数据清洗的方法：
设置阈值去掉异常值
随机森林预测去掉点的数值加进去
onehot编码（不适用于决策树和随机森林）：
先将一个属性分成几个类别
然后再将样本的数据变成矩阵01，1表示其所在类别
会导致特征数增多
数据清洗代码实现
import numpy as np
import pandas as pd
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
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()
def correct_state(row):
if row['state'] != 0:
state = process.extractOne(row['state'], states, score_cutoff=80)
if state:
state_name = state[0]
return ' '.join(map(capital, state_name.split(' ')))
return row['state']
def fill_state_code(row):
if row['state'] != 0:
state = process.extractOne(row['state'], states, score_cutoff=80)
if state:
state_name = state[0]
return state_to_code[state_name]
return ''
if __name__ == "__main__":
pd.set_option('display.width', 200)
data = pd.read_excel('sales.xlsx', sheetname='sheet1', header=0)
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,
print
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)
# 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)
data = data.rename(index={15:'Total'})
print data.tail()
# apply的使用
print '==============apply的使用=========='
data.apply(enum_row, axis=1)
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')
print process.extract('Mississippi', states)
print process.extract('Mississipi', states, limit=1)
print process.extractOne('Mississipi', states)
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)
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)
主成分分析PCA代码实现：
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegressionCV
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
def extend(a, b):
return 1.05*a-0.05*b, 1.05*b-0.05*a
if __name__ == '__main__':
pd.set_option('display.width', 200)
data = pd.read_csv('iris.data', header=None)
columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'type']
data.rename(columns=dict(zip(np.arange(5), columns)), inplace=True)
data['type'] = pd.Categorical(data['type']).codes
print data.head(5)
x = data.loc[:, columns[:-1]]
y = data['type']
pca = PCA(n_components=2, whiten=True, random_state=0)
x = pca.fit_transform(x)
print '各方向方差：', pca.explained_variance_
print '方差所占比例：', pca.explained_variance_ratio_
print x[:5]
cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
mpl.rcParams['font.sans-serif'] = u'SimHei'
mpl.rcParams['axes.unicode_minus'] = False
plt.figure(facecolor='w')
plt.scatter(x[:, 0], x[:, 1], s=30, c=y, marker='o', cmap=cm_dark)
plt.grid(b=True, ls=':')
plt.xlabel(u'组份1', fontsize=14)
plt.ylabel(u'组份2', fontsize=14)
plt.title(u'鸢尾花数据PCA降维', fontsize=18)
# plt.savefig('1.png')
plt.show()
x, x_test, y, y_test = train_test_split(x, y, train_size=0.7)
model = Pipeline([
('poly', PolynomialFeatures(degree=2, include_bias=True)),
('lr', LogisticRegressionCV(Cs=np.logspace(-3, 4, 8), cv=5, fit_intercept=False))
])
model.fit(x, y)
print '最优参数：', model.get_params('lr')['lr'].C_
y_hat = model.predict(x)
print '训练集精确度：', metrics.accuracy_score(y, y_hat)
y_test_hat = model.predict(x_test)
print '测试集精确度：', metrics.accuracy_score(y_test, y_test_hat)
N, M = 500, 500 # 横纵各采样多少个值
x1_min, x1_max = extend(x[:, 0].min(), x[:, 0].max()) # 第0列的范围
x2_min, x2_max = extend(x[:, 1].min(), x[:, 1].max()) # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点
x_show = np.stack((x1.flat, x2.flat), axis=1) # 测试点
y_hat = model.predict(x_show) # 预测值
y_hat = y_hat.reshape(x1.shape) # 使之与输入的形状相同
plt.figure(facecolor='w')
plt.pcolormesh(x1, x2, y_hat, cmap=cm_light) # 预测值的显示
plt.scatter(x[:, 0], x[:, 1], s=30, c=y, edgecolors='k', cmap=cm_dark) # 样本的显示
plt.xlabel(u'组份1', fontsize=14)
plt.ylabel(u'组份2', fontsize=14)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid(b=True, ls=':')
patchs = [mpatches.Patch(color='#77E0A0', label='Iris-setosa'),
mpatches.Patch(color='#FF8080', label='Iris-versicolor'),
mpatches.Patch(color='#A0A0FF', label='Iris-virginica')]
plt.legend(handles=patchs, fancybox=True, framealpha=0.8, loc='lower right')
plt.title(u'鸢尾花Logistic回归分类效果', fontsize=17)
# plt.savefig('2.png')
plt.show()
本文地址：https://blog.csdn.net/CoderMateng/article/details/107135442
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