Find two consecutive quarters of GDP decline, and ending with two consecutive quarters of GDP growth - python-3.x
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)
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.
Here is my try:
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])
But with the function above, I am only able to get the start and end quarter of the first recession in 1947.
Any idea?
This is probably overkill for this particular example... In a nutshell this is a bit more complicated than #zaq's answer but also much faster (about 9x here, and the difference would be much bigger on larger datasets) because it's vectorized instead of looped. But for this very small dataset here, clearly you would go with the simpler answer since even the slower way is fast enough. Finally, it stores the data in the dataframe itself rather than as a tuple (which could be an advantage or disadvantage, depending on the situation).
Thanks to #zaq for pointing out that I misread the question initially. I believe this now gives the same answer as zaq's except we have different implicit assumptions about the initial state of the world (beginning in recession or not) which is indeterminate in the data provided.
df['change'] = df.diff() # change in GDP from prior quarter
start = (df.change<0) & (df.change.shift(-1)<0) # potential start
end = (df.change>0) & (df.change.shift(-1)>0) # potential end
df['recess' ] = np.nan
df.loc[ start, 'recess' ] = -1
df.loc[ end, 'recess' ] = 1
df['recess'] = df.recess.ffill() # if the current row doesn't fit the
# definition of a start or end, then
# fill it with the prior row value
df['startend'] = np.nan
df.loc[ (df.recess==-1) & (df.recess.shift()== 1), 'startend'] = -1 # start
df.loc[ (df.recess== 1) & (df.recess.shift()==-1), 'startend'] = 1 # end
df[df.startend.notnull()]
GDP change recess startend
quarter
1947q4 1960.7 30.4 1.0 1.0
1949q1 2007.5 -27.8 -1.0 -1.0
1950q1 2084.6 79.9 1.0 1.0
1953q3 2578.9 -14.6 -1.0 -1.0
1954q2 2530.7 2.7 1.0 1.0
1957q4 2846.4 -29.5 -1.0 -1.0
1958q2 2790.9 18.2 1.0 1.0
1969q4 4715.5 -20.6 -1.0 -1.0
1970q2 4715.4 8.3 1.0 1.0
1974q3 5378.7 -52.6 -1.0 -1.0
1975q2 5333.2 40.8 1.0 1.0
1980q2 6392.6 -132.3 -1.0 -1.0
1980q4 6501.2 118.3 1.0 1.0
1981q4 6585.1 -77.8 -1.0 -1.0
1982q4 6493.1 6.3 1.0 1.0
1990q4 8907.4 -76.5 -1.0 -1.0
1991q2 8934.4 68.8 1.0 1.0
2008q3 14891.6 -71.8 -1.0 -1.0
2009q3 14402.5 46.9 1.0 1.0
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.
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)))
Related
Calculation of stock values with yfinance and python
I would like to make some calculations on stock prices in Python 3 and I have installed the module yfinance. I try to get an individual value like this: import yfinance as yf #define the ticker symbol tickerSymbol = 'MSFT' #get data on this ticker tickerData = yf.Ticker(tickerSymbol) #get the historical prices for this ticker tickerDf = tickerData.history(period='1d', start='2015-1-1', end='2020-12-30') row_date = tickerDf[tickerDf['Date']=='2020-12-30'] value = row_date.Open.item() #see your data print (value) But when I run this, it says: KeyError: 'Date' Which is strange because when I do this, it works well and I have the column Date: import yfinance as yf #define the ticker symbol tickerSymbol = 'MSFT' #get data on this ticker tickerData = yf.Ticker(tickerSymbol) #get the historical prices for this ticker tickerDf = tickerData.history(period='1d', start='2015-1-1', end='2020-12-30') #row_date = tickerDf[tickerDf['Date']=='2020-12-30'] #value = row_date.Open.item() #see your data print (tickerDf) I get the following result: G:\python> python test.py Open High Low Close Volume Dividends Stock Splits Date 2014-12-31 41.512481 42.143207 41.263744 41.263744 21552500 0.0 0 2015-01-02 41.450302 42.125444 41.343701 41.539135 27913900 0.0 0 2015-01-05 41.192689 41.512495 41.086088 41.157158 39673900 0.0 0 2015-01-06 41.201567 41.530255 40.455355 40.553074 36447900 0.0 0 2015-01-07 40.846223 41.272629 40.410934 41.068310 29114100 0.0 0 ... ... ... ... ... ... ... ... 2020-12-22 222.690002 225.630005 221.850006 223.940002 22612200 0.0 0 2020-12-23 223.110001 223.559998 220.800003 221.020004 18699600 0.0 0 2020-12-24 221.419998 223.610001 221.199997 222.750000 10550600 0.0 0 2020-12-28 224.449997 226.029999 223.020004 224.960007 17933500 0.0 0 2020-12-29 226.309998 227.179993 223.580002 224.149994 17403200 0.0 0 [1510 rows x 7 columns]
Under the hood, yfinance uses a Pandas data frame to create a Ticker. In this dataframe, Date isn't an ordinary column, but is instead a name given to the index (see line 240 in base.py of yfinance). The index column behaves differently than other columns and actually can't be referenced by name. You can access it using TickerDf.index=='2020-12-30' or by turning it into a regular column using reset_index as explained in another question. Searching through an index is faster than searching a regular column, so if you are looking through a lot of data, it will be to your advantage to leave it as an index.
Counting the number of times the values are more than the mean for a specific column in Dataframe
I'm trying to find the number of times the value in a certain column (in this case under "AveragePrice") is more than its mean & median. I calculated the mean using the below: mean_AveragePrice = avocadodf["AveragePrice"].mean(axis = 0) median_AveragePrice = avocadodf["AveragePrice"].median(axis = 0) how do I count the number of times the values were more than the mean? Sample of the Dataframe: Date AveragePrice Total Volume PLU4046 PLU4225 PLU4770 Total Bags 0 27/12/2015 1.33 64236.62 1036.74 54454.85 48.16 8696.87 1 20/12/2015 1.35 54876.98 674.28 44638.81 58.33 9505.56 2 13/12/2015 0.93 118220.22 794.70 109149.67 130.50 8145.35 3 06/12/2015 1.08 78992.15 1132.00 71976.41 72.58 5811.16 4 29/11/2015 1.28 51039.60 941.48 43838.39 75.78 6183.95 5 22/11/2015 1.26 55979.78 1184.27 48067.99 43.61 6683.91 6 15/11/2015 0.99 83453.76 1368.92 73672.72 93.26 8318.86 7 08/11/2015 0.98 109428.33 703.75 101815.36 80.00 6829.22 8 01/11/2015 1.02 99811.42 1022.15 87315.57 85.34 11388.36
import numpy as np mean_AveragePrice = avocadodf["AveragePrice"].mean(axis = 0) median_AveragePrice = avocadodf["AveragePrice"].median(axis = 0) where_bigger = np.where((avocadodf["AveragePrice"] > mean_AveragePrice) & (avocadodf["AveragePrice"] > median_AveragePrice), 1, 0 ) where_bigger.sum() So you got the data you need and now you need the test. np.where will help you out
Axis is specified but .drop() errors out
I've a data set with 3 columns: UserID ProductID Ratings 0 AKM1MP6P0OYPR 0132793040 5.0 1 A2CX7LUOHB2NDG 0321732944 5.0 2 A2NWSAGRHCP8N5 0439886341 1.0 3 A2WNBOD3WNDNKT 0439886341 3.0 4 A1GI0U4ZRJA8WN 0439886341 1.0 I'm trying to drop the rows where the UserID count is les than 50, that is if the user has rated less than 50 products. So I ran the code below: for i in data.UserID.unique(): print (i) if(data['UserID'].loc[data['UserID'] == i].value_counts().values < 50): data.drop(data.loc[data['UserID'] == i], axis = 0, inplace = True) But I receive an error message stating the labels are not contained in an axis like so: KeyError: "labels ['UserID' 'ProductID' 'Ratings'] not contained in axis" Could anyone please tell me what I'm getting wrong? Thanks!
Simple datetime conversion from integer or string
Is there a simple way to convert a start and end time input into a list of evenly separated times? the input can be string or integer with format 1000,"1000",or "10:00" in 2400hr format. I've managed to accomplish this in a messy looking way, is there a tighter more efficient way to create this list? As you'll notice I created an array first and then called .tolist() to make the time transformation iteration easier. The problem is that an input of 1030 or 1015 would need to be translated into 1050 or 1025 to create the right spacing but if there were a way I could call a datetime.timedelta or something and cleanly make the array?
start="1000"
end="1600"
total_minutes=(int(end[:2])*60)+int(end[2:])-(int(start[:2])*60)-
int(start[2:])
dog=list(range(0,int(total_minutes),25))
walk=dog_df["Walk Length"][dog_df.index[dog_df["Name"]==self.name][0]]
if walk=='half':
self.dogarr=np.array([(x-25,x,x+25,x+50) for x in dog])
elif walk=='full':
self.dogarr=np.array([(x-25,x,x+25,x+50,x+75,x+100) for x in dog])
else:
self.dogarr=np.array([(x,x+25,x+50) for x in dog])
if int(start[2])!=0:
start=start[:2]+str(int(int(start[2:])*1.667))
self.dogarr+=(int(start))
self.dogarr=self.dogarr.tolist()
z=0
while z<len(self.dogarr):
for timespot in self.dogarr[z].copy():
self.dogarr[z][self.dogarr[z].index(timespot)]=time.strftime('%H%M', time.gmtime(self.dogarr[z][self.dogarr[z].index(timespot)]*36))
z+=1
self.dogarr=np.array(self.dogarr)```
array([['1115', '1130', '1145', '1200'],
['1130', '1145', '1200', '1215'],
['1145', '1200', '1215', '1230'],
['1200', '1215', '1230', '1245'],
['1215', '1230', '1245', '1300']], dtype='<U4')
I'm sure you can figure out to parse times from any number of existing questions. The crux of your question seems to be how to create evenly separated times within a range. Here's a simple way: start = datetime.datetime(2018,12,20,10) # or use strptime etc. end = datetime.datetime(2018,12,24,18) count = 10 interval = (end - start) / count dt = start while dt <= end: print(dt) dt += interval The output is: 2018-12-20 10:00:00 2018-12-20 20:24:00 2018-12-21 06:48:00 2018-12-21 17:12:00 2018-12-22 03:36:00 2018-12-22 14:00:00 2018-12-23 00:24:00 2018-12-23 10:48:00 2018-12-23 21:12:00 2018-12-24 07:36:00 2018-12-24 18:00:00
svm train output file has less lines than that of the input file
I am currently building a binary classification model and have created an input file for svm-train (svm_input.txt). This input file has 453 lines, 4 No. features and 2 No. classes [0,1]. i.e 0 1:15.0 2:40.0 3:30.0 4:15.0 1 1:22.73 2:40.91 3:36.36 4:0.0 1 1:31.82 2:27.27 3:22.73 4:18.18 0 1:22.73 2:13.64 3:36.36 4:27.27 1 1:30.43 2:39.13 3:13.04 4:17.39 ...................... My problem is that when I count the number of lines in the output model generated by svm-train (svm_train_model.txt), this has 12 fewer lines than that of the input file. The line count here shows 450, although there are obviously also 9 lines at the beginning showing the various parameters generated i.e. svm_type c_svc kernel_type rbf gamma 1 nr_class 2 total_sv 441 rho -0.156449 label 0 1 nr_sv 228 213 SV Therefore 12 lines in total from the original input of 453 have gone. I am new to svm and was hoping that someone could shed some light on why this might have happened? Thanks in advance Updated......... I now believe that in generating the model, it has removed lines whereby the labels and all the parameters are exactly the same. To explain............... My input is a set of miRNAs which have been classified as 1 and 0 depending on their involvement in a particular process or not (i.e 1=Yes & 0=No). The input file looks something like....... 0 1:22 2:30 3:14 4:16 1 1:26 2:15 3:17 4:25 0 1:22 2:30 3:14 4:16 Whereby, lines one and three are exactly the same and as a result will be removed from the output model. My question is then both why the output model would do this and how I can get around this (whilst using the same features)? Whilst both SOME OF the labels and their corresponding feature values are identical within the input file, these are still different miRNAs. NOTE: The Input file does not have a feature for miRNA name (and this would clearly show the differences in each line) however, in terms of the features used (i.e Nucleotide Percentage Content), some of the miRNAs do have exactly the same percentage content of A,U,G & C and as a result are viewed as duplicates and then removed from the output model as it obviously views them as duplicates even though they are not (hence there are less lines in the output model). the format of the input file is: Where: Column 0 - label (i.e 1 or 0): 1=Yes & 0=No Column 1 - Feature 1 = Percentage Content "A" Column 2 - Feature 2 = Percentage Content "U" Column 3 - Feature 3 = Percentage Content "G" Column 4 - Feature 4 = Percentage Content "C" The input file actually looks something like (See the very first two lines below), as they appear identical, however each line represents a different miRNA): 1 1:23 2:36 3:23 4:18 1 1:23 2:36 3:23 4:18 0 1:36 2:32 3:5 4:27 1 1:14 2:41 3:36 4:9 1 1:18 2:50 3:18 4:14 0 1:36 2:23 3:23 4:18 0 1:15 2:40 3:30 4:15 In terms of software, I am using libsvm-3.22 and python 2.7.5
Align your input file properly, is my first observation. The code for libsvm doesnt look for exactly 4 features. I identifies by the string literals you have provided separating the features from the labels. I suggest manually converting your input file to create the desired input argument.
Try the following code in python to run
Requirements - h5py, if your input is from matlab. (.mat file)
pip install h5py
import h5py
f = h5py.File('traininglabel.mat', 'r')# give label.mat file for training
variables = f.items()
labels = []
c = []
import numpy as np
for var in variables:
data = var[1]
lables = (data.value[0])
trainlabels= []
for i in lables:
trainlabels.append(str(i))
finaltrain = []
trainlabels = np.array(trainlabels)
for i in range(0,len(trainlabels)):
if trainlabels[i] == '0.0':
trainlabels[i] = '0'
if trainlabels[i] == '1.0':
trainlabels[i] = '1'
print trainlabels[i]
f = h5py.File('training_features.mat', 'r') #give features here
variables = f.items()
lables = []
file = open('traindata.txt', 'w+')
for var in variables:
data = var[1]
lables = data.value
for i in range(0,1000): #no of training samples in file features.mat
file.write(str(trainlabels[i]))
file.write(' ')
for j in range(0,49):
file.write(str(lables[j][i]))
file.write(' ')
file.write('\n')