python time series data Resampling

List of articles

• Downsampling
• L sampling

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Resampling ： It refers to the process of changing time series data from one frequency to another .
Resampling can be divided into

Downsampling ： High frequency to low frequency .

L sampling ： Low frequency to high frequency .

Missing values will be generated after L sampling . In contrast, downsampling is more commonly used .

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Downsampling

import pandas as pd
import numpy as np
# Create daily data 100 strip 
data1 = pd.DataFrame(np.random.uniform(10, 50, (100, 1)), index=pd.date_range('20220101', periods=100), columns=["value"])
print(data1)

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Downsampling , To 10 Daily data sum

data2 = data1.resample('10D').sum()
print(data2)

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Downsampling , Convert to monthly data And sum up

data3 = data1.resample('M').sum()
print(data3)

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L sampling

After L sampling, it is necessary to use asfreq() Method , Only in this way can the data after L sampling be converted to DataFrame Format , All the newly added data are displayed as null values .

First prepare a set of data .

data4 = pd.DataFrame(np.random.randint(1000,4000,size=(4,4)),index=pd.date_range('1/1/2022', periods=4, freq='W-WED'), columns=[" Beijing "," Shanghai "," Guangzhou "," Shenzhen "])
print(data4)

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print(data4.resample('D').asfreq())

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