如何平均每月数据以获得 Python 中的年度值？

Question

I have a dataset that looks like this:

Date	Value
1871-01	4.5
1871-02	10.7
1871-03	8.9
1871-04	1.3

all the way to 2021-12.

how do I get the average value for each year in Python? For example, the 1871 average would be the average of all of the values from 1871-01 to 1871-1 and I would like it for all years from 1871-2021. ... ...

Answer 1

given your data is in a pandas dataframe called df:

>>> df
    Date        Value
0   1871-01     4.5
1   1871-02     10.7
2   1871-03     8.9
3   1871-04     1.3
4   1872-02     1.5
5   1872-03     15.9
6   1872-04     7.3
>>> year_df = df.set_index(pd.to_datetime(df['Date'])).groupby(pd.Grouper(freq='Y')).mean()
>>> year_df.index = year_df.index.year
>>> year_df
Date    Value
1871    6.35
1872    8.233333333333333

Answer 2

Depends on the what format the data is being given to you. Is it json? csv? If you already know how to import and read the data with python.. you just need to assign the years to variables and average them. (x1 + x2 + x3) / (number of averaged variables)

Answer 3

Make a numpy array with the values, reshape and use np.mean.

Example with only 3 years worth of "data"

import numpy as np

values=np.random.normal(0,1,36)
yearly_avgs=np.mean(values.reshape((len(values)//12,12)),axis=1)

Answer 4

This will help you to get average of all the data according to monthly average for years. In this method there's no need to set date as index and will return single level dataframe as shown in output.

import pandas as pd
import numpy as np
df=pd.DataFrame({"date":pd.date_range("1871-01","2021-12",freq="M"),"val":np.random.randint(10,100,[1811])}) # 1811 months
df[df["date"].dt.year==1871].mean() # 57.666667
df.groupby(pd.PeriodIndex(df["date"],freq="y"))["val"].mean().reset_index()

Above method will return same output even if date feature is of str data type.

Following below will return the same output given the column/feature is date type.

df.groupby(df["date"].dt.year)["val"].mean().reset_index()

Output .head() :

	date	val
0	1871	57.666667
1	1872	58.916667
2	1873	52.416667
3	1874	41.666667
4	1875	57.583333