entityMap|blocks|key|9ktvj|text|假设每个公司每年有1个价值。您可以将日期减少为年份。这要简单得多。不需要groupby或apply。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|8fgsr|假设您的数据帧名为df。首先，将日期减少到年份：|offset|length|style|CODE|ej625|df['year']+=+df['Date'].dt.year|code-block|syntax|javascript|ep1sr|第二，添加year%2B5|4dp58|df['year%2B5']+=+df['year']+%2B+5|4060t|第三，将'df‘与自身合并：|bl21l|df_new+=+pandas.merge(df,+df,+how='inner',+left_on=['Instrument',+'year'],+right_on=['Instrument','year%2B5'],+suffixes=['_start',+'_end'])|6d8p|最后，计算滚动CAGR|h209|df_new['CAGR']+=+(df_new['Company+Market+Cap_end']/df_new['Company+Market+Cap_start'])**(0.2)-1|qtni^0|0|9|2|0|0|0|0|0|0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|10|9|@]|A|@]|B|$]]|$2|C|4|D|6|7|8|11|9|@$E|12|F|13|G|H]]|A|@]|B|$]]|$2|I|4|J|6|K|8|14|9|@]|A|@]|B|$L|M]]|$2|N|4|O|6|7|8|15|9|@]|A|@]|B|$]]|$2|P|4|Q|6|K|8|16|9|@]|A|@]|B|$L|M]]|$2|R|4|S|6|7|8|17|9|@]|A|@]|B|$]]|$2|T|4|U|6|K|8|18|9|@]|A|@]|B|$L|M]]|$2|V|4|W|6|7|8|19|9|@]|A|@]|B|$]]|$2|X|4|Y|6|K|8|1A|9|@]|A|@]|B|$L|M]]|$2|Z|4|-4|6|7|8|1B|9|@]|A|@]|B|$]]]]

Assuming there is 1 value per company per year. You can reduce the date to year. This is a lot simpler. No need for groupby or apply.

Say your dataframe is name <code>df</code>. First, reduce date to year:

<pre><code>df['year'] = df['Date'].dt.year
</code></pre>

Second, add year+5

<pre><code>df['year+5'] = df['year'] + 5
</code></pre>

Third, merge the 'df' with itself:

<pre><code>df_new = pandas.merge(df, df, how='inner', left_on=['Instrument', 'year'], right_on=['Instrument','year+5'], suffixes=['_start', '_end'])
</code></pre>

Finally, calculate rolling CAGR

<pre><code>df_new['CAGR'] = (df_new['Company Market Cap_end']/df_new['Company Market Cap_start'])**(0.2)-1
</code></pre>

entityMap|blocks|key|dl4i8|text|设置一个玩具示例：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|b9810|import+numpy+as+np
import+pandas+as+pd

idx_level_0+=+np.repeat(["company1",+"company2",+"company3"],+5)
idx_level_1+=+np.tile([2015,+2016,+2017,+2018,+2019],+3)
values+=+np.random.randint(low=1,+high=100,+size=15)

df+=+pd.DataFrame({"values":+values},+index=[idx_level_0,+idx_level_1])
df.index.names+=+["company",+"year"]
print(df)

+++++++++++++++values
company++year++++++++
company1+2015++++++19
+++++++++2016++++++61
+++++++++2017++++++87
+++++++++2018++++++55
+++++++++2019++++++46
company2+2015+++++++1
+++++++++2016++++++68
+++++++++2017++++++50
+++++++++2018++++++93
+++++++++2019++++++84
company3+2015++++++11
+++++++++2016++++++84
+++++++++2017++++++54
+++++++++2018++++++21
+++++++++2019++++++55|code-block|syntax|javascript|4bi00|我建议使用groupby对单个公司进行分组。然后，您可以通过lambda函数应用您的计算。结果基本上是一行代码。|offset|length|style|CODE|8485i|#+actual+computation+for+a+two-year+period
cagr_period+=+2
df["cagr"]+=+df.groupby("company").apply(lambda+x,+period:+((x.pct_change(period)+%2B+1)+**+(1/period))+-+1,+cagr_period)
print(df)


+++++++++++++++values++++++cagr
company++year++++++++++++++++++
company1+2015++++++19+++++++NaN
+++++++++2016++++++61+++++++NaN
+++++++++2017++++++87++1.139848
+++++++++2018++++++55+-0.050453
+++++++++2019++++++46+-0.272858
company2+2015+++++++1+++++++NaN
+++++++++2016++++++68+++++++NaN
+++++++++2017++++++50++6.071068
+++++++++2018++++++93++0.169464
+++++++++2019++++++84++0.296148
company3+2015++++++11+++++++NaN
+++++++++2016++++++84+++++++NaN
+++++++++2017++++++54++1.215647
+++++++++2018++++++21+-0.500000
+++++++++2019++++++55++0.009217|35rvb^0|0|0|5|7|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|Q|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|R|9|@]|A|@]|B|$F|G]]|$2|H|4|I|6|7|8|S|9|@$J|T|K|U|L|M]]|A|@]|B|$]]|$2|N|4|O|6|E|8|V|9|@]|A|@]|B|$F|G]]|$2|P|4|-4|6|7|8|W|9|@]|A|@]|B|$]]]]

Setting up a toy example:
<pre><code>import numpy as np
import pandas as pd

idx_level_0 = np.repeat([&quot;company1&quot;, &quot;company2&quot;, &quot;company3&quot;], 5)
idx_level_1 = np.tile([2015, 2016, 2017, 2018, 2019], 3)
values = np.random.randint(low=1, high=100, size=15)

df = pd.DataFrame({&quot;values&quot;: values}, index=[idx_level_0, idx_level_1])
df.index.names = [&quot;company&quot;, &quot;year&quot;]
print(df)

 values
company year 
company1 2015 19
 2016 61
 2017 87
 2018 55
 2019 46
company2 2015 1
 2016 68
 2017 50
 2018 93
 2019 84
company3 2015 11
 2016 84
 2017 54
 2018 21
 2019 55
</code></pre>
I suggest to use <code>groupby</code> to group by individual companies. You then could apply your computation via a lambda function. The result is basically a one-liner.
<pre><code># actual computation for a two-year period
cagr_period = 2
df[&quot;cagr&quot;] = df.groupby(&quot;company&quot;).apply(lambda x, period: ((x.pct_change(period) + 1) ** (1/period)) - 1, cagr_period)
print(df)


 values cagr
company year 
company1 2015 19 NaN
 2016 61 NaN
 2017 87 1.139848
 2018 55 -0.050453
 2019 46 -0.272858
company2 2015 1 NaN
 2016 68 NaN
 2017 50 6.071068
 2018 93 0.169464
 2019 84 0.296148
company3 2015 11 NaN
 2016 84 NaN
 2017 54 1.215647
 2018 21 -0.500000
 2019 55 0.009217
</code></pre>

I have a dataframe with historical market caps for which I need to compute their 5-year compound annual growth rates (CAGRs). However, the dataframe has hundreds of companies with 20 years of values each, so I need to be able to isolate each company's data to compute their CAGRs. How do I go about doing this? 

The function to calculate a CAGR is: <code>(end/start)^(1/# years)-1</code>. I have never used <code>.groupby()</code> or <code>.apply()</code>, so I don't know how to implement the CAGR equation for rolling values.

Here is a screenshot of part of the dataframe so you have a visual representation of what I am trying to use:
<a href="https://i.stack.imgur.com/TkBiG.png" rel="nofollow noreferrer">Screeshot of dataframe.</a>

Any guidance would be greatly appreciated!

Python: Calculate 5-year rolling CAGR of values that need to be grouped from a dataframe

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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 我有一个包含历史市值的数据框架，我需要计算它们的5年复合年增长率(CAGR)。但是，dataframe有数百家公司，每家公司的价值都是20年，所以我需要能够隔离每个公司的数据来计算它们的CAGR。我该怎么做呢？ 计算复合增长率的函数是：(end/start)^(1/# years)-1。我从未使用过.groupby(...

问Python:计算需要从数据帧中分组的值的5年滚动CAGR
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