={15:'Total'})   #将刚加入的第十五行变成Total

    # apply的使用
    print('==============apply的使用==========')
    data.apply(enum_row, axis=1)
    pd.set_option('display.max_columns',None)
    print('加入state的data',data)

    state_to_code = {"VERMONT": "VT", "GEORGIA": "GA", "IOWA": "IA", "Armed Forces Pacific": "AP", "GUAM": "GU",
                     "KANSAS": "KS", "FLORIDA": "FL", "AMERICAN SAMOA": "AS", "NORTH CAROLINA": "NC", "HAWAII": "HI",
                     "NEW YORK": "NY", "CALIFORNIA": "CA", "ALABAMA": "AL", "IDAHO": "ID",
                     "FEDERATED STATES OF MICRONESIA": "FM",
                     "Armed Forces Americas": "AA", "DELAWARE": "DE", "ALASKA": "AK", "ILLINOIS": "IL",
                     "Armed Forces Africa": "AE", "SOUTH DAKOTA": "SD", "CONNECTICUT": "CT", "MONTANA": "MT",
                     "MASSACHUSETTS": "MA",
                     "PUERTO RICO": "PR", "Armed Forces Canada": "AE", "NEW HAMPSHIRE": "NH", "MARYLAND": "MD",
                     "NEW MEXICO": "NM",
                     "MISSISSIPPI": "MS", "TENNESSEE": "TN", "PALAU": "PW", "COLORADO": "CO",
                     "Armed Forces Middle East": "AE",
                     "NEW JERSEY": "NJ", "UTAH": "UT", "MICHIGAN": "MI", "WEST VIRGINIA": "WV", "WASHINGTON": "WA",
                     "MINNESOTA": "MN", "OREGON": "OR", "VIRGINIA": "VA", "VIRGIN ISLANDS": "VI",
                     "MARSHALL ISLANDS": "MH",
                     "WYOMING": "WY", "OHIO": "OH", "SOUTH CAROLINA": "SC", "INDIANA": "IN", "NEVADA": "NV",
                     "LOUISIANA": "LA",
                     "NORTHERN MARIANA ISLANDS": "MP", "NEBRASKA": "NE", "ARIZONA": "AZ", "WISCONSIN": "WI",
                     "NORTH DAKOTA": "ND",
                     "Armed Forces Europe": "AE", "PENNSYLVANIA": "PA", "OKLAHOMA": "OK", "KENTUCKY": "KY",
                     "RHODE ISLAND": "RI",
                     "DISTRICT OF COLUMBIA": "DC", "ARKANSAS": "AR", "MISSOURI": "MO", "TEXAS": "TX", "MAINE": "ME"}
    states = state_to_code.keys()
    print(fuzz.ratio('Python Package', 'PythonPackage'))  #全匹配，输出相似度
    print('-'*100)
    print(process.extract('Mississippi', states))  #不输入limit的话就是默认为5
    #process就是进程函数的意思,是fuzzyeuzzy带的
    print('-'*100)
    print(process.extract('Mississipi', states, limit=1))
    print(process.extract('Mississipi', states, limit=3))
    #limit：返回的元素数的可选最大值。默认为到五点。
    print('-'*100)
    print(process.extractOne('Mississipi', states))    #etractOne 就选出1个
    print('-'*100)
    data.apply(find_state_code, axis=1)

    print('Before Correct State:\n', data['state'])
    data['state'] = data.apply(correct_state, axis=1)
    print('After Correct State:\n', data['state'])
    data.insert(5, 'State Code', np.nan)  #5的意思是插到第五个位置
    print(data)
    # print('state:',data['State Code'])
    # print(data)
    data['State Code'] = data.apply(fill_state_code, axis=1)
    print(data)

    # group by
    print('==============group by================')
    print(data.groupby('State Code'))
    print('All Columns:\n')
    print(data.groupby('State Code').sum())
    print('Short Columns:\n')
    print(data[['State Code', 'Jan', 'Feb', 'Mar', 'total']].groupby('State Code').sum())

    # 写入文件
    data.to_excel('sales_result.xls', sheet_name='Sheet1', index=False)