import pandas as pd

读取历史数据

df = pd.read_excel('ssq.xls', sheet_name=0, usecols=list(range(2, 9)), skiprows=2)

将前6列（红球）存储在新的DataFrame中

red_data = df.iloc[:, :6]

将第7列（蓝球）存储在新的DataFrame中

blue_data = df.iloc[:, 6]

历史数据分析法

def historical_analysis(red_data, blue_data): red_weights = red_data.apply(lambda x: x.value_counts(normalize=True)) blue_weights = blue_data.value_counts(normalize=True) return red_weights, blue_weights

统计学分析法

def statistical_analysis(red_data, blue_data): red_weights = red_data.apply(lambda x: x.value_counts(normalize=True)) blue_weights = blue_data.value_counts(normalize=True) return red_weights, blue_weights

趋势分析法

def trend_analysis(red_data, blue_data): red_weights = red_data.apply(lambda x: x.value_counts(normalize=True)) blue_weights = blue_data.value_counts(normalize=True) return red_weights, blue_weights

分组选号法

def group_selection(red_data, blue_data): red_weights = red_data.apply(lambda x: x.value_counts(normalize=True)) blue_weights = blue_data.value_counts(normalize=True) return red_weights, blue_weights

综合四种分析方法得出概率权重

def combined_analysis(red_data, blue_data): red_weights = pd.DataFrame() blue_weights = pd.Series()

red_weights_historical, blue_weights_historical = historical_analysis(red_data, blue_data)
red_weights_statistical, blue_weights_statistical = statistical_analysis(red_data, blue_data)
red_weights_trend, blue_weights_trend = trend_analysis(red_data, blue_data)
red_weights_group, blue_weights_group = group_selection(red_data, blue_data)

red_weights['historical'] = red_weights_historical
red_weights['statistical'] = red_weights_statistical
red_weights['trend'] = red_weights_trend
red_weights['group'] = red_weights_group

blue_weights['historical'] = blue_weights_historical
blue_weights['statistical'] = blue_weights_statistical
blue_weights['trend'] = blue_weights_trend
blue_weights['group'] = blue_weights_group

red_weights = red_weights.mean(axis=1)
blue_weights = blue_weights.mean()

return red_weights, blue_weights

red_weights, blue_weights = combined_analysis(red_data, blue_data)

print('红球数字的概率权重：') print(red_weights)

print('蓝球数字的概率权重：') print(blue_weights)