pandas 如何在时间序列数据上“get_dummies"

问题描述

如果我有一些时间序列数据:(弥补)

If I had some time series data: (make some up)

import numpy as np
import pandas as pd
np.random.seed(11)

rows,cols = 50000,2
data = np.random.rand(rows,cols) 
tidx = pd.date_range('2019-01-01', periods=rows, freq='H') 
df = pd.DataFrame(data, columns=['Temperature','Value'], index=tidx)

我如何使用 get_dummies?只看Pandas 文档，我不知道我是否可以申请我如何制作一种热编码.

How could I utilize get_dummies? Just looking at the Pandas documentation, I dont know if I can apply to how I am making one hot encoding.

例如，我知道如何制作一个虚拟变量来表示每周时间变量的唯一方法是一种非常笨拙的冗余代码方法.有人可以给我建议如何更好地做到这一点吗?

For example, the only way I know how to make a dummy variable to represent time-of-week variables is a very clunky redundant code method. Can someone give me advise on how to do this better?

#create dummy variables
df['month'] = df.index.month
df['year'] = df.index.year
df['day_of_week'] = df.index.dayofweek
df['hour'] = df.index.strftime('%H').astype('int')

df['hour_0'] = np.where(df['hour'].isin([0]), 1, 0)
df['hour_1'] = np.where(df['hour'].isin([1]), 1, 0)
df['hour_2'] = np.where(df['hour'].isin([2]), 1, 0)
df['hour_3'] = np.where(df['hour'].isin([3]), 1, 0)
df['hour_4'] = np.where(df['hour'].isin([4]), 1, 0)
df['hour_5'] = np.where(df['hour'].isin([5]), 1, 0)
df['hour_6'] = np.where(df['hour'].isin([6]), 1, 0)
df['hour_7'] = np.where(df['hour'].isin([7]), 1, 0)
df['hour_8'] = np.where(df['hour'].isin([8]), 1, 0)
df['hour_9'] = np.where(df['hour'].isin([9]), 1, 0)
df['hour_10'] = np.where(df['hour'].isin([10]), 1, 0)
df['hour_11'] = np.where(df['hour'].isin([11]), 1, 0)
df['hour_12'] = np.where(df['hour'].isin([12]), 1, 0)
df['hour_13'] = np.where(df['hour'].isin([13]), 1, 0)
df['hour_14'] = np.where(df['hour'].isin([14]), 1, 0)
df['hour_15'] = np.where(df['hour'].isin([15]), 1, 0)
df['hour_16'] = np.where(df['hour'].isin([16]), 1, 0)
df['hour_17'] = np.where(df['hour'].isin([17]), 1, 0)
df['hour_18'] = np.where(df['hour'].isin([18]), 1, 0)
df['hour_19'] = np.where(df['hour'].isin([19]), 1, 0)
df['hour_20'] = np.where(df['hour'].isin([20]), 1, 0)
df['hour_21'] = np.where(df['hour'].isin([21]), 1, 0)
df['hour_22'] = np.where(df['hour'].isin([22]), 1, 0)
df['hour_23'] = np.where(df['hour'].isin([23]), 1, 0)

df['monday'] = np.where(df['day_of_week'].isin([0]), 1, 0)
df['tuesday'] = np.where(df['day_of_week'].isin([1]), 1, 0)
df['wednesday'] = np.where(df['day_of_week'].isin([2]), 1, 0)
df['thursday'] = np.where(df['day_of_week'].isin([3]), 1, 0)
df['friday'] = np.where(df['day_of_week'].isin([4]), 1, 0)
df['saturday'] = np.where(df['day_of_week'].isin([5]), 1, 0)
df['sunday'] = np.where(df['day_of_week'].isin([6]), 1, 0)

df['january'] = np.where(df['month'].isin([1]), 1, 0)
df['february'] = np.where(df['month'].isin([2]), 1, 0)
df['march'] = np.where(df['month'].isin([3]), 1, 0)
df['april'] = np.where(df['month'].isin([4]), 1, 0)
df['may'] = np.where(df['month'].isin([5]), 1, 0)
df['june'] = np.where(df['month'].isin([6]), 1, 0)
df['july'] = np.where(df['month'].isin([7]), 1, 0)
df['august'] = np.where(df['month'].isin([8]), 1, 0)
df['september'] = np.where(df['month'].isin([9]), 1, 0)
df['october'] = np.where(df['month'].isin([10]), 1, 0)
df['november'] = np.where(df['month'].isin([11]), 1, 0)
df['december'] = np.where(df['month'].isin([12]), 1, 0)

df['year19'] = np.where(df['year'].isin([2019]), 1, 0)
df['year20'] = np.where(df['year'].isin([2020]), 1, 0)
df['year21'] = np.where(df['year'].isin([2021]), 1, 0)
df['year22'] = np.where(df['year'].isin([2022]), 1, 0)
df['year23'] = np.where(df['year'].isin([2023]), 1, 0)
df['year24'] = np.where(df['year'].isin([2024]), 1, 0)

然后我正在试验 ML 算法的最终数据框将是:

And then my final dataframe which I am experimenting with ML algorithms would be:

df2 = df[['Temperature', 'Value', 
            'hour_0' , 'hour_1' , 'hour_2' , 'hour_3' , 'hour_4' , 'hour_5' , 'hour_6' ,
            'hour_7' , 'hour_8' , 'hour_9' , 'hour_10' , 'hour_11' , 'hour_12' , 'hour_13' , 
            'hour_14' , 'hour_15' , 'hour_16' , 'hour_17' , 'hour_18' , 'hour_19' , 'hour_20' , 
            'hour_21' , 'hour_22' , 'hour_23' , 
            'monday' , 'tuesday' , 'wednesday' , 'thursday' , 'friday' , 'saturday' , 'sunday' , 
            'january' , 'february' , 'march' , 'april' , 'may' , 'june' , 'july' , 'august' , 
            'september' , 'october' , 'november' , 'december' , 
            'year19' , 'year20' , 'year21' , 'year22' , 'year23' , 'year24']]

编辑更新代码尝试

EDIT UPDATED CODE ATTEMPT

import numpy as np
import pandas as pd
np.random.seed(11)

rows,cols = 50000,2
data = np.random.rand(rows,cols) 
tidx = pd.date_range('2019-01-01', periods=rows, freq='H') 
df = pd.DataFrame(data, columns=['Temperature','Value'], index=tidx)

df['hour'] = df.index.strftime('%H').astype('int')
df['day_of_week'] = df.index.dayofweek
df['month'] = df.index.month
df['year'] = df.index.year

hour_dummies = pd.get_dummies(df['hour'], prefix='hour')

day_mapping = {0: 'monday', 1: 'tuesday', 2: 'wednesday', 3: 'thursday', 4: 'friday', 5: 'saturday', 6: 'sunday'}
day_dummies = pd.get_dummies(df['day_of_week'].map(day_mapping))

month_mapping = {0: 'jan', 1: 'feb', 2: 'mar', 3: 'apr', 4: 'may', 5: 'jun', 6: 'jul',
                 7: 'aug', 8: 'sep', 9: 'oct', 10: 'nov', 11: 'dec'}
month_dummies = pd.get_dummies(df['month'].map(month_mapping))

year_mapping = {0: 'year_2019', 1: 'year_2020', 2: 'year_2021', 3: 'year_2022', 4: 'year_2023', 5: 'year_2024'}
year_dummies = pd.get_dummies(df['year'].map(year_mapping))

df = df.join(hour_dummies)
df = df.join(day_dummies)
df = df.join(month_dummies)
df = df.join(year_dummies)

推荐答案

可以从时间索引中提取相应的信息，然后使用pd.get_dummies.例如

You can extract the corresponding information from the time index, then use pd.get_dummies. For example

# day name
day_names = pd.get_dummies(df.index.day_name())

# hours
hours = pd.get_dummies(df.index.hour, prefix='hour')

# months
months = pd.get_dummies(df.index.month_name())

# year
years = pd.get_dummies(df.index.year, prefix='year')

然后concat:

df = pd.concat((df, hours, day_names), axis=1)

这篇关于 pandas 如何在时间序列数据上“get_dummies"的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持html5模板网！