55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
# 统计特征频度
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# 读取文件
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import pandas as pd
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df = pd.read_csv("D:\code\huarongqiming\碳酸锂合并数据.csv",encoding='gbk')
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df['ds'] = pd.to_datetime(df['ds'])
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# 按ds正序排序，重置索引
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df = df.sort_values(by='ds', ascending=True).reset_index(drop=True)
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# 统计特征频度
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# 每列随机抽取6个值，计算出5个时间间隔，统计每个时间间隔的频度
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columns = df.columns.to_list()
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columns.remove('ds')
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count_dict = {}
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for column in columns:
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# 获取每列时间间隔
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values = df[[column,'ds']]
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values.dropna(inplace=True,axis=0)
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values=values.reset_index(drop=True)
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# 抽取10个值
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value = values.sample(10)
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index = value.index
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next_index = index + 1
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count = []
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for i,j in zip(index, next_index):
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#通过索引计算日期差
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try:
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count.append((values.loc[j,'ds'] - values.loc[i,'ds']).days)
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except:
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pass
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# 把31 换成 30
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count = [30 if i == 31 else i for i in count]
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# 保留count中出现次数最多的数
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count = max(set(count), key=count.count)
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# 存储到字典中
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count_dict[column] = count
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df = pd.DataFrame(count_dict,index=['count']).T
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pindu_dfs = pd.DataFrame()
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# 根据count分组
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# 输出特征频度统计
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pindudict = {'1':'日度','7':'周度','30':'月度','90':'季度','180':'半年度','365':'年度'}
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for i in df.groupby('count'):
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# 获取 i[1] 的索引值
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index = i[1].index
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pindu_df = pd.DataFrame()
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pindu_df[pindudict[str(i[0])]+f'({len(i[1])})'] = index
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# 合并到pindu_dfs
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pindu_dfs = pd.concat([pindu_dfs,pindu_df],axis=1)
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# nan替换为 ' '
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pindu_dfs = pindu_dfs.fillna('')
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pindu_dfs.to_csv('D:\code\huarongqiming\pindu.csv',index=False)
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print(pindu_dfs)
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print('*'*200) |