查找连续两个季度的GDP下降，并以连续两个季度的GDP增长结束 [英] Find two consecutive quarters of GDP decline, and ending with two consecutive quarters of GDP growth

问题描述

我有以下df数据，其中包含从1947年1季度到2016年2季度的美国季度GDP(按数十亿美元的环比2009年美元汇率):

I have the following df with data about the American quarterly GDP in billions of chained 2009 dollars, from 1947q1 to 2016q2:

df = pd.DataFrame(data = [1934.5, 1932.3, 1930.3, 1960.7, 1989.5, 2021.9, 2033.2, 2035.3, 2007.5, 2000.8, 2022.8, 2004.7, 2084.6, 2147.6, 2230.4, 2273.4, 2304.5, 2344.5, 2392.8, 2398.1, 2423.5, 2428.5, 2446.1, 2526.4, 2573.4, 2593.5, 2578.9, 2539.8, 2528.0, 2530.7, 2559.4, 2609.3, 2683.8, 2727.5, 2764.1, 2780.8, 2770.0, 2792.9, 2790.6, 2836.2, 2854.5, 2848.2, 2875.9, 2846.4, 2772.7, 2790.9, 2855.5, 2922.3, 2976.6, 3049.0, 3043.1, 3055.1, 3123.2, 3111.3, 3119.1, 3081.3, 3102.3, 3159.9, 3212.6, 3277.7, 3336.8, 3372.7, 3404.8, 3418.0, 3456.1, 3501.1, 3569.5, 3595.0, 3672.7, 3716.4, 3766.9, 3780.2, 3873.5, 3926.4, 4006.2, 4100.6, 4201.9, 4219.1, 4249.2, 4285.6, 4324.9, 4328.7, 4366.1, 4401.2, 4490.6, 4566.4, 4599.3, 4619.8, 4691.6, 4706.7, 4736.1, 4715.5, 4707.1, 4715.4, 4757.2, 4708.3, 4834.3, 4861.9, 4900.0, 4914.3, 5002.4, 5118.3, 5165.4, 5251.2, 5380.5, 5441.5, 5411.9, 5462.4, 5417.0, 5431.3, 5378.7, 5357.2, 5292.4, 5333.2, 5421.4, 5494.4, 5618.5, 5661.0, 5689.8, 5732.5, 5799.2, 5913.0, 6017.6, 6018.2, 6039.2, 6274.0, 6335.3, 6420.3, 6433.0, 6440.8, 6487.1, 6503.9, 6524.9, 6392.6, 6382.9, 6501.2, 6635.7, 6587.3, 6662.9, 6585.1, 6475.0, 6510.2, 6486.8, 6493.1, 6578.2, 6728.3, 6860.0, 7001.5, 7140.6, 7266.0, 7337.5, 7396.0, 7469.5, 7537.9, 7655.2, 7712.6, 7784.1, 7819.8, 7898.6, 7939.5, 7995.0, 8084.7, 8158.0, 8292.7, 8339.3, 8449.5, 8498.3, 8610.9, 8697.7, 8766.1, 8831.5, 8850.2, 8947.1, 8981.7, 8983.9, 8907.4, 8865.6, 8934.4, 8977.3, 9016.4, 9123.0, 9223.5, 9313.2, 9406.5, 9424.1, 9480.1, 9526.3, 9653.5, 9748.2, 9881.4, 9939.7, 10052.5, 10086.9, 10122.1, 10208.8, 10281.2, 10348.7, 10529.4, 10626.8, 10739.1, 10820.9, 10984.2, 11124.0, 11210.3, 11321.2, 11431.0, 11580.6, 11770.7, 11864.7, 11962.5, 12113.1, 12323.3, 12359.1, 12592.5, 12607.7, 12679.3, 12643.3, 12710.3, 12670.1, 12705.3, 12822.3, 12893.0, 12955.8, 12964.0, 13031.2, 13152.1, 13372.4, 13528.7, 13606.5, 13706.2, 13830.8, 13950.4, 14099.1, 14172.7, 14291.8, 14373.4, 14546.1, 14589.6, 14602.6, 14716.9, 14726.0, 14838.7, 14938.5, 14991.8, 14889.5, 14963.4, 14891.6, 14577.0, 14375.0, 14355.6, 14402.5, 14541.9, 14604.8, 14745.9, 14845.5, 14939.0, 14881.3, 14989.6, 15021.1, 15190.3, 15291.0, 15362.4, 15380.8, 15384.3, 15491.9, 15521.6, 15641.3, 15793.9, 15747.0, 15900.8, 16094.5, 16186.7, 16269.0, 16374.2, 16454.9, 16490.7, 16525.0, 16583.1],
index = ['1947q1', '1947q2', '1947q3', '1947q4', '1948q1', '1948q2', '1948q3',
   '1948q4', '1949q1', '1949q2', '1949q3', '1949q4', '1950q1', '1950q2',
   '1950q3', '1950q4', '1951q1', '1951q2', '1951q3', '1951q4', '1952q1',
   '1952q2', '1952q3', '1952q4', '1953q1', '1953q2', '1953q3', '1953q4',
   '1954q1', '1954q2', '1954q3', '1954q4', '1955q1', '1955q2', '1955q3',
   '1955q4', '1956q1', '1956q2', '1956q3', '1956q4', '1957q1', '1957q2',
   '1957q3', '1957q4', '1958q1', '1958q2', '1958q3', '1958q4', '1959q1',
   '1959q2', '1959q3', '1959q4', '1960q1', '1960q2', '1960q3', '1960q4',
   '1961q1', '1961q2', '1961q3', '1961q4', '1962q1', '1962q2', '1962q3',
   '1962q4', '1963q1', '1963q2', '1963q3', '1963q4', '1964q1', '1964q2',
   '1964q3', '1964q4', '1965q1', '1965q2', '1965q3', '1965q4', '1966q1',
   '1966q2', '1966q3', '1966q4', '1967q1', '1967q2', '1967q3', '1967q4',
   '1968q1', '1968q2', '1968q3', '1968q4', '1969q1', '1969q2', '1969q3',
   '1969q4', '1970q1', '1970q2', '1970q3', '1970q4', '1971q1', '1971q2',
   '1971q3', '1971q4', '1972q1', '1972q2', '1972q3', '1972q4', '1973q1',
   '1973q2', '1973q3', '1973q4', '1974q1', '1974q2', '1974q3', '1974q4',
   '1975q1', '1975q2', '1975q3', '1975q4', '1976q1', '1976q2', '1976q3',
   '1976q4', '1977q1', '1977q2', '1977q3', '1977q4', '1978q1', '1978q2',
   '1978q3', '1978q4', '1979q1', '1979q2', '1979q3', '1979q4', '1980q1',
   '1980q2', '1980q3', '1980q4', '1981q1', '1981q2', '1981q3', '1981q4',
   '1982q1', '1982q2', '1982q3', '1982q4', '1983q1', '1983q2', '1983q3',
   '1983q4', '1984q1', '1984q2', '1984q3', '1984q4', '1985q1', '1985q2',
   '1985q3', '1985q4', '1986q1', '1986q2', '1986q3', '1986q4', '1987q1',
   '1987q2', '1987q3', '1987q4', '1988q1', '1988q2', '1988q3', '1988q4',
   '1989q1', '1989q2', '1989q3', '1989q4', '1990q1', '1990q2', '1990q3',
   '1990q4', '1991q1', '1991q2', '1991q3', '1991q4', '1992q1', '1992q2',
   '1992q3', '1992q4', '1993q1', '1993q2', '1993q3', '1993q4', '1994q1',
   '1994q2', '1994q3', '1994q4', '1995q1', '1995q2', '1995q3', '1995q4',
   '1996q1', '1996q2', '1996q3', '1996q4', '1997q1', '1997q2', '1997q3',
   '1997q4', '1998q1', '1998q2', '1998q3', '1998q4', '1999q1', '1999q2',
   '1999q3', '1999q4', '2000q1', '2000q2', '2000q3', '2000q4', '2001q1',
   '2001q2', '2001q3', '2001q4', '2002q1', '2002q2', '2002q3', '2002q4',
   '2003q1', '2003q2', '2003q3', '2003q4', '2004q1', '2004q2', '2004q3',
   '2004q4', '2005q1', '2005q2', '2005q3', '2005q4', '2006q1', '2006q2',
   '2006q3', '2006q4', '2007q1', '2007q2', '2007q3', '2007q4', '2008q1',
   '2008q2', '2008q3', '2008q4', '2009q1', '2009q2', '2009q3', '2009q4',
   '2010q1', '2010q2', '2010q3', '2010q4', '2011q1', '2011q2', '2011q3',
   '2011q4', '2012q1', '2012q2', '2012q3', '2012q4', '2013q1', '2013q2',
   '2013q3', '2013q4', '2014q1', '2014q2', '2014q3', '2014q4', '2015q1',
   '2015q2', '2015q3', '2015q4', '2016q1', '2016q2'])
df.columns = ['GDP in billions of chained 2009 dollars']
df.index.rename('quarter', inplace = True)

衰退期定义为从GDP连续两个季度下降开始，以GDP连续两个季度结束结束.目标是创建一个函数"get_recession_periods()"，该函数返回1947q1至2016q2之间的所有衰退期.输出可能是具有两列(开始和结束)的数据框，或者是具有所有衰退期的元组列表((开始和结束)，...]).

A recession period is defined as starting with two consecutive quarters of GDP decline, and ending with two consecutive quarters of GDP growth. The goal is to create a function 'get_recession_periods()' that returns all of the recession periods between 1947q1 and 2016q2. The output could a dataframe with two columns (start and end) or a list of tuples [(start and end), ...] with all the recession periods found.

这是我的尝试:

get_recession_periods()
    lst_start = []
    for i in range(0,len(df['GDP in billions of chained 2009 dollars'])-2):
    if df['GDP in billions of chained 2009 dollars'][i] < df['GDP in billions of chained 2009 dollars'][i-1] and df['GDP in billions of chained 2009 dollars'][i+1] < df['GDP in billions of chained 2009 dollars'][i]:
        lst_start.append(df.index[i])
    start = lst_start[0]
    lst_end = []
    for j in range(df.index.get_loc(start),len(df['GDP in billions of chained 2009 dollars'])-2):
        if df['GDP in billions of chained 2009 dollars'][j] > df['GDP in billions of chained 2009 dollars'][j-1] and df['GDP in billions of chained 2009 dollars'][j+1] > df['GDP in billions of chained 2009 dollars'][j]:
            lst_end.append(df.index[j])
    return (lst_start[0], lst_end[0])

但是使用上面的功能，我只能得到1947年第一次经济衰退的开始和结束季度.

But with the function above, I am only able to get the start and end quarter of the first recession in 1947.

有什么主意吗?

推荐答案

您的代码存在一个问题，即您没有跟踪经济的当前状况.如果GDP连续20个季度下降，则您的代码将报告18个衰退开始.如果有20个季度的增长，即使没有开始，它也将报告18场衰退结束.

One issue with your code is that you are not tracking the current status of the economy. If there are 20 consecutive quarters of GDP decline, your code will report 18 recessions beginning. And if there are 20 quarters of growth, it will report 18 recessions ending even if there wasn't one to begin with.

因此，我引入一个布尔值recession来指示我们当前是否处于衰退之中.其他更改:像a < b < c这样的链式不等式在Python中可以正常工作，并提高了可读性；同样，您的列名太冗长，以至于我使用位置索引iloc来使if语句中的条件易于理解.

So, I introduce a Boolean recession to indicate whether we are in recession currently. Other changes: chained inequalities like a < b < c work in Python as expected, and improve readability; also, your column name is so verbose that I used positional indexing iloc instead, to have readable conditions in if-statements.

lst_start = []
lst_end = []
recession = False
for i in range(1, len(df)-1):
    if not recession and (df.iloc[i-1, 0] > df.iloc[i, 0] > df.iloc[i+1, 0]):
        recession = True
        lst_start.append(df.index[i])
    elif recession and (df.iloc[i-1, 0] < df.iloc[i, 0] < df.iloc[i+1, 0]):
        recession = False
        lst_end.append(df.index[i])    
print(list(zip(lst_start, lst_end)))

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